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16. The method of claim 1, further comprising the step, prior to said identifying step, of applying a set of heuristic rules to said stored character string to identify a character-transition in said first segment of said stored character string, said identification of a character-transition reducing the number of possible character combinations forming words in said stored character string.
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16. The method of claim 1, further comprising the step, prior to said identifying step, of applying a set of heuristic rules to said stored character string to identify a character-transition in said first segment of said stored character string, said identification of a character-transition reducing the number of possible character combinations forming words in said stored character string. 23. The method of claim 16, wherein said step of applying the set of heuristic rules further comprises locating identifying particles in said stored character string, and identifying a character-transition that precedes and a character-transition that follows said located particles.
| 0.811753 |
1. A method for providing a keyword recommendation for a user to access content from a global textsite platform (GTP), comprising: obtaining a first registered unique keyword, of a plurality of registered unique keywords, from a user message sent to the GTP by the user, wherein the plurality of registered unique keywords are used by a plurality of GTP users to access content from the GTP based on a text messaging service (TMS); selecting a keyword recommendation algorithm from a plurality of keyword recommendation algorithms based on a pre-determined selection sequence assigned to the plurality of keyword recommendation algorithms and a previously selected keyword recommendation algorithm; analyzing, by a computer processor using the keyword recommendation algorithm and based at least on the first registered unique keyword, a GTP usage pattern to select a recommended keyword from the plurality of registered unique keywords, wherein the GTP usage pattern comprises statistical information of the plurality of GTP users using the plurality of registered unique keywords to access content from the GTP; and sending the recommended keyword to the GTP, wherein the GTP sends, to the user in response to the user message, a GTP message comprising a keyword recommendation that identifies the recommended keyword, and wherein the user message and the GTP message comprise a TMS message.
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1. A method for providing a keyword recommendation for a user to access content from a global textsite platform (GTP), comprising: obtaining a first registered unique keyword, of a plurality of registered unique keywords, from a user message sent to the GTP by the user, wherein the plurality of registered unique keywords are used by a plurality of GTP users to access content from the GTP based on a text messaging service (TMS); selecting a keyword recommendation algorithm from a plurality of keyword recommendation algorithms based on a pre-determined selection sequence assigned to the plurality of keyword recommendation algorithms and a previously selected keyword recommendation algorithm; analyzing, by a computer processor using the keyword recommendation algorithm and based at least on the first registered unique keyword, a GTP usage pattern to select a recommended keyword from the plurality of registered unique keywords, wherein the GTP usage pattern comprises statistical information of the plurality of GTP users using the plurality of registered unique keywords to access content from the GTP; and sending the recommended keyword to the GTP, wherein the GTP sends, to the user in response to the user message, a GTP message comprising a keyword recommendation that identifies the recommended keyword, and wherein the user message and the GTP message comprise a TMS message. 6. The method of claim 1 , further comprises: obtaining a pre-determined ranking of a registered unique keyword among the plurality of registered unique keywords; and wherein analyzing the GTP usage pattern comprises: selecting, in response to the pre-determined ranking meeting a pre-determined criterion, the registered unique keyword as the recommended keyword.
| 0.860367 |
11. A computer system comprising: a computer processor; computer memory connected to the computer processor, the computer memory storing a page rank module programmed to: receive a search query from a user; identify one or more result pages that pertain to the search query; determine, for each of the one or more result pages, a raw page ranking; determine a metric of direct evidence of user interest for at least one result page of the one or more result pages by which is a result of a mathematical function which expresses how many users historically have visited the at least one result page subtracted from how many users have recently visited the at least one result page all divided by how many users historically have visited the at least one result page; adjust the raw page ranking of the at least one result page among the one or more result pages based on the metric of direct evidence of user interest for the at least one result page to produce an adjusted page ranking for the at least one result page; and present, as search results, the at least one result page to the user in accordance with the adjusted page ranking; wherein: determine the metric of direct evidence of user interest for the at least one result page by determining a measure of how often users traverse to or from the at least one result page while browsing.
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11. A computer system comprising: a computer processor; computer memory connected to the computer processor, the computer memory storing a page rank module programmed to: receive a search query from a user; identify one or more result pages that pertain to the search query; determine, for each of the one or more result pages, a raw page ranking; determine a metric of direct evidence of user interest for at least one result page of the one or more result pages by which is a result of a mathematical function which expresses how many users historically have visited the at least one result page subtracted from how many users have recently visited the at least one result page all divided by how many users historically have visited the at least one result page; adjust the raw page ranking of the at least one result page among the one or more result pages based on the metric of direct evidence of user interest for the at least one result page to produce an adjusted page ranking for the at least one result page; and present, as search results, the at least one result page to the user in accordance with the adjusted page ranking; wherein: determine the metric of direct evidence of user interest for the at least one result page by determining a measure of how often users traverse to or from the at least one result page while browsing. 15. The system of claim 11 , wherein page rank module is programmed to: adjust the raw page ranking of the at least one result page among the one or more result pages based on the metric of direct evidence of user interest for the at least one result page to produce the adjusted page ranking by scaling a number of user traversals to the at least one result page from other result pages.
| 0.556236 |
18. The computer-implemented system of claim 17 , wherein the issue library metadata entity further comprises issue text of the individual issue, wherein the issue text is representative of the individual issue and is to be presented to an end-user researching the individual issue.
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18. The computer-implemented system of claim 17 , wherein the issue library metadata entity further comprises issue text of the individual issue, wherein the issue text is representative of the individual issue and is to be presented to an end-user researching the individual issue. 19. The computer-implemented system of claim 18 , wherein: the issue text comprises a selected reason-for-citing or cited-text-area that is selected from the reasons-for-citing or cited-text-areas of the individual issue; and the selected reason-for-citing or cited-text-area is selected based at least in part on linguistic rules.
| 0.912565 |
12. The system of claim 11 , wherein the search-result item is a web page.
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12. The system of claim 11 , wherein the search-result item is a web page. 13. The system of claim 12 , wherein the classification is spam and the classification value quantifies a confidence that the web page includes spam.
| 0.922843 |
1. A method for determining speech effectiveness, the method comprising: receiving, by one or more computer processors, speech input; determining, by the one or more computer processors, based, at least in part, on the received speech input, a speaking mode of the received speech input, the speaking mode being one of: a conversation with words spoken by two or more people during a predetermined time interval, and a presentation with words spoken by one person and not any other person during a predetermined time interval; detecting, by the one or more computer processors, at least one problem with the speech input; if the speaking mode of the received speech input is determined to be a presentation and not a conversation, weighting the detected at least one problem with the speech input using a first factor while using the at least one or more computer processors, and, if the speaking mode of the received speech input is determined to be a conversation and not a presentation, weighting the detected at least one problem with the speech input using a second factor while using the at least one or more computer processors; and notifying a user, by the one or more computer processors, of the detected at least one problem with the speech input, if a rate of occurrence of the detected at least one problem exceeds a pre-defined threshold of the at least one problem with the speech input, wherein the rate of occurrence is based on the weighting of the at least one problem with the speech input and a base value of occurrence of the at least one problem with the speech input.
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1. A method for determining speech effectiveness, the method comprising: receiving, by one or more computer processors, speech input; determining, by the one or more computer processors, based, at least in part, on the received speech input, a speaking mode of the received speech input, the speaking mode being one of: a conversation with words spoken by two or more people during a predetermined time interval, and a presentation with words spoken by one person and not any other person during a predetermined time interval; detecting, by the one or more computer processors, at least one problem with the speech input; if the speaking mode of the received speech input is determined to be a presentation and not a conversation, weighting the detected at least one problem with the speech input using a first factor while using the at least one or more computer processors, and, if the speaking mode of the received speech input is determined to be a conversation and not a presentation, weighting the detected at least one problem with the speech input using a second factor while using the at least one or more computer processors; and notifying a user, by the one or more computer processors, of the detected at least one problem with the speech input, if a rate of occurrence of the detected at least one problem exceeds a pre-defined threshold of the at least one problem with the speech input, wherein the rate of occurrence is based on the weighting of the at least one problem with the speech input and a base value of occurrence of the at least one problem with the speech input. 2. The method of claim 1 , further comprising: performing, by the one or more computer processors, an analysis of the detected at least one problem with the speech; storing, by the one or more computer processors, the analysis of the detected at least one problem; receiving, by the one or more computer processors, a request for feedback from a user; responsive to receiving, by the one or more computer processors, a request for feedback from the user, retrieving, by the one or more computer processors, the analysis of the detected at least one problem; determining, by the one or more computer processors, based, at least in part, on the request for feedback and the analysis of the detected at least one problem, feedback for the user; and providing, by the one or more computer processors, the feedback associated with the analysis of the detected at least one problem to the user.
| 0.613718 |
1. A machine translation system comprising: translation processing means for processing an input original sentence so as to obtain translated-word possibilities and translation data associated therewith corresponding to translation processing units of the original sentence, thereby producing a translated-sentence possibility, formed of selected translation processing units, corresponding to the original sentence, said translation processing means including syntax determining means for determining improper combinations of translated words and producing said translated sentence possibility only in accordance with predetermined grammatical rules; display means for displaying the translated-sentence possibility produced by said translation processing means; division display control means, operated in response to the processing of said translation processing means, for controlling said display means so that the displayed translated-sentence possibility is divided into the translation processing units in said translation processing means and is displayed on said display means; identifying display control means, operated in respose to the processing of said translation processing means, for controlling said display means in such a manner that, if portions of the original sentence have other translation processing units in addition to the displayed translation processing units, the translated-sentence possibility is displayed so that positions of said portions of the original sentence having the other translation processing units and classes of the other translation-processing units are identified; and selection means to enable an operator to select an appropriate translation processing unit while displaying the other translation processing units on said display means.
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1. A machine translation system comprising: translation processing means for processing an input original sentence so as to obtain translated-word possibilities and translation data associated therewith corresponding to translation processing units of the original sentence, thereby producing a translated-sentence possibility, formed of selected translation processing units, corresponding to the original sentence, said translation processing means including syntax determining means for determining improper combinations of translated words and producing said translated sentence possibility only in accordance with predetermined grammatical rules; display means for displaying the translated-sentence possibility produced by said translation processing means; division display control means, operated in response to the processing of said translation processing means, for controlling said display means so that the displayed translated-sentence possibility is divided into the translation processing units in said translation processing means and is displayed on said display means; identifying display control means, operated in respose to the processing of said translation processing means, for controlling said display means in such a manner that, if portions of the original sentence have other translation processing units in addition to the displayed translation processing units, the translated-sentence possibility is displayed so that positions of said portions of the original sentence having the other translation processing units and classes of the other translation-processing units are identified; and selection means to enable an operator to select an appropriate translation processing unit while displaying the other translation processing units on said display means. 6. A system according to claim 1, wherein said identifying display control means comprises: means for controlling display so that, when a plurality of classes of the other translation processing units must be identified, at least some of the classes are selectively identified and displayed in accordance with a priority of the classes.
| 0.628855 |
48. The method of claim 47 further comprising: maintaining the social graph comprising: using activity information stored at the storage server, determining a plurality of nodes representing persons specified in a plurality of activity information stored at the storage server; generating a plurality of edges in the social graph, wherein each edge represents at least one activity information of the plurality of activity information; and using the plurality of edges to couple the plurality of nodes.
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48. The method of claim 47 further comprising: maintaining the social graph comprising: using activity information stored at the storage server, determining a plurality of nodes representing persons specified in a plurality of activity information stored at the storage server; generating a plurality of edges in the social graph, wherein each edge represents at least one activity information of the plurality of activity information; and using the plurality of edges to couple the plurality of nodes. 49. The method of claim 48 wherein the determining the plurality of nodes representing persons specified in a plurality of activity information stored at the storage server comprises disallowing access to personally identifiable information of the persons.
| 0.817344 |
1. A customer analysis method for analyzing electronic customer communication data and generating behavioral assessment data, which method comprises: receiving electronic customer communication data of two or more types by one or more servers configured to provide a user interface comprising a web site, web portal, or virtual portal, wherein at least one of the two or more types of electronic customer communication data comprises social media data, update status, media feed, social media review, social media data stream, social media profile or social media account setup; identifying a customer associated with the electronic customer communication data received by the one or more servers; analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data for that identified customer; generating behavioral assessment data based on the analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data for that identified customer; and displaying instructions to a user via a reporting engine, wherein the instructions are based on the generated behavioral assessment data, wherein the user includes the identified customer or a customer service agent.
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1. A customer analysis method for analyzing electronic customer communication data and generating behavioral assessment data, which method comprises: receiving electronic customer communication data of two or more types by one or more servers configured to provide a user interface comprising a web site, web portal, or virtual portal, wherein at least one of the two or more types of electronic customer communication data comprises social media data, update status, media feed, social media review, social media data stream, social media profile or social media account setup; identifying a customer associated with the electronic customer communication data received by the one or more servers; analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data for that identified customer; generating behavioral assessment data based on the analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data for that identified customer; and displaying instructions to a user via a reporting engine, wherein the instructions are based on the generated behavioral assessment data, wherein the user includes the identified customer or a customer service agent. 2. The customer analysis method of claim 1 , wherein at least one of the two or more types of electronic customer communication data comprises social media data, update status, media feed, social media review, or a social media data stream.
| 0.709052 |
1. An application server for matching a plurality of users within a domain, said application server configured to: (A) implement a social network having a plurality of users; (B) observe network behaviors of at least some of said plurality of users of said social network; (C) develop profiles of at least some of said plurality of users within at least one of a plurality of domains using a profile function, wherein (i) said profile function defines a level of relevance of network behaviors for said domain, (ii) said profile function maps said observed network behaviors to said profiles for said domain and (iii) each of said profiles stores a descriptor representing one of said plurality of users for said domain; and (D) compute matches of two or more of said plurality of users with respect to one of said domains, wherein said matches are based on a relation of common descriptors of said profiles for said domain.
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1. An application server for matching a plurality of users within a domain, said application server configured to: (A) implement a social network having a plurality of users; (B) observe network behaviors of at least some of said plurality of users of said social network; (C) develop profiles of at least some of said plurality of users within at least one of a plurality of domains using a profile function, wherein (i) said profile function defines a level of relevance of network behaviors for said domain, (ii) said profile function maps said observed network behaviors to said profiles for said domain and (iii) each of said profiles stores a descriptor representing one of said plurality of users for said domain; and (D) compute matches of two or more of said plurality of users with respect to one of said domains, wherein said matches are based on a relation of common descriptors of said profiles for said domain. 2. The application server according to claim 1 , wherein said plurality of domains comprises at least one of an activity, a goal, a need and an interest.
| 0.602103 |
1. A method comprising: recognizing, via a processor, received speech with a plurality of domain-specific speech recognizers without knowledge of a domain of the received speech, the plurality of domain-specific speech recognizers comprising two domain-specific speech recognizers from different domains and two domain-specific speech recognizers from a specific domain, wherein each domain-specific speech recognizer of the plurality of domain-specific speech recognizers recognizes the received speech, to yield respective speech recognition outputs; determining a speech recognition confidence score for each of the respective speech recognition outputs; selecting speech recognition candidates from segments of the respective speech recognition outputs based on the speech recognition confidence score for the respective speech recognition outputs; combining, via a machine-learning algorithm, the speech recognition candidates, to yield a combination of the speech recognition candidates; and generating text based on the combination.
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1. A method comprising: recognizing, via a processor, received speech with a plurality of domain-specific speech recognizers without knowledge of a domain of the received speech, the plurality of domain-specific speech recognizers comprising two domain-specific speech recognizers from different domains and two domain-specific speech recognizers from a specific domain, wherein each domain-specific speech recognizer of the plurality of domain-specific speech recognizers recognizes the received speech, to yield respective speech recognition outputs; determining a speech recognition confidence score for each of the respective speech recognition outputs; selecting speech recognition candidates from segments of the respective speech recognition outputs based on the speech recognition confidence score for the respective speech recognition outputs; combining, via a machine-learning algorithm, the speech recognition candidates, to yield a combination of the speech recognition candidates; and generating text based on the combination. 7. The method of claim 1 , wherein selecting the speech recognition candidates further comprises: dividing the received speech into substrings; and selecting a best speech recognition candidate for each substring.
| 0.663843 |
8. A system comprising: one or more computers and one or more storage devices storing instructions that are configured, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a search query; generating a potential substitute term that is related to a query term of the search query; identifying an original set of documents that are responsive to the search query; weighting each potential substitute term that appears in a document in the original set based on a prevalence of the potential substitute term in the original set of documents; producing a pruned set of terms whose weight satisfies a condition; determining that the potential substitute term is a member of the pruned set of terms; and in response to determining that the potential substitute term is a member of the pruned set of terms, modifying the search query to include the potential substitute term.
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8. A system comprising: one or more computers and one or more storage devices storing instructions that are configured, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a search query; generating a potential substitute term that is related to a query term of the search query; identifying an original set of documents that are responsive to the search query; weighting each potential substitute term that appears in a document in the original set based on a prevalence of the potential substitute term in the original set of documents; producing a pruned set of terms whose weight satisfies a condition; determining that the potential substitute term is a member of the pruned set of terms; and in response to determining that the potential substitute term is a member of the pruned set of terms, modifying the search query to include the potential substitute term. 12. The system of claim 8 , wherein a number of documents in the original set of documents is limited to a specific number of the most relevant documents.
| 0.653425 |
1. A speech analysis system for analyzing speech, comprising: at least one hardware processor that: converts inputted speech received from a speaker to text; displays the text in a textual interface displayed on at least one display device; automatically generates feedback information, that comprises results of an analysis of the inputted speech and/or text, by automatically analyzing the inputted speech and/or text; and automatically outputs the feedback information as annotations in the textual interface, wherein the annotations are distinct from the inputted speech and text.
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1. A speech analysis system for analyzing speech, comprising: at least one hardware processor that: converts inputted speech received from a speaker to text; displays the text in a textual interface displayed on at least one display device; automatically generates feedback information, that comprises results of an analysis of the inputted speech and/or text, by automatically analyzing the inputted speech and/or text; and automatically outputs the feedback information as annotations in the textual interface, wherein the annotations are distinct from the inputted speech and text. 9. The speech analysis system of claim 1 , wherein the at least one hardware processor displays feedback in a display on the at least one display device, the display selected from the group consisting of: a dashboard and a teleprompter.
| 0.621406 |
1. A computer-implemented method for identifying items in response to a query, comprising: under control of one or more computer systems configured with executable instructions, receiving a first query from a first user, the first query including one or more query terms; generating a first query result including one or more items corresponding to the first query; for each respective item in the first query result, determining a ranking value using one or more rating scores, each rating score corresponding to a respective query term and depending at least in part on a first frequency with which selection actions are performed by previous users with respect to the respective item, the selection actions being performed against previous query results generated in response to previous queries that include the respective query term; and presenting at least a portion of the first query result for display to the first user according to the ranking values.
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1. A computer-implemented method for identifying items in response to a query, comprising: under control of one or more computer systems configured with executable instructions, receiving a first query from a first user, the first query including one or more query terms; generating a first query result including one or more items corresponding to the first query; for each respective item in the first query result, determining a ranking value using one or more rating scores, each rating score corresponding to a respective query term and depending at least in part on a first frequency with which selection actions are performed by previous users with respect to the respective item, the selection actions being performed against previous query results generated in response to previous queries that include the respective query term; and presenting at least a portion of the first query result for display to the first user according to the ranking values. 10. The computer-implemented method of claim 1 , wherein determining comprises computing a sum of the one or more rating scores.
| 0.65157 |
1. A method of protecting an original plain text file which comprises the steps of: a) encrypting the original plain text file and making the original plain text file available to a user as a protected file; b) issuing to said user a user program and a user license, operable on a computer, to enable said user to decrypt the protected file and view the original plain text file as an image of the original plain text file whilst protecting the image of the original plain text file from being copied to any file, other than a further protected file, wherein the image of the original plain text file cannot be found by other programs on the computer; and c) arranging that the user program comprises an editor program that allows the user to (i) edit the image of the original plain text file to create an edited image and (ii) to save changes made to the image of the original plain text file in an encrypted form, separate from the original plain text file, wherein the editor program enables the use to create the edited image of the original plain text file from the protected file and a difference file using the editor program and user license, wherein, before allowing the user to view or edit the image of the original plain text file, the user program checks its own validity by checking a digital signature to ensure the user program has not been modified, and wherein parts of the original plain text file are marked as non-editable, and the editor program prevents such parts being edited so that they will always be present in any image created from the original plain text file and any difference file or files.
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1. A method of protecting an original plain text file which comprises the steps of: a) encrypting the original plain text file and making the original plain text file available to a user as a protected file; b) issuing to said user a user program and a user license, operable on a computer, to enable said user to decrypt the protected file and view the original plain text file as an image of the original plain text file whilst protecting the image of the original plain text file from being copied to any file, other than a further protected file, wherein the image of the original plain text file cannot be found by other programs on the computer; and c) arranging that the user program comprises an editor program that allows the user to (i) edit the image of the original plain text file to create an edited image and (ii) to save changes made to the image of the original plain text file in an encrypted form, separate from the original plain text file, wherein the editor program enables the use to create the edited image of the original plain text file from the protected file and a difference file using the editor program and user license, wherein, before allowing the user to view or edit the image of the original plain text file, the user program checks its own validity by checking a digital signature to ensure the user program has not been modified, and wherein parts of the original plain text file are marked as non-editable, and the editor program prevents such parts being edited so that they will always be present in any image created from the original plain text file and any difference file or files. 5. The method as claimed in claim 1 in which said changes are stored in said protected file or in the difference file related to the protected file.
| 0.547953 |
14. A method for text input comprising: receiving input identifying a touch point at a first position wherein said input is a touch input identifying a virtual key and wherein said touch point is the point of touch for the touch input; displaying a first set of candidates comprising a plurality of candidates, the candidates comprising candidate wordstems at a second position offset from said touch point, said first position and said second position both being in a common display area, and interpreting subsequent touch input originating from the touch point as having an offset position at a projected touch point originating at the second position wherein the offset of the projected touch point and an offset of the subsequent touch input are related, wherein at least one of the wordstems comprises a word; receiving input referring to a first candidate being comprised in said first set; receiving a select command of said first candidate; and inputting said selected candidate as text.
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14. A method for text input comprising: receiving input identifying a touch point at a first position wherein said input is a touch input identifying a virtual key and wherein said touch point is the point of touch for the touch input; displaying a first set of candidates comprising a plurality of candidates, the candidates comprising candidate wordstems at a second position offset from said touch point, said first position and said second position both being in a common display area, and interpreting subsequent touch input originating from the touch point as having an offset position at a projected touch point originating at the second position wherein the offset of the projected touch point and an offset of the subsequent touch input are related, wherein at least one of the wordstems comprises a word; receiving input referring to a first candidate being comprised in said first set; receiving a select command of said first candidate; and inputting said selected candidate as text. 24. The method according to claim 14 , wherein the first set of candidates comprises a candidate that is associated with a prediction.
| 0.621941 |
2. The method of claim 1 , wherein the search-result item is a web page.
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2. The method of claim 1 , wherein the search-result item is a web page. 5. The method of claim 2 , further comprising: determining that the search-result item includes a particular language identified from the user query.
| 0.923858 |
1. A system for facilitating the analysis of software code, the system comprising: a decompiler and analysis subsystem operating on a processor, the decompiler and analysis subsystem comprising; means for separating the executable software code into a code section and a data section; means for generating one or more signature files from an input set of libraries comprising at least one of industry standard libraries and analyst-generated libraries; and means for comparing the code section of the executable software code to the one or more signature files; and means for generating a data-flow graph from at least a portion of the code section that does not match with any of the one of more signature files, the data-flow generation comprising variablization and variable type determination.
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1. A system for facilitating the analysis of software code, the system comprising: a decompiler and analysis subsystem operating on a processor, the decompiler and analysis subsystem comprising; means for separating the executable software code into a code section and a data section; means for generating one or more signature files from an input set of libraries comprising at least one of industry standard libraries and analyst-generated libraries; and means for comparing the code section of the executable software code to the one or more signature files; and means for generating a data-flow graph from at least a portion of the code section that does not match with any of the one of more signature files, the data-flow generation comprising variablization and variable type determination. 2. The system of claim 1 wherein the decompiler and analysis subsystem further comprises means for creating an intermediate representation of the executable software code comprising a complete model of the executable software code based on a data section and the code sections.
| 0.5 |
11. One or more computer storage media according to claim 9 , wherein said step of managing the natural language entry includes: displaying the natural language entry in a list of one or more natural language entries within the interface, the list contains entries associated with the plurality of the types of PIM data which include task PIM data, contact PIM data and calendar PIM data.
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11. One or more computer storage media according to claim 9 , wherein said step of managing the natural language entry includes: displaying the natural language entry in a list of one or more natural language entries within the interface, the list contains entries associated with the plurality of the types of PIM data which include task PIM data, contact PIM data and calendar PIM data. 13. One or more computer storage media according to claim 11 , wherein said natural language entry is a text entry, the identifying which type of PIM data is associated with each natural language entry includes determining the type of PIM data by parsing the text if there is no indicator that identifies the type of PIM data.
| 0.834485 |
7. A system for processing a hierarchically structured document, comprising: means for creating a first state variable table having a first set of state variables; means for creating a first reference to the first state variable table, said first reference to the first state variable table being associated with a structure portion of a predetermined hierarchical level of the document; means for creating, for said predetermined hierarchical level of the document when a content portion of said predetermined hierarchical level is processed, a second state variable table and copying the first set of state variables to a second set of state variables of the second state variable table; means for processing said content portion of said predetermined hierarchical level using a second reference to said second state variable table; means for determining if processing of content for said predetermined hierarchical level is finished; means for processing a structure portion of a first subsequent hierarchical level, wherein said first subsequent hierarchical level is lower in the hierarchical structure of the document than the predetermined hierarchical level; means for copying one of the first and second references to a third reference associated with the structure portion of the first subsequent hierarchical level; means for processing a structure portion of a second subsequent hierarchical level, wherein said second subsequent hierarchical level is lower in the hierarchical structure of the document than the first subsequent hierarchical level; means for creating, for said second subsequent hierarchical level of the document when a content portion of said second subsequent hierarchical level is processed, a third state variable table referenced by a fourth reference and copying the set of state variables referred to by the third reference to a third set of state variables in the third state variable table; means for processing said content portion of said second subsequent hierarchical level using the fourth reference to said third set of state variables; means for determining if processing of said content portion of said second subsequent hierarchical level is finished; and means for copying the fourth reference to the third reference, when said processing of said content portion of said second subsequent hierarchical level is determined to be finished.
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7. A system for processing a hierarchically structured document, comprising: means for creating a first state variable table having a first set of state variables; means for creating a first reference to the first state variable table, said first reference to the first state variable table being associated with a structure portion of a predetermined hierarchical level of the document; means for creating, for said predetermined hierarchical level of the document when a content portion of said predetermined hierarchical level is processed, a second state variable table and copying the first set of state variables to a second set of state variables of the second state variable table; means for processing said content portion of said predetermined hierarchical level using a second reference to said second state variable table; means for determining if processing of content for said predetermined hierarchical level is finished; means for processing a structure portion of a first subsequent hierarchical level, wherein said first subsequent hierarchical level is lower in the hierarchical structure of the document than the predetermined hierarchical level; means for copying one of the first and second references to a third reference associated with the structure portion of the first subsequent hierarchical level; means for processing a structure portion of a second subsequent hierarchical level, wherein said second subsequent hierarchical level is lower in the hierarchical structure of the document than the first subsequent hierarchical level; means for creating, for said second subsequent hierarchical level of the document when a content portion of said second subsequent hierarchical level is processed, a third state variable table referenced by a fourth reference and copying the set of state variables referred to by the third reference to a third set of state variables in the third state variable table; means for processing said content portion of said second subsequent hierarchical level using the fourth reference to said third set of state variables; means for determining if processing of said content portion of said second subsequent hierarchical level is finished; and means for copying the fourth reference to the third reference, when said processing of said content portion of said second subsequent hierarchical level is determined to be finished. 8. A system according to claim 7, further comprising: means for creating an entry in a stack for the predetermined and the first subsequent hierarchical levels of the document, each entry in the stack including a reference to a set of references corresponding to structure of a corresponding hierarchical level and used during processing of the corresponding hierarchical level, and including a reference to a set of references corresponding to content of a corresponding hierarchical level and used during processing of a content portion of the corresponding hierarchical level.
| 0.5 |
1. A method of adapting a speech system, comprising: logging speech data from the speech system; processing the speech data, by a plurality of characteristic detector modules, to detect a plurality of user characteristics from the speech data, the plurality of characteristic detector modules each map the speech data into at least one category associated with at least one user characteristic of the plurality of user characteristics, the user characteristics comprise characteristics that are specific to behavior of a user of a vehicle when saying a command to an automated system; tracking a frequency of each of the plurality of user characteristics; and when the frequency of at least one of the plurality of user characteristics reaches a certain frequency, selecting a language model associated with the user of the vehicle, and updating the language model based on the categories of the plurality of the user characteristics.
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1. A method of adapting a speech system, comprising: logging speech data from the speech system; processing the speech data, by a plurality of characteristic detector modules, to detect a plurality of user characteristics from the speech data, the plurality of characteristic detector modules each map the speech data into at least one category associated with at least one user characteristic of the plurality of user characteristics, the user characteristics comprise characteristics that are specific to behavior of a user of a vehicle when saying a command to an automated system; tracking a frequency of each of the plurality of user characteristics; and when the frequency of at least one of the plurality of user characteristics reaches a certain frequency, selecting a language model associated with the user of the vehicle, and updating the language model based on the categories of the plurality of the user characteristics. 7. The method of claim 1 wherein the user characteristics comprise at least one of an age, a dialect, and a gender.
| 0.838889 |
11. The software design system according to claim 1 , further comprising an editor module arranged to enable user editing of the formal and informal specifications via data input screens.
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11. The software design system according to claim 1 , further comprising an editor module arranged to enable user editing of the formal and informal specifications via data input screens. 12. The software design system according to claim 11 , wherein the editor module is arranged to store data as data files.
| 0.970243 |
10. The method of monitoring an electronic communication as claimed in claim 6 , further comprising the steps of: accumulating each match of an expression with a stored expression in a results collection; and determining a score for the results collection in accordance with predetermined rules, where the determined score corresponds to an aggregate alert level for the matched expressions and wherein the aggregate alert level is utilized to facilitate monitoring of the electronic communication.
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10. The method of monitoring an electronic communication as claimed in claim 6 , further comprising the steps of: accumulating each match of an expression with a stored expression in a results collection; and determining a score for the results collection in accordance with predetermined rules, where the determined score corresponds to an aggregate alert level for the matched expressions and wherein the aggregate alert level is utilized to facilitate monitoring of the electronic communication. 11. The method as claimed in claim 10 , wherein the predetermined rules for determining a score are based on criteria relating to the relationship of words in an expression of one section of the dictionary to words in an expression of another section of the dictionary.
| 0.88149 |
1. The method of providing language usage guidance with respect to a passage of a document being edited by a user, comprising: generating a hash of the passage being edited; comparing the hash against entries in a database to determine likelihood that the passage is written as intended by the user; and providing alternative language suggestions to the user based in part upon entries in the database; and allowing the user to modify the document by accepting a selected one of the suggestions; a program when run on a local computer to compute a triplet of hashes, wherein each hash consists of a hash for each word in the document, a hash for the word and its preceding word, and a hash for the word and its following word.
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1. The method of providing language usage guidance with respect to a passage of a document being edited by a user, comprising: generating a hash of the passage being edited; comparing the hash against entries in a database to determine likelihood that the passage is written as intended by the user; and providing alternative language suggestions to the user based in part upon entries in the database; and allowing the user to modify the document by accepting a selected one of the suggestions; a program when run on a local computer to compute a triplet of hashes, wherein each hash consists of a hash for each word in the document, a hash for the word and its preceding word, and a hash for the word and its following word. 2. The method of claim 1 , wherein the step of providing alternative suggestions comprises providing first, and second suggestions.
| 0.55234 |
13. The apparatus of claim 12 , further configured to examine the textual data to determine a violation of the policy.
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13. The apparatus of claim 12 , further configured to examine the textual data to determine a violation of the policy. 14. The apparatus of claim 13 , further configured to block a software application from network resources based on the violation.
| 0.922816 |
34. The non-transitory computer readable storage medium of claim 30 , wherein determining if the first metadata item is relevant to the content object comprises: generating a relevance score based upon a similarity of the first metadata item to the content object, wherein the first metadata item is relevant to the content object in response to the relevance score being at least a threshold value.
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34. The non-transitory computer readable storage medium of claim 30 , wherein determining if the first metadata item is relevant to the content object comprises: generating a relevance score based upon a similarity of the first metadata item to the content object, wherein the first metadata item is relevant to the content object in response to the relevance score being at least a threshold value. 35. The non-transitory computer readable storage medium of claim 34 , wherein the threshold value is based upon a type of the activity history.
| 0.918263 |
13. The system of claim 8 , further comprising obtaining information for the media-specific search results from a third-party structured database of media content, and from a non-media-directed database.
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13. The system of claim 8 , further comprising obtaining information for the media-specific search results from a third-party structured database of media content, and from a non-media-directed database. 14. The system of claim 13 , wherein obtaining the information from the non-media-directed database includes modifying the query by adding a term descriptive of media and submitting the modified query to the search engine to query the non-media-directed database with the modified query to limit the information from the non-media-directed database to media-related results.
| 0.933404 |
2. The computer-implemented method of claim 1 , wherein the local context includes either i) at least one preceding word from a current position in the dialog that was uttered by the first user or ii) a start of sentence token that indicates that the next word is a first word being uttered by the first user.
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2. The computer-implemented method of claim 1 , wherein the local context includes either i) at least one preceding word from a current position in the dialog that was uttered by the first user or ii) a start of sentence token that indicates that the next word is a first word being uttered by the first user. 3. The computer-implemented method of claim 2 , wherein: the first portion of the input layer comprises a plurality of nodes that represent a plurality of words, and applying the local context as part of the input to the input layer comprises providing a predetermined value to a portion of the plurality of nodes that correspond to the at least one preceding word that comprises the local context, the predetermined value indicating that a word is part of the local context for the phrase.
| 0.883003 |
1. A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving, by a server comprising one or more computers, first endorsement information characterizing a first member's rating of a first local product or service provider, wherein the first member is in a member network and is provided with a financial incentive to endorse the first local product or service provider; receiving, by the server from a second member in the member network, a local search query comprising information identifying one or more items to be found and a geographic locale to be searched; identifying, using a member network engine available to the server, that there is an association between the first member and the second member, wherein the association comprises an explicit relationship between the first member and the second member or a common membership of the first member and the second member in a community of the member network, wherein the first member is explicitly related to at least one other member in the member network; ranking items responsive to the local search query based on a type of the association between the second member and the first member in the member network; and responding, by the server, to the local search query with information describing a result set responsive to the local search query, the response set including the ranked items.
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1. A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving, by a server comprising one or more computers, first endorsement information characterizing a first member's rating of a first local product or service provider, wherein the first member is in a member network and is provided with a financial incentive to endorse the first local product or service provider; receiving, by the server from a second member in the member network, a local search query comprising information identifying one or more items to be found and a geographic locale to be searched; identifying, using a member network engine available to the server, that there is an association between the first member and the second member, wherein the association comprises an explicit relationship between the first member and the second member or a common membership of the first member and the second member in a community of the member network, wherein the first member is explicitly related to at least one other member in the member network; ranking items responsive to the local search query based on a type of the association between the second member and the first member in the member network; and responding, by the server, to the local search query with information describing a result set responsive to the local search query, the response set including the ranked items. 19. The computer storage medium of claim 1 , wherein the information describing the result set includes at least one identifier personally identifying the first member and indicating that endorsement information characterizing the first member's rating of the first local product or service provider has been received.
| 0.761799 |
31. A method according to claim 19, wherein the computer display information comprises one or more data fields.
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31. A method according to claim 19, wherein the computer display information comprises one or more data fields. 32. A method according to claim 31, wherein at least one data field comprises an unprotected input field, further comprising: converting the unprotected input field into a text input field within the markup language document using a translator within the host extension of the server application framework of the server computer.
| 0.918593 |
1. A method for operating an intelligent automated assistant, the method comprising: at an electronic device with a processor and memory storing one or more programs for execution by the processor: receiving, from a user, a speech input containing a heteronym and one or more additional words; processing the speech input using an automatic speech recognition system to determine at least one of: a phonemic string corresponding to the heteronym as pronounced by the user in the speech input; and a frequency of occurrence of an n-gram with respect to a corpus, wherein the n-gram includes the heteronym and the one or more additional words; determining a correct pronunciation of the heteronym based on at least one of the phonemic string and the frequency of occurrence of the n-gram; generating a dialogue response to the speech input, wherein the dialogue response includes the heteronym; and outputting the dialogue response as a speech output, wherein the heteronym in the dialogue response is pronounced in the speech output according to the determined correct pronunciation.
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1. A method for operating an intelligent automated assistant, the method comprising: at an electronic device with a processor and memory storing one or more programs for execution by the processor: receiving, from a user, a speech input containing a heteronym and one or more additional words; processing the speech input using an automatic speech recognition system to determine at least one of: a phonemic string corresponding to the heteronym as pronounced by the user in the speech input; and a frequency of occurrence of an n-gram with respect to a corpus, wherein the n-gram includes the heteronym and the one or more additional words; determining a correct pronunciation of the heteronym based on at least one of the phonemic string and the frequency of occurrence of the n-gram; generating a dialogue response to the speech input, wherein the dialogue response includes the heteronym; and outputting the dialogue response as a speech output, wherein the heteronym in the dialogue response is pronounced in the speech output according to the determined correct pronunciation. 11. The method of claim 1 , further comprising: annotating the heteronym in the dialogue response with a tag to identify the correct pronunciation of the heteronym, wherein outputting the dialogue response includes synthesizing the heteronym in the dialogue response using a speech synthesizer, and wherein the heteronym in the dialogue response is synthesized based on the tag.
| 0.658741 |
1. A computer-implemented method, comprising: selecting a plurality of search queries; grouping the plurality of search queries into one or more clusters, wherein grouping further comprises: assigning each of the plurality of search queries to a cluster in a total number of clusters; designating one of the search queries assigned to each cluster as a cluster center for the each cluster; and adjusting one or more of (i) the total number of clusters, (ii) an assignment of search queries to the clusters, and (iii) a designation of cluster centers for the clusters, to minimize an aggregated metric of all search queries, where a metric of a search query is between the search query and the cluster center of the cluster comprising the search query; selecting a representative query for each cluster; associating each cluster with a respective representative category; assigning a respective rank to each of the clusters, the assigning being based on a cluster popularity score of each cluster and a category popularity score of each cluster's respective representative category; and presenting the selected representative queries in order according to the ranks of their respective clusters, wherein assigning a respective rank to each of the clusters is performed on one or more processors.
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1. A computer-implemented method, comprising: selecting a plurality of search queries; grouping the plurality of search queries into one or more clusters, wherein grouping further comprises: assigning each of the plurality of search queries to a cluster in a total number of clusters; designating one of the search queries assigned to each cluster as a cluster center for the each cluster; and adjusting one or more of (i) the total number of clusters, (ii) an assignment of search queries to the clusters, and (iii) a designation of cluster centers for the clusters, to minimize an aggregated metric of all search queries, where a metric of a search query is between the search query and the cluster center of the cluster comprising the search query; selecting a representative query for each cluster; associating each cluster with a respective representative category; assigning a respective rank to each of the clusters, the assigning being based on a cluster popularity score of each cluster and a category popularity score of each cluster's respective representative category; and presenting the selected representative queries in order according to the ranks of their respective clusters, wherein assigning a respective rank to each of the clusters is performed on one or more processors. 2. The method of claim 1 wherein selecting the representative query for each query cluster further comprises: selecting the representative query based on a query popularity score for each search query in the cluster, where the query popularity score for a search query is based on a rate of increase in search volume for the search query or a total search volume for the search query.
| 0.618327 |
19. The system for managing user contributed data extraction templates using weighted ranking score analysis of claim 18 further comprising: transforming the data extraction template ranking score associated with the data extraction template used to extract data from the source document to reflect a decrease in the data acceptance ratio term data whenever data extracted from the source document using the corresponding data extraction template is not accepted by the user.
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19. The system for managing user contributed data extraction templates using weighted ranking score analysis of claim 18 further comprising: transforming the data extraction template ranking score associated with the data extraction template used to extract data from the source document to reflect a decrease in the data acceptance ratio term data whenever data extracted from the source document using the corresponding data extraction template is not accepted by the user. 20. The system for managing user contributed data extraction templates using weighted ranking score analysis of claim 19 further comprising: transforming the data extraction template ranking score associated with the data extraction template used to extract data from the source document whenever data extracted from the source using the template is deemed as correct and accepted by the user to reflect the increase in the data acceptance count associated with the data extraction template used to extract accepted data.
| 0.904434 |
24. A computer program product comprising: a memory having microcontroller-readable code embedded therein for reasoning about data using fuzzy logic, comprising: (i) code means for accessing the data; (ii) code means for determining a type of the data from a group consisting of numeric, linguistic and a hybrid combination thereof; (iii) code means for selecting a rule for firing based on the determined type of the data; (iv) code means for obtaining fuzzy membership grades; (v) code means for aggregating the fuzzy membership grades by invoking a parametric formulation; (vi) code means for applying a compositional rule of inference parametrically to extract a consequent to obtain a fuzzy output; and (vii) code means for defuzifying the fuzzy output.
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24. A computer program product comprising: a memory having microcontroller-readable code embedded therein for reasoning about data using fuzzy logic, comprising: (i) code means for accessing the data; (ii) code means for determining a type of the data from a group consisting of numeric, linguistic and a hybrid combination thereof; (iii) code means for selecting a rule for firing based on the determined type of the data; (iv) code means for obtaining fuzzy membership grades; (v) code means for aggregating the fuzzy membership grades by invoking a parametric formulation; (vi) code means for applying a compositional rule of inference parametrically to extract a consequent to obtain a fuzzy output; and (vii) code means for defuzifying the fuzzy output. 31. The computer program product according to claim 24 , wherein the parametric formulation ranges between the Formal Logical extreme to the Mamdani extreme.
| 0.5 |
1. A method for storing at least one file generated by a distributed application in a parallel computing system, wherein said file comprises a plurality of sub-files, said method comprising the steps of: obtaining a user specification of semantic information related to said file; determining semantically meaningful sub-file boundaries for said plurality of sub-files based on said semantic information; providing said semantic information as a data structure description to a data formatting library write function, wherein said semantic information is dependent upon a content of said file; and storing said semantic information related to said file with additional metadata related to said file and with one or more of said sub-files using said determined semantically meaningful sub-file boundaries in one or more storage nodes of said parallel computing system.
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1. A method for storing at least one file generated by a distributed application in a parallel computing system, wherein said file comprises a plurality of sub-files, said method comprising the steps of: obtaining a user specification of semantic information related to said file; determining semantically meaningful sub-file boundaries for said plurality of sub-files based on said semantic information; providing said semantic information as a data structure description to a data formatting library write function, wherein said semantic information is dependent upon a content of said file; and storing said semantic information related to said file with additional metadata related to said file and with one or more of said sub-files using said determined semantically meaningful sub-file boundaries in one or more storage nodes of said parallel computing system. 8. The method of claim 1 , wherein said semantic information related to a given sub-file is stored with said corresponding sub-file.
| 0.71087 |
2. The method as recited in claim 1 , wherein, regardless of which characters are assigned to the convenient key by a predetermined way, said reassigning one of the characters that is most likely to follow the initial input character causes the predetermined way to change so that only said one of the characters is now assigned to the convenient key.
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2. The method as recited in claim 1 , wherein, regardless of which characters are assigned to the convenient key by a predetermined way, said reassigning one of the characters that is most likely to follow the initial input character causes the predetermined way to change so that only said one of the characters is now assigned to the convenient key. 3. The method as recited in claim 2 , wherein the convenient key is located in a center of the standard telephone keypad.
| 0.874514 |
5. The method of claim 1 further comprising: constructing, by the processor, a document vector for each of a plurality of documents from the corpus, the document vector including a first plurality of components corresponding to words and a second plurality of components corresponding to at least some of the meaningful phrases; and forming, by the processor, a plurality of clusters of documents based at least in part on the document vectors.
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5. The method of claim 1 further comprising: constructing, by the processor, a document vector for each of a plurality of documents from the corpus, the document vector including a first plurality of components corresponding to words and a second plurality of components corresponding to at least some of the meaningful phrases; and forming, by the processor, a plurality of clusters of documents based at least in part on the document vectors. 6. The method of claim 5 wherein selecting the meaningful phrases includes: computing a phrase weight for each candidate phrase, the phrase weight being based on a number of documents in the corpus that contain the candidate phrase; and selecting the meaningful phrases based on the phrase weights.
| 0.91784 |
36. A system comprising: one or more computers programmed to perform operations comprising: calculating one or more time trend statistics for a plurality of quality of result statistics for a first document as a search result for a first query, each of the quality of result statistics corresponding to a different time period and being an estimate of a respective portion of users that found the first document relevant to the first query out of a total number of users who viewed the first document as a search result for the first query during the time period, the one or more time trend statistics estimating changes in the quality of result statistics over time; generating a first modified quality of result statistic by modifying a first quality of result statistic for the first document as a search result for the first query by a factor, where the factor is based on the one or more time trend statistics; and providing the first modified quality of result statistic as an input to a document ranking process for the first document and the first query.
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36. A system comprising: one or more computers programmed to perform operations comprising: calculating one or more time trend statistics for a plurality of quality of result statistics for a first document as a search result for a first query, each of the quality of result statistics corresponding to a different time period and being an estimate of a respective portion of users that found the first document relevant to the first query out of a total number of users who viewed the first document as a search result for the first query during the time period, the one or more time trend statistics estimating changes in the quality of result statistics over time; generating a first modified quality of result statistic by modifying a first quality of result statistic for the first document as a search result for the first query by a factor, where the factor is based on the one or more time trend statistics; and providing the first modified quality of result statistic as an input to a document ranking process for the first document and the first query. 39. The system of claim 36 , wherein the factor is further based on how statistically significant the quality of result difference is.
| 0.702415 |
6. The data storage and/or retrieval system of claim 1 , wherein the plurality of entity type data tunnels comprise a plurality of combinative data tunnels, the plurality of attribute data tunnels comprise a plurality of combinative data tunnels, and the plurality of data cells of said combinative data tunnels are combinative data cells, wherein each respective instance of an attribute has one respective combinative data cell in all respective combinative data tunnels, wherein each of the combinative data cells has data for which a respective bounding operator evaluates to a boolean result which indicates either the likely possibility or the impossibility of the attribute instance corresponding to said combinative data cell being bounded for a given set of one or more operands, and wherein the storage engine accesses one or more of the plurality of combinative data tunnels based on one or more of said bounding operator and a given set of one or more operands.
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6. The data storage and/or retrieval system of claim 1 , wherein the plurality of entity type data tunnels comprise a plurality of combinative data tunnels, the plurality of attribute data tunnels comprise a plurality of combinative data tunnels, and the plurality of data cells of said combinative data tunnels are combinative data cells, wherein each respective instance of an attribute has one respective combinative data cell in all respective combinative data tunnels, wherein each of the combinative data cells has data for which a respective bounding operator evaluates to a boolean result which indicates either the likely possibility or the impossibility of the attribute instance corresponding to said combinative data cell being bounded for a given set of one or more operands, and wherein the storage engine accesses one or more of the plurality of combinative data tunnels based on one or more of said bounding operator and a given set of one or more operands. 7. The data storage and/or retrieval system of claim 6 , wherein each instance of a respective attribute is expressed in unitary scale and as a significand with respect to a fixed radix point, each of the combinative data tunnels is respective to part of the significand, all of the combinative data tunnels are mutually exclusive in respect of the signficand, and all of the combinative data tunnels enclose the whole of the signficand.
| 0.820953 |
9. A computer-implemented method performed by a computer system for processing an interaction, the interaction including an utterance requiring recognition before being usable for further computer-implemented processing, the computer-implemented method comprising: receiving data representing an utterance from a computer application, the utterance received from a device of a customer over a computer network; identifying a grammar to which the utterance is expected to conform; determining a time length of the utterance; dynamically selecting, based at least in part on the identified grammar and the time length of the utterance, one or more recognizers from: an automated speech recognizer (ASR), and a second type of recognizer, different from the automated speech recognizer, and communicating over a computer network with devices located at locations remote from the computer system; and providing a recognition result responsive to results of processing by the one or more recognizers.
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9. A computer-implemented method performed by a computer system for processing an interaction, the interaction including an utterance requiring recognition before being usable for further computer-implemented processing, the computer-implemented method comprising: receiving data representing an utterance from a computer application, the utterance received from a device of a customer over a computer network; identifying a grammar to which the utterance is expected to conform; determining a time length of the utterance; dynamically selecting, based at least in part on the identified grammar and the time length of the utterance, one or more recognizers from: an automated speech recognizer (ASR), and a second type of recognizer, different from the automated speech recognizer, and communicating over a computer network with devices located at locations remote from the computer system; and providing a recognition result responsive to results of processing by the one or more recognizers. 13. The computer-implemented method of claim 9 , wherein said dynamically selecting favors selection of the automated speech recognizer relative to the second type of recognizer based on recognition cost factors.
| 0.616796 |
20. The apparatus of claim 19 , further comprising one or more computer programs for determining a subpredicate combined selectivity estimate of the unapplied eligible predicates using column distribution statistics of the pre-defined query.
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20. The apparatus of claim 19 , further comprising one or more computer programs for determining a subpredicate combined selectivity estimate of the unapplied eligible predicates using column distribution statistics of the pre-defined query. 21. The apparatus of claim 20 , wherein a cardinality ratio comprises a ratio of a cardinality of the pre-defined query to a product of cardinalities of base tables referenced in the pre-defined query and the query.
| 0.786836 |
19. An apparatus for training a prosody statistic model with a raw corpus that includes a plurality of sentences with punctuation, comprising: a tokenization unit configured to transform said plurality of sentences in said raw corpus into a plurality of token sequences respectively; a counter configured to count frequency of each adjacent token pair occurring in said plurality of token sequences and frequency of punctuation that represents a pause occurring at associated positions of said each token pair; a pause probability calculator configured to calculate pause probabilities at said associated positions of said each token pair, based on the frequency of each adjacent token pair and the frequency of punctuation; and a prosody statistic model constructor configured to construct said prosody statistic model based on said token pairs and said pause probabilities at associated positions thereof.
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19. An apparatus for training a prosody statistic model with a raw corpus that includes a plurality of sentences with punctuation, comprising: a tokenization unit configured to transform said plurality of sentences in said raw corpus into a plurality of token sequences respectively; a counter configured to count frequency of each adjacent token pair occurring in said plurality of token sequences and frequency of punctuation that represents a pause occurring at associated positions of said each token pair; a pause probability calculator configured to calculate pause probabilities at said associated positions of said each token pair, based on the frequency of each adjacent token pair and the frequency of punctuation; and a prosody statistic model constructor configured to construct said prosody statistic model based on said token pairs and said pause probabilities at associated positions thereof. 20. The apparatus for training a prosody statistic model according to claim 19 , wherein said associated positions of said each token pair include: before, after and amid said token pair.
| 0.554199 |
15. The computer system of claim 13 , wherein identifying the higher frequency of user interactions with the first set of notifications comprises: identifying patterns in the user interactions with the first set of notifications, the patterns describing the characteristics of the interactions of the user with the first set of notifications.
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15. The computer system of claim 13 , wherein identifying the higher frequency of user interactions with the first set of notifications comprises: identifying patterns in the user interactions with the first set of notifications, the patterns describing the characteristics of the interactions of the user with the first set of notifications. 17. The computer system of claim 15 , wherein identifying the patterns in the user interactions comprises: identifying a type of notifications from the first set of notifications most frequently interacted with by the user; and providing the second set of notifications of third-party content objects of the identified type of notifications to the client device.
| 0.877282 |
1. A system comprising: a computer-readable memory storing executable instructions; and one or more processors in communication with the computer-readable memory, wherein the one or more processors are programmed by the executable instructions to at least: receive text corresponding to at least a portion of a user input; generate a first natural language understanding (“NLU”) result using the text and a first NLU module, wherein the first NLU module is associated with a first subject matter, and wherein the first NLU result is associated with a first score indicative of a confidence in the first NLU result; generate a second NLU result using the text and a second NLU module, wherein the second NLU module is associated with a second subject matter, and wherein the second NLU result is associated with a second score indicative of a confidence in the second NLU result; select the first NLU result based at least partly on the first score and the second score; and generate a response based at least partly on the first NLU result.
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1. A system comprising: a computer-readable memory storing executable instructions; and one or more processors in communication with the computer-readable memory, wherein the one or more processors are programmed by the executable instructions to at least: receive text corresponding to at least a portion of a user input; generate a first natural language understanding (“NLU”) result using the text and a first NLU module, wherein the first NLU module is associated with a first subject matter, and wherein the first NLU result is associated with a first score indicative of a confidence in the first NLU result; generate a second NLU result using the text and a second NLU module, wherein the second NLU module is associated with a second subject matter, and wherein the second NLU result is associated with a second score indicative of a confidence in the second NLU result; select the first NLU result based at least partly on the first score and the second score; and generate a response based at least partly on the first NLU result. 9. The system of claim 1 , wherein the first NLU module being associated with the first subject matter comprises the first NLU module being configured to generate NLU results comprising intents associated with the first subject matter and not NLU results comprising intents associated with the second subject matter.
| 0.605242 |
15. A computer-implemented method comprising computer-implemented operations for: instantiating at least one iterator in response to a search request, wherein the iterator includes fixed state information that remains constant over a life of the iterator, and includes dynamic state information that is updated over the life of the iterator; creating a storage structure associated with the iterator and storing a representation of the dynamic state information in the storage structure; creating a stack structure that includes a plurality of entries, further storing the representation of the dynamic state information in one of the entries, and further storing at least a further representation of a further instance of the dynamic state information in a further one of the entries; traversing the iterator through at least a portion of at least one postings list in connection with performing a search in response to the search request; updating at least one instance of the dynamic state information, as the iterator traverses through at least the portion of the postings list while performing the search; and evaluating whether to create a checkpoint of the iterator at one or more points during the iterator traversing through at least the portion of the postings list while performing the search, wherein the checkpoint includes at least a representation of the dynamic state information at one of the points.
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15. A computer-implemented method comprising computer-implemented operations for: instantiating at least one iterator in response to a search request, wherein the iterator includes fixed state information that remains constant over a life of the iterator, and includes dynamic state information that is updated over the life of the iterator; creating a storage structure associated with the iterator and storing a representation of the dynamic state information in the storage structure; creating a stack structure that includes a plurality of entries, further storing the representation of the dynamic state information in one of the entries, and further storing at least a further representation of a further instance of the dynamic state information in a further one of the entries; traversing the iterator through at least a portion of at least one postings list in connection with performing a search in response to the search request; updating at least one instance of the dynamic state information, as the iterator traverses through at least the portion of the postings list while performing the search; and evaluating whether to create a checkpoint of the iterator at one or more points during the iterator traversing through at least the portion of the postings list while performing the search, wherein the checkpoint includes at least a representation of the dynamic state information at one of the points. 16. The method of claim 15 , further comprising storing the representation of only the dynamic state information, as associated with the checkpoint.
| 0.593624 |
1. A method for multimedia model retrieval, comprising: acquiring parameters of a first multimedia model to be retrieved; performing a projection on the first multimedia model according to the parameters of the first multimedia model so as to obtain a projection image; performing a feature extraction on the projection image to generate a first feature extraction result; comparing the first feature extraction result with a plurality of feature extraction results corresponding to a plurality of multimedia models stored in a database so as to obtain a retrieval result, wherein the retrieval result comprises a number of multimedia models; labeling by a user each of the multimedia models in the retrieval result with a probability value; training a support vector machine with the labeled multimedia models; performing a probability-based classification on the plurality of multimedia models stored in the database by using the support vector machine to obtain a classification result; and updating the retrieval result with the classification result.
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1. A method for multimedia model retrieval, comprising: acquiring parameters of a first multimedia model to be retrieved; performing a projection on the first multimedia model according to the parameters of the first multimedia model so as to obtain a projection image; performing a feature extraction on the projection image to generate a first feature extraction result; comparing the first feature extraction result with a plurality of feature extraction results corresponding to a plurality of multimedia models stored in a database so as to obtain a retrieval result, wherein the retrieval result comprises a number of multimedia models; labeling by a user each of the multimedia models in the retrieval result with a probability value; training a support vector machine with the labeled multimedia models; performing a probability-based classification on the plurality of multimedia models stored in the database by using the support vector machine to obtain a classification result; and updating the retrieval result with the classification result. 8. The method according to claim 1 , further comprising: sorting the retrieval result obtained by comparing or the retrieval result obtained after updating.
| 0.627279 |
28. A non-transitory computer-readable medium including: contents that, when executed, cause a computing system to recommend content, by performing a method comprising: extracting a quotation from a text document in a corpus of text documents; identifying one or more entities that are referenced by the text document, each of the determined entities being electronically represented by the content recommendation system, wherein identifying the one or more entities includes linking together multiple mentions of a same entity across the text document, the linking including resolving pronoun coreference to identify an entity that is a speaker of the quotation; attributing the quotation to the speaker of the quotation by storing data that associates the quotation with the identified entity that is the speaker of the quotation; and providing the quotation by transmitting text that represents the quotation and the attributed speaker.
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28. A non-transitory computer-readable medium including: contents that, when executed, cause a computing system to recommend content, by performing a method comprising: extracting a quotation from a text document in a corpus of text documents; identifying one or more entities that are referenced by the text document, each of the determined entities being electronically represented by the content recommendation system, wherein identifying the one or more entities includes linking together multiple mentions of a same entity across the text document, the linking including resolving pronoun coreference to identify an entity that is a speaker of the quotation; attributing the quotation to the speaker of the quotation by storing data that associates the quotation with the identified entity that is the speaker of the quotation; and providing the quotation by transmitting text that represents the quotation and the attributed speaker. 29. The computer-readable medium of claim 28 wherein the computer-readable medium is at least one of a memory in a computing device or a data transmission medium transmitting a generated signal containing the contents.
| 0.599852 |
6. The touch-typable device as claimed in claim 1 , wherein said movable keymask is superimposed over a character map comprising of said textual input choices, further wherein each of said plurality of cells of said movable keymask superimposes over a textual input choice of said character map.
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6. The touch-typable device as claimed in claim 1 , wherein said movable keymask is superimposed over a character map comprising of said textual input choices, further wherein each of said plurality of cells of said movable keymask superimposes over a textual input choice of said character map. 7. The touch-typable device as claimed in claim 6 , wherein said movable keymask is navigated over the said character map to access said textual input choices using said touch-typable device.
| 0.923607 |
7. A system for recognizing a gesture, the system comprising: one or more processors configured to: determine a first set of metrics to differentiate gestures from among only a first subset of gestures of a plurality of gestures, the first subset of gestures recognizable as valid input in a particular context of a user interface environment of the system; receive user input that causes a gesture classification context to be applied from a plurality of gesture classification contexts available for a gesture analysis engine, wherein the gesture classification context indicates the first subset of gestures; apply the gesture classification context to the gesture analysis engine; after applying the gesture classification context, receive data indicative of the gesture performed by a user; and identify, based on the first set of metrics, using the gesture analysis engine, the gesture in accordance with the applied gesture classification context, wherein identifying includes identifying the gesture from only the first subset of gestures of the plurality of gestures indicated by the applied gesture classification context while the gesture classification context is applied; determine a second subset of gestures from the plurality of gestures, wherein each gesture of the second subset of gestures is valid in a second gesture classification context; calculate a second set of metrics for the second subset of gestures to differentiate gestures from among only the second subset of gestures, wherein: only the second subset of gestures are eligible to be identified when the second gesture classification context is applied, and at least one gesture of the second subset of gestures is not in the first subset of gestures; receive user input that causes the second gesture classification context to be applied to the gesture analysis engine; after applying the second gesture classification context, receive data indicative of a second gesture performed by the user; and identify, based on the second set of metrics, the second gesture in accordance with the applied second gesture classification context, wherein identifying includes identifying the second gesture from only the second subset of gestures indicated by the applied second gesture classification context.
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7. A system for recognizing a gesture, the system comprising: one or more processors configured to: determine a first set of metrics to differentiate gestures from among only a first subset of gestures of a plurality of gestures, the first subset of gestures recognizable as valid input in a particular context of a user interface environment of the system; receive user input that causes a gesture classification context to be applied from a plurality of gesture classification contexts available for a gesture analysis engine, wherein the gesture classification context indicates the first subset of gestures; apply the gesture classification context to the gesture analysis engine; after applying the gesture classification context, receive data indicative of the gesture performed by a user; and identify, based on the first set of metrics, using the gesture analysis engine, the gesture in accordance with the applied gesture classification context, wherein identifying includes identifying the gesture from only the first subset of gestures of the plurality of gestures indicated by the applied gesture classification context while the gesture classification context is applied; determine a second subset of gestures from the plurality of gestures, wherein each gesture of the second subset of gestures is valid in a second gesture classification context; calculate a second set of metrics for the second subset of gestures to differentiate gestures from among only the second subset of gestures, wherein: only the second subset of gestures are eligible to be identified when the second gesture classification context is applied, and at least one gesture of the second subset of gestures is not in the first subset of gestures; receive user input that causes the second gesture classification context to be applied to the gesture analysis engine; after applying the second gesture classification context, receive data indicative of a second gesture performed by the user; and identify, based on the second set of metrics, the second gesture in accordance with the applied second gesture classification context, wherein identifying includes identifying the second gesture from only the second subset of gestures indicated by the applied second gesture classification context. 11. The system for recognizing the gesture of claim 7 , wherein: determining the second subset of gestures from the plurality of gestures and determining the second set of metrics for the second subset of gestures to differentiate gestures from among only the second subset of gestures occur in response to an application being installed on the system.
| 0.557768 |
12. A method for use in recognizing continuous speech, the method comprising: accepting signals corresponding to interspersed speech elements including text elements corresponding to text to be recognized and command elements corresponding to commands to be executed, wherein a particular one of the speech elements may correspond to a text element in one context and to a command element in another context, recognizing the speech elements, when a recognized one of the speech elements may be either a command element or a text element, designating the recognized speech element as corresponding to a text element or to a command element based on a context in which the recognized speech element appears, and if the recognized speech element includes a command element, displaying information associated with the command element.
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12. A method for use in recognizing continuous speech, the method comprising: accepting signals corresponding to interspersed speech elements including text elements corresponding to text to be recognized and command elements corresponding to commands to be executed, wherein a particular one of the speech elements may correspond to a text element in one context and to a command element in another context, recognizing the speech elements, when a recognized one of the speech elements may be either a command element or a text element, designating the recognized speech element as corresponding to a text element or to a command element based on a context in which the recognized speech element appears, and if the recognized speech element includes a command element, displaying information associated with the command element. 13. The method of claim 12 in which the displaying includes displaying a command element that would be difficult to undo after execution of the command.
| 0.510024 |
1. A method employing a portable user device having at least one microphone that captures audio, and at least one image sensor for capturing imagery, the method comprising the acts: (a) capturing imagery with the image sensor, the captured image depicting one or more physical subjects within an environment of said user, and capturing user speech with the microphone; (b) sending, to a speech recognition module, audio data corresponding to the user speech, and receiving recognized user speech data corresponding thereto; (c) applying a computer-implemented cognition process to the imagery, said cognition process also employing information from the recognized user speech data as a clue to help identify a physical subject within the captured imagery that is of interest to said user; and (d) presenting a set of plural response options to the user, for user selection therebetween; wherein the set of plural response options presented to the user varies based on said identified physical subject.
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1. A method employing a portable user device having at least one microphone that captures audio, and at least one image sensor for capturing imagery, the method comprising the acts: (a) capturing imagery with the image sensor, the captured image depicting one or more physical subjects within an environment of said user, and capturing user speech with the microphone; (b) sending, to a speech recognition module, audio data corresponding to the user speech, and receiving recognized user speech data corresponding thereto; (c) applying a computer-implemented cognition process to the imagery, said cognition process also employing information from the recognized user speech data as a clue to help identify a physical subject within the captured imagery that is of interest to said user; and (d) presenting a set of plural response options to the user, for user selection therebetween; wherein the set of plural response options presented to the user varies based on said identified physical subject. 17. The method of claim 1 in which the set of response options presented to the user includes at least two selected from the group consisting of: (i) sending information to a social network service for posting, (ii) starting a text communication with a person, (iii) identifying a person, (iv) sending a friend invitation on a social network service, (v) making a purchase, (vi) identifying nutritional information, and (vii) identifying a local store.
| 0.5 |
12. The medium of claim 9 , wherein the medium further holds instructions for receiving an indication of the plurality of entities.
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12. The medium of claim 9 , wherein the medium further holds instructions for receiving an indication of the plurality of entities. 13. The medium of claim 12 , wherein the medium further holds instructions for receiving a selection from a user of the plurality of entities.
| 0.930994 |
13. A speech recognition method, comprising: determining a speed level of a moving object by using a noise signal at an initial time of speech recognition; if the speed level is equal to or lower than a specific level, enhancing sound quality of an input speech signal by extracting voice activity detection features robust against vehicular noise and applying to the input speech signal a Wiener filter using the extracted voice activity detection features; detecting start and end points of a signal having been subjected to the sound quality enhancement; eliminating sudden noise components occurring when the moving object moves based on a sudden noise Gaussian mixture model; and decoding a signal having been subjected to the sudden noise elimination by using a low-speed/medium-speed acoustic model database, and if the speed level is higher than the specific level, enhancing the sound quality of the input speech signal of the speech recognition by applying a Wiener filter using a Gaussian mixture model; detecting start and end points of a signal having been subjected to the sound quality enhancement; compensating distorted harmonic components of the signal having been subjected to end point detection based on a harmonic Gaussian mixture model; eliminating sudden noise components occurring when the moving object moves based on a sudden noise Gaussian mixture model; and decoding a signal having been subjected to the compensation and the sudden noise elimination by using a high-speed acoustic model database.
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13. A speech recognition method, comprising: determining a speed level of a moving object by using a noise signal at an initial time of speech recognition; if the speed level is equal to or lower than a specific level, enhancing sound quality of an input speech signal by extracting voice activity detection features robust against vehicular noise and applying to the input speech signal a Wiener filter using the extracted voice activity detection features; detecting start and end points of a signal having been subjected to the sound quality enhancement; eliminating sudden noise components occurring when the moving object moves based on a sudden noise Gaussian mixture model; and decoding a signal having been subjected to the sudden noise elimination by using a low-speed/medium-speed acoustic model database, and if the speed level is higher than the specific level, enhancing the sound quality of the input speech signal of the speech recognition by applying a Wiener filter using a Gaussian mixture model; detecting start and end points of a signal having been subjected to the sound quality enhancement; compensating distorted harmonic components of the signal having been subjected to end point detection based on a harmonic Gaussian mixture model; eliminating sudden noise components occurring when the moving object moves based on a sudden noise Gaussian mixture model; and decoding a signal having been subjected to the compensation and the sudden noise elimination by using a high-speed acoustic model database. 18. The speech recognition method of claim 13 , wherein said enhancing the sound quality if the speed level is higher than the specific level includes: estimating a de-noised power spectral density of the input speech signal; calculating a zero-mean power spectral density by using the de-noised power spectral density; calculating a first clean speech power spectral density by using a power spectral density of clean speech and the zero-mean power spectral density; calculating a second clean speech power spectral density by using the de-noised power spectral density and the first clean speech power spectral density; acquiring frequency response characteristics of the Wiener filter using the Gaussian mixture model by using the second clean speech power spectral density; and enhancing the sound quality of the input speech signal based on the frequency response characteristics.
| 0.5 |
16. A non-transitory computer readable medium comprising executable instructions encoded thereon operable on a computerized device to perform processing comprising: instructions for receiving at least one search criteria from a first source; instructions for identifying multiple portions of indexed content, each respective portion of indexed portion matching at least one characteristic of the search criteria, each respective portion of indexed content referencing metadata; instructions for determining a relevance of each respective portion of indexed content to the first source who provided the search criteria, the relevance based on user feedback associated with an online version of the portion of indexed content; instructions for ranking the multiple portions of indexed content according to their respective relevance to the first source who provided the search criteria; and instructions for creating a search result based on ranking the multiple portions of indexed content.
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16. A non-transitory computer readable medium comprising executable instructions encoded thereon operable on a computerized device to perform processing comprising: instructions for receiving at least one search criteria from a first source; instructions for identifying multiple portions of indexed content, each respective portion of indexed portion matching at least one characteristic of the search criteria, each respective portion of indexed content referencing metadata; instructions for determining a relevance of each respective portion of indexed content to the first source who provided the search criteria, the relevance based on user feedback associated with an online version of the portion of indexed content; instructions for ranking the multiple portions of indexed content according to their respective relevance to the first source who provided the search criteria; and instructions for creating a search result based on ranking the multiple portions of indexed content. 18. The non-transitory computer readable medium as in claim 16 , wherein the instructions for determining the relevance of each respective portion of indexed content to the first source include: for each respective portion of indexed content: (i) instructions for identifying a plurality of user ratings provided to the online version of the portion of indexed content; (ii) instructions for determining a metric based on an aggregation of the plurality of user ratings; and (iii) instructions for determining the relevance of the portion of indexed content based at least on the metric.
| 0.658183 |
2. The method of claim 1 , further comprising: receiving an image of the monitored scene divided into a plurality of segments.
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2. The method of claim 1 , further comprising: receiving an image of the monitored scene divided into a plurality of segments. 8. The method of claim 2 , further comprising: identifying characteristics of the plurality of segments and refining the plurality of segments based on the characteristics.
| 0.90237 |
7. The method of claim 6 , further comprising: (i) calculating a semantic distance (SD) value between said one of said N concepts and one of the remaining N−1 concepts utilizing the formula:
SD=w 1 F+w 2 C+w 3 A; wherein: F represents said factual semantic relationship value; C represents said co-occurrence semantic relationship value; A represents said associative semantic relationship value; and w 1 , w 2 , w 3 are weights assigned to the F, C, and A semantic relationship values, respectively; whereby said SD value is indicative of how strongly associated said one of N concepts is to said one of the remaining N−1 concepts.
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7. The method of claim 6 , further comprising: (i) calculating a semantic distance (SD) value between said one of said N concepts and one of the remaining N−1 concepts utilizing the formula:
SD=w 1 F+w 2 C+w 3 A; wherein: F represents said factual semantic relationship value; C represents said co-occurrence semantic relationship value; A represents said associative semantic relationship value; and w 1 , w 2 , w 3 are weights assigned to the F, C, and A semantic relationship values, respectively; whereby said SD value is indicative of how strongly associated said one of N concepts is to said one of the remaining N−1 concepts. 8. The method of claim 7 , further comprising: (j) receiving a query from a user containing said one of said N concepts; and (k) presenting to said user, via a graphical user interface, said SD value.
| 0.836889 |
1. A computer-implemented method of representing a software application to be coded in a procedural language, comprising receiving an initial UML class diagram modelizing the software application; identifying data definition classes within the initial UML class diagram; identifying class operations within the initial UML class diagram; modifying, by a processor, the initial UML class diagram to generate an extended UML class diagram by applying a data object stereotype to the identified data definition classes, and applying a program stereotype to the identified class operations, wherein the data definition classes represent logical data, and the class operations represents programs; for each parameter of the identified class operations, identifying whether the parameter is a resource or external data; and upon the parameter being identified as a resource, adding a resource class to the extended UML class diagram, and changing a type of the identified parameter from the data object class to the resource class.
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1. A computer-implemented method of representing a software application to be coded in a procedural language, comprising receiving an initial UML class diagram modelizing the software application; identifying data definition classes within the initial UML class diagram; identifying class operations within the initial UML class diagram; modifying, by a processor, the initial UML class diagram to generate an extended UML class diagram by applying a data object stereotype to the identified data definition classes, and applying a program stereotype to the identified class operations, wherein the data definition classes represent logical data, and the class operations represents programs; for each parameter of the identified class operations, identifying whether the parameter is a resource or external data; and upon the parameter being identified as a resource, adding a resource class to the extended UML class diagram, and changing a type of the identified parameter from the data object class to the resource class. 4. The method of claim 1 , wherein the procedural language is COBOL.
| 0.649956 |
186. The method of claim 181 , wherein the required term of experience is rounded up to a unit of time.
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186. The method of claim 181 , wherein the required term of experience is rounded up to a unit of time. 187. The method of claim 186 , wherein the unit of time is a number of seconds, minutes, hours, days, weeks, months, years, or decades.
| 0.966727 |
10. The method of claim 1 , wherein the step of determining a quality of recognition of a search query further comprises the steps of: determining a quality of recognition of one or more entities in the search content in accordance with the knowledge base; and determining a quality of associations between the one or more entities in the search content in accordance with the knowledge base.
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10. The method of claim 1 , wherein the step of determining a quality of recognition of a search query further comprises the steps of: determining a quality of recognition of one or more entities in the search content in accordance with the knowledge base; and determining a quality of associations between the one or more entities in the search content in accordance with the knowledge base. 11. The method of claim 10 , wherein the step of determining a quality of recognition of one or more entities in the search content further comprises the steps of: identifying one or more content elements in the search content; assigning all content elements an expected weight corresponding to complete recognition; determining for each content element, a knowledge base entity corresponding thereto; decrementing the expected weight associated with a particular content element when it is determined that the particular content element is indirectly resolved, in accordance with one or more knowledge base components, to the corresponding knowledge base entity; and combining the weights for all content elements that are associated with a single knowledge entity corresponding to the knowledge base.
| 0.850894 |
1. A method for use in managing application features, the method comprising: extracting a first extensible markup language (XML) file from a first java archive (JAR) file; extracting a second XML file from a second JAR file; based on an identifier that is common to the first and second XML files and a directory structure that is common to the first and second JAR files, executing a merging operation at runtime on the first and second XML files, wherein the first and second XML files are processed together as if they were part of the same XML file and functionality, wherein the first XML file and the second XML file are from different JAR files having the same path so that the first and second XML files can be treated as being located in the same directory, and the first XML file and the second XML file have the same directory structure, wherein the first and second XML files share a common identifier so that a first portion of the first XML file is treated as incorporating a second portion of the second XML file for the purposes of producing a GUI section; and deriving an application feature dynamically from the results of the merging operation such that disjoint application plug-ins extend upon each other without having to interact before the merging operation, wherein the application feature is dynamically derived after the application for which it was derived for has been deployed.
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1. A method for use in managing application features, the method comprising: extracting a first extensible markup language (XML) file from a first java archive (JAR) file; extracting a second XML file from a second JAR file; based on an identifier that is common to the first and second XML files and a directory structure that is common to the first and second JAR files, executing a merging operation at runtime on the first and second XML files, wherein the first and second XML files are processed together as if they were part of the same XML file and functionality, wherein the first XML file and the second XML file are from different JAR files having the same path so that the first and second XML files can be treated as being located in the same directory, and the first XML file and the second XML file have the same directory structure, wherein the first and second XML files share a common identifier so that a first portion of the first XML file is treated as incorporating a second portion of the second XML file for the purposes of producing a GUI section; and deriving an application feature dynamically from the results of the merging operation such that disjoint application plug-ins extend upon each other without having to interact before the merging operation, wherein the application feature is dynamically derived after the application for which it was derived for has been deployed. 6. The method of claim 1 , wherein in deploying an application based on Java, the application has multiple separate JAR files which are included in a runtime path.
| 0.729373 |
1. A computer implemented method for accessing information from a plurality of searchable information sources, comprising: analyzing a search query to determine subject matter of the query; and selecting a subset of information sources from the plurality of information sources based upon the subject matter of the query; wherein the subject matter of the query is derived by comparing the search query against a knowledge-base, the knowledge base including a taxonomy of subject matters and a set of terms for at least some of the respective subject matters, the set of terms representing information likely to be found in the respective subject matters, and wherein comparing includes comparing at least portions of the search query against the sets of terms in the knowledge base to determine the respective subject matters of matching terms.
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1. A computer implemented method for accessing information from a plurality of searchable information sources, comprising: analyzing a search query to determine subject matter of the query; and selecting a subset of information sources from the plurality of information sources based upon the subject matter of the query; wherein the subject matter of the query is derived by comparing the search query against a knowledge-base, the knowledge base including a taxonomy of subject matters and a set of terms for at least some of the respective subject matters, the set of terms representing information likely to be found in the respective subject matters, and wherein comparing includes comparing at least portions of the search query against the sets of terms in the knowledge base to determine the respective subject matters of matching terms. 5. The computer implemented method of claim 1 , further comprising building the knowledge base, wherein building includes: defining the taxonomy of subject matters; for at least some of the subject matters in the taxonomy, providing at least one example document that represents content typically found for the respective subject matter; generating a set of terms from the example document; and linking the set of terms to the respective subject matter.
| 0.506633 |
14. An apparatus, comprising: a processor configured to be operatively coupled to a memory and configured to execute a determination module; and the determination module configured to be operatively coupled to (1) a first database that includes a first data object, the first data object including at least one of a first identifier type, a second identifier type, and a first characteristic type, (2) a second database that includes a second data object, the second data object including at least one of the second identifier type, a third identifier type, and a second characteristic type, and (3) a third database that includes a third data object, the third data object including at least one of the third identifier type, and a third characteristic type; the determination module configured to receive an indication of the selection of (1) the first database, the second database and the third database, and (2) a business rule, the business rule being a logical expression indicating a relationship between a first concept and a second concept, the first concept being a logical expression indicative of the presence of a selected value of the first characteristic type and the second concept being a logical expression indicative of the presence of a selected value of the second characteristic type; the determination module configured to, in response the indication of the selection, apply the business rule to the first database, the second database, and the third database; the determination module configure to send over a communications network, in response to (1) the first data object satisfying the first concept and (2) the second data object satisfying the second concept, to the first database, a report indicating a relationship between the first data object, the second data object and the third data object, the report configured for display on a graphical user interface, the report including at least one alert corresponding to an inconsistency when at least one inconsistency is identified by the determination module.
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14. An apparatus, comprising: a processor configured to be operatively coupled to a memory and configured to execute a determination module; and the determination module configured to be operatively coupled to (1) a first database that includes a first data object, the first data object including at least one of a first identifier type, a second identifier type, and a first characteristic type, (2) a second database that includes a second data object, the second data object including at least one of the second identifier type, a third identifier type, and a second characteristic type, and (3) a third database that includes a third data object, the third data object including at least one of the third identifier type, and a third characteristic type; the determination module configured to receive an indication of the selection of (1) the first database, the second database and the third database, and (2) a business rule, the business rule being a logical expression indicating a relationship between a first concept and a second concept, the first concept being a logical expression indicative of the presence of a selected value of the first characteristic type and the second concept being a logical expression indicative of the presence of a selected value of the second characteristic type; the determination module configured to, in response the indication of the selection, apply the business rule to the first database, the second database, and the third database; the determination module configure to send over a communications network, in response to (1) the first data object satisfying the first concept and (2) the second data object satisfying the second concept, to the first database, a report indicating a relationship between the first data object, the second data object and the third data object, the report configured for display on a graphical user interface, the report including at least one alert corresponding to an inconsistency when at least one inconsistency is identified by the determination module. 20. The apparatus of claim 14 , wherein the first concept is a logical expression indicative of the presence of any value of the first characteristic type as a text string in an unstructured data object or as data code stored in a structured data object.
| 0.702853 |
14. The computer system of claim 11 , wherein the first data comprises first annotation set data representing a first annotation set; wherein the first UALI comprises a first Unique Set Identifier (USI) that uniquely identifies the first annotation set; wherein (A) comprises receiving a request containing the first USI; and wherein (C) comprises, in response to the request containing the first USI, manifesting the first annotation set data in connection with a manifestation of the second instance of the first XML document.
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14. The computer system of claim 11 , wherein the first data comprises first annotation set data representing a first annotation set; wherein the first UALI comprises a first Unique Set Identifier (USI) that uniquely identifies the first annotation set; wherein (A) comprises receiving a request containing the first USI; and wherein (C) comprises, in response to the request containing the first USI, manifesting the first annotation set data in connection with a manifestation of the second instance of the first XML document. 15. The computer system of claim 14 , wherein the first annotation set further contains second annotation data for annotating the first instance of the first XML document; and wherein (C) further comprises, in response to the request containing the first USI, manifesting the second annotation data in connection with the manifestation of the second instance of the first XML document.
| 0.886858 |
28. An apparatus comprising a computer-readable medium tangibly storing instructions executable by a computer processor to perform a method comprising: (A) deriving, from a region of a document and a corresponding region of a spoken audio stream, a likelihood score representing a likelihood that the region of the document correctly represents content in the corresponding region of the spoken audio stream; (B) selecting a relevance score representing a measure of relevance of the region of the spoken audio stream, the measure of relevance representing a measure of importance that the region of the spoken audio stream be brought to the attention of a human proofreader; and (C) deriving, by performing a calculation on the likelihood score and the relevance score, an emphasis factor for modifying emphasis placed on the region of the spoken audio stream when played back.
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28. An apparatus comprising a computer-readable medium tangibly storing instructions executable by a computer processor to perform a method comprising: (A) deriving, from a region of a document and a corresponding region of a spoken audio stream, a likelihood score representing a likelihood that the region of the document correctly represents content in the corresponding region of the spoken audio stream; (B) selecting a relevance score representing a measure of relevance of the region of the spoken audio stream, the measure of relevance representing a measure of importance that the region of the spoken audio stream be brought to the attention of a human proofreader; and (C) deriving, by performing a calculation on the likelihood score and the relevance score, an emphasis factor for modifying emphasis placed on the region of the spoken audio stream when played back. 42. The apparatus of claim 28 , wherein the method further comprises: (D) generating the document based on the spoken audio stream.
| 0.717633 |
31. The apparatus of claim 28 , wherein the method further comprises: (D) modifying an emphasis of the region of the spoken audio stream in accordance with the emphasis factor to produce an emphasis-adjusted audio stream.
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31. The apparatus of claim 28 , wherein the method further comprises: (D) modifying an emphasis of the region of the spoken audio stream in accordance with the emphasis factor to produce an emphasis-adjusted audio stream. 32. The apparatus of claim 31 , wherein the method further comprises: (E) modifying an emphasis of the region of the document in accordance with the emphasis factor to produce an emphasis-adjusted document region.
| 0.918136 |
10. A system comprising: one or more computers programmed to perform operations comprising: receiving first text submitted by a first user; for each of a plurality of different previously submitted texts, calculating a text similarity score based on a textual similarity of the first text to the previously submitted text, and calculating a sentiment similarity score based on a similarity between sentiment of the first text and sentiment of the previously submitted text; based on the scores, selecting one or more of the previously submitted texts having closest similarity to the first text; for each of the selected texts, identifying an emoticon previously selected by a user for insertion into the selected text; and providing the identified emoticons for selection by the first user.
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10. A system comprising: one or more computers programmed to perform operations comprising: receiving first text submitted by a first user; for each of a plurality of different previously submitted texts, calculating a text similarity score based on a textual similarity of the first text to the previously submitted text, and calculating a sentiment similarity score based on a similarity between sentiment of the first text and sentiment of the previously submitted text; based on the scores, selecting one or more of the previously submitted texts having closest similarity to the first text; for each of the selected texts, identifying an emoticon previously selected by a user for insertion into the selected text; and providing the identified emoticons for selection by the first user. 17. The system of claim 10 wherein the operations further comprise: calculating a language similarity score based on a similarity between a language of the first text and a language of a second text.
| 0.633003 |
18. The method of claim 16 , further comprising selecting, by the one or more computing devices, the particular executable computational analysis.
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18. The method of claim 16 , further comprising selecting, by the one or more computing devices, the particular executable computational analysis. 20. The method of claim 18 , wherein selecting the particular executable computational analysis comprises selecting the particular executable computational analysis based on at least one of: at least a subset of the data set attribute data, a result of performing another computational analysis on at least some of the data values included in the data set or on data values of another data set, an identity of the another executable computational analysis, a parameter, or a user input corresponding to at least one of (i) the selecting of the particular executable computational analysis, or (ii) selecting the data set.
| 0.911497 |
7. A computer program product for optimizing generation of a regular expression utilized for entity extraction, the computer program product comprising: a computer readable tangible storage device and program instructions stored on the computer readable tangible storage device, the program instructions include: program instructions to receive at a server, an input from a user of the server, the input enabling at least a first performance optimization parameter; program instructions to receive, from a user of a client computer, a query comprising a plain text word; program instructions to receive, at the server, data extracted from an electronic repository that is communicatively connected to the server, the data describing probabilities of spelling errors based, at least in part, on a number of syllables in the plain text word; program instructions to initialize, at the server, the first performance optimization parameter based, at least in part, on the received data and the input enabling at least the first performance optimization parameter; program instructions to optimize performance of generating the regular expression, at the server, by, at least in part, executing program instructions to identify, using the first performance optimization parameter, a syllable within the plain text word that has a high probability of at least one of an incorrectly substituted and transposed character within a spelling of a word having a same number of syllables as the plain text word; program instructions to select, at the server, a character in the syllable identified; program instructions to identify, at the server, a group of characters from a confusion matrix that are commonly confused with the character selected; program instructions to generate, at the server, a set of characters for the character selected, wherein the set of characters begins with the character selected followed by and ending with the group of characters from the confusion matrix; program instructions to determine, at the server, that a probability of omitting the character selected exceeds a threshold, and in response, associating, at the server, a tag with the set of characters; program instructions to generate, at the server, a regular expression by concatenating the set of characters with one or more additional sets of characters; program instructions to, based, at least in part, on the tag and the regular expression, search, at the server, the electronic repository for text data describing a spelling of the plain text word in which at least one of (i) the character selected and (ii) one or more characters of the group of characters from the confusion matrix is omitted; and program instructions to provide, to the user of the client computer, search results based on the regular expression.
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7. A computer program product for optimizing generation of a regular expression utilized for entity extraction, the computer program product comprising: a computer readable tangible storage device and program instructions stored on the computer readable tangible storage device, the program instructions include: program instructions to receive at a server, an input from a user of the server, the input enabling at least a first performance optimization parameter; program instructions to receive, from a user of a client computer, a query comprising a plain text word; program instructions to receive, at the server, data extracted from an electronic repository that is communicatively connected to the server, the data describing probabilities of spelling errors based, at least in part, on a number of syllables in the plain text word; program instructions to initialize, at the server, the first performance optimization parameter based, at least in part, on the received data and the input enabling at least the first performance optimization parameter; program instructions to optimize performance of generating the regular expression, at the server, by, at least in part, executing program instructions to identify, using the first performance optimization parameter, a syllable within the plain text word that has a high probability of at least one of an incorrectly substituted and transposed character within a spelling of a word having a same number of syllables as the plain text word; program instructions to select, at the server, a character in the syllable identified; program instructions to identify, at the server, a group of characters from a confusion matrix that are commonly confused with the character selected; program instructions to generate, at the server, a set of characters for the character selected, wherein the set of characters begins with the character selected followed by and ending with the group of characters from the confusion matrix; program instructions to determine, at the server, that a probability of omitting the character selected exceeds a threshold, and in response, associating, at the server, a tag with the set of characters; program instructions to generate, at the server, a regular expression by concatenating the set of characters with one or more additional sets of characters; program instructions to, based, at least in part, on the tag and the regular expression, search, at the server, the electronic repository for text data describing a spelling of the plain text word in which at least one of (i) the character selected and (ii) one or more characters of the group of characters from the confusion matrix is omitted; and program instructions to provide, to the user of the client computer, search results based on the regular expression. 8. The computer program product of claim 7 , the program instructions further comprising: program instruction to determine whether a second performance optimization parameter is configured to optimize performance of generating the regular expression, wherein the second performance optimization parameter is a boolean edit distance parameter that is used to determine whether to associate a configurable numerical value to the set of characters.
| 0.545835 |
13. One or more non-transitory computer-readable storage media comprising a plurality of instructions that in response to being executed cause an augmented reality device to: capture an image; analyze the image to recognize a subject represented in the image and a background item in the image; access a character profile based on the subject; determine, based on the character profile, whether the background item is associated with the subject; determine, based on the character profile and in response to a determination that the background item is associated with the subject, a theme of the image; determine a context associated with the subject based on the image and the background item, wherein the context is related to the theme, wherein to determine the context comprises to classify a type of an accessory object represented in the image that is related to the subject; select a virtual object based on the context of the subject and the background item, wherein the virtual object comprises an accessory object that is composable with the virtual character and is of the same type as the accessory object represented in the image, and wherein to select the virtual object comprises to select the virtual object based on similarity between the virtual object and a feature of the subject; apply the virtual object to a virtual character; and render an augmented reality scene based on the captured image and including the virtual character with the virtual object applied thereto.
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13. One or more non-transitory computer-readable storage media comprising a plurality of instructions that in response to being executed cause an augmented reality device to: capture an image; analyze the image to recognize a subject represented in the image and a background item in the image; access a character profile based on the subject; determine, based on the character profile, whether the background item is associated with the subject; determine, based on the character profile and in response to a determination that the background item is associated with the subject, a theme of the image; determine a context associated with the subject based on the image and the background item, wherein the context is related to the theme, wherein to determine the context comprises to classify a type of an accessory object represented in the image that is related to the subject; select a virtual object based on the context of the subject and the background item, wherein the virtual object comprises an accessory object that is composable with the virtual character and is of the same type as the accessory object represented in the image, and wherein to select the virtual object comprises to select the virtual object based on similarity between the virtual object and a feature of the subject; apply the virtual object to a virtual character; and render an augmented reality scene based on the captured image and including the virtual character with the virtual object applied thereto. 16. The one or more non-transitory computer-readable storage media of claim 13 , wherein to determine the context comprises to determine a current activity of the subject.
| 0.549797 |
1. A computer implemented method, comprising: identifying a plurality of classification terms indicative of a classification; identifying a corpus of communications from one or more databases, the corpus of communications including a plurality of communications that are not labeled with an association to the classification; determining a cluster of the communications based on occurrence of one or more of the classification terms in the communications of the cluster; subsequent to determining the cluster, determining a feature set based on the communications of the cluster, wherein determining the feature set comprises: determining one or more features that are based on content that appears in a plurality of the communications of the cluster, wherein the content is in addition to the classification terms used in determining the cluster, and wherein determining the features based on the content that is in addition to the classification terms comprises determining the features based on the content appearing in the plurality of the communications of the cluster; assigning the feature set to an indication of the classification; and using the assigned feature set to classify an additional communication with the classification or using the assigned feature set to select a data extraction parser, for the classification, for the additional communication.
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1. A computer implemented method, comprising: identifying a plurality of classification terms indicative of a classification; identifying a corpus of communications from one or more databases, the corpus of communications including a plurality of communications that are not labeled with an association to the classification; determining a cluster of the communications based on occurrence of one or more of the classification terms in the communications of the cluster; subsequent to determining the cluster, determining a feature set based on the communications of the cluster, wherein determining the feature set comprises: determining one or more features that are based on content that appears in a plurality of the communications of the cluster, wherein the content is in addition to the classification terms used in determining the cluster, and wherein determining the features based on the content that is in addition to the classification terms comprises determining the features based on the content appearing in the plurality of the communications of the cluster; assigning the feature set to an indication of the classification; and using the assigned feature set to classify an additional communication with the classification or using the assigned feature set to select a data extraction parser, for the classification, for the additional communication. 18. The computer implemented method of claim 1 , wherein the communications of the cluster include structured communications, and wherein determining the feature set comprises: determining structural features of the features based on hierarchical structure of structural paths of the structured communications of the cluster, the structural paths being structural paths of eXtensible Markup Language or Hypertext Markup Language.
| 0.611986 |
1. A modular learning device comprising of: a base board having a frame with plurality of sides, a complementary joining construction on sides of the base board, a front plane, a rear plane, a network of tracks having a spinal track, several dormant tracks, several solution tracks, several parking tracks, the dormant tracks and the parking tracks and the solution tracks are connected to the spinal track, a recess, wherein some parking tracks are also solution tracks; a plurality of sliding blocks having a tread, a stopper and a shaft, the shaft having a first end and a second end; and a plurality of wisdom cards having an identifier section, a margin having up to same number of sides as the base board, a plurality of zones in the margin, a middle area and corresponding solutions in the zones randomly, a solution code for problems or situations, each wisdom card containing different problems or situations on a plurality of subjects, an indicator for each problem or situation, wherein the indicator is a hint or a prompt to arrive at correct provided solution; different base boards unifiable by the complementary joining construction on the sides of the base board, the plurality of wisdom cards mounted, either one at a time, or in combination, one in each recess of the base board, the plurality of sliding blocks mounted on the base board with the head on the front plane of the base board and the stopper on the rear plane of the base board, the plurality of sliding blocks can move freely in the network of tracks and cannot get dislodged unless the stopper is separated or manoeuvred using a minimum force.
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1. A modular learning device comprising of: a base board having a frame with plurality of sides, a complementary joining construction on sides of the base board, a front plane, a rear plane, a network of tracks having a spinal track, several dormant tracks, several solution tracks, several parking tracks, the dormant tracks and the parking tracks and the solution tracks are connected to the spinal track, a recess, wherein some parking tracks are also solution tracks; a plurality of sliding blocks having a tread, a stopper and a shaft, the shaft having a first end and a second end; and a plurality of wisdom cards having an identifier section, a margin having up to same number of sides as the base board, a plurality of zones in the margin, a middle area and corresponding solutions in the zones randomly, a solution code for problems or situations, each wisdom card containing different problems or situations on a plurality of subjects, an indicator for each problem or situation, wherein the indicator is a hint or a prompt to arrive at correct provided solution; different base boards unifiable by the complementary joining construction on the sides of the base board, the plurality of wisdom cards mounted, either one at a time, or in combination, one in each recess of the base board, the plurality of sliding blocks mounted on the base board with the head on the front plane of the base board and the stopper on the rear plane of the base board, the plurality of sliding blocks can move freely in the network of tracks and cannot get dislodged unless the stopper is separated or manoeuvred using a minimum force. 24. The modular learning device as claimed in claim 1 , wherein the solution code is a set of geometric figures.
| 0.562534 |
1. An apparatus for determining, based on speech waveform data, a portion representing a feature of the speech waveform, comprising: an acoustic/prosodic analysis unit which calculates, from said data, a distribution of energy of a prescribed frequency range of said speech waveform along a time axis, and extracts, among various syllables, a first portion of said speech waveform that is generated stably by a source of said speech waveform, based on the distribution of energy and pitch of said speech waveform; a cepstral analysis unit which calculates, from said data, a frequency spectrum distribution of said speech waveform along the time axis, and estimates, based on the frequency spectrum distribution, a second portion of said speech waveform, for which change is well controlled by said source; and a pseudo-syllabic center extracting unit which determines the portion representing the feature of said speech waveform based on the first portion extracted by the acoustic/prosodic analysis unit and the second portion estimated by the cepstral analysis unit, wherein said cepstral analysis unit includes: a linear prediction analysis unit which performs linear prediction analysis on said speech waveform and outputting an estimated value of formant frequency; a cepstral distance calculating unit which calculates, using said data, a distribution of cepstral distance on the time axis based on the estimated value of formant frequency provided by said linear prediction analysis unit; an inter-frame variance calculating unit which calculates, based on an output from said linear prediction analysis unit, distribution of local variance of magnitude of delta cepstrum of said speech waveform on the time axis; and a reliability center candidate output unit which estimates, based both on said distribution of cepstral distance on the time axis based on the estimated value of formant frequency calculated by said cepstral distance calculating unit and on said distribution on the time axis of local variance of magnitude of delta cepstrum of said speech waveform calculated by said inter-frame variance calculating unit, a range in which change in the speech waveform is well controlled by said source.
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1. An apparatus for determining, based on speech waveform data, a portion representing a feature of the speech waveform, comprising: an acoustic/prosodic analysis unit which calculates, from said data, a distribution of energy of a prescribed frequency range of said speech waveform along a time axis, and extracts, among various syllables, a first portion of said speech waveform that is generated stably by a source of said speech waveform, based on the distribution of energy and pitch of said speech waveform; a cepstral analysis unit which calculates, from said data, a frequency spectrum distribution of said speech waveform along the time axis, and estimates, based on the frequency spectrum distribution, a second portion of said speech waveform, for which change is well controlled by said source; and a pseudo-syllabic center extracting unit which determines the portion representing the feature of said speech waveform based on the first portion extracted by the acoustic/prosodic analysis unit and the second portion estimated by the cepstral analysis unit, wherein said cepstral analysis unit includes: a linear prediction analysis unit which performs linear prediction analysis on said speech waveform and outputting an estimated value of formant frequency; a cepstral distance calculating unit which calculates, using said data, a distribution of cepstral distance on the time axis based on the estimated value of formant frequency provided by said linear prediction analysis unit; an inter-frame variance calculating unit which calculates, based on an output from said linear prediction analysis unit, distribution of local variance of magnitude of delta cepstrum of said speech waveform on the time axis; and a reliability center candidate output unit which estimates, based both on said distribution of cepstral distance on the time axis based on the estimated value of formant frequency calculated by said cepstral distance calculating unit and on said distribution on the time axis of local variance of magnitude of delta cepstrum of said speech waveform calculated by said inter-frame variance calculating unit, a range in which change in the speech waveform is well controlled by said source. 4. An apparatus as recited in claim 1 , wherein said cepstral analysis unit is configured to calculate, from said data, a frequency spectrum distribution of said speech waveform along the time axis, and estimate the second portion, based on the frequency spectrum distribution, as a portion where local variance of changes of the frequency spectrum is at a local minimum.
| 0.789185 |
16. The method of claim 1 , wherein the first object comprises content stored separately from the set of objects.
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16. The method of claim 1 , wherein the first object comprises content stored separately from the set of objects. 17. The method of claim 16 , wherein a first file stores the set of objects and a second file, different from the first file, stores the items associated with the first object.
| 0.920394 |
3. A method for defining an interface for a system to receive voice commands to connect a first user to a second user over a network, comprising the steps of: a) receiving a request to define said interface; b) receiving a first information item; c) searching at least one database for a second information item indexed by said first information item; and d) constructing a grammar for said interface using said first and second information items.
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3. A method for defining an interface for a system to receive voice commands to connect a first user to a second user over a network, comprising the steps of: a) receiving a request to define said interface; b) receiving a first information item; c) searching at least one database for a second information item indexed by said first information item; and d) constructing a grammar for said interface using said first and second information items. 8. The method of claim 3, wherein said grammar is used by a natural language interface.
| 0.85034 |
5. The method of claim 4 wherein one of the components is a contacts view component for displaying the user's present contacts, each of the present contacts having contact information such that, when a contact is selected, the selected contact's information is displayed on the graphical user interface.
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5. The method of claim 4 wherein one of the components is a contacts view component for displaying the user's present contacts, each of the present contacts having contact information such that, when a contact is selected, the selected contact's information is displayed on the graphical user interface. 7. The method of claim 5 wherein one of the components is a forum view component wherein, when a contact is selected, the selected contact's forum is displayed along with the selected contact's information.
| 0.880926 |
2. A speech recognition circuit according to claim 1 , wherein said lexical memory means stores said lexical tree data structure as an n phone model of words, where n is an integer, and said components comprise n phones.
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2. A speech recognition circuit according to claim 1 , wherein said lexical memory means stores said lexical tree data structure as an n phone model of words, where n is an integer, and said components comprise n phones. 3. A speech recognition circuit according to claim 2 , wherein said lexical memory means stores each lexical tree data structure as mono phone models of words, and said lexical tree processors are arranged to use said mono phone models to generate context dependant phone models of words dynamically for use in processing the speech parameters.
| 0.865898 |
1. A method comprising: capturing an image of a scene that includes a portion of a diagram representing functional blocks, wherein the functional blocks include at least a first functional block associated with a first computer operation; applying functional block recognition rules to image data of the image to recognize the functional blocks; determining whether the functional blocks comply with functional block syntax rules, wherein the functional block syntax rules indicate a hierarchy of operations associated with the functional blocks; and computer-generating a functional graph corresponding to the diagram based on the functional blocks complying with the functional block syntax rules, wherein the functional graph includes a graphical representation of the functional blocks.
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1. A method comprising: capturing an image of a scene that includes a portion of a diagram representing functional blocks, wherein the functional blocks include at least a first functional block associated with a first computer operation; applying functional block recognition rules to image data of the image to recognize the functional blocks; determining whether the functional blocks comply with functional block syntax rules, wherein the functional block syntax rules indicate a hierarchy of operations associated with the functional blocks; and computer-generating a functional graph corresponding to the diagram based on the functional blocks complying with the functional block syntax rules, wherein the functional graph includes a graphical representation of the functional blocks. 22. The method of claim 1 , wherein the image is captured at a mobile device, and further comprising generating, at the mobile device, program code corresponding to the functional graph.
| 0.677395 |
36. One or more non-transitory computer-readable media having stored thereon executable instructions configured to, when executed, cause an apparatus at least to: receive a syntax element to be encoded as a code word, wherein the syntax element relates to at least one of a coded block pattern, a transform split pattern, or motion partition information; determine a mapping between the syntax element and the code word on the basis of a hierarchy level in a tree structure, wherein the tree structure relates to at least one of a transform block size, a prediction block size, or a block size, and wherein the hierarchy level represents division of a block into smaller blocks; use the mapping to obtain the code word; and update the mapping.
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36. One or more non-transitory computer-readable media having stored thereon executable instructions configured to, when executed, cause an apparatus at least to: receive a syntax element to be encoded as a code word, wherein the syntax element relates to at least one of a coded block pattern, a transform split pattern, or motion partition information; determine a mapping between the syntax element and the code word on the basis of a hierarchy level in a tree structure, wherein the tree structure relates to at least one of a transform block size, a prediction block size, or a block size, and wherein the hierarchy level represents division of a block into smaller blocks; use the mapping to obtain the code word; and update the mapping. 39. The one or more non-transitory computer-readable media according to claim 36 having stored thereon further executable instructions configured to, when executed, cause the apparatus to: use at least two sorting tables, and use the hierarchy level to select a sorting table from said at least two sorting tables.
| 0.595833 |
14. The system of claim 3 , further comprising a plurality of data repositories that store the knowledge data, the plurality of data repositories comprising data repositories of more than one repository type, wherein enabling the user to specify the subset of knowledge data comprises enabling the user to select one or more data repositories from a plurality of data repositories, and wherein the specified subset of knowledge data is stored in the selected one or more data repositories.
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14. The system of claim 3 , further comprising a plurality of data repositories that store the knowledge data, the plurality of data repositories comprising data repositories of more than one repository type, wherein enabling the user to specify the subset of knowledge data comprises enabling the user to select one or more data repositories from a plurality of data repositories, and wherein the specified subset of knowledge data is stored in the selected one or more data repositories. 15. The system of claim 14 , wherein the more than one repository type comprises two or more of a repository type provided by Lotus Notes, a repository type provided by Lotus QuickPlace, a repository type provided by Domino.Doc type, an electronic mail type, or a web file system type.
| 0.846841 |
20. An electronic device for comparing documents, the electronic device comprising: a memory; and a processor communicatively coupled to the memory, the processor configured to: associate a respective weight with each of a plurality of information types including text-based information, graphical information, audio information, or video information; identify, for each of the first document and the second document, one or more segments each corresponding to one of the plurality of information types; and estimate a similarity value between the first document and the second document, by comparing each segment of the first document with a segment of the second document that correspond to a same information type, wherein the similarity value is based on a distance, in a semantic hierarchy, between a first semantic class associated with the first document and a common ancestor, in the semantic hierarchy, of the first semantic class and a second semantic class associated with a second document; and combine results of the comparison based on the respective associated weights.
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20. An electronic device for comparing documents, the electronic device comprising: a memory; and a processor communicatively coupled to the memory, the processor configured to: associate a respective weight with each of a plurality of information types including text-based information, graphical information, audio information, or video information; identify, for each of the first document and the second document, one or more segments each corresponding to one of the plurality of information types; and estimate a similarity value between the first document and the second document, by comparing each segment of the first document with a segment of the second document that correspond to a same information type, wherein the similarity value is based on a distance, in a semantic hierarchy, between a first semantic class associated with the first document and a common ancestor, in the semantic hierarchy, of the first semantic class and a second semantic class associated with a second document; and combine results of the comparison based on the respective associated weights. 23. The electronic device of claim 20 , wherein the processor is further configured to: perform optical character recognition (OCR) on the one or more segments that include text in the form of graphical information.
| 0.523346 |
7. A non-transitory computer readable storage medium having computer readable program instructions stored thereon for causing a computer system to perform instructions, the instructions comprising the steps of: creating a list of candidate probe words; for each candidate probe word, counting a number of application descriptions that contain the candidate probe word; choosing q probe words from the candidate probe words whose word count is closest to |D i |/(q+1), where D i is a set of remaining application descriptions for an ith repetition, and q is a number of probe words presented for user selection and is an integer greater than 1; presenting the q probe words for user selection; pruning the list of candidate probe words to eliminate application descriptions that include non-selected ones of the q probe words to create a pruned list of candidate probe words, wherein the pruned list of candidate probe words includes a probe word selected by the user from the q probe words; and repeating counting the number of application descriptions, choosing q probe words from the pruned list of candidate probe words, presenting the q probe words for selection and pruning the list of probe words until a final list of applications remain.
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7. A non-transitory computer readable storage medium having computer readable program instructions stored thereon for causing a computer system to perform instructions, the instructions comprising the steps of: creating a list of candidate probe words; for each candidate probe word, counting a number of application descriptions that contain the candidate probe word; choosing q probe words from the candidate probe words whose word count is closest to |D i |/(q+1), where D i is a set of remaining application descriptions for an ith repetition, and q is a number of probe words presented for user selection and is an integer greater than 1; presenting the q probe words for user selection; pruning the list of candidate probe words to eliminate application descriptions that include non-selected ones of the q probe words to create a pruned list of candidate probe words, wherein the pruned list of candidate probe words includes a probe word selected by the user from the q probe words; and repeating counting the number of application descriptions, choosing q probe words from the pruned list of candidate probe words, presenting the q probe words for selection and pruning the list of probe words until a final list of applications remain. 8. The non-transitory computer readable storage medium having computer readable program instructions recited in claim 7 , the instructions further comprising presenting the final list of remaining applications.
| 0.585771 |
4. The system of claim 3 further comprising a modified problem manifest generated by the server, wherein the server generates a random identifier that is unique to a current session between the server and the problem publisher, wherein an identifier field of the modified problem manifest includes the random identifier, and wherein the server maintains a mapping between the random identifier and the unique identifier.
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4. The system of claim 3 further comprising a modified problem manifest generated by the server, wherein the server generates a random identifier that is unique to a current session between the server and the problem publisher, wherein an identifier field of the modified problem manifest includes the random identifier, and wherein the server maintains a mapping between the random identifier and the unique identifier. 5. The system of claim 4 wherein the server applies a presentation template to the modified problem manifest resulting in the presentation of the common sense problem.
| 0.802395 |
1. A system comprising at least one server, wherein the at least one server includes: memory; and one or more processors to execute one or more programs stored in the memory, wherein the system is configured to: access Internet usage data for a particular individual computer user, wherein the usage data include a plurality of search queries previously submitted by the particular individual computer user; identify, without human intervention by the particular individual computer user, from at least some of the Internet usage data, a search query previously submitted by the particular individual computer user that meets one or more predefined query selection criteria; automatically rerun, without human intervention by the particular individual computer user, the identified search query in its entirety, wherein the identified search query is a search query previously submitted by the particular individual computer user; and send at least some search results from the rerun query to a computer associated with the particular individual computer user for display.
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1. A system comprising at least one server, wherein the at least one server includes: memory; and one or more processors to execute one or more programs stored in the memory, wherein the system is configured to: access Internet usage data for a particular individual computer user, wherein the usage data include a plurality of search queries previously submitted by the particular individual computer user; identify, without human intervention by the particular individual computer user, from at least some of the Internet usage data, a search query previously submitted by the particular individual computer user that meets one or more predefined query selection criteria; automatically rerun, without human intervention by the particular individual computer user, the identified search query in its entirety, wherein the identified search query is a search query previously submitted by the particular individual computer user; and send at least some search results from the rerun query to a computer associated with the particular individual computer user for display. 15. The system of claim 1 , wherein at least one of the predefined query selection criteria is based on a duration of a query session by the particular individual computer user within which the previously submitted query occurred.
| 0.665653 |
13. A system comprising: at least one processor; and an information carrier storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: identifying a data record to be archived, the data record comprising a plurality of data record attributes and originally residing in a database; creating an archive record, the archive record comprising a first subset of the plurality of data record attributes, the first subset comprising at least some of the plurality of data record attributes; storing the archive record in a data archive that is separate from the database; creating a new archive index record, the new archive index record comprising a reference to a location of the archive record in the data archive and a second plurality of attribute of the plurality of data record attributes, the second subset comprising selected attributes from the plurality of data record attributes, the selected attributes being those identified as necessary for access by users of the database; adding the new archive index record to a dictionary-based archive index, the dictionary-based archive index comprising a plurality of archive index records, being stored separately from the database, and comprising a dictionary storing every term used in the plurality of index records; deleting the data record from the database; and deleting, after a selected period, the values of all but a selected subset of the plurality of data record attributes from the dictionary-based archive index, the selected subset of the plurality of data record attributes comprising an index key to the archived record.
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13. A system comprising: at least one processor; and an information carrier storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: identifying a data record to be archived, the data record comprising a plurality of data record attributes and originally residing in a database; creating an archive record, the archive record comprising a first subset of the plurality of data record attributes, the first subset comprising at least some of the plurality of data record attributes; storing the archive record in a data archive that is separate from the database; creating a new archive index record, the new archive index record comprising a reference to a location of the archive record in the data archive and a second plurality of attribute of the plurality of data record attributes, the second subset comprising selected attributes from the plurality of data record attributes, the selected attributes being those identified as necessary for access by users of the database; adding the new archive index record to a dictionary-based archive index, the dictionary-based archive index comprising a plurality of archive index records, being stored separately from the database, and comprising a dictionary storing every term used in the plurality of index records; deleting the data record from the database; and deleting, after a selected period, the values of all but a selected subset of the plurality of data record attributes from the dictionary-based archive index, the selected subset of the plurality of data record attributes comprising an index key to the archived record. 16. The system of claim 13 , wherein the operations further comprise: accepting a query from a user for the data record, the query requesting at least one requested attribute not included in the second subset of the plurality of data record attributes; performing a search of the archive index to find the location of the archive record based on the reference in the new archive index record; accessing the archive record from the data archive to retrieve the at least one attribute; and returning the at least one requested attribute in response to the query.
| 0.709524 |
1. A method of displaying a list of a display apparatus, the method comprising: receiving a user command to display a list of multimedia content; determining at least one multimedia content which matches a pre-stored keyword from among a plurality of multimedia content included in the list, in response to the user command; and displaying the list including the plurality of multimedia content based on the result of the determination, wherein the displayed list displays the pre-stored keyword as overlapping the least one multimedia content matching the pre-stored keyword from among the plurality of multimedia content, and wherein the display apparatus displays a list of information related to a broadcast content which is currently displayed and stores information selected from the list as the pre-stored keyword.
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1. A method of displaying a list of a display apparatus, the method comprising: receiving a user command to display a list of multimedia content; determining at least one multimedia content which matches a pre-stored keyword from among a plurality of multimedia content included in the list, in response to the user command; and displaying the list including the plurality of multimedia content based on the result of the determination, wherein the displayed list displays the pre-stored keyword as overlapping the least one multimedia content matching the pre-stored keyword from among the plurality of multimedia content, and wherein the display apparatus displays a list of information related to a broadcast content which is currently displayed and stores information selected from the list as the pre-stored keyword. 11. The method of claim 1 , wherein the pre-stored keyword is selected according to user input.
| 0.555051 |
1. Apparatus for providing facial image animation comprising: a plurality of image forming units each with at least a portion of a facial expression corresponding to a spoken phoneme; means for visually displaying images received from said forming units; and means for selectively activating different pluralities of said image forming units in any of a plurality of different orders to provide correspondingly different facial expression sequences.
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1. Apparatus for providing facial image animation comprising: a plurality of image forming units each with at least a portion of a facial expression corresponding to a spoken phoneme; means for visually displaying images received from said forming units; and means for selectively activating different pluralities of said image forming units in any of a plurality of different orders to provide correspondingly different facial expression sequences. 2. Apparatus as in claim 1 where said image forming units include a rotary disk having image negatives of various facial expressions around its periphery.
| 0.5 |
5. A computer-implemented method for providing an information navigation system for searching a set of materials having navigation states, the method comprising: providing information including: the set of materials, a plurality of attributes characterizing the materials, a plurality of values describing the materials, each value of the values having an association with at least one attribute of the attributes, each association defining an attribute-value pair, and a plurality of navigation states, each navigation state corresponding to a particular set of attribute-value pairs and to a particular subset of materials, wherein the particular subset of materials consists of materials in the information navigation system that are described by every attribute-value pair in the particular set of attribute-value pairs corresponding to said each navigation state, such that each material of materials in the particular subset of materials is described by every attribute-value pair in the particular set of attribute-value pairs corresponding to said each navigation state; generating a partial set of pre-computed navigation states by using a computer, wherein a first navigation state of the partial set of pre-computed navigation states includes a first attribute-value pair having a first attribute, in which the first attribute-value pair does not describe all the materials in the information navigation system that the first attribute characterizes, wherein a second navigation state of the partial set of pre-computed navigation states includes a second attribute-value pair having the first attribute, in which the second attribute-value pair refines the first attribute-value pair, and wherein at least one of the first navigation state and the second navigation state includes a third attribute-value pair having a third attribute, which is not the same as the first attribute, wherein the third attribute-value pair is mutually incomparable with the first attribute-value pair and is mutually incomparable with the second attribute-value pair, and the third attribute-value pair does not describe all the materials in the information navigation system that the third attribute characterizes; storing the partial set of pre-computed navigation states in a data structure in a memory, the partial set of pre-computed navigation states stored in the data structure including at least one of the first navigation state and the second navigation state; providing an interface to the information navigation system, the interface including a free-text search tool for and the interface providing a plurality of transitions, each transition providing a direct path, with no intervening navigation states, between two of the navigation states, wherein said each transition represents a change from a set of attribute-value pairs corresponding to an originating navigation to the set of attribute-value pairs corresponding to a destination navigation state, wherein a series of one or more transitions provides a path between any two navigation states, wherein the interface provides a direct path, with no intervening navigation states, between the first navigation state and the second navigation state, wherein the interface includes a guided search tool for enabling navigation from a current navigation state based on the plurality of transitions among the plurality of navigation states, and wherein the interface operates in an XML-based environment; searching, by using the computer, descriptive information associated with the set of materials, based at least in part on a free-text query accepted from the free-text search tool of the provided interface, to produce a set of free-text query interpretations; accepting a query to the navigation system based at least in part on the free-text query interpretations; and returning, to a user, a responsive navigation state by retrieving a pre-computed navigation state from the data structure based on the query.
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5. A computer-implemented method for providing an information navigation system for searching a set of materials having navigation states, the method comprising: providing information including: the set of materials, a plurality of attributes characterizing the materials, a plurality of values describing the materials, each value of the values having an association with at least one attribute of the attributes, each association defining an attribute-value pair, and a plurality of navigation states, each navigation state corresponding to a particular set of attribute-value pairs and to a particular subset of materials, wherein the particular subset of materials consists of materials in the information navigation system that are described by every attribute-value pair in the particular set of attribute-value pairs corresponding to said each navigation state, such that each material of materials in the particular subset of materials is described by every attribute-value pair in the particular set of attribute-value pairs corresponding to said each navigation state; generating a partial set of pre-computed navigation states by using a computer, wherein a first navigation state of the partial set of pre-computed navigation states includes a first attribute-value pair having a first attribute, in which the first attribute-value pair does not describe all the materials in the information navigation system that the first attribute characterizes, wherein a second navigation state of the partial set of pre-computed navigation states includes a second attribute-value pair having the first attribute, in which the second attribute-value pair refines the first attribute-value pair, and wherein at least one of the first navigation state and the second navigation state includes a third attribute-value pair having a third attribute, which is not the same as the first attribute, wherein the third attribute-value pair is mutually incomparable with the first attribute-value pair and is mutually incomparable with the second attribute-value pair, and the third attribute-value pair does not describe all the materials in the information navigation system that the third attribute characterizes; storing the partial set of pre-computed navigation states in a data structure in a memory, the partial set of pre-computed navigation states stored in the data structure including at least one of the first navigation state and the second navigation state; providing an interface to the information navigation system, the interface including a free-text search tool for and the interface providing a plurality of transitions, each transition providing a direct path, with no intervening navigation states, between two of the navigation states, wherein said each transition represents a change from a set of attribute-value pairs corresponding to an originating navigation to the set of attribute-value pairs corresponding to a destination navigation state, wherein a series of one or more transitions provides a path between any two navigation states, wherein the interface provides a direct path, with no intervening navigation states, between the first navigation state and the second navigation state, wherein the interface includes a guided search tool for enabling navigation from a current navigation state based on the plurality of transitions among the plurality of navigation states, and wherein the interface operates in an XML-based environment; searching, by using the computer, descriptive information associated with the set of materials, based at least in part on a free-text query accepted from the free-text search tool of the provided interface, to produce a set of free-text query interpretations; accepting a query to the navigation system based at least in part on the free-text query interpretations; and returning, to a user, a responsive navigation state by retrieving a pre-computed navigation state from the data structure based on the query. 7. The method of claim 5 , wherein the descriptive information includes text distinct from attribute-value pairs associated with the set of materials.
| 0.50407 |
10. The computer-implemented method of claim 8 , wherein the underlying fields include an encrypted data field, and a protected range field.
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10. The computer-implemented method of claim 8 , wherein the underlying fields include an encrypted data field, and a protected range field. 13. The computer-implemented method of claim 10 , wherein a search key to access the protected range field is different than an access key to access the encrypted data field, the search key permitting a user to request a range of protected data without the ability to access the protected data.
| 0.93049 |
1. A computer-implemented speech recognition system, comprising: a microphone that receives user speech; a speech recognition engine coupled to the microphone, the speech recognition engine recognizing the user speech and providing a corresponding textual output on a user interface; a change recognition component that automatically assigns a categorization to a user-initiated change to the corresponding textual output, the categorization being automatically assigned based at least in part upon a measurement of time indicative of how long it took the user to initiate the change, upon whether or not the user utilized an alternate list to generate the user initiated change, upon an acoustic similarity between the original textual output and the change to the textual output, and upon a number of words that is changed between the original textual output and the change to the textual output; and wherein automatically assigning the categorization comprises automatically identifying the user-initiated change as being either a correction or an edit operation, wherein the user-initiated change is identified as the correction upon the measurement of time indicating that there was a relatively short amount of time between providing the original textual output and the user initiating the change, wherein the user-initiated change is identified as the correction upon the user utilizing the alternate list, wherein the user-initiated change is identified as the correction upon the original textual output and the change to the textual output being acoustically similar, and wherein the user-initiated change is identified as the correction upon the number of words that is changed between the original textual output and the change to the textual output is determined to be insignificant, and wherein otherwise the categorization is determined to be the edit operation; and an adaptation component that selectively adapts the speech recognition engine depending upon the categorization.
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1. A computer-implemented speech recognition system, comprising: a microphone that receives user speech; a speech recognition engine coupled to the microphone, the speech recognition engine recognizing the user speech and providing a corresponding textual output on a user interface; a change recognition component that automatically assigns a categorization to a user-initiated change to the corresponding textual output, the categorization being automatically assigned based at least in part upon a measurement of time indicative of how long it took the user to initiate the change, upon whether or not the user utilized an alternate list to generate the user initiated change, upon an acoustic similarity between the original textual output and the change to the textual output, and upon a number of words that is changed between the original textual output and the change to the textual output; and wherein automatically assigning the categorization comprises automatically identifying the user-initiated change as being either a correction or an edit operation, wherein the user-initiated change is identified as the correction upon the measurement of time indicating that there was a relatively short amount of time between providing the original textual output and the user initiating the change, wherein the user-initiated change is identified as the correction upon the user utilizing the alternate list, wherein the user-initiated change is identified as the correction upon the original textual output and the change to the textual output being acoustically similar, and wherein the user-initiated change is identified as the correction upon the number of words that is changed between the original textual output and the change to the textual output is determined to be insignificant, and wherein otherwise the categorization is determined to be the edit operation; and an adaptation component that selectively adapts the speech recognition engine depending upon the categorization. 3. The system of claim 1 , wherein selectively adapting comprises adapting or not adapting depending upon whether the categorization is indicates that the user-initiated change is a correction or an edit operation.
| 0.579722 |
147. A method comprising: receiving first activity information for a sender of a first link to at least one recipient collected by a collection resource at a Web site, wherein no personally identifiable information of the sender is collected in the first activity information; storing the first activity information at a storage server; receiving second activity information when a recipient accesses the first link sent by the sender corresponding to the first activity information stored at the storage server, wherein no personally identifiable information of the recipient is collected in the second activity information; using at least one processor, using the first activity information to identify a first node in a social graph as being representative of the sender further comprising: extracting a user identifier from the first activity data; and if a match for the user identifier is not identified in the social graph, performing a deterministic fingerprinting approach using an IP address; using the second activity information to identify a second node in the social graph as being representative of the recipient; determining a category for the first link as a first category type; in the social graph, identifying a first edge between the first and second nodes as being representative of the first category type; and updating a first value associated with the first edge to a second value based on a time elapsed from at least one of the first activity information or second activity information.
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147. A method comprising: receiving first activity information for a sender of a first link to at least one recipient collected by a collection resource at a Web site, wherein no personally identifiable information of the sender is collected in the first activity information; storing the first activity information at a storage server; receiving second activity information when a recipient accesses the first link sent by the sender corresponding to the first activity information stored at the storage server, wherein no personally identifiable information of the recipient is collected in the second activity information; using at least one processor, using the first activity information to identify a first node in a social graph as being representative of the sender further comprising: extracting a user identifier from the first activity data; and if a match for the user identifier is not identified in the social graph, performing a deterministic fingerprinting approach using an IP address; using the second activity information to identify a second node in the social graph as being representative of the recipient; determining a category for the first link as a first category type; in the social graph, identifying a first edge between the first and second nodes as being representative of the first category type; and updating a first value associated with the first edge to a second value based on a time elapsed from at least one of the first activity information or second activity information. 150. The method of claim 147 comprising making a real-time bid on a content serving opportunity to the recipient based on the second value associated with the first edge.
| 0.641133 |
23. The system of claim 1 , further comprising a viewer that filters e-mail according to the assigned priorities.
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23. The system of claim 1 , further comprising a viewer that filters e-mail according to the assigned priorities. 25. The system of claim 23 , wherein the message subsystem provides summaries of messages according to the assigned priorities.
| 0.880952 |
1. A method of rasterising a document using a plurality of threads, the method comprising: interpreting objects of the document by performing interpreting tasks; establishing a plurality of rasterising tasks associated with the performed interpreting tasks, each performed interpreting task establishing a plurality of rasterising tasks; estimating an amount of parallelisable work available to be performed using the plurality of threads, the amount of parallelisable work being estimated using the established plurality of rasterising tasks and an expected number of interpreting tasks to be performed, wherein the parallelizable work comprises one or more of the rasterizing tasks from the established plurality of rasterizing tasks together with one or more of the interpreting tasks to be performed; selecting, based on the estimated amount of parallelisable work, one of: (i) an interpreting task to interpret objects of the document, and (ii) a rasterising task from the established plurality of rasterising tasks; and executing the selected task using at least one thread to rasterize the document.
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1. A method of rasterising a document using a plurality of threads, the method comprising: interpreting objects of the document by performing interpreting tasks; establishing a plurality of rasterising tasks associated with the performed interpreting tasks, each performed interpreting task establishing a plurality of rasterising tasks; estimating an amount of parallelisable work available to be performed using the plurality of threads, the amount of parallelisable work being estimated using the established plurality of rasterising tasks and an expected number of interpreting tasks to be performed, wherein the parallelizable work comprises one or more of the rasterizing tasks from the established plurality of rasterizing tasks together with one or more of the interpreting tasks to be performed; selecting, based on the estimated amount of parallelisable work, one of: (i) an interpreting task to interpret objects of the document, and (ii) a rasterising task from the established plurality of rasterising tasks; and executing the selected task using at least one thread to rasterize the document. 2. A method according to claim 1 , comprising selecting and executing the interpreting tasks to increase the amount of parallelisable work.
| 0.588914 |
1. A computer-implemented method of assigning a document identification tag to a new document, the new document to be added to a collection of documents, the method comprising: subdividing a predetermined set of monotonically ordered document identification tags into a plurality of tiers, wherein each tier is associated with a respective subset of the set of document identification tags, and wherein the plurality of tiers are monotonically ordered with respect to a query-independent document importance metric; receiving query-independent information about the new document, the information including the query-independent document importance metric; selecting, based at least on the query-independent information, one of the tiers; assigning to the new document a document identification tag from the respective subset of document identification tags associated with the selected tier, the assigned document identification tag not previously assigned to any of the documents in the collection of documents; and storing an assignment of the document identification tag from the respective subset of document identification tags associated with the selected tier to the new document in a computer-readable medium.
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1. A computer-implemented method of assigning a document identification tag to a new document, the new document to be added to a collection of documents, the method comprising: subdividing a predetermined set of monotonically ordered document identification tags into a plurality of tiers, wherein each tier is associated with a respective subset of the set of document identification tags, and wherein the plurality of tiers are monotonically ordered with respect to a query-independent document importance metric; receiving query-independent information about the new document, the information including the query-independent document importance metric; selecting, based at least on the query-independent information, one of the tiers; assigning to the new document a document identification tag from the respective subset of document identification tags associated with the selected tier, the assigned document identification tag not previously assigned to any of the documents in the collection of documents; and storing an assignment of the document identification tag from the respective subset of document identification tags associated with the selected tier to the new document in a computer-readable medium. 11. The method of claim 1 , further comprising; when a flush condition is satisfied, performing a flush operation, the flush operation including building a first sorted map and a second sorted map; wherein the first sorted map is keyed and sorted by globally unique identifiers, and includes for each globally unique identifier a corresponding document identification tag; and wherein the second sorted map is keyed and sorted by document identification tags assigned to documents since a prior flush operation, and includes for each such document identification tag a corresponding globally unique identifier.
| 0.609038 |
10. A system for creating mappings between taxonomies, the system comprising: at least one data source comprising a master taxonomy of documents and a taxonomy of documents; and a processor configured with logic to: classify documents from a category of the taxonomy to one or more categories within the master taxonomy based on a statistical model and classification score values, wherein the classification score values indicate whether the documents belong in a corresponding category of the master taxonomy; analyze the document classifications to determine a mapping between the taxonomy category and a corresponding category of the master taxonomy, a rule-based mapping is automatically created that maps the taxonomy category to the corresponding category in the master taxonomy in response to classification score values for the documents being above a threshold and in response to a majority of documents of the taxonomy category being classified within a single corresponding category of the master taxonomy; analyze the document classifications to identify outlier documents within the taxonomy category to determine whether the outlier documents should belong to the corresponding category; and update the statistical model when the outlier documents should belong to the corresponding category.
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10. A system for creating mappings between taxonomies, the system comprising: at least one data source comprising a master taxonomy of documents and a taxonomy of documents; and a processor configured with logic to: classify documents from a category of the taxonomy to one or more categories within the master taxonomy based on a statistical model and classification score values, wherein the classification score values indicate whether the documents belong in a corresponding category of the master taxonomy; analyze the document classifications to determine a mapping between the taxonomy category and a corresponding category of the master taxonomy, a rule-based mapping is automatically created that maps the taxonomy category to the corresponding category in the master taxonomy in response to classification score values for the documents being above a threshold and in response to a majority of documents of the taxonomy category being classified within a single corresponding category of the master taxonomy; analyze the document classifications to identify outlier documents within the taxonomy category to determine whether the outlier documents should belong to the corresponding category; and update the statistical model when the outlier documents should belong to the corresponding category. 11. The system of claim 10 , wherein the taxonomy category is mapped by the processor to the corresponding category in the master taxonomy, and the processor is further configured with logic to: identify the outlier documents from the documents having insufficient classification score values indicating that the documents have classification score values that are insufficient for further evaluation for classification into the corresponding category in the master taxonomy.
| 0.51481 |
2. The system of claim 1 , wherein each of the templates includes an issue description related to a different problem, the issue description including an ordered arrangement of nodes including field nodes, each field node corresponding to a field, at least some of the fields being fillable by a user and at least some of the fields being fillable by the at least one problem detector, the issue description further including production and morphological rules, the production rules each modeling how the value of one of the nodes influences at least one other of the nodes, the morphological rules each describing a syntactic relationship between at least two of the nodes.
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2. The system of claim 1 , wherein each of the templates includes an issue description related to a different problem, the issue description including an ordered arrangement of nodes including field nodes, each field node corresponding to a field, at least some of the fields being fillable by a user and at least some of the fields being fillable by the at least one problem detector, the issue description further including production and morphological rules, the production rules each modeling how the value of one of the nodes influences at least one other of the nodes, the morphological rules each describing a syntactic relationship between at least two of the nodes. 4. The system of claim 2 , wherein the nodes of the issue description further include text nodes which support the generation of syntactically correct text strings in the problem statement, in which text content of the text node is filled or modified in response to features of other nodes wherein the problem statement is based on the content of at least some of the field nodes and text nodes.
| 0.835476 |
1. A method for creating or updating a data set stored as a record in a database, wherein a plurality of data sets are stored in the database, wherein each data set in the plurality of data sets is defined to include a plurality of fields corresponding to a plurality of predefined entities, the method comprising: searching through a plurality of documents for current information about the data set; upon locating a search result document, in the plurality of documents, containing the current information about the data set, copying and storing a data string having a plurality of tokens from content of the search result document containing the current information about the data set; extracting a sequence of tokens corresponding to the data string; recognizing a first set of tokens in the sequence of tokens as a first entity based on entity recognition probabilistic scoring derived from a machine evaluation of a training set of entities; recognizing a second set of tokens in the sequence of tokens as a second entity based on identifying the first entity as a first node in a tree-like structure and identifying the second entity as by a second node in the tree-like structure, the first node connected to the second node by an arc representing a probability that the first entity is followed by the second entity in a probable entity sequence, the first node connected to another node by another arc representing another probability that the first entity is followed by another entity in another probable entity sequence, the tree-like structure created by a machine evaluation of a training set of input strings; aligning one or more tokens of the first set of tokens as one of a plurality of probable entities using the probabilistic scoring of the first set of tokens and grammatical rules; assigning the aligned one or more tokens to one entity field of the plurality of predefined entity fields of the data set; and creating and storing a new record for the data set if none exists, or updating an existing record for the data set, using the assigned aligned one or more tokens.
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1. A method for creating or updating a data set stored as a record in a database, wherein a plurality of data sets are stored in the database, wherein each data set in the plurality of data sets is defined to include a plurality of fields corresponding to a plurality of predefined entities, the method comprising: searching through a plurality of documents for current information about the data set; upon locating a search result document, in the plurality of documents, containing the current information about the data set, copying and storing a data string having a plurality of tokens from content of the search result document containing the current information about the data set; extracting a sequence of tokens corresponding to the data string; recognizing a first set of tokens in the sequence of tokens as a first entity based on entity recognition probabilistic scoring derived from a machine evaluation of a training set of entities; recognizing a second set of tokens in the sequence of tokens as a second entity based on identifying the first entity as a first node in a tree-like structure and identifying the second entity as by a second node in the tree-like structure, the first node connected to the second node by an arc representing a probability that the first entity is followed by the second entity in a probable entity sequence, the first node connected to another node by another arc representing another probability that the first entity is followed by another entity in another probable entity sequence, the tree-like structure created by a machine evaluation of a training set of input strings; aligning one or more tokens of the first set of tokens as one of a plurality of probable entities using the probabilistic scoring of the first set of tokens and grammatical rules; assigning the aligned one or more tokens to one entity field of the plurality of predefined entity fields of the data set; and creating and storing a new record for the data set if none exists, or updating an existing record for the data set, using the assigned aligned one or more tokens. 6. The method of claim 1 , wherein extracting the sequence of tokens corresponding to the data string includes: removing a trailing period if present; adding a space before an apostrophe or a comma; and splitting the data string into a plurality of entities at each space added before the apostrophe or the comma.
| 0.517219 |
1. A system for portlet-based translation of web content, comprising a configuration system stored on a memory device, the configuration system for designating a set of specifications for translating web content within an individual portlet of a portal page directly from a first natural language to a second natural language, wherein the set of specifications includes a translation paradigm, and wherein the translation paradigm dictates whether translation of the web content will be viewer initiated, automatic or both viewer initiated and automatic, and wherein the set of specifications further includes: an address of a translation system for translating the web content; a set of target languages into which the web content can be translated; a subject area indicating a type of informational content included in the web content; an address of a user dictionary that supplements the translation system; and a language style for translating the web content, and wherein the configuration system is configured to translate the web content of a portlet of the portal page into a natural language and to translate the web content of a different portlet of the portal page into a different natural language, wherein the source language of the web content of the portlet is different from the source language of the web content of the different portlet.
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1. A system for portlet-based translation of web content, comprising a configuration system stored on a memory device, the configuration system for designating a set of specifications for translating web content within an individual portlet of a portal page directly from a first natural language to a second natural language, wherein the set of specifications includes a translation paradigm, and wherein the translation paradigm dictates whether translation of the web content will be viewer initiated, automatic or both viewer initiated and automatic, and wherein the set of specifications further includes: an address of a translation system for translating the web content; a set of target languages into which the web content can be translated; a subject area indicating a type of informational content included in the web content; an address of a user dictionary that supplements the translation system; and a language style for translating the web content, and wherein the configuration system is configured to translate the web content of a portlet of the portal page into a natural language and to translate the web content of a different portlet of the portal page into a different natural language, wherein the source language of the web content of the portlet is different from the source language of the web content of the different portlet. 2. The system of claim 1 , wherein the configuration system provides an interface for designating the set of specifications.
| 0.536344 |
14. The method according to claim 13 , wherein the pixel dimensions are calculated based on: an available space in the display screen for the frameset, and parameters defined for the frame in the instructions.
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14. The method according to claim 13 , wherein the pixel dimensions are calculated based on: an available space in the display screen for the frameset, and parameters defined for the frame in the instructions. 15. The method according to claim 14 , wherein the calculating step calculates a height and width for the frame, according to the following:
S H =δ H ×(α H /β H ), and
S W =δ W ×(α W /(β W ), and where S H and S W are a calculated height and width for the frame, respectively, which are calculated in terms of pixels, δ H and δ W are a height and width for the frame, respectively, which are defined in terms of pixels, according to parameters in the instructions α H and α W are an available screen height and width for the frameset in the display screen, which are defined in terms of pixels, and β H and β W are a default display height and width, which are defined in terms of pixels, according to the instructions.
| 0.801544 |
34. The method of claim 27 , wherein the crowd-sourced hints list is refined by the hints server based on UE-specific hint list criteria for the target UE.
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34. The method of claim 27 , wherein the crowd-sourced hints list is refined by the hints server based on UE-specific hint list criteria for the target UE. 35. The method of claim 34 , wherein the UE-specific hint list criteria includes a list of web pages for which the target UE already has locally available web page resource information, and wherein the crowd-sourced hints list is refined to exclude at least a portion of the web page resource information for the listed web pages.
| 0.842083 |
33. The method of claim 22 wherein the complex-adaptive system comprises a negative-feedback mechanism that controls the varying of the faceted data set and dimensional concept taxonomy in response to the dimensional concept taxonomy information.
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33. The method of claim 22 wherein the complex-adaptive system comprises a negative-feedback mechanism that controls the varying of the faceted data set and dimensional concept taxonomy in response to the dimensional concept taxonomy information. 34. The method of claim 33 wherein the negative-feedback mechanism comprises at least one of statistical hurdles and pattern-matching constraints to the facets, facet attributes, and facet attribute hierarchies for the facet attributes derived from the dimensional concept taxonomy information.
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