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1. A method, comprising: crawling a computer network to identify documents that name an individual; generating, by a system having a processor, summaries of the documents with articles in an encyclopedia; building, by the system, a profile of the individual with the summaries; and searching the profile to provide responses to search queries.
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1. A method, comprising: crawling a computer network to identify documents that name an individual; generating, by a system having a processor, summaries of the documents with articles in an encyclopedia; building, by the system, a profile of the individual with the summaries; and searching the profile to provide responses to search queries. 7. The method of claim 1 further comprising: aggregating names of individuals with titles of articles from the encyclopedia to construct a database; searching the database for individuals with a specific expertise.
| 0.780612 |
4. The augmented reality device of claim 1 , wherein the context comprises a current activity of the subject.
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4. The augmented reality device of claim 1 , wherein the context comprises a current activity of the subject. 5. The augmented reality device of claim 4 , wherein to select the virtual object comprises to select a virtual object associated with the current activity of the subject.
| 0.950203 |
14. A method comprising: receiving, at a playback device, a content asset; determining a location of one or more triggers in the content asset, wherein the one or more triggers are associated with a command for a voice activated device; and inserting, prior to playback of the content asset, a signal marker of a plurality of signal markers at a location in the content asset corresponding to the one or more triggers, the signal marker being configured to prevent the voice activated device from causing execution of the command, wherein the plurality of signal markers comprises a first signal marker configured to prevent the voice activated device from causing execution of the command for a first time duration and a second signal marker configured to prevent the voice activated device from causing execution of the command for a second time duration.
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14. A method comprising: receiving, at a playback device, a content asset; determining a location of one or more triggers in the content asset, wherein the one or more triggers are associated with a command for a voice activated device; and inserting, prior to playback of the content asset, a signal marker of a plurality of signal markers at a location in the content asset corresponding to the one or more triggers, the signal marker being configured to prevent the voice activated device from causing execution of the command, wherein the plurality of signal markers comprises a first signal marker configured to prevent the voice activated device from causing execution of the command for a first time duration and a second signal marker configured to prevent the voice activated device from causing execution of the command for a second time duration. 16. The method of claim 14 , further comprising receiving an instruction to insert the signal marker at the location of the one or more triggers.
| 0.5 |
1. A method for an electronic device comprising: detecting a first touch input representing a selection of multiple rows of text displayed on a touch-sensitive display of the electronic device, wherein a start row or an end row of the selected text is not a complete row of selected text; detecting a second touch input representing a selection completing a row of text for the start row or the end row, wherein the selection of the second touch input completing the row of text comprises an expanded selection from the selection associated with the first touch input toward either a beginning of the start row of selected text or an end of the end row of selected text to select an entire row of the selected text for the start row or the end row; in response to a determination that the start row or the end row of the selected text is a complete row of selected text after receiving the second touch input, displaying a paragraph selection handle proximal to the complete row of the corresponding start row or end row of the selected text; detecting a third touch input related to the paragraph selection handle; and in response to detecting the third touch input, enabling a paragraph selection mode for the selection of one or more paragraphs for the electronic device.
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1. A method for an electronic device comprising: detecting a first touch input representing a selection of multiple rows of text displayed on a touch-sensitive display of the electronic device, wherein a start row or an end row of the selected text is not a complete row of selected text; detecting a second touch input representing a selection completing a row of text for the start row or the end row, wherein the selection of the second touch input completing the row of text comprises an expanded selection from the selection associated with the first touch input toward either a beginning of the start row of selected text or an end of the end row of selected text to select an entire row of the selected text for the start row or the end row; in response to a determination that the start row or the end row of the selected text is a complete row of selected text after receiving the second touch input, displaying a paragraph selection handle proximal to the complete row of the corresponding start row or end row of the selected text; detecting a third touch input related to the paragraph selection handle; and in response to detecting the third touch input, enabling a paragraph selection mode for the selection of one or more paragraphs for the electronic device. 8. A method according to claim 1 , comprising displaying a paragraph selection handle proximal each of the start row and the end row when both the start row and the end row of the text selected by the first touch input are complete.
| 0.554362 |
12. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: receiving a first specification that identifies program code behavior associated with a plurality of documents, wherein the specification comprises an input-output pair including a first data entity and a second data entity; for each module of program code of a plurality of modules of program code, supplying to a constraint solver one or more input-output constraints based on the input-output pair of the first specification and one or more code constraints based on the module of program code; receiving from the constraint solver, for each module of program code, a result indicating whether the code constraints based on the module of program code are satisfiable with the input-output constraints; and generating search results referencing one or more modules of program code having a positive result from the constraint solver and providing the search results to a user.
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12. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: receiving a first specification that identifies program code behavior associated with a plurality of documents, wherein the specification comprises an input-output pair including a first data entity and a second data entity; for each module of program code of a plurality of modules of program code, supplying to a constraint solver one or more input-output constraints based on the input-output pair of the first specification and one or more code constraints based on the module of program code; receiving from the constraint solver, for each module of program code, a result indicating whether the code constraints based on the module of program code are satisfiable with the input-output constraints; and generating search results referencing one or more modules of program code having a positive result from the constraint solver and providing the search results to a user. 16. The computer-readable medium of claim 12 , wherein the first data entity is one or more database tables and the second data entity is a portion of the one or more database tables.
| 0.616906 |
17. A method for extracting an attacking packet signature candidate comprising: configuring a processor to perform the steps of: separating a network packet into a header and a payload; parsing the header information; generating traffic information based on the parsed value; measuring a frequency of appearing substrings with a predetermined length in the separated payload for a constant observation period, and extracting a substring having a frequency higher than a predetermined setup value by updating the measured frequency information to a substring frequency table, wherein the updating includes increasing said frequency information of the substring that is less frequently shown previously by a larger increment amount; extracting a substring with a predetermined length based on information about the updated substring frequency table; generating a signature by collecting the extracted substring information of the substring frequency table and the generated traffic information, and updating the generated signature information to a signature frequency table; and extracting a signature candidate with reference to information of the signature frequency table.
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17. A method for extracting an attacking packet signature candidate comprising: configuring a processor to perform the steps of: separating a network packet into a header and a payload; parsing the header information; generating traffic information based on the parsed value; measuring a frequency of appearing substrings with a predetermined length in the separated payload for a constant observation period, and extracting a substring having a frequency higher than a predetermined setup value by updating the measured frequency information to a substring frequency table, wherein the updating includes increasing said frequency information of the substring that is less frequently shown previously by a larger increment amount; extracting a substring with a predetermined length based on information about the updated substring frequency table; generating a signature by collecting the extracted substring information of the substring frequency table and the generated traffic information, and updating the generated signature information to a signature frequency table; and extracting a signature candidate with reference to information of the signature frequency table. 18. The method according to claim 17 , further comprising interrupting a related process before measuring the frequency of the substring in the substring extractor if the extracted substring information is identical to a pre-stored substring information by storing information about an allowable substring.
| 0.594185 |
1. A system for providing an automated media service, comprising: one or more memories designed to store computer program code; and one or more processors designed to execute the computer program code stored in the one or more memories, the computer program code designed to cause the one or more processors to perform at least the following: enable users and producers to subscribe to the media service at a website on the Internet; search content on the Internet in order to identify a topic, the topic indicative of relevant news or events, the topic indicative of a type of media content that will be requested for uploading from the producers; publish the topic to the producers; receive and store media content uploaded from the producers that relate to the topic in the one or more memories; enable the users to select and download the media content from the website over the Internet; enable the users to upload media content ratings for the media content to the website over the Internet; determine a producer rating for each of the producers based at least in part upon the media content ratings; and prevent the download of the media content from a first producer from the website over the Internet based at least in part upon the producer rating associated with the first producer; and permit the download of the media content from a second producer from the website over the Internet based at least in part upon the producer rating associated with the second producer.
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1. A system for providing an automated media service, comprising: one or more memories designed to store computer program code; and one or more processors designed to execute the computer program code stored in the one or more memories, the computer program code designed to cause the one or more processors to perform at least the following: enable users and producers to subscribe to the media service at a website on the Internet; search content on the Internet in order to identify a topic, the topic indicative of relevant news or events, the topic indicative of a type of media content that will be requested for uploading from the producers; publish the topic to the producers; receive and store media content uploaded from the producers that relate to the topic in the one or more memories; enable the users to select and download the media content from the website over the Internet; enable the users to upload media content ratings for the media content to the website over the Internet; determine a producer rating for each of the producers based at least in part upon the media content ratings; and prevent the download of the media content from a first producer from the website over the Internet based at least in part upon the producer rating associated with the first producer; and permit the download of the media content from a second producer from the website over the Internet based at least in part upon the producer rating associated with the second producer. 9. The system of claim 1 , wherein the computer program code is further designed to cause the one or more processors to: download the media content to the users via streaming.
| 0.557091 |
11. An intelligent speech recognition system comprising: at least one audio input; at least one memory; a family of grammar-based language models stored within the at least one memory; a family of statistical language models stored within the at least one memory; and at least one processor operably connected to the at least one audio input and the at least one memory and configured to (i) perform a first speech recognition using the family of grammar-based language models, (ii) perform a second speech recognition using the family of statistical language models, and (iii) determine a recognized speech based upon the first speech recognition and the second speech recognition, wherein the family of grammar-based language models is generated based upon a generated frequency count of each utterance in a plurality of utterances identified as being in a high-frequency segment of the plurality of utterances based upon a predetermined frequency threshold; and the family of statistical language models is generated based upon a generated frequency count of each utterance in a plurality of utterances identified as being in a low-frequency segment of the plurality of utterances based upon the predetermined frequency threshold.
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11. An intelligent speech recognition system comprising: at least one audio input; at least one memory; a family of grammar-based language models stored within the at least one memory; a family of statistical language models stored within the at least one memory; and at least one processor operably connected to the at least one audio input and the at least one memory and configured to (i) perform a first speech recognition using the family of grammar-based language models, (ii) perform a second speech recognition using the family of statistical language models, and (iii) determine a recognized speech based upon the first speech recognition and the second speech recognition, wherein the family of grammar-based language models is generated based upon a generated frequency count of each utterance in a plurality of utterances identified as being in a high-frequency segment of the plurality of utterances based upon a predetermined frequency threshold; and the family of statistical language models is generated based upon a generated frequency count of each utterance in a plurality of utterances identified as being in a low-frequency segment of the plurality of utterances based upon the predetermined frequency threshold. 13. The system of claim 11 , wherein at least a portion of the intelligent speech recognition system is located within or remotely from one or more client devices.
| 0.636845 |
1. A machine implemented method comprising: receiving a first user input comprising a search query; displaying, by a data processing system, in a search interface accessible across a plurality of computer application programs, a plurality of results from a search performed across files and programs accessible by the data processing system, wherein the plurality of results matches the search query and are categorized into a plurality of categories, only a first subset of the plurality of results are displayed for each of the plurality of categories, and the results in the first subset are displayed grouped together in proximity to a displayed representation of a corresponding category; receiving a second user input comprising a selection of one of the displayed representations of the plurality of categories; and in response to the second user input, displaying, in the search interface, a second subset of the plurality of results, wherein the second subset matches the search query and is categorized into a plurality of subcategories of the selected category, and the results categorized into each subcategory is displayed grouped together in proximity to a displayed representation of a corresponding subcategory.
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1. A machine implemented method comprising: receiving a first user input comprising a search query; displaying, by a data processing system, in a search interface accessible across a plurality of computer application programs, a plurality of results from a search performed across files and programs accessible by the data processing system, wherein the plurality of results matches the search query and are categorized into a plurality of categories, only a first subset of the plurality of results are displayed for each of the plurality of categories, and the results in the first subset are displayed grouped together in proximity to a displayed representation of a corresponding category; receiving a second user input comprising a selection of one of the displayed representations of the plurality of categories; and in response to the second user input, displaying, in the search interface, a second subset of the plurality of results, wherein the second subset matches the search query and is categorized into a plurality of subcategories of the selected category, and the results categorized into each subcategory is displayed grouped together in proximity to a displayed representation of a corresponding subcategory. 3. The method of claim 1 , further comprising: in response to the second user input, displaying, in the search interface, more results matching the search query in the selected category than were initially displayed for the selected category.
| 0.705811 |
1. A system comprising: a data funnel coupled to a processor, wherein the data funnel collates input data from a plurality of sources, wherein the input data is semantically uncorrelated three-space data of an instantaneous spatial and geometric state of an object in a frame of reference of the object, wherein the plurality of sources comprise disparate sources, wherein the data funnel conforms the input data into a stream of spatiotemporal data, wherein the spatiotemporal data of the stream is uniformly represented; a gesture engine coupled to the data funnel, wherein the gesture engine generates gestural events from the spatiotemporal data using a plurality of gesture descriptions, wherein the gesture engine represents the gestural events in a protoevent comprising a data format that is application-neutral and fully articulated; and a distributor coupled to the gesture engine, wherein the distributor provides access to the gestural events by at least one event consumer via corresponding protoevents in a spatial-semantic frame of reference of the at least one event consumer.
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1. A system comprising: a data funnel coupled to a processor, wherein the data funnel collates input data from a plurality of sources, wherein the input data is semantically uncorrelated three-space data of an instantaneous spatial and geometric state of an object in a frame of reference of the object, wherein the plurality of sources comprise disparate sources, wherein the data funnel conforms the input data into a stream of spatiotemporal data, wherein the spatiotemporal data of the stream is uniformly represented; a gesture engine coupled to the data funnel, wherein the gesture engine generates gestural events from the spatiotemporal data using a plurality of gesture descriptions, wherein the gesture engine represents the gestural events in a protoevent comprising a data format that is application-neutral and fully articulated; and a distributor coupled to the gesture engine, wherein the distributor provides access to the gestural events by at least one event consumer via corresponding protoevents in a spatial-semantic frame of reference of the at least one event consumer. 15. The system of claim 1 , wherein the input data is received from an electric field sensing system.
| 0.613287 |
56. A mobile telephone having a touch interface and a graphical user interface, wherein the mobile telephone includes the computer system of claim 41 .
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56. A mobile telephone having a touch interface and a graphical user interface, wherein the mobile telephone includes the computer system of claim 41 . 58. The mobile telephone of claim 56 wherein the indication of the accuracy of the practice gesture currently being performed includes one or more items selected from the group consisting of: an animated hand, one or more motion trails, an iconographic representation, a textual description of the gesture currently being performed, a textual description of a command corresponding to the gesture currently being performed, a positive feedback indicator, and a negative feedback indicator.
| 0.77551 |
1. At least one non-transitory machine-readable media having machine-executable instructions encoded thereon which, when executed by a data processing system, cause the data processing system to perform a method of grouping axes of information elements, the method comprising: providing a first plurality of information elements adapted to be displayed in a first axis of information elements; providing a second plurality of information elements adapted to be displayed in a second axis of information elements, the first axis of information elements and the second axis of information elements being adapted to be acted upon independently from one another, the first axis of information elements and the second axis of information elements being further adapted to be grouped together on a basis of a user input; grouping the first axis of information elements and the second axis of information elements in a group of axes of information elements, the group of axes being adapted to collectively perform an action on the first axis of information elements and the second axis of information elements; and displaying the group of axes of information elements, the first axis of information elements being adapted to graphically represent information elements along a first substantially rectilinear arrangement and the second axis of information elements being adapted to graphically represent information elements along a second substantially rectilinear arrangement, the first axis of information elements and the second axis of information elements from the group of axes of information elements being adapted to be ungrouped on a basis of a user input.
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1. At least one non-transitory machine-readable media having machine-executable instructions encoded thereon which, when executed by a data processing system, cause the data processing system to perform a method of grouping axes of information elements, the method comprising: providing a first plurality of information elements adapted to be displayed in a first axis of information elements; providing a second plurality of information elements adapted to be displayed in a second axis of information elements, the first axis of information elements and the second axis of information elements being adapted to be acted upon independently from one another, the first axis of information elements and the second axis of information elements being further adapted to be grouped together on a basis of a user input; grouping the first axis of information elements and the second axis of information elements in a group of axes of information elements, the group of axes being adapted to collectively perform an action on the first axis of information elements and the second axis of information elements; and displaying the group of axes of information elements, the first axis of information elements being adapted to graphically represent information elements along a first substantially rectilinear arrangement and the second axis of information elements being adapted to graphically represent information elements along a second substantially rectilinear arrangement, the first axis of information elements and the second axis of information elements from the group of axes of information elements being adapted to be ungrouped on a basis of a user input. 9. The at least one non-transitory machine-readable media of claim 1 , wherein, when the first axis of information elements and the second axis of information elements are grouped, the first axis of information elements is adapted to be longitudinally scrolled in respect with the second axis of information elements in the group of axes of information elements.
| 0.641952 |
10. A computer-implemented arrangement within memory of a computer system for detecting indicators of misleading content in a markup language coded document, said computer-implemented arrangement comprising: a classifier module, said classifier module being configured to identify and group documents with markup language, wherein said classifier module including a tag extractor, said tag extractor being configured to extract a set of tags from said markup language coded document, said set of tags having been included in said markup language coded document before said markup language coded document is received by said computer system, a tag structure signature generator, said tag structure signature generator being configured to create a tag structure signature by combining tags of said set of tags, said tag structure signature being an n-gram signature including a set of n-grams, each n-gram of said set of n-grams including at least two tags from said set of tags, said tag structure signature generator being further configured to add a copy of a symbol between any two tags in said each n-gram of said set of n-grams, and a comparison module, said comparison module being configured to calculated a similarity value between said tag structure signature and a set known tag structure signatures.
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10. A computer-implemented arrangement within memory of a computer system for detecting indicators of misleading content in a markup language coded document, said computer-implemented arrangement comprising: a classifier module, said classifier module being configured to identify and group documents with markup language, wherein said classifier module including a tag extractor, said tag extractor being configured to extract a set of tags from said markup language coded document, said set of tags having been included in said markup language coded document before said markup language coded document is received by said computer system, a tag structure signature generator, said tag structure signature generator being configured to create a tag structure signature by combining tags of said set of tags, said tag structure signature being an n-gram signature including a set of n-grams, each n-gram of said set of n-grams including at least two tags from said set of tags, said tag structure signature generator being further configured to add a copy of a symbol between any two tags in said each n-gram of said set of n-grams, and a comparison module, said comparison module being configured to calculated a similarity value between said tag structure signature and a set known tag structure signatures. 15. The computer-implemented of claim 10 wherein said tag structure signature generator is further configured to remove attribute names from said tags of said set of tags before combining said tags of said set of tags.
| 0.656806 |
1. A method of creating a keyboard state table mapping a chording keyboard state k to a symbol s, for a chording keyboard comprising K keyboards states and a symbol set of S symbols, comprising the steps of: a) constructing an array of K*(K−1) entries wherein each entry represents a keyboard state transition of the chording keyboard from a first keyboard state k 1 to a second keyboard state k 2 ; b) constructing an exercise adapted to be performed by users of the chording keyboard wherein the users use the chording keyboard to generate a series of keyboard state transitions; c) measuring the difficulty with which a user generates each transition, such difficulty, as a single scalar, the, “psychomotor cost” of that transition; d) aggregating the psychomotor costs from different users of the exercise, for each transition in the array; e) entering the aggregated psychomotor cost for each transition in the array; f) identifying a text corpus comprising sequential symbols from the symbol set; g) creating a first keyboard state table that associates with each of the K keyboard states one symbol from the set of S symbols; assigning the first keyboard state table a keyboard state table under test; h) encoding the text corpus using the keyboard state table under test; i) summing the total psychomotor costs for all of the sequential transitions to complete step h, using the array to determine the psychomotor cost for each transition used in step h); this sum being the total psychomotor cost for the keyboard state table under test using the corpus; k) creating a set of alternative keyboard state tables by permuting a different subset of table lines from the keyboard state table under test; wherein the size of the subset is Z table lines; l) performing steps h) and i) for each table in the set of alternative keyboard state tables; wherein each table in the set of alternative keyboard state tables now becomes one keyboard state table under test; thus creating multiple keyboard state tables under test and copies of steps of this method; m) selecting the one table from the set of alternative keyboard state tables that has the lowest total psychomotor cost; the selected table becoming a new keyboard state table under test; n) repeating steps k) through m) until a terminating condition is reached; o) using a last selected table as the keyboard state table created by this method; wherein the chording keyboard is free of a requirement to release all keys between chords.
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1. A method of creating a keyboard state table mapping a chording keyboard state k to a symbol s, for a chording keyboard comprising K keyboards states and a symbol set of S symbols, comprising the steps of: a) constructing an array of K*(K−1) entries wherein each entry represents a keyboard state transition of the chording keyboard from a first keyboard state k 1 to a second keyboard state k 2 ; b) constructing an exercise adapted to be performed by users of the chording keyboard wherein the users use the chording keyboard to generate a series of keyboard state transitions; c) measuring the difficulty with which a user generates each transition, such difficulty, as a single scalar, the, “psychomotor cost” of that transition; d) aggregating the psychomotor costs from different users of the exercise, for each transition in the array; e) entering the aggregated psychomotor cost for each transition in the array; f) identifying a text corpus comprising sequential symbols from the symbol set; g) creating a first keyboard state table that associates with each of the K keyboard states one symbol from the set of S symbols; assigning the first keyboard state table a keyboard state table under test; h) encoding the text corpus using the keyboard state table under test; i) summing the total psychomotor costs for all of the sequential transitions to complete step h, using the array to determine the psychomotor cost for each transition used in step h); this sum being the total psychomotor cost for the keyboard state table under test using the corpus; k) creating a set of alternative keyboard state tables by permuting a different subset of table lines from the keyboard state table under test; wherein the size of the subset is Z table lines; l) performing steps h) and i) for each table in the set of alternative keyboard state tables; wherein each table in the set of alternative keyboard state tables now becomes one keyboard state table under test; thus creating multiple keyboard state tables under test and copies of steps of this method; m) selecting the one table from the set of alternative keyboard state tables that has the lowest total psychomotor cost; the selected table becoming a new keyboard state table under test; n) repeating steps k) through m) until a terminating condition is reached; o) using a last selected table as the keyboard state table created by this method; wherein the chording keyboard is free of a requirement to release all keys between chords. 7. The method of claim 1 comprising the additional step of: (p) expanding the final keyboard state table to comprise an additional state assigned to a null symbol s-n not contained in the set S.
| 0.819109 |
1. A method for the spotting of keywords in a handwritten document, comprising the steps of : inputting an image of the handwritten document; performing word segmentation on the image to obtain segmented words; performing word matching, consisting in the sub-steps of : performing character segmentation on the segmented words; performing character recognition on the segmented characters; performing distance computations on the recognized characters using a Generalized Hidden Markov Model with ergodic topology to identify words based on character models; performing non-keyword rejection on the identified words using a classifier based on a combination of Gaussian Mixture Models, Hidden Markov Models and Support Vector Machines, the non-rejected identified words being identified as spotted keywords; outputting the spotted keywords.
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1. A method for the spotting of keywords in a handwritten document, comprising the steps of : inputting an image of the handwritten document; performing word segmentation on the image to obtain segmented words; performing word matching, consisting in the sub-steps of : performing character segmentation on the segmented words; performing character recognition on the segmented characters; performing distance computations on the recognized characters using a Generalized Hidden Markov Model with ergodic topology to identify words based on character models; performing non-keyword rejection on the identified words using a classifier based on a combination of Gaussian Mixture Models, Hidden Markov Models and Support Vector Machines, the non-rejected identified words being identified as spotted keywords; outputting the spotted keywords. 15. The method of claim 1 , wherein the sub-step of performing character recognition on the segmented characters is based on a background skeletal graph.
| 0.651327 |
1. A method, in a data processing system, for performing load balancing of question processing in a Question and Answer (QA) system, implemented by the data processing system, having a plurality of QA system pipelines, the method comprising: receiving, by the data processing system, an input question for processing by the QA system; determining, by the data processing system, a predicted question difficulty for generating an answer to the input question based on at least one feature extracted from the input question and a correlation of the at least one feature with a predicted level of question difficulty, wherein the predicted question difficulty is indicative of a predicted amount of time required to process the input question to generate an answer to the input question via a QA system pipeline in the plurality of QA system pipelines; and performing load balancing of question processing at least by: selecting, by the data processing system, a QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty; routing, by the data processing system, the input question to the selected QA system pipeline for processing; and processing, by the data processing system, the input question by the selected QA system pipeline to generate an answer for the input question.
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1. A method, in a data processing system, for performing load balancing of question processing in a Question and Answer (QA) system, implemented by the data processing system, having a plurality of QA system pipelines, the method comprising: receiving, by the data processing system, an input question for processing by the QA system; determining, by the data processing system, a predicted question difficulty for generating an answer to the input question based on at least one feature extracted from the input question and a correlation of the at least one feature with a predicted level of question difficulty, wherein the predicted question difficulty is indicative of a predicted amount of time required to process the input question to generate an answer to the input question via a QA system pipeline in the plurality of QA system pipelines; and performing load balancing of question processing at least by: selecting, by the data processing system, a QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty; routing, by the data processing system, the input question to the selected QA system pipeline for processing; and processing, by the data processing system, the input question by the selected QA system pipeline to generate an answer for the input question. 2. The method of claim 1 , wherein selecting the QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty further comprises selecting the QA system pipeline based on a current load of each of the QA system pipelines in the plurality of QA system pipelines.
| 0.64759 |
5. The method of claim 1 , wherein the second request comprises at least one of a user identifier, document identifier, portion identifier, or a version identifier.
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5. The method of claim 1 , wherein the second request comprises at least one of a user identifier, document identifier, portion identifier, or a version identifier. 6. The method of claim 5 , wherein determining whether the user is authorized to access the document comprises: calculating a hash of the user identifier, the file identifier and the version identifier; and comparing a stored cryptographic hash with the generated hash.
| 0.84959 |
10. The computer system of claim 8 , wherein said family of devices is a family of multifunction printers (MFPs).
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10. The computer system of claim 8 , wherein said family of devices is a family of multifunction printers (MFPs). 11. The computer system of claim 10 , wherein said firmware components comprise: a print engine firmware component; a copier firmware component; and a scanner firmware component; and wherein the XML description file includes, for each said component, an identification of the firmware for each of said components and a location of a component firmware file for each of said components.
| 0.878077 |
1. A method for processing a data stream comprising: receiving a data stream input at a scanner component, the data stream input representing a plurality of program elements; and scanning the data stream input for annotations of the program elements, the scanning of the data stream input including: encountering a data type description for a Java class type, informing a handler component regarding the data type description, determining whether to process program elements within the class type, the determination being based at least in part on any messages received from the handler component indicating that the class type is not of interest, scanning the program elements within the class type to identify annotated program elements and annotation values of the identified annotated program elements based on the determination to process the program elements within the class type, and skipping the program elements within the class type and annotation values of the program elements within the class type based on the determination not to process the program elements within the class type.
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1. A method for processing a data stream comprising: receiving a data stream input at a scanner component, the data stream input representing a plurality of program elements; and scanning the data stream input for annotations of the program elements, the scanning of the data stream input including: encountering a data type description for a Java class type, informing a handler component regarding the data type description, determining whether to process program elements within the class type, the determination being based at least in part on any messages received from the handler component indicating that the class type is not of interest, scanning the program elements within the class type to identify annotated program elements and annotation values of the identified annotated program elements based on the determination to process the program elements within the class type, and skipping the program elements within the class type and annotation values of the program elements within the class type based on the determination not to process the program elements within the class type. 7. The method of claim 1 , wherein the plurality of program elements comprises Java class file program elements.
| 0.603324 |
46. A method performed by a system comprising one or more computers, the method comprising: determining that a first search query includes a respective text reference to each of one or more predetermined attributes, wherein each attribute is associated with a first entity type; obtaining search results for the first search query from a search engine, each search result identifying a respective resource; for each of a plurality of the obtained search results, determining an initial score for each of a plurality of entities of the first entity type based on occurrences of names of the entity in the resource identified by the search result; generating a final score for each of the plurality of entities based on the initial scores; selecting one or more names of entities of the first entity type to include in a response to the first search query based on the final scores: and generating one or more attribute suggestions to include in the response to the search query, each attribute suggestion identifying a respective additional attribute associated with the first entity type, wherein generating the one or more attribute suggestions comprises: identifying one or more associated attributes for each of the selected entities; and selecting the associated attributes that maximally refine the selected entities as being attribute suggestions.
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46. A method performed by a system comprising one or more computers, the method comprising: determining that a first search query includes a respective text reference to each of one or more predetermined attributes, wherein each attribute is associated with a first entity type; obtaining search results for the first search query from a search engine, each search result identifying a respective resource; for each of a plurality of the obtained search results, determining an initial score for each of a plurality of entities of the first entity type based on occurrences of names of the entity in the resource identified by the search result; generating a final score for each of the plurality of entities based on the initial scores; selecting one or more names of entities of the first entity type to include in a response to the first search query based on the final scores: and generating one or more attribute suggestions to include in the response to the search query, each attribute suggestion identifying a respective additional attribute associated with the first entity type, wherein generating the one or more attribute suggestions comprises: identifying one or more associated attributes for each of the selected entities; and selecting the associated attributes that maximally refine the selected entities as being attribute suggestions. 56. The method of claim 46 , further comprising: in response to a user input indicating that a first attribute suggestion matches the first search query, generating an additional search query that includes the first search query and the first attribute suggestion.
| 0.597023 |
8. An apparatus comprising: a processor; a memory storing machine readable code executable by the processor, the machine readable code comprising: an input reception module configured to detect a gesture from a single touch input item on a touch-enabled display of an input device, the gesture input comprising a swipe gesture; a characteristic determination module configured to determine a location on the touch-enabled display where the swipe gesture begins and a direction of the swipe gesture; a subdividing module configured to dynamically subdivide a view area of the touch-enabled display into one or more segments based on the location and the direction of the swipe gesture, wherein each segment represents a particular word length, and wherein visible boundaries representing each segment are displayed on the touch-enabled display; a word presentation module configured to present a list of one or more words having word lengths determined in response to detecting the swipe gesture move into a first segment of the one or more segments; and an update module configured to dynamically update the presented list of one or more words with one or more different words having different word lengths indicated by the second segment in response to detecting the swipe gesture move into a second segment of the one or more segments and in response to a period of time elapsing after modification of the swipe gesture.
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8. An apparatus comprising: a processor; a memory storing machine readable code executable by the processor, the machine readable code comprising: an input reception module configured to detect a gesture from a single touch input item on a touch-enabled display of an input device, the gesture input comprising a swipe gesture; a characteristic determination module configured to determine a location on the touch-enabled display where the swipe gesture begins and a direction of the swipe gesture; a subdividing module configured to dynamically subdivide a view area of the touch-enabled display into one or more segments based on the location and the direction of the swipe gesture, wherein each segment represents a particular word length, and wherein visible boundaries representing each segment are displayed on the touch-enabled display; a word presentation module configured to present a list of one or more words having word lengths determined in response to detecting the swipe gesture move into a first segment of the one or more segments; and an update module configured to dynamically update the presented list of one or more words with one or more different words having different word lengths indicated by the second segment in response to detecting the swipe gesture move into a second segment of the one or more segments and in response to a period of time elapsing after modification of the swipe gesture. 14. The apparatus of claim 8 , wherein the input is received from a virtual keyboard, the virtual keyboard presented on a touch-enabled display of an information handling device and wherein the one or more words presented in the list begin with one or more characters entered on the virtual keyboard, an order of the one or more entered characters being preserved in the one or more listed words.
| 0.5 |
1. A non-transitory computer readable medium storing computer readable program code for causing a computer to perform steps of: presenting a graphical user interface; receiving input from a user using the graphical user interface; receiving a first graphical manipulation of a closing expression from the user using the graphical user interface; reconfiguring a text based parser for a text based test language based on the user input and the first graphical manipulations; parsing a source test program file using the reconfigured text based parser, and controlling control data extraction from the source test program file based on the first graphical manipulation, wherein the closing expression acts as a bookend to extract data located inside starting and ending lines of the source test program file.
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1. A non-transitory computer readable medium storing computer readable program code for causing a computer to perform steps of: presenting a graphical user interface; receiving input from a user using the graphical user interface; receiving a first graphical manipulation of a closing expression from the user using the graphical user interface; reconfiguring a text based parser for a text based test language based on the user input and the first graphical manipulations; parsing a source test program file using the reconfigured text based parser, and controlling control data extraction from the source test program file based on the first graphical manipulation, wherein the closing expression acts as a bookend to extract data located inside starting and ending lines of the source test program file. 4. The non-transitory computer readable medium of claim 1 storing computer readable program code for causing a computer to perform the steps further comprising: presenting to the user configurable data fields for storing and extracting parsed data.
| 0.747976 |
13. A system according to claim 1, wherein the computer display information comprises one or more data fields.
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13. A system according to claim 1, wherein the computer display information comprises one or more data fields. 18. A system according to claim 13, further comprising: a translator within the host extension converting the one or more data fields into one or more function key fields within the markup language document.
| 0.948844 |
15. A computer system for generating a composite of images in multiple languages when a request is made to capture an image of a screen, the computer system comprising: one or more computer processors, one or more computer readable storage media, and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to receive a request to capture a first graphical user interface (GUI) display of an application in a first language available to the application; program instructions to iterate over GUI elements of the first GUI display to locate bundle keys from a first language bundle for a second language; and program instructions to generate a second GUI display for the first language available to the application, wherein the second GUI display is a recreation of images within the first GUI display, and wherein generating the second GUI comprises: program instructions to retrieve a bundle name and a first bundle key from the first language bundle for the second language and a second bundle key from a second language bundle for the first language; and program instructions to run the application a second time, using the second bundle key from the second language bundle for the first language in place of the first bundle key from the first language bundle for the second language.
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15. A computer system for generating a composite of images in multiple languages when a request is made to capture an image of a screen, the computer system comprising: one or more computer processors, one or more computer readable storage media, and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to receive a request to capture a first graphical user interface (GUI) display of an application in a first language available to the application; program instructions to iterate over GUI elements of the first GUI display to locate bundle keys from a first language bundle for a second language; and program instructions to generate a second GUI display for the first language available to the application, wherein the second GUI display is a recreation of images within the first GUI display, and wherein generating the second GUI comprises: program instructions to retrieve a bundle name and a first bundle key from the first language bundle for the second language and a second bundle key from a second language bundle for the first language; and program instructions to run the application a second time, using the second bundle key from the second language bundle for the first language in place of the first bundle key from the first language bundle for the second language. 18. The computer system of claim 15 , wherein program instructions to generate the second GUI display comprise: program instructions to generate alternative GUI displays with different languages, using substituted language strings from the second language bundle.
| 0.583141 |
16. The system according to claim 11 , wherein an algorithm weight is assigned to each algorithm.
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16. The system according to claim 11 , wherein an algorithm weight is assigned to each algorithm. 17. The system according to claim 16 , wherein at least one algorithm weight is modified in response to feedback regarding the personnel needs.
| 0.939369 |
1. A computer-implemented method for defining a structure for items to be identified in loosely-structured data, comprising: defining a layout for a composite data definition, where the layout indicates at least one of positional relationship of data items to each other and positional information for data items in the loosely-structured data; arranging data items in the layout, where each data item in the layout has a common meaning for applications that use the data item; creating an identification order list for the composite data definition, where the identification order list includes the data items in the layout and specifies an order in which the data items comprising the composite data definition are to be identified within the loosely-structured data; receiving loosely-structured input data; and parsing the input data to identify data items therein using the layout and the identification order list of the composite data definition, where the data items in the layout are searched for in the order specified by the identification order list and the order specified in the identification order list differs from the arrangement of the data items in the layout of the composite data definition and the method is implemented by computer-executable instructions executed by a computer processor.
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1. A computer-implemented method for defining a structure for items to be identified in loosely-structured data, comprising: defining a layout for a composite data definition, where the layout indicates at least one of positional relationship of data items to each other and positional information for data items in the loosely-structured data; arranging data items in the layout, where each data item in the layout has a common meaning for applications that use the data item; creating an identification order list for the composite data definition, where the identification order list includes the data items in the layout and specifies an order in which the data items comprising the composite data definition are to be identified within the loosely-structured data; receiving loosely-structured input data; and parsing the input data to identify data items therein using the layout and the identification order list of the composite data definition, where the data items in the layout are searched for in the order specified by the identification order list and the order specified in the identification order list differs from the arrangement of the data items in the layout of the composite data definition and the method is implemented by computer-executable instructions executed by a computer processor. 3. The method of claim 1 further comprises assigning an identification property to a given data item in the layout after the given data item has been arranged in the layout.
| 0.725042 |
7. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed on the processor, cause the processor to perform operations comprising: identifying, via a processor, a first segment of a dialog turn associated with soliciting a first probable user response as part of a dialog with a dialog system; identifying, via the processor, a second segment of the dialog turn associated with soliciting a second probable user response, wherein the first segment and the second segment of the dialog turn are further identified based on a first timing of the first probable user response and a second timing of the second probable user response; activating a first weighted grammar for the first segment of the dialog for processing speech received during the first segment, to yield a first activated weighted grammar, wherein the first weighted grammar is weighted based on a user profile which consists of information about a number called from, demographic information, account information, a time of day, and a date; activating a second weighted grammar for the second segment of the dialog for processing speech received during the second segment, to yield a second activated weighted grammar; recognizing user speech received during the first segment of the dialog using the first activated weighted grammar; and recognizing user speech received during the second segment of the dialog using the second activated weighted grammar.
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7. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed on the processor, cause the processor to perform operations comprising: identifying, via a processor, a first segment of a dialog turn associated with soliciting a first probable user response as part of a dialog with a dialog system; identifying, via the processor, a second segment of the dialog turn associated with soliciting a second probable user response, wherein the first segment and the second segment of the dialog turn are further identified based on a first timing of the first probable user response and a second timing of the second probable user response; activating a first weighted grammar for the first segment of the dialog for processing speech received during the first segment, to yield a first activated weighted grammar, wherein the first weighted grammar is weighted based on a user profile which consists of information about a number called from, demographic information, account information, a time of day, and a date; activating a second weighted grammar for the second segment of the dialog for processing speech received during the second segment, to yield a second activated weighted grammar; recognizing user speech received during the first segment of the dialog using the first activated weighted grammar; and recognizing user speech received during the second segment of the dialog using the second activated weighted grammar. 12. The system of claim 7 , the computer-readable storage medium having additional instructions stored which, when executed by the processor, result in operations comprising presenting a menu to a user using the dialog system prior to receiving the first user speech or the second user speech.
| 0.633185 |
1. A method for generating applications from candidates interested in attending an educational institution, said method comprising the steps of: (a) accessing a candidate database containing personal information; (b) profiling the candidates according to criteria established by the educational institution; (c) segmenting the profiled candidates into a target group; (d) providing a web site containing links to a survey and to a partial application; wherein the survey and partial applications are distinct from each other; (e) assigning a unique access number (“PIN”) to each candidate in the target group; (f) electronically mailing each candidate in the target group the assigned PIN and an invitation to use the PIN to access the web site; (g) providing each candidate accessing the web site and indicating a continuing interest in the educational institution with electronic access to the partial application; wherein the indication of a continuing interest is determined from at least the survey; (h) for each candidate electronically accessing the partial application, customizing the partial application with personal information from the database; (i) compiling the partial applications which have been electronically completed; (j) transmitting the completed partial applications to the educational institution; (k) providing a personalized acknowledgement of each completed partial application received; (l) updating the database with information from the completed partial application; (m) inviting each candidate to submit a full application; (n) providing electronic access through use of the PIN to a full application customized with personal information from the updated database; and (o) offering each candidate invited to submit the full application an incentive to submit the full application, wherein the full application includes questions that are not in the partial application.
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1. A method for generating applications from candidates interested in attending an educational institution, said method comprising the steps of: (a) accessing a candidate database containing personal information; (b) profiling the candidates according to criteria established by the educational institution; (c) segmenting the profiled candidates into a target group; (d) providing a web site containing links to a survey and to a partial application; wherein the survey and partial applications are distinct from each other; (e) assigning a unique access number (“PIN”) to each candidate in the target group; (f) electronically mailing each candidate in the target group the assigned PIN and an invitation to use the PIN to access the web site; (g) providing each candidate accessing the web site and indicating a continuing interest in the educational institution with electronic access to the partial application; wherein the indication of a continuing interest is determined from at least the survey; (h) for each candidate electronically accessing the partial application, customizing the partial application with personal information from the database; (i) compiling the partial applications which have been electronically completed; (j) transmitting the completed partial applications to the educational institution; (k) providing a personalized acknowledgement of each completed partial application received; (l) updating the database with information from the completed partial application; (m) inviting each candidate to submit a full application; (n) providing electronic access through use of the PIN to a full application customized with personal information from the updated database; and (o) offering each candidate invited to submit the full application an incentive to submit the full application, wherein the full application includes questions that are not in the partial application. 2. The method of claim 1 wherein the incentive is selected using criteria established by the educational institution for the target group.
| 0.5 |
13. A non-transitory computer storage device encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: identifying a click count for an image and a search query pair, the click count being based on a number of times that an image search result that includes a representation of the image has been selected when provided in response to the search query; identifying a hover count for the image and the search query pair, the hover count being based on a number of times that the representation of the image has been hovered over by a pointer when the image search result has been provided in response to the search query; adjusting the hover count using a hover weighting to determine an adjusted hover count, the hover weighting being based on a total click count for the search query; determining a quality measure for the image with respect to the search query, the quality measure being based at least on the click count and the adjusted hover count; ranking the image relative to a plurality of images for the search query based at least in part on the quality measure for the image; selecting one or more images from a set of images that includes the image and the plurality of images based on the ranking, wherein each particular image in the set of images is ranked based on a respective quality measure for the particular image; generating search results that reference the one or more images; and transmitting the search results for presentation.
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13. A non-transitory computer storage device encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: identifying a click count for an image and a search query pair, the click count being based on a number of times that an image search result that includes a representation of the image has been selected when provided in response to the search query; identifying a hover count for the image and the search query pair, the hover count being based on a number of times that the representation of the image has been hovered over by a pointer when the image search result has been provided in response to the search query; adjusting the hover count using a hover weighting to determine an adjusted hover count, the hover weighting being based on a total click count for the search query; determining a quality measure for the image with respect to the search query, the quality measure being based at least on the click count and the adjusted hover count; ranking the image relative to a plurality of images for the search query based at least in part on the quality measure for the image; selecting one or more images from a set of images that includes the image and the plurality of images based on the ranking, wherein each particular image in the set of images is ranked based on a respective quality measure for the particular image; generating search results that reference the one or more images; and transmitting the search results for presentation. 14. The non-transitory computer storage device of claim 13 , wherein the hover weighting is inversely proportional to the total click count.
| 0.852475 |
1. A method for parsing a semi-structured document having a plurality of document lines on which a series of items are listed, the listing of each item spanning one or more document lines, said method comprising: obtaining a plurality of candidate records, each candidate record spanning one or more lines of the document; defining a term representing an optimal cost of selecting a number n of candidate records to span the document lines up to a given ending document line i; efficiently evaluating the term over a first range of values for n and a second range of values for i; selecting a subset of the plurality of candidate records as a global optimal parse of the document, wherein the subset selected is based on the evaluation of the term; constraining the selection of the subset such that no two selected candidate records in the subset spans the same document line; establishing a matrix having a plurality of entries into which values for the evaluated terms are entered, said matrix being defined by a plurality of columns and a plurality of rows which intersect one another to define the entries; and employing the matrix to determine which of the plurality of candidate records are selected for the subset.
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1. A method for parsing a semi-structured document having a plurality of document lines on which a series of items are listed, the listing of each item spanning one or more document lines, said method comprising: obtaining a plurality of candidate records, each candidate record spanning one or more lines of the document; defining a term representing an optimal cost of selecting a number n of candidate records to span the document lines up to a given ending document line i; efficiently evaluating the term over a first range of values for n and a second range of values for i; selecting a subset of the plurality of candidate records as a global optimal parse of the document, wherein the subset selected is based on the evaluation of the term; constraining the selection of the subset such that no two selected candidate records in the subset spans the same document line; establishing a matrix having a plurality of entries into which values for the evaluated terms are entered, said matrix being defined by a plurality of columns and a plurality of rows which intersect one another to define the entries; and employing the matrix to determine which of the plurality of candidate records are selected for the subset. 11. The method of claim 1 , wherein at least one of the obtained plurality of candidate records spans at least two lines of the document.
| 0.572054 |
3. The method recited in claim 1 further comprising the step of: providing in said message model data collection message models of informational and error messages generated by said interactive program, said method substituting messages in the national language of a user for the informational and error messages generated by said interactive program.
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3. The method recited in claim 1 further comprising the step of: providing in said message model data collection message models of informational and error messages generated by said interactive program, said method substituting messages in the national language of a user for the informational and error messages generated by said interactive program. 4. The method according to claim 3 wherein the step of composing includes the step of: reproducing the informational or error message of the interactive program in the event that no data is found in said data collection corresponding to said primary and secondary keys.
| 0.946492 |
15. A system for protecting a programming code through watermarking, the system comprising: a memory storage; and a processing unit coupled to the memory storage, wherein the processing unit is configured to: determine at least one portion of a programming code to be watermarked; determine a watermarking mechanism to be applied to the at least one portion of the programming code; determine a size of repeated binary format patterns associated with the watermarking mechanism, the watermarking mechanism being repeatedly encoded throughout the programming code; and encode the watermarking mechanism to the at least one portion of the programming code by modifying a text stream of the programming code with a repeated binary format pattern.
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15. A system for protecting a programming code through watermarking, the system comprising: a memory storage; and a processing unit coupled to the memory storage, wherein the processing unit is configured to: determine at least one portion of a programming code to be watermarked; determine a watermarking mechanism to be applied to the at least one portion of the programming code; determine a size of repeated binary format patterns associated with the watermarking mechanism, the watermarking mechanism being repeatedly encoded throughout the programming code; and encode the watermarking mechanism to the at least one portion of the programming code by modifying a text stream of the programming code with a repeated binary format pattern. 19. The system of claim 15 , wherein the processing unit being configured to determine the watermarking mechanism comprises the processing unit being configured to select from a plurality of watermarking mechanisms that include patterned use of at least one of syntax modification, contractions, abbreviations, white spaces, blank line runs, spaces before tabs at start of line, spaces at end of line, line break types, banner comment character runs, and ordered variables.
| 0.675809 |
1. A method, comprising: providing a design interface by a computing system programmed to provide an application development environment, the design interface depicting a runtime appearance of a plurality of interface elements of an application under development, each of the interface elements defined in source code accessible by the application development environment, the source code including declarative expressions defining the interface elements, the source code expressed in a declarative markup language; receiving, through the design interface, data representing a selection of a plurality of the interface elements; identifying the expressions corresponding to the selected interface elements in one or more code segments of the source code for the application under development; defining a new declarative expression; generating at least one code segment using the identified expressions; and storing the generated code segment in a computer readable medium, the stored code segment associated with the new declarative expression.
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1. A method, comprising: providing a design interface by a computing system programmed to provide an application development environment, the design interface depicting a runtime appearance of a plurality of interface elements of an application under development, each of the interface elements defined in source code accessible by the application development environment, the source code including declarative expressions defining the interface elements, the source code expressed in a declarative markup language; receiving, through the design interface, data representing a selection of a plurality of the interface elements; identifying the expressions corresponding to the selected interface elements in one or more code segments of the source code for the application under development; defining a new declarative expression; generating at least one code segment using the identified expressions; and storing the generated code segment in a computer readable medium, the stored code segment associated with the new declarative expression. 7. The method set forth in claim 1 , wherein identifying comprises parsing the source code for the application under development to locate those expressions that generate a property of the selected visual element, the property comprising at least one of: the selected visual element's visual appearance; a behavior of the selected visual element; or a data provider used by the selected element.
| 0.614929 |
10. A system comprising one or more processors and memory operably coupled to the one or more processors, wherein the memory stores instructions that, in response to execution by the one or more processors, cause the one or more processors to perform the following operations: receiving, at a first client device that executes a first portion of a virtual assistant, input from a user, wherein the input is received during a first session between the user and the virtual assistant, and the input is based on user interface input generated by the user via one or more input devices of the client device; semantically processing, by the virtual assistant, the input from the user to determine a state expressed by the user to the virtual assistant; storing, by the virtual assistant in memory hosted on a cloud infrastructure that is accessible to the first client device and at least a second client device of the user, an indication of the state expressed by the user during the first session for future use by the virtual assistant; determining, by the virtual assistant based on one or more signals, that a second session between the user and the virtual assistant that is distinct from the first session is underway, wherein the one or more signals include the user invoking at least a second portion of the virtual assistant on the second client device; forming, by a third portion of the virtual assistant that executes on the cloud infrastructure or the second portion of the automated assistant, based on the stored indication of the state expressed by the user, a natural language output from a plurality of candidate words, phrases, or statements, wherein the natural language output raises the state expressed by the user during the first session; and outputting, by the virtual assistant via one or more output devices of the second client device, as part of the second session, the natural language output.
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10. A system comprising one or more processors and memory operably coupled to the one or more processors, wherein the memory stores instructions that, in response to execution by the one or more processors, cause the one or more processors to perform the following operations: receiving, at a first client device that executes a first portion of a virtual assistant, input from a user, wherein the input is received during a first session between the user and the virtual assistant, and the input is based on user interface input generated by the user via one or more input devices of the client device; semantically processing, by the virtual assistant, the input from the user to determine a state expressed by the user to the virtual assistant; storing, by the virtual assistant in memory hosted on a cloud infrastructure that is accessible to the first client device and at least a second client device of the user, an indication of the state expressed by the user during the first session for future use by the virtual assistant; determining, by the virtual assistant based on one or more signals, that a second session between the user and the virtual assistant that is distinct from the first session is underway, wherein the one or more signals include the user invoking at least a second portion of the virtual assistant on the second client device; forming, by a third portion of the virtual assistant that executes on the cloud infrastructure or the second portion of the automated assistant, based on the stored indication of the state expressed by the user, a natural language output from a plurality of candidate words, phrases, or statements, wherein the natural language output raises the state expressed by the user during the first session; and outputting, by the virtual assistant via one or more output devices of the second client device, as part of the second session, the natural language output. 13. The system of claim 10 , wherein the natural language output is formed on the cloud infrastructure.
| 0.696498 |
5. The method of claim 1 , wherein the speech recognition resource includes a local speech recognition grammar.
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5. The method of claim 1 , wherein the speech recognition resource includes a local speech recognition grammar. 7. The method of claim 5 , further comprising the step of recursively updating the local speech recognition grammar.
| 0.962998 |
15. A computer-readable storage medium having instructions stored which, when executed by a computing device, result in the computing device performing operations comprising: partitioning, via a processor, a speech recognizer output into independent clauses; identifying, independent of domain, a dialog act for each of the independent clauses; identifying, dependent on domain, an object within each of the independent clauses; and recursively generating, for each independent clause in the independent clauses, a semantic representation using the dialog act and the object of each independent clause.
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15. A computer-readable storage medium having instructions stored which, when executed by a computing device, result in the computing device performing operations comprising: partitioning, via a processor, a speech recognizer output into independent clauses; identifying, independent of domain, a dialog act for each of the independent clauses; identifying, dependent on domain, an object within each of the independent clauses; and recursively generating, for each independent clause in the independent clauses, a semantic representation using the dialog act and the object of each independent clause. 19. The computer-readable storage medium of claim 15 , wherein identifying the object comprises using a domain specific classifier.
| 0.723092 |
2. The method of claim 1 , wherein mapping the identified nodes into the logical structure comprises mapping the nodes into a representation of named lists of nodes and creating a structured document view using the logical structure.
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2. The method of claim 1 , wherein mapping the identified nodes into the logical structure comprises mapping the nodes into a representation of named lists of nodes and creating a structured document view using the logical structure. 3. The method of claim 2 , wherein the structured document is an XML document and the list of nodes is used in creating an XPath document view.
| 0.949784 |
14. The method set forth in claim 2 wherein the step of using the computer system to make a probability determination includes the steps of: for words in the sequence, obtaining weights from a table stored in the computer system which indicates the weights of certain words for determining whether the word/sense pair has a sense which is lexically appropriate, the weights being computed using the Bayesian discrimination technique, and summing the weights to determine a likelihood that the word/sense pair is lexically appropriate to the given position in the text.
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14. The method set forth in claim 2 wherein the step of using the computer system to make a probability determination includes the steps of: for words in the sequence, obtaining weights from a table stored in the computer system which indicates the weights of certain words for determining whether the word/sense pair has a sense which is lexically appropriate, the weights being computed using the Bayesian discrimination technique, and summing the weights to determine a likelihood that the word/sense pair is lexically appropriate to the given position in the text. 15. The method set forth in claim 14 wherein: there is plurality of the word/sense pairs; the steps of obtaining the weights and summing the weights are performed for each word/sense pair; and the method includes the further step performed in the computer system of selecting the word/sense pair having the summed weights which indicate the greatest likelihood that the word/sense pair is lexically appropriate for the given position in the text.
| 0.67199 |
15. The apparatus according to claim 10 , wherein the speech input is received by a speech application, the speech application further comprising a text dialog interface, the text representation being transferred to the text dialog interface and the text representation being communicated by the speech application from the text dialog interface.
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15. The apparatus according to claim 10 , wherein the speech input is received by a speech application, the speech application further comprising a text dialog interface, the text representation being transferred to the text dialog interface and the text representation being communicated by the speech application from the text dialog interface. 16. The apparatus according to claim 15 , wherein the text dialog interface is in a separate application and the speech application transfers the text representation to said text dialog interface.
| 0.872556 |
18. The processor-readable medium of claim 16 , the instructions further comprising instructions to: obtain an indication that the pre-registered bundle is not available; provide a request to register a new bundle, upon obtaining the indication that the pre-registered bundle is not available, the request including the input string; and obtain an indication that the new bundle has been registered.
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18. The processor-readable medium of claim 16 , the instructions further comprising instructions to: obtain an indication that the pre-registered bundle is not available; provide a request to register a new bundle, upon obtaining the indication that the pre-registered bundle is not available, the request including the input string; and obtain an indication that the new bundle has been registered. 19. The processor-readable medium of claim 18 , the instructions further comprising instructions to: obtain a bundle identification corresponding to the new bundle, upon obtaining the indication that the bundle has been registered; replace the input string in the API call with the bundle identification of the new bundle; and store the new bundle and the bundle identification corresponding to the new bundle in the database.
| 0.848256 |
2. The method of claim 1 , wherein the determining the at least one relationship is based on determining a measure of relatedness based on a collection of relationship distances of entities, wherein the entities include at least one of (a) a content item, (b) an element of the at least one previous search, and (c) an element of the present input, and wherein the relatedness measure is based on one of the relationship distances.
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2. The method of claim 1 , wherein the determining the at least one relationship is based on determining a measure of relatedness based on a collection of relationship distances of entities, wherein the entities include at least one of (a) a content item, (b) an element of the at least one previous search, and (c) an element of the present input, and wherein the relatedness measure is based on one of the relationship distances. 8. The method of claim 2 , wherein the determining the at least one relationship is based further on user preferences acquired over time, and wherein the relatedness measure is modified based on the user preferences.
| 0.929158 |
11. The method of claim 8 , wherein rewriting is performed by the first system of the search site.
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11. The method of claim 8 , wherein rewriting is performed by the first system of the search site. 13. The method of claim 11 , wherein the memory is a database in a memory system of the search site, and the steps of mapping and determining are performed by the memory system.
| 0.963495 |
8. A computer system for developing user profiles within a profile corpus, the system comprising a non-transitory computer readable medium storing instructions to: determine topics associated with digital content items; organize user profiles of users into a plurality of demographic groups based on demographics of the users; retrieve access data indicating interactions of users in a demographic group from one of the plurality of demographic groups with a plurality of the digital content items; identify the topics associated with the plurality of digital content items interacted with by the users from the demographic group as candidate topics to include in user profiles of the users in the demographic group based on the access data; for each candidate topic, identify topics included in the user profiles of the users in the demographic group that co-occur in the user profiles with that candidate topic; select, by a computer, a candidate topic from the candidate topics to include in a target user profile of a target user from the demographic group based on co-occurrence of the candidate topic with one or more topics included in the target user profile that were identified from the user profiles of the users in the demographic group as co-occurring with the candidate topic; and add the selected candidate topic to the target user profile of the target user from the demographic group.
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8. A computer system for developing user profiles within a profile corpus, the system comprising a non-transitory computer readable medium storing instructions to: determine topics associated with digital content items; organize user profiles of users into a plurality of demographic groups based on demographics of the users; retrieve access data indicating interactions of users in a demographic group from one of the plurality of demographic groups with a plurality of the digital content items; identify the topics associated with the plurality of digital content items interacted with by the users from the demographic group as candidate topics to include in user profiles of the users in the demographic group based on the access data; for each candidate topic, identify topics included in the user profiles of the users in the demographic group that co-occur in the user profiles with that candidate topic; select, by a computer, a candidate topic from the candidate topics to include in a target user profile of a target user from the demographic group based on co-occurrence of the candidate topic with one or more topics included in the target user profile that were identified from the user profiles of the users in the demographic group as co-occurring with the candidate topic; and add the selected candidate topic to the target user profile of the target user from the demographic group. 11. The computer system of claim 8 , further comprising instructions to: calculate, for each of the candidate topics, a count at which the candidate topic co-occurs with the topics identified from the user profiles of the users in the demographic group; rank, by the computer, the candidate topics for the target user profile based on the count at which each of the candidate topics co-occurs with topics included in the target user profile; and wherein select the candidate topic to add to the target user profile of the target user from the demographic group is based on a rank of the candidate topic in the ranking of candidate topics for the target user profile.
| 0.517218 |
1. A method comprising: receiving social object data including a plurality of metadata tags; receiving electronic program guide information including a plurality of television program identifiers; generating a graph data structure comprising a plurality of nodes and plurality of edges, each node representing a metadata tag of the plurality of metadata tags or a television program identifier of the plurality of television program identifiers, each edge connecting two nodes, each edge including a timestamp based on the social object data; receiving information about user-selected television shows; querying the graph data structure with a selected metadata tag of the plurality of metadata tags corresponding to a social object of the social object data; receiving a set of television program identifiers associated with the selected metadata tag by traversing, with a timestamp within a predetermined timeframe, at least a portion of the plurality of edges of the graph data structure; selecting a subset of the set of television program identifiers most closely related to the selected metadata tag by comparing the set of television program identifiers with the information about user-selected television shows; and responsive to one of the television program identifiers of the subset corresponding to at least one of the user-selected television shows, selecting the social object for removal from the social object data.
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1. A method comprising: receiving social object data including a plurality of metadata tags; receiving electronic program guide information including a plurality of television program identifiers; generating a graph data structure comprising a plurality of nodes and plurality of edges, each node representing a metadata tag of the plurality of metadata tags or a television program identifier of the plurality of television program identifiers, each edge connecting two nodes, each edge including a timestamp based on the social object data; receiving information about user-selected television shows; querying the graph data structure with a selected metadata tag of the plurality of metadata tags corresponding to a social object of the social object data; receiving a set of television program identifiers associated with the selected metadata tag by traversing, with a timestamp within a predetermined timeframe, at least a portion of the plurality of edges of the graph data structure; selecting a subset of the set of television program identifiers most closely related to the selected metadata tag by comparing the set of television program identifiers with the information about user-selected television shows; and responsive to one of the television program identifiers of the subset corresponding to at least one of the user-selected television shows, selecting the social object for removal from the social object data. 9. The method of claim 1 , wherein the social object is not selected for removal if the selected metadata tag is part of a whitelist.
| 0.647526 |
10. A system comprising one or more computers programmed to perform operations comprising: obtaining a group of query pairs, each query pair including a first query and a second query; determining, using one or more computers, a quality score for each query pair in the group of query pairs; determining a diversity score for each query pair in the group of query pairs having a quality score satisfying a quality threshold; and associating, for each query pair having a quality score satisfying the quality threshold and a diversity score satisfying a diversity threshold, the second query of the query pair with the first query of the query pair as a candidate refinement for the first query; selecting a group of candidate refinements associated with a candidate query, the group of candidate refinements ordered according to an order; processing one or more of the candidate refinements according to the order and determining, for at least one additional candidate refinement in the one or more processed candidate refinements, that the additional candidate refinement has an intra-suggestion diversity score satisfying an intra-suggestion diversity threshold, the intra-suggestion diversity score estimating diversity between a first group of top documents responsive to the additional candidate refinement and a group of seen documents; associating the additional candidate refinement with the candidate query as a confirmed refinement; and adding the first group of top documents to the group of seen documents.
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10. A system comprising one or more computers programmed to perform operations comprising: obtaining a group of query pairs, each query pair including a first query and a second query; determining, using one or more computers, a quality score for each query pair in the group of query pairs; determining a diversity score for each query pair in the group of query pairs having a quality score satisfying a quality threshold; and associating, for each query pair having a quality score satisfying the quality threshold and a diversity score satisfying a diversity threshold, the second query of the query pair with the first query of the query pair as a candidate refinement for the first query; selecting a group of candidate refinements associated with a candidate query, the group of candidate refinements ordered according to an order; processing one or more of the candidate refinements according to the order and determining, for at least one additional candidate refinement in the one or more processed candidate refinements, that the additional candidate refinement has an intra-suggestion diversity score satisfying an intra-suggestion diversity threshold, the intra-suggestion diversity score estimating diversity between a first group of top documents responsive to the additional candidate refinement and a group of seen documents; associating the additional candidate refinement with the candidate query as a confirmed refinement; and adding the first group of top documents to the group of seen documents. 15. The system of claim 10 , wherein the quality score for each query pair is determined from second quality of result statistics for a second plurality of documents as search results for the second query in the query pair, the second plurality of documents being responsive to the first query in the query pair and the second query in the query pair.
| 0.544221 |
15. A system for ranking search results, comprising: a processor; and memory including instructions that, when executed by the processor, cause the processor to: obtain an initial set of search results and a query used to generate the initial set of search results, each search result corresponding to an instance of content, the query including at least one term; for each term in the query, determine a frequency with which users previously performed a selection action with respect to an instance of content in response to prior search results presented for a prior query that included the term; utilize the determined frequency for each term of the query, with respect to each of a plurality of instances of content having frequency information for at least one term of the query, to generate a ranking value for each of at least a portion of the instances of content; generate an updated set of search results including at least a portion of the initial set of search results with a ranking based at least in part upon the determined ranking values; and provide the updated set of search results to a source of the query, wherein the updated set of search results is capable of including additional search results, corresponding to one or more instances of content not included in the initial set of search results, based at least in part upon a determined ranking value for each of the additional instances of content.
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15. A system for ranking search results, comprising: a processor; and memory including instructions that, when executed by the processor, cause the processor to: obtain an initial set of search results and a query used to generate the initial set of search results, each search result corresponding to an instance of content, the query including at least one term; for each term in the query, determine a frequency with which users previously performed a selection action with respect to an instance of content in response to prior search results presented for a prior query that included the term; utilize the determined frequency for each term of the query, with respect to each of a plurality of instances of content having frequency information for at least one term of the query, to generate a ranking value for each of at least a portion of the instances of content; generate an updated set of search results including at least a portion of the initial set of search results with a ranking based at least in part upon the determined ranking values; and provide the updated set of search results to a source of the query, wherein the updated set of search results is capable of including additional search results, corresponding to one or more instances of content not included in the initial set of search results, based at least in part upon a determined ranking value for each of the additional instances of content. 16. The system of claim 15 , wherein the initial set of search results is received from a search facility, the search facility providing at least a portion of a full set of search results at a time, and wherein the updated set of search results is capable of including search results not provided in a first portion of search results from the search facility.
| 0.513173 |
8. A speech recognition apparatus comprising a cluster storing means in which a feature vector is to be classified, a membership degree calculating means for calculating, with respect to vectors y and z to be compared, a membership degree of each of the vectors to each of the clusters or a posterior probability of each of the clusters to each of the vectors, and calculating membership degree vectors a and b having the membership degrees of the respective vectors to the respective clusters as elements and a similarity degree calculating means for calculating a distance or a similarity degree between the membership degree vectors, wherein the distance or the similarity degree is rendered a distance or a similarity degree of the feature vectors x and y.
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8. A speech recognition apparatus comprising a cluster storing means in which a feature vector is to be classified, a membership degree calculating means for calculating, with respect to vectors y and z to be compared, a membership degree of each of the vectors to each of the clusters or a posterior probability of each of the clusters to each of the vectors, and calculating membership degree vectors a and b having the membership degrees of the respective vectors to the respective clusters as elements and a similarity degree calculating means for calculating a distance or a similarity degree between the membership degree vectors, wherein the distance or the similarity degree is rendered a distance or a similarity degree of the feature vectors x and y. 25. The speech recognition apparatus according to claim 8, wherein the similarity degree of a t-th frame of an input pattern and a j-th frame of a reference pattern is specified as follows, ##EQU85## with respect to b.sub.jm calculated for all the clusters or N of b.sub.j,g(j,1),b.sub.j,g(j,2), . . . ,b.sub.j,g(j,N) calculated to establish b.sub.j,g(j,1) + . . .+b.sub.j,g(j,N) =1 in correspondence with an order of largeness among b.sub.j1, . . . ,b.sub.jM (g(j,n) designates a label of a n-th largest cluster at the frame j of the reference pattern, N.ltoreq.M), K of u.sub.t,h(t,1),u.sub.t,h(t,2), . . . ,u.sub.t,h(t,K) taken from u.sub.t1, . . . ,u.sub.tM in an order of largeness (h(t,k) designates a label of a k-th largest cluster at the frame t of the input pattern, K.ltoreq.M) and a value u.sub.o calculated to establish u.sub.t,h(t,1) + . . .+u.sub.t,h(t,K) +u.sub.o (M-K)=1.
| 0.697447 |
14. An apparatus having a central processing unit (CPU) and a memory coupled to said CPU comprising: training logic configured to train a probabilistic latent semantic analysis (PLSA) model, the training logic further configured to apply an expectation maximization algorithm utilizing a set of expectation maximization equations corresponding to a PLSA model based on using P(z|w); term identification logic configured to identify at least one new term w from a document d to be added to said trained PLSA model; term addition logic configured to incrementally add said at least one new term to said trained PLSA model, the term addition logic further configured to apply said expectation maximization algorithm utilizing only a subset comprising at least one of said expectation maximization equations, wherein parameters dependent on the new term w and the document d are used by the term addition logic, the set of expectation maximization equations comprising: P ( z ❘ d , w ) = P ( z ❘ d ) P ( z ❘ w ) / P ( z ) ∑ z ′ P ( z ′ ❘ d ) P ( z ′ ❘ w ) / P { z ′ ) ; P ( z ❘ w ) = ∑ d f ( d , w ) P ( z ❘ d , w ) ∑ d , z ′ f ( d , w ) P ( z ′ ❘ d , w ) ; P ( z ❘ d ) = ∑ w f ( d , w ) P ( z ❘ d , w ) ∑ w , z ′ f ( d , w ) P ( z ′ ❘ d , w ) ; and P ( z ) = ∑ d , w f ( d , w ) P ( z ❘ d , w ) ∑ d , w f ( d , w ) , wherein P(z) represents a probability of a latent class z, P(z|d) represents a probability of a latent class z given a document d, P(z|w) represents a probability of a latent class z given a term w, and f(d,w) represents the number of times the term w occurs in the document d, and wherein the term addition logic is further configured to keep track of a total count N when incrementally adding said at least one new term, wherein: N adj = N old + ∑ d new ∈ D new ∑ w ∈ W old ⋃ W new f ( d new , w ) ; P adj ( z ) = N old P old ( z ) + ∑ d new ∈ D new ∑ w ∈ W old ⋃ W new f ( d new , w ) P ( z ❘ d new , w ) N adj ; and wherein N adj represents an adjusted value of N, N old represents the previous value of N, d new represents the new document d, D new represents a collection of new documents D, W old represents a previous set of terms W, W new represents a new set of terms which replaces W, P old (z) represents the previous value of P(z), and P adj (z) represents a new value of P(z) which replaces the previous value of P(z); and presentation logic configured to present a model parameter from said trained PLSA model, the presented model parameter including at least one of an updated P(z|d) value and an updated P(z|w) value.
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14. An apparatus having a central processing unit (CPU) and a memory coupled to said CPU comprising: training logic configured to train a probabilistic latent semantic analysis (PLSA) model, the training logic further configured to apply an expectation maximization algorithm utilizing a set of expectation maximization equations corresponding to a PLSA model based on using P(z|w); term identification logic configured to identify at least one new term w from a document d to be added to said trained PLSA model; term addition logic configured to incrementally add said at least one new term to said trained PLSA model, the term addition logic further configured to apply said expectation maximization algorithm utilizing only a subset comprising at least one of said expectation maximization equations, wherein parameters dependent on the new term w and the document d are used by the term addition logic, the set of expectation maximization equations comprising: P ( z ❘ d , w ) = P ( z ❘ d ) P ( z ❘ w ) / P ( z ) ∑ z ′ P ( z ′ ❘ d ) P ( z ′ ❘ w ) / P { z ′ ) ; P ( z ❘ w ) = ∑ d f ( d , w ) P ( z ❘ d , w ) ∑ d , z ′ f ( d , w ) P ( z ′ ❘ d , w ) ; P ( z ❘ d ) = ∑ w f ( d , w ) P ( z ❘ d , w ) ∑ w , z ′ f ( d , w ) P ( z ′ ❘ d , w ) ; and P ( z ) = ∑ d , w f ( d , w ) P ( z ❘ d , w ) ∑ d , w f ( d , w ) , wherein P(z) represents a probability of a latent class z, P(z|d) represents a probability of a latent class z given a document d, P(z|w) represents a probability of a latent class z given a term w, and f(d,w) represents the number of times the term w occurs in the document d, and wherein the term addition logic is further configured to keep track of a total count N when incrementally adding said at least one new term, wherein: N adj = N old + ∑ d new ∈ D new ∑ w ∈ W old ⋃ W new f ( d new , w ) ; P adj ( z ) = N old P old ( z ) + ∑ d new ∈ D new ∑ w ∈ W old ⋃ W new f ( d new , w ) P ( z ❘ d new , w ) N adj ; and wherein N adj represents an adjusted value of N, N old represents the previous value of N, d new represents the new document d, D new represents a collection of new documents D, W old represents a previous set of terms W, W new represents a new set of terms which replaces W, P old (z) represents the previous value of P(z), and P adj (z) represents a new value of P(z) which replaces the previous value of P(z); and presentation logic configured to present a model parameter from said trained PLSA model, the presented model parameter including at least one of an updated P(z|d) value and an updated P(z|w) value. 25. apparatus of claim 14 , further comprising retraining logic configured to retrain said trained PLSA model using said at least one new term.
| 0.599223 |
15. A gesture recognition method for a gesture entered via a virtual keyboard interface of a computing device, the method comprising: receiving, via the virtual keyboard interface, a continuous stroke having a start-point and an end-point; selecting a list of word candidates by comparing the received continuous stroke with a set of template patterns; determining a best-match template pattern; and displaying, over the virtual keyboard, and near a location defined by the endpoint of the continuous stroke, one or more characters that represent the best-match template pattern.
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15. A gesture recognition method for a gesture entered via a virtual keyboard interface of a computing device, the method comprising: receiving, via the virtual keyboard interface, a continuous stroke having a start-point and an end-point; selecting a list of word candidates by comparing the received continuous stroke with a set of template patterns; determining a best-match template pattern; and displaying, over the virtual keyboard, and near a location defined by the endpoint of the continuous stroke, one or more characters that represent the best-match template pattern. 16. The method of claim 15 wherein: the selecting comprises comparing the received continuous stroke with a first set of template patterns in a core lexicon and comparing the continuous stroke with a second set of template patterns in an extended lexicon; and the determining a best-match template pattern comprises selecting a best-match template pattern from the core lexicon.
| 0.860148 |
1. A method comprising: at a computer system with a display and an input device: displaying a user interface on the display; while displaying the user interface on the display, detecting an input on the input device, wherein the input includes a motion component and a pressure component; and in response to detecting the input: determining whether the pressure component of the input is above a pressure threshold; in accordance with a determination that the pressure component of the input is above the pressure threshold, performing a first operation in the user interface displayed on the display in accordance with the motion component of the input, wherein the first operation is scrolling content in the user interface at a variable scroll rate that increases and then decays over time, and performing the first operation in the user interface includes causing the user interface to rapidly scroll through content for an initial predetermined time interval and subsequently reduce a scroll rate over a second subsequent predetermined time interval gradually decaying the scroll rate to zero; and in accordance with a determination that the pressure component of the input is below the pressure threshold, performing a second operation in the user interface displayed on the display in accordance with the motion component of the input, wherein the second operation is different from the first operation and is scrolling content in the user interface at a predetermined scroll rate.
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1. A method comprising: at a computer system with a display and an input device: displaying a user interface on the display; while displaying the user interface on the display, detecting an input on the input device, wherein the input includes a motion component and a pressure component; and in response to detecting the input: determining whether the pressure component of the input is above a pressure threshold; in accordance with a determination that the pressure component of the input is above the pressure threshold, performing a first operation in the user interface displayed on the display in accordance with the motion component of the input, wherein the first operation is scrolling content in the user interface at a variable scroll rate that increases and then decays over time, and performing the first operation in the user interface includes causing the user interface to rapidly scroll through content for an initial predetermined time interval and subsequently reduce a scroll rate over a second subsequent predetermined time interval gradually decaying the scroll rate to zero; and in accordance with a determination that the pressure component of the input is below the pressure threshold, performing a second operation in the user interface displayed on the display in accordance with the motion component of the input, wherein the second operation is different from the first operation and is scrolling content in the user interface at a predetermined scroll rate. 4. The method of claim 1 , wherein the input is interpreted as a predetermined motion when the pressure component of the input is below the pressure threshold.
| 0.792746 |
1. A non-transitory computer readable medium storing a program that when executed by a processor performs a method of managing decision logic, the method comprising: receiving data; storing the data in a computer memory; receiving a set of rules for the decision logic; generating a decision based at least in part on the data and on the set of rules, the decision being a part of the decision logic; providing an interface to a first user, the interface enabling editing of the set of rules in context of the data; managing the decision logic in a first mode or a second mode; and when in the first mode or the second mode, managing the set of rules for the decision logic in the context of the data by the first user; wherein the managing the set of rules includes reviewing the set of rules for the decision logic in the context of the data; and wherein the managing the set of rules includes the editing the set of rules for the decision logic in the context of the data, the editing done by at least one of (i) modifying a rule in the set of rules and (ii) creating another rule and adding it to the set of rules.
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1. A non-transitory computer readable medium storing a program that when executed by a processor performs a method of managing decision logic, the method comprising: receiving data; storing the data in a computer memory; receiving a set of rules for the decision logic; generating a decision based at least in part on the data and on the set of rules, the decision being a part of the decision logic; providing an interface to a first user, the interface enabling editing of the set of rules in context of the data; managing the decision logic in a first mode or a second mode; and when in the first mode or the second mode, managing the set of rules for the decision logic in the context of the data by the first user; wherein the managing the set of rules includes reviewing the set of rules for the decision logic in the context of the data; and wherein the managing the set of rules includes the editing the set of rules for the decision logic in the context of the data, the editing done by at least one of (i) modifying a rule in the set of rules and (ii) creating another rule and adding it to the set of rules. 11. The method of claim 1 , the method further comprising: when in the first mode, receiving a selection of selected contextual data from the user; calculating an impact of the selected contextual data on the decision logic; and displaying a graphical indicator, the graphical indicator showing the impact of the selected contextual data on the decision logic; wherein the impact includes at least one of (i) whether the selected contextual data is used in a rule, the rule being included in the set of rules, (ii) a frequency of usage of the selected contextual data in the decision logic, (iii) a role of the selected contextual data in determining the decision, and (iv) usage information indicating an importance and correlation of the selected contextual data with respect to the decision.
| 0.500313 |
7. A schedule management method for managing user's schedules and tasks, comprising: storing a context estimation rule for use in estimation of contexts of spare time blocks, wherein the context estimation rule is used to generate a context by applying the context estimation rule to the spare time blocks and the context estimation rule generates contexts corresponding to schedule types before spare time blocks whose contexts are estimated, and wherein the generated context varies based on dates and times of the spare time blocks and the generated context indicates conditions of the user; listing spare time blocks when there is no schedule set by the user; applying the context estimation rule to the listed spare time blocks, thereby estimating contexts of the spare time blocks; storing a task template that represents tasks and subtasks associated with the tasks, the task template including respective metadata of the subtasks; inputting a task; dividing the task into the associated subtasks based on the task template; storing a task recommendation rule for recommending tasks to do in accordance with the relation between contexts and metadata of tasks; applying, by a computer processor, the task recommendation rule to the listed spare time blocks, and recommending tasks to do in the spare time blocks on the basis of the contexts of the spare time blocks and the metadata of the subtasks; and managing the recommended tasks as schedules.
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7. A schedule management method for managing user's schedules and tasks, comprising: storing a context estimation rule for use in estimation of contexts of spare time blocks, wherein the context estimation rule is used to generate a context by applying the context estimation rule to the spare time blocks and the context estimation rule generates contexts corresponding to schedule types before spare time blocks whose contexts are estimated, and wherein the generated context varies based on dates and times of the spare time blocks and the generated context indicates conditions of the user; listing spare time blocks when there is no schedule set by the user; applying the context estimation rule to the listed spare time blocks, thereby estimating contexts of the spare time blocks; storing a task template that represents tasks and subtasks associated with the tasks, the task template including respective metadata of the subtasks; inputting a task; dividing the task into the associated subtasks based on the task template; storing a task recommendation rule for recommending tasks to do in accordance with the relation between contexts and metadata of tasks; applying, by a computer processor, the task recommendation rule to the listed spare time blocks, and recommending tasks to do in the spare time blocks on the basis of the contexts of the spare time blocks and the metadata of the subtasks; and managing the recommended tasks as schedules. 11. A schedule management method according to claim 7 , further comprising: setting a level of importance to the task recommendation rule.
| 0.665877 |
1. A method, comprising: receiving a request to modify a data file by an application program at a client device; determining a file type for the data file where the file type is one of an immutable file type, a locking required file type, and a locking preferred file type; accessing a set of file access rules based on the file type; and determining whether the application program has permission to modify the data file based on the file access rules, wherein the file access rules require a network connection to a locking server for editing when the file type is the locking required file type, the file access rules do not require the network connection to the locking server for editing when the file type is the immutable file type, and the file access rules for the locking preferred file type only require the network connection to the locking server for editing when the network connection to the locking server is available, wherein a lock is granted to the application program if the file type is the locking preferred file type and the network connection to the locking server is not available.
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1. A method, comprising: receiving a request to modify a data file by an application program at a client device; determining a file type for the data file where the file type is one of an immutable file type, a locking required file type, and a locking preferred file type; accessing a set of file access rules based on the file type; and determining whether the application program has permission to modify the data file based on the file access rules, wherein the file access rules require a network connection to a locking server for editing when the file type is the locking required file type, the file access rules do not require the network connection to the locking server for editing when the file type is the immutable file type, and the file access rules for the locking preferred file type only require the network connection to the locking server for editing when the network connection to the locking server is available, wherein a lock is granted to the application program if the file type is the locking preferred file type and the network connection to the locking server is not available. 2. The method of claim 1 , comprising: determining the file type is an immutable file type; and granting permission to the application program to modify the file by the client.
| 0.858626 |
24. A mobile system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to: receive an image of a screen captured on the mobile system, the screen being displayed on a display device of the mobile system, determine areas of actionable content in the image, determine that a density of areas of actionable content in a first portion of the image exceeds a threshold, the first portion including a first area of actionable content and a second area of actionable content, select the first area based on determining that content represented by the first area has a higher relevancy ranking than content represented by the second area of actionable content, for at least the first area of actionable content, determine an action associated with the first area, generate annotation data, the annotation data including a visual cue that corresponds to the first area of actionable content, the visual cue being actionable to initiate the action when selected, and display the annotation data with a screen being displayed on the display device.
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24. A mobile system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to: receive an image of a screen captured on the mobile system, the screen being displayed on a display device of the mobile system, determine areas of actionable content in the image, determine that a density of areas of actionable content in a first portion of the image exceeds a threshold, the first portion including a first area of actionable content and a second area of actionable content, select the first area based on determining that content represented by the first area has a higher relevancy ranking than content represented by the second area of actionable content, for at least the first area of actionable content, determine an action associated with the first area, generate annotation data, the annotation data including a visual cue that corresponds to the first area of actionable content, the visual cue being actionable to initiate the action when selected, and display the annotation data with a screen being displayed on the display device. 29. The mobile system of claim 24 , wherein the first area of actionable content is associated with a logo included in the image.
| 0.641083 |
10. A computerized device comprising: a processor; and a user interface operatively connected to said processor, said user interface receiving a question comprising question terms, said processor automatically searching sources of data containing passages using a processor of said computerized device to produce candidate answers to said question, said searching being based on said question terms, and said searching identifying sources of evidence that support each of said candidate answers based on scoring features that indicate whether said candidate answers are correct answers to said question, said sources of evidence comprising passages, said processor automatically creating a scoring feature-specific matrix for each scoring feature of said scoring features, each said scoring feature-specific matrix specifying all different combinations of said passages, said candidate answers, and said question terms and comprising score fields for score values for each specific question term with respect to a specific passage and a specific candidate answer, each score field containing a score value indicating how a passage term of said specific passage aligns with said specific question term to support said specific candidate answer as being a correct answer to said question with respect to said scoring feature, and multiple ones of said different combinations of said passages, said candidate answers, and said question terms forming vectors, said processor automatically combining said vectors by calculating a statistical measure of said vectors to produce a collapsed score for each of said question terms, said processor automatically combining collapsed scores for each of said question terms to produce a combined score for each of said candidate answers, and said processor automatically ranking said candidate answers based on said combined score for each of said candidate answers.
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10. A computerized device comprising: a processor; and a user interface operatively connected to said processor, said user interface receiving a question comprising question terms, said processor automatically searching sources of data containing passages using a processor of said computerized device to produce candidate answers to said question, said searching being based on said question terms, and said searching identifying sources of evidence that support each of said candidate answers based on scoring features that indicate whether said candidate answers are correct answers to said question, said sources of evidence comprising passages, said processor automatically creating a scoring feature-specific matrix for each scoring feature of said scoring features, each said scoring feature-specific matrix specifying all different combinations of said passages, said candidate answers, and said question terms and comprising score fields for score values for each specific question term with respect to a specific passage and a specific candidate answer, each score field containing a score value indicating how a passage term of said specific passage aligns with said specific question term to support said specific candidate answer as being a correct answer to said question with respect to said scoring feature, and multiple ones of said different combinations of said passages, said candidate answers, and said question terms forming vectors, said processor automatically combining said vectors by calculating a statistical measure of said vectors to produce a collapsed score for each of said question terms, said processor automatically combining collapsed scores for each of said question terms to produce a combined score for each of said candidate answers, and said processor automatically ranking said candidate answers based on said combined score for each of said candidate answers. 12. The computerized device according to claim 10 , said passages comprising text passages.
| 0.524921 |
1. A method for monitoring consumer-generated media, implemented on at least one computer having a display, comprising: discovering consumer generated media using a plurality of keywords from a set of keywords configured to return consumer generated media embedded in a digital location; collecting consumer generated media. from a plurality of sources using a plurality of robots configured to collect media from the discovered consumer generated media; determining a sentiment of the collected consumer generated media based on the semantics of the language in the collected consumer generated media; generating a graphical user interface illustrating the determined sentiment for a selected time period and the determined sentiment for a time period previous to the selected time period, wherein the determined sentiment comprises at least one of a good, bad, neutral and good/bad sentiment value; displaying the graphical user interface on the display; identifying a specific consumer generated media to which a reply should be posted; in response to identifying the specific consumer generated media, automatedly posting to a source of the collected consumer generated media a first reply to the identified consumer generated media; collecting a consumer-generated response to the at automatedly posted first reply; and automatedly posting to the source of the collected consumer generated media a second reply based on the consumer-generated response to the automatedly posted first reply.
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1. A method for monitoring consumer-generated media, implemented on at least one computer having a display, comprising: discovering consumer generated media using a plurality of keywords from a set of keywords configured to return consumer generated media embedded in a digital location; collecting consumer generated media. from a plurality of sources using a plurality of robots configured to collect media from the discovered consumer generated media; determining a sentiment of the collected consumer generated media based on the semantics of the language in the collected consumer generated media; generating a graphical user interface illustrating the determined sentiment for a selected time period and the determined sentiment for a time period previous to the selected time period, wherein the determined sentiment comprises at least one of a good, bad, neutral and good/bad sentiment value; displaying the graphical user interface on the display; identifying a specific consumer generated media to which a reply should be posted; in response to identifying the specific consumer generated media, automatedly posting to a source of the collected consumer generated media a first reply to the identified consumer generated media; collecting a consumer-generated response to the at automatedly posted first reply; and automatedly posting to the source of the collected consumer generated media a second reply based on the consumer-generated response to the automatedly posted first reply. 5. The method of claim 1 , wherein the graphical user interface further comprises filtering consumer generated media based on predetermined content settings.
| 0.655245 |
11. A system, comprising: a processor; and memory having stored therein instructions which, when executed by the processor, cause the processor to perform steps comprising: receiving, from a user device, a request to download a desired application, the desired application being localized in different languages; determining, by an online store, a local language associated with the user device, wherein determining the local language comprises: determining a plurality of factors associated with the user device, wherein each factor is associated with a secondary language and a weight; grouping the plurality of factors based on the secondary language to form a list of secondary languages; ranking the languages in the list of secondary languages according to the weight of each factor, and assigning a secondary language from the list of secondary languages as the local language according to the ranking; and presenting, by the online store, an interface to download a version of the desired application in the local language when the local language is one of the different languages.
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11. A system, comprising: a processor; and memory having stored therein instructions which, when executed by the processor, cause the processor to perform steps comprising: receiving, from a user device, a request to download a desired application, the desired application being localized in different languages; determining, by an online store, a local language associated with the user device, wherein determining the local language comprises: determining a plurality of factors associated with the user device, wherein each factor is associated with a secondary language and a weight; grouping the plurality of factors based on the secondary language to form a list of secondary languages; ranking the languages in the list of secondary languages according to the weight of each factor, and assigning a secondary language from the list of secondary languages as the local language according to the ranking; and presenting, by the online store, an interface to download a version of the desired application in the local language when the local language is one of the different languages. 15. The system of claim 11 , wherein the factors include a language associated with a geographic location of the user device.
| 0.597105 |
11. The method of claim 1 , further comprising: expressing the set of instructions using a formal language by providing either necessary information or references to where the necessary information is found, to solve equations of motion describing aircraft flight and so compute a trajectory of the aircraft.
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11. The method of claim 1 , further comprising: expressing the set of instructions using a formal language by providing either necessary information or references to where the necessary information is found, to solve equations of motion describing aircraft flight and so compute a trajectory of the aircraft. 13. The method of claim 11 , comprising providing a graphical display of the set of instructions expressed using the formal language.
| 0.940933 |
1. A computer-implemented method for associating an avatar with a user identity, the method comprising: detecting a selection by a user, the selection being at least one of an avatar selection and a wallpaper selection for use in an instant messaging environment, wherein the selected avatar or wallpaper comprise one or more attributes; inferring, using at least one processor, one or more user profile attributes for the user based on the detected user selection, wherein the inferred user profile attributes are not identical to the one or more attributes of the selected avatar or wallpaper; storing the inferred user profile attributes in a user profile of the user, wherein the user profile is viewable by one or more other users within the instant messaging environment; accessing stored attributes for multiple avatars that are potential candidates for selection by the user to represent the user in a communications session; identifying a subset of less than all of the multiple avatars based on a comparison between the inferred user profile attributes located is the stored user profile and the accessed avatar attributes; and presenting the identified subset of avatars for selection by the user.
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1. A computer-implemented method for associating an avatar with a user identity, the method comprising: detecting a selection by a user, the selection being at least one of an avatar selection and a wallpaper selection for use in an instant messaging environment, wherein the selected avatar or wallpaper comprise one or more attributes; inferring, using at least one processor, one or more user profile attributes for the user based on the detected user selection, wherein the inferred user profile attributes are not identical to the one or more attributes of the selected avatar or wallpaper; storing the inferred user profile attributes in a user profile of the user, wherein the user profile is viewable by one or more other users within the instant messaging environment; accessing stored attributes for multiple avatars that are potential candidates for selection by the user to represent the user in a communications session; identifying a subset of less than all of the multiple avatars based on a comparison between the inferred user profile attributes located is the stored user profile and the accessed avatar attributes; and presenting the identified subset of avatars for selection by the user. 4. The method of claim 1 , wherein identifying a subset of less than all of the multiple avatars based on a comparison between the inferred user profile information and the accessed attributes for multiple avatars includes comparing an interest in the inferred user profile information to the accessed attributes for multiple avatars.
| 0.616434 |
18. The method of claim 1 , wherein objects of the set of objects editable in the first editing context and available for association with the first object are graphically distinguished from the items already associated with the first object that are editable in the second editing context.
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18. The method of claim 1 , wherein objects of the set of objects editable in the first editing context and available for association with the first object are graphically distinguished from the items already associated with the first object that are editable in the second editing context. 19. The method of claim 18 , wherein objects of the set of objects editable in the first editing context and available for association with the first object appear grayed out.
| 0.853677 |
1. An ecommerce system having a database comprising multiple data items, wherein the data items represent products sold in the ecommerce system, the system comprising: a processor; a processor-implemented candidate list generator module to: generate a list of keywords from search query information; generate a set of token pairs including a keyword from the list of keywords and a token, the token being a synonym of the keyword; receive demand information and supply information, wherein the demand information is information retrieved from query logs maintained for user-provided query entries stored in the database and the supply information is seller-provided descriptor information for the data items stored in the database; and apply candidate selection rules to token pairs across a plurality of categories of data items in the database using the demand information and the supply information, generating a set of candidate token pairs; a processor-implemented validation module to: calculate a divergence value for each candidate token pair across the plurality of categories, wherein calculation of the divergence value comprises: performing a search using a keyword of a candidate token pair as a search criteria; determining a first distribution (P) of search results for the keyword, the first distribution over the plurality of categories; performing a search using a token of the candidate token pair as a search criteria; determining a second distribution (Q) of search results for the token, the second distribution over the plurality of categories; calculating the divergence measure as an Information Radius (IRad) value as:
IRad=0.5[ D ( P ∥avg( P,Q ))+ D ( Q ∥avg( P,Q ))], wherein D is Kullback-Liebler divergence calculated as:
D ( P∥Q )=Σ P ( i )log( P ( i )/ Q ( i )), wherein avg (P,Q) is an average of the first distribution and the second distribution, and wherein an index i=1, 2, . . . N, and N is the number of categories in the plurality of categories; and validate the candidate token pairs based on the divergence value, wherein the candidate token pairs having divergence values exceeding a divergence threshold are valid token pairs; and a processor-implemented data dictionary module to: receive the validated token pairs as entries in a vocabulary, and provide the validated token pairs in response to a search query containing a keyword corresponding to at least one entry in the vocabulary.
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1. An ecommerce system having a database comprising multiple data items, wherein the data items represent products sold in the ecommerce system, the system comprising: a processor; a processor-implemented candidate list generator module to: generate a list of keywords from search query information; generate a set of token pairs including a keyword from the list of keywords and a token, the token being a synonym of the keyword; receive demand information and supply information, wherein the demand information is information retrieved from query logs maintained for user-provided query entries stored in the database and the supply information is seller-provided descriptor information for the data items stored in the database; and apply candidate selection rules to token pairs across a plurality of categories of data items in the database using the demand information and the supply information, generating a set of candidate token pairs; a processor-implemented validation module to: calculate a divergence value for each candidate token pair across the plurality of categories, wherein calculation of the divergence value comprises: performing a search using a keyword of a candidate token pair as a search criteria; determining a first distribution (P) of search results for the keyword, the first distribution over the plurality of categories; performing a search using a token of the candidate token pair as a search criteria; determining a second distribution (Q) of search results for the token, the second distribution over the plurality of categories; calculating the divergence measure as an Information Radius (IRad) value as:
IRad=0.5[ D ( P ∥avg( P,Q ))+ D ( Q ∥avg( P,Q ))], wherein D is Kullback-Liebler divergence calculated as:
D ( P∥Q )=Σ P ( i )log( P ( i )/ Q ( i )), wherein avg (P,Q) is an average of the first distribution and the second distribution, and wherein an index i=1, 2, . . . N, and N is the number of categories in the plurality of categories; and validate the candidate token pairs based on the divergence value, wherein the candidate token pairs having divergence values exceeding a divergence threshold are valid token pairs; and a processor-implemented data dictionary module to: receive the validated token pairs as entries in a vocabulary, and provide the validated token pairs in response to a search query containing a keyword corresponding to at least one entry in the vocabulary. 7. The system of claim 1 , wherein the vocabulary is a stemming and transliteration vocabulary.
| 0.534551 |
16. The computer-readable storage device of claim 15 , wherein the social network context is based on one of a social network profile, a caller utterance, or a social graph.
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16. The computer-readable storage device of claim 15 , wherein the social network context is based on one of a social network profile, a caller utterance, or a social graph. 17. The computer-readable storage device of claim 16 , wherein the caller utterance comprises one of a post or a comment to a post.
| 0.915878 |
7. The system of claim 1 , wherein the operations further comprise: associating words and word phrases with the plurality of categories; and assigning association strengths to the word and word phrase associations.
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7. The system of claim 1 , wherein the operations further comprise: associating words and word phrases with the plurality of categories; and assigning association strengths to the word and word phrase associations. 8. The system of claim 7 , wherein the operations further comprise: calculating the search criteria-categories score using a degree of match for the search criteria based on the association strengths of the word and word phrase associations.
| 0.911746 |
1. A combined computer/human method for automatically identifying actionable insights for a process, the method comprising: a computer system automatically and iteratively performing the steps of: calculating metrics for data within a data set, wherein the data in the data set was generated by the process; identifying norms for the process based on the calculated metrics; identifying potentially valuable patterns in the process, comprising: calculating metrics for different samples of the process, and identifying potentially valuable patterns in the process based on which samples of the process have calculated metrics that vary from the calculated metrics for the identified norms for the process, whereby the identified potentially valuable patterns are patterns in the process that may cause deviations from the norms for the process; requesting from statistically untrained humans feedback regarding whether the potentially valuable patterns are patterns that actually produce deviations from the norms; and receiving said feedback from the statistically untrained humans; and iteratively developing actionable insights for changing the process based on said steps taken by the computer system.
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1. A combined computer/human method for automatically identifying actionable insights for a process, the method comprising: a computer system automatically and iteratively performing the steps of: calculating metrics for data within a data set, wherein the data in the data set was generated by the process; identifying norms for the process based on the calculated metrics; identifying potentially valuable patterns in the process, comprising: calculating metrics for different samples of the process, and identifying potentially valuable patterns in the process based on which samples of the process have calculated metrics that vary from the calculated metrics for the identified norms for the process, whereby the identified potentially valuable patterns are patterns in the process that may cause deviations from the norms for the process; requesting from statistically untrained humans feedback regarding whether the potentially valuable patterns are patterns that actually produce deviations from the norms; and receiving said feedback from the statistically untrained humans; and iteratively developing actionable insights for changing the process based on said steps taken by the computer system. 11. The method of claim 1 wherein the process is a process of web interaction analytics.
| 0.605121 |
47. A combination as defined in claim 46, wherein the data characteristic statistics generated by the interrogation processor include a frequency distribution of characters found in the scanned record, a rate of occurrence for each character in the frequency distribution, and an entropy value; and the interrogation processor further comprises: means for completing a stabilization procedure comprising repeating the steps of selecting a current record block for scanning and generating a current set of the data characteristic statistics, selecting a new record block, different from the current block, for scanning and generating a new set of the data characteristic statistics, and comparing the current set of statistics to the new set of statistics, until the entropy value of the new set is below a predetermined entropy threshold and the number of scanned record blocks equals a predetermined scanning limit value, and thereupon storing the new block in an interrogation buffer and storing the set of new data characteristic statistics.
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47. A combination as defined in claim 46, wherein the data characteristic statistics generated by the interrogation processor include a frequency distribution of characters found in the scanned record, a rate of occurrence for each character in the frequency distribution, and an entropy value; and the interrogation processor further comprises: means for completing a stabilization procedure comprising repeating the steps of selecting a current record block for scanning and generating a current set of the data characteristic statistics, selecting a new record block, different from the current block, for scanning and generating a new set of the data characteristic statistics, and comparing the current set of statistics to the new set of statistics, until the entropy value of the new set is below a predetermined entropy threshold and the number of scanned record blocks equals a predetermined scanning limit value, and thereupon storing the new block in an interrogation buffer and storing the set of new data characteristic statistics. 62. A combination as defined in claim 47, wherein the dictionary build processor produces a dictionary token comprising the dictionary identifiers ordered in a sequence in accordance with the display representation of the identifiers of the respective dictionary segments.
| 0.833333 |
9. A computer-readable storage medium comprising: computer-readable program code for receiving addressing information identifying a website on the Internet; computer-readable program code for processing the addressing information to generate a keyword; computer-readable program code for performing a search on the keyword to generate a search result; and computer-readable program code for presenting an end-user the search result responsive to the keyword that is based on the addressing information when the end-user navigates to the website using a client computer.
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9. A computer-readable storage medium comprising: computer-readable program code for receiving addressing information identifying a website on the Internet; computer-readable program code for processing the addressing information to generate a keyword; computer-readable program code for performing a search on the keyword to generate a search result; and computer-readable program code for presenting an end-user the search result responsive to the keyword that is based on the addressing information when the end-user navigates to the website using a client computer. 14. The computer-readable storage medium of claim 9 wherein the search result is to be integrated in a browser window along with a content from a web page.
| 0.530657 |
14. A method for bi-directional sign language/speech translation in real time performed by an apparatus for bi-directional sign language/speech translation in real time, the method comprising: analyzing a used pattern of sign language by a user who uses the apparatus for bi-directional sign language/speech translation in real time in view of current surrounding environment information of the user to generate a sign category information; recognizing a first sign or a first speech externally made by the user through a camera or a microphone, respectively; identifying a second sign corresponding to the first speech or a second speech corresponding to the first sign using the sign category information; and outputting the second sign or the second speech through a different translation path based on a result of the identifying, wherein the analyzing comprises: comparing the sign category information with the second sign corresponding to the first speech or the second speech corresponding to the first sign to determine whether the sign category information is correct, and transmitting a signal related to whether the sign category information is correct to the speech-sign outputter or the sign-speech outputter to block the second sign or the second speech corresponding to the sign category information.
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14. A method for bi-directional sign language/speech translation in real time performed by an apparatus for bi-directional sign language/speech translation in real time, the method comprising: analyzing a used pattern of sign language by a user who uses the apparatus for bi-directional sign language/speech translation in real time in view of current surrounding environment information of the user to generate a sign category information; recognizing a first sign or a first speech externally made by the user through a camera or a microphone, respectively; identifying a second sign corresponding to the first speech or a second speech corresponding to the first sign using the sign category information; and outputting the second sign or the second speech through a different translation path based on a result of the identifying, wherein the analyzing comprises: comparing the sign category information with the second sign corresponding to the first speech or the second speech corresponding to the first sign to determine whether the sign category information is correct, and transmitting a signal related to whether the sign category information is correct to the speech-sign outputter or the sign-speech outputter to block the second sign or the second speech corresponding to the sign category information. 18. The method for bi-directional sign language/speech translation in real time of claim 14 , wherein the outputting the second sign or the second speech comprises: recognizing the first sign externally made by the user; generating a sign index to translate into the second speech corresponding to the recognized first sign; and outputting the second speech corresponding to the recognized first sign based on the generated sign index.
| 0.583003 |
9. An apparatus of claim 8 , wherein to construct the decision diagram, the apparatus is further caused to: serialize the resource description framework graph into variables of a predetermined format; determine a bit size of the variables by calculating sizes of the variables or using a fixed size; and construct a representation of the reduced ordered binary decision diagram from the variables.
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9. An apparatus of claim 8 , wherein to construct the decision diagram, the apparatus is further caused to: serialize the resource description framework graph into variables of a predetermined format; determine a bit size of the variables by calculating sizes of the variables or using a fixed size; and construct a representation of the reduced ordered binary decision diagram from the variables. 10. An apparatus of claim 9 , wherein to compute the hash identifier, the apparatus is further caused to: select a hash function for obtaining unique hash identifiers; and feed the representation into the hash function thereby computing the hash identifier.
| 0.719219 |
2. The computer-readable memory of claim 1 , further comprising instructions that, when executed, cause the one or more processors to perform an act of generating synthesized speech for an input text using the transformed target speech waveforms.
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2. The computer-readable memory of claim 1 , further comprising instructions that, when executed, cause the one or more processors to perform an act of generating synthesized speech for an input text using the transformed target speech waveforms. 3. The computer-readable memory of claim 2 , instructions that, when executed, cause the one or more processors to perform an act of estimating the LPC spectrums of the source speech waveforms using a Speech Transformation and Representation using Adaptive Interpolation of Weighted Spectrum (STRAIGHT) speech analysis.
| 0.870121 |
12. A non-transitory computer readable medium including executable instructions to generate procedural language code for extracting data from an operational system, comprising executable instructions to: accept a declarative specification; and generate procedural language code from the declarative specification to execute a data extraction, transformation and loading process defined by the declarative specification.
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12. A non-transitory computer readable medium including executable instructions to generate procedural language code for extracting data from an operational system, comprising executable instructions to: accept a declarative specification; and generate procedural language code from the declarative specification to execute a data extraction, transformation and loading process defined by the declarative specification. 17. The computer readable medium of claim 12 , wherein the generating includes generating ABAP code to read and load R/3 tables, files and IDOC intermediate documents.
| 0.650997 |
1. A method comprising: providing an online advertisement linked to content; obtaining, by a computer system, a macro-context numerically quantifying the affinity of the content for each subject matter domain of a plurality of subject matter domains; obtaining, by the computer system, a personalization vector numerically quantifying the affinity of a user for each subject matter domain of the plurality of subject matter domains; selecting, by the user, the online advertisement; directing, by the computer system in direct response to the selecting, the user to the first content; calculating, by the computer system, a match value quantifying the similarity between the personalization vector and the macro-context.
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1. A method comprising: providing an online advertisement linked to content; obtaining, by a computer system, a macro-context numerically quantifying the affinity of the content for each subject matter domain of a plurality of subject matter domains; obtaining, by the computer system, a personalization vector numerically quantifying the affinity of a user for each subject matter domain of the plurality of subject matter domains; selecting, by the user, the online advertisement; directing, by the computer system in direct response to the selecting, the user to the first content; calculating, by the computer system, a match value quantifying the similarity between the personalization vector and the macro-context. 15. The method of claim 1 , wherein calculating a match value comprises multiplying together the macro-context and the personalization vector.
| 0.709641 |
12. A method for providing computerized training to a student, said method comprising: providing a training station connected with a computer system with computer-accessible data storage supporting a rules engine thereon; storing lesson data in the data storage so as to be accessed continuously by the rules engine, said lesson data comprising learning object data defining a number of learning objects that each, when activated by the rules engine, cause the training station to output visual imagery, audio or other output, and rules data defining a plurality of rules on which the rules engine operates so as to administer the computerized training, said rules each having a data condition part and an action part, the data condition part defining a state of data in the data storage that, when present, causes the rules engine to direct the computerized system to take a predetermined action substantially immediately, at least some of said actions comprising activating at least some of the learning objects to interact with the student at the training station; storing student state data in the data storage, said student state data including data defining an assessment measure of training of the student; providing the computerized training to the student at the training station with the rules engine administering the training according to the rules stored in the data storage; determining repeatedly or continually the assessment measure for the student based on input received from the student at the training station; storing the determined assessment measure in the student state data; wherein the rules data defines at least one rule that initiates the action thereof when a data condition that the student state data in the data storage defines an assessment measure below a predetermined value is present, the action including initiating operation on the training station of one of the stored learning objects; and wherein the training station and the computer system with the rules engine are connected by a network operating pursuant to communications software that controls the communication on the network such that computers on the network publish data packets each including a respective data field defining a topic name thereof that is transmitted only to other computers on the network that have subscribed to receive said data packets having data fields defining one or more specified topic names; said computer system with the rules engine subscribing to data published by said training station and receiving data therefrom; and the method further comprising storing data received over the network from the training station in the computer accessible data storage of the computer system with the rules engine, wherein one or more of the rules have if-portions defining a condition of data that is based at least partly on some of the received data; and the determining of the assessment measure of the student including a determination of a KSA gap between knowledge, skill and ability of the student based on some of the data received over the network and a level of knowledge, skill or ability defined by the objective data, and storing of data indicative of said KSA gap in the student state data; wherein the rules engine has one or more rules that, responsive to a condition of the data indicative of said KSA gap, initiates remedial action comprising initiating one of the learning objects so as to output media content stored in the learning object to the student, the rules data including a plurality of rules each having a respective remedial action and a respective if-portion initiating the associated remedial action based on a different assessment of the KSA gap for the student in regard to the instructional area; wherein one of said plurality of rules, when the KSA gap data shows a gap in knowledge, activates a knowledge-directed remedial learning object, another of said rules, when the KSA gap data shows a gap in skill, activates a skill-directed remedial learning object, and a third of said rules, when the KSA gap data shows a gap in ability, activates an ability-directed remedial learning object.
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12. A method for providing computerized training to a student, said method comprising: providing a training station connected with a computer system with computer-accessible data storage supporting a rules engine thereon; storing lesson data in the data storage so as to be accessed continuously by the rules engine, said lesson data comprising learning object data defining a number of learning objects that each, when activated by the rules engine, cause the training station to output visual imagery, audio or other output, and rules data defining a plurality of rules on which the rules engine operates so as to administer the computerized training, said rules each having a data condition part and an action part, the data condition part defining a state of data in the data storage that, when present, causes the rules engine to direct the computerized system to take a predetermined action substantially immediately, at least some of said actions comprising activating at least some of the learning objects to interact with the student at the training station; storing student state data in the data storage, said student state data including data defining an assessment measure of training of the student; providing the computerized training to the student at the training station with the rules engine administering the training according to the rules stored in the data storage; determining repeatedly or continually the assessment measure for the student based on input received from the student at the training station; storing the determined assessment measure in the student state data; wherein the rules data defines at least one rule that initiates the action thereof when a data condition that the student state data in the data storage defines an assessment measure below a predetermined value is present, the action including initiating operation on the training station of one of the stored learning objects; and wherein the training station and the computer system with the rules engine are connected by a network operating pursuant to communications software that controls the communication on the network such that computers on the network publish data packets each including a respective data field defining a topic name thereof that is transmitted only to other computers on the network that have subscribed to receive said data packets having data fields defining one or more specified topic names; said computer system with the rules engine subscribing to data published by said training station and receiving data therefrom; and the method further comprising storing data received over the network from the training station in the computer accessible data storage of the computer system with the rules engine, wherein one or more of the rules have if-portions defining a condition of data that is based at least partly on some of the received data; and the determining of the assessment measure of the student including a determination of a KSA gap between knowledge, skill and ability of the student based on some of the data received over the network and a level of knowledge, skill or ability defined by the objective data, and storing of data indicative of said KSA gap in the student state data; wherein the rules engine has one or more rules that, responsive to a condition of the data indicative of said KSA gap, initiates remedial action comprising initiating one of the learning objects so as to output media content stored in the learning object to the student, the rules data including a plurality of rules each having a respective remedial action and a respective if-portion initiating the associated remedial action based on a different assessment of the KSA gap for the student in regard to the instructional area; wherein one of said plurality of rules, when the KSA gap data shows a gap in knowledge, activates a knowledge-directed remedial learning object, another of said rules, when the KSA gap data shows a gap in skill, activates a skill-directed remedial learning object, and a third of said rules, when the KSA gap data shows a gap in ability, activates an ability-directed remedial learning object. 14. A method according to claim 12 , wherein the data storage further stores objective data that includes data defining input that should be received from the student interacting with the training station if training of the student is effective; and said determination of the KSA gap including providing computerized output of a learning object to the student and processing a reaction input or absence of reaction input from the student, said output comprising media presenting a test question output to the student, and the reaction input comprising an answering input, or the output comprising a presentation of an simulated condition of a simulated vehicle requiring the student to react by providing control input, the control input comprising an input using a simulated vehicle control panel or cockpit of the training station.
| 0.551916 |
1. A computer-implemented method of allocating an advertising budget among a fixed set of keywords, each keyword having a bid, a bid intensity, and a utility associated therewith, the method comprising: raising the bid intensities associated with selected ones of the keywords using one or more computing devices such that the advertising budget is not exceeded, the selected keywords having the highest utilities among the fixed set of keywords; and when the bid intensities associated with the selected keywords reach maximum values, raising the bids associated with first ones of the selected keywords using the one or more computing devices such that the advertising budget is not exceeded, the first selected keywords having the highest utilities among the selected keywords.
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1. A computer-implemented method of allocating an advertising budget among a fixed set of keywords, each keyword having a bid, a bid intensity, and a utility associated therewith, the method comprising: raising the bid intensities associated with selected ones of the keywords using one or more computing devices such that the advertising budget is not exceeded, the selected keywords having the highest utilities among the fixed set of keywords; and when the bid intensities associated with the selected keywords reach maximum values, raising the bids associated with first ones of the selected keywords using the one or more computing devices such that the advertising budget is not exceeded, the first selected keywords having the highest utilities among the selected keywords. 2. The method of claim 1 further comprising lowering the bid intensities associated with second ones of the keywords in conjunction with raising the bids associated with the first selected keywords to ensure that the advertising budget is not exceeded.
| 0.595668 |
1. A method of operating a computer to perform linguistic analysis, comprising the steps of: splitting an input text into words and sentences; for each sentence, comparing phrases in each sentence with known phrases stored in a database, as follows: for each word in the sentence, comparing a value thereof and values of the words following it with values of words of stored phrases; in the event a match is found between the value of at least two consecutive words and the value of words of a stored phrase, labelling the matched at least two consecutive words with an overphrase that describes the matched value; after a penultimate word has been compared, recasting the sentence by replacing the matched phrases by respective overphrases; and then repeating the step of comparing phrases with the recast sentence until there is no further recasting by the step of using the overphrase in the comparison as a word until there is no further match found.
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1. A method of operating a computer to perform linguistic analysis, comprising the steps of: splitting an input text into words and sentences; for each sentence, comparing phrases in each sentence with known phrases stored in a database, as follows: for each word in the sentence, comparing a value thereof and values of the words following it with values of words of stored phrases; in the event a match is found between the value of at least two consecutive words and the value of words of a stored phrase, labelling the matched at least two consecutive words with an overphrase that describes the matched value; after a penultimate word has been compared, recasting the sentence by replacing the matched phrases by respective overphrases; and then repeating the step of comparing phrases with the recast sentence until there is no further recasting by the step of using the overphrase in the comparison as a word until there is no further match found. 5. A method according to claim 1 , wherein in a first comparison in the step of comparing a value, the value corresponds to one of: a literal meaning, a grammatical use and an attribute, and wherein in a second comparison in the step of comparing a value, the value is either of the two remaining values.
| 0.504394 |
9. A system of clustering a set of documents considered to be relevant to a subject, comprising: one or more processors; and a storage device having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: select a set of documents considered to be relevant to a subject, wherein each document is an electronic document that comprises two or more distinct fields including at least a first field and a second field, each of the two or more distinct fields providing data corresponding to a category of data about the respective document; cluster documents in the set of documents into a cluster hierarchy according to data contained in the first field, the cluster hierarchy comprising a plurality of branches, each branch having a sub-tree; merge a received number of levels of the cluster hierarchy; and re-cluster the merged levels according to data contained in the second field.
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9. A system of clustering a set of documents considered to be relevant to a subject, comprising: one or more processors; and a storage device having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: select a set of documents considered to be relevant to a subject, wherein each document is an electronic document that comprises two or more distinct fields including at least a first field and a second field, each of the two or more distinct fields providing data corresponding to a category of data about the respective document; cluster documents in the set of documents into a cluster hierarchy according to data contained in the first field, the cluster hierarchy comprising a plurality of branches, each branch having a sub-tree; merge a received number of levels of the cluster hierarchy; and re-cluster the merged levels according to data contained in the second field. 14. The system of claim 9 , wherein the step of clustering according to data contained in a first field is performed according to a partitional approach or agglomerative approach.
| 0.694737 |
1. A method for use in converting textual data from a source form to a target form, where said target form differs from said source form with respect to at least one of linguistics and syntax, said method comprising the steps of: providing a computer-based processing tool operating on a computer system for transliterating textual data from a first form to a second form, said computer-based processing tool being configurable to associate first elements of said first form with second elements of said second form so as to establish a conversion model; first operating said computer-based processing tool by a first user to establish a first association of a first set of said first elements with a second set of said second elements; second operating said computer-based processing tool by a second user to establish a second association of a third set of said first elements with a fourth set of said second elements, wherein said first set and said third set of first elements include at least a common one of said first elements thereby defining an overlap, and wherein said common one of said first elements is associated with a first one of said second elements by said first association by said first user and a second one of said second elements by said second association by said second user thereby defining an inconsistency between said first and second associations for said common one of said first elements; and third operating said computer-based processing tool to process one of said first association and said second association so as to address said inconsistency.
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1. A method for use in converting textual data from a source form to a target form, where said target form differs from said source form with respect to at least one of linguistics and syntax, said method comprising the steps of: providing a computer-based processing tool operating on a computer system for transliterating textual data from a first form to a second form, said computer-based processing tool being configurable to associate first elements of said first form with second elements of said second form so as to establish a conversion model; first operating said computer-based processing tool by a first user to establish a first association of a first set of said first elements with a second set of said second elements; second operating said computer-based processing tool by a second user to establish a second association of a third set of said first elements with a fourth set of said second elements, wherein said first set and said third set of first elements include at least a common one of said first elements thereby defining an overlap, and wherein said common one of said first elements is associated with a first one of said second elements by said first association by said first user and a second one of said second elements by said second association by said second user thereby defining an inconsistency between said first and second associations for said common one of said first elements; and third operating said computer-based processing tool to process one of said first association and said second association so as to address said inconsistency. 5. A method as set forth in claim 1 , wherein said second form comprises a semantic metadata model that provides a standardized basis for textual data conversion.
| 0.603302 |
9. A client device that performs a method for automatically generating a set of suggested search terms, the method comprising: aggregating a user's search behavior; establishing a user profile based upon the aggregation, wherein the user profile is automatically refined upon detecting the user's search behavior; receiving a search input; parsing the search input on a real time character-by-character basis incident to each keystroke during entry of the search input; generating a set of suggested search terms by interrogating the user profile with the parsed search input; ordering the set of suggested search terms by evaluating the relevance of each search term in the set of suggested search terms against the search input in real time; rendering the set of suggested search terms at a UI display, wherein the set of suggested search terms is updated in real time on a character-by-character basis; and rendering a set of selectable edit options at the UI display that, when selected, modify one or more of the rendered set of suggested search terms, wherein modifications to one or more of the rendered set of suggested search terms are automatically transmitted to the user profile that incorporates those modifications in prospective sets of suggested search terms.
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9. A client device that performs a method for automatically generating a set of suggested search terms, the method comprising: aggregating a user's search behavior; establishing a user profile based upon the aggregation, wherein the user profile is automatically refined upon detecting the user's search behavior; receiving a search input; parsing the search input on a real time character-by-character basis incident to each keystroke during entry of the search input; generating a set of suggested search terms by interrogating the user profile with the parsed search input; ordering the set of suggested search terms by evaluating the relevance of each search term in the set of suggested search terms against the search input in real time; rendering the set of suggested search terms at a UI display, wherein the set of suggested search terms is updated in real time on a character-by-character basis; and rendering a set of selectable edit options at the UI display that, when selected, modify one or more of the rendered set of suggested search terms, wherein modifications to one or more of the rendered set of suggested search terms are automatically transmitted to the user profile that incorporates those modifications in prospective sets of suggested search terms. 15. A method according to claim 9 , wherein interrogating the user profile with the parsed search input comprises accessing aggregate usage data to generate the set of suggested search terms.
| 0.725918 |
15. Apparatus for transmitting human speech in real time over a narrow-band channel, comprising: (a) speech input means for receiving a speech signal; (b) cusp detector means operatively connected to said speech input means for detecting cusps in the energy curve of said speech signal; (c) correlation detector means operatively connected to said cusp detector means for producing an output indicative of the regularity of occurrence of said cusps; (d) synch pulse generating means operatively connected to said cusp detector means and said correlation detector means for generating pulses in synchronism with said cusps when the occurrence of said cusps is substantially regular, and pulses at predetermined intervals when it is not; (e) transform converter means operatively connected to said speech input means and said synch pulse generating means for producing an indication of the approximate frequency and decay rate of at least the most dominant frequency component of said speech signal between two adjacent synch pulses; and (f) transmission means operatively connected to said transform converter means and said correlation detector means for transmitting, for each synch pulse interval, signals indicative of at least the regularity of said cusp occurrence and the approximate frequency and decay rate of said most dominant frequency component.
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15. Apparatus for transmitting human speech in real time over a narrow-band channel, comprising: (a) speech input means for receiving a speech signal; (b) cusp detector means operatively connected to said speech input means for detecting cusps in the energy curve of said speech signal; (c) correlation detector means operatively connected to said cusp detector means for producing an output indicative of the regularity of occurrence of said cusps; (d) synch pulse generating means operatively connected to said cusp detector means and said correlation detector means for generating pulses in synchronism with said cusps when the occurrence of said cusps is substantially regular, and pulses at predetermined intervals when it is not; (e) transform converter means operatively connected to said speech input means and said synch pulse generating means for producing an indication of the approximate frequency and decay rate of at least the most dominant frequency component of said speech signal between two adjacent synch pulses; and (f) transmission means operatively connected to said transform converter means and said correlation detector means for transmitting, for each synch pulse interval, signals indicative of at least the regularity of said cusp occurrence and the approximate frequency and decay rate of said most dominant frequency component. 16. The apparatus of claim 15, in which said transform converter means also produce an indication of the decay rates and approximate frequencies of the second most dominant and third-most dominant frequency components of said speech signal between two adjacent synch pulses, said apparatus further comprising frequency selection means operatively connected to said transform converter means for selecting said three most dominant frequency components and producing outputs indicative of the approximate frequency and amplitude of each of said most dominant frequency components; said transmission means being also operatively connected to said frequency selection means and being arranged to additionally transmit signals indicative of the approximate frequencies and amplitudes of at least said second-most and third-most dominant frequency components.
| 0.5 |
22. A system configured to provide a responsive communication to a communicant, which system comprises one or more associated processors that function as a means to: receive an electronic voice communication from a communicant; separating the electronic voice communication into at least first constituent data and second constituent data, the first constituent data being generated by the communicant, wherein the separating further comprises: identifying a communication protocol associated with an electronic voice communication; recording the electronic voice communication to a first electronic data file comprising a first and second audio track, the first constituent voice data being automatically recorded on the first audio track based on the identified communication protocol, and the second constituent voice data being automatically recorded on the second audio track based on the identified communication protocol; and separating at least one of the first and second constituent voice data recorded on the corresponding first and second track from the first electronic data file; transcribe the electronic voice communication to text; analyze the text of the separated first and second constituent data by mining the separated one of the first and second constituent data of the electronic voice communication and applying a predetermined linguistic-based psychological behavioral model to one of the separated first and second constituent data; generate behavioral assessment data including a personality type corresponding to the analyzed text of one of the separated first and second constituent data of the electronic voice communication; generate event data based on the analyzed text of the electronic voice communication; and generate a communication responsive to the communicant that is based on the event data.
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22. A system configured to provide a responsive communication to a communicant, which system comprises one or more associated processors that function as a means to: receive an electronic voice communication from a communicant; separating the electronic voice communication into at least first constituent data and second constituent data, the first constituent data being generated by the communicant, wherein the separating further comprises: identifying a communication protocol associated with an electronic voice communication; recording the electronic voice communication to a first electronic data file comprising a first and second audio track, the first constituent voice data being automatically recorded on the first audio track based on the identified communication protocol, and the second constituent voice data being automatically recorded on the second audio track based on the identified communication protocol; and separating at least one of the first and second constituent voice data recorded on the corresponding first and second track from the first electronic data file; transcribe the electronic voice communication to text; analyze the text of the separated first and second constituent data by mining the separated one of the first and second constituent data of the electronic voice communication and applying a predetermined linguistic-based psychological behavioral model to one of the separated first and second constituent data; generate behavioral assessment data including a personality type corresponding to the analyzed text of one of the separated first and second constituent data of the electronic voice communication; generate event data based on the analyzed text of the electronic voice communication; and generate a communication responsive to the communicant that is based on the event data. 23. The system of claim 22 , wherein the responsive communication generated is in voice format responsive to the electronic voice communication.
| 0.930489 |
18. The system of claim 16 , further comprising a cache-sensitive array trie index or a cache-sensitive prefix trie index used on top of the shared-leaves data structures as the encode index.
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18. The system of claim 16 , further comprising a cache-sensitive array trie index or a cache-sensitive prefix trie index used on top of the shared-leaves data structures as the encode index. 19. The system of claim 18 , wherein the cache-sensitive array trie index comprises an array to store the plurality of variable-length string values.
| 0.921418 |
1. A method for adapting a speech recognition system, the method comprising: receiving a first utterance from a user; determining an amount of time of the first utterance from the user is below a predetermined duration threshold; identifying at least one further utterance from the user, wherein the at least one further utterance provides additional information, the additional information comprising contextual language information, the at least one further utterance being identified in response to determining that the amount of time of the first utterance is below the predetermined duration threshold; generating a concatenated utterance by concatenating the first utterance with the at least one further utterance; transmitting the concatenated utterance to a speech recognition server; receiving a transcription of the concatenated utterance from the speech recognition server, wherein the transcription of the concatenated utterance includes a transcription of the first utterance, and wherein the transcription of the first utterance is based on the additional information provided by the at least one further utterance; extracting the transcription of the first utterance from the transcription of the concatenated utterance; and sending the extracted transcription to a computer device of the user, the computer device communicating with the speech recognition server.
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1. A method for adapting a speech recognition system, the method comprising: receiving a first utterance from a user; determining an amount of time of the first utterance from the user is below a predetermined duration threshold; identifying at least one further utterance from the user, wherein the at least one further utterance provides additional information, the additional information comprising contextual language information, the at least one further utterance being identified in response to determining that the amount of time of the first utterance is below the predetermined duration threshold; generating a concatenated utterance by concatenating the first utterance with the at least one further utterance; transmitting the concatenated utterance to a speech recognition server; receiving a transcription of the concatenated utterance from the speech recognition server, wherein the transcription of the concatenated utterance includes a transcription of the first utterance, and wherein the transcription of the first utterance is based on the additional information provided by the at least one further utterance; extracting the transcription of the first utterance from the transcription of the concatenated utterance; and sending the extracted transcription to a computer device of the user, the computer device communicating with the speech recognition server. 7. The method according to claim 1 , wherein identifying the at least one further utterance comprises: selecting the at least one further utterance for concatenation from one or more additional utterances from the user.
| 0.641341 |
1. A method comprising: receiving real time live data that includes one or more contexts from multiple data systems on the Internet (cloud) configured to deliver the data to a mobile device; correlating the data based on the one or more contexts of the data; determining a subset of the data based on a context of the mobile device after correlating the data from the multiple data systems based on the one or more contexts of the data, the context of the mobile device based on a calendar entry of a user of the mobile device and a location of the mobile device; delivering in real time, by a push engine, the subset of the data to the mobile device based on a push mechanism.
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1. A method comprising: receiving real time live data that includes one or more contexts from multiple data systems on the Internet (cloud) configured to deliver the data to a mobile device; correlating the data based on the one or more contexts of the data; determining a subset of the data based on a context of the mobile device after correlating the data from the multiple data systems based on the one or more contexts of the data, the context of the mobile device based on a calendar entry of a user of the mobile device and a location of the mobile device; delivering in real time, by a push engine, the subset of the data to the mobile device based on a push mechanism. 2. The method of claim 1 , wherein the subset of the data includes service lead data.
| 0.926496 |
2. The method of claim 1 , further comprising: associating each of one or more of the selected model implementations with a node in a directed graph, wherein for one or more ordered pairs of nodes in the graph the prediction output of a model implementation associated with a tail node in the pair serves as input to a model implementation associated with a head node in the pair.
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2. The method of claim 1 , further comprising: associating each of one or more of the selected model implementations with a node in a directed graph, wherein for one or more ordered pairs of nodes in the graph the prediction output of a model implementation associated with a tail node in the pair serves as input to a model implementation associated with a head node in the pair. 3. The method of claim 2 , further comprising executing each model implementation in an order prescribed by the directed graph.
| 0.94822 |
6. The method of claim 1 , wherein the plurality of concept instances further comprises a third concept instance extracted from the first software system artifact and the one or more relationships further comprise at least one relationship between the first concept instance and the third concept instance.
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6. The method of claim 1 , wherein the plurality of concept instances further comprises a third concept instance extracted from the first software system artifact and the one or more relationships further comprise at least one relationship between the first concept instance and the third concept instance. 7. The method of claim 6 , further comprising: extracting at least the third concept instance from the first software system artifact; and storing the third second concept instance in the one or more repositories.
| 0.884262 |
6. A non-transitory computer readable medium having stored thereon instructions for validating data transformed from a source repository to a target repository comprising machine executable code that when executed by a processor, causes the processor to perform steps comprising: receiving a data transformation specification from a user; analyzing the data transformation specification to determine data transformation rules, wherein the data transformation rules are indicative of a relationship between corresponding fields of the source repository and the target repository; generating test cases and test scripts based on the data transformation rules; determining a number of records on which the test cases and the test scripts are to be executed; executing the test cases and the test scripts in an order based on the data transformation rules, on the source repository and the target repository to validate the relationship between the corresponding fields of the source repository and the target repository, wherein execution of the test cases and the test scripts is initiated serially when the determined number of records is less than a critical threshold, and wherein the execution of the test cases and the test scripts is initiated in parallel when the determined number of records is more than a critical threshold; and generating a log file indicative of the outcome of the execution of the test cases and the test scripts.
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6. A non-transitory computer readable medium having stored thereon instructions for validating data transformed from a source repository to a target repository comprising machine executable code that when executed by a processor, causes the processor to perform steps comprising: receiving a data transformation specification from a user; analyzing the data transformation specification to determine data transformation rules, wherein the data transformation rules are indicative of a relationship between corresponding fields of the source repository and the target repository; generating test cases and test scripts based on the data transformation rules; determining a number of records on which the test cases and the test scripts are to be executed; executing the test cases and the test scripts in an order based on the data transformation rules, on the source repository and the target repository to validate the relationship between the corresponding fields of the source repository and the target repository, wherein execution of the test cases and the test scripts is initiated serially when the determined number of records is less than a critical threshold, and wherein the execution of the test cases and the test scripts is initiated in parallel when the determined number of records is more than a critical threshold; and generating a log file indicative of the outcome of the execution of the test cases and the test scripts. 10. The medium as set forth in claim 6 further having stored thereon at least one additional instruction that when executed by the processor causes the processor to perform at least one additional step comprising: receiving an identifier from a user, wherein the identifier is indicative of an application whose associated data repositories are to be validated; identifying the source repository and the target repository based on the identifier; receiving from the user configuration parameters of the source repository and the target repository, wherein the configuration parameters are indicative of at least one of data formats, structure, and schema of the source repository and the target repository; receiving from the user connection parameters of the source repository and the target repository, wherein the connection parameters are indicative of the authentication details associated with accessing source repository and the target repository; establishing connection with the source repository and the target repository, based on the connection parameters; identifying the corresponding tables and fields of the source repository and the target repository; and prompting the user to confirm the identified corresponding tables and fields of the source repository and the target repository.
| 0.574663 |
1. A method of creating a statistical classification model for use with a natural language understanding system, the method comprising: via a processor, processing training data using an existing statistical classification model; via the processor, selecting sentences of the training data correctly classified into a selected class of the existing statistical classification model; via the processor, assigning each selected sentence of the training data to a fringe group or a core group according to confidence score; via the processor, updating the training data by associating the fringe group with a fringe subclass of the selected class and the core group with a core subclass of the selected class; via the processor, building a new statistical classification model from the updated training data; and via the processor, outputting the new statistical classification model.
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1. A method of creating a statistical classification model for use with a natural language understanding system, the method comprising: via a processor, processing training data using an existing statistical classification model; via the processor, selecting sentences of the training data correctly classified into a selected class of the existing statistical classification model; via the processor, assigning each selected sentence of the training data to a fringe group or a core group according to confidence score; via the processor, updating the training data by associating the fringe group with a fringe subclass of the selected class and the core group with a core subclass of the selected class; via the processor, building a new statistical classification model from the updated training data; and via the processor, outputting the new statistical classification model. 2. The method of claim 1 , wherein at runtime the method further comprises: via the processor, classifying a text input into the fringe subclass or the core subclass of the selected class according to the new statistical classification model; and via the processor, outputting an indication that the text input belongs to the selected class.
| 0.689218 |
11. A method of analyzing relationships between documents, the method comprising the acts of: extracting text from source documents received from an external document source; storing the extracted text; creating an index of the extracted text; computing a document vector for each source document using the extracted text automatically when the extracted text is stored; storing the document vectors for each source document; extracting text from profile documents received from an external document source; storing the extracted text from the profile documents; computing a document vector for each profile document using the extracted text automatically when the extracted text is stored; computing a combined profile document vector from the profile document vectors of selected profile documents associated with a query; receiving a selection of a plurality of source documents as a target dataset and parameters of a query via a user interface; generating a result dataset containing documents of interest from the target dataset based on the query by evaluating similarities between the combined profile document vector and the document vector calculated for each source document in the target dataset; and automatically selecting a visualization model for clustering the documents of interest based the number of documents of interest in the result dataset and rendering the result set using selected visualization model in a user interface.
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11. A method of analyzing relationships between documents, the method comprising the acts of: extracting text from source documents received from an external document source; storing the extracted text; creating an index of the extracted text; computing a document vector for each source document using the extracted text automatically when the extracted text is stored; storing the document vectors for each source document; extracting text from profile documents received from an external document source; storing the extracted text from the profile documents; computing a document vector for each profile document using the extracted text automatically when the extracted text is stored; computing a combined profile document vector from the profile document vectors of selected profile documents associated with a query; receiving a selection of a plurality of source documents as a target dataset and parameters of a query via a user interface; generating a result dataset containing documents of interest from the target dataset based on the query by evaluating similarities between the combined profile document vector and the document vector calculated for each source document in the target dataset; and automatically selecting a visualization model for clustering the documents of interest based the number of documents of interest in the result dataset and rendering the result set using selected visualization model in a user interface. 16. The method of claim 11 further comprising the acts of: extracting text from profile documents received from an external document source; storing the extracted text from the profile documents; computing a document vector for each profile document using the extracted text automatically when the extracted text is stored; and generating a result dataset by evaluating similarities between each profile document vector and the document vector calculated for each source document in the target dataset.
| 0.535204 |
13. A memory device having stored thereon instructions that, when executed, result in: receiving entries in a plurality of web page fields of a single web page form; inspecting the received entries; and according to the inspection, causing a first level of on-line support to be provided for a first one of the plurality of web page fields responsive to the inspection and causing a second different level of on-line support to be provided responsive to the inspection for a second one of the plurality of web page fields; wherein the first level of on-line support includes human support, and wherein the second different level of on-line support does not include human support.
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13. A memory device having stored thereon instructions that, when executed, result in: receiving entries in a plurality of web page fields of a single web page form; inspecting the received entries; and according to the inspection, causing a first level of on-line support to be provided for a first one of the plurality of web page fields responsive to the inspection and causing a second different level of on-line support to be provided responsive to the inspection for a second one of the plurality of web page fields; wherein the first level of on-line support includes human support, and wherein the second different level of on-line support does not include human support. 23. The memory device according to claim 13 , further comprising instructions that, when executed, result in: tracking what web page fields in the web page form have been completed; and displaying a completion indicator that shows what amount of the web page form is completed.
| 0.72154 |
1. 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 collecting the first activity information, and the personally identifiable information of the sender comprises an e-mail address of the sender; 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 collected in collecting the second activity information; using at least one processor, attempting to identify a first node representative of the sender in a social graph; when a first node representative of the sender in a social graph is not identified and after receiving the second activity information, creating a second node to represent the sender in the social graph; and based on at least information associated with the second node in the social graph, selecting a personalized digital content for delivery to the sender.
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1. 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 collecting the first activity information, and the personally identifiable information of the sender comprises an e-mail address of the sender; 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 collected in collecting the second activity information; using at least one processor, attempting to identify a first node representative of the sender in a social graph; when a first node representative of the sender in a social graph is not identified and after receiving the second activity information, creating a second node to represent the sender in the social graph; and based on at least information associated with the second node in the social graph, selecting a personalized digital content for delivery to the sender. 9. The method of claim 1 wherein the plurality of activity data comprises activity data collected from an instant messaging application.
| 0.628335 |
1. A computer system for a narrational media organizer to transform digital media into a personal, memorable story with minimal user input, the system comprising: a) one or more processors; b) a non-transitory machine readable medium coupled to the one or more processors; c) a set of computer instructions stored in the machine readable medium and operable on the one or more processors for creating a narrational media organizer (NMO) environment causing performance of operations comprising: displaying digital media files of a first set as graphical representations of each of the digital media files in an arrangement along a timeline in a work area; receiving from a first user, a first command to shift the arrangement, including a user selection of an annotation location between two of the displayed graphical representations of the digital media files and a textual annotation; in response to the first command, placing a graphical instance of the textual annotation at the selected annotation location and shifting the arrangement of graphical representations to make room for the textual annotation; obtaining one or more digital media files of a second set of a second user, each of the one or more digital media files of the second set associated with a timestamp, wherein a first digital media file of the one or more digital media files of the second set was non-destructively excluded from the NMO environment; and merging the digital media files of the second set with the first set of digital media files, wherein: a first graphical representation of at least one of the digital media files of the second set is automatically inserted between two of the displayed graphical representations of the digital media files of the first set into the arrangement along the timeline based on the timestamp associated with the at least one of the digital media files of the second set, and a second graphical representation of the first digital media file of the second set is automatically inserted along the timeline and the second graphical representation includes a minimized version of the first digital media file; d) a user interface operably connected to the one or more processors, the user interface effective to transmit one or more commands including the first command to the one or more processors; and e) a storage operably connected to the one or more processors, the storage effective for storing a data structure, wherein the data structure includes the digital media files of the first set, the digital media files of the second set, and the textual annotation.
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1. A computer system for a narrational media organizer to transform digital media into a personal, memorable story with minimal user input, the system comprising: a) one or more processors; b) a non-transitory machine readable medium coupled to the one or more processors; c) a set of computer instructions stored in the machine readable medium and operable on the one or more processors for creating a narrational media organizer (NMO) environment causing performance of operations comprising: displaying digital media files of a first set as graphical representations of each of the digital media files in an arrangement along a timeline in a work area; receiving from a first user, a first command to shift the arrangement, including a user selection of an annotation location between two of the displayed graphical representations of the digital media files and a textual annotation; in response to the first command, placing a graphical instance of the textual annotation at the selected annotation location and shifting the arrangement of graphical representations to make room for the textual annotation; obtaining one or more digital media files of a second set of a second user, each of the one or more digital media files of the second set associated with a timestamp, wherein a first digital media file of the one or more digital media files of the second set was non-destructively excluded from the NMO environment; and merging the digital media files of the second set with the first set of digital media files, wherein: a first graphical representation of at least one of the digital media files of the second set is automatically inserted between two of the displayed graphical representations of the digital media files of the first set into the arrangement along the timeline based on the timestamp associated with the at least one of the digital media files of the second set, and a second graphical representation of the first digital media file of the second set is automatically inserted along the timeline and the second graphical representation includes a minimized version of the first digital media file; d) a user interface operably connected to the one or more processors, the user interface effective to transmit one or more commands including the first command to the one or more processors; and e) a storage operably connected to the one or more processors, the storage effective for storing a data structure, wherein the data structure includes the digital media files of the first set, the digital media files of the second set, and the textual annotation. 32. The system of claim 1 , wherein the data structure comprises: a) one or more first fields that store a first list of digital media elements information; b) one or more second fields that store annotation information; and c) one or more third fields that store paragraph information.
| 0.594265 |
18. An implantable spine stabilization device comprising: an elongated bone anchor having a distal end and a proximal end; a post having a distal end and a proximal end; a joint which secures the distal end of the post to the proximal end of the bone anchor such that the post may pivot relative to the bone anchor; a tubular extension of the bone anchor which extends over a distal portion of the post; and a compliant member disposed between the distal portion of the post and the tubular extension of the bone anchor whereby the compliant member biases the post into alignment with the bone anchor; and wherein said compliant member is a polymer disc having an outer diameter sized to fit with the tubular extension and a central aperture sized to receive the post.
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18. An implantable spine stabilization device comprising: an elongated bone anchor having a distal end and a proximal end; a post having a distal end and a proximal end; a joint which secures the distal end of the post to the proximal end of the bone anchor such that the post may pivot relative to the bone anchor; a tubular extension of the bone anchor which extends over a distal portion of the post; and a compliant member disposed between the distal portion of the post and the tubular extension of the bone anchor whereby the compliant member biases the post into alignment with the bone anchor; and wherein said compliant member is a polymer disc having an outer diameter sized to fit with the tubular extension and a central aperture sized to receive the post. 19. The spine stabilization device of claim 18 , further comprising: a limit surface associated with the tubular extension housing and positioned to contact the post when the post pivots through a first angle from alignment with the bone anchor; and wherein the limit surface resists pivoting of said post beyond said first angle.
| 0.875283 |
1. A method of delivering promotional content, the method comprising: receiving an audio query; recognizing that the audio query matches an audio reference; following a link between the audio reference and promotional content; delivering the promotional content through a network; and billing a campaign manager user for the delivering of the promotional content.
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1. A method of delivering promotional content, the method comprising: receiving an audio query; recognizing that the audio query matches an audio reference; following a link between the audio reference and promotional content; delivering the promotional content through a network; and billing a campaign manager user for the delivering of the promotional content. 3. The method of claim 1 , further comprising: receiving geo-location information; and comparing the geo-location information to a geographic location associated with the promotional content, wherein the delivering of the promotional content is responsive to the geo-location information matching the geographic location associated with the promotional content.
| 0.667279 |
4. The thermostat of claim 2 , wherein the stored appliance data over time is compared with reference appliance data stored in a database provided by at least one of the thermostat, the remote maintenance server, and the remote controller.
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4. The thermostat of claim 2 , wherein the stored appliance data over time is compared with reference appliance data stored in a database provided by at least one of the thermostat, the remote maintenance server, and the remote controller. 5. The thermostat of claim 4 , wherein the stored appliance data is representative for a current status of a component of the appliance and the database includes reference appliance data representative for a specification of the component of the appliance.
| 0.869702 |
5. The method according to claim 1 , wherein at least one semantic word is associated with the received query as an implicit semantic concept, the probabilistic framework is a semantic probabilistic framework in which multimedia works are mapped to semantic concepts, and the at least one semantic word is used to search the mapped multimedia works within the probabilistic semantic framework.
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5. The method according to claim 1 , wherein at least one semantic word is associated with the received query as an implicit semantic concept, the probabilistic framework is a semantic probabilistic framework in which multimedia works are mapped to semantic concepts, and the at least one semantic word is used to search the mapped multimedia works within the probabilistic semantic framework. 6. The method according to claim 5 , wherein a plurality of words belong to at least one respective semantic concept, such that the association of the respective multimedia work with the plurality of semantic concepts is through automatically generated annotation words and analysis of the joint probability distribution.
| 0.958644 |
9. The method according to claim 1 , wherein the corpus comprises at least a part of the World Wide Web, and the documents comprise Web pages, and wherein searching the corpus comprises conveying the query to one or more Web search engines.
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9. The method according to claim 1 , wherein the corpus comprises at least a part of the World Wide Web, and the documents comprise Web pages, and wherein searching the corpus comprises conveying the query to one or more Web search engines. 10. The method according to claim 9 , wherein inputting the first query comprises receiving the query from a user of a pervasive device, and wherein searching the corpus comprises searching while the device is disconnected from the Web.
| 0.850456 |
1. A method of incorporating query results into an abstract database, comprising: receiving a first set of query results produced by executing a first abstract query using a first data abstraction model against a first database; determining one or more mappings between the first set of query results and one or more logical fields in a second data abstraction model, wherein the second data abstraction model models underlying physical data in a manner making a schema of the physical data transparent to a user of the second data abstraction model, further comprising: determining similarities between at least a portion of the first set of query results and at least one field in the second database; and determining at least one logical field that maps to the at least one field in the second database; and modifying one or more logical field definitions within the second data abstraction model to further map to the at least a portion of the first set of query results, based on the determined one or more mappings, wherein the one or more logical field definitions correspond to the one or more logical fields, such that abstract queries can be executed against both the second database and the first set of query results using the modified second data abstraction model, wherein the first database is distinct from the second database, and wherein the first data abstraction model is distinct from the second data abstraction model.
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1. A method of incorporating query results into an abstract database, comprising: receiving a first set of query results produced by executing a first abstract query using a first data abstraction model against a first database; determining one or more mappings between the first set of query results and one or more logical fields in a second data abstraction model, wherein the second data abstraction model models underlying physical data in a manner making a schema of the physical data transparent to a user of the second data abstraction model, further comprising: determining similarities between at least a portion of the first set of query results and at least one field in the second database; and determining at least one logical field that maps to the at least one field in the second database; and modifying one or more logical field definitions within the second data abstraction model to further map to the at least a portion of the first set of query results, based on the determined one or more mappings, wherein the one or more logical field definitions correspond to the one or more logical fields, such that abstract queries can be executed against both the second database and the first set of query results using the modified second data abstraction model, wherein the first database is distinct from the second database, and wherein the first data abstraction model is distinct from the second data abstraction model. 6. The method of claim 1 , further comprising: transmitting a subscription request for query results from the first database, wherein the first set of query results were transmitted upon execution of a first abstract query against the first database and further responsive to the subscription request.
| 0.604734 |
1. A method for operating an Electronic Book (e-book) in a mobile device, the method comprising: displaying e-book content; receiving a first anchor interaction at a first location corresponding to a start location of corresponding text or a location close to the start location in text of the e-book content; displaying a first plurality of candidate anchors at a plurality of candidate locations between first text items of the e-book content based on the first location; determining a first candidate anchor from the first plurality of candidate anchors to be a first definite anchor, when the first candidate anchor is selected; removing remaining candidate anchors of the first plurality of candidate anchors, except the first candidate anchor for a start location of a block; receiving a second anchor interaction at a second location corresponding to a start location of corresponding text or a location close to the start location in the text of the e-book content; displaying a second plurality of candidate anchors at a plurality of candidate locations between second text items of the e-book content based on the second location; determining a second candidate anchor from the second plurality of candidate anchors to be a second definite anchor, when the second candidate anchor is selected; removing remaining candidate anchors of the second plurality of candidate anchors, except the second candidate anchor for an end location of the block; and forming a block enclosing text between the first definite anchor and the second definite anchor.
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1. A method for operating an Electronic Book (e-book) in a mobile device, the method comprising: displaying e-book content; receiving a first anchor interaction at a first location corresponding to a start location of corresponding text or a location close to the start location in text of the e-book content; displaying a first plurality of candidate anchors at a plurality of candidate locations between first text items of the e-book content based on the first location; determining a first candidate anchor from the first plurality of candidate anchors to be a first definite anchor, when the first candidate anchor is selected; removing remaining candidate anchors of the first plurality of candidate anchors, except the first candidate anchor for a start location of a block; receiving a second anchor interaction at a second location corresponding to a start location of corresponding text or a location close to the start location in the text of the e-book content; displaying a second plurality of candidate anchors at a plurality of candidate locations between second text items of the e-book content based on the second location; determining a second candidate anchor from the second plurality of candidate anchors to be a second definite anchor, when the second candidate anchor is selected; removing remaining candidate anchors of the second plurality of candidate anchors, except the second candidate anchor for an end location of the block; and forming a block enclosing text between the first definite anchor and the second definite anchor. 14. The method of claim 1 , further comprising: displaying the first or second plurality of candidate anchors based on two start points of words and one start point of a sentence, with respect to a location where the first or second anchor interaction is input.
| 0.716766 |
1. A computer-implemented method, comprising: based on an attempt by an online endorser to generate an endorsement at a target, receiving a request for a token; in response to the request, generating the token, and providing the target with an encrypted version of the token; receiving the encrypted version of the token and an endorsement action from the target; generating an endorsement log comprising an endorser identifier, a target identifier, endorsement data, and the token indicative of the endorsement event, wherein the generating the endorsement log comprises: receiving the request for the token from the endorser identifier; generating, encoding and encrypting a signal, including the token, in response to the request; transmitting the signal and an endorsement object to the endorser identifier; and in response to an endorsement action by the endorsement identifier that occurs after the attempt, providing an indication of endorsement and passing the endorsement action and the token from the endorser identifier to the server; analyzing the endorsement log with respect to a user aggregator and a target aggregator, to generate an online endorsement abuse candidate associated with endorsement abuse, wherein the endorsement abuse comprises an endorsement that identifies an online user as a source of the endorsement, without authorization by the online user; generating an endorsement overlap graph for the online endorsement abuse candidate based on the endorsement log, the user aggregator and the target aggregator, wherein nodes of the endorsement overlap graph represent ones of the online endorsement abuse candidate, and edges represent a number of shared endorsements; and determining whether the endorsement event is authorized based an analysis of the endorsement overlap graph and an orthogonal signal at least one of transmitted and received by the ones of the online endorsement abuse candidate.
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1. A computer-implemented method, comprising: based on an attempt by an online endorser to generate an endorsement at a target, receiving a request for a token; in response to the request, generating the token, and providing the target with an encrypted version of the token; receiving the encrypted version of the token and an endorsement action from the target; generating an endorsement log comprising an endorser identifier, a target identifier, endorsement data, and the token indicative of the endorsement event, wherein the generating the endorsement log comprises: receiving the request for the token from the endorser identifier; generating, encoding and encrypting a signal, including the token, in response to the request; transmitting the signal and an endorsement object to the endorser identifier; and in response to an endorsement action by the endorsement identifier that occurs after the attempt, providing an indication of endorsement and passing the endorsement action and the token from the endorser identifier to the server; analyzing the endorsement log with respect to a user aggregator and a target aggregator, to generate an online endorsement abuse candidate associated with endorsement abuse, wherein the endorsement abuse comprises an endorsement that identifies an online user as a source of the endorsement, without authorization by the online user; generating an endorsement overlap graph for the online endorsement abuse candidate based on the endorsement log, the user aggregator and the target aggregator, wherein nodes of the endorsement overlap graph represent ones of the online endorsement abuse candidate, and edges represent a number of shared endorsements; and determining whether the endorsement event is authorized based an analysis of the endorsement overlap graph and an orthogonal signal at least one of transmitted and received by the ones of the online endorsement abuse candidate. 2. The computer-implemented method of claim 1 , further comprising taking an action in response to a determination that the endorsement event is not authorized.
| 0.82538 |
28. The matching service system of claim 24 wherein the at least one processor further: verifies at least one piece of information provided by at least one of the end user clients of the successfully paired end user clients, wherein the first plurality of successfully paired end user clients is a subset of a larger number of successfully paired end user clients, the first plurality of successfully paired end user clients including the end user clients for which the at least one piece of information has been verified.
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28. The matching service system of claim 24 wherein the at least one processor further: verifies at least one piece of information provided by at least one of the end user clients of the successfully paired end user clients, wherein the first plurality of successfully paired end user clients is a subset of a larger number of successfully paired end user clients, the first plurality of successfully paired end user clients including the end user clients for which the at least one piece of information has been verified. 33. The matching service system of claim 28 wherein the at least one processor verifies the at least one piece of information associated with a first user profile set up by the respective end user client with a second user profile set up by the respective end user client.
| 0.858821 |
18. A non-transitory computer-readable storage medium storing computer executable instructions, which when executed by a computer system implement a method of modeling, the method comprising: managing a plurality of computer-implemented model components, wherein each model component is configured to implement a modeling function using a set of standard execution rules; managing a set of models, wherein each model is configured to simulate a system, and wherein each model includes at least one of the plurality of model components; managing attribute data for each of the set of models and each of the plurality of model components, the attribute data including evaluation data for the corresponding model or model component, wherein the evaluation data comprises at least one metric associated with an effectiveness of the corresponding model or model component, and wherein the at least one metric is based on at least one of: prior user feedback for the corresponding model or model component or third party citations of the corresponding model or model component; obtaining a selected model from a user, wherein the obtaining includes providing the evaluation data for presentation to the user; obtaining initialization data for the selected model; executing the selected model using the initialization data; and storing result data for the model execution.
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18. A non-transitory computer-readable storage medium storing computer executable instructions, which when executed by a computer system implement a method of modeling, the method comprising: managing a plurality of computer-implemented model components, wherein each model component is configured to implement a modeling function using a set of standard execution rules; managing a set of models, wherein each model is configured to simulate a system, and wherein each model includes at least one of the plurality of model components; managing attribute data for each of the set of models and each of the plurality of model components, the attribute data including evaluation data for the corresponding model or model component, wherein the evaluation data comprises at least one metric associated with an effectiveness of the corresponding model or model component, and wherein the at least one metric is based on at least one of: prior user feedback for the corresponding model or model component or third party citations of the corresponding model or model component; obtaining a selected model from a user, wherein the obtaining includes providing the evaluation data for presentation to the user; obtaining initialization data for the selected model; executing the selected model using the initialization data; and storing result data for the model execution. 19. The storage medium of claim 18 , wherein the evaluation data includes evaluation data for each of a plurality of tiers of users, and wherein each user tier includes a set of users having a common perspective in using and evaluating the set of models and the plurality of model components.
| 0.530681 |
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