Datasets:

patent

/

AIPD_nlp_sentence_dataset_v2

like 1

5. The process as claimed in claim 1 , including generating linked document data for displaying said hierarchy of resource clusters.

5. The process as claimed in claim 1 , including generating linked document data for displaying said hierarchy of resource clusters. 7. The process as claimed in claim 5 , wherein said linked document data includes metadata of said plurality of information resources.

6. A method of collaborating across a computing network comprising: generating a collaborative gateway graphical user interface on a terminal that includes a display of at least one collaboration application, the at least one collaboration application including at least one of a plurality of collaboration options; identifying, at the terminal, a subject matter related context in which the terminal is being used by a user of the terminal; detecting a change in a focus to a newly active document at the terminal; recognizing at the terminal that the newly active document is new, or unscanned, or unparsed; detecting at the terminal whether a context of the newly active document is known in response to detection that the newly active document is new, or unscanned, or unparsed; pattern matching, at the terminal, a content of the newly active document to detect a contextual pattern match for the content of the active document in response to detection that the newly active document is unknown; in response to detection diction of the contextual pattern match at the terminal, determining, at the terminal, the context of the newly active document based upon the content of the newly active document and the detected contextual pattern match performed by the terminal at the terminal; retrieving, by the terminal via the computer network, a user context profile from a database, wherein the user context profile includes user defined relevant context information, and wherein the user context profile is at least partially defined by the user; comparing, at the terminal, the determined context of the newly active document to the user defined relevant context information in response to determination of the context of the newly active document to detect whether the determined context is relevant to the at least one collaboration application displayed on the terminal; determining, at the terminal, whether the determined context of the newly active document is different from the identified subject matter related context in response to detection that the determined context is relevant to the at least one collaboration application; detecting a context change in use of the terminal by the user in response to determination that the determined context of the newly active document is different from the identified subject matter related context; transmitting a context message from the terminal to a collaboration assistant application resident on a server in response to detection of the context change, wherein the context message is indicative of a change in the identified subject matter related context, wherein the context message includes a user identity and a detected subject matter related context indication; and adjusting the display at the terminal of the at least one collaboration option in the collaborative gateway graphical user interface in response to receipt of an update indication transmitted by the collaboration assistant application that reflects the detected subject matter related context indication.

6. A method of collaborating across a computing network comprising: generating a collaborative gateway graphical user interface on a terminal that includes a display of at least one collaboration application, the at least one collaboration application including at least one of a plurality of collaboration options; identifying, at the terminal, a subject matter related context in which the terminal is being used by a user of the terminal; detecting a change in a focus to a newly active document at the terminal; recognizing at the terminal that the newly active document is new, or unscanned, or unparsed; detecting at the terminal whether a context of the newly active document is known in response to detection that the newly active document is new, or unscanned, or unparsed; pattern matching, at the terminal, a content of the newly active document to detect a contextual pattern match for the content of the active document in response to detection that the newly active document is unknown; in response to detection diction of the contextual pattern match at the terminal, determining, at the terminal, the context of the newly active document based upon the content of the newly active document and the detected contextual pattern match performed by the terminal at the terminal; retrieving, by the terminal via the computer network, a user context profile from a database, wherein the user context profile includes user defined relevant context information, and wherein the user context profile is at least partially defined by the user; comparing, at the terminal, the determined context of the newly active document to the user defined relevant context information in response to determination of the context of the newly active document to detect whether the determined context is relevant to the at least one collaboration application displayed on the terminal; determining, at the terminal, whether the determined context of the newly active document is different from the identified subject matter related context in response to detection that the determined context is relevant to the at least one collaboration application; detecting a context change in use of the terminal by the user in response to determination that the determined context of the newly active document is different from the identified subject matter related context; transmitting a context message from the terminal to a collaboration assistant application resident on a server in response to detection of the context change, wherein the context message is indicative of a change in the identified subject matter related context, wherein the context message includes a user identity and a detected subject matter related context indication; and adjusting the display at the terminal of the at least one collaboration option in the collaborative gateway graphical user interface in response to receipt of an update indication transmitted by the collaboration assistant application that reflects the detected subject matter related context indication. 11. The method of claim 6 , further comprising: in response to detection that the newly active document includes a known context, the terminal comparing the known context of the newly active document to the user context profile to detect whether the known context is relevant to the plurality of collaboration applications displayed on the collaborative gateway graphical user interface.

3. The computerized method as recited in claim 1 , wherein said receiving step comprises: receiving text data.

3. The computerized method as recited in claim 1 , wherein said receiving step comprises: receiving text data. 5. The computerized method as recited in claim 3 , wherein said receiving step comprises: receiving one or more phrases of text data.

15. One or more non-transitory computer-readable media storing processor-executable instructions that when executed cause one or more processors to perform a method comprising: obtaining a webpage having hypertext markup language (HTML) elements; translating the HTML elements into graphical representations of the HTML elements, wherein the graphical representations include a current graphical element on the webpage and a target graphical element on the webpage; determining spatial locations of the current and target graphical elements on the webpage; and determining a spatial relationship between the current and target graphical elements on the webpage; generating a data structure having the markup language elements; assigning a keyboard shortcut to the determined spatial relationship; augmenting the generated data structure with data representing the assigned-keyboard shortcut; storing the augmented data structure; obtaining a user selection of the keyboard-shortcut that indicates that the user wants to change focus on the webpage from the current graphical element to the target graphical element; and using the determined spatial locations, the determined spatial relationship and the stored augmented data structure, changing the focus on the webpage from the current graphical element to the target graphical element in response to the keyboard-shortcut selection.

15. One or more non-transitory computer-readable media storing processor-executable instructions that when executed cause one or more processors to perform a method comprising: obtaining a webpage having hypertext markup language (HTML) elements; translating the HTML elements into graphical representations of the HTML elements, wherein the graphical representations include a current graphical element on the webpage and a target graphical element on the webpage; determining spatial locations of the current and target graphical elements on the webpage; and determining a spatial relationship between the current and target graphical elements on the webpage; generating a data structure having the markup language elements; assigning a keyboard shortcut to the determined spatial relationship; augmenting the generated data structure with data representing the assigned-keyboard shortcut; storing the augmented data structure; obtaining a user selection of the keyboard-shortcut that indicates that the user wants to change focus on the webpage from the current graphical element to the target graphical element; and using the determined spatial locations, the determined spatial relationship and the stored augmented data structure, changing the focus on the webpage from the current graphical element to the target graphical element in response to the keyboard-shortcut selection. 17. One or more non-transitory computer-readable media according to claim 15 , wherein determining the spatial location of the current and target graphical elements includes using Cartesian coordinates of the webpage.

19. A computer method, comprising: processing automatically medical text in a radiology report using a computer, the processing comprising: identifying medical phrases contained within the medical text; extracting the medical phrases from the medical text; determining which of the medical phrases are valued medical phrases, a valued medical phrase includes content having a corresponding value in a lexicon of relevant terminology in radiology, the valued medical phrase being either a medical finding or a medical recommendation; reducing each valued medical phrase to one or more radiology codes in radiology from at least one of Current Procedural Terminology, Fourth Revision (CPT4), International Classification of Diseases, Ninth Revision (ICD9), Systematized Nomenclature of Medicine (SNOWMED), a lexicon of radiological terms, or Unified Medical Language System (UMLS); the one or more medical codes being medical identifiers; and rendering in a first group medical codes corresponding to the medical finding; and rendering in a second group separate from the first group medical codes corresponding to the medical recommendation, wherein processing automatically the medical text in the radiology report further comprises generating a lexicon-based hierarchical decision tree for radiology by assigning each valued medical phrase to a location in the lexicon-based hierarchical decision tree.

19. A computer method, comprising: processing automatically medical text in a radiology report using a computer, the processing comprising: identifying medical phrases contained within the medical text; extracting the medical phrases from the medical text; determining which of the medical phrases are valued medical phrases, a valued medical phrase includes content having a corresponding value in a lexicon of relevant terminology in radiology, the valued medical phrase being either a medical finding or a medical recommendation; reducing each valued medical phrase to one or more radiology codes in radiology from at least one of Current Procedural Terminology, Fourth Revision (CPT4), International Classification of Diseases, Ninth Revision (ICD9), Systematized Nomenclature of Medicine (SNOWMED), a lexicon of radiological terms, or Unified Medical Language System (UMLS); the one or more medical codes being medical identifiers; and rendering in a first group medical codes corresponding to the medical finding; and rendering in a second group separate from the first group medical codes corresponding to the medical recommendation, wherein processing automatically the medical text in the radiology report further comprises generating a lexicon-based hierarchical decision tree for radiology by assigning each valued medical phrase to a location in the lexicon-based hierarchical decision tree. 20. The computer method of claim 19 wherein rendering in the first group medical codes corresponding to the medical finding comprises displaying on a screen the first group, wherein rendering in the second group medical codes corresponding to the medical recommendation comprises displaying on the screen the second group.

3. The computer program product as recited in claim 2 , wherein the program code further comprises the programming instructions for: prioritizing said identified key concepts using a weighting algorithm which are presented to a user for approval.

3. The computer program product as recited in claim 2 , wherein the program code further comprises the programming instructions for: prioritizing said identified key concepts using a weighting algorithm which are presented to a user for approval. 4. The computer program product as recited in claim 3 , wherein the program code further comprises the programming instructions for: receiving acceptance of said prioritized identified key concepts or one or more of edits to said prioritized identified key concepts and additional key concepts to form said list of key concepts.

1. A method comprising: receiving, by a computer-based system, a body of text from a data source, wherein the body of text is an electronic text and is one of an email, a website chat room, an internet forum, or a text message; identifying, by the computer-based system, structured contextual information based on a known format of the body of text, wherein the structured contextual information includes at least one of a sender email address, one or more recipient email addresses, a subject field, a message date and time stamp, or an attachment title; tokenizing, by the computer-based system, the body of text by splitting the body of text into individual tokens; resolving, by the computer-based system and based on the tokenizing, the individual tokens having a pronoun grammatical role with corresponding noun phrases; wherein the resolving the individual tokens comprises weighting the individual tokens having a pronoun grammatical role based on the structured contextual information, analyzing structured contextual information to facilitate a homophora resolution; integrating, in response to the analyzing and in response to the weighting of the individual tokens having a pronoun grammatical role based on the structured contextual information, the homophora resolution into an anaphora resolution algorithm by substituting the structured contextual information into the body of text to create a substituted body of text; translating, by the computer-based system and based on the integrating, semantic concepts of the substituted body of text into one or more semantic tags; conducting, by the computer-based system, in response to the translating and using the one or more semantic tags, semantic reasoning to facilitate pattern identification within a group of documents, wherein the pattern identification includes analyzing implied relationships of the text within the group of documents to identify a specific topic, wherein the pattern identification is based on at least one of progress or consensus of the substituted body of text within the group of documents; and displaying, by the computer-based system, in response to the conducting and to a user interface, the specific identified topic of the substituted body of text.

1. A method comprising: receiving, by a computer-based system, a body of text from a data source, wherein the body of text is an electronic text and is one of an email, a website chat room, an internet forum, or a text message; identifying, by the computer-based system, structured contextual information based on a known format of the body of text, wherein the structured contextual information includes at least one of a sender email address, one or more recipient email addresses, a subject field, a message date and time stamp, or an attachment title; tokenizing, by the computer-based system, the body of text by splitting the body of text into individual tokens; resolving, by the computer-based system and based on the tokenizing, the individual tokens having a pronoun grammatical role with corresponding noun phrases; wherein the resolving the individual tokens comprises weighting the individual tokens having a pronoun grammatical role based on the structured contextual information, analyzing structured contextual information to facilitate a homophora resolution; integrating, in response to the analyzing and in response to the weighting of the individual tokens having a pronoun grammatical role based on the structured contextual information, the homophora resolution into an anaphora resolution algorithm by substituting the structured contextual information into the body of text to create a substituted body of text; translating, by the computer-based system and based on the integrating, semantic concepts of the substituted body of text into one or more semantic tags; conducting, by the computer-based system, in response to the translating and using the one or more semantic tags, semantic reasoning to facilitate pattern identification within a group of documents, wherein the pattern identification includes analyzing implied relationships of the text within the group of documents to identify a specific topic, wherein the pattern identification is based on at least one of progress or consensus of the substituted body of text within the group of documents; and displaying, by the computer-based system, in response to the conducting and to a user interface, the specific identified topic of the substituted body of text. 10. The method of claim 1 , further comprising identifying, by the computer-based system, named entities within the body of text.

10. The apparatus of claim 9 further comprising the processing system configured to execute the program instructions, wherein the program instructions further direct the processing system to identify contextual information associated with at least the other report artifact and communicate the contextual information for surfacing in the spreadsheet workbook in visual association with the suggestion.

10. The apparatus of claim 9 further comprising the processing system configured to execute the program instructions, wherein the program instructions further direct the processing system to identify contextual information associated with at least the other report artifact and communicate the contextual information for surfacing in the spreadsheet workbook in visual association with the suggestion. 11. The apparatus of claim 10 wherein the report artifact comprises a data connection that connects the spreadsheet workbook to an external data source and comprises a set of information for accessing the external data source.

15. The method of claim 1 , further comprising presenting to a user indicators on input fields of a web form indicating the tag associated with each of the input fields in accordance with the at least one inbound tagging rule.

15. The method of claim 1 , further comprising presenting to a user indicators on input fields of a web form indicating the tag associated with each of the input fields in accordance with the at least one inbound tagging rule. 16. The method of claim 15 , wherein the indicators are presented via a plug-in in the user's web browser.

21. The method of claim 1, wherein said one or more occluder nodes is represented as a one dimensional line, and an object is represented as a two dimensional bounding square in a two dimensional case.

21. The method of claim 1, wherein said one or more occluder nodes is represented as a one dimensional line, and an object is represented as a two dimensional bounding square in a two dimensional case. 22. The method of claim 21, further comprising the steps of: calculating bounding planes which form a bounding volume in which said object is occluded; and testing if a camera is inside an occlusion volume associated with an object, such that said object is visible to said camera, wherein an object not visible to said camera is not rendered.

1. A method, comprising: receiving, by a system including a processor, a plurality of user-generated comments associated with media content, wherein the plurality of user-generated comments are annotated to the media content during presentations of the media content by a group of communication devices; identifying, by the system from the plurality of user-generated comments, a cluster of comments associated with a segment of the media content based on a frequency of the plurality of user-generated comments; filtering, by the system, the cluster of comments based on subject matter to generate a filtered cluster of comments; identifying, by the system, a sample segment according to a range of the segment of the media content that is to be transmitted to a recipient device; and transmitting, by the system, the filtered cluster of comments and the sample segment to the recipient device.

1. A method, comprising: receiving, by a system including a processor, a plurality of user-generated comments associated with media content, wherein the plurality of user-generated comments are annotated to the media content during presentations of the media content by a group of communication devices; identifying, by the system from the plurality of user-generated comments, a cluster of comments associated with a segment of the media content based on a frequency of the plurality of user-generated comments; filtering, by the system, the cluster of comments based on subject matter to generate a filtered cluster of comments; identifying, by the system, a sample segment according to a range of the segment of the media content that is to be transmitted to a recipient device; and transmitting, by the system, the filtered cluster of comments and the sample segment to the recipient device. 4. The method of claim 1 , further comprising: receiving user input; determining the range of the segment based on the user input; and transmitting a client program to the group of communication devices operating in an interactive television network, wherein the client program presents an overlay that is superimposed onto the media content, wherein annotations are presented along a time line in the overlay, and wherein each of the annotations is representative of a corresponding one of the plurality of user-generated comments.

1. A method for inputting text comprising: detecting user contact simultaneously with at least two of a plurality of discrete of touch sensitive areas, wherein input of a text character requires simultaneous contact by a user with at least two of the touch sensitive areas; determining a text character based on the detected simultaneous user contact with at least two touch sensitive areas; and confirming user selection of the text character based on additional user contact with a touch-sensitive area.

1. A method for inputting text comprising: detecting user contact simultaneously with at least two of a plurality of discrete of touch sensitive areas, wherein input of a text character requires simultaneous contact by a user with at least two of the touch sensitive areas; determining a text character based on the detected simultaneous user contact with at least two touch sensitive areas; and confirming user selection of the text character based on additional user contact with a touch-sensitive area. 3. The method of claim 1 further comprising providing feedback following at least one of the determination of the text character or confirming the user selection.

1. A method for training a duration prediction model, comprising: generating an initial duration prediction model with a plurality of attributes related to duration prediction and at least part of possible attribute combinations of said plurality of attributes, in which each of said plurality of attributes and said attribute combinations is included as an item; calculating importance of each item in said duration prediction model with a computer processor; deleting the item having a lowest importance calculated; re-generating a duration prediction model with remaining items; determining whether said re-generated duration prediction model is an optimal model; and repeating said step of calculating importance and the steps following said step of calculating importance with a newly re-generated duration prediction model, if said duration prediction model is determined as not an optimal model.

1. A method for training a duration prediction model, comprising: generating an initial duration prediction model with a plurality of attributes related to duration prediction and at least part of possible attribute combinations of said plurality of attributes, in which each of said plurality of attributes and said attribute combinations is included as an item; calculating importance of each item in said duration prediction model with a computer processor; deleting the item having a lowest importance calculated; re-generating a duration prediction model with remaining items; determining whether said re-generated duration prediction model is an optimal model; and repeating said step of calculating importance and the steps following said step of calculating importance with a newly re-generated duration prediction model, if said duration prediction model is determined as not an optimal model. 2. The method for training a duration prediction model according to claim 1 , wherein said plurality of attributes related to duration prediction include: attributes of language type and speech type.

15. A non-transitory computer-readable medium having instructions stored thereon which, when executed by one or more processors of a server, causes the server to perform operations comprising: outputting, to a developer of a web page in a source language, an offer to opt-in to a translation feature that enables one or more other users to translate the web page to a different target language; receiving, from the developer, a first request to opt-in to the translation feature; and in response to receiving the first request to opt-in to the translation feature: generating and storing a copy of the web page; obtaining, from the one or more other users, translations of at least a portion of the web page from the source language to the target language; modifying the web page copy based on the obtained translations to obtain a translated web page, the translated web page being a translated version of the web page; detecting a second request for the web page from a computing device associated with the target language; and in response to detecting the second request, outputting, to the computing device, the translated web page with additional content relevant to the computing device or a user associated with the computing device.

15. A non-transitory computer-readable medium having instructions stored thereon which, when executed by one or more processors of a server, causes the server to perform operations comprising: outputting, to a developer of a web page in a source language, an offer to opt-in to a translation feature that enables one or more other users to translate the web page to a different target language; receiving, from the developer, a first request to opt-in to the translation feature; and in response to receiving the first request to opt-in to the translation feature: generating and storing a copy of the web page; obtaining, from the one or more other users, translations of at least a portion of the web page from the source language to the target language; modifying the web page copy based on the obtained translations to obtain a translated web page, the translated web page being a translated version of the web page; detecting a second request for the web page from a computing device associated with the target language; and in response to detecting the second request, outputting, to the computing device, the translated web page with additional content relevant to the computing device or a user associated with the computing device. 18. The computer-readable medium of claim 15 , wherein the operations further comprise, based on user interaction with the additional content, coordinating monetary compensation for the developer of the web page.

9. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: obtain question information identifying extracted features of an input question and a first source communication device that is a source of the input question; perform a clustering operation to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions; perform an operation based on results of the clustering of the input question with the one or more other questions, wherein the operation comprises automatically initiating a communication between the first source communication device and a second source communication device that is a source of another question in the cluster at least by one of automatically establishing a communication connection between the first source communication device and the second source communication device or automatically initiating a collaboration session, in a computer-implemented collaboration system, between the first source communication device and the second source communication device, wherein the input question is a question input to a Question and Answer (QA) system which processes the input question to generate an answer to the input question based on a corpus of information, and further generates one or more supporting evidence passages supporting the answer as being a correct answer for the input question; and generate, for the input question, one or more question (Q)-Answer (A)-evidence Passage (P) triplets, wherein performing the clustering operation comprises performing clustering on features of the one or more QAP triplets.

9. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: obtain question information identifying extracted features of an input question and a first source communication device that is a source of the input question; perform a clustering operation to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions; perform an operation based on results of the clustering of the input question with the one or more other questions, wherein the operation comprises automatically initiating a communication between the first source communication device and a second source communication device that is a source of another question in the cluster at least by one of automatically establishing a communication connection between the first source communication device and the second source communication device or automatically initiating a collaboration session, in a computer-implemented collaboration system, between the first source communication device and the second source communication device, wherein the input question is a question input to a Question and Answer (QA) system which processes the input question to generate an answer to the input question based on a corpus of information, and further generates one or more supporting evidence passages supporting the answer as being a correct answer for the input question; and generate, for the input question, one or more question (Q)-Answer (A)-evidence Passage (P) triplets, wherein performing the clustering operation comprises performing clustering on features of the one or more QAP triplets. 13. The computer program product of claim 9 , wherein the first source communication device and second source communication device are communication devices associated with users, and wherein the operation further comprises at least one of: sending targeted advertising, by a third party source communication device, to the first source communication device and the second source communication device, providing information about the first source communication device and the second source communication device to the third party source communication device, performing, by a third party system, data mining of user information for the cluster, initiating a targeted processing of user information of the first source communication device or second source communication device to analyze other activities by the first source communication device or second source communication device, and identifying, by a government organization system, the first source communication device and the second source communication device as potentially engaged in illegal activity and targeting the first source communication device and second source communication device for further investigation of illegal activity.

1. A system, comprising: a citation search and analytics engine that includes a processor, which in operation, retrieves from a social network a plurality of content items composed by a plurality of subjects citing a plurality of objects that fit searching criteria specified by a user; and an object selection engine that includes a processor, which in operation, receives a user-provided query with selection criteria including a separate media-affiliation status preference, identifies media affiliation status of the plurality of subjects of the plurality of content items meeting the query selection criteria, uses a whitelist and a trained probabilistic media type classifier either separately or in combination to assign a subject to each of the content items either as a media type or non-media type author, wherein the object selection derives the whitelist from a public list of social media sources and their respective verified accounts, which classify the users/owners of those accounts as either media type or non-media type authors engine, utilizes the media affiliation status of the plurality of subjects as a preference to select at least one of the plurality of content items or the cited objects such that those items/objects for which the plurality of subjects media affiliation status matches the user-provided media affiliation status preference, wherein the media affiliation status for one of the plurality of subjects is whether that subject is associated with a commercial news source, provides this selection in an un-ranked search result, computes and provides aggregated metrics based on this selection, and ranks the selected content items or cited object higher than other content items/cited objects in a ranked search result, based on whether the corresponding subject for the selected content items or cited object is associated with media affiliation status preference, wherein one of the selected content items or cited objects that is associated with the corresponding subject matching the media affiliation status preference is ranked higher than another one of the content items or cited objects associated with the corresponding subject not matching the media affiliation status preference.

1. A system, comprising: a citation search and analytics engine that includes a processor, which in operation, retrieves from a social network a plurality of content items composed by a plurality of subjects citing a plurality of objects that fit searching criteria specified by a user; and an object selection engine that includes a processor, which in operation, receives a user-provided query with selection criteria including a separate media-affiliation status preference, identifies media affiliation status of the plurality of subjects of the plurality of content items meeting the query selection criteria, uses a whitelist and a trained probabilistic media type classifier either separately or in combination to assign a subject to each of the content items either as a media type or non-media type author, wherein the object selection derives the whitelist from a public list of social media sources and their respective verified accounts, which classify the users/owners of those accounts as either media type or non-media type authors engine, utilizes the media affiliation status of the plurality of subjects as a preference to select at least one of the plurality of content items or the cited objects such that those items/objects for which the plurality of subjects media affiliation status matches the user-provided media affiliation status preference, wherein the media affiliation status for one of the plurality of subjects is whether that subject is associated with a commercial news source, provides this selection in an un-ranked search result, computes and provides aggregated metrics based on this selection, and ranks the selected content items or cited object higher than other content items/cited objects in a ranked search result, based on whether the corresponding subject for the selected content items or cited object is associated with media affiliation status preference, wherein one of the selected content items or cited objects that is associated with the corresponding subject matching the media affiliation status preference is ranked higher than another one of the content items or cited objects associated with the corresponding subject not matching the media affiliation status preference. 8. The system of claim 1 , wherein: the media type of an author of a content item originated from a professional reporting or news agency is classified as a media type author.

9. A computer program product comprising a non-transitory computer-readable storage medium storing computer-executable code comprising instructions for: storing a plurality of social network groups and user interactions performed by users of a social networking system with the plurality of social network groups; associating a set of social network groups of the plurality of social network groups with a category; selecting a set of users associated with the set of social network groups, comprising, for each social network group from the set of social network groups: identifying users performing user interactions with the social network group; for each identified user, determining a measure of user interactions of the user with the social network group; and including the user in the set of users, responsive to the measure of user interactions of the user with the social network group exceeding a threshold; selecting a set of candidate social network groups associated with the set of users, based on user interactions of users from the set of users with the candidate social network groups; receiving, by a computer, keywords associated with the category; searching for keyword occurrences in content associated with each candidate social network group provided by the users of the social networking system; determining whether each candidate social network group is associated with the category based on the keyword occurrences; storing information describing the category associated with the one or more of the candidate social network groups; providing information describing a particular social network group to a user for performing an action, the information provided based on the category.

9. A computer program product comprising a non-transitory computer-readable storage medium storing computer-executable code comprising instructions for: storing a plurality of social network groups and user interactions performed by users of a social networking system with the plurality of social network groups; associating a set of social network groups of the plurality of social network groups with a category; selecting a set of users associated with the set of social network groups, comprising, for each social network group from the set of social network groups: identifying users performing user interactions with the social network group; for each identified user, determining a measure of user interactions of the user with the social network group; and including the user in the set of users, responsive to the measure of user interactions of the user with the social network group exceeding a threshold; selecting a set of candidate social network groups associated with the set of users, based on user interactions of users from the set of users with the candidate social network groups; receiving, by a computer, keywords associated with the category; searching for keyword occurrences in content associated with each candidate social network group provided by the users of the social networking system; determining whether each candidate social network group is associated with the category based on the keyword occurrences; storing information describing the category associated with the one or more of the candidate social network groups; providing information describing a particular social network group to a user for performing an action, the information provided based on the category. 12. The computer program product of claim 9 , wherein an interaction of a user with a social network group comprises one of the user uploading information associated with the social network group, the user retrieving information associated with the social network group, or the user establishing connections in the social networking system with other users associated with the social network group.

10. The method of claim 1 , wherein determining personalization information including the first search term and the second search term further comprises identifying a search term explicitly defined by the at least one community.

10. The method of claim 1 , wherein determining personalization information including the first search term and the second search term further comprises identifying a search term explicitly defined by the at least one community. 12. The method of claim 10 , wherein the search term explicitly defined by the at least one community is defined by the members of the at least one community.

10. Apparatus for transforming a natural language request for modifying a set of subscriptions for a publish/subscribe topic string, the apparatus comprising: at least one processing device operable to (A) receive a natural language request for modifying a set of subscriptions for one or more topics in a publish/subscribe topic hierarchy, the natural language request comprising a topic in the hierarchy and a predetermined natural language element; (B) transform the natural language request into a publish/subscribe topic string, wherein the predetermined natural language element is transformed into a publish/subscribe symbol, the symbol identifying a hierarchical relationship between the topic in the natural language request and one or more other topics in the topic hierarchy; and (C) modify one or more subscriptions to include one or more topics having the identified hierarchical relationship to the topic in the natural language request based on the transformed topic string.

10. Apparatus for transforming a natural language request for modifying a set of subscriptions for a publish/subscribe topic string, the apparatus comprising: at least one processing device operable to (A) receive a natural language request for modifying a set of subscriptions for one or more topics in a publish/subscribe topic hierarchy, the natural language request comprising a topic in the hierarchy and a predetermined natural language element; (B) transform the natural language request into a publish/subscribe topic string, wherein the predetermined natural language element is transformed into a publish/subscribe symbol, the symbol identifying a hierarchical relationship between the topic in the natural language request and one or more other topics in the topic hierarchy; and (C) modify one or more subscriptions to include one or more topics having the identified hierarchical relationship to the topic in the natural language request based on the transformed topic string. 17. Apparatus according to claim 10 , where the apparatus is provided by a publish/subscribe message broker.

7. The method of claim 6 wherein combining by progressive interpolation further comprises morphing the rendered image depicting the active movements corresponding to the graphical rendition into another rendered image corresponding to the progressed image.

7. The method of claim 6 wherein combining by progressive interpolation further comprises morphing the rendered image depicting the active movements corresponding to the graphical rendition into another rendered image corresponding to the progressed image. 8. The method of claim 7 wherein morphing the rendered image corresponding to the graphical rendition into another rendered image corresponding to the progressed image includes transitioning the rendered image, the rendered image corresponding to a first base image, to correspond to a second base image.

1. A method comprising, by a computing device: accessing a prior structured query previously selected by a first user of an online social network, the prior structured query corresponding to a first set of search results, wherein the prior structured query comprises references to one or more objects associated with the online social network; identifying changes to the first set of search results corresponding to the prior structured query; and sending, to a client system of the first user, one or more suggested structured queries for display to the first user, wherein at least one of the suggested structured queries is a dynamic query comprising at least a portion of the prior structured query and a reference to the identified changes to the first set of search results corresponding to the prior structured query.

1. A method comprising, by a computing device: accessing a prior structured query previously selected by a first user of an online social network, the prior structured query corresponding to a first set of search results, wherein the prior structured query comprises references to one or more objects associated with the online social network; identifying changes to the first set of search results corresponding to the prior structured query; and sending, to a client system of the first user, one or more suggested structured queries for display to the first user, wherein at least one of the suggested structured queries is a dynamic query comprising at least a portion of the prior structured query and a reference to the identified changes to the first set of search results corresponding to the prior structured query. 17. The method of claim 1 , wherein the one or more changes to the first set of search results comprise changes performed by one or more second users within a threshold degree of separation of the first user on the online social network.

15. The method of claim 12 : wherein performing the matching sites search that compares the at least one search term to the electronic information store that includes content extracted from different web pages from different web sites comprises comparing the at least one search term to content which is accessible by select users; and wherein performing the world wide web search using the at least one search term to identify additional matches for the at least one search term to those identified by performing the matching sites search comprises comparing the at least one search term to content available to users over the world wide web.

15. The method of claim 12 : wherein performing the matching sites search that compares the at least one search term to the electronic information store that includes content extracted from different web pages from different web sites comprises comparing the at least one search term to content which is accessible by select users; and wherein performing the world wide web search using the at least one search term to identify additional matches for the at least one search term to those identified by performing the matching sites search comprises comparing the at least one search term to content available to users over the world wide web. 16. The method of claim 15 : wherein comparing the at least one search term to content which is accessible by select users comprises comparing the at least one search term to content maintained by a web searching host for which access is provided only to select users of the web searching host; and wherein comparing the at least one search term to content available to users over the world wide web comprises comparing the at least one search term to content maintained by a source external to the web searching host and accessible to any user over the world wide web.

70. A computer system for presenting an electronic document to a viewer to facilitate comprehension and control display and speed of delivery, the computer system including a computer program product, the computer program product comprising: program instructions that group selected words into a cognitive cluster to be treated as a single word; program instructions that assign an emphasis value to each word and cognitive cluster in the electronic document using a knowledge database thereby generating a first tagged file of assigned emphasis values for each word and cognitive cluster; program instructions that process the first tagged file, including deriving emphasis values for recognizability and comprehensibility, to generate a second tagged file of derived emphasis values; program instructions that process the second tagged file, including facilitating editing of properties of selected words and cognitive clusters in the electronic document, to generate a property deliverable file that dynamically controls the presentation of the electronic document to the viewer; a printer or an electronic display device; and program instructions that present the electronic document to the viewer on the electronic display device or to the printer.

70. A computer system for presenting an electronic document to a viewer to facilitate comprehension and control display and speed of delivery, the computer system including a computer program product, the computer program product comprising: program instructions that group selected words into a cognitive cluster to be treated as a single word; program instructions that assign an emphasis value to each word and cognitive cluster in the electronic document using a knowledge database thereby generating a first tagged file of assigned emphasis values for each word and cognitive cluster; program instructions that process the first tagged file, including deriving emphasis values for recognizability and comprehensibility, to generate a second tagged file of derived emphasis values; program instructions that process the second tagged file, including facilitating editing of properties of selected words and cognitive clusters in the electronic document, to generate a property deliverable file that dynamically controls the presentation of the electronic document to the viewer; a printer or an electronic display device; and program instructions that present the electronic document to the viewer on the electronic display device or to the printer. 84. The computer system for presenting an electronic document of claim 70 wherein the computer program product further comprises program instructions that determine a number of characters in each word and assign a character count value to each word.

1. A computer-implemented method: a) initiating formation of a query using controlled vocabulary of ItemSelectors by presenting a plurality of such ItemSelectors to a user for selection on a database interface machine display, each ItemSelector thus presented to the user i) having a Boolean property associated therewith, and ii) having been determined to describe at least one data Item in the database; b) incorporating an ItemSelector selected by the user from among those presented as part of currently selected ItemSelectors; c) changing the ItemSelectors presented to the user as necessary after each user selection such that each ItemSelector presented, when combined according to the corresponding Boolean properties with all other currently selected ItemSelectors, is determined to describe at least one data Item in the database; d) repeating (b) and (c) until a plurality of ItemSelectors are currently selected, including i) at least a first ItemSelector having a first Boolean property associated therewith, and ii) at least a second ItemSelector having a different second Boolean property associated therewith; e) deriving from the selected ItemSelectors a Boolean expression encompassing the first and second ItemSelectors and reflecting the corresponding associated Boolean properties of each ItemSelector; f) associating each of a multiplicity of data Items of a database with a unique corresponding value of a first matrix variable; associating each of a multiplicity of ItemSelectors of the database with a unique corresponding value of a second matrix variable; determining all ItemSelectors associated with each data Item and/or all data Items associated with each ItemSelector; and g) storing the determined associations between ItemSelectors and data Items in an Item-ItemSelector Association array in an electronically readable memory module coupled to the interface machine as a pair of indices of the Item-ItemSelector Association array.

1. A computer-implemented method: a) initiating formation of a query using controlled vocabulary of ItemSelectors by presenting a plurality of such ItemSelectors to a user for selection on a database interface machine display, each ItemSelector thus presented to the user i) having a Boolean property associated therewith, and ii) having been determined to describe at least one data Item in the database; b) incorporating an ItemSelector selected by the user from among those presented as part of currently selected ItemSelectors; c) changing the ItemSelectors presented to the user as necessary after each user selection such that each ItemSelector presented, when combined according to the corresponding Boolean properties with all other currently selected ItemSelectors, is determined to describe at least one data Item in the database; d) repeating (b) and (c) until a plurality of ItemSelectors are currently selected, including i) at least a first ItemSelector having a first Boolean property associated therewith, and ii) at least a second ItemSelector having a different second Boolean property associated therewith; e) deriving from the selected ItemSelectors a Boolean expression encompassing the first and second ItemSelectors and reflecting the corresponding associated Boolean properties of each ItemSelector; f) associating each of a multiplicity of data Items of a database with a unique corresponding value of a first matrix variable; associating each of a multiplicity of ItemSelectors of the database with a unique corresponding value of a second matrix variable; determining all ItemSelectors associated with each data Item and/or all data Items associated with each ItemSelector; and g) storing the determined associations between ItemSelectors and data Items in an Item-ItemSelector Association array in an electronically readable memory module coupled to the interface machine as a pair of indices of the Item-ItemSelector Association array. 8. The method of claim 1 , wherein (a) comprises presenting to the user a plurality of groups of ItemSelectors including: i) a first group consisting of ItemSelectors associated with the first Boolean property, and ii) a second group consisting of ItemSelectors associated with the second Boolean property.

16. A system, comprising: a programmable processor; a memory storage system in data communication with the programmable processor and storing instructions implementing a machine learning module that cause the programmable processor to perform operations comprising: providing a base model having observed variables and first conceptually related variables related to the observed variables, providing a candidate model having the observed variables and second conceptually related variables related to the observed variables, a comparator for receiving observations assigned to a subset of the observed variables, and for each observation: evaluating the observation by the base model to produce a base assessment of the observation including a subset of the first conceptually related variables, evaluating the observation by the candidate model to produce a second assessment of the observation including a subset of the second conceptually related variables, and determining a similarity measure of the assessment of the observation based on the base assessment and the second assessment, and a sorting module for selecting a subset of observations having similarity scores below a threshold for use in evaluating performance of the candidate model.

16. A system, comprising: a programmable processor; a memory storage system in data communication with the programmable processor and storing instructions implementing a machine learning module that cause the programmable processor to perform operations comprising: providing a base model having observed variables and first conceptually related variables related to the observed variables, providing a candidate model having the observed variables and second conceptually related variables related to the observed variables, a comparator for receiving observations assigned to a subset of the observed variables, and for each observation: evaluating the observation by the base model to produce a base assessment of the observation including a subset of the first conceptually related variables, evaluating the observation by the candidate model to produce a second assessment of the observation including a subset of the second conceptually related variables, and determining a similarity measure of the assessment of the observation based on the base assessment and the second assessment, and a sorting module for selecting a subset of observations having similarity scores below a threshold for use in evaluating performance of the candidate model. 22. The system of claim 16 in which the sorting module determines the similarity measure by computing first and second weighted observations based on the base assessment and second assessment, respectively, of the observation, the weighted observations being significance measures of the subset of observed variables.

2. The method of claim 1 , wherein the generating of the theme vector comprises: extracting feature candidates from the music title; selecting a feature from the extracted feature candidates; and generating the theme vector by assigning a feature value of the selected feature.

2. The method of claim 1 , wherein the generating of the theme vector comprises: extracting feature candidates from the music title; selecting a feature from the extracted feature candidates; and generating the theme vector by assigning a feature value of the selected feature. 11. The method of claim 2 , further comprising maintaining the music title in a music title database.

1. A method comprising: receiving, at a server, a message comprising a subject, a first sentence and a second sentence; processing the message to yield a processed subject based on the subject, a processed first sentence based on the first sentence, and a processed second sentence based on the second sentence, wherein processing the message comprises identifying words in the message that are of a predefined word type; and selecting exactly one of the first sentence or the second sentence as a summary text, which summarizes the message, based on: (i) a first number of words of the predefined word type in the processed first sentence; (ii) a second number of words of the predefined word type in the processed second sentence; (iii) first overlapping words, of the predefined word type, occurring both in the processed subject and the processed first sentence; and (iv) second overlapping words, of the predefined word type, occurring both in the processed subject and the processed second sentence.

1. A method comprising: receiving, at a server, a message comprising a subject, a first sentence and a second sentence; processing the message to yield a processed subject based on the subject, a processed first sentence based on the first sentence, and a processed second sentence based on the second sentence, wherein processing the message comprises identifying words in the message that are of a predefined word type; and selecting exactly one of the first sentence or the second sentence as a summary text, which summarizes the message, based on: (i) a first number of words of the predefined word type in the processed first sentence; (ii) a second number of words of the predefined word type in the processed second sentence; (iii) first overlapping words, of the predefined word type, occurring both in the processed subject and the processed first sentence; and (iv) second overlapping words, of the predefined word type, occurring both in the processed subject and the processed second sentence. 2. The method of claim 1 , wherein the summary text is a first summary text, and wherein processing the message further yields a processed message based on the message, the method further comprising: receiving a follow-up message to the message, the follow-up message comprising a third sentence and a fourth sentence; processing the follow-up message to yield a processed third sentence based on the third sentence and a processed fourth sentence based on the fourth sentence; and selecting only one of the third sentence or the fourth sentence as a second summary text, which summarizes the follow-up message, based on: (i) third overlapping words, of the predefined word type, occurring both in the processed message and the processed third sentence; and (ii) fourth overlapping words, of the predefined word type, occurring both in the processed message and the processed fourth sentence.

13. A system for facilitating advertisement selection using advertising units, the system comprising: an entity referencing component that references an entity comprising a sequence of two or more words; an advertisable unit determining component that determines that the entity is an advertisable unit that is a sequence of two or more words that functions as a unit for purposes of selecting an advertisement for display, the advertisable unit determining component comparing search data associated with the entity to search data associated with each substring of the entity to make the determination, the search data comprising at least one of click numbers, click-through rates, clicked Uniform Resource Locators, or search snippets that correspond with search results presented in response to one or more user search queries; an advertisement selecting component that selects one or more advertisements to display to a user in accordance with the advertisable unit.

13. A system for facilitating advertisement selection using advertising units, the system comprising: an entity referencing component that references an entity comprising a sequence of two or more words; an advertisable unit determining component that determines that the entity is an advertisable unit that is a sequence of two or more words that functions as a unit for purposes of selecting an advertisement for display, the advertisable unit determining component comparing search data associated with the entity to search data associated with each substring of the entity to make the determination, the search data comprising at least one of click numbers, click-through rates, clicked Uniform Resource Locators, or search snippets that correspond with search results presented in response to one or more user search queries; an advertisement selecting component that selects one or more advertisements to display to a user in accordance with the advertisable unit. 17. The system of claim 13 , wherein the search data comprises at least one of click numbers, clicked Uniform Resource Locators, and search result snippets.

15. A computer-readable storage device storing computer-executable instructions that, when executed by a server computer, cause the server computer to perform a method, comprising: providing, on the server computer, a programming model for providing a web service that supports defining a concrete custom query builder subclass derived from a query builder base class included in the programming model and defining a concrete arguments class that extends an arguments base class included in the programming model, wherein: the query builder base class is configured to use query parameters of the arguments base class to create queries; the concrete arguments class includes one or more custom query parameters specific to the concrete custom query builder subclass; the concrete custom query builder subclass is configured to dynamically create a custom query at runtime using parameter values of the one or more custom query parameters which are to be bound to the concrete custom query builder subclass at runtime; the concrete custom query builder subclass uses the custom query to dynamically create a query run at runtime, wherein the query run is configured to execute the custom query for returning query results based on the one or more custom query parameters; and the query builder base class includes a method for initializing an instance of the custom query builder subclass at runtime and a method for retrieving the query run created by the concrete query builder subclass; providing, on the server computer, a query service that provides an interface to expose data access functionality via the query builder base class included in the programming model; accepting, by the query service from a consumer application at runtime, a query request that includes a reference to the concrete custom query builder subclass; invoking, by the query service, the concrete custom query builder subclass to initialize the instance of the concrete custom query builder subclass and retrieve the query run created by the concrete custom query builder subclass; executing, by the query service, the query run created by the concrete custom query builder subclass for retrieving the query results based on the one or more custom query parameters; and returning, by the query service, the query results to the consumer application at runtime.

15. A computer-readable storage device storing computer-executable instructions that, when executed by a server computer, cause the server computer to perform a method, comprising: providing, on the server computer, a programming model for providing a web service that supports defining a concrete custom query builder subclass derived from a query builder base class included in the programming model and defining a concrete arguments class that extends an arguments base class included in the programming model, wherein: the query builder base class is configured to use query parameters of the arguments base class to create queries; the concrete arguments class includes one or more custom query parameters specific to the concrete custom query builder subclass; the concrete custom query builder subclass is configured to dynamically create a custom query at runtime using parameter values of the one or more custom query parameters which are to be bound to the concrete custom query builder subclass at runtime; the concrete custom query builder subclass uses the custom query to dynamically create a query run at runtime, wherein the query run is configured to execute the custom query for returning query results based on the one or more custom query parameters; and the query builder base class includes a method for initializing an instance of the custom query builder subclass at runtime and a method for retrieving the query run created by the concrete query builder subclass; providing, on the server computer, a query service that provides an interface to expose data access functionality via the query builder base class included in the programming model; accepting, by the query service from a consumer application at runtime, a query request that includes a reference to the concrete custom query builder subclass; invoking, by the query service, the concrete custom query builder subclass to initialize the instance of the concrete custom query builder subclass and retrieve the query run created by the concrete custom query builder subclass; executing, by the query service, the query run created by the concrete custom query builder subclass for retrieving the query results based on the one or more custom query parameters; and returning, by the query service, the query results to the consumer application at runtime. 20. The computer-readable storage device of claim 15 , wherein the custom query is created at runtime by modifying a pre-existing query which is selected from a set of pre-existing queries based on the one or more custom query parameters.

18. The method as claimed in claim 15 , wherein generating the segmentation rules including the series of filters comprises: providing a higher level user interface that provides higher level conceptual metaphors that allow the user to generate rules; and generating rules based on user's input through the higher level user interface.

18. The method as claimed in claim 15 , wherein generating the segmentation rules including the series of filters comprises: providing a higher level user interface that provides higher level conceptual metaphors that allow the user to generate rules; and generating rules based on user's input through the higher level user interface. 19. The method as claimed in claim 18 , wherein providing the higher level user interface comprises: providing an interactive graphical representation of data to allow the user to visually set segments.

1. A method comprising: instantiating an application on a plurality of computers including a first computer implementing a thin client, a second computer implementing a mobile client and a third computer implementing a connected client; said application comprising multiple layers including an object manager layer˜in data communication with business objects in an object the object repository comprises a plurality of business objects that are organized into projects, the projects that can be locked, checked-out or checked-in; said layers of the application comprise interfaces for passing data and commands between the layers; and instantiating the application comprises extracting attribute-value data from a meta data repository for the application.

1. A method comprising: instantiating an application on a plurality of computers including a first computer implementing a thin client, a second computer implementing a mobile client and a third computer implementing a connected client; said application comprising multiple layers including an object manager layer˜in data communication with business objects in an object the object repository comprises a plurality of business objects that are organized into projects, the projects that can be locked, checked-out or checked-in; said layers of the application comprise interfaces for passing data and commands between the layers; and instantiating the application comprises extracting attribute-value data from a meta data repository for the application. 3. The method of claim 1 wherein the multiple layers includes a data manager layer.

1. A computer accessible storage hardware having thereon stored a system for providing application-layer functionality between one or more database clients and one or more database servers, the system comprising: one or more decoders residing at a decoding layer above a network layer, the decoders residing at a first network location between one or more database clients residing at one or more second network locations and one or more database servers residing at one or more third network locations, the decoders being operable to: receive database messages communicated from the database clients and intended for the database servers and database messages communicated from the database servers and intended for the database clients; decode the database messages; extract query-language statements from the database messages; a caching application residing at an application layer above the decoding layer, the caching application residing at the first network location, the caching application being operable to: receive query-language statements extracted at the decoders comprising queries; receive query-language statements extracted at the decoders comprising query results corresponding to the queries; record the queries and the query results corresponding to the queries in a cache residing at the first network location wherein the queries are associated with a corresponding query result; a monitoring application operable to receive query-language statements extracted at the decoders and record observations on the database messages based at least in part on the query-language statements extracted at the decoders, wherein at least one observation is associated with a query result stored in the cache and communicate one or more observations associated with the queries to one or more computing systems at a fourth network location according to the needs of the one or more computer systems, wherein at least one of the computer systems maintains a web cache and is operable to modify the web cache based upon the communicated observations wherein observations on the database messages based at least in part on the query-language statement extracted at the decoders comprise the following: subject database instances of the query-language statements; network protocols and versions of the network protocols used to communicate the database messages; devices hosting the subject database instances of the query-language statements; hostnames, Internet Protocol (IP) addresses, Media Access Control (MAC) addresses, and network ports of the database servers; operating systems (OSs), versions of the OSs, and attributes of the OSs of devices hosting the subject database instances; devices hosting the clients; and a number of queries communicated from each of the clients to each of one or more database instances; and an application residing at an application layer above the decoding layer, the application residing at the first network location, the application being operable to receive and process query-language statements extracted at the decoders.

1. A computer accessible storage hardware having thereon stored a system for providing application-layer functionality between one or more database clients and one or more database servers, the system comprising: one or more decoders residing at a decoding layer above a network layer, the decoders residing at a first network location between one or more database clients residing at one or more second network locations and one or more database servers residing at one or more third network locations, the decoders being operable to: receive database messages communicated from the database clients and intended for the database servers and database messages communicated from the database servers and intended for the database clients; decode the database messages; extract query-language statements from the database messages; a caching application residing at an application layer above the decoding layer, the caching application residing at the first network location, the caching application being operable to: receive query-language statements extracted at the decoders comprising queries; receive query-language statements extracted at the decoders comprising query results corresponding to the queries; record the queries and the query results corresponding to the queries in a cache residing at the first network location wherein the queries are associated with a corresponding query result; a monitoring application operable to receive query-language statements extracted at the decoders and record observations on the database messages based at least in part on the query-language statements extracted at the decoders, wherein at least one observation is associated with a query result stored in the cache and communicate one or more observations associated with the queries to one or more computing systems at a fourth network location according to the needs of the one or more computer systems, wherein at least one of the computer systems maintains a web cache and is operable to modify the web cache based upon the communicated observations wherein observations on the database messages based at least in part on the query-language statement extracted at the decoders comprise the following: subject database instances of the query-language statements; network protocols and versions of the network protocols used to communicate the database messages; devices hosting the subject database instances of the query-language statements; hostnames, Internet Protocol (IP) addresses, Media Access Control (MAC) addresses, and network ports of the database servers; operating systems (OSs), versions of the OSs, and attributes of the OSs of devices hosting the subject database instances; devices hosting the clients; and a number of queries communicated from each of the clients to each of one or more database instances; and an application residing at an application layer above the decoding layer, the application residing at the first network location, the application being operable to receive and process query-language statements extracted at the decoders. 2. The computer accessible storage hardware of claim 1 , wherein the caching application is further operable to communicate query results corresponding to the queries from the cache to the clients.

17. A non-transitory computer-readable storage medium storing computer code for searching content, said computer code upon execution performing: maintaining a log of queries; dividing said queries into individual query terms; analyzing said queries and partitioning the individual query terms into collections based on co-occurrence of the individual query terms within the queries, wherein co-occurrence of given ones of the individual query terms in a particular one of the queries comprises the given individual query terms co-occurring in a particular query; representing said query terms as nodes within a construct, with edges connecting the nodes, wherein each of said edges is assigned a frequency weight for representing co-occurrence in the queries of query terms represented by the nodes connected by a corresponding edge; partitioning said construct into sets based on said edges; distributing linking indices for the query terms to different index servers, depending upon the sets in which said query terms are located; maintaining a map indicating which of the index servers store corresponding ones of the linking indices for individual query terms; and distributing query terms in a new query to the index servers based on said map.

17. A non-transitory computer-readable storage medium storing computer code for searching content, said computer code upon execution performing: maintaining a log of queries; dividing said queries into individual query terms; analyzing said queries and partitioning the individual query terms into collections based on co-occurrence of the individual query terms within the queries, wherein co-occurrence of given ones of the individual query terms in a particular one of the queries comprises the given individual query terms co-occurring in a particular query; representing said query terms as nodes within a construct, with edges connecting the nodes, wherein each of said edges is assigned a frequency weight for representing co-occurrence in the queries of query terms represented by the nodes connected by a corresponding edge; partitioning said construct into sets based on said edges; distributing linking indices for the query terms to different index servers, depending upon the sets in which said query terms are located; maintaining a map indicating which of the index servers store corresponding ones of the linking indices for individual query terms; and distributing query terms in a new query to the index servers based on said map. 18. The non-transitory computer-readable storage medium as set forth in claim 17 wherein said partitioning further takes into account how often the terms are found in the content.

2. A computer-implemented method, comprising: storing data identifying for each of a plurality of queries, a plurality of training images for the query, wherein each of the training images is classified as being in a positive group of images for the query or a negative group of images for the query according to a respective query-specific preference measure for the image; selecting one of the queries as a selected query; iteratively evaluating a scoring model with the selected query, the evaluation iteration comprising: selecting a first image from either the positive group of images or the negative group of images for the selected query, and applying a scoring model to the first image and the selected query to determine a score for the first image; selecting a plurality of candidate images from the other group of images for the selected query; applying the scoring model to each of the candidate images and the selected query to determine a respective score for each candidate image and the selected query, and then selecting a second image from the candidate images, the second image having a highest score; and determining that the scores for the first image and the second image for the selected query fail to satisfy a criterion, wherein the criterion requires that a result of the score of the image selected from the positive group of images for the selected query minus the score of the image selected from the negative group of images for the selected query exceeds a threshold, and in response updating the scoring model, storing the updated scoring model, and selecting another one of the queries as a selected query for another evaluation iteration.

2. A computer-implemented method, comprising: storing data identifying for each of a plurality of queries, a plurality of training images for the query, wherein each of the training images is classified as being in a positive group of images for the query or a negative group of images for the query according to a respective query-specific preference measure for the image; selecting one of the queries as a selected query; iteratively evaluating a scoring model with the selected query, the evaluation iteration comprising: selecting a first image from either the positive group of images or the negative group of images for the selected query, and applying a scoring model to the first image and the selected query to determine a score for the first image; selecting a plurality of candidate images from the other group of images for the selected query; applying the scoring model to each of the candidate images and the selected query to determine a respective score for each candidate image and the selected query, and then selecting a second image from the candidate images, the second image having a highest score; and determining that the scores for the first image and the second image for the selected query fail to satisfy a criterion, wherein the criterion requires that a result of the score of the image selected from the positive group of images for the selected query minus the score of the image selected from the negative group of images for the selected query exceeds a threshold, and in response updating the scoring model, storing the updated scoring model, and selecting another one of the queries as a selected query for another evaluation iteration. 14. The computer-implemented method of claim 2 , wherein the respective query-specific preference measure for each image is derived from a number of times users select the image in response to being presented with a search result for the query that includes the image.

1. A computer-implemented method, comprising: selecting a regular expression from a set of regular expressions used to identify a text portion of a message; retrieving a set of one or more configuration parameters arranged to limit expansion of features of the regular expression into a set of potential regular expression key terms, at least one configuration parameter operative to determine which feature to expand and which feature not to expand; identifying a set of one or more features within the regular expression enabled by the set of configuration parameters; and generating a set of one or more identified regular expression key terms from the enabled features of the regular expression.

1. A computer-implemented method, comprising: selecting a regular expression from a set of regular expressions used to identify a text portion of a message; retrieving a set of one or more configuration parameters arranged to limit expansion of features of the regular expression into a set of potential regular expression key terms, at least one configuration parameter operative to determine which feature to expand and which feature not to expand; identifying a set of one or more features within the regular expression enabled by the set of configuration parameters; and generating a set of one or more identified regular expression key terms from the enabled features of the regular expression. 9. The computer-implemented method of claim 1 , comprising extracting single characters with repetition operators from the regular expression.

3. The method of claim 1 wherein the compiling the arbitrary formula into the arbitrary code sequence comprises compiling instructions for weak second-order pattern matching.

3. The method of claim 1 wherein the compiling the arbitrary formula into the arbitrary code sequence comprises compiling instructions for weak second-order pattern matching. 4. The method of claim 3 wherein the compiling the arbitrary code sequence further comprises executing by the at least one processor, a check-bound instruction.

1. A method for processing an ontological query for data from any of a plurality of different databases on a network coupled to a computer, comprising: loading a ontological data model that comprises a plurality of logical models based on data from the plurality of different databases; compiling the ontological query and optimizing the compiled ontological query according to join and combination rules based on the logical models and describing meta-properties of the data and meta-relationships based on the meta-properties between the data from the plurality of different databases; and processing logical operations on the compiled ontological query, wherein the data from the plurality of different databases defines a hierarchy of ontologies in parent-child relationship, wherein the hierarchy of ontologies includes: a first child ontology accessing data from a first of the plurality of different databases; a second child ontology accessing data from a second of the plurality of different databases; and a parent ontology; wherein the first and second child ontologies contribute to the parent ontology such that the parent ontology accesses data from both the first and second databases.

1. A method for processing an ontological query for data from any of a plurality of different databases on a network coupled to a computer, comprising: loading a ontological data model that comprises a plurality of logical models based on data from the plurality of different databases; compiling the ontological query and optimizing the compiled ontological query according to join and combination rules based on the logical models and describing meta-properties of the data and meta-relationships based on the meta-properties between the data from the plurality of different databases; and processing logical operations on the compiled ontological query, wherein the data from the plurality of different databases defines a hierarchy of ontologies in parent-child relationship, wherein the hierarchy of ontologies includes: a first child ontology accessing data from a first of the plurality of different databases; a second child ontology accessing data from a second of the plurality of different databases; and a parent ontology; wherein the first and second child ontologies contribute to the parent ontology such that the parent ontology accesses data from both the first and second databases. 4. The method of claim 1 , wherein the ontological model monolithically stores logical models and metadata from the plurality of different databases.

5. The computer-implemented method of claim 1 , wherein initiating the speech recognition process includes assigning a confidence level to an identification of the voice command, the confidence level indicating how confident the system is of accurate recognition of the voice command; wherein in response to an assigned confidence level determined to be at or above a predefined level, automatically constructing the application command; wherein in response to the assigned confidence level determined to be below the predefined level, routing the voice command to an interface for manual review and transcription of the voice command; wherein transmitting the application command to the wireless communication device includes transmitting a manually-constructed command to the wireless communication device.

5. The computer-implemented method of claim 1 , wherein initiating the speech recognition process includes assigning a confidence level to an identification of the voice command, the confidence level indicating how confident the system is of accurate recognition of the voice command; wherein in response to an assigned confidence level determined to be at or above a predefined level, automatically constructing the application command; wherein in response to the assigned confidence level determined to be below the predefined level, routing the voice command to an interface for manual review and transcription of the voice command; wherein transmitting the application command to the wireless communication device includes transmitting a manually-constructed command to the wireless communication device. 6. The computer-implemented method of claim 5 , further comprising: training the speech recognition process to better identify voice commands in response to received manual input; routing to a manual interface based on transcriber criteria, wherein the transcriber criteria includes a type of voice command, a workload of a particular transcriber, and a measure of typing ability of the particular transcriber; and in response to routing the voice command to the manual interface, automatically displaying historical data associated with the user to expedite manual processing of the voice command.

11. A non-transitory computer-readable storage medium whose contents cause a computer system to perform a method for scheduling storage operations on a cloud storage site, the method comprising: receiving multiple new requests for cloud storage from one or more clients, wherein the multiple new requests each include a request for data storage, and wherein the multiple new requests each include information associated with the data storage requested; determining a current capacity of the cloud storage site, wherein the current capacity of the cloud storage site is determined based at least in part on: (i) a capacity policy, wherein the capacity policy specifies preferences and criteria associated with allocating system resources for the cloud storage site, and (ii) at least one of: a quotation policy, wherein the quotation policy includes a set of preferences and criteria associated with generating a quote in response to received client requests, and a scheduled job, wherein the scheduled job is associated with a quote for cloud storage accepted by a client, a quoted job, wherein the quoted job is associated with a quote for cloud storage provided to a client, and queued requests, wherein queued requests include requests by clients for cloud storage for which the respective clients have not been provided a quote; identifying one or more approved requests, wherein the one or more approved requests are identified from pending requests based at least in part on preferences and criteria specified in the accessed quotation policy and the current capacity, wherein pending requests comprise the received multiple new requests and queued requests; generating a responsive quote for each approved request, wherein the responsive quotes are generated based at least in part on preferences and criteria specified in the accessed quotation policy, and, wherein each responsive quote includes one or more pricing values; sending a generated responsive quote to a client associated with an approved request that the responsive quote was generated for; and receiving from the client that was sent the generated responsive quote an indication of acceptance of the generated responsive quote.

11. A non-transitory computer-readable storage medium whose contents cause a computer system to perform a method for scheduling storage operations on a cloud storage site, the method comprising: receiving multiple new requests for cloud storage from one or more clients, wherein the multiple new requests each include a request for data storage, and wherein the multiple new requests each include information associated with the data storage requested; determining a current capacity of the cloud storage site, wherein the current capacity of the cloud storage site is determined based at least in part on: (i) a capacity policy, wherein the capacity policy specifies preferences and criteria associated with allocating system resources for the cloud storage site, and (ii) at least one of: a quotation policy, wherein the quotation policy includes a set of preferences and criteria associated with generating a quote in response to received client requests, and a scheduled job, wherein the scheduled job is associated with a quote for cloud storage accepted by a client, a quoted job, wherein the quoted job is associated with a quote for cloud storage provided to a client, and queued requests, wherein queued requests include requests by clients for cloud storage for which the respective clients have not been provided a quote; identifying one or more approved requests, wherein the one or more approved requests are identified from pending requests based at least in part on preferences and criteria specified in the accessed quotation policy and the current capacity, wherein pending requests comprise the received multiple new requests and queued requests; generating a responsive quote for each approved request, wherein the responsive quotes are generated based at least in part on preferences and criteria specified in the accessed quotation policy, and, wherein each responsive quote includes one or more pricing values; sending a generated responsive quote to a client associated with an approved request that the responsive quote was generated for; and receiving from the client that was sent the generated responsive quote an indication of acceptance of the generated responsive quote. 17. The non-transitory computer-readable storage medium of claim 11 , further comprising at least one of the following: generating a responsive quote, to be sent to a client, having at least one term that is different from a term in a request received from the client; and queuing a received request for later evaluation.

13. The computer-readable storage media recited in claim 9 , further comprising, in response to a user input, altering the proximity between the first and second digital objects.

13. The computer-readable storage media recited in claim 9 , further comprising, in response to a user input, altering the proximity between the first and second digital objects. 14. The computer-readable storage media recited in claim 13 , further comprising, in response to the altered proximity between the first and second digital objects, altering the logical connection.

14. An electronic device comprising: a memory; a depth sensor; one or more processors, communicatively coupled to the memory, wherein the memory stores instructions to cause the one or more processors to: acquire a depth image of a scene in a vicinity of the electronic device using the depth sensor, the depth image having a first plurality of pixels, each pixel having a value indicative of a distance; store the depth image in the memory; develop a scene geometry based upon the depth image; determine that a user is engaging the electronic device; identify a human hand in a region of space based on the first plurality of values; identify a three-dimensional region about the human hand, wherein the three-dimensional region includes at least some of the first plurality of pixels; partition the three-dimensional region about the human hand into a second plurality of sub-regions, each sub-region having a corresponding value and size, wherein the value of a particular sub-region comprises a number of human hand pixels within the particular sub-region, wherein the sizes of the sub-regions are configured so that the number of human hand pixels within each sub-region is approximately equal, and wherein the sizes of the sub-regions are non-uniform; generate a feature vector for the human hand based on the values of the second plurality of sub-regions; apply the feature vector to a classifier; determine that the human hand is making an identified gesture based on output from the classifier; and cause an action to be taken by the electronic device, based, at least in part, upon the identified gesture and the scene geometry.

14. An electronic device comprising: a memory; a depth sensor; one or more processors, communicatively coupled to the memory, wherein the memory stores instructions to cause the one or more processors to: acquire a depth image of a scene in a vicinity of the electronic device using the depth sensor, the depth image having a first plurality of pixels, each pixel having a value indicative of a distance; store the depth image in the memory; develop a scene geometry based upon the depth image; determine that a user is engaging the electronic device; identify a human hand in a region of space based on the first plurality of values; identify a three-dimensional region about the human hand, wherein the three-dimensional region includes at least some of the first plurality of pixels; partition the three-dimensional region about the human hand into a second plurality of sub-regions, each sub-region having a corresponding value and size, wherein the value of a particular sub-region comprises a number of human hand pixels within the particular sub-region, wherein the sizes of the sub-regions are configured so that the number of human hand pixels within each sub-region is approximately equal, and wherein the sizes of the sub-regions are non-uniform; generate a feature vector for the human hand based on the values of the second plurality of sub-regions; apply the feature vector to a classifier; determine that the human hand is making an identified gesture based on output from the classifier; and cause an action to be taken by the electronic device, based, at least in part, upon the identified gesture and the scene geometry. 19. The electronic device of claim 14 , wherein the three-dimensional region about the human hand comprises those pixels from the first plurality of pixels corresponding to a spatial location that is within a specified distance from the human hand.

11. A computer-implemented method for determining string similarity, implemented by a hardware processor, the method comprising executing on the hardware processor the steps of: receiving over a computer network a first string of characters and a second string of characters from domain name system (DNS) query packets originating from a particular computing device, the second string of characters being different from the first string of characters; generating a first syntax string by replacing each character of the first string with one of a plurality of metacharacters, each of the plurality of metacharacters representing a category of characters that is different from each other category of characters represented by each other metacharacter in the plurality of metacharacters; generating a second syntax string by replacing each character of the second string with one of the plurality of metacharacters; and generating network anomaly data for the particular computing device by determining a measure of similarity between the first string and the second string using a syntactic edit distance between the first string and the second string, the syntactic edit distance between first string and the second string being determined based on the first syntax string and second syntax string.

11. A computer-implemented method for determining string similarity, implemented by a hardware processor, the method comprising executing on the hardware processor the steps of: receiving over a computer network a first string of characters and a second string of characters from domain name system (DNS) query packets originating from a particular computing device, the second string of characters being different from the first string of characters; generating a first syntax string by replacing each character of the first string with one of a plurality of metacharacters, each of the plurality of metacharacters representing a category of characters that is different from each other category of characters represented by each other metacharacter in the plurality of metacharacters; generating a second syntax string by replacing each character of the second string with one of the plurality of metacharacters; and generating network anomaly data for the particular computing device by determining a measure of similarity between the first string and the second string using a syntactic edit distance between the first string and the second string, the syntactic edit distance between first string and the second string being determined based on the first syntax string and second syntax string. 12. The method of claim 11 , further comprising: identifying the particular computing device as a potential source of malicious software based on the measure of similarity between the first string and the second string.

2. The security analysis tool of claim 1 , wherein the computer-executable components further comprising an interface component that generates a description of one or more industrial controllers in the automation system.

2. The security analysis tool of claim 1 , wherein the computer-executable components further comprising an interface component that generates a description of one or more industrial controllers in the automation system. 8. The security analysis tool of claim 2 , wherein the description comprises a model of one or more industrial automation assets to be protected and one or more associated network pathways to access the one or more industrial automation assets.

17. A system for performing an integration of an origin data set into a target data set, the system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to: receive a target data set ontology, the target data set ontology defining data objects of the target data set; access an origin data set schema, the origin data set schema including an organizational structure of the origin data set; generate, according to the origin data set schema and the target data set ontology, a domain-specific transform programming language specific to the origin data set schema and the target data set ontology; receive transform instructions from a user input device programmed in the domain-specific transform programming language; and transfer the transform instructions to a remote computer system for integrating the origin data set into the target data set according to the received transform instructions.

17. A system for performing an integration of an origin data set into a target data set, the system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to: receive a target data set ontology, the target data set ontology defining data objects of the target data set; access an origin data set schema, the origin data set schema including an organizational structure of the origin data set; generate, according to the origin data set schema and the target data set ontology, a domain-specific transform programming language specific to the origin data set schema and the target data set ontology; receive transform instructions from a user input device programmed in the domain-specific transform programming language; and transfer the transform instructions to a remote computer system for integrating the origin data set into the target data set according to the received transform instructions. 18. The system of claim 17 , wherein the system is further caused to provide a preview target data set based on the received transform instructions.

2. The method of claim 1 , wherein a plurality of the containers are further loosely coupled by one or more soft links that represents a relationship between two fine-grained software items.

2. The method of claim 1 , wherein a plurality of the containers are further loosely coupled by one or more soft links that represents a relationship between two fine-grained software items. 3. The method of claim 2 , wherein the plurality of the containers are different versions of the software related items and the soft link is used to determine the appropriate version at query time.

15. The computer-readable storage medium of claim 14 , wherein the method further comprises determining emission probabilities for the HMM based on the calculated passage similarities.

15. The computer-readable storage medium of claim 14 , wherein the method further comprises determining emission probabilities for the HMM based on the calculated passage similarities. 16. The computer-readable storage medium of claim 15 , wherein the method further comprises setting an emission probability for an additional state, which corresponds to creation of a new passage, based on a maximum emission probability of other states belonging to the HMM.

1. A computer-implemented method, comprising: identifying a starting point and a destination point for travel by a user including any constraints of the user regarding the travel; determining one or more initial routes between the starting point and the destination point; for each initial route, segmenting the route into a plurality of segments based on one or more criteria; and determining attribute oriented routes using the segments including: determining one or more entities associated with each segment; identifying attributes for each determined entity; aggregating and ranking the attributes along all the determined initial routes and determining one or more emerging attributes; determining one or more attribute oriented routes based on the emerging attributes including identifying a theme for an attribute oriented route based on one or more of the emerging attributes, identifying a set of entities from the determined entities that are associated with the one or more emerging attributes, and creating the attribute oriented route that passes through a region that includes the set of entities, wherein determining one or more attribute oriented routes includes scoring a particular region based on matching emerging attributes of the particular region compared to one or more goals and determining routes that maximize an aggregate rank based on the scoring under the constraints; and providing at least one attribute oriented route and information related to the theme to a device associated with the user.

1. A computer-implemented method, comprising: identifying a starting point and a destination point for travel by a user including any constraints of the user regarding the travel; determining one or more initial routes between the starting point and the destination point; for each initial route, segmenting the route into a plurality of segments based on one or more criteria; and determining attribute oriented routes using the segments including: determining one or more entities associated with each segment; identifying attributes for each determined entity; aggregating and ranking the attributes along all the determined initial routes and determining one or more emerging attributes; determining one or more attribute oriented routes based on the emerging attributes including identifying a theme for an attribute oriented route based on one or more of the emerging attributes, identifying a set of entities from the determined entities that are associated with the one or more emerging attributes, and creating the attribute oriented route that passes through a region that includes the set of entities, wherein determining one or more attribute oriented routes includes scoring a particular region based on matching emerging attributes of the particular region compared to one or more goals and determining routes that maximize an aggregate rank based on the scoring under the constraints; and providing at least one attribute oriented route and information related to the theme to a device associated with the user. 4. The method of claim 1 wherein the one or more criteria is a distance criterion, and wherein segmenting includes segmenting the route into plural substantially equal segments.

4. The metadata repository according to claim 1 , the storage system to further store: third metadata defining a second object model to define a view on a result node of the first object model, wherein the second object model is an instance of a multi-dimensional analytical view metadata model.

4. The metadata repository according to claim 1 , the storage system to further store: third metadata defining a second object model to define a view on a result node of the first object model, wherein the second object model is an instance of a multi-dimensional analytical view metadata model. 5. The metadata repository according to claim 4 , wherein the business object view metadata model, the multi-dimensional analytical view metadata model and the business object metadata model are instances of the same meta-metadata model.

7. The method of claim 6 , further comprising performing spelling to sound mapping which includes applying a predetermined set of rules to each word in a word string of a textual corpus, with pronunciations of words being obtained from the information indicating a correspondence between baseform components and word parts, wherein baseforms reflected in the information indicating a correspondence between baseform components and word parts are collected in said acoustic vocabulary.

7. The method of claim 6 , further comprising performing spelling to sound mapping which includes applying a predetermined set of rules to each word in a word string of a textual corpus, with pronunciations of words being obtained from the information indicating a correspondence between baseform components and word parts, wherein baseforms reflected in the information indicating a correspondence between baseform components and word parts are collected in said acoustic vocabulary. 8. The method of claim 7 , wherein the information indicating a correspondence between baseform components and word parts applies spelling to sound mapping to strings of components, said strings of components being obtained by filtering words of said textual corpus.

1. A method comprising: receiving current context information related to a first device; accessing a context profile and a security profile associated with the first device; developing a composite context tree based on at least a portion of the current context information related to the first device and context information related to at least one other device based at least in part on the context profile defining, for a current context of the first device, aspects of the current context information to be utilized for the developing of the composite context tree, wherein developing the composite context tree comprises generating a schema based on the security profile and the context profile in which the schema defines the portion of the current context information and applying the schema to a context tree including the current context information such that only a portion of the context tree defined by the schema is visible to an entity developing the composite context tree; determining a context change has occurred with respect to the first device; determining whether the context change to the composite context tree is allowed based on the security profile and the context profile; wherein in an instance in which the context change is determined to be allowed, the context tree is updated for the context change; and wherein in an instance in which the context change is determined to not be allowed, the context tree is not updated for the context change.

1. A method comprising: receiving current context information related to a first device; accessing a context profile and a security profile associated with the first device; developing a composite context tree based on at least a portion of the current context information related to the first device and context information related to at least one other device based at least in part on the context profile defining, for a current context of the first device, aspects of the current context information to be utilized for the developing of the composite context tree, wherein developing the composite context tree comprises generating a schema based on the security profile and the context profile in which the schema defines the portion of the current context information and applying the schema to a context tree including the current context information such that only a portion of the context tree defined by the schema is visible to an entity developing the composite context tree; determining a context change has occurred with respect to the first device; determining whether the context change to the composite context tree is allowed based on the security profile and the context profile; wherein in an instance in which the context change is determined to be allowed, the context tree is updated for the context change; and wherein in an instance in which the context change is determined to not be allowed, the context tree is not updated for the context change. 3. The method of claim 1 , further comprising distributing the composite context tree to the first device and the other device.

8. The method of claim 6 , further comprising: customizing at least one buffer feature of said buffer.

8. The method of claim 6 , further comprising: customizing at least one buffer feature of said buffer. 9. The method of claim 8 , wherein said at least one buffer feature comprises a size or a number of warning characters.

10. The method of claim 1 further comprising, given a key matching rule having at least one dimension, storing in the memory a value associated with the at least one dimension, the value being stored as dimension data of the multi-rule.

10. The method of claim 1 further comprising, given a key matching rule having at least one dimension, storing in the memory a value associated with the at least one dimension, the value being stored as dimension data of the multi-rule. 11. The method of claim 10 wherein storing the dimension data of the multi-rule further includes for a given key matching rule of the chunk, storing a priority value at the end of the dimension data stored for the rule in the multi-rule.

15. A computer readable memory encoded with a set of program instructions that, when executed, causes a processor to execute a method, the method comprising: receiving a search request from an electronic device, the search request including one or more search criteria; searching a database in accordance with the one or more search criteria to obtain search results, the database including digital asset information pertaining to a plurality of digital media assets and the search results corresponding to different digital media assets; monitoring usage of the electronic device to determine usage data, wherein monitoring the usage includes determining a level of completion of a digital media asset consumed by the electronic device; determining, based on the level of completion of the digital media asset consumed by the electronic device, that a particular type of digital media asset is of more interest to a user of the electronic device compared to another type of digital media asset when the level of completion of the digital media asset consumed by the electronic device has exceeded a trigger point of the digital media asset, wherein the trigger point indicates a position in the digital media asset; ranking the search results based at least in part on the usage data and the particular type of digital media asset determined to be of more interest to the user compared to the other type of digital media asset, wherein ranking the search results includes increasing a ranking for digital media assets belonging to the determined particular type of digital media assets compared to digital media assets of the other type within the search results; and presenting the ranked search results via the electronic device.

15. A computer readable memory encoded with a set of program instructions that, when executed, causes a processor to execute a method, the method comprising: receiving a search request from an electronic device, the search request including one or more search criteria; searching a database in accordance with the one or more search criteria to obtain search results, the database including digital asset information pertaining to a plurality of digital media assets and the search results corresponding to different digital media assets; monitoring usage of the electronic device to determine usage data, wherein monitoring the usage includes determining a level of completion of a digital media asset consumed by the electronic device; determining, based on the level of completion of the digital media asset consumed by the electronic device, that a particular type of digital media asset is of more interest to a user of the electronic device compared to another type of digital media asset when the level of completion of the digital media asset consumed by the electronic device has exceeded a trigger point of the digital media asset, wherein the trigger point indicates a position in the digital media asset; ranking the search results based at least in part on the usage data and the particular type of digital media asset determined to be of more interest to the user compared to the other type of digital media asset, wherein ranking the search results includes increasing a ranking for digital media assets belonging to the determined particular type of digital media assets compared to digital media assets of the other type within the search results; and presenting the ranked search results via the electronic device. 16. The computer readable storage medium of claim 15 , wherein the search results includes resultant digital asset information pertaining to a subset of the plurality of digital media assets.

6. The computer-readable storage medium of claim 2 , wherein the updating comprises applying a machine learning algorithm to the authentication code and the one or more previous authentication codes in the authentication code preference model.

6. The computer-readable storage medium of claim 2 , wherein the updating comprises applying a machine learning algorithm to the authentication code and the one or more previous authentication codes in the authentication code preference model. 7. The computer-readable storage medium of claim 6 , wherein the machine learning algorithm comprises: using at least one natural language processing technique to identify one or more authentication code elements; using at least one named entity recognition technique to further identify the user; translating the authentication code elements into one or more pattern sequences; updating elements of the authentication code preference model based on the pattern sequences; generating one or more model scores based on a distance between one or more pattern sequences from the authentication code to one or more pattern sequences of one or more previous authentication codes for the user; using a Bayesian model to compute a first probability score of the authentication code on the user and a second probability score on all users; using the second probability score as a priori to smooth the first probability score; and using one or more score fusion techniques to combine the one or more model scores, the first probability score, and the second probability scores to generate an overall score for the authentication code.

7. A computer program product comprising computer-readable program code to be executed by one or more processors when retrieved from a non-transitory computer-readable medium, the program code including instructions to: generate a match rule key based on a match rule, wherein the match rule specifies whether two objects match; create a plurality of candidate keys by applying the match rule key to a plurality of data objects; create a probe key by applying the match rule key to a probe object; determine whether the probe key matches a candidate key of the plurality of candidate keys; determine whether the probe object matches a candidate object based on applying the match rule to the probe object and the candidate object in response to a determination that the probe key matches the candidate key corresponding to the candidate object; and identify the probe object and the candidate object as matching based on the match rule in response to a determination that the probe object matches the candidate object, the match rule identifying a match of the probe object and the candidate object unless both the probe object and the candidate object have corresponding non-empty fields that lack a match.

7. A computer program product comprising computer-readable program code to be executed by one or more processors when retrieved from a non-transitory computer-readable medium, the program code including instructions to: generate a match rule key based on a match rule, wherein the match rule specifies whether two objects match; create a plurality of candidate keys by applying the match rule key to a plurality of data objects; create a probe key by applying the match rule key to a probe object; determine whether the probe key matches a candidate key of the plurality of candidate keys; determine whether the probe object matches a candidate object based on applying the match rule to the probe object and the candidate object in response to a determination that the probe key matches the candidate key corresponding to the candidate object; and identify the probe object and the candidate object as matching based on the match rule in response to a determination that the probe object matches the candidate object, the match rule identifying a match of the probe object and the candidate object unless both the probe object and the candidate object have corresponding non-empty fields that lack a match. 12. The computer program product of claim 7 , wherein the plurality of data objects comprises a plurality of customer relationship management objects.

2. A data gathering system in accordance with claim 1 wherein each definition is accompanied into the system by a designator which serves as a variable name, and wherein the system includes means for operating said computer system to establish a directory of designators each of which designators is stored within the directory together with the computer system address of at least a portion of the corresponding variable definition.

2. A data gathering system in accordance with claim 1 wherein each definition is accompanied into the system by a designator which serves as a variable name, and wherein the system includes means for operating said computer system to establish a directory of designators each of which designators is stored within the directory together with the computer system address of at least a portion of the corresponding variable definition. 5. A data gathering system in accordance with claim 2 and which includes job definitions at least some of which contain triggering references to variable designators; which further includes means for operating said computer system to accept said job definitions, to locate linked portions of the variable definitions corresponding to designators contained within such job definitions using said directory, and to include such job definitions in the linkage of definition portions with the job definition linked immediately following the portion relating to the triggering variable; and further including means for operating said computer system to execute the job defined by said definitions when such a definition is encountered during the processing of a linkage of definition portions.

12. A computer-implemented method of accessing an interactive manual system of a device, the interactive manual system including a model package database storing (i) a set of at least one of phrases and sentences, (ii) an object file that includes data objects that include information regarding an appearance of, and spatial characteristics of, a plurality of structures of the device and that include data representing visual displays that are associated with the structures of the device, and (iii) a grammar table that identifies associations between (a) the information and data and (b) the respective ones of the set of the at least one of phrases and sentences, the method comprising: receiving, by a computer processor, a speech input from a user; converting, by the computer processor executing a speech engine application, the speech input into a word sequence and determining the word sequence's meaning, which is associated with one or more of the set of the at least one of phrases and sentences; identifying, by the computer processor, a grammar category to which the word sequence as a whole conforms; extracting, by the computer processor a subset of the information regarding the appearance of, and spatial characteristics of, the one or more structures of the device; and outputting, by the computer processor, via an output arrangement what has been extracted; wherein the extraction is based on the identified grammar category, the one or more of the set of the at least one of phrases and sentences to which the determined meaning of the word sequence corresponds, and the associations of the grammar table, such that the extraction includes collecting from the data objects a plurality of data objects that concern a particular one of the structures of the device associated by the grammar table with the one or more of the set of the at least one of phrases and sentences that are associated with the determined meaning of the word sequence, different ones of the data objects that concern the particular structure being included as part of the collected plurality of data objects depending on the category to which the processor has identified the word sequence as a whole conforms.

12. A computer-implemented method of accessing an interactive manual system of a device, the interactive manual system including a model package database storing (i) a set of at least one of phrases and sentences, (ii) an object file that includes data objects that include information regarding an appearance of, and spatial characteristics of, a plurality of structures of the device and that include data representing visual displays that are associated with the structures of the device, and (iii) a grammar table that identifies associations between (a) the information and data and (b) the respective ones of the set of the at least one of phrases and sentences, the method comprising: receiving, by a computer processor, a speech input from a user; converting, by the computer processor executing a speech engine application, the speech input into a word sequence and determining the word sequence's meaning, which is associated with one or more of the set of the at least one of phrases and sentences; identifying, by the computer processor, a grammar category to which the word sequence as a whole conforms; extracting, by the computer processor a subset of the information regarding the appearance of, and spatial characteristics of, the one or more structures of the device; and outputting, by the computer processor, via an output arrangement what has been extracted; wherein the extraction is based on the identified grammar category, the one or more of the set of the at least one of phrases and sentences to which the determined meaning of the word sequence corresponds, and the associations of the grammar table, such that the extraction includes collecting from the data objects a plurality of data objects that concern a particular one of the structures of the device associated by the grammar table with the one or more of the set of the at least one of phrases and sentences that are associated with the determined meaning of the word sequence, different ones of the data objects that concern the particular structure being included as part of the collected plurality of data objects depending on the category to which the processor has identified the word sequence as a whole conforms. 14. The method of claim 12 , wherein the output arrangement includes a speech synthesis arrangement.

1. An information processing apparatus for recognizing a user's emotion, said apparatus comprising: obtaining means for obtaining meta-information concerning content; predicting means for predicting an emotion of a user who is viewing the content from the meta-information obtained by the obtaining means; and recognizing means for recognizing an emotion of the user using the emotion predicted by the predicting means and user information acquired from the user, wherein said recognizing means comprises a prediction information obtaining means, a recognition-model selection means, a recognition-model holding means, and a matching means, wherein a method for recognizing a user's emotion is based on the Bayes decision rule, wherein said prediction information obtaining means obtains prediction information from said predicting means and supplies said prediction information to said recognition-model selection means to select appropriate recognition model(s) from said recognition-model holding means based on said prediction information, and supplies the selected recognition model(s) to said matching means, wherein an emotion recognition result output from said emotion recognizing means is fed back to said emotion predicting means so that said emotion predicting means can be adapted to said user, and wherein the emotion recognition result is used as information indicating the user's preference to obtain information suited to the preference of the user when obtaining information via a network, wherein the emotion is an emotion of a user who is viewing the content.

1. An information processing apparatus for recognizing a user's emotion, said apparatus comprising: obtaining means for obtaining meta-information concerning content; predicting means for predicting an emotion of a user who is viewing the content from the meta-information obtained by the obtaining means; and recognizing means for recognizing an emotion of the user using the emotion predicted by the predicting means and user information acquired from the user, wherein said recognizing means comprises a prediction information obtaining means, a recognition-model selection means, a recognition-model holding means, and a matching means, wherein a method for recognizing a user's emotion is based on the Bayes decision rule, wherein said prediction information obtaining means obtains prediction information from said predicting means and supplies said prediction information to said recognition-model selection means to select appropriate recognition model(s) from said recognition-model holding means based on said prediction information, and supplies the selected recognition model(s) to said matching means, wherein an emotion recognition result output from said emotion recognizing means is fed back to said emotion predicting means so that said emotion predicting means can be adapted to said user, and wherein the emotion recognition result is used as information indicating the user's preference to obtain information suited to the preference of the user when obtaining information via a network, wherein the emotion is an emotion of a user who is viewing the content. 10. The information processing apparatus according to claim 1 , wherein: the predicting means includes a table used to predict the emotion of the user; the emotion of the user recognized by the recognizing means is supplied to the predicting means; and the predicting means updates the table in response to the supplied recognized emotion of the user.

1. A computer-implemented method comprising: at a server having one or more processors and memory storing one or more programs for execution by the one or more processors, collecting information containing product reviews for a plurality of products, wherein a respective product review provides a critical, subjective evaluation of a corresponding product by a human in electronic form; automatically extracting product reviews from the collected information; for at least some of the extracted product reviews, identifying a particular product that is associated with the extracted product review; and for each particular product in at least a subset of the plurality of products, generating aggregated review information for the particular product based on extracted product reviews that are associated with the particular product; storing the extracted product reviews and the aggregated review information; receiving a request from a client for an aggregated review of a product, the aggregated review of the product including portions of extracted product reviews of the product; and sending the aggregated review of the product in response to the request, wherein the aggregated review of the product includes a list of server-suggested search terms that are automatically selected from extracted product reviews of the product in accordance with their respective weighted occurrences in the extracted product reviews of the product.

1. A computer-implemented method comprising: at a server having one or more processors and memory storing one or more programs for execution by the one or more processors, collecting information containing product reviews for a plurality of products, wherein a respective product review provides a critical, subjective evaluation of a corresponding product by a human in electronic form; automatically extracting product reviews from the collected information; for at least some of the extracted product reviews, identifying a particular product that is associated with the extracted product review; and for each particular product in at least a subset of the plurality of products, generating aggregated review information for the particular product based on extracted product reviews that are associated with the particular product; storing the extracted product reviews and the aggregated review information; receiving a request from a client for an aggregated review of a product, the aggregated review of the product including portions of extracted product reviews of the product; and sending the aggregated review of the product in response to the request, wherein the aggregated review of the product includes a list of server-suggested search terms that are automatically selected from extracted product reviews of the product in accordance with their respective weighted occurrences in the extracted product reviews of the product. 9. The computer-implemented method of claim 1 , wherein the identifying a particular product that is associated with the extracted product review comprises associating a unique number in the extracted product review with a particular product.

53. The non-transitory computer-readable medium of claim 50 , wherein the first answer includes answer content and a confidence score indicating a likelihood that the answer content accurately answers the first question instance.

53. The non-transitory computer-readable medium of claim 50 , wherein the first answer includes answer content and a confidence score indicating a likelihood that the answer content accurately answers the first question instance. 54. The non-transitory computer-readable medium of claim 53 , wherein the instructions to automatically generate a first answer comprises instructions to automatically generate the confidence score based on a degree of agreement between the answer content and at least one previous answer to the first question instance received from a human.

17. The article of manufacture of claim 16 , wherein the operations further comprise: processing a qualified mnemonic in the computer program comprising the mnemonic and a qualification character; interpreting the qualified mnemonic according to the user defined definition for the mnemonic in response to a last processed qualifier command comprising a first qualifier command associating the qualification character with the user defined definition; and interpreting the qualified mnemonic according to the translator definition for the mnemonic in response to the last processed qualifier command comprising a second qualifier command associating the qualification character with the translator definition.

17. The article of manufacture of claim 16 , wherein the operations further comprise: processing a qualified mnemonic in the computer program comprising the mnemonic and a qualification character; interpreting the qualified mnemonic according to the user defined definition for the mnemonic in response to a last processed qualifier command comprising a first qualifier command associating the qualification character with the user defined definition; and interpreting the qualified mnemonic according to the translator definition for the mnemonic in response to the last processed qualifier command comprising a second qualifier command associating the qualification character with the translator definition. 18. The article of manufacture of claim 17 , wherein the first and second qualifier commands indicate that the qualification character is either applied as a prefix, suffix or quotation to the mnemonic being qualified.

1. A method for authenticating a user in a heterogeneous computer environment, the method comprising: utilizing one or more computers to perform: a. defining a set of unique prefixes, each prefix identifying a type of user repository; b. defining a set of abstract repository names, each abstract repository name identifying an address of a user repository, wherein each abstract repository name is mapped to a user repository, and is used to retrieve required address information that must be used to communicate with the repository, wherein said defining comprises defining an Abstract Repository Name Catalogue, which maps references to abstract repository names and abstract repository names to physical addresses; and c. identifying the user in the heterogeneous computer environment by assigning a sequence comprising a unique prefix, a reference to an abstract repository name and a unique identifier for the user within the user repository indicated by the reference to the abstract repository name, wherein the user repository is indicated via the mapping by the Abstract Repository Name Catalogue of the reference to the abstract repository name, and of the abstract repository name to the physical address of the user repository, wherein the unique identifier for the user uniquely identifies the user based on one or more rules of the user repository, and wherein authenticating the user comprises verifying that the identified user is authenticated for the indicated user repository based on the one or more rules; wherein more than one physical address is provided for a single abstract repository name, if the respective repository can be accessed via more than one protocol, and wherein the Abstract Repository Name Catalogue selects the physical address to return to the requestor based on the protocol required by the requestor to access the repository.

1. A method for authenticating a user in a heterogeneous computer environment, the method comprising: utilizing one or more computers to perform: a. defining a set of unique prefixes, each prefix identifying a type of user repository; b. defining a set of abstract repository names, each abstract repository name identifying an address of a user repository, wherein each abstract repository name is mapped to a user repository, and is used to retrieve required address information that must be used to communicate with the repository, wherein said defining comprises defining an Abstract Repository Name Catalogue, which maps references to abstract repository names and abstract repository names to physical addresses; and c. identifying the user in the heterogeneous computer environment by assigning a sequence comprising a unique prefix, a reference to an abstract repository name and a unique identifier for the user within the user repository indicated by the reference to the abstract repository name, wherein the user repository is indicated via the mapping by the Abstract Repository Name Catalogue of the reference to the abstract repository name, and of the abstract repository name to the physical address of the user repository, wherein the unique identifier for the user uniquely identifies the user based on one or more rules of the user repository, and wherein authenticating the user comprises verifying that the identified user is authenticated for the indicated user repository based on the one or more rules; wherein more than one physical address is provided for a single abstract repository name, if the respective repository can be accessed via more than one protocol, and wherein the Abstract Repository Name Catalogue selects the physical address to return to the requestor based on the protocol required by the requestor to access the repository. 4. The method of claim 1 , wherein there are more than one reference for a single abstract repository name.

8. The method of claim 1 , wherein each type of document action request is processed by a different sub-process, and further wherein each sub-process is associated with a time that the sub-processed last processed a document action request.

8. The method of claim 1 , wherein each type of document action request is processed by a different sub-process, and further wherein each sub-process is associated with a time that the sub-processed last processed a document action request. 9. The method of claim 8 , wherein the time that the sub-processed last processed a document action request is stored.

1. A method for voice communication, accomplished by a communication sheet and a digital voice signal processing device, the communication sheet comprising a plurality of communication units and a plurality of function units for a user to click with the digital voice signal processing device, the plurality of function units comprising a whole sentence unit, the method comprising a method for performing a function of emitting the sounds of a whole sentence, which comprises the following steps: (A) receiving a plurality of sounds of words selected from the plurality of communication units by the user; (B) searching for voice files each respectively corresponding to each of the sounds of words; (C) receiving a whole sentence command generated after the user clicks the whole sentence unit; (D) sequentially playing each of the voice files; wherein the plurality of function units further comprise a memory unit, and the method further comprises a method for performing a memory function, which comprises the following steps: (E) receiving at least one sound of a word selected from the plurality of communication units by the user; (F) searching for voice files each respectively corresponding to each of the sounds of words; (G) receiving a memory command generated after the user clicks the memory unit; and (H) sequentially recording recognition information of each of the voice files; wherein the plurality of communication units further comprise a plurality of storage location units, and step (G) further comprises the following steps: (G1) receiving a memory message generated after the user clicks at least one storage location unit of the plurality of storage location units; and (G2) assigning a storage location according to the memory message; wherein step (H) substantially and sequentially records the recognition information of each of the voice files into the storage location.

1. A method for voice communication, accomplished by a communication sheet and a digital voice signal processing device, the communication sheet comprising a plurality of communication units and a plurality of function units for a user to click with the digital voice signal processing device, the plurality of function units comprising a whole sentence unit, the method comprising a method for performing a function of emitting the sounds of a whole sentence, which comprises the following steps: (A) receiving a plurality of sounds of words selected from the plurality of communication units by the user; (B) searching for voice files each respectively corresponding to each of the sounds of words; (C) receiving a whole sentence command generated after the user clicks the whole sentence unit; (D) sequentially playing each of the voice files; wherein the plurality of function units further comprise a memory unit, and the method further comprises a method for performing a memory function, which comprises the following steps: (E) receiving at least one sound of a word selected from the plurality of communication units by the user; (F) searching for voice files each respectively corresponding to each of the sounds of words; (G) receiving a memory command generated after the user clicks the memory unit; and (H) sequentially recording recognition information of each of the voice files; wherein the plurality of communication units further comprise a plurality of storage location units, and step (G) further comprises the following steps: (G1) receiving a memory message generated after the user clicks at least one storage location unit of the plurality of storage location units; and (G2) assigning a storage location according to the memory message; wherein step (H) substantially and sequentially records the recognition information of each of the voice files into the storage location. 2. The method for voice communication as claimed in claim 1 , wherein the method for performing the memory function further comprises the following step: (I) emitting a notification sound.

19. A method, comprising: initiating a search request to retrieve one or more types of contact information for a plurality of organizations across the Internet based on a telecommunications identifier associated with an organization of the plurality of organizations; determining an access level for the search request, wherein the access level identifies which of the one or more types of contact information are accessible to the search request; receiving the one or more types of contact information for the organization that are determined as accessible to the search request based on the access level and associated with the telecommunications identifier in response to said initiating a search request; displaying the received one or more types of contact information for the organization; indicating that an action is available for an item of the received one or more types of contact information for the organization; and invoking a communication based on the received one or more types of contact information for the organization.

19. A method, comprising: initiating a search request to retrieve one or more types of contact information for a plurality of organizations across the Internet based on a telecommunications identifier associated with an organization of the plurality of organizations; determining an access level for the search request, wherein the access level identifies which of the one or more types of contact information are accessible to the search request; receiving the one or more types of contact information for the organization that are determined as accessible to the search request based on the access level and associated with the telecommunications identifier in response to said initiating a search request; displaying the received one or more types of contact information for the organization; indicating that an action is available for an item of the received one or more types of contact information for the organization; and invoking a communication based on the received one or more types of contact information for the organization. 23. The method of claim 19 , wherein the telecommunications identifier includes a telephone number associated with the organization.

11. A system, comprising: a processor; and a non-transitory computer-readable storage medium containing instructions configured to cause the processor to perform operations including: receiving raw data on a computing device; dividing the raw data into a set of time stamped searchable events; storing the set of events in an indexed data store; applying an extraction rule to the set of events, wherein an extraction rule defines a field within an event from which to extract a value; extracting a value from a field within an event, wherein the value is extracted using the extraction rule; and displaying the event in a graphical interface, wherein the value extracted from the field within the event is emphasized.

11. A system, comprising: a processor; and a non-transitory computer-readable storage medium containing instructions configured to cause the processor to perform operations including: receiving raw data on a computing device; dividing the raw data into a set of time stamped searchable events; storing the set of events in an indexed data store; applying an extraction rule to the set of events, wherein an extraction rule defines a field within an event from which to extract a value; extracting a value from a field within an event, wherein the value is extracted using the extraction rule; and displaying the event in a graphical interface, wherein the value extracted from the field within the event is emphasized. 14. The system of claim 11 , wherein the extraction rule includes a regular expression automatically generated to extract a value selected from an event.

2. An electronic device for searching information stored therein comprising: memory means having a first area for storing a plurality of words of information, and having a second area; first searching means connected to said memory means for searching said first area at least once for a desired word of information of said plurality of words of information, wherein said second area of said memory means stores a number of times each said desired word of information is searched by said first searching means; incrementing means connected to said first searching means and to said second area of said memory means and being responsive to operation of said first searching means to search for said desired word of information for incrementing the number of times searches are made for said desired word of information stored in said second memory means; second searching means, connected to said first area and said second area of said memory means, for searching for a desired word of information of said plurality of words of information on the basis of said stored number corresponding to the number of times said desired word has been searched; means for visually displaying the number of times each said desired word of information has been searched by said first searching means, stored in said second area of said memory means, said visually displaying means being operative to display the number of times as aforesaid in response to each search for said desired word of information by said first searching means; and incrementing means, responsive to operation of said searching means for said desired word of information, for incrementing the number of searches made for said desired word of information stored in said second area of said memory means.

2. An electronic device for searching information stored therein comprising: memory means having a first area for storing a plurality of words of information, and having a second area; first searching means connected to said memory means for searching said first area at least once for a desired word of information of said plurality of words of information, wherein said second area of said memory means stores a number of times each said desired word of information is searched by said first searching means; incrementing means connected to said first searching means and to said second area of said memory means and being responsive to operation of said first searching means to search for said desired word of information for incrementing the number of times searches are made for said desired word of information stored in said second memory means; second searching means, connected to said first area and said second area of said memory means, for searching for a desired word of information of said plurality of words of information on the basis of said stored number corresponding to the number of times said desired word has been searched; means for visually displaying the number of times each said desired word of information has been searched by said first searching means, stored in said second area of said memory means, said visually displaying means being operative to display the number of times as aforesaid in response to each search for said desired word of information by said first searching means; and incrementing means, responsive to operation of said searching means for said desired word of information, for incrementing the number of searches made for said desired word of information stored in said second area of said memory means. 3. An electronic device for searching information stored therein according to claim 2, further comprising entering means for entering information for searching said desired word of information by said first searching means.

1. A method comprising: storing in a database a first annotation and a second annotation, the first annotation relating to a first content unit and comprising a first semantic label and first content, the first semantic label comprising a term that does not appear in the first content and indicating a semantic classification of the first content, the second annotation relating to a second content unit and comprising a second semantic label and second content, the second semantic label comprising a term that does not appear in the second content and indicating a semantic classification of the second content, the semantic classification of the second content being different from the semantic classification of the first content, wherein the semantic classification of the first content indicates a meaning of the first content in context of the first content unit, wherein the first content does not explicitly appear in the first content unit, the term that indicates the semantic classification of the first content indicating a meaning of the first content in context of the first content unit from which the first content was determined, wherein the second content is a text excerpt of text of the second content unit, wherein the semantic classification of the second content indicates that the second content is an organizational and/or grammatical element of the text of the second content unit, wherein the storing comprises storing the first semantic label for the first annotation and the second semantic label for the second annotation in a first table of the database, and storing the first content of the first annotation and the second content of the second annotation in at least one second table of the database different from the first table.

1. A method comprising: storing in a database a first annotation and a second annotation, the first annotation relating to a first content unit and comprising a first semantic label and first content, the first semantic label comprising a term that does not appear in the first content and indicating a semantic classification of the first content, the second annotation relating to a second content unit and comprising a second semantic label and second content, the second semantic label comprising a term that does not appear in the second content and indicating a semantic classification of the second content, the semantic classification of the second content being different from the semantic classification of the first content, wherein the semantic classification of the first content indicates a meaning of the first content in context of the first content unit, wherein the first content does not explicitly appear in the first content unit, the term that indicates the semantic classification of the first content indicating a meaning of the first content in context of the first content unit from which the first content was determined, wherein the second content is a text excerpt of text of the second content unit, wherein the semantic classification of the second content indicates that the second content is an organizational and/or grammatical element of the text of the second content unit, wherein the storing comprises storing the first semantic label for the first annotation and the second semantic label for the second annotation in a first table of the database, and storing the first content of the first annotation and the second content of the second annotation in at least one second table of the database different from the first table. 3. The method of claim 1 , wherein the storing the first semantic label and the second semantic label in the first table of the database comprises storing the first semantic label and the second semantic label in one data structure in at least one computer-readable storage medium, the data structure specifying an organization of the first table.

22. The system of claim 14 , further comprising a verifier configured to: create one or more lists of identifiers from the one or more header files; and remove each identifier in the one or more lists of identifiers based upon whether the respective identifier exists in the tokenized form.

22. The system of claim 14 , further comprising a verifier configured to: create one or more lists of identifiers from the one or more header files; and remove each identifier in the one or more lists of identifiers based upon whether the respective identifier exists in the tokenized form. 23. The system of claim 22 , wherein the serializer is further configured to serialize in a modular form the one or more lists of identifiers to a storage device.

9. A non-transitory computer readable medium comprising a set of instructions which, when executed by a computer, is configured to: receive, through an application program interface, from an advertiser, input data defining an advertising campaign of the advertiser, the advertising campaign including one or more contracts, each respective contract defining one or more advertising placements, each respective advertising placement being associated with one or more predicate configurations; using the input data defining the advertising campaign, construct, by an index construction module, in memory, a targeting dimension dictionary that is user extensible to new attribute type declarations, the targeting dimension dictionary comprising at least three targeting dimensions including a dimension of web pages a history of hits associated with each of the web pages, each targeting dimension comprising at least two attribute types, each attribute type comprising one or more attribute values to make the targeting dimension dictionary scalable and extensible to newly added attribute type declarations and number of values against each attribute type; receive, from a user, advertiser input data defining a new targeting dimension, a new dimension attribute type or a new dimension attribute value corresponding to new targeting criteria introduced by the user; access the targeting dimension dictionary; dynamically update, by the index construction module, the targeting dimension dictionary, using an index construction module, by adding at least one of the new targeting dimension, the new dimension attribute type, or the new dimension attribute value based on the advertiser input data provided by the user without modifying the application programming interface, the one or more contracts including one or more newly added attribute type declarations or number of values against each attribute type; receive, by a campaign authoring tool, a contract of an advertiser; construct, based on clauses of the contract, a target predicate for the contract, at least in part using the updated targeting dimension dictionary; determine, by a predicate configuration manager module, whether the target predicate for the contract covers a target market based on the history of hits associated with the attribute values; adjust, by the predicate configuration manager module, the target predicate when the target predicate for the contract does not cover a target market; identify an impression available for booking at a web page of the web pages; construct, by the predicate configuration manager module, a placement opportunity profile predicate for impression at least in part using the targeting dimension dictionary, the placement opportunity profile predicate being constructed based on impression attributes of the history of hits for the web page; determine whether the target predicate matches the placement opportunity profile predicate; responsive to the target predicate being matched to the placement opportunity profile predicate, book an advertisement associated with the contract to the impression associated with the placement opportunity profile predicate; and serve the advertisement associated with the contract to the impression to a client system over a network.

9. A non-transitory computer readable medium comprising a set of instructions which, when executed by a computer, is configured to: receive, through an application program interface, from an advertiser, input data defining an advertising campaign of the advertiser, the advertising campaign including one or more contracts, each respective contract defining one or more advertising placements, each respective advertising placement being associated with one or more predicate configurations; using the input data defining the advertising campaign, construct, by an index construction module, in memory, a targeting dimension dictionary that is user extensible to new attribute type declarations, the targeting dimension dictionary comprising at least three targeting dimensions including a dimension of web pages a history of hits associated with each of the web pages, each targeting dimension comprising at least two attribute types, each attribute type comprising one or more attribute values to make the targeting dimension dictionary scalable and extensible to newly added attribute type declarations and number of values against each attribute type; receive, from a user, advertiser input data defining a new targeting dimension, a new dimension attribute type or a new dimension attribute value corresponding to new targeting criteria introduced by the user; access the targeting dimension dictionary; dynamically update, by the index construction module, the targeting dimension dictionary, using an index construction module, by adding at least one of the new targeting dimension, the new dimension attribute type, or the new dimension attribute value based on the advertiser input data provided by the user without modifying the application programming interface, the one or more contracts including one or more newly added attribute type declarations or number of values against each attribute type; receive, by a campaign authoring tool, a contract of an advertiser; construct, based on clauses of the contract, a target predicate for the contract, at least in part using the updated targeting dimension dictionary; determine, by a predicate configuration manager module, whether the target predicate for the contract covers a target market based on the history of hits associated with the attribute values; adjust, by the predicate configuration manager module, the target predicate when the target predicate for the contract does not cover a target market; identify an impression available for booking at a web page of the web pages; construct, by the predicate configuration manager module, a placement opportunity profile predicate for impression at least in part using the targeting dimension dictionary, the placement opportunity profile predicate being constructed based on impression attributes of the history of hits for the web page; determine whether the target predicate matches the placement opportunity profile predicate; responsive to the target predicate being matched to the placement opportunity profile predicate, book an advertisement associated with the contract to the impression associated with the placement opportunity profile predicate; and serve the advertisement associated with the contract to the impression to a client system over a network. 10. The non-transitory computer readable medium of claim 9 , wherein the set of instructions is further configured to provide the targeting dimension dictionary with an application programming interface, wherein the application programming interface allows the targeting dimension dictionary to be accessed by multiple applications.

17. A computer system providing taxonomy for enterprise data management, the system comprising: interface to a computer network, said computer network including data stored in devices attached to it; means to access said data; means to access data stored in devices directly attached to said computer system; means to access data in a cloud computing environment; means to extract metadata; means to perform full text search; classification means to classify data accessible to said computer system, said classification means are applied to metadata and said classification means are applied to result of full text search; means to create a hierarchy of enterprise data management taxonomy nodes, wherein each of said nodes includes: classification criteria used by said classification means to classify data belonging to the parent node in the hierarchy; classified data created by said classification means using said classification criteria; means to create data management policies, wherein some policies ensure compliance with federal regulations; means to apply said data management policies to classified data; wherein one or more of said nodes include data management policy; and wherein in each node that includes a data management policy said management policy is applied to data included in said each node.

17. A computer system providing taxonomy for enterprise data management, the system comprising: interface to a computer network, said computer network including data stored in devices attached to it; means to access said data; means to access data stored in devices directly attached to said computer system; means to access data in a cloud computing environment; means to extract metadata; means to perform full text search; classification means to classify data accessible to said computer system, said classification means are applied to metadata and said classification means are applied to result of full text search; means to create a hierarchy of enterprise data management taxonomy nodes, wherein each of said nodes includes: classification criteria used by said classification means to classify data belonging to the parent node in the hierarchy; classified data created by said classification means using said classification criteria; means to create data management policies, wherein some policies ensure compliance with federal regulations; means to apply said data management policies to classified data; wherein one or more of said nodes include data management policy; and wherein in each node that includes a data management policy said management policy is applied to data included in said each node. 18. The system of claim 17 , further comprising: means for creating a plurality of themes relating to data management and analysis, wherein for each theme a taxonomy is built by classifying data related to the theme; means for creating data management policies related to the theme of said taxonomy, wherein said data management policies are applied to data included in one or more nodes of said taxonomy; and means for data analysis related to the theme of said taxonomy, wherein said means for data analysis perform analysis of data included in one or more nodes of said taxonomy.

34. The search engine server of claim 33 , where the evaluation model is trained further based on information associated with human generated metrics.

34. The search engine server of claim 33 , where the evaluation model is trained further based on information associated with human generated metrics. 35. The search engine server of claim 34 , where the information associated with the human generated metrics include information associated with a ranking, by a human evaluator, of the input document/search query pairs based on a relevance of a document to a corresponding search query.

61. A computer system for processing a stored document, comprising: a document index input device including a scanner to scan a coversheet having a document index to provide an image of the document index; a marked check box locator, coupled to the document input index device, to identify at least one action from a plurality of actions set forth in the image by identifying a location of a ark in an action indication area on the image, wherein the plurality of actions includes printing, faxing, sending by electronic mail, and grouping, wherein the action indication area is associated with the plurality of actions, to identify a location on the document index image of at least one indication area having a mark therein, the at least one indication area being associated with at least one document out of a plurality of document set forth in the image; a document identifier, coupled to the marked check box locator, to identify the at least one document from the plurality of document set forth in the image based on the location of the at least one indication area having the mark therein, wherein the at least one action and the at least one document are identified by scanning the coversheet; and a document processor, coupled to the document identifier, to perform the at least one action identified from the plurality of action presented on the document index on the at least one document identified from the plurality of documents presented on the document index.

61. A computer system for processing a stored document, comprising: a document index input device including a scanner to scan a coversheet having a document index to provide an image of the document index; a marked check box locator, coupled to the document input index device, to identify at least one action from a plurality of actions set forth in the image by identifying a location of a ark in an action indication area on the image, wherein the plurality of actions includes printing, faxing, sending by electronic mail, and grouping, wherein the action indication area is associated with the plurality of actions, to identify a location on the document index image of at least one indication area having a mark therein, the at least one indication area being associated with at least one document out of a plurality of document set forth in the image; a document identifier, coupled to the marked check box locator, to identify the at least one document from the plurality of document set forth in the image based on the location of the at least one indication area having the mark therein, wherein the at least one action and the at least one document are identified by scanning the coversheet; and a document processor, coupled to the document identifier, to perform the at least one action identified from the plurality of action presented on the document index on the at least one document identified from the plurality of documents presented on the document index. 63. The computer system of claim 61 wherein the indication area is located on top of a portion of a graphic representing at least one document in a collection.

7. A method as claimed in claim 5 , wherein an entropy threshold is assigned to each subset, the method comprising selecting, as a cluster attractor, the respective probability distribution of one or more terms from each subset having an entropy that satisfies the respective entropy threshold.

7. A method as claimed in claim 5 , wherein an entropy threshold is assigned to each subset, the method comprising selecting, as a cluster attractor, the respective probability distribution of one or more terms from each subset having an entropy that satisfies the respective entropy threshold. 8. A method as claimed in claim 7 , comprising selecting, as a cluster attractor, the respective probability distribution of one or more terms from each subset having an entropy that is less than or equal to the respective entropy threshold.

5. The method of claim 1 , wherein each audio file transcription comprises a word lattice and further comprising creating at least one confusion network form the word lattice.

5. The method of claim 1 , wherein each audio file transcription comprises a word lattice and further comprising creating at least one confusion network form the word lattice. 6. The method of claim 5 , wherein the at least one confusion network is created by applying a minimum Bayes risk decoder to the word lattice.

11. The system of claim 8 , wherein the operations further comprise obtaining price information for the book from a products corpus, wherein the rich result comprises the price information for the book.

11. The system of claim 8 , wherein the operations further comprise obtaining price information for the book from a products corpus, wherein the rich result comprises the price information for the book. 12. The system of claim 11 , wherein obtaining price information for the book from a products corpus comprises: obtaining products results from the products corpus using an ISBN corresponding to the book; and determining a price for the book using the products results.

1. A computational system for performing an online choice model, the system comprising: at least one processor; at least one memory operatively coupled to the at least one processor; and a plurality of modules, each of the modules comprising instructions for execution by the at least one processor, the plurality of modules comprising: a problem definition module comprising a problem definition user interface for receiving a plurality of attributes from a user wherein each attribute has an associated plurality of attribute levels; an online choice model survey module comprising: an experimental design generator module for generating a survey experimental design and an associated plurality of treatments; comprising a library of experimental designs, wherein the experimental design generator module determines the signature of the attribute space from the received plurality of attributes and associated attribute levels; and for one or more experimental designs in the library of experimental designs, performs one or more transformations until the signature of the transformed experimental design matches the signature of the attribute space to obtain one or more matching transformed experimental designs; wherein each transformation preserves the information properties of the untransformed experimental design; wherein the experimental design generator module selects the survey experimental design from the one or more matching transformed experimental designs; and wherein the experimental design generator module obtains a set of treatments from the selected survey experimental design; an online survey assembly module which receives the plurality of treatments and assembles an online survey from one or more survey templates pages, and a plurality of treatment representations created using the plurality of treatments received from the experimental design module; a data collection and sampling module for conducting the assembled online survey, wherein the data collection and sampling module allocates treatments to survey respondents and collects responses using the assembled online survey; a model generation module for receiving the data collected by the data collection and sampling module and building a model to obtain a plurality of model parameter estimates and errors from which a utility estimate can be obtained for each attribute level; and a model explorer module comprising a model explorer user interface for allowing the user to enter one or more attribute levels and obtain a model prediction of the expected utility; wherein the one or more transformations comprise one or more of the following group of transformations: factorial splitting of a factor F into two sub factors A, B where A×B=F and A or B match at least one unmatched factor in the signature; factorial expansion of a factor F into a new factor A×F where A×F matches at least one unmatched factor in the signature; factor truncation of a factor F into a new factor F−A where F−A matches at least one unmatched factor in the signature; full factorization by generation of a new factor F where F matches at least one unmatched factor in the signature; and deleting a factor F when all the other factors in the signature are matched.

1. A computational system for performing an online choice model, the system comprising: at least one processor; at least one memory operatively coupled to the at least one processor; and a plurality of modules, each of the modules comprising instructions for execution by the at least one processor, the plurality of modules comprising: a problem definition module comprising a problem definition user interface for receiving a plurality of attributes from a user wherein each attribute has an associated plurality of attribute levels; an online choice model survey module comprising: an experimental design generator module for generating a survey experimental design and an associated plurality of treatments; comprising a library of experimental designs, wherein the experimental design generator module determines the signature of the attribute space from the received plurality of attributes and associated attribute levels; and for one or more experimental designs in the library of experimental designs, performs one or more transformations until the signature of the transformed experimental design matches the signature of the attribute space to obtain one or more matching transformed experimental designs; wherein each transformation preserves the information properties of the untransformed experimental design; wherein the experimental design generator module selects the survey experimental design from the one or more matching transformed experimental designs; and wherein the experimental design generator module obtains a set of treatments from the selected survey experimental design; an online survey assembly module which receives the plurality of treatments and assembles an online survey from one or more survey templates pages, and a plurality of treatment representations created using the plurality of treatments received from the experimental design module; a data collection and sampling module for conducting the assembled online survey, wherein the data collection and sampling module allocates treatments to survey respondents and collects responses using the assembled online survey; a model generation module for receiving the data collected by the data collection and sampling module and building a model to obtain a plurality of model parameter estimates and errors from which a utility estimate can be obtained for each attribute level; and a model explorer module comprising a model explorer user interface for allowing the user to enter one or more attribute levels and obtain a model prediction of the expected utility; wherein the one or more transformations comprise one or more of the following group of transformations: factorial splitting of a factor F into two sub factors A, B where A×B=F and A or B match at least one unmatched factor in the signature; factorial expansion of a factor F into a new factor A×F where A×F matches at least one unmatched factor in the signature; factor truncation of a factor F into a new factor F−A where F−A matches at least one unmatched factor in the signature; full factorization by generation of a new factor F where F matches at least one unmatched factor in the signature; and deleting a factor F when all the other factors in the signature are matched. 4. The computational system as claimed in claim 1 , wherein each transformation preserves the information properties of the untransformed experimental design.

1. A computer-implemented method, comprising: receiving at a computing device within a phrase detection system a set of phrase terms of a phrase, the phrase terms being in first ordinal positions, and the phrase terms are indicative of data leakage; generating a set of first hashes by the phrase detection system, the set of first hashes including a first hash of each of the phrase terms; generating concatenated hashes from the set of first hashes by the phrase detection system, the concatenated hashes including a concatenation of the set of first hashes according the first ordinal positions of the phrase terms, and concatenations of proper subsets of the set of first hashes according to the first ordinal positions of the phrase terms; using the first set of hashes and the concatenated hashes by the phrase detection system for phrase detection in content with noise terms intermixed between phrase terms in the content, wherein the noise terms comprise terms, words and other data that when hashed do not match one of the first set of hashes, and wherein the noise terms are ignored in the phrase detection; receiving content, the content including content terms in second ordinal positions; generating a set of second hashes, the set of second hashes includes a second hash for each of the content terms; selecting the second hashes according to an increasing order of the second ordinal positions and sub-phrase scores; comparing the selected second hashes of the content terms to the concatenated hashes and the first hashes; and determining a phrase detection of the phrase has occurred if selected second hashes match at least one comparison to the concatenated hashes or first hashes: wherein selecting the second hashes according to the increasing order of the second ordinal positions and the sub-phrase scores comprises: comparing the cardinality of the set of second hashes to the cardinality of the set of first hashes; selecting the set of second hashes when the cardinality of the set of second hashes is equal to the cardinality of the set of first hashes; and selecting the set of the second hashes and selecting proper subsets of the set of second hashes according to the second ordinal positions and the sub-phrase scores when the cardinality of the set of second hashes is greater than the cardinality of the set of first hashes.

1. A computer-implemented method, comprising: receiving at a computing device within a phrase detection system a set of phrase terms of a phrase, the phrase terms being in first ordinal positions, and the phrase terms are indicative of data leakage; generating a set of first hashes by the phrase detection system, the set of first hashes including a first hash of each of the phrase terms; generating concatenated hashes from the set of first hashes by the phrase detection system, the concatenated hashes including a concatenation of the set of first hashes according the first ordinal positions of the phrase terms, and concatenations of proper subsets of the set of first hashes according to the first ordinal positions of the phrase terms; using the first set of hashes and the concatenated hashes by the phrase detection system for phrase detection in content with noise terms intermixed between phrase terms in the content, wherein the noise terms comprise terms, words and other data that when hashed do not match one of the first set of hashes, and wherein the noise terms are ignored in the phrase detection; receiving content, the content including content terms in second ordinal positions; generating a set of second hashes, the set of second hashes includes a second hash for each of the content terms; selecting the second hashes according to an increasing order of the second ordinal positions and sub-phrase scores; comparing the selected second hashes of the content terms to the concatenated hashes and the first hashes; and determining a phrase detection of the phrase has occurred if selected second hashes match at least one comparison to the concatenated hashes or first hashes: wherein selecting the second hashes according to the increasing order of the second ordinal positions and the sub-phrase scores comprises: comparing the cardinality of the set of second hashes to the cardinality of the set of first hashes; selecting the set of second hashes when the cardinality of the set of second hashes is equal to the cardinality of the set of first hashes; and selecting the set of the second hashes and selecting proper subsets of the set of second hashes according to the second ordinal positions and the sub-phrase scores when the cardinality of the set of second hashes is greater than the cardinality of the set of first hashes. 2. The method of claim 1 , further comprising associating sub-phrase scores to the first hashes and the concatenations of the proper subsets of the set of first hashes.

9. The tangible computer-readable medium of claim 8 , the instructions further comprising assigning the verbs be and have as special predicates.

9. The tangible computer-readable medium of claim 8 , the instructions further comprising assigning the verbs be and have as special predicates. 10. The tangible computer-readable medium of claim 9 , wherein assigning the verbs distinguishes the verbs from utterances which do not have a predicate.

15. A non-transitory computer-usable storage media having machine-readable instructions stored thereon and configured to cause a processor to perform a method, the method comprising: for two or more portions of a set of items of known classification, classifying members of each portion using a particular classifier engine; selecting a portion of the set of items whose classifications satisfy a first criteria; classifying members of the selected portion of the set of items using two or more classifier engines; and selecting a classifier engine whose classification of the selected portion of the set of items satisfies a second criteria.

15. A non-transitory computer-usable storage media having machine-readable instructions stored thereon and configured to cause a processor to perform a method, the method comprising: for two or more portions of a set of items of known classification, classifying members of each portion using a particular classifier engine; selecting a portion of the set of items whose classifications satisfy a first criteria; classifying members of the selected portion of the set of items using two or more classifier engines; and selecting a classifier engine whose classification of the selected portion of the set of items satisfies a second criteria. 18. The non-transitory computer-usable storage media of claim 15 , wherein the method further comprises: classifying an item outside members of the selected portion of the set of items using the selected classifier engine.

1. A computing system, comprising: a processor; a memory configured to store instructions for controlling the processor; a user interface device configured to receive natural language speech input; a graphic user interface comprising an array of facets arranged in a tiled configuration, at least one of the facets being associated with at least one application; and a natural language speech parser configured to parse the received natural language speech input according to instruction grammars to determine instructions and respective parameters of the determined instructions, wherein, upon receipt of natural language speech input comprising a first instruction and a first parameter of the first instruction, the processor is configured to cause a focus to shift to a facet associated with a first application associated with the respective first parameter of the first instruction and to vary the first facet from a first size to a second size, the second size occupying substantially an entire viewable portion of the graphic user interface, and pass the first instruction and respective first parameter for processing by the first application associated with the facet, and upon subsequent receipt of natural language speech input comprising a second instruction, the processor is configured to cause the focus to shift away from the facet associated with the first application associated with the first parameter and return the first facet to the first size, and wherein, upon receipt of natural language speech input comprising the first instruction and a second parameter, the processor is configured to cause the focus to shift to a second facet associated with a second application associated with the second parameter of the first instruction, to vary the second facet from a third size to the second size, and upon subsequent receipt of natural language speech input comprising the second instruction, to cause the focus to shift away from the second facet associated with the second parameter and to return the second facet to the third size.

1. A computing system, comprising: a processor; a memory configured to store instructions for controlling the processor; a user interface device configured to receive natural language speech input; a graphic user interface comprising an array of facets arranged in a tiled configuration, at least one of the facets being associated with at least one application; and a natural language speech parser configured to parse the received natural language speech input according to instruction grammars to determine instructions and respective parameters of the determined instructions, wherein, upon receipt of natural language speech input comprising a first instruction and a first parameter of the first instruction, the processor is configured to cause a focus to shift to a facet associated with a first application associated with the respective first parameter of the first instruction and to vary the first facet from a first size to a second size, the second size occupying substantially an entire viewable portion of the graphic user interface, and pass the first instruction and respective first parameter for processing by the first application associated with the facet, and upon subsequent receipt of natural language speech input comprising a second instruction, the processor is configured to cause the focus to shift away from the facet associated with the first application associated with the first parameter and return the first facet to the first size, and wherein, upon receipt of natural language speech input comprising the first instruction and a second parameter, the processor is configured to cause the focus to shift to a second facet associated with a second application associated with the second parameter of the first instruction, to vary the second facet from a third size to the second size, and upon subsequent receipt of natural language speech input comprising the second instruction, to cause the focus to shift away from the second facet associated with the second parameter and to return the second facet to the third size. 2. The system of claim 1 , wherein the first instruction comprises an instruction adapted to open at least one of the first application and the second application.

1. A method of automatic speech recognition, comprising the steps of: (a) receiving an utterance from a user via a microphone that converts the utterance into a speech signal; (b) pre-processing the speech signal using a processor to extract acoustic data from the received speech signal; (c) identifying at least one user-specific characteristic in response to the extracted acoustic data, wherein the at least one user-specific characteristic comprises a plurality of confidence scores associated with failed attempts of the user to store a nametag; and (d) determining a user-specific confidence threshold responsive to the at least one user-specific characteristic, wherein the determination is carried out by calculating an average of the plurality of confidence scores and setting the user-specific confidence threshold to a value greater than or equal to the calculated average.

1. A method of automatic speech recognition, comprising the steps of: (a) receiving an utterance from a user via a microphone that converts the utterance into a speech signal; (b) pre-processing the speech signal using a processor to extract acoustic data from the received speech signal; (c) identifying at least one user-specific characteristic in response to the extracted acoustic data, wherein the at least one user-specific characteristic comprises a plurality of confidence scores associated with failed attempts of the user to store a nametag; and (d) determining a user-specific confidence threshold responsive to the at least one user-specific characteristic, wherein the determination is carried out by calculating an average of the plurality of confidence scores and setting the user-specific confidence threshold to a value greater than or equal to the calculated average. 6. The method of claim 1 , wherein the at least one user-specific characteristic includes at least one formant of the utterance.

1. An optical system for communicating optical signals to and from a subscriber unit, comprises: a first set of selected wavelengths; a second set of selected wavelengths offset from the first set of selected wavelengths; a first subsystem configured to transmit to the subscriber unit, the optical signals of one or more wavelengths from the first set of selected wavelengths over an optical fiber; a second subsystem configured to transmit to the subscriber unit the optical signals of one wavelength selected from the second set of selected wavelengths in statistical time division multiplexing over the optical fiber; a third subsystem at the subscriber unit configured to receive the optical signals of the one or more wavelengths from the first set of selected wavelengths and the optical signals of one wavelength from the second set of selected wavelengths over the optical fiber; a fourth subsystem at a head-end configured to receive looped back optical signals of one wavelength from the second set of selected wavelengths from the subscriber unit to the head-end over the optical fiber; a cyclic arrayed waveguide grating router capable of routing more than one wavelength to the subscriber unit; and a phase modulator, an intensity modulator, an amplifier and a looped back configuration within the subscriber unit.

1. An optical system for communicating optical signals to and from a subscriber unit, comprises: a first set of selected wavelengths; a second set of selected wavelengths offset from the first set of selected wavelengths; a first subsystem configured to transmit to the subscriber unit, the optical signals of one or more wavelengths from the first set of selected wavelengths over an optical fiber; a second subsystem configured to transmit to the subscriber unit the optical signals of one wavelength selected from the second set of selected wavelengths in statistical time division multiplexing over the optical fiber; a third subsystem at the subscriber unit configured to receive the optical signals of the one or more wavelengths from the first set of selected wavelengths and the optical signals of one wavelength from the second set of selected wavelengths over the optical fiber; a fourth subsystem at a head-end configured to receive looped back optical signals of one wavelength from the second set of selected wavelengths from the subscriber unit to the head-end over the optical fiber; a cyclic arrayed waveguide grating router capable of routing more than one wavelength to the subscriber unit; and a phase modulator, an intensity modulator, an amplifier and a looped back configuration within the subscriber unit. 11. An optical system as in claim 1 , further comprising a plurality of intensity modulators for modulating light intensities from the laser sources.

8. A computer-implemented method of providing electronic patent information, the method being executed on a computer and comprising: providing, in a computer processor, a contract document from the electronic storage; subdividing, in the computer processor responsive to a user, the contract document into sections; entering, in the computer processor responsive to the user, an annotation regarding a relationship between (i) at least one section within the contract document and (ii) at least one other section within the contract document or at least one other section within another electronic document; inputting, in response to the user, edits to the document; and storing, by the computer processor, the annotation in the electronic storage for later retrieval, the contract documents are stored in the electronic storage separately from the annotations, wherein each annotation is different from other annotations in the electronic storage, wherein the annotation stored in the electronic storage: indicates the relationship between (i) the at least one section within the contract document and (ii) the at least one other section within the contract document or the at least one other section within the another electronic document, the annotation regarding the relationship further includes specific to the at least one section of the contract document to which the at least one annotations is applied and the at least one other section, a pre-defined conflict indication user-selected from at least two of pass, possible and fail, indicates a predetermined data portion within the contract document as stored in the electronic storage to which the annotation is related as indicated by a document-image-independent data schema, retrieving the at least one contract document from the electronic storage as document data, said document data including at least one element corresponding to the location of the at least one annotation within said document; retrieving the annotation to be applied to said at least one contract document from the electronic storage as annotation data; and combining the document data and the annotation data to form a unitary single logical document displaying the annotation embedded at the location in the document data that is indicated by the document-image-independent data schema; extracting the annotation data and the document data from the edited document, and determine the predetermined data portion for the annotation data within the edited document; store the extracted annotation data that is unedited as a same version in the electronic annotations; and updating said document data from the extracted document data as a next version in the electronic contract documents for later retrieval, the electronic annotations are stored separately from the electronic contract documents; receiving, from the user, an indication of a search request with annotation search criteria, the annotation search criteria is at least one of the pre-defined conflict indications; and searching, responsive to the annotation search criteria, in the electronic storage, for annotations that satisfy the annotation search criteria and to output, as a search result the contracts indicated by the annotations that satisfy the annotation search criteria.

8. A computer-implemented method of providing electronic patent information, the method being executed on a computer and comprising: providing, in a computer processor, a contract document from the electronic storage; subdividing, in the computer processor responsive to a user, the contract document into sections; entering, in the computer processor responsive to the user, an annotation regarding a relationship between (i) at least one section within the contract document and (ii) at least one other section within the contract document or at least one other section within another electronic document; inputting, in response to the user, edits to the document; and storing, by the computer processor, the annotation in the electronic storage for later retrieval, the contract documents are stored in the electronic storage separately from the annotations, wherein each annotation is different from other annotations in the electronic storage, wherein the annotation stored in the electronic storage: indicates the relationship between (i) the at least one section within the contract document and (ii) the at least one other section within the contract document or the at least one other section within the another electronic document, the annotation regarding the relationship further includes specific to the at least one section of the contract document to which the at least one annotations is applied and the at least one other section, a pre-defined conflict indication user-selected from at least two of pass, possible and fail, indicates a predetermined data portion within the contract document as stored in the electronic storage to which the annotation is related as indicated by a document-image-independent data schema, retrieving the at least one contract document from the electronic storage as document data, said document data including at least one element corresponding to the location of the at least one annotation within said document; retrieving the annotation to be applied to said at least one contract document from the electronic storage as annotation data; and combining the document data and the annotation data to form a unitary single logical document displaying the annotation embedded at the location in the document data that is indicated by the document-image-independent data schema; extracting the annotation data and the document data from the edited document, and determine the predetermined data portion for the annotation data within the edited document; store the extracted annotation data that is unedited as a same version in the electronic annotations; and updating said document data from the extracted document data as a next version in the electronic contract documents for later retrieval, the electronic annotations are stored separately from the electronic contract documents; receiving, from the user, an indication of a search request with annotation search criteria, the annotation search criteria is at least one of the pre-defined conflict indications; and searching, responsive to the annotation search criteria, in the electronic storage, for annotations that satisfy the annotation search criteria and to output, as a search result the contracts indicated by the annotations that satisfy the annotation search criteria. 10. The method of claim 8 , wherein the annotation indicates how the at least one section relates to the at least one other section within the contract document or the at least one other section within another electronic document.

1. A method performed by a communications system including a communications server in communication with one or more communications devices for automatically optimizing a message text, the method comprising: receiving on the communications server a message text comprising a plurality of words or word phrases that combine together as non-overlapping parts of the message text; treating the non-overlapping words or word phrases of the message text as multiple independent variables that are reduced to a message vector having each of the multiple independent variables as components of the message vector; automatically creating on the communications server a plurality of lexical variants of the message text, wherein the lexical variants are created by replacing a word or word phrase for each of the multiple independent variables with one or more alternate words or word phrases based on one or more value-changing rules being applied to the received word or word phrase in the message text, the lexical variants for each of the multiple independent variables being reduced to a lexical vector such that the message vector is made up of variable-sized lexical vectors; sending each of the plurality of created lexical variants of the message text to the one or more communications devices; measuring a response rate for each sent lexical variant of the message text; identifying one or more lexical variants having the best performing measured response rates for each of the lexical vectors; automatically creating on the communications server syntactical variants of the identified best performing lexical variants by rearranging the lexical vectors within the message vector based on one or more position-changing rules; sending a plurality of the syntactical variants of the identified best performing lexical variants to the one or more communications devices, wherein only grammatically-correct syntactical variants are sent; measuring a response rate for each of the sent syntactical variants; and identifying a message text having the highest measured response rate for the sent syntactical variants.

1. A method performed by a communications system including a communications server in communication with one or more communications devices for automatically optimizing a message text, the method comprising: receiving on the communications server a message text comprising a plurality of words or word phrases that combine together as non-overlapping parts of the message text; treating the non-overlapping words or word phrases of the message text as multiple independent variables that are reduced to a message vector having each of the multiple independent variables as components of the message vector; automatically creating on the communications server a plurality of lexical variants of the message text, wherein the lexical variants are created by replacing a word or word phrase for each of the multiple independent variables with one or more alternate words or word phrases based on one or more value-changing rules being applied to the received word or word phrase in the message text, the lexical variants for each of the multiple independent variables being reduced to a lexical vector such that the message vector is made up of variable-sized lexical vectors; sending each of the plurality of created lexical variants of the message text to the one or more communications devices; measuring a response rate for each sent lexical variant of the message text; identifying one or more lexical variants having the best performing measured response rates for each of the lexical vectors; automatically creating on the communications server syntactical variants of the identified best performing lexical variants by rearranging the lexical vectors within the message vector based on one or more position-changing rules; sending a plurality of the syntactical variants of the identified best performing lexical variants to the one or more communications devices, wherein only grammatically-correct syntactical variants are sent; measuring a response rate for each of the sent syntactical variants; and identifying a message text having the highest measured response rate for the sent syntactical variants. 2. The method of claim 1 , wherein only a subset of the created lexical variants of the message text is sent to the one or more communication devices and a D-optimal design is used to determine the subset.

20. The mobile device of claim 15 further comprising a memory coupled to the processor for maintaining a store of portions of text and respective translations, wherein the translation is determined using the store.

20. The mobile device of claim 15 further comprising a memory coupled to the processor for maintaining a store of portions of text and respective translations, wherein the translation is determined using the store. 21. The mobile device of claim 20 wherein the store of portions of text and respective translations is defined by prior translations.

10. A computer implemented method for predicting future trends of terms taxonomies of users generated content, comprising: crawling one or more sources of users generated content to collect phrases mentioned by users of the one or more data sources; periodically analyzing one or more term taxonomies to determine at least a trend of at least a non-sentiment phrase with respect to a plurality of sentiment phrases, wherein each of the sentiment phrases includes one or more words describing a sentiment, the sentiment being any one of: a positive sentiment, a neutral sentiment, and a negative sentiment, and wherein a term taxonomy is an association between the non-sentiment phrase and at least one of the plurality of sentiment phrases; and generating a prediction of future behavior of the at least trend with respect of the one or more term taxonomies.

10. A computer implemented method for predicting future trends of terms taxonomies of users generated content, comprising: crawling one or more sources of users generated content to collect phrases mentioned by users of the one or more data sources; periodically analyzing one or more term taxonomies to determine at least a trend of at least a non-sentiment phrase with respect to a plurality of sentiment phrases, wherein each of the sentiment phrases includes one or more words describing a sentiment, the sentiment being any one of: a positive sentiment, a neutral sentiment, and a negative sentiment, and wherein a term taxonomy is an association between the non-sentiment phrase and at least one of the plurality of sentiment phrases; and generating a prediction of future behavior of the at least trend with respect of the one or more term taxonomies. 11. The computer implemented method of claim 10 , further comprises: associating between a non-sentiment phrase and a sentiment phrase of the collected phrases to create a term taxonomy.

11. A system comprising: one or more computer processors; and one or more non-transitory computer readable devices that include instructions that, when executed by the one or more computer processors, causes the processors to perform operations, the operations comprising: receiving a first electronic document; determining an entropy value for the first electronic document; determining a first information gain value associated with a first line that divides the first electronic document into a first portion and a second portion, comprising: a) determining an entropy value for the first portion of the first electronic document and an entropy value for the second portion of the first electronic document, b) based on the entropy value for the first portion of the first electronic document and the entropy value for the second portion of the first electronic document, determining an entropy value associated with the first line, and c) determining the first information gain value by determining a difference between i) the entropy value for the first electronic document and ii) the entropy value associated with the first line; determining a second information gain value associated with a second line that divides the first electronic document into a third portion and a fourth portion, comprising: a) determining an entropy value for the third portion of the first electronic document and an entropy value for the fourth portion of the first electronic document, b) based on the entropy value for the third portion of the first electronic document and the entropy value for the fourth portion of the first electronic document, determining an entropy value associated with the second line, and c) determining the second information gain value by determining a difference between i) the entropy value for the first electronic document and ii) the entropy value associated with the second line, wherein each of the entropy values is based at least in part on document objects in the respective portions of the first electronic document; determining which of the first information gain value and second information gain value is greater; in response to determining that the first information gain value is greater, generating a second electronic document that includes at least a portion defined by the first line and using the first information gain value to recursively divide the portions defined by the first line; in response to determining that the second information gain value is greater, generating a third electronic document that includes at least a portion defined by the second line and using the second information gain value to recursively divide the portions defined by the second line, wherein the entropy value for the first portion of the first electronic document and the entropy value for the second portion of the first electronic document are based at least on a variation in pixel intensity for pixels that the first line intersects in the first electronic document, and the entropy value for the third portion of the first electronic document and the entropy value for the fourth portion of the first electronic document are based at least on a variation in pixel intensity for pixels that the second line intersects in the first electronic document.

11. A system comprising: one or more computer processors; and one or more non-transitory computer readable devices that include instructions that, when executed by the one or more computer processors, causes the processors to perform operations, the operations comprising: receiving a first electronic document; determining an entropy value for the first electronic document; determining a first information gain value associated with a first line that divides the first electronic document into a first portion and a second portion, comprising: a) determining an entropy value for the first portion of the first electronic document and an entropy value for the second portion of the first electronic document, b) based on the entropy value for the first portion of the first electronic document and the entropy value for the second portion of the first electronic document, determining an entropy value associated with the first line, and c) determining the first information gain value by determining a difference between i) the entropy value for the first electronic document and ii) the entropy value associated with the first line; determining a second information gain value associated with a second line that divides the first electronic document into a third portion and a fourth portion, comprising: a) determining an entropy value for the third portion of the first electronic document and an entropy value for the fourth portion of the first electronic document, b) based on the entropy value for the third portion of the first electronic document and the entropy value for the fourth portion of the first electronic document, determining an entropy value associated with the second line, and c) determining the second information gain value by determining a difference between i) the entropy value for the first electronic document and ii) the entropy value associated with the second line, wherein each of the entropy values is based at least in part on document objects in the respective portions of the first electronic document; determining which of the first information gain value and second information gain value is greater; in response to determining that the first information gain value is greater, generating a second electronic document that includes at least a portion defined by the first line and using the first information gain value to recursively divide the portions defined by the first line; in response to determining that the second information gain value is greater, generating a third electronic document that includes at least a portion defined by the second line and using the second information gain value to recursively divide the portions defined by the second line, wherein the entropy value for the first portion of the first electronic document and the entropy value for the second portion of the first electronic document are based at least on a variation in pixel intensity for pixels that the first line intersects in the first electronic document, and the entropy value for the third portion of the first electronic document and the entropy value for the fourth portion of the first electronic document are based at least on a variation in pixel intensity for pixels that the second line intersects in the first electronic document. 14. The system of claim 11 , wherein the entropy values for each portion of the first electronic document are each based at least on an area of document objects within the respective portion of the first electronic document.

18. The computer program product of claim 15 , wherein the portion of data is obtained from a received XML instance.

18. The computer program product of claim 15 , wherein the portion of data is obtained from a received XML instance. 19. The computer program product of claim 18 , wherein accessing the layout information comprises identifying the schema definition and converting the portion of data to a string representation.

12. A system for retrieving web content relating to one or more items of video content maintained on a storage media item, the system comprising: a content acquisition module operative to retrieve the digital video content maintained on the storage media item; an extraction module operative to: extract at least one of caption and subtitle content associated with the one or more items of digital video content, segment the extracted at least one of caption and subtitle content into one or more text segments, and generate a description characterizing the video content based on the one or more text segments; and a search engine operative to: receive a query for one or more items of the digital video content from a user, retrieve the description and at least one text segment based on the query, retrieve one or more related items of web content corresponding to the at least one text segment, and provide the one or more related items of web content and the video content with the description and the at least one text segment to the user.

12. A system for retrieving web content relating to one or more items of video content maintained on a storage media item, the system comprising: a content acquisition module operative to retrieve the digital video content maintained on the storage media item; an extraction module operative to: extract at least one of caption and subtitle content associated with the one or more items of digital video content, segment the extracted at least one of caption and subtitle content into one or more text segments, and generate a description characterizing the video content based on the one or more text segments; and a search engine operative to: receive a query for one or more items of the digital video content from a user, retrieve the description and at least one text segment based on the query, retrieve one or more related items of web content corresponding to the at least one text segment, and provide the one or more related items of web content and the video content with the description and the at least one text segment to the user. 15. The system of claim 12 wherein the extraction module is operative to: identify preexisting divisions in a given digital video content; and segment the extracted at least one of caption and subtitle content from the digital video content according to the preexisting divisions.

9. The computing system set forth in claim 8 , wherein at least one identified expression references a data provider for the selected interface element.

9. The computing system set forth in claim 8 , wherein at least one identified expression references a data provider for the selected interface element. 10. The computing system set forth in claim 9 , wherein the refactoring module configures the computing system to store the identified expression referencing the data provider based on input data specifying whether the data provider should be included in the generated code segment.

5. A data processing system comprising a processor and accessible memory, the data processing system particularly configured to perform the steps of: loading a first language definition and a second language definition; loading a transformation definition corresponding to the first language definition and the second language definition, wherein the transformation definition maps a first construct of the first language definition to a second construct of the second language definition; loading a validation rule definition, wherein the validation rule definition defines a successful transformation between the first construct and the second construct and indicates whether the transformation definition produces a valid transformation between the first construct and the second construct; and applying the validation rule definition to the transformation definition to produce a validation result indicating whether the transformation definition produces a valid transformation between the first language definition and the second language definition; and storing the validation result.

5. A data processing system comprising a processor and accessible memory, the data processing system particularly configured to perform the steps of: loading a first language definition and a second language definition; loading a transformation definition corresponding to the first language definition and the second language definition, wherein the transformation definition maps a first construct of the first language definition to a second construct of the second language definition; loading a validation rule definition, wherein the validation rule definition defines a successful transformation between the first construct and the second construct and indicates whether the transformation definition produces a valid transformation between the first construct and the second construct; and applying the validation rule definition to the transformation definition to produce a validation result indicating whether the transformation definition produces a valid transformation between the first language definition and the second language definition; and storing the validation result. 7. The data processing system of claim 5 , wherein the first language definition is document type definition.

12. The system of claim 1, wherein the group leader terminal further comprises means for selecting an unanticipated multi-character response of a participant terminal and comparing the unanticipated multi-character response with the multi-character responses from other participant terminals.

12. The system of claim 1, wherein the group leader terminal further comprises means for selecting an unanticipated multi-character response of a participant terminal and comparing the unanticipated multi-character response with the multi-character responses from other participant terminals. 15. The system of claim 12 wherein the participant terminals are provided with reinforcement means, for reinforcing a favorable comparison between the selected unanticipated response on the group leader terminal and the responses from other participant terminals.

3. The method of claim 1 , wherein for each of the first process level tags, the corresponding second process level tags and process element identifiers in combination indicate the ordered sequence of the process elements corresponding to the first process level tag.

3. The method of claim 1 , wherein for each of the first process level tags, the corresponding second process level tags and process element identifiers in combination indicate the ordered sequence of the process elements corresponding to the first process level tag. 8. The method of claim 3 , wherein the second process level tag corresponding to the SIPOC identifier of the first process level tag is from the group further consisting of: a SIPOC process identifier; and wherein the ordered sequence includes a first process element assigned the SIPOC identifier and the SIPOC process identifier followed by a second process element assigned the SIPOC identifier.

17. The one or more non-transitory computer-readable storage media of claim 16 , wherein the range of values within the field having been are separated into discrete range segments and stored in the list of discrete values.

17. The one or more non-transitory computer-readable storage media of claim 16 , wherein the range of values within the field having been are separated into discrete range segments and stored in the list of discrete values. 19. The one or more non-transitory computer-readable storage media of claim 17 , wherein the list is recalculated after a threshold of changes have been made to the field.

10. A system for identifying a cover song from a query song, the system comprising: a hardware processor that: generates a beat-synchronized chroma matrix of a plurality of chroma vectors each having a plurality of chroma bins for a portion of the query song; identifies landmarks in the beat-synchronized chroma matrix, wherein a first prominent pitch at a first time in the portion of the query song and a second prominent pitch in the portion of the query song each correspond to an identified landmark; calculates a query song jumpcode based on differences between successive pairs of the identified landmarks in a time window of a size less than the size of the portion of the song identifies a plurality of reference song jumpcodes for a reference song, wherein each of the reference song jumpcodes is based at least in part on a change in prominent pitch between two times in a portion of the reference song; determines if the query song jumpcode matches any of the plurality of reference song jumpcodes; and upon determining that the query song jumpcode matches at least one of the plurality of reference song jumpcodes, generates an indication that the reference song is a cover song of the query song.

10. A system for identifying a cover song from a query song, the system comprising: a hardware processor that: generates a beat-synchronized chroma matrix of a plurality of chroma vectors each having a plurality of chroma bins for a portion of the query song; identifies landmarks in the beat-synchronized chroma matrix, wherein a first prominent pitch at a first time in the portion of the query song and a second prominent pitch in the portion of the query song each correspond to an identified landmark; calculates a query song jumpcode based on differences between successive pairs of the identified landmarks in a time window of a size less than the size of the portion of the song identifies a plurality of reference song jumpcodes for a reference song, wherein each of the reference song jumpcodes is based at least in part on a change in prominent pitch between two times in a portion of the reference song; determines if the query song jumpcode matches any of the plurality of reference song jumpcodes; and upon determining that the query song jumpcode matches at least one of the plurality of reference song jumpcodes, generates an indication that the reference song is a cover song of the query song. 15. The system of claim 10 , wherein the processor is further configured to: limit the number of landmarks identified at a time frame to a maximum number of landmarks.

1. A method programmed in a non-transitory memory of a device comprising: a. automatically fact checking target information by comparing the target information with source information to generate a result, wherein the target information comprises web page information or social networking information, wherein comparing includes at least one of: i. searching for an exact match of the target information in the source information and returning the result of the exact match search if the exact match is found; ii. utilizing pattern matching for fact checking and returning the result of the pattern matching fact check if a pattern matching result confidence score is above a pattern matching result confidence threshold; and iii. utilizing a natural language search for fact checking and returning the result of the natural language fact check if a natural language result confidence score is above a natural language result confidence threshold; and b. automatically affecting the target information and presenting a status of the target information in real-time based on the result of the comparison of the target information with the source information, wherein searching for the exact match begins searching the source information located on a fastest access time hardware device and continues to slower access time hardware devices; wherein utilizing pattern matching begins utilizing the source information located on the fastest access time hardware device and continues to the slower access time hardware devices; and wherein the natural language search begins searching the source information located on the fastest access time hardware device and continues to the slower access time hardware devices, wherein searching for the exact match begins searching the source information classified by a plurality of keywords found in the target information, then using the source information classified by a single keyword found in the target information, and then using the source information classified by keywords related to the keywords found in the target information; wherein utilizing pattern matching begins utilizing the source information classified by the plurality of keywords found in the target information, then using the source information classified by the single keyword found in the target information, and then using the source information classified by the keywords related to the keywords found in the target information; and wherein the natural language search begins searching the source information classified by the plurality of keywords found in the target information, then using the source information classified by the single keyword found in the target information, and then using the source information classified by the keywords related to the keywords found in the target information.

1. A method programmed in a non-transitory memory of a device comprising: a. automatically fact checking target information by comparing the target information with source information to generate a result, wherein the target information comprises web page information or social networking information, wherein comparing includes at least one of: i. searching for an exact match of the target information in the source information and returning the result of the exact match search if the exact match is found; ii. utilizing pattern matching for fact checking and returning the result of the pattern matching fact check if a pattern matching result confidence score is above a pattern matching result confidence threshold; and iii. utilizing a natural language search for fact checking and returning the result of the natural language fact check if a natural language result confidence score is above a natural language result confidence threshold; and b. automatically affecting the target information and presenting a status of the target information in real-time based on the result of the comparison of the target information with the source information, wherein searching for the exact match begins searching the source information located on a fastest access time hardware device and continues to slower access time hardware devices; wherein utilizing pattern matching begins utilizing the source information located on the fastest access time hardware device and continues to the slower access time hardware devices; and wherein the natural language search begins searching the source information located on the fastest access time hardware device and continues to the slower access time hardware devices, wherein searching for the exact match begins searching the source information classified by a plurality of keywords found in the target information, then using the source information classified by a single keyword found in the target information, and then using the source information classified by keywords related to the keywords found in the target information; wherein utilizing pattern matching begins utilizing the source information classified by the plurality of keywords found in the target information, then using the source information classified by the single keyword found in the target information, and then using the source information classified by the keywords related to the keywords found in the target information; and wherein the natural language search begins searching the source information classified by the plurality of keywords found in the target information, then using the source information classified by the single keyword found in the target information, and then using the source information classified by the keywords related to the keywords found in the target information. 16. The method of claim 1 wherein searching for the exact match begins searching the source information controlled by a media company, then using crowdsourced data as the source information, and then using world wide web data for fact checking; wherein utilizing pattern matching begins utilizing the source information controlled by the media company, then using the crowdsourced data as the source information, and then using the world wide web data for fact checking; and wherein the natural language search begins searching the source information controlled by the media company, then using the crowdsourced data as the source information, and then using the world wide web data for fact checking.

14. A system for facilitating communications for a user transaction, the system comprising: a determining module configured to determine a goal transaction for a user to provide input to a human-to-machine interface, said determining module further configured to analyze the human-to-machine interface to determine one or more states and one or more navigation paths of a state machine of the human-to-machine interface defining available interactions for the user to interact with the human-to-machine interface, said determining module further configured to account for at least a subset of the states and associate the goal transaction with at least the one or more navigation paths of the state machine, the user reaching at least one end state of the one or more navigation paths via an individual interaction with a visual representation of a voice input parameter, the state machine operating in a manner consistent with the at least the subset of the states; a constructing module configured to construct and present the visual representation of the voice input parameter for the goal transaction based on at least the subset of the states, the visual representation of the voice input parameter representing multiple operations of the goal transaction optionally available for the user to employ to achieve interaction with the human-to-machine interface in fewer stages than through individual interactions with the human-to-machine interface; and wherein the visual representations enable user interaction with the human-to-machine interface.

14. A system for facilitating communications for a user transaction, the system comprising: a determining module configured to determine a goal transaction for a user to provide input to a human-to-machine interface, said determining module further configured to analyze the human-to-machine interface to determine one or more states and one or more navigation paths of a state machine of the human-to-machine interface defining available interactions for the user to interact with the human-to-machine interface, said determining module further configured to account for at least a subset of the states and associate the goal transaction with at least the one or more navigation paths of the state machine, the user reaching at least one end state of the one or more navigation paths via an individual interaction with a visual representation of a voice input parameter, the state machine operating in a manner consistent with the at least the subset of the states; a constructing module configured to construct and present the visual representation of the voice input parameter for the goal transaction based on at least the subset of the states, the visual representation of the voice input parameter representing multiple operations of the goal transaction optionally available for the user to employ to achieve interaction with the human-to-machine interface in fewer stages than through individual interactions with the human-to-machine interface; and wherein the visual representations enable user interaction with the human-to-machine interface. 15. The system of claim 14 further comprising: a tracking module configured to track one or more individual transactions from the user with the human-to-machine interface; a data store configured to store the one or more individual transactions from the user with the human-to-machine interface; and the determining module further configured to determine the goal transaction for the user based on the one or more stored individual transactions from the user.

7. The electronic computing device of claim 5 , wherein updating the probable words dictionary comprises: determining whether each word in the list of words is present in the probable words dictionary; and for each word not found in the probable words dictionary, adding the word to the probable words dictionary.

7. The electronic computing device of claim 5 , wherein updating the probable words dictionary comprises: determining whether each word in the list of words is present in the probable words dictionary; and for each word not found in the probable words dictionary, adding the word to the probable words dictionary. 8. The electronic computing device of claim 7 , wherein updating the probable words dictionary further comprises, for each word from the list of words found in the probable words dictionary, increasing a priority weighting factor for said word.

61. A method of teaching a student to read, comprising: constructing sight reading development tests that include one or more interactive processes each with one or more interactive process types; presenting the sight reading development tests to the student; inputting a response from the student; determining student performance according to a response time measured between said presenting and said inputting steps; adjusting the difficulty level according to the student performance; and iterating said constructing, presenting, inputting, determining and adjusting wherein said constructing step constructs a next sight reading development test according to the adjusted difficulty level.

61. A method of teaching a student to read, comprising: constructing sight reading development tests that include one or more interactive processes each with one or more interactive process types; presenting the sight reading development tests to the student; inputting a response from the student; determining student performance according to a response time measured between said presenting and said inputting steps; adjusting the difficulty level according to the student performance; and iterating said constructing, presenting, inputting, determining and adjusting wherein said constructing step constructs a next sight reading development test according to the adjusted difficulty level. 62. The method according to claim 61, said determining step further including determining student performance according to a difficulty level of the sight reading drill.