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8,817,188 | 11 | 14 | 11. The method of claim 10 , further comprising allowing a user to manually select an aspect ratio for the video, the edges of the video determined according to the aspect ratio selected by the user. | 11. The method of claim 10 , further comprising allowing a user to manually select an aspect ratio for the video, the edges of the video determined according to the aspect ratio selected by the user. 14. The method of claim 11 , wherein the user manually selects an aspect ratio when the video is available in widescreen format, the user selecting from one of: 2.35:1, 1.85:1, 1.78:1, and 2.40:1. | 0.657343 |
8,340,974 | 6 | 15 | 6. An audio data processing system for use by a plurality of users to control a device, said audio data processing system comprising: a microphone operable to detect speech from a first user of the plurality of users and to generate speech data based on the detected speech; a user demographic profiles database capable of having demographic data stored therein; a content database being capable of having at least one of content data and advertisement data stored therein; a voice recognition portion operable to process user instructions from the first user, based on a command speech data that comprises at least one command by the first user for controlling the device; a voice analysis portion operable to determine characteristics of the first user based on the speech data; and a speech to text portion operable to determine interests of the first user based on a conversational speech data that comprises at least a portion of a monitored conversation between the first user and at least a second user of the plurality of users, and further wherein the portion does not comprise a command by the first user for controlling the device. | 6. An audio data processing system for use by a plurality of users to control a device, said audio data processing system comprising: a microphone operable to detect speech from a first user of the plurality of users and to generate speech data based on the detected speech; a user demographic profiles database capable of having demographic data stored therein; a content database being capable of having at least one of content data and advertisement data stored therein; a voice recognition portion operable to process user instructions from the first user, based on a command speech data that comprises at least one command by the first user for controlling the device; a voice analysis portion operable to determine characteristics of the first user based on the speech data; and a speech to text portion operable to determine interests of the first user based on a conversational speech data that comprises at least a portion of a monitored conversation between the first user and at least a second user of the plurality of users, and further wherein the portion does not comprise a command by the first user for controlling the device. 15. The audio data processing system of claim 6 , wherein said voice recognition portion is further operable to process user instructions from the first user and the second user, wherein said voice analysis portion is further operable to determine characteristics of the first user and the second user, and wherein said speech to text portion is further operable to determine interests of the first user and the second user. | 0.5 |
9,564,122 | 8 | 9 | 8. The method according to claim 7 , wherein the determination of semantically closeness of the page content and the seed comprises mapping the page content and the seed to vectors V[page] and V[seed], respectively, and measuring the degree of relatedness between said two vectors by a similarity function mathematically defined as a mapping of said two vectors to a real number. | 8. The method according to claim 7 , wherein the determination of semantically closeness of the page content and the seed comprises mapping the page content and the seed to vectors V[page] and V[seed], respectively, and measuring the degree of relatedness between said two vectors by a similarity function mathematically defined as a mapping of said two vectors to a real number. 9. The method according to claim 8 , wherein the page content is determined to have passed a semantic test when the value of the similarity function applied on V[page] and V[seed] is larger than a threshold that is a positive value smaller than 1. | 0.537453 |
8,650,561 | 17 | 23 | 17. A non-transitory computer-readable medium storing a computer program having instructions for localizing display of applications for download, the instructions 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. | 17. A non-transitory computer-readable medium storing a computer program having instructions for localizing display of applications for download, the instructions 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. 23. The non-transitory computer-readable medium of claim 17 , further comprising: receiving, from the user, a request for a desired localized version of the desired application; determining, by the online store, that the desired localized version is not one of the different languages; recording statistics based on the determination; and reporting the statistics to a developer of the application. | 0.5 |
8,630,854 | 7 | 11 | 7. A non-transitory computer-readable memory storing logic, the logic operable when executed by one or more processors to: match human speech of a videoconference to writable symbols, the human speech encoded in audio data of the videoconference; determine a probability that a portion of the human speech matches a profile of a participant of a plurality of participants of the videoconference, the profile stored in tangible computer-readable memory; if the probability is less than a predetermined threshold, use video data of the videoconference to determine which participant of the plurality of participants of the videoconference is the most likely source of the portion of the human speech; and generate a transcription of the videoconference that identifies for each statement the determination of which participant of the plurality of participants of the videoconference is most likely the source of the statement. | 7. A non-transitory computer-readable memory storing logic, the logic operable when executed by one or more processors to: match human speech of a videoconference to writable symbols, the human speech encoded in audio data of the videoconference; determine a probability that a portion of the human speech matches a profile of a participant of a plurality of participants of the videoconference, the profile stored in tangible computer-readable memory; if the probability is less than a predetermined threshold, use video data of the videoconference to determine which participant of the plurality of participants of the videoconference is the most likely source of the portion of the human speech; and generate a transcription of the videoconference that identifies for each statement the determination of which participant of the plurality of participants of the videoconference is most likely the source of the statement. 11. The non-transitory computer-readable memory of claim 7 , wherein the logic is further operable when executed by the one or more processors to generate at least a portion of the profile before the videoconference begins. | 0.515217 |
9,009,045 | 7 | 8 | 7. The method of claim 1 , further comprising collecting the digital interview data of the data set, wherein the collecting the digital interview data comprises collecting post-interview data, wherein the post-interview data comprises at least one of timing data, audio data, or video data. | 7. The method of claim 1 , further comprising collecting the digital interview data of the data set, wherein the collecting the digital interview data comprises collecting post-interview data, wherein the post-interview data comprises at least one of timing data, audio data, or video data. 8. The method of claim 7 , wherein the collecting the timing data comprises at least one of: determining a time metric representative of the respective interviewing candidate's timeliness on starting and completing an interview; or determining whether the respective interviewing candidate missed a deadline or requested additional time to complete the interview. | 0.693412 |
9,436,680 | 12 | 13 | 12. The method according to claim 11 , further comprising: referencing the screen character string control table, and inserting the master table identifier associated with the incorporating screen character string into the user-interface by associating with the character string. | 12. The method according to claim 11 , further comprising: referencing the screen character string control table, and inserting the master table identifier associated with the incorporating screen character string into the user-interface by associating with the character string. 13. The method according to claim 12 , further comprising: recording a translated character strings, into the screen character string control table wherein the screen character string control table includes a set of the master identifiers associated with the character string before modification; associating the translated screen character strings with respective master identifiers that are associated with the character strings corresponding to the translated screen character strings; associating the category information associated with the character strings before modification with the corresponding translated screen character strings; associating the first appearance positional information associated with the character strings before modification with the corresponding translated screen character strings; and associating the second appearance positional information associated with the screen character string before modification with the corresponding translated screen character strings; and wherein maintaining consistency comprises replacing the character string before modification that is associated with the acquired second appearance positional information with the translated screen character string. | 0.701806 |
8,812,474 | 3 | 4 | 3. The method of claim 1 , wherein the act (A) comprises receiving the query from a device, the act (D) comprises identifying a plurality of search engines to which a representation of the query is to be submitted, and wherein the method further comprises an act of: (E) defining an order of presentation of results from the plurality of search engines on the device. | 3. The method of claim 1 , wherein the act (A) comprises receiving the query from a device, the act (D) comprises identifying a plurality of search engines to which a representation of the query is to be submitted, and wherein the method further comprises an act of: (E) defining an order of presentation of results from the plurality of search engines on the device. 4. The method of claim 3 , wherein the act (E) comprises defining the order of presentation based at least in part on the content. | 0.5 |
9,135,000 | 10 | 15 | 10. A method, comprising: embedding, by a system including a processor, a first set of functionalities with a first object and a second set of functionalities with a second object; receiving, by the system, a request for execution of the first object and the second object; determining, by the system, a use context for the first object and the second object based on the request; masking, by the system, at least one functionality of the first set of functionalities or the second set of functionalities based on the use context to yield a customized set of functionalities; executing, by the system, at least one of the first object and the second object; displaying, by the system, the customized set of functionalities as a result of the executing; receiving, by the system, a second request to initiate a workflow, wherein at least one of the first object or the second object is executed based on parameters of the workflow; verifying, by the system, interconnectability of the first object and the second object based on an evaluation of the workflow; and recommending, by the system, an additional functionality to be associated with at least one of the first object or the second object based on the evaluation of the workflow. | 10. A method, comprising: embedding, by a system including a processor, a first set of functionalities with a first object and a second set of functionalities with a second object; receiving, by the system, a request for execution of the first object and the second object; determining, by the system, a use context for the first object and the second object based on the request; masking, by the system, at least one functionality of the first set of functionalities or the second set of functionalities based on the use context to yield a customized set of functionalities; executing, by the system, at least one of the first object and the second object; displaying, by the system, the customized set of functionalities as a result of the executing; receiving, by the system, a second request to initiate a workflow, wherein at least one of the first object or the second object is executed based on parameters of the workflow; verifying, by the system, interconnectability of the first object and the second object based on an evaluation of the workflow; and recommending, by the system, an additional functionality to be associated with at least one of the first object or the second object based on the evaluation of the workflow. 15. The method of claim 10 , wherein the determining the use context comprises determining at least one of an intended use of the first object and the second object or a user identity associated with the request. | 0.808664 |
6,076,061 | 31 | 32 | 31. The speech recognition method according to claim 27, wherein, in said selecting step, a maximum value is assigned to the weight for a class of the recognition information related to one of the plurality of areas in which the viewpoint stays, and a minimum value is assigned to the weight for other classes of the recognition information. | 31. The speech recognition method according to claim 27, wherein, in said selecting step, a maximum value is assigned to the weight for a class of the recognition information related to one of the plurality of areas in which the viewpoint stays, and a minimum value is assigned to the weight for other classes of the recognition information. 32. The speech recognition method according to claim 31, wherein, in a case where the viewpoint exits from the area where it has stayed, the weight for the class of the recognition information related to the area in which the viewpoint had stayed is changed to the minimum value, after a predetermined time period has elapsed, in said selecting step. | 0.5 |
7,831,869 | 1 | 36 | 1. A method of writing a block of user data to a tape storage medium, said method comprising: arranging said block of user data into an array of bytes, said array comprising a plurality of rows and a plurality of columns of said bytes; applying an error correction code to individual ones of said rows of bytes, such that said error correction coded rows each comprise four code words, wherein each of the four code words in each corresponding error correction coded row includes data bytes and error correcting bytes; in each of said error correction coded rows, interleaving said four code words of the row; and writing said error correction coded rows each comprising four code words into a diagonal track that extends diagonally across a width of the tape storage medium. | 1. A method of writing a block of user data to a tape storage medium, said method comprising: arranging said block of user data into an array of bytes, said array comprising a plurality of rows and a plurality of columns of said bytes; applying an error correction code to individual ones of said rows of bytes, such that said error correction coded rows each comprise four code words, wherein each of the four code words in each corresponding error correction coded row includes data bytes and error correcting bytes; in each of said error correction coded rows, interleaving said four code words of the row; and writing said error correction coded rows each comprising four code words into a diagonal track that extends diagonally across a width of the tape storage medium. 36. The method of claim 1 , wherein applying the error correction code to each individual row of bytes results in the individual row having four code words. | 0.829694 |
9,619,597 | 11 | 17 | 11. A non-transitory computer-readable storage medium for electronic design verification, the computer-readable storage medium having stored thereon instructions that when executed by a machine result in one or more operations, the operations comprising: providing an electronic design including, at least in part, one or more hardware description languages and one or more software programming languages; calculating, using one or more processors, configuration information without analyzing the electronic design, wherein the configuration information includes one or more memory elements configured to control a mode of operation of the electronic design; determining a change in the one or more memory elements; and altering a function associated with the electronic design verification based upon, at least in part, the determined change. | 11. A non-transitory computer-readable storage medium for electronic design verification, the computer-readable storage medium having stored thereon instructions that when executed by a machine result in one or more operations, the operations comprising: providing an electronic design including, at least in part, one or more hardware description languages and one or more software programming languages; calculating, using one or more processors, configuration information without analyzing the electronic design, wherein the configuration information includes one or more memory elements configured to control a mode of operation of the electronic design; determining a change in the one or more memory elements; and altering a function associated with the electronic design verification based upon, at least in part, the determined change. 17. The computer-readable storage medium of claim 11 , further comprising: enabling, via a graphical user interface, a display of one or more constraints connected to one or more design-under-test modules. | 0.647766 |
7,644,054 | 1 | 15 | 1. A computer-implemented user-interface system for incrementally finding and presenting one or more content items in response to keystrokes entered by a user on an input device having a known layout of overloaded keys selected from a set of key layouts, each overloaded key having a corresponding set of alphanumeric symbols, the system comprising: a database stored in a computer memory, the database containing content items and corresponding descriptive terms that characterize the content items; a computer memory comprising instructions for causing a computer system to: receive keystrokes from the user and build a string corresponding to incremental entries by the user, each item in the string having the set of alphanumeric symbols associated with a corresponding keystroke; map the string to the database to find the most likely content items corresponding to the incremental entries, the mapping being in accordance with a defined error model, the error model corresponding to the known layout of overloaded keys of the input device; wherein the error model associates the string with: (i) suggested corrections for typographic errors corresponding to incremental user entries, wherein suggested corrections are derived by replacing characters in the string resulting from one or more accidently pressed adjacent keys; (ii) suggested corrections for orthographic errors corresponding to incremental user entries, wherein suggested corrections are derived by replacing one or more characters in the string resulting from phonetic substitutions; and wherein the most likely content items are ordered and presented on a display device in accordance with defined ordering criteria; such that the user-interface system receives ambiguous entries from the user and presents the most likely matching content items. | 1. A computer-implemented user-interface system for incrementally finding and presenting one or more content items in response to keystrokes entered by a user on an input device having a known layout of overloaded keys selected from a set of key layouts, each overloaded key having a corresponding set of alphanumeric symbols, the system comprising: a database stored in a computer memory, the database containing content items and corresponding descriptive terms that characterize the content items; a computer memory comprising instructions for causing a computer system to: receive keystrokes from the user and build a string corresponding to incremental entries by the user, each item in the string having the set of alphanumeric symbols associated with a corresponding keystroke; map the string to the database to find the most likely content items corresponding to the incremental entries, the mapping being in accordance with a defined error model, the error model corresponding to the known layout of overloaded keys of the input device; wherein the error model associates the string with: (i) suggested corrections for typographic errors corresponding to incremental user entries, wherein suggested corrections are derived by replacing characters in the string resulting from one or more accidently pressed adjacent keys; (ii) suggested corrections for orthographic errors corresponding to incremental user entries, wherein suggested corrections are derived by replacing one or more characters in the string resulting from phonetic substitutions; and wherein the most likely content items are ordered and presented on a display device in accordance with defined ordering criteria; such that the user-interface system receives ambiguous entries from the user and presents the most likely matching content items. 15. The system of claim 1 , wherein the input device is a wireless communication device, a mobile phone, a PDA, a personal media player, or a television remote control. | 0.833333 |
7,586,654 | 21 | 23 | 21. A set of computer-readable instructions stored on a medium for storing computer readable instructions, said instructions, when executed by a processor of a scanner, causing the processor to: receive an electronic representation of a hardcopy document from a scanner bed of the scanner; present a menu of pre-established messages or graphics using a user interface; receive a selection from said menu indicating material to be added to a scanned document; and add said material to said electronic representation of the hardcopy document to produce an annotated electronic document; display an image of the hardcopy document on a display device of the user interface; and add said material to said annotated electronic document at a location corresponding to a location on the displayed image of the hardcopy document referenced during input of said material. | 21. A set of computer-readable instructions stored on a medium for storing computer readable instructions, said instructions, when executed by a processor of a scanner, causing the processor to: receive an electronic representation of a hardcopy document from a scanner bed of the scanner; present a menu of pre-established messages or graphics using a user interface; receive a selection from said menu indicating material to be added to a scanned document; and add said material to said electronic representation of the hardcopy document to produce an annotated electronic document; display an image of the hardcopy document on a display device of the user interface; and add said material to said annotated electronic document at a location corresponding to a location on the displayed image of the hardcopy document referenced during input of said material. 23. The instructions of claim 21 , further causing the processor to transmit said annotated electronic document as a facsimile. | 0.637143 |
9,213,705 | 12 | 16 | 12. A non-transitory computer-readable medium storing computer executable instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising: receiving a request for playback of narration audio content; in response to the request for playback of the narration audio content: determining a first keyword associated with a first portion of textual content, wherein the textual content represents words included in the narration audio content; determining a second keyword associated with a second portion of the textual content; retrieving, from one or more data stores, a first visual content item associated with the first keyword and a second visual content item associated with the second keyword; and automatically presenting (a) the first visual content item during playback of a portion of the narration audio content corresponding to the first portion of the textual content and (b) the second visual content item during playback of a portion of the narration audio content corresponding to the second portion of the textual content. | 12. A non-transitory computer-readable medium storing computer executable instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising: receiving a request for playback of narration audio content; in response to the request for playback of the narration audio content: determining a first keyword associated with a first portion of textual content, wherein the textual content represents words included in the narration audio content; determining a second keyword associated with a second portion of the textual content; retrieving, from one or more data stores, a first visual content item associated with the first keyword and a second visual content item associated with the second keyword; and automatically presenting (a) the first visual content item during playback of a portion of the narration audio content corresponding to the first portion of the textual content and (b) the second visual content item during playback of a portion of the narration audio content corresponding to the second portion of the textual content. 16. The non-transitory computer-readable medium of claim 12 , wherein the operations further comprise: receiving user feedback regarding at least one of the first visual content item or the second visual content item. | 0.526201 |
8,798,986 | 1 | 12 | 1. A method of providing a portable, real time voice translation, the method comprising: making a translation system available to a user for use on a single unit, portable device having a processor and a memory, the translation system having a computer program that is operable in executing the following functions: accessing a multilanguage database embodied in a non-transitory computer readable storage medium including a plurality of phrase templates associated with a respective plurality of phrases; selecting a source language from a plurality of source languages and a destination language from a plurality of destination languages; inputting a source phrase; transmitting the source phrase (i) to a speech recognition module embodied in a non-transitory computer readable storage medium for converting the spoken source phrase into a recognized source phrase; (ii) a translation engine embodied in a non-transitory computer readable storage medium for translating the recognized source phrase in the selected source language into a destination phrase in a destination language selected from multiple languages, wherein the plurality of phrases in the multilanguage database include the destination phrase; (iii) a template look-up engine embodied in a non-transitory computer readable storage medium for finding the phrase template associated with the destination phrase from among the multiple languages; and, outputting the spoken translation in the selected destination language; the translation system having a total time between the input of the source phrase and output of the destination phrase that is no slower than 0.010 seconds. | 1. A method of providing a portable, real time voice translation, the method comprising: making a translation system available to a user for use on a single unit, portable device having a processor and a memory, the translation system having a computer program that is operable in executing the following functions: accessing a multilanguage database embodied in a non-transitory computer readable storage medium including a plurality of phrase templates associated with a respective plurality of phrases; selecting a source language from a plurality of source languages and a destination language from a plurality of destination languages; inputting a source phrase; transmitting the source phrase (i) to a speech recognition module embodied in a non-transitory computer readable storage medium for converting the spoken source phrase into a recognized source phrase; (ii) a translation engine embodied in a non-transitory computer readable storage medium for translating the recognized source phrase in the selected source language into a destination phrase in a destination language selected from multiple languages, wherein the plurality of phrases in the multilanguage database include the destination phrase; (iii) a template look-up engine embodied in a non-transitory computer readable storage medium for finding the phrase template associated with the destination phrase from among the multiple languages; and, outputting the spoken translation in the selected destination language; the translation system having a total time between the input of the source phrase and output of the destination phrase that is no slower than 0.010 seconds. 12. The method of claim 1 , wherein the executing includes accessing a plurality of phrase templates that represent languages that include at least a combination of English, French, and Spanish; English, Japanese, and Mandarin; English, French, and Portuguese; English, Russian, and Mandarin; English, Hindustani, and Japanese; or, English, Arabic, and Russian. | 0.752401 |
8,606,808 | 9 | 11 | 9. A non-transitory computer-readable medium comprising a plurality of instructions, that instructions comprising: instructions to automatically extract a plurality of groups of words from a set comprising a first document, wherein each group of the plurality of groups comprises a word; instructions to automatically determine a plurality of first counts of a number of times said each of the groups of words in said plurality matches said set; instructions to automatically determine a plurality of second counts of the number of times said each group of words in said plurality matches a corpus of second documents; instructions to obtain a weight of said each group of words based on the first counts and second counts, comprising automatically performing function fitting on at least first counts of said plurality of groups of words and corresponding second counts of said plurality of groups of words to obtain a fitted function, and using at least one processor in automatically comparing a first count of said each group of words in the plurality of first counts to an evaluation of said fitted function at a second count of said each group of words in the plurality of second counts, to obtain a weight of said each group of words; instructions to automatically rank based on said weight, said at least one group of words relative to another group of words in said plurality of groups; and instructions to select a plurality of first groups of words from the plurality of groups, based on said weights; instructions to display the plurality of first groups of words in a browser for display to a user; instructions to construct a new combination of search terms using more than one user-selected word groups, wherein the user-selected word groups are selected by the user from the plurality of first groups of words displayed to the user; instructions to invoke a search service with the new combination of search terms; instructions to display results of the search service using the new combination of search terms in the browser; and instructions to store the results in memory coupled to a processor. | 9. A non-transitory computer-readable medium comprising a plurality of instructions, that instructions comprising: instructions to automatically extract a plurality of groups of words from a set comprising a first document, wherein each group of the plurality of groups comprises a word; instructions to automatically determine a plurality of first counts of a number of times said each of the groups of words in said plurality matches said set; instructions to automatically determine a plurality of second counts of the number of times said each group of words in said plurality matches a corpus of second documents; instructions to obtain a weight of said each group of words based on the first counts and second counts, comprising automatically performing function fitting on at least first counts of said plurality of groups of words and corresponding second counts of said plurality of groups of words to obtain a fitted function, and using at least one processor in automatically comparing a first count of said each group of words in the plurality of first counts to an evaluation of said fitted function at a second count of said each group of words in the plurality of second counts, to obtain a weight of said each group of words; instructions to automatically rank based on said weight, said at least one group of words relative to another group of words in said plurality of groups; and instructions to select a plurality of first groups of words from the plurality of groups, based on said weights; instructions to display the plurality of first groups of words in a browser for display to a user; instructions to construct a new combination of search terms using more than one user-selected word groups, wherein the user-selected word groups are selected by the user from the plurality of first groups of words displayed to the user; instructions to invoke a search service with the new combination of search terms; instructions to display results of the search service using the new combination of search terms in the browser; and instructions to store the results in memory coupled to a processor. 11. The non-transitory computer-readable medium of claim 9 , further comprising refining the search service comprising extracting additional word groups from a response to the invoked search service, and adding, by the user, the additional word groups to the search service. | 0.5 |
9,720,948 | 1 | 2 | 1. A computing system for identifying records, comprising: at least one hardware processor; and at least one storage device configured to store software instructions configured for execution by the at least one hardware processor to cause the computing system to: provide one alphanumeric key to any record of a plurality of records not including an alphanumeric key; provide one text description comprising at least one word to any record of the plurality of records not including a text description comprising at least one word; establish a multidimensional index comprising at least one four element index associated with each record, the four element index comprising: a pointer pointing from one alphanumeric key to one associated record; a text description pointer pointing from each text description to the one associated record; a first reverse word index pointer pointing from each word to one alphanumeric key; and a second reverse word index pointer pointing from each word to one text description; and search the plurality of records using the multidimensional index. | 1. A computing system for identifying records, comprising: at least one hardware processor; and at least one storage device configured to store software instructions configured for execution by the at least one hardware processor to cause the computing system to: provide one alphanumeric key to any record of a plurality of records not including an alphanumeric key; provide one text description comprising at least one word to any record of the plurality of records not including a text description comprising at least one word; establish a multidimensional index comprising at least one four element index associated with each record, the four element index comprising: a pointer pointing from one alphanumeric key to one associated record; a text description pointer pointing from each text description to the one associated record; a first reverse word index pointer pointing from each word to one alphanumeric key; and a second reverse word index pointer pointing from each word to one text description; and search the plurality of records using the multidimensional index. 2. The computing system of claim 1 , wherein each alphanumeric key in the multidimensional index is unique. | 0.788538 |
7,865,016 | 1 | 6 | 1. A computer implemented method for recognition of a handwritten pattern comprising one or more curves, said method comprising: receiving, by a computational device, sample data representing the handwritten pattern; segmenting, by the computational device, the handwritten pattern by detecting segmentation points on each curve and by dividing the handwritten pattern into segments, wherein detecting segmentation points further comprises detecting the segmentation points as local extreme points which are below a predetermined threshold, such that a particular gradient function of the curve is exceeded; and comparing, by the computational device, the handwritten pattern to templates wherein the comparing comprises: normalizing, by the computational device, said segments according to a scheme which is independent of the templates to which the segments are to be compared, and determining, by the computational device, matching measures for selecting at least one sequence of templates representing a recognition candidate of the handwritten pattern. | 1. A computer implemented method for recognition of a handwritten pattern comprising one or more curves, said method comprising: receiving, by a computational device, sample data representing the handwritten pattern; segmenting, by the computational device, the handwritten pattern by detecting segmentation points on each curve and by dividing the handwritten pattern into segments, wherein detecting segmentation points further comprises detecting the segmentation points as local extreme points which are below a predetermined threshold, such that a particular gradient function of the curve is exceeded; and comparing, by the computational device, the handwritten pattern to templates wherein the comparing comprises: normalizing, by the computational device, said segments according to a scheme which is independent of the templates to which the segments are to be compared, and determining, by the computational device, matching measures for selecting at least one sequence of templates representing a recognition candidate of the handwritten pattern. 6. The computer implemented method according to claim 1 , wherein said matching measures comprise connective matching measures comparing connective features between segments in the handwritten pattern to connective features of templates. | 0.5 |
9,268,866 | 1 | 4 | 1. A method of providing rewards based on interactions with annotations, the method being implemented by a computer system that includes one or more physical processors executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, at the computer system, a first annotation provided by a first user during a presentation of a first content item, wherein the first annotation corresponds to a reference time at which a product or service appears in the first content item, and wherein the first annotation is provided by the first user via a user device different from the computer system; identifying, by the computer system, in the first annotation, a reference referring to the product or service; modifying, by the computer system, the first annotation to include a mechanism that enables a transaction related to the product or service, wherein the first annotation is modified to include the mechanism based on the identification of the reference; associating, by the computer system, the modified first annotation with a first user account of the first user; providing, by the computer system, based on the corresponding reference time, a presentation of the modified first annotation during one or more presentations of the first content item to other users such that the mechanism is available for use by the other users via the modified first annotation during the one or more presentations of the first content item; monitoring, by the computer system, during the one or more presentations of the first content item, use of the mechanism by the other users via the modified first annotation; and determining, by the computer system, a reward to be provided to the first user account based on the monitored use of the mechanism by the other users via the modified first annotation. | 1. A method of providing rewards based on interactions with annotations, the method being implemented by a computer system that includes one or more physical processors executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, at the computer system, a first annotation provided by a first user during a presentation of a first content item, wherein the first annotation corresponds to a reference time at which a product or service appears in the first content item, and wherein the first annotation is provided by the first user via a user device different from the computer system; identifying, by the computer system, in the first annotation, a reference referring to the product or service; modifying, by the computer system, the first annotation to include a mechanism that enables a transaction related to the product or service, wherein the first annotation is modified to include the mechanism based on the identification of the reference; associating, by the computer system, the modified first annotation with a first user account of the first user; providing, by the computer system, based on the corresponding reference time, a presentation of the modified first annotation during one or more presentations of the first content item to other users such that the mechanism is available for use by the other users via the modified first annotation during the one or more presentations of the first content item; monitoring, by the computer system, during the one or more presentations of the first content item, use of the mechanism by the other users via the modified first annotation; and determining, by the computer system, a reward to be provided to the first user account based on the monitored use of the mechanism by the other users via the modified first annotation. 4. The method of claim 1 , wherein the reference referring to the product or service comprises a product or service identifier associated with the product or service, and wherein modifying the first annotation comprises supplementing, based on identification of the product or service identifier, the product or service identifier with the mechanism in the first annotation. | 0.714067 |
10,127,277 | 1 | 5 | 1. A computer program product for processing a structured query language (SQL) statement, the SQL statement comprising at least an OUTER JOIN operation, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising program instructions to: determine whether a first query and a second query are equivalent, the first and second queries being respectively the left side and the right side operands of the OUTER JOIN operation; determine whether an output of the right side of the OUTER JOIN operation contains an output of the left side of the OUTER JOIN operation; determine whether partitioning columns of a GROUP BY operation are the same as partitioning columns of the OUTER JOIN operation; determine whether columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations; determine whether there are no filter predicates or having clause in the GROUP BY operation; determine whether a SELECT output of the SQL statement does not refer to database columns from the left side of the OUTER JOIN operation which are not also partitioning columns of the OUTER JOIN operation; and responsive to determining that: (i) output of the first side of the OUTER JOIN operation contains the output of the left side of the OUTER JOIN operation, (ii) the partitioning columns of the GROUP BY operation are the same as the partitioning columns of the OUTER JOIN operation, (iii) the columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations, (iv) there are no filter predicates or having clause in the GROUP BY operation, (v) the first query and the second query are equivalent, and (vi) the SELECT output of the SQL statement does not refer to database columns from the left side of the OUTER JOIN operation which are not also partitioning columns of the OUTER JOIN operation, transform the SQL statement into an optimized query SQL statement by removing the OUTER JOIN operation. | 1. A computer program product for processing a structured query language (SQL) statement, the SQL statement comprising at least an OUTER JOIN operation, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising program instructions to: determine whether a first query and a second query are equivalent, the first and second queries being respectively the left side and the right side operands of the OUTER JOIN operation; determine whether an output of the right side of the OUTER JOIN operation contains an output of the left side of the OUTER JOIN operation; determine whether partitioning columns of a GROUP BY operation are the same as partitioning columns of the OUTER JOIN operation; determine whether columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations; determine whether there are no filter predicates or having clause in the GROUP BY operation; determine whether a SELECT output of the SQL statement does not refer to database columns from the left side of the OUTER JOIN operation which are not also partitioning columns of the OUTER JOIN operation; and responsive to determining that: (i) output of the first side of the OUTER JOIN operation contains the output of the left side of the OUTER JOIN operation, (ii) the partitioning columns of the GROUP BY operation are the same as the partitioning columns of the OUTER JOIN operation, (iii) the columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations, (iv) there are no filter predicates or having clause in the GROUP BY operation, (v) the first query and the second query are equivalent, and (vi) the SELECT output of the SQL statement does not refer to database columns from the left side of the OUTER JOIN operation which are not also partitioning columns of the OUTER JOIN operation, transform the SQL statement into an optimized query SQL statement by removing the OUTER JOIN operation. 5. The computer program product of claim 1 , wherein the OUTER JOIN operation is represented by a RIGHT OUTER JOIN, and further comprising program instructions to: determine whether the output quantified by the left side of the OUTER JOIN does not match the output quantified by the right side of the OUTER JOIN; determine whether the output of the right side of the OUTER JOIN operation subsumes the output of the left side of the OUTER JOIN operation; determine whether partitioning columns of a GROUP BY operation are the same as the portioning columns of the OUTER JOIN operation; determine whether there are no filter predicates or having clause in the GROUP BY operation; and responsive to determining that the output quantified by the left side of the OUTER JOIN does not match the output quantified by the right side of the OUTER JOIN, that the output of the right side of the OUTER JOIN operation contains the output of the left side of the OUTER JOIN operation, that the partitioning columns of the GROUP BY operation are the same as the OUTER JOIN partitioning columns and that the columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations, and that there are no filter predicates or having clause in the GROUP BY operation, transform the SQL statement into an optimized query SQL statement by removing the OUTER JOIN operation. | 0.5 |
9,378,654 | 1 | 4 | 1. A method for rendering music notation comprising: receiving, by a server, a request for electronic content, the request being communicated to the server by a client; obtaining, by the server, one or more files associated with the electronic content from a storage unit; parsing, by the server, one or more files associated with the electronic content to determine a music notation element; translating, by the server, the music notation from a first format to a second format that is supported by a browser application; creating, by the server, a music notation object based at least in part on the translation; and sending, by the server, the music notation object to the client, wherein the client renders the music notation object via the browser application. | 1. A method for rendering music notation comprising: receiving, by a server, a request for electronic content, the request being communicated to the server by a client; obtaining, by the server, one or more files associated with the electronic content from a storage unit; parsing, by the server, one or more files associated with the electronic content to determine a music notation element; translating, by the server, the music notation from a first format to a second format that is supported by a browser application; creating, by the server, a music notation object based at least in part on the translation; and sending, by the server, the music notation object to the client, wherein the client renders the music notation object via the browser application. 4. The method of claim 1 , wherein the music notation represents a multi-part music composition. | 0.799163 |
8,938,449 | 5 | 6 | 5. The method of claim 1 , wherein the one or more proximity metric values include a location that represents the geographic location in which the image was first recorded. | 5. The method of claim 1 , wherein the one or more proximity metric values include a location that represents the geographic location in which the image was first recorded. 6. The method of claim 5 , wherein determining the proximity score for the second image comprises determining the proximity score based on a distance between the location of the first image and the location of the second image. | 0.5 |
8,380,722 | 14 | 17 | 14. A computing device comprising: a processor; and a memory coupled to the processor, the memory comprising computer-program instructions executable by the processor for: determining a number of links on a source site that point to a destination site; calculating a destination site weight of an anchor text on the source site that links to the destination site based at least in part on the determined number of links; determining a number of duplicated links between the source site and multiple other source sites that also link to the destination site; determining a number of links between the source site and the multiple other source sites that are not duplicated links; calculating a source site weight of the anchor text based at least in part on the determined number of duplicated links and the determined number of links that are not duplicated links, wherein the source site weight of the anchor text is relatively higher when the determined number of duplicated links is relatively lower and the source site weight of the anchor text is relatively lower when the determined number of duplicated links is relatively higher; calculating a final weight of the anchor text based at least in part on the destination site and source site weights; storing the final weight of the anchor text in association with the anchor text and a destination page of the destination site to which the anchor text links; and re-ranking the destination page within a list of destination pages produced by search engine results based at least in part on the final weight. | 14. A computing device comprising: a processor; and a memory coupled to the processor, the memory comprising computer-program instructions executable by the processor for: determining a number of links on a source site that point to a destination site; calculating a destination site weight of an anchor text on the source site that links to the destination site based at least in part on the determined number of links; determining a number of duplicated links between the source site and multiple other source sites that also link to the destination site; determining a number of links between the source site and the multiple other source sites that are not duplicated links; calculating a source site weight of the anchor text based at least in part on the determined number of duplicated links and the determined number of links that are not duplicated links, wherein the source site weight of the anchor text is relatively higher when the determined number of duplicated links is relatively lower and the source site weight of the anchor text is relatively lower when the determined number of duplicated links is relatively higher; calculating a final weight of the anchor text based at least in part on the destination site and source site weights; storing the final weight of the anchor text in association with the anchor text and a destination page of the destination site to which the anchor text links; and re-ranking the destination page within a list of destination pages produced by search engine results based at least in part on the final weight. 17. The computing device of claim 14 , wherein the source site and the multiple other source sites each link to a common page on a destination site. | 0.859848 |
9,602,444 | 1 | 17 | 1. A method comprising: at a computing system having one or more processors and a memory storing one or more programs for execution by the one or more processors so as to perform the method of: identifying an ongoing conversation including a first user and a second user, wherein both the first user and the second user are conversation participants in the ongoing conversation, wherein the first user has delivered at least one message to the second user in the ongoing conversation; generating, without user intervention, a conversation profile that is specific to the first user for the ongoing conversation, wherein the conversation profile includes (i) a set of terms specific to the first user that are included in messages of the ongoing conversation, and (ii) context weights that correspond to the set of terms specific to the first user that are included in the messages, wherein the context weights are based on user-specific term weights included in a user profile of the first user; using content of other ongoing conversations in which the first user is an active participant to generate user profiles of other users not included in the ongoing conversation, wherein the user profiles are further individualized to the first user by adjusting respective counted occurrences of one or more terms based on the user-specific term weights corresponding to the first user; comparing, during the ongoing conversation, the generated user profiles to the generated conversation profile to identify a third user not currently participating in the ongoing conversation whose generated user profile matches the conversation profile to a threshold degree; generating, without user intervention, a suggestion for the first user to add the third user to the ongoing conversation; and formatting the suggestion for display to the first user. | 1. A method comprising: at a computing system having one or more processors and a memory storing one or more programs for execution by the one or more processors so as to perform the method of: identifying an ongoing conversation including a first user and a second user, wherein both the first user and the second user are conversation participants in the ongoing conversation, wherein the first user has delivered at least one message to the second user in the ongoing conversation; generating, without user intervention, a conversation profile that is specific to the first user for the ongoing conversation, wherein the conversation profile includes (i) a set of terms specific to the first user that are included in messages of the ongoing conversation, and (ii) context weights that correspond to the set of terms specific to the first user that are included in the messages, wherein the context weights are based on user-specific term weights included in a user profile of the first user; using content of other ongoing conversations in which the first user is an active participant to generate user profiles of other users not included in the ongoing conversation, wherein the user profiles are further individualized to the first user by adjusting respective counted occurrences of one or more terms based on the user-specific term weights corresponding to the first user; comparing, during the ongoing conversation, the generated user profiles to the generated conversation profile to identify a third user not currently participating in the ongoing conversation whose generated user profile matches the conversation profile to a threshold degree; generating, without user intervention, a suggestion for the first user to add the third user to the ongoing conversation; and formatting the suggestion for display to the first user. 17. The method of claim 1 , further comprising: receiving selection of the suggestion by the first user; and in response to receiving the selection from the first user, adding the third user to the ongoing conversation. | 0.594444 |
8,192,469 | 1 | 17 | 1. A dynamic spine stabilization system comprising: a first horizontal rod having a first end and a second end; a mount extending from the first horizontal rod midway between the first end and the second end; a first bone anchor connectable to the first end of the first horizontal rod and configured to engage a first pedicle of a first vertebra, and a second bone anchor connectable to the second end of the second horizontal rod and configured to engage a second pedicle of the first vertebra; a second horizontal rod having a third end and a fourth end; a third bone anchor connectable to the third end of the second horizontal rod and configured to engage a third pedicle of a second vertebra, and a fourth bone anchor connectable to the fourth end of the second horizontal rod and configured to engage a fourth pedicle of the second vertebra; a deflection rod connected to the mount of the first horizontal rod, the deflection rod having a fifth end extending away from the mount substantially parallel to the first horizontal rod; and a vertical rod connected between the fifth end of the deflection rod and the second horizontal rod, and a pivotable joint located where the vertical rod is connected to the fifth end of the deflection rod; such that, when secured between the first vertebra and second vertebra, the vertical rod transmits load from the second vertebra to the deflection rod; the deflection rod transmits load to the mount; the mount transmits load to the first horizontal rod; and the first horizontal rod distributes said load to the first pedicle and the second pedicle of the first vertebra via the first bone anchor and the second bone anchor. | 1. A dynamic spine stabilization system comprising: a first horizontal rod having a first end and a second end; a mount extending from the first horizontal rod midway between the first end and the second end; a first bone anchor connectable to the first end of the first horizontal rod and configured to engage a first pedicle of a first vertebra, and a second bone anchor connectable to the second end of the second horizontal rod and configured to engage a second pedicle of the first vertebra; a second horizontal rod having a third end and a fourth end; a third bone anchor connectable to the third end of the second horizontal rod and configured to engage a third pedicle of a second vertebra, and a fourth bone anchor connectable to the fourth end of the second horizontal rod and configured to engage a fourth pedicle of the second vertebra; a deflection rod connected to the mount of the first horizontal rod, the deflection rod having a fifth end extending away from the mount substantially parallel to the first horizontal rod; and a vertical rod connected between the fifth end of the deflection rod and the second horizontal rod, and a pivotable joint located where the vertical rod is connected to the fifth end of the deflection rod; such that, when secured between the first vertebra and second vertebra, the vertical rod transmits load from the second vertebra to the deflection rod; the deflection rod transmits load to the mount; the mount transmits load to the first horizontal rod; and the first horizontal rod distributes said load to the first pedicle and the second pedicle of the first vertebra via the first bone anchor and the second bone anchor. 17. The system of claim 1 wherein said deflection rod is tapered. | 0.940909 |
9,654,616 | 10 | 11 | 10. The message displaying apparatus of claim 9 , wherein the controller is further configured to: detect a selection on an input unit of a group text message sent to the group of recipients; and display a list of the recipients in the group, in response to the selection. | 10. The message displaying apparatus of claim 9 , wherein the controller is further configured to: detect a selection on an input unit of a group text message sent to the group of recipients; and display a list of the recipients in the group, in response to the selection. 11. The message displaying apparatus of claim 10 , wherein the controller is further configured to control display, on the display unit, of a predetermined number of most recent responses to the group text message by a selected recipient in the list. | 0.676166 |
9,262,768 | 11 | 12 | 11. A method for identifying content for users within a social network, the method steps comprising: a server obtaining via network-based communication with remote, distributed devices inputs from a plurality of users, the inputs including ratings for items from the users and their corresponding contextual moods; wherein the ratings are user-specified and dependent upon a content of uniform resource indicators (URIs); wherein the moods are user-specified rater states independent of the URI content, and wherein the moods denote a context-specific preference; the server communicating with a client device including a graphical user interface (GUI) over an electronic social network and the server providing addressable URIs over the network; the server automatically receiving and storing the ratings from the GUI, tracking the URIs, using the ratings to create a preference model for the URIs, using social graph data, and using the preference model to suggest additional URIs; wherein the stored ratings include at least one record having a rated item URI, a rated item rater having a unique identification, at least one rated item rating value provided by the rated item rater, and at least one metadatum including the moods for creating subsets of ratings; and wherein the social graph data includes information about what content of URIs a user has shared with their friends, what content of URIs their friends have shared with the user, what content of URIs the user or their friends have liked or disliked, personal connections, business connections, and combinations thereof; wherein all ratings and social graph data are available to the preference model; the server combining the sets of ratings to form a combined datasets; the server identifying the items where ratings collisions exist; the server treating ratings collisions automatically to generate a treated data subset of the combined dataset; the server identifying items to suggest to users or enabling access to specific content distribution. | 11. A method for identifying content for users within a social network, the method steps comprising: a server obtaining via network-based communication with remote, distributed devices inputs from a plurality of users, the inputs including ratings for items from the users and their corresponding contextual moods; wherein the ratings are user-specified and dependent upon a content of uniform resource indicators (URIs); wherein the moods are user-specified rater states independent of the URI content, and wherein the moods denote a context-specific preference; the server communicating with a client device including a graphical user interface (GUI) over an electronic social network and the server providing addressable URIs over the network; the server automatically receiving and storing the ratings from the GUI, tracking the URIs, using the ratings to create a preference model for the URIs, using social graph data, and using the preference model to suggest additional URIs; wherein the stored ratings include at least one record having a rated item URI, a rated item rater having a unique identification, at least one rated item rating value provided by the rated item rater, and at least one metadatum including the moods for creating subsets of ratings; and wherein the social graph data includes information about what content of URIs a user has shared with their friends, what content of URIs their friends have shared with the user, what content of URIs the user or their friends have liked or disliked, personal connections, business connections, and combinations thereof; wherein all ratings and social graph data are available to the preference model; the server combining the sets of ratings to form a combined datasets; the server identifying the items where ratings collisions exist; the server treating ratings collisions automatically to generate a treated data subset of the combined dataset; the server identifying items to suggest to users or enabling access to specific content distribution. 12. The method of claim 11 , further including the step of identifying users to receive the identified suggested items. | 0.875523 |
8,200,834 | 1 | 3 | 1. A method for controlling access to protected resources within a distributed data processing system, the method comprising: using a processor configured to perform data processing operations; receiving at a first server from a client a first single-use domain token associated with said client and a request to access a protected resource; validating said single-use domain token; generating a client authorization credential request; sending to a second server said client authorization credential request, said first single-use domain token, and a second single-use domain token associated with said first server, wherein said first server and said second server are operated within a common domain; receiving a single-use service token from said second server, said single-use service token associated with said client; processing said single-use service token to generate a response to said request; refreshing said single-use service token; and providing said response and said refreshed single-use service token to said client. | 1. A method for controlling access to protected resources within a distributed data processing system, the method comprising: using a processor configured to perform data processing operations; receiving at a first server from a client a first single-use domain token associated with said client and a request to access a protected resource; validating said single-use domain token; generating a client authorization credential request; sending to a second server said client authorization credential request, said first single-use domain token, and a second single-use domain token associated with said first server, wherein said first server and said second server are operated within a common domain; receiving a single-use service token from said second server, said single-use service token associated with said client; processing said single-use service token to generate a response to said request; refreshing said single-use service token; and providing said response and said refreshed single-use service token to said client. 3. The method of claim 1 , wherein said first single-use domain token comprises session information for performing session management with respect to said client. | 0.821192 |
7,739,102 | 10 | 11 | 10. The method of claim 9 , further comprising employing a graphical user interface for performing the multi-language text analysis. | 10. The method of claim 9 , further comprising employing a graphical user interface for performing the multi-language text analysis. 11. The method of claim 10 , further comprising providing in the graphical user interface a search topic field for entering a search topic, a topic clarification field for entering a word for clarifying the search topic, means for selecting a directory to search, means for selecting a language for the multi-language text analysis, means for starting the multi-language text analysis, and means for viewing a report for listing a text matching the search topic. | 0.5 |
9,536,193 | 13 | 15 | 13. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: performing a biological meaningfulness analysis on a biological relationship graph that has a plurality of paths through the graph, wherein each of the plurality of paths includes a plurality of connected nodes, and wherein the biological meaningfulness analysis is based on a process similarity calculation of gene ontologies of the nodes in the paths and a contextual similarity calculation of word occurrences from a plurality of documents in a corpus where a reference to the respective nodes are found; performing a biological interestingness analysis on the biological relationship graph that is based on a path diversity value calculated for each of the paths and a path rarity value calculated for each of the paths, wherein the path diversity value is based on a number of distinct documents in each of the paths and the number of connections in the respective paths, and wherein the path rarity value is based a total degrees of nodes that form each of the paths; and screening the plurality of paths in the biological relationship graph based on the biological meaningfulness analysis and the biological interestingness analysis, wherein the screened plurality of paths are displayed to a user, and wherein the screening further comprises actions of: identifying one or more meaningful paths through the biological relationship graph based on comparing a path meaningfulness value (PMV) with a threshold; and ranking the meaningful paths by a path interestingness value (PIV) corresponding to each of the meaningful paths. | 13. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: performing a biological meaningfulness analysis on a biological relationship graph that has a plurality of paths through the graph, wherein each of the plurality of paths includes a plurality of connected nodes, and wherein the biological meaningfulness analysis is based on a process similarity calculation of gene ontologies of the nodes in the paths and a contextual similarity calculation of word occurrences from a plurality of documents in a corpus where a reference to the respective nodes are found; performing a biological interestingness analysis on the biological relationship graph that is based on a path diversity value calculated for each of the paths and a path rarity value calculated for each of the paths, wherein the path diversity value is based on a number of distinct documents in each of the paths and the number of connections in the respective paths, and wherein the path rarity value is based a total degrees of nodes that form each of the paths; and screening the plurality of paths in the biological relationship graph based on the biological meaningfulness analysis and the biological interestingness analysis, wherein the screened plurality of paths are displayed to a user, and wherein the screening further comprises actions of: identifying one or more meaningful paths through the biological relationship graph based on comparing a path meaningfulness value (PMV) with a threshold; and ranking the meaningful paths by a path interestingness value (PIV) corresponding to each of the meaningful paths. 15. The computer program product of claim 13 wherein performing the biological meaningfulness analysis further comprises actions of: identifying a plurality of the nodes that exhibit a contextual similarity with each other by: selecting a plurality of documents where the nodes appear in the corpus; setting a plurality of word vector values corresponding to a plurality of biologically significant words for each the nodes based on a frequency that the biologically significant words appear in the same documents as each of the nodes; calculating a plurality of term frequency-inverse document frequency (TFIDF) averages corresponding to each of the biologically significant words for each of the nodes; and calculating a contextual similarity value (CSV) corresponding to a plurality of connections between the plurality of nodes by combining the TFIDF averages calculated for each of the nodes included in each of the connections. | 0.5 |
7,813,916 | 1 | 6 | 1. A method for associating anaphors with antecedents in a written work, the method comprising: processing a training corpus containing textual documents that are topically related to the written work, said processing producing interpretive information useful to categorize noun phrases of the training corpus as independent or potentially anaphoric; identifying noun phrases within the written work; using the interpretive information, filtering those identified noun phrases to exclude noun phrases that can be identified to be independent in nature; identifying a set of potentially anaphoric noun phrases occurring in the written work; following said identifying a set of potentially anaphoric noun phrases, recognizing cases of unambiguous coreferences in the set of potentially anaphoric noun phrases, said recognizing associating a noun phrase with an antecedent for each case; following said recognizing, identifying coreference combinations for unrecognized noun phrases from the set of potentially anaphoric noun phrases, each coreference combination including an unrecognized noun phrase and a potential antecedent; applying a plurality of general knowledge sources and contextual role knowledge sources to the coreference combinations, wherein the contextual role knowledge sources include events and a manner of participation in the events to identify relatedness for the coreference combination at a thematic role level, the contextual role knowledge sources including lexical expectations and semantic expectations to resolve the coreference combination by comparing the lexical expectations and the semantic expectations of the coreference combination to the antecedent, said applying producing evidentiary values for the coreference combinations; applying a factor to each of the produced evidentiary values to favor more credible knowledge sources; for each unrecognized noun phrase, applying a probabilistic model to the produced evidentiary values associated with the noun phrase; and for each application of the probabilistic model to an unrecognized noun phrase, selecting either an antecedent for that unrecognized noun phrase, if the coreference of that antecedent has a corresponding evidentiary value above a selected threshold value, or no antecedent otherwise. | 1. A method for associating anaphors with antecedents in a written work, the method comprising: processing a training corpus containing textual documents that are topically related to the written work, said processing producing interpretive information useful to categorize noun phrases of the training corpus as independent or potentially anaphoric; identifying noun phrases within the written work; using the interpretive information, filtering those identified noun phrases to exclude noun phrases that can be identified to be independent in nature; identifying a set of potentially anaphoric noun phrases occurring in the written work; following said identifying a set of potentially anaphoric noun phrases, recognizing cases of unambiguous coreferences in the set of potentially anaphoric noun phrases, said recognizing associating a noun phrase with an antecedent for each case; following said recognizing, identifying coreference combinations for unrecognized noun phrases from the set of potentially anaphoric noun phrases, each coreference combination including an unrecognized noun phrase and a potential antecedent; applying a plurality of general knowledge sources and contextual role knowledge sources to the coreference combinations, wherein the contextual role knowledge sources include events and a manner of participation in the events to identify relatedness for the coreference combination at a thematic role level, the contextual role knowledge sources including lexical expectations and semantic expectations to resolve the coreference combination by comparing the lexical expectations and the semantic expectations of the coreference combination to the antecedent, said applying producing evidentiary values for the coreference combinations; applying a factor to each of the produced evidentiary values to favor more credible knowledge sources; for each unrecognized noun phrase, applying a probabilistic model to the produced evidentiary values associated with the noun phrase; and for each application of the probabilistic model to an unrecognized noun phrase, selecting either an antecedent for that unrecognized noun phrase, if the coreference of that antecedent has a corresponding evidentiary value above a selected threshold value, or no antecedent otherwise. 6. A method according to claim 1 , wherein said recognizing cases of unambiguous coreferences applies a set of syntactic heuristics. | 0.880435 |
9,646,614 | 27 | 28 | 27. The computer-readable storage medium of claim 22 , wherein the first phoneme-independent representation is further decomposed into at least one content input sequence, and wherein determining that the spoken utterance is spoken by the registered user further comprises determining that the spoken utterance is spoken by the registered user if the at least one content input sequence is similar to at least one content reference sequence previously trained by the registered speaker. | 27. The computer-readable storage medium of claim 22 , wherein the first phoneme-independent representation is further decomposed into at least one content input sequence, and wherein determining that the spoken utterance is spoken by the registered user further comprises determining that the spoken utterance is spoken by the registered user if the at least one content input sequence is similar to at least one content reference sequence previously trained by the registered speaker. 28. The computer-readable storage medium of claim 27 , further comprising instructions for causing the one or more processor to: determine similarity based on a distance calculated between the at least one content input sequence and the at least one content reference sequence. | 0.5 |
7,835,902 | 12 | 22 | 12. A computer-implemented system for assessing an editorial quality of a textual unit, the system comprising: a processor; and a feature extraction component, executed by the processor, configured to generate a plurality of training-time feature vectors by automatically extracting features, which include grammar and spelling features, word n-grams and linguistic analysis features based on automatic syntactic and semantic analysis, from first versions of training documents that represent a first class of text and last versions of training documents that represent a second class of text, and configured to combine the extracted grammar and spelling features, the extracted word n-grams and the extracted linguistic analysis features to form the plurality of training-time feature vectors, and further configured to generate a run-time feature vector for the textual unit to be assessed by automatically extracting features from the textual unit; and a machine-learned classifier, trained based on the plurality of training-time feature vectors with the help of the processor, configured to evaluate the run-time feature vector and to provide an assessment of the editorial quality of the textual unit based on a degree of similarity in quality of the textual unit to either the first versions of the training documents that represent the first class of text or the last versions of the training documents that represent the second class of text, wherein the first versions of the training documents are unedited documents and wherein the last versions of the training documents are edited documents, and wherein the linguistic analysis features include at least one logical form feature, and wherein each of the plurality of training-time feature vectors includes a designator of the editorial quality of a training document, of the training documents, to which it corresponds. | 12. A computer-implemented system for assessing an editorial quality of a textual unit, the system comprising: a processor; and a feature extraction component, executed by the processor, configured to generate a plurality of training-time feature vectors by automatically extracting features, which include grammar and spelling features, word n-grams and linguistic analysis features based on automatic syntactic and semantic analysis, from first versions of training documents that represent a first class of text and last versions of training documents that represent a second class of text, and configured to combine the extracted grammar and spelling features, the extracted word n-grams and the extracted linguistic analysis features to form the plurality of training-time feature vectors, and further configured to generate a run-time feature vector for the textual unit to be assessed by automatically extracting features from the textual unit; and a machine-learned classifier, trained based on the plurality of training-time feature vectors with the help of the processor, configured to evaluate the run-time feature vector and to provide an assessment of the editorial quality of the textual unit based on a degree of similarity in quality of the textual unit to either the first versions of the training documents that represent the first class of text or the last versions of the training documents that represent the second class of text, wherein the first versions of the training documents are unedited documents and wherein the last versions of the training documents are edited documents, and wherein the linguistic analysis features include at least one logical form feature, and wherein each of the plurality of training-time feature vectors includes a designator of the editorial quality of a training document, of the training documents, to which it corresponds. 22. The system of claim 12 wherein the textual unit is a document to be classified, and wherein, in addition to being configured to provide the assessment of the editorial quality of the document, the machine-learned classification component is further configured to provide at least one of an editorial quality assessment of sentences and paragraphs within the document. | 0.5 |
7,904,478 | 8 | 10 | 8. The non-transitory computer-readable storage medium of claim 7 , wherein the method further comprises displaying path expressions which are associated with the determined paths, wherein a path expression is an alternating sequence of entity names and relationship names. | 8. The non-transitory computer-readable storage medium of claim 7 , wherein the method further comprises displaying path expressions which are associated with the determined paths, wherein a path expression is an alternating sequence of entity names and relationship names. 10. The non-transitory computer-readable storage medium of claim 8 , wherein the entities in the data model are associated with tables in a relational database, and wherein the method further comprises using the displayed path expressions to determine a join statement in the relational database. | 0.5 |
10,147,421 | 1 | 3 | 1. has been amended as: A device, comprising: one or more processors; a user interface (UI) for interacting with a user of the device using graphics and audio; and a memory device storing code associated with one or more applications and computer-readable instructions which, when executed by the one or more processors, perform a method comprising the steps of: exposing a digital assistant on the device configured for maintaining context-awareness for a device user by monitoring user behaviors and interactions with the device, the digital assistant further interacting with the device user using voice interactions through the UI, the one or more applications each being distinct from the digital assistant, registering, after launch and during runtime of an application from the one or more applications, a plurality of manifests of commands with the digital assistant, wherein respective manifests of commands are application-specific with respective applications among a plurality of applications currently in runtime from the one or more applications; listening for voice commands from the device user, determining, at the digital assistant and after receiving a voice command, which of the plurality of applications to direct the voice command according to; the application-specific manifests of commands, and the context awareness, and delivering the voice command to the determined application for handling, wherein, after receiving the delivered voice command, the determined application handles operations associated with the voice command and controls the digital assistant to provide voice output to the user that is responsive to the user's voice command. | 1. has been amended as: A device, comprising: one or more processors; a user interface (UI) for interacting with a user of the device using graphics and audio; and a memory device storing code associated with one or more applications and computer-readable instructions which, when executed by the one or more processors, perform a method comprising the steps of: exposing a digital assistant on the device configured for maintaining context-awareness for a device user by monitoring user behaviors and interactions with the device, the digital assistant further interacting with the device user using voice interactions through the UI, the one or more applications each being distinct from the digital assistant, registering, after launch and during runtime of an application from the one or more applications, a plurality of manifests of commands with the digital assistant, wherein respective manifests of commands are application-specific with respective applications among a plurality of applications currently in runtime from the one or more applications; listening for voice commands from the device user, determining, at the digital assistant and after receiving a voice command, which of the plurality of applications to direct the voice command according to; the application-specific manifests of commands, and the context awareness, and delivering the voice command to the determined application for handling, wherein, after receiving the delivered voice command, the determined application handles operations associated with the voice command and controls the digital assistant to provide voice output to the user that is responsive to the user's voice command. 3. The device of claim 1 further including enabling the one or more applications to load application-specific voice commands from a manifest into a runtime environment for execution. | 0.510753 |
8,812,602 | 36 | 38 | 36. A method of identifying conversations of a social network system having relevance to a first file, comprising: identifying a plurality of conversations within the social network system, wherein the plurality of conversations each have a relationship with the first file, wherein the social network system provides a platform for storing and sharing conversation, and each conversation includes a conversation and associated information; generating, by a system server, a first list of inquiries based on the plurality of conversations, wherein the list of inquiries includes search terms used in a search that identified the first file and the plurality of conversations, thereby establishing the relationship between the first file and the plurality of conversations by text analysis or filtering; providing, by the system server, the first list of inquiries to at least one sender of the first file, wherein the sender is provided with write-privilege to the first file; receiving from the at least one sender of the plurality of conversations at least one first response to the first list of inquiries; generating, by the system server, a second list of inquiries based on the plurality of conversations; providing, by the system server, the second list of inquiries to at least one second sender of the first file, wherein the second sender is provided with write-privilege to the first file; receiving from the at least one second sender at least one second response to the second list of inquiries; selecting a subset of the plurality of conversations based on the at least one response and the at least one second response; storing information related to the selected subset of the plurality of conversations for access if the first file is selected; providing, by the system server, the selected subset of the plurality of conversations to a user that selects the first file; and identifying the at least one sender and the at least one second sender to the user. | 36. A method of identifying conversations of a social network system having relevance to a first file, comprising: identifying a plurality of conversations within the social network system, wherein the plurality of conversations each have a relationship with the first file, wherein the social network system provides a platform for storing and sharing conversation, and each conversation includes a conversation and associated information; generating, by a system server, a first list of inquiries based on the plurality of conversations, wherein the list of inquiries includes search terms used in a search that identified the first file and the plurality of conversations, thereby establishing the relationship between the first file and the plurality of conversations by text analysis or filtering; providing, by the system server, the first list of inquiries to at least one sender of the first file, wherein the sender is provided with write-privilege to the first file; receiving from the at least one sender of the plurality of conversations at least one first response to the first list of inquiries; generating, by the system server, a second list of inquiries based on the plurality of conversations; providing, by the system server, the second list of inquiries to at least one second sender of the first file, wherein the second sender is provided with write-privilege to the first file; receiving from the at least one second sender at least one second response to the second list of inquiries; selecting a subset of the plurality of conversations based on the at least one response and the at least one second response; storing information related to the selected subset of the plurality of conversations for access if the first file is selected; providing, by the system server, the selected subset of the plurality of conversations to a user that selects the first file; and identifying the at least one sender and the at least one second sender to the user. 38. The method of claim 36 , further comprises providing the opinion of the at least one sender to a user that selects the first file. | 0.702222 |
9,141,606 | 1 | 2 | 1. A method for machine translation, comprising: receiving a translation request comprising user identification information, an indication of a source language, an indication of a target language, and a source string in the source language; decoding the translation request using at least one processor, wherein the source string is spliced into multiple source strings if the source string is determined to exceed a character limit set by the translator; normalizing the source string to produce a normalized source string; tokenizing the normalized source string to produce a tokenized source string; communicating the tokenized source string to a translator; obtaining a translated string from the translator, the translated string being at least a partial translation of the tokenized source string and comprising any temporary textual elements inserted during tokenization; and generating an output string using at least one processor, the generation comprising replacing all temporary textual elements in the translated string with associated target textual elements. | 1. A method for machine translation, comprising: receiving a translation request comprising user identification information, an indication of a source language, an indication of a target language, and a source string in the source language; decoding the translation request using at least one processor, wherein the source string is spliced into multiple source strings if the source string is determined to exceed a character limit set by the translator; normalizing the source string to produce a normalized source string; tokenizing the normalized source string to produce a tokenized source string; communicating the tokenized source string to a translator; obtaining a translated string from the translator, the translated string being at least a partial translation of the tokenized source string and comprising any temporary textual elements inserted during tokenization; and generating an output string using at least one processor, the generation comprising replacing all temporary textual elements in the translated string with associated target textual elements. 2. The method of claim 1 , wherein the decoding comprises: using the user identification information to identify at least one memory comprising user-specific translation data, the user-specific translation data comprising at least one of: glossary data, translation memory data, normalization data and source-identification rule data. | 0.5 |
9,213,946 | 2 | 3 | 2. The method of claim 1 , wherein: the information about relationships among the one or more variables of the first generative model comprises information about relationships among first hidden variables and first observable variables; and the information about relationships among the one or more variables of the second generative model comprises information about relationships among second hidden variables and second observable variables. | 2. The method of claim 1 , wherein: the information about relationships among the one or more variables of the first generative model comprises information about relationships among first hidden variables and first observable variables; and the information about relationships among the one or more variables of the second generative model comprises information about relationships among second hidden variables and second observable variables. 3. The method of claim 2 , wherein: the first generative model includes a first set of clusters, each cluster of the first set including one or more of the first observable variables and one or more of the first hidden variables, the first observable variables being represented as terminal nodes and the first hidden variables being represented as cluster nodes; and the second generative model includes a second set of clusters, each cluster of the second set including one or more of the second observable variables and one or more of the second hidden variables, the second observable variables being represented as terminal nodes and the second hidden variables being represented as cluster nodes, wherein the terminal nodes and cluster nodes are coupled together by weighted links, so that when an incoming link from a node is activated, a cluster node may be caused to activate with a probability proportional to the weight of the incoming link, and wherein an outgoing link from the cluster node to another node causes the other node to activate with a probability proportionate to the weight of the outgoing link, otherwise the other node is not activated. | 0.5 |
9,323,485 | 1 | 11 | 1. A verifiable document security system comprising: a validation center for generating a security architecture for a secured document and transmitting print instructions for printing the security architecture on a document in a form of a check payable to a recipient, the validation center communicating with an issuer and the recipient to receive and validate transaction information, wherein the recipient receives the secured document after printing; wherein the security architecture comprises a plurality of security elements comprising indicia printed with a print media and in a print configuration; wherein the print media is selected from the group consisting of a transparent magnetizable fluid, an ultraviolet (UV) excitable fluid, an infrared (IR) fluid, an x-ray excitable fluid, a gamma ray excitable fluid, an electron beam (EB) excitable fluid, an alternative energy excitable fluid, and a fluorescent fluid, further wherein at least one of the security elements is printed with the transparent magnetizable fluid; wherein the print configuration is selected from the group consisting of at least two sizes, a non-linear configuration, a curved configuration and an angled configuration; and a printing means comprising an ink suite, which comprises the print media, wherein the printing means is capable of receiving print instructions and capable of printing the security architecture on the document. | 1. A verifiable document security system comprising: a validation center for generating a security architecture for a secured document and transmitting print instructions for printing the security architecture on a document in a form of a check payable to a recipient, the validation center communicating with an issuer and the recipient to receive and validate transaction information, wherein the recipient receives the secured document after printing; wherein the security architecture comprises a plurality of security elements comprising indicia printed with a print media and in a print configuration; wherein the print media is selected from the group consisting of a transparent magnetizable fluid, an ultraviolet (UV) excitable fluid, an infrared (IR) fluid, an x-ray excitable fluid, a gamma ray excitable fluid, an electron beam (EB) excitable fluid, an alternative energy excitable fluid, and a fluorescent fluid, further wherein at least one of the security elements is printed with the transparent magnetizable fluid; wherein the print configuration is selected from the group consisting of at least two sizes, a non-linear configuration, a curved configuration and an angled configuration; and a printing means comprising an ink suite, which comprises the print media, wherein the printing means is capable of receiving print instructions and capable of printing the security architecture on the document. 11. The verifiable document system according to claim 1 , wherein the security architecture further comprises a colored document security background. | 0.851594 |
9,990,398 | 18 | 23 | 18. One or more non-transitory computer-readable storage media storing sequences of instructions which, when executed by one or more processors, cause: storing a query that references multiple tables; analyzing operations the query specifies on the multiple tables to detect at least one of: two particular tables of the multiple tables could be dimension tables by detecting a join of the two particular tables of said multiple tables that does not comprise an equijoin, or one or more particular tables of the multiple tables could be fact tables by detecting at least one of: a minimum operation, a maximum operation, an average operation, a summation operation, an online analytical processing (OLAP) function based on a table of said multiple tables, or a group by operation based on multiple columns of a table of said multiple tables; identifying one or more candidate dimension tables of the multiple tables at least in part by determining, based at least in part on content of the query, that a particular candidate fact table of the multiple tables appears in one or more equijoins with the one or more candidate dimension tables; based at least in part on determining which of the multiple tables could be fact tables, transforming the query to a transformed query that operates on the particular candidate fact table and the one or more candidate dimension tables; wherein the transformed query, when executed, processes at least some data from at least one dimension using at least one of the one or more candidate dimension tables before processing at least some data from the particular candidate fact table. | 18. One or more non-transitory computer-readable storage media storing sequences of instructions which, when executed by one or more processors, cause: storing a query that references multiple tables; analyzing operations the query specifies on the multiple tables to detect at least one of: two particular tables of the multiple tables could be dimension tables by detecting a join of the two particular tables of said multiple tables that does not comprise an equijoin, or one or more particular tables of the multiple tables could be fact tables by detecting at least one of: a minimum operation, a maximum operation, an average operation, a summation operation, an online analytical processing (OLAP) function based on a table of said multiple tables, or a group by operation based on multiple columns of a table of said multiple tables; identifying one or more candidate dimension tables of the multiple tables at least in part by determining, based at least in part on content of the query, that a particular candidate fact table of the multiple tables appears in one or more equijoins with the one or more candidate dimension tables; based at least in part on determining which of the multiple tables could be fact tables, transforming the query to a transformed query that operates on the particular candidate fact table and the one or more candidate dimension tables; wherein the transformed query, when executed, processes at least some data from at least one dimension using at least one of the one or more candidate dimension tables before processing at least some data from the particular candidate fact table. 23. The one or more non-transitory computer-readable storage media of claim 18 , wherein the sequence of instructions includes instructions that, when executed by one or more processors, further cause: determining that the query includes a maximum operation or minimum operation that references a group by operation and a particular column of a table of the one or more candidate dimension tables; and based at least in part on determining that the query includes the maximum operation or minimum operation that references the particular column, moving the particular column out of the maximum operation or minimum operation and into the group by operation. | 0.541201 |
8,775,403 | 1 | 11 | 1. A method of scheduling document indexing, comprising: at a computing system having one or more processors and memory storing programs for execution by the one or more processors: retrieving a number of document identifiers, each document identifier identifying a corresponding document on a network; and for each retrieved document identifier and its corresponding document, determining a query-independent score indicative of a rank of the corresponding document relative to other documents in a set of documents; determining a first score for the document identifier that is a function of the determined query-independent score, a determined content change frequency of the corresponding document, and an age of the corresponding document; comparing the first score against a threshold value thereby obtaining a result, wherein the threshold value is a function of a speed of the engine crawler system; and conditionally scheduling the corresponding document for indexing based on the result. | 1. A method of scheduling document indexing, comprising: at a computing system having one or more processors and memory storing programs for execution by the one or more processors: retrieving a number of document identifiers, each document identifier identifying a corresponding document on a network; and for each retrieved document identifier and its corresponding document, determining a query-independent score indicative of a rank of the corresponding document relative to other documents in a set of documents; determining a first score for the document identifier that is a function of the determined query-independent score, a determined content change frequency of the corresponding document, and an age of the corresponding document; comparing the first score against a threshold value thereby obtaining a result, wherein the threshold value is a function of a speed of the engine crawler system; and conditionally scheduling the corresponding document for indexing based on the result. 11. The method of claim 1 , wherein the conditionally scheduling the corresponding document for indexing based on the result assigns the document to a periodic crawl layer when the result is deemed satisfactory. | 0.604869 |
9,544,402 | 10 | 13 | 10. The method of claim 1 further comprising, given a key matching rule having at least one dimension, storing in the memory a value associated with the at least one dimension, the value being stored as dimension data of the multi-rule. | 10. The method of claim 1 further comprising, given a key matching rule having at least one dimension, storing in the memory a value associated with the at least one dimension, the value being stored as dimension data of the multi-rule. 13. The method of claim 10 wherein storing the dimension data of the multi-rule includes, given a dimension of the subject key matching rule is a mask field with a value and a mask, interleaving the value with the mask to form an interleaved value; and storing in the memory the interleaved value of the mask field associated with the subject key matching rule. | 0.54534 |
9,390,418 | 9 | 13 | 9. A non-transitory computer-readable medium storing instructions executable by one or more processors, the instructions comprising: one or more instructions that, when executed by the one or more processors, cause the one or more processors to: generate a first event record for one or more telephone calls handled by one or more telecommunications systems in one or more networks; apply, using a first fraud detection test, a first fraud detection rule of a plurality of fraud detection rules to the first event record, the first event record being of an account and corresponding to information associated with suspected fraud at a first time; generate, based on applying the first fraud detection rule, a first fraud alarm; generate a second event record for the one or more telephone calls handled by the one or more telecommunications systems in the one or more networks, the second event record being of the account and corresponding to the information associated with the suspected fraud at a second time; apply, using a second fraud detection test, a dynamically reconfigured fraud detection rule, of a plurality of dynamically reconfigured fraud detection rules, to the second event record; generate, based on applying the dynamically reconfigured fraud detection rule, a second fraud alarm, the second fraud alarm being different than the first fraud alarm; obtain first information from a plurality of devices; obtain an enhanced first fraud alarm by enhancing the first fraud alarm based on the first information, the first information being based on a first type of alarm associated with the first fraud alarm, and the first information including additional information and information indicating how the additional information is to be added to the first fraud alarm to obtain the enhanced first fraud alarm; obtain second information from the plurality of devices; obtain an enhanced second fraud alarm by enhancing the second fraud alarm based on the second information, the second information being based on a second type of alarm associated with the second fraud alarm, and the second information including other information and information indicating how the other information is to be added to the second fraud alarm to obtain the enhanced second fraud alarm; correlate the enhanced first fraud alarm with the enhanced second fraud alarm into a fraud case for the account; and institute one or more switch-based automatic number identification (ANI) blocks based on correlating the enhanced first fraud alarm with the enhanced second fraud alarm. | 9. A non-transitory computer-readable medium storing instructions executable by one or more processors, the instructions comprising: one or more instructions that, when executed by the one or more processors, cause the one or more processors to: generate a first event record for one or more telephone calls handled by one or more telecommunications systems in one or more networks; apply, using a first fraud detection test, a first fraud detection rule of a plurality of fraud detection rules to the first event record, the first event record being of an account and corresponding to information associated with suspected fraud at a first time; generate, based on applying the first fraud detection rule, a first fraud alarm; generate a second event record for the one or more telephone calls handled by the one or more telecommunications systems in the one or more networks, the second event record being of the account and corresponding to the information associated with the suspected fraud at a second time; apply, using a second fraud detection test, a dynamically reconfigured fraud detection rule, of a plurality of dynamically reconfigured fraud detection rules, to the second event record; generate, based on applying the dynamically reconfigured fraud detection rule, a second fraud alarm, the second fraud alarm being different than the first fraud alarm; obtain first information from a plurality of devices; obtain an enhanced first fraud alarm by enhancing the first fraud alarm based on the first information, the first information being based on a first type of alarm associated with the first fraud alarm, and the first information including additional information and information indicating how the additional information is to be added to the first fraud alarm to obtain the enhanced first fraud alarm; obtain second information from the plurality of devices; obtain an enhanced second fraud alarm by enhancing the second fraud alarm based on the second information, the second information being based on a second type of alarm associated with the second fraud alarm, and the second information including other information and information indicating how the other information is to be added to the second fraud alarm to obtain the enhanced second fraud alarm; correlate the enhanced first fraud alarm with the enhanced second fraud alarm into a fraud case for the account; and institute one or more switch-based automatic number identification (ANI) blocks based on correlating the enhanced first fraud alarm with the enhanced second fraud alarm. 13. The non-transitory computer-readable medium of claim 9 , where the one or more instructions to cause the one or more processors to generate the first fraud alarm include one or more instructions to cause the one or more processors to: compare at least a portion of the first event record to a profile detection rule, and generate the first fraud alarm when the first event record violates the profile detection rule. | 0.524887 |
6,167,409 | 1 | 7 | 1. A computer system for making available digital documents of different types and content, and responsive to a request for one of the documents received from another computer according to a communication protocol, the computer system comprising: means for receiving an indication of a requested document; means for accessing a definition of additional content for a type of the requested document; means for generating an additional content component according to the definition of additional content for the type of the requested document; means for selecting at least a portion of the content of the requested document; means for combining the additional content component with the content of the selected portion of the requested document to obtain an output document; and means for packaging and transmitting the output document to the other computer according to the communication protocol. | 1. A computer system for making available digital documents of different types and content, and responsive to a request for one of the documents received from another computer according to a communication protocol, the computer system comprising: means for receiving an indication of a requested document; means for accessing a definition of additional content for a type of the requested document; means for generating an additional content component according to the definition of additional content for the type of the requested document; means for selecting at least a portion of the content of the requested document; means for combining the additional content component with the content of the selected portion of the requested document to obtain an output document; and means for packaging and transmitting the output document to the other computer according to the communication protocol. 7. The computer system of claim 1, further comprising: means for transforming the portion of the requested document according to an identity of the other computer. | 0.700368 |
4,194,301 | 1 | 2 | 1. An educational device for use in paired associate learning situations, comprising: a base; a top sheet having a plurality of viewing holes defined therein and being removably positioned on said base, said base having a plurality of blind holes defined therein to be aligned with said top sheet viewing holes; a plurality of stimulus discs removably held in said base blind holes, said stimulus discs including selected stimuli located thereon to be aligned with individual viewing holes of said plurality of viewing holes and visible through said individual holes, said selected stimuli on said stimulus discs each including one of a plurality of associated components, a component on one disc being combinable with a component on another disc to form a selected unit used with teaching techniques involved in paired associate learning situations; and a plurality of stimulus covers releasably mounted on said top sheet to cover said viewing holes and prevent viewing therethrough so that viewing of said selected stimuli via said viewing holes is prevented by said viewing hole covering stimulus covers, said stimulus covers each being removable from a viewing hole covering position to expose a selected stimulus aligned with such uncovered viewing hole so that removal of a plurality of stimulus covers exposes components involved in paired associate learning situations. | 1. An educational device for use in paired associate learning situations, comprising: a base; a top sheet having a plurality of viewing holes defined therein and being removably positioned on said base, said base having a plurality of blind holes defined therein to be aligned with said top sheet viewing holes; a plurality of stimulus discs removably held in said base blind holes, said stimulus discs including selected stimuli located thereon to be aligned with individual viewing holes of said plurality of viewing holes and visible through said individual holes, said selected stimuli on said stimulus discs each including one of a plurality of associated components, a component on one disc being combinable with a component on another disc to form a selected unit used with teaching techniques involved in paired associate learning situations; and a plurality of stimulus covers releasably mounted on said top sheet to cover said viewing holes and prevent viewing therethrough so that viewing of said selected stimuli via said viewing holes is prevented by said viewing hole covering stimulus covers, said stimulus covers each being removable from a viewing hole covering position to expose a selected stimulus aligned with such uncovered viewing hole so that removal of a plurality of stimulus covers exposes components involved in paired associate learning situations. 2. The educational device defined in claim 1 wherein said selected stimuli include single consonants. | 0.667763 |
8,359,204 | 12 | 27 | 12. A computer program product, comprising a non-transitory computer readable storage medium storing computer executable code classifying speech data including one or more words, the computer executable code performing the steps of: storing a plurality of predefined commands, each predefined command including one or more words and each predefined command associated with an action; receiving one or more alternate formats associated with each predefined command from a data source, each alternate format including one or more words and an identifier associating the alternate format with a predefined command; for each predefined command, representing the predefined command with a sparse vector comprising the term frequency-inverse document frequency (“TFIDF”) weights for each word of the predefined command and the one or more alternate formats associated with the predefined command; receiving the speech data; generating a term frequency vector associated with the speech data; determining, for each predefined command, the probability that the speech data is associated with the predefined command based on the sparse vector associated with each predefined command and the term frequency vector associated with the speech data; associating the speech data with a predefined command based on the determined probabilities; and executing the action associated with the predefined command associated with the speech data. | 12. A computer program product, comprising a non-transitory computer readable storage medium storing computer executable code classifying speech data including one or more words, the computer executable code performing the steps of: storing a plurality of predefined commands, each predefined command including one or more words and each predefined command associated with an action; receiving one or more alternate formats associated with each predefined command from a data source, each alternate format including one or more words and an identifier associating the alternate format with a predefined command; for each predefined command, representing the predefined command with a sparse vector comprising the term frequency-inverse document frequency (“TFIDF”) weights for each word of the predefined command and the one or more alternate formats associated with the predefined command; receiving the speech data; generating a term frequency vector associated with the speech data; determining, for each predefined command, the probability that the speech data is associated with the predefined command based on the sparse vector associated with each predefined command and the term frequency vector associated with the speech data; associating the speech data with a predefined command based on the determined probabilities; and executing the action associated with the predefined command associated with the speech data. 27. The system of claim 12 , further comprising a generative modeling module configured to: generate a language model for each predefined command; generate a language model for the set of predefined commands; and determine, for each predefined command, the probability that the speech data is associated with the predefined command further based on an estimation of the probability that each term in the term frequency vector associated with the speech data is associated with the predefined command based on the language model for the predefined command and the language model for the set of predefined commands. | 0.545252 |
10,055,767 | 10 | 13 | 10. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by a computing device and from an advertiser, a textual representation of a candidate adword for use in a bidding process for selecting advertisements based on a transcription of a speech query; generating, by a text to speech module of the computing device, data that represents an audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query; based on the data that represents the audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query, generating a transcription of the audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query; applying an acoustic model for a speech recognizer to the audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query; applying a language model for the speech recognizer to the transcription of the audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query; based on applying the acoustic model for the speech recognizer to the audible representation of the textual representation of the candidate adword and based on applying a language model for the speech recognizer to the transcription of the audible representation of the textual representation of the candidate adword, generating, for the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query, a score that reflects a likelihood of the speech recognizer generating, in response to receiving an utterance of the candidate adword, a transcription that includes a word that is associated with the textual representation of the candidate adword; and based at least on the score that reflects the likelihood of the speech recognizer generating, in response to receiving the utterance of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query, the transcription that includes the word that is associated with the textual representation of the candidate adword, classifying the textual representation of the candidate adword as an appropriate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query or as not an appropriate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query. | 10. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by a computing device and from an advertiser, a textual representation of a candidate adword for use in a bidding process for selecting advertisements based on a transcription of a speech query; generating, by a text to speech module of the computing device, data that represents an audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query; based on the data that represents the audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query, generating a transcription of the audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query; applying an acoustic model for a speech recognizer to the audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query; applying a language model for the speech recognizer to the transcription of the audible representation of the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query; based on applying the acoustic model for the speech recognizer to the audible representation of the textual representation of the candidate adword and based on applying a language model for the speech recognizer to the transcription of the audible representation of the textual representation of the candidate adword, generating, for the textual representation of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query, a score that reflects a likelihood of the speech recognizer generating, in response to receiving an utterance of the candidate adword, a transcription that includes a word that is associated with the textual representation of the candidate adword; and based at least on the score that reflects the likelihood of the speech recognizer generating, in response to receiving the utterance of the candidate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query, the transcription that includes the word that is associated with the textual representation of the candidate adword, classifying the textual representation of the candidate adword as an appropriate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query or as not an appropriate adword for use in the bidding process for selecting advertisements based on the transcription of the speech query. 13. The system of claim 10 , wherein the operations further comprise: receiving, from the advertiser, a bid that is associated with a transcription of the audible representation of the textual representation of the candidate adword. | 0.817035 |
9,330,195 | 1 | 6 | 1. A computer-implemented method performed by a data processing apparatus, the method comprising: accessing command input logs storing data defining user device sessions; identifying, from the command input logs, user device sessions that each respectively store: a sequence of two or more command inputs, each command input specifying one or more parameter values, and each command input having a respective ordinal position in the sequence, and wherein the sequence includes at least one pair of a first command input that precedes a second command input in ordinal position in the sequence; first operation data indicating a first operation performed on data from a first resource property in response to the first command input; second operation data indicating a second operation performed on data from a second resource property in response to the second command input; identifying pairs of first and second command inputs from respective user device sessions for which the first and second operation data are indicative of a first operation failure and a second operation success; and determining, from the identified pairs of first and second command inputs, command inputs for which a parsing rule that is associated with the second operation is to be generated. | 1. A computer-implemented method performed by a data processing apparatus, the method comprising: accessing command input logs storing data defining user device sessions; identifying, from the command input logs, user device sessions that each respectively store: a sequence of two or more command inputs, each command input specifying one or more parameter values, and each command input having a respective ordinal position in the sequence, and wherein the sequence includes at least one pair of a first command input that precedes a second command input in ordinal position in the sequence; first operation data indicating a first operation performed on data from a first resource property in response to the first command input; second operation data indicating a second operation performed on data from a second resource property in response to the second command input; identifying pairs of first and second command inputs from respective user device sessions for which the first and second operation data are indicative of a first operation failure and a second operation success; and determining, from the identified pairs of first and second command inputs, command inputs for which a parsing rule that is associated with the second operation is to be generated. 6. The computer-implemented method of claim 1 , wherein determining command inputs for which a parsing rule that is associated with the second operation is to be generated comprises determining a parsing rule that is associated with the second operation is to be generated from the first command inputs of the identified pairs of first and second command inputs. | 0.76615 |
9,864,973 | 4 | 6 | 4. The method of claim 2 , wherein the data describing the edit to the first element is expressed in terms of one or more machine-readable change primitives. | 4. The method of claim 2 , wherein the data describing the edit to the first element is expressed in terms of one or more machine-readable change primitives. 6. The method of claim 4 , wherein a module of the second media processing application reads the one or more change primitives and provides information to the second application indicating a portion of the media project associated with the edit to the first element. | 0.5 |
9,299,041 | 8 | 13 | 8. A non-transitory computer readable storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving unstructured data that includes text; identifying an attribute associated with a structured data collection; obtaining historical data associated with the attribute and two or more different types of additional data associated with a user of the computing system, wherein each additional datum is associated with an accuracy modifier based on the type of the additional datum; identifying one or more terms from the unstructured data as being associated with the attribute based on at least one of the historical data and the additional data, wherein identifying the one or more terms includes resolving ambiguities in the identified terms based on the accuracy modifier associated with each additional datum; storing the identified one or more terms in a data record of the structured data collection; and preparing for display the data record of the structure data collection. | 8. A non-transitory computer readable storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving unstructured data that includes text; identifying an attribute associated with a structured data collection; obtaining historical data associated with the attribute and two or more different types of additional data associated with a user of the computing system, wherein each additional datum is associated with an accuracy modifier based on the type of the additional datum; identifying one or more terms from the unstructured data as being associated with the attribute based on at least one of the historical data and the additional data, wherein identifying the one or more terms includes resolving ambiguities in the identified terms based on the accuracy modifier associated with each additional datum; storing the identified one or more terms in a data record of the structured data collection; and preparing for display the data record of the structure data collection. 13. The non-transitory computer readable storage medium of claim 8 , wherein the operations further comprise: receiving an input from the user to associate the one or more terms with the attribute; and including the one or more terms in the historical data associated with the attribute. | 0.8269 |
8,719,733 | 10 | 13 | 10. A system for implementing website navigation, comprising: a processor configured to: derive a confidence level for at least one leaf node of a website navigation category diagram using historical user operation data, wherein the confidence level for the at least one lead node is determined based at least in part on historical search behavior data; generate a plurality of navigation hierarchical structure diagrams based on the website navigation category diagram, wherein for at least one of the plurality of navigation hierarchical structure diagrams, leaf nodes with a corresponding confidence level greater than a preset threshold value are included in a predetermined level of the navigation hierarchical structure diagram; determine a searching cost associated with each of the plurality of navigation hierarchical structure diagrams; determine the navigation hierarchical structure diagram associated with the lowest to searching cost; and implement at least in part a website navigation process using the navigation hierarchical structure diagram associated with the lowest searching cost; and a memory coupled to the processor and configured to provide the processor with instructions. | 10. A system for implementing website navigation, comprising: a processor configured to: derive a confidence level for at least one leaf node of a website navigation category diagram using historical user operation data, wherein the confidence level for the at least one lead node is determined based at least in part on historical search behavior data; generate a plurality of navigation hierarchical structure diagrams based on the website navigation category diagram, wherein for at least one of the plurality of navigation hierarchical structure diagrams, leaf nodes with a corresponding confidence level greater than a preset threshold value are included in a predetermined level of the navigation hierarchical structure diagram; determine a searching cost associated with each of the plurality of navigation hierarchical structure diagrams; determine the navigation hierarchical structure diagram associated with the lowest to searching cost; and implement at least in part a website navigation process using the navigation hierarchical structure diagram associated with the lowest searching cost; and a memory coupled to the processor and configured to provide the processor with instructions. 13. The system of claim 10 , wherein the processor is further configured to display leaf nodes associated with a navigation hierarchical structure diagram in an order based on corresponding confidence levels of the leaf nodes. | 0.859627 |
9,972,305 | 7 | 10 | 7. A method of normalizing input data of an acoustic model, the method comprising: extracting windows of frame data to be input to the acoustic model from frame data of a speech to be recognized; and normalizing the frame data to be input to the acoustic model in units of the extracted windows, wherein the normalizing of the frame data comprises normalizing frames belonging to a current window in consideration of frames belonging to preceding windows of the current window. | 7. A method of normalizing input data of an acoustic model, the method comprising: extracting windows of frame data to be input to the acoustic model from frame data of a speech to be recognized; and normalizing the frame data to be input to the acoustic model in units of the extracted windows, wherein the normalizing of the frame data comprises normalizing frames belonging to a current window in consideration of frames belonging to preceding windows of the current window. 10. The method of claim 7 , the normalizing of the frame data comprises normalizing the frames belonging to the current window in consideration of the frames belonging to the preceding windows and frames of training data in response to a total number of the frames belonging to the current window and of the frames belonging to the preceding windows being insufficient for speech recognition. | 0.5 |
7,827,172 | 1 | 21 | 1. A method comprising the steps of: receiving a particular query that comprises one or more terms; in response to receiving the particular query: making a first plurality of comparisons, wherein each comparison in the first plurality of comparisons is based on the particular query and a different query of a plurality of queries, wherein the number of the first plurality of comparisons and the number of the plurality of queries is the same; making a second plurality of comparisons, wherein each comparison in the second plurality of comparisons is based on the particular query and a different query of the plurality of queries; based on the first plurality of comparisons, generating a first numerical value; based on the second plurality of comparisons, generating a second numerical value that is different than the first numerical value; determining a ratio between the first numerical value and the second numerical value; and ranking documents in a document set based, at least in part, on the ratio; wherein the steps are performed by one or more computer systems. | 1. A method comprising the steps of: receiving a particular query that comprises one or more terms; in response to receiving the particular query: making a first plurality of comparisons, wherein each comparison in the first plurality of comparisons is based on the particular query and a different query of a plurality of queries, wherein the number of the first plurality of comparisons and the number of the plurality of queries is the same; making a second plurality of comparisons, wherein each comparison in the second plurality of comparisons is based on the particular query and a different query of the plurality of queries; based on the first plurality of comparisons, generating a first numerical value; based on the second plurality of comparisons, generating a second numerical value that is different than the first numerical value; determining a ratio between the first numerical value and the second numerical value; and ranking documents in a document set based, at least in part, on the ratio; wherein the steps are performed by one or more computer systems. 21. A volatile or non-volatile machine-readable medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 1 . | 0.719451 |
8,291,475 | 11 | 12 | 11. The method of claim 1 , further comprising: determining which type of domain code authorization a browser on the website uses; and exchanging data between the website and the untrusted website based on the type of domain code authorization of the browser. | 11. The method of claim 1 , further comprising: determining which type of domain code authorization a browser on the website uses; and exchanging data between the website and the untrusted website based on the type of domain code authorization of the browser. 12. The method of claim 11 , further comprising: determining that the browser uses a static type of domain code authorization; and using a callback technique or a polling technique to exchange data between the website and the untrusted website. | 0.5 |
7,890,438 | 11 | 12 | 11. The method of claim 1 , wherein the data elements are modeled as nodes in the relational dependency network, each node being linked to at least one other data element. | 11. The method of claim 1 , wherein the data elements are modeled as nodes in the relational dependency network, each node being linked to at least one other data element. 12. The method of claim 11 , wherein the types of data include at least a first type of data associated with a first level of nodes and a second type of data associated with lower level nodes. | 0.5 |
9,870,554 | 1 | 5 | 1. A method for managing computer-based documents, comprising: identifying, by a computing device using calendar information from a calendar program associated with a first participant, related events in the calendar program associated with the first participant, wherein the related events include a first event and a second event, wherein the first event is associated with a first time period on a specified day, wherein the first time period occurs in the present or in the future, wherein the second event is associated with a second time period, wherein the second time period occurred in the past, wherein the calendar program includes, for the first participant and for the specified day, first participant events including the first event, wherein the calendar information includes at least one of event title, event participant list, event tag, or event topic, and wherein the identification of the related events is made when at least some of the calendar information associated with the second event matches at least some of the calendar information associated with the first event; identifying a first document associated with the second event, wherein the first document was created, accessed, or modified by the first participant during the second time period, and wherein the first document is inaccessible by a second participant of the first event; creating, in a folder of a directory, a link to the first document, wherein the folder is a folder of the first participant and the folder is inaccessible to the second participant of the first event, wherein the folder is associated with the first event, and wherein the directory is part of a program other than the calendar program, is associated with the specified day, and includes respective folders for some of the first participant events including the folder associated with the first event; responsive to a selection of the specified day in the program, displaying the directory, the respective folders including the folder associated with the first event, and the link to the first document; and sharing, with a permission of the first participant, the folder and the link with the second participant of the first event. | 1. A method for managing computer-based documents, comprising: identifying, by a computing device using calendar information from a calendar program associated with a first participant, related events in the calendar program associated with the first participant, wherein the related events include a first event and a second event, wherein the first event is associated with a first time period on a specified day, wherein the first time period occurs in the present or in the future, wherein the second event is associated with a second time period, wherein the second time period occurred in the past, wherein the calendar program includes, for the first participant and for the specified day, first participant events including the first event, wherein the calendar information includes at least one of event title, event participant list, event tag, or event topic, and wherein the identification of the related events is made when at least some of the calendar information associated with the second event matches at least some of the calendar information associated with the first event; identifying a first document associated with the second event, wherein the first document was created, accessed, or modified by the first participant during the second time period, and wherein the first document is inaccessible by a second participant of the first event; creating, in a folder of a directory, a link to the first document, wherein the folder is a folder of the first participant and the folder is inaccessible to the second participant of the first event, wherein the folder is associated with the first event, and wherein the directory is part of a program other than the calendar program, is associated with the specified day, and includes respective folders for some of the first participant events including the folder associated with the first event; responsive to a selection of the specified day in the program, displaying the directory, the respective folders including the folder associated with the first event, and the link to the first document; and sharing, with a permission of the first participant, the folder and the link with the second participant of the first event. 5. The method of claim 1 wherein the first event and the second event are from a recurring series of related events. | 0.858191 |
8,908,969 | 9 | 10 | 9. A non-transitory computer-readable medium having stored thereon a sequence of instructions which when executed by a system, causes the system to perform a method, comprising: obtaining an image of a document; detecting image objects on the image; matching by a processor the image objects to a predetermined document type, wherein image objects distinguish the document type from other document types, and wherein image objects include anchor elements; generating by the processor a flexible structure description corresponding to the predetermined document type based on the detected image objects, wherein the flexible structure description includes a set of search elements for each data field in the image of the document, each search element having an associated search criterion; searching, via a search algorithm, additional document images to determine a respective document type of the additional document images, wherein each of the additional document images are of a document type corresponding to the predetermined document type; modifying the flexible structure description based on said searching of additional document images, wherein the search algorithm is configured to detect data fields based on the flexible structure description, said data fields corresponding to the predetermined document type; and repeating said searching and modifying of the flexible structure description until a defined level of precision is achieved or exceeded. | 9. A non-transitory computer-readable medium having stored thereon a sequence of instructions which when executed by a system, causes the system to perform a method, comprising: obtaining an image of a document; detecting image objects on the image; matching by a processor the image objects to a predetermined document type, wherein image objects distinguish the document type from other document types, and wherein image objects include anchor elements; generating by the processor a flexible structure description corresponding to the predetermined document type based on the detected image objects, wherein the flexible structure description includes a set of search elements for each data field in the image of the document, each search element having an associated search criterion; searching, via a search algorithm, additional document images to determine a respective document type of the additional document images, wherein each of the additional document images are of a document type corresponding to the predetermined document type; modifying the flexible structure description based on said searching of additional document images, wherein the search algorithm is configured to detect data fields based on the flexible structure description, said data fields corresponding to the predetermined document type; and repeating said searching and modifying of the flexible structure description until a defined level of precision is achieved or exceeded. 10. The non-transitory computer-readable medium of claim 9 , wherein said modifying of the flexible document description includes adding a type element to the set of search elements for a data field corresponding to a data type associated with the data field. | 0.799225 |
6,018,708 | 2 | 3 | 2. A speech recognition system as defined in claim 1, comprising second processing unit for processing said augmented list for selecting an orthography from said augmented list as a possible candidate to the spoken utterance. | 2. A speech recognition system as defined in claim 1, comprising second processing unit for processing said augmented list for selecting an orthography from said augmented list as a possible candidate to the spoken utterance. 3. A speech recognition system as defined in claim 2, wherein said second processing unit includes a re-scoring unit to rank orthographies in said augmented list on a basis of acoustic match with the spoken utterance. | 0.551653 |
8,739,058 | 1 | 13 | 1. A computer-readable storage memory having stored on the memory a data structure for use with a host user interface associated with a host application, said data structure defining a representation of a secondary user interface of a secondary application within a sidebar area of the host user interface, said data structure comprising: a plurality of part object parameters, each of said part object parameters representing a property of the secondary user interface; a part settings object parameter of each part object parameter, said part settings object parameter representing a configuration of the secondary user interface, said part settings object parameter selected from the group consisting of Read((String)PropertyName) and IWrite((String)PropertyName, (String)PropertyValue); a display object parameter representing at least one of an event and a property associated with the secondary user interface; a display screen object parameter representing at least one of a property and method of the secondary user interface; and an element object parameter representing at least one of an event, method or property of the secondary user interface. | 1. A computer-readable storage memory having stored on the memory a data structure for use with a host user interface associated with a host application, said data structure defining a representation of a secondary user interface of a secondary application within a sidebar area of the host user interface, said data structure comprising: a plurality of part object parameters, each of said part object parameters representing a property of the secondary user interface; a part settings object parameter of each part object parameter, said part settings object parameter representing a configuration of the secondary user interface, said part settings object parameter selected from the group consisting of Read((String)PropertyName) and IWrite((String)PropertyName, (String)PropertyValue); a display object parameter representing at least one of an event and a property associated with the secondary user interface; a display screen object parameter representing at least one of a property and method of the secondary user interface; and an element object parameter representing at least one of an event, method or property of the secondary user interface. 13. The computer-readable storage media of claim 1 further comprising an event object parameter representing a property of the secondary user interface and selected from the group consisting of ClickCount, MouseWheelDelta, Handled, is KeyDown, Key, keyState, modifierKeys, and MouseKeyStates. | 0.857838 |
8,594,995 | 14 | 15 | 14. A computer-readable storage device having stored thereon computer-executable instructions that, when executed by at least one processor, perform a method of multilingual asynchronous communications, the method comprising: receiving, by a recipient multilingual communications application of a recipient computing device, from a sender multilingual communications application of a sender computing device, a first speech message recorded in a first digital media file, wherein the first speech message is received via the sender multilingual communications application from a first user; converting, by the recipient multilingual communications application, the first speech message to a text representation of the first speech message; identifying, by the recipient multilingual communications application, that the text representation of the first speech message is in a source language that is not a predetermined target language; translating, by the recipient multilingual communications application in the recipient computing device of a second user, the text representation of the first speech message in the source language to a translated text representation of the first speech message in the target language, wherein the recipient multilingual communications application in the recipient computing device of the second user translates the text representation of the first speech message to the translated text representation of the first speech message in the target language based on the received first digital media file; converting, by the recipient multilingual communications application, the translated text representation of the first speech message to synthesized speech in the target language; recording, by the recipient multilingual communications application, the synthesized speech in the target language in a second digital media file; playing the second digital media file thereby rendering the synthesized speech to the second user, receiving, by the recipient multilingual communications application, from the second user, a second speech message in the target language comprising a response to the synthesized speech; and transmitting the second speech message to the sender multilingual communications application. | 14. A computer-readable storage device having stored thereon computer-executable instructions that, when executed by at least one processor, perform a method of multilingual asynchronous communications, the method comprising: receiving, by a recipient multilingual communications application of a recipient computing device, from a sender multilingual communications application of a sender computing device, a first speech message recorded in a first digital media file, wherein the first speech message is received via the sender multilingual communications application from a first user; converting, by the recipient multilingual communications application, the first speech message to a text representation of the first speech message; identifying, by the recipient multilingual communications application, that the text representation of the first speech message is in a source language that is not a predetermined target language; translating, by the recipient multilingual communications application in the recipient computing device of a second user, the text representation of the first speech message in the source language to a translated text representation of the first speech message in the target language, wherein the recipient multilingual communications application in the recipient computing device of the second user translates the text representation of the first speech message to the translated text representation of the first speech message in the target language based on the received first digital media file; converting, by the recipient multilingual communications application, the translated text representation of the first speech message to synthesized speech in the target language; recording, by the recipient multilingual communications application, the synthesized speech in the target language in a second digital media file; playing the second digital media file thereby rendering the synthesized speech to the second user, receiving, by the recipient multilingual communications application, from the second user, a second speech message in the target language comprising a response to the synthesized speech; and transmitting the second speech message to the sender multilingual communications application. 15. The computer-readable storage device of claim 14 , wherein the method further comprises: storing the second digital media file on a digital audio player; and playing the second digital media file on the digital audio player thereby rendering the synthesized speech in the target language. | 0.5 |
9,460,709 | 13 | 14 | 13. The system of claim 8 , the computer-readable storage medium having additional instructions stored which result in operations comprising normalizing the automatic speech recognition data using frequency warping. | 13. The system of claim 8 , the computer-readable storage medium having additional instructions stored which result in operations comprising normalizing the automatic speech recognition data using frequency warping. 14. The system of claim 13 , wherein the frequency warping is performed by selecting a singular linear warping function using normalized automatic speech recognition data. | 0.5 |
8,312,034 | 8 | 14 | 8. A method of generating a query for a search engine, comprising: deriving concept terms by extracting significant terms from search text and inferring relevant terms from the extracted significant terms using a concept matrix, the concept matrix derived from a plurality of sample documents using singular value decomposition of a term-document matrix, the concept matrix identifying a latent pattern of word usage in the plurality of sample documents around a concept, the concept matrix further providing a term-term similarity between the search text and terms in the plurality of sample documents; and generating a query for a search engine, the query comprising a search expression having at least one of the derived concept terms. | 8. A method of generating a query for a search engine, comprising: deriving concept terms by extracting significant terms from search text and inferring relevant terms from the extracted significant terms using a concept matrix, the concept matrix derived from a plurality of sample documents using singular value decomposition of a term-document matrix, the concept matrix identifying a latent pattern of word usage in the plurality of sample documents around a concept, the concept matrix further providing a term-term similarity between the search text and terms in the plurality of sample documents; and generating a query for a search engine, the query comprising a search expression having at least one of the derived concept terms. 14. The method of claim 8 , wherein the concept matrix comprises a compressed matrix derived by compressing a term by document matrix using singular value decomposition, the term by document matrix derived from the plurality of sample documents using latent semantic analysis and comprising weighted teen frequencies. | 0.5 |
7,904,452 | 1 | 5 | 1. An information providing server communicably connected to a first apparatus and a second apparatus, the first apparatus storing content received from an external apparatus and recorded according to an instruction from a user in association with record information related to the content and a keyword representing the content, the second apparatus searching for information related to a search phrase specified by the user through a web browser, the information providing server comprising: a record information storage module configured to store a keyword representing content recorded on the first apparatus in association with user information identifying a user who instructs to record the content; a search information storage module configured to store a search phrase used for search by the second apparatus in association with user information identifying a user who instructs the search; a search word handler configured to extract a predetermined number of words having high search frequency from search phrases stored in the search information storage module in association with user information identifying a specific user to generate a word list; a record information handler configured to extract keywords stored in association with the user information identifying the specific user from the record information storage module to generate a keyword list; a ranking processor configured to generate ranking information indicating a word in the word list which matches a keyword in the keyword list; and an information provider configured to provide the ranking information to any of the first apparatus and the second apparatus as requested, for subsequent display of record information of content, among contents stored in the first apparatus, associated with a keyword corresponding to a word contained in the ranking information. | 1. An information providing server communicably connected to a first apparatus and a second apparatus, the first apparatus storing content received from an external apparatus and recorded according to an instruction from a user in association with record information related to the content and a keyword representing the content, the second apparatus searching for information related to a search phrase specified by the user through a web browser, the information providing server comprising: a record information storage module configured to store a keyword representing content recorded on the first apparatus in association with user information identifying a user who instructs to record the content; a search information storage module configured to store a search phrase used for search by the second apparatus in association with user information identifying a user who instructs the search; a search word handler configured to extract a predetermined number of words having high search frequency from search phrases stored in the search information storage module in association with user information identifying a specific user to generate a word list; a record information handler configured to extract keywords stored in association with the user information identifying the specific user from the record information storage module to generate a keyword list; a ranking processor configured to generate ranking information indicating a word in the word list which matches a keyword in the keyword list; and an information provider configured to provide the ranking information to any of the first apparatus and the second apparatus as requested, for subsequent display of record information of content, among contents stored in the first apparatus, associated with a keyword corresponding to a word contained in the ranking information. 5. The information providing server of claim 1 , wherein the search word handler is configured to generate the word list in which a word indicating a proper noun is placed at higher order. | 0.848631 |
8,631,071 | 1 | 7 | 1. A method for concurrently supporting multiple versions of an industry model repository (IMR) at runtime, the method comprising the steps of: semantically searching for available computing resources for a plurality of businesses conducting commerce in different industries, the available computing resources being searched using an industry model repository (IMR) architecture system comprising: (a) a first layer of abstraction comprising business specific model assets, (b) a second layer of abstraction comprising a plurality of topic maps, each of the topic maps comprising a set of topics of capturing characteristics and relationships among the business specific model assets and other topics, associations having a specific association type and association role played by a designated topic, and occurrences defining business specific instances of the business specific model assets, each of the occurrences having an occurrence type that links to a specific one of the topics and an occurrence locator indicating an accessible network location in a federated asset repository where a corresponding specific instance is stored; and (c) a third layer of abstraction comprising service oriented architecture (“SOA”) program services that utilize the topic maps of the second layer to semantically search for the business specific model assets, wherein one or more of the services of the third layer of abstraction and linkages between the topic maps of the second layer of abstraction change over time as indicated by different versions of the industry model repository (IMR) of the second layer, wherein the searching step searches the computing resources based on linkages specific to a particular one of the different versions of the industry model repository (IMR), wherein a plurality of different versions are concurrently active at runtime and are used by different ones of the plurality of different businesses to access the business specific model assets of the first layer using the SOA program services of the third layer; a first computing device executing a first SOA service available for the searching and use at a first time when a first version of the different versions of the industry model repository (IMR) is available to the first computing device and a second SOA available for the searching and use at a second, later time using a second version of the industry model repository but not available for the searching and use at the first time; a second computing device executing the second SOA service at approximately the first time when the first version of the industry model repository is available to the second computing device, the second computing device executing the second SOA service at approximately the second time; and at least one computing device of the industry model repository (IMR) enabling the first computing device to execute the first SOA service at the first time, enabling the first computing device to execute the second SOA at the second time, enabling the second computing device to execute the second SOA service at approximately the first and at approximately the second time, thereby permitting concurrent use of different versions of the industry model repository (IMR) by different devices without modification or redeployment of the industry model repository (IMR). | 1. A method for concurrently supporting multiple versions of an industry model repository (IMR) at runtime, the method comprising the steps of: semantically searching for available computing resources for a plurality of businesses conducting commerce in different industries, the available computing resources being searched using an industry model repository (IMR) architecture system comprising: (a) a first layer of abstraction comprising business specific model assets, (b) a second layer of abstraction comprising a plurality of topic maps, each of the topic maps comprising a set of topics of capturing characteristics and relationships among the business specific model assets and other topics, associations having a specific association type and association role played by a designated topic, and occurrences defining business specific instances of the business specific model assets, each of the occurrences having an occurrence type that links to a specific one of the topics and an occurrence locator indicating an accessible network location in a federated asset repository where a corresponding specific instance is stored; and (c) a third layer of abstraction comprising service oriented architecture (“SOA”) program services that utilize the topic maps of the second layer to semantically search for the business specific model assets, wherein one or more of the services of the third layer of abstraction and linkages between the topic maps of the second layer of abstraction change over time as indicated by different versions of the industry model repository (IMR) of the second layer, wherein the searching step searches the computing resources based on linkages specific to a particular one of the different versions of the industry model repository (IMR), wherein a plurality of different versions are concurrently active at runtime and are used by different ones of the plurality of different businesses to access the business specific model assets of the first layer using the SOA program services of the third layer; a first computing device executing a first SOA service available for the searching and use at a first time when a first version of the different versions of the industry model repository (IMR) is available to the first computing device and a second SOA available for the searching and use at a second, later time using a second version of the industry model repository but not available for the searching and use at the first time; a second computing device executing the second SOA service at approximately the first time when the first version of the industry model repository is available to the second computing device, the second computing device executing the second SOA service at approximately the second time; and at least one computing device of the industry model repository (IMR) enabling the first computing device to execute the first SOA service at the first time, enabling the first computing device to execute the second SOA at the second time, enabling the second computing device to execute the second SOA service at approximately the first and at approximately the second time, thereby permitting concurrent use of different versions of the industry model repository (IMR) by different devices without modification or redeployment of the industry model repository (IMR). 7. The method of claim 1 , wherein the business specific assets of the first layer of abstraction comprise at least three different types of business specific assets from a group of: a framework asset; an industry model asset; a business model; a Unified Modeling Language (UML) design application; a data model; a business service; a service component; and a technical service. | 0.841176 |
9,179,250 | 1 | 8 | 1. A method of creating a customized recommendation agent for a user, the method comprising: obtaining a plurality of labelled context slices derived from context data associated with a user, each labelled context slice including a time and a user context label and having one of a stay type and a travel type; identifying a plurality of transitions between the labelled context slices, each transition from a source context slice to a destination context slice, a time of the source context slice being within a threshold time of a time of the destination context slice, and identified in response to determining that the source context slice and the destination context slice both have stay types and both have durations equaling or exceeding a threshold duration; determining a plurality of transition rules based on the identified plurality of transitions, each transition rule corresponding to a probability of transition from a source context label to a destination context label; and creating the customized recommendation agent incorporating the plurality of transition rules, the customized recommendation agent configured to provide a recommendation to the user responsive to a current context slice comprising a current time and a current context label. | 1. A method of creating a customized recommendation agent for a user, the method comprising: obtaining a plurality of labelled context slices derived from context data associated with a user, each labelled context slice including a time and a user context label and having one of a stay type and a travel type; identifying a plurality of transitions between the labelled context slices, each transition from a source context slice to a destination context slice, a time of the source context slice being within a threshold time of a time of the destination context slice, and identified in response to determining that the source context slice and the destination context slice both have stay types and both have durations equaling or exceeding a threshold duration; determining a plurality of transition rules based on the identified plurality of transitions, each transition rule corresponding to a probability of transition from a source context label to a destination context label; and creating the customized recommendation agent incorporating the plurality of transition rules, the customized recommendation agent configured to provide a recommendation to the user responsive to a current context slice comprising a current time and a current context label. 8. The method of claim 1 , wherein determining the plurality of transition rules comprises, for each transition rule of the plurality: identifying a first number of transitions from a particular source context label to a particular destination context label; identifying a second number of transitions from the particular source context label to any destination context label; and determining the probability from the first number of transitions given the second number of transitions. | 0.609501 |
8,285,652 | 1 | 6 | 1. A method for virtually integrating a bot with an online search service, the method comprising the steps of: surfacing bot content for indexing by a search engine associated with the search service, the surfaced bot content being includable in a link contained in a results page returned by the search engine to a user of the online search service responsively to a query; responsively to the link being activated by the user, launching the bot to engage in a conversation with the user, enabling the user to converse with the bot using a natural language interface; and receiving at least a portion of the query from the search engine to enable the bot to begin the conversation with the user using bot content that has relevance to the query by identifying the user with a unique identifier that is shared with the bot so that the bot may utilize the unique identifier to pull a last known query input by the user. | 1. A method for virtually integrating a bot with an online search service, the method comprising the steps of: surfacing bot content for indexing by a search engine associated with the search service, the surfaced bot content being includable in a link contained in a results page returned by the search engine to a user of the online search service responsively to a query; responsively to the link being activated by the user, launching the bot to engage in a conversation with the user, enabling the user to converse with the bot using a natural language interface; and receiving at least a portion of the query from the search engine to enable the bot to begin the conversation with the user using bot content that has relevance to the query by identifying the user with a unique identifier that is shared with the bot so that the bot may utilize the unique identifier to pull a last known query input by the user. 6. The method of claim 1 in which the surfacing comprises locating a database of indexed bot content in a hosted platform that is integrated with a back end of the search engine. | 0.502793 |
6,012,633 | 5 | 14 | 5. An automatic transaction apparatus according to claim 4, wherein said guidance means has a guide for guiding the financial document. | 5. An automatic transaction apparatus according to claim 4, wherein said guidance means has a guide for guiding the financial document. 14. An automatic transaction apparatus according to claim 5, wherein said guide is disposed in each of upper and lower portions of said depository inlet. | 0.5 |
9,355,190 | 18 | 19 | 18. The non-transitory computer readable storage medium of claim 15 , wherein the first category is represented by at least one image file. | 18. The non-transitory computer readable storage medium of claim 15 , wherein the first category is represented by at least one image file. 19. The non-transitory computer readable storage medium of claim 18 , wherein the image file is an assigned image file. | 0.5 |
8,979,538 | 1 | 8 | 1. A method for utilizing game elements within a productivity application, the method executing on a processor of a computer, comprising: tracking features utilized by a user within the productivity application, the productivity application being a word-processing application, a presentation application, a spreadsheet application, a database application, or a programming application; for each of the tracked features, determining a component score for the user based on a difficulty level of the tracked feature; accumulating component scores to derive a user score; determining challenges to provide to the user based at least in part on the tracked features utilized by the user; wherein the challenges include additional features of the application for the user to learn, the additional features being used by a group of additional users of the productivity application but not used by the user; displaying a user interface including categories of features within the productivity application, a user's progress towards accomplishing the challenges associated with features in each of the categories, and the challenges associated with additional features of the productivity application; receiving a selection of one of the challenges from the user; providing the selected challenge to the user; providing a hint button during the selected challenge that directly links help content that provides one or more steps to complete the selected challenge, wherein the help content is provided to the user upon selection of a hint button during the selected challenge; repeating the tracking, determining, and accumulating operations with respect to the additional features related to the selected challenge; and comparing the accumulated user score to a score of at least one user from the group of additional users and providing a display of the comparison to the user. | 1. A method for utilizing game elements within a productivity application, the method executing on a processor of a computer, comprising: tracking features utilized by a user within the productivity application, the productivity application being a word-processing application, a presentation application, a spreadsheet application, a database application, or a programming application; for each of the tracked features, determining a component score for the user based on a difficulty level of the tracked feature; accumulating component scores to derive a user score; determining challenges to provide to the user based at least in part on the tracked features utilized by the user; wherein the challenges include additional features of the application for the user to learn, the additional features being used by a group of additional users of the productivity application but not used by the user; displaying a user interface including categories of features within the productivity application, a user's progress towards accomplishing the challenges associated with features in each of the categories, and the challenges associated with additional features of the productivity application; receiving a selection of one of the challenges from the user; providing the selected challenge to the user; providing a hint button during the selected challenge that directly links help content that provides one or more steps to complete the selected challenge, wherein the help content is provided to the user upon selection of a hint button during the selected challenge; repeating the tracking, determining, and accumulating operations with respect to the additional features related to the selected challenge; and comparing the accumulated user score to a score of at least one user from the group of additional users and providing a display of the comparison to the user. 8. The method of claim 1 , wherein comparing the accumulated user score to the score of the at least one other user comprises displaying the score of the at least one other user in relation to the accumulated user score. | 0.508929 |
8,364,725 | 18 | 19 | 18. A computer program product for navigating model objects, the computer program product comprising: a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising: computer readable program code, when executed by a processor of a computer, configured to perform: displaying the model objects in models in a models stack; in response to a model object in a model of the models stack being selected as an initial context, displaying one or more navigation paths associated with the selected model object, wherein each of the navigation paths has nodes represented as graphical components that are built in real time and that represent the selected model object and other model objects from the models in the models stack; and in response to a user selecting a node in one of the one or more navigation paths, navigating to a new model object represented by the selected node in the models stack in one of a forward direction and a backward direction to provide bidirectional navigation between the model objects in the models without loosing the initial context; and displaying one or more navigation paths associated with the new model object and at least one appended node that represents a previously traversed model object. | 18. A computer program product for navigating model objects, the computer program product comprising: a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising: computer readable program code, when executed by a processor of a computer, configured to perform: displaying the model objects in models in a models stack; in response to a model object in a model of the models stack being selected as an initial context, displaying one or more navigation paths associated with the selected model object, wherein each of the navigation paths has nodes represented as graphical components that are built in real time and that represent the selected model object and other model objects from the models in the models stack; and in response to a user selecting a node in one of the one or more navigation paths, navigating to a new model object represented by the selected node in the models stack in one of a forward direction and a backward direction to provide bidirectional navigation between the model objects in the models without loosing the initial context; and displaying one or more navigation paths associated with the new model object and at least one appended node that represents a previously traversed model object. 19. The computer program product of claim 18 , wherein the models comprise industry models and wherein the computer readable program code, when executed by the processor of the computer, is configured to perform: caching the one or more navigation paths between the model objects in a models stack. | 0.626566 |
7,739,221 | 9 | 16 | 9. A computer-implemented method of performing a multi-dimensional search, comprising: employing a processor to execute computer executable instructions stored on a computer readable storage medium to perform the following acts: receiving an input, the input comprises visual data and at least one of text or audio data, wherein the visual data is from an image file; extracting a plurality of features from the input comprising: utilizing a pattern recognition component to extract a plurality of features from the image file, wherein the features comprise physical attributes that are visually identifiable in the image file; establishing a plurality of search terms based at least in part upon a subset of the extracted features; retrieving a plurality of results based at least in part upon a subset of the search terms; collecting and indexing search-related information across a plurality of dimensions, and building an index based upon features associated with the input; automatically and dynamically extracting features from searchable items and making the features available for search based upon a particular query or set of queries; learning, based on context information, historical data, or feedback, what results are desired in view of a determined and inferred query, as well as how the results should be rendered, and generating a statistical model with the results; and filtering a subset of the plurality of results in accordance with at least one of a user context, a user preference, a relevancy factor with respect to the input, or a device context. | 9. A computer-implemented method of performing a multi-dimensional search, comprising: employing a processor to execute computer executable instructions stored on a computer readable storage medium to perform the following acts: receiving an input, the input comprises visual data and at least one of text or audio data, wherein the visual data is from an image file; extracting a plurality of features from the input comprising: utilizing a pattern recognition component to extract a plurality of features from the image file, wherein the features comprise physical attributes that are visually identifiable in the image file; establishing a plurality of search terms based at least in part upon a subset of the extracted features; retrieving a plurality of results based at least in part upon a subset of the search terms; collecting and indexing search-related information across a plurality of dimensions, and building an index based upon features associated with the input; automatically and dynamically extracting features from searchable items and making the features available for search based upon a particular query or set of queries; learning, based on context information, historical data, or feedback, what results are desired in view of a determined and inferred query, as well as how the results should be rendered, and generating a statistical model with the results; and filtering a subset of the plurality of results in accordance with at least one of a user context, a user preference, a relevancy factor with respect to the input, or a device context. 16. The method of claim 9 , further comprising generating results that include a combination of text, visual or audible data. | 0.788851 |
9,477,709 | 1 | 4 | 1. A computer-implemented method comprising: receiving (i) a natural language query submitted by a user that requests information relating to a context associated with a prior consumption of a media item, and (ii) ambient environmental data obtained from an environment of a user; identifying a particular media item based on detecting a match between one or more features of the ambient environmental data obtained from the environment of the user and one or more features of the particular media item; determining that the particular media item is identified in a media consumption database that identifies media items that are identified as having been previously consumed by the user; accessing, at the media consumption database, information that (i) identifies a context associated with the previous consumption of the particular media item by the user, and that is (ii) identified as being responsive to the natural language query submitted by the user; and providing a response to the natural language query submitted by the user that includes at least a portion of the information accessed at the media consumption database that (i) identifies the context associated with the previous consumption of the particular media item by the user, and that is (ii) identified as being responsive to the natural language query submitted by the user. | 1. A computer-implemented method comprising: receiving (i) a natural language query submitted by a user that requests information relating to a context associated with a prior consumption of a media item, and (ii) ambient environmental data obtained from an environment of a user; identifying a particular media item based on detecting a match between one or more features of the ambient environmental data obtained from the environment of the user and one or more features of the particular media item; determining that the particular media item is identified in a media consumption database that identifies media items that are identified as having been previously consumed by the user; accessing, at the media consumption database, information that (i) identifies a context associated with the previous consumption of the particular media item by the user, and that is (ii) identified as being responsive to the natural language query submitted by the user; and providing a response to the natural language query submitted by the user that includes at least a portion of the information accessed at the media consumption database that (i) identifies the context associated with the previous consumption of the particular media item by the user, and that is (ii) identified as being responsive to the natural language query submitted by the user. 4. The method of claim 1 , wherein identifying the particular media item based on detecting the match between one or more features of the ambient environmental data obtained from the environment of the user and one or more features of the particular media item comprises: obtaining audio fingerprints of the ambient environmental data obtained from the environment of the user; comparing the audio fingerprints of the ambient environmental data obtained from the environment of the user to audio fingerprints of the particular media item; and determining that the audio fingerprints of the ambient environmental data obtained from the environment of the user match audio fingerprints of the particular media item. | 0.655556 |
7,779,009 | 1 | 4 | 1. A method, performed at least partially on a computer, for enabling satisfaction of a search responsive to a query based on classification of the query, the method comprising: receiving, from user input, a query phrase; parsing the received query phrase into at least a first constituent part and a second constituent part; determining definitional information of the first constituent part; determining a first category associated with the received query phrase by performing a first classification process that uses the determined definitional information of the first constituent part and the second constituent part, the first classification process including: accessing, from classification information stored in a computer storage medium that includes patterns, a pattern that is associated with at least one category, the pattern including a first part and a second part; comparing the determined definitional information of the first constituent part with the first part included in the accessed pattern and comparing the second constituent part with the second part included in the accessed pattern; based on the comparison results, determining whether the second constituent part and the determined definitional information of the first constituent part correspond to at least a subportion of the accessed pattern; based on a determination that the second constituent part and the determined definitional information of the first constituent part correspond to at least a subportion of the accessed pattern, identifying the category that is associated with the pattern as the first category associated with the received query phrase; determining a second category associated with the received query phrase by performing a second classification process that uses the determined definitional information of the first constituent part and the second constituent part, the second classification process being different than the first classification process; determining whether the first category determined by the first classification process matches the second category determined by the second classification process; in response to a determination that the first category matches the second category, associating the received query phrase with a category that corresponds to the first category and the second category; in response to a determination that the first category does not match the second category: selecting, from among the first category and the second category, a single category; and associating the received query phrase with the single category selected; identifying at least one search resource for satisfying the received query phrase based on the associated category; and routing the received query phrase to the at least one identified search resource. | 1. A method, performed at least partially on a computer, for enabling satisfaction of a search responsive to a query based on classification of the query, the method comprising: receiving, from user input, a query phrase; parsing the received query phrase into at least a first constituent part and a second constituent part; determining definitional information of the first constituent part; determining a first category associated with the received query phrase by performing a first classification process that uses the determined definitional information of the first constituent part and the second constituent part, the first classification process including: accessing, from classification information stored in a computer storage medium that includes patterns, a pattern that is associated with at least one category, the pattern including a first part and a second part; comparing the determined definitional information of the first constituent part with the first part included in the accessed pattern and comparing the second constituent part with the second part included in the accessed pattern; based on the comparison results, determining whether the second constituent part and the determined definitional information of the first constituent part correspond to at least a subportion of the accessed pattern; based on a determination that the second constituent part and the determined definitional information of the first constituent part correspond to at least a subportion of the accessed pattern, identifying the category that is associated with the pattern as the first category associated with the received query phrase; determining a second category associated with the received query phrase by performing a second classification process that uses the determined definitional information of the first constituent part and the second constituent part, the second classification process being different than the first classification process; determining whether the first category determined by the first classification process matches the second category determined by the second classification process; in response to a determination that the first category matches the second category, associating the received query phrase with a category that corresponds to the first category and the second category; in response to a determination that the first category does not match the second category: selecting, from among the first category and the second category, a single category; and associating the received query phrase with the single category selected; identifying at least one search resource for satisfying the received query phrase based on the associated category; and routing the received query phrase to the at least one identified search resource. 4. The method of claim 1 further comprising: comparing the category associated with the received query phrase with attributes associated with search resources; and wherein the routing includes modifying the received query phrase and routing a modified version of the received query phrase to a subset of the search resources based on a result of the comparison of the category with the attributes. | 0.845765 |
8,601,023 | 1 | 12 | 1. A computer implemented method performed by a processor, comprising: observing usage patterns by one or more users in an on-line community in connection with an on-line asset; identifying usefulness of the on-line asset by observing user implicit behaviors in connection with the usage patterns of the on-line asset and by extracting behavioral patterns from the user implicit behaviors; refining the identified on-line asset usefulness by context, wherein a context of the on-line asset is automatically detected based on one or more observed terms obtained by observing user implicit behaviors with respect to the identified on-line asset, wherein the identified on-line asset has a plurality of term vectors; assigning a term vector entry of a term vector of the plurality of term vectors that describes a degree to which the identified on-line asset has an affinity with the observed terms, wherein each term vector of the plurality of term vectors is associated with a different user of the one or more users; identifying for each user of the one or more users an expertise vector by identifying on-line assets with respect to which the each user has engaged in one or more of the user implicit behaviors; and generating the expertise vector by summing the plurality of term vectors for the identified on-line assets; receive query including query terms; obtaining search result documents; determining that at least one search result document is an identified on-line asset for an expert user of the one or more users; and ranking the at least one search result based on a relationship of the query terms to the expertise vector of the expert user. | 1. A computer implemented method performed by a processor, comprising: observing usage patterns by one or more users in an on-line community in connection with an on-line asset; identifying usefulness of the on-line asset by observing user implicit behaviors in connection with the usage patterns of the on-line asset and by extracting behavioral patterns from the user implicit behaviors; refining the identified on-line asset usefulness by context, wherein a context of the on-line asset is automatically detected based on one or more observed terms obtained by observing user implicit behaviors with respect to the identified on-line asset, wherein the identified on-line asset has a plurality of term vectors; assigning a term vector entry of a term vector of the plurality of term vectors that describes a degree to which the identified on-line asset has an affinity with the observed terms, wherein each term vector of the plurality of term vectors is associated with a different user of the one or more users; identifying for each user of the one or more users an expertise vector by identifying on-line assets with respect to which the each user has engaged in one or more of the user implicit behaviors; and generating the expertise vector by summing the plurality of term vectors for the identified on-line assets; receive query including query terms; obtaining search result documents; determining that at least one search result document is an identified on-line asset for an expert user of the one or more users; and ranking the at least one search result based on a relationship of the query terms to the expertise vector of the expert user. 12. The computer implemented method of claim 1 , further comprising: providing a visualization for understanding groups of related experts and relationships between groups using the expert vectors of the one or more users. | 0.81058 |
9,767,184 | 1 | 12 | 1. A method of searching an electronic database, the method comprising: receiving from a user interface a first search query that includes first and second search terms; identifying a set of multiple documents in the electronic database that each include both the first and second search terms; identifying within the set of multiple documents a first proximity window about instances of the first search term, and a second proximity window about instances of the second search term; identifying additional terms from within the first and second proximity windows that are semantically unrelated to each of the first and second search terms; generating a ranked concordance of at least some of the additional terms along with a relative frequency of each additional term within the first and second proximity windows; presenting the ranked concordance of the additional terms found within the first and second proximity windows to the user interface; receiving a third search term that the user selected from the ranked concordance; and present to the user interface a subset of the set of multiple documents having the third search term within at least one of the first proximity window about instances of the first search term and the second proximity window about instances of the second search term. | 1. A method of searching an electronic database, the method comprising: receiving from a user interface a first search query that includes first and second search terms; identifying a set of multiple documents in the electronic database that each include both the first and second search terms; identifying within the set of multiple documents a first proximity window about instances of the first search term, and a second proximity window about instances of the second search term; identifying additional terms from within the first and second proximity windows that are semantically unrelated to each of the first and second search terms; generating a ranked concordance of at least some of the additional terms along with a relative frequency of each additional term within the first and second proximity windows; presenting the ranked concordance of the additional terms found within the first and second proximity windows to the user interface; receiving a third search term that the user selected from the ranked concordance; and present to the user interface a subset of the set of multiple documents having the third search term within at least one of the first proximity window about instances of the first search term and the second proximity window about instances of the second search term. 12. The method of claim 1 further comprising receiving from the user a designation of a size of the first proximity window, designating a number of words on either side of the first search term. | 0.512563 |
4,584,597 | 6 | 10 | 6. A method for encoding a digital luminance signal and two digital color difference signals having high bit-rates and composed of first words representing color picture elements into an encoded digital luminance signal and an encoded digital color difference signal having low bit-rates and composed of second words, wherein a predetermined peak-to-peak luminance amplitude is split into luminance regions that are each associated with a table of second words of said encoded color difference signal representative of predetermined variation sub-regions in the two color differences for luminances lying within one of said luminance regions, and wherein two of said first words of said two color difference signals representing a picture element are encoded into a second word that is selected from the respective table associated with the luminance region including the luminance of said picture element and that is representative of the sub-region in said luminance region including the two color differences of said picture element. | 6. A method for encoding a digital luminance signal and two digital color difference signals having high bit-rates and composed of first words representing color picture elements into an encoded digital luminance signal and an encoded digital color difference signal having low bit-rates and composed of second words, wherein a predetermined peak-to-peak luminance amplitude is split into luminance regions that are each associated with a table of second words of said encoded color difference signal representative of predetermined variation sub-regions in the two color differences for luminances lying within one of said luminance regions, and wherein two of said first words of said two color difference signals representing a picture element are encoded into a second word that is selected from the respective table associated with the luminance region including the luminance of said picture element and that is representative of the sub-region in said luminance region including the two color differences of said picture element. 10. The method claimed in claim 6 wherein prior to encoding, two analog color difference signals are corrected into two corrected color difference signals having peak-to-peak amplitude equal to said peak-to-peak amplitude of an analog luminance signal, and said analog luminance signal and said two corrected color difference signals are converted into said digital luminance signal and said two digital color difference signals in accordance with a same sampling quantification scale. | 0.5 |
9,646,338 | 13 | 14 | 13. A non-transitory computer-readable storage device storing instructions that, in response to execution by a computer system, cause the computer system to perform operations comprising: receiving, via a network connection, a plurality of submissions from a plurality of entities, wherein the plurality of submissions each comprise a textual description of an attribute of a product; matching a set of the plurality of submissions such that the set of matching submissions include textual descriptions of a same product; performing a similarity comparison for each submission of the set of matching submissions, wherein performing the similarity comparison comprises: comparing a particular submission of the set of matching submissions to a plurality of other submissions of the set of matching submissions; and determining a plurality of data quality scores for the particular submission, wherein each data quality score of the plurality of data quality scores is based on a measure of similarity between the textual description of the attribute of the product included in the particular submission and a textual description of the attribute of the product included in a respective one of the other submissions of the set of matching submissions; calculating, based on the plurality of data quality scores a calculated data quality score for the quality of the textual description of the attribute of the product included in the submission from the particular entity; rating the particular entity based, at least in part, on the calculated data quality score of the textual description submitted from the particular entity, wherein said rating comprises: combining the calculated data quality score with one or more previously calculated data quality scores of textual descriptions in submissions previously received from the same particular entity to determine a cumulative data quality score of textual descriptions in submissions received from the same particular entity, wherein the particular entity is distinct from other entities of the plurality of entities; and assigning a rating to the particular entity, wherein the rating indicates the particular entity's propensity to provide quality product descriptions; selecting a textual description of the attribute of the product, to be displayed via a network-based interface implemented via a server in a distributed computing environment that includes the computer system that performs the operations, from the plurality of textual descriptions of the attribute of the product included in the plurality of submissions based, at least in part, on the rating of the particular entity that submitted the particular submission. | 13. A non-transitory computer-readable storage device storing instructions that, in response to execution by a computer system, cause the computer system to perform operations comprising: receiving, via a network connection, a plurality of submissions from a plurality of entities, wherein the plurality of submissions each comprise a textual description of an attribute of a product; matching a set of the plurality of submissions such that the set of matching submissions include textual descriptions of a same product; performing a similarity comparison for each submission of the set of matching submissions, wherein performing the similarity comparison comprises: comparing a particular submission of the set of matching submissions to a plurality of other submissions of the set of matching submissions; and determining a plurality of data quality scores for the particular submission, wherein each data quality score of the plurality of data quality scores is based on a measure of similarity between the textual description of the attribute of the product included in the particular submission and a textual description of the attribute of the product included in a respective one of the other submissions of the set of matching submissions; calculating, based on the plurality of data quality scores a calculated data quality score for the quality of the textual description of the attribute of the product included in the submission from the particular entity; rating the particular entity based, at least in part, on the calculated data quality score of the textual description submitted from the particular entity, wherein said rating comprises: combining the calculated data quality score with one or more previously calculated data quality scores of textual descriptions in submissions previously received from the same particular entity to determine a cumulative data quality score of textual descriptions in submissions received from the same particular entity, wherein the particular entity is distinct from other entities of the plurality of entities; and assigning a rating to the particular entity, wherein the rating indicates the particular entity's propensity to provide quality product descriptions; selecting a textual description of the attribute of the product, to be displayed via a network-based interface implemented via a server in a distributed computing environment that includes the computer system that performs the operations, from the plurality of textual descriptions of the attribute of the product included in the plurality of submissions based, at least in part, on the rating of the particular entity that submitted the particular submission. 14. The non-transitory computer-readable storage device of claim 13 , wherein the one or more previously determined data quality scores of textual descriptions are selected based on a number of most recent submissions received from the particular entity. | 0.5 |
7,644,066 | 20 | 33 | 20. A machine-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, causes: storing said collection of XML documents in one or more base database structures managed by a database system for storing said collection of XML documents; wherein each XML document of said collection of XML documents is stored, within the one or more base database structures, in an unshredded form; based on pattern data that indicates elements defined for a particular XML document type, creating a table for the particular XML document type separate from said one or more base database structures in which said collection of XML documents are stored; wherein said table includes a plurality of columns; wherein each column of said plurality of columns corresponds to a different element indicated in the pattern data; wherein each column contains only values of the element of the XML document type to which the column corresponds; wherein each row of said table corresponds to a corresponding XML document in said collection; wherein each row of said table stores values for elements from the corresponding XML document; wherein said pattern data indicates, for each column of said plurality of columns, the different element, of the particular XML document type, that corresponds to the column; using said table to answer a first query requesting data from said collection of XML documents; responding to said first query without accessing said one or more base database structures; and responding to a second query requesting data from said collection of XML documents by providing one or more unshredded XML documents from said one or more base database structures. | 20. A machine-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, causes: storing said collection of XML documents in one or more base database structures managed by a database system for storing said collection of XML documents; wherein each XML document of said collection of XML documents is stored, within the one or more base database structures, in an unshredded form; based on pattern data that indicates elements defined for a particular XML document type, creating a table for the particular XML document type separate from said one or more base database structures in which said collection of XML documents are stored; wherein said table includes a plurality of columns; wherein each column of said plurality of columns corresponds to a different element indicated in the pattern data; wherein each column contains only values of the element of the XML document type to which the column corresponds; wherein each row of said table corresponds to a corresponding XML document in said collection; wherein each row of said table stores values for elements from the corresponding XML document; wherein said pattern data indicates, for each column of said plurality of columns, the different element, of the particular XML document type, that corresponds to the column; using said table to answer a first query requesting data from said collection of XML documents; responding to said first query without accessing said one or more base database structures; and responding to a second query requesting data from said collection of XML documents by providing one or more unshredded XML documents from said one or more base database structures. 33. The machine-readable storage medium of claim 20 , further comprising instructions which, when executed by one or more processors, causes: creating a set of secondary indexes for the table. | 0.820225 |
8,989,216 | 11 | 13 | 11. A network node comprising a Diameter protocol command dictionary comprising: a definition for a Diameter protocol command, wherein the Diameter protocol command comprises a command default definition and a command first context specific definition; and a definition for a Diameter protocol attribute value pair (AVP), wherein the Diameter protocol AVP comprises an AVP default definition and an AVP first context specific definition and the Diameter protocol dictionary supports multiple versions of a standard, where said Diameter protocol command dictionary interoperates with a Diameter protocol to perform functions for processing Diameter messages. | 11. A network node comprising a Diameter protocol command dictionary comprising: a definition for a Diameter protocol command, wherein the Diameter protocol command comprises a command default definition and a command first context specific definition; and a definition for a Diameter protocol attribute value pair (AVP), wherein the Diameter protocol AVP comprises an AVP default definition and an AVP first context specific definition and the Diameter protocol dictionary supports multiple versions of a standard, where said Diameter protocol command dictionary interoperates with a Diameter protocol to perform functions for processing Diameter messages. 13. The network node of claim 11 , wherein the Diameter protocol AVP further comprises AVP second context specific definition. | 0.692683 |
9,921,814 | 15 | 17 | 15. A program product for making a computer system execute an analysis of a control flow graph, the program product comprising a non-transitory computer readable media including a memory of the computer system, and having, recorded therein, a program for analyzing the control flow graph, the program comprising the program codes of: extracting a program state of the program from a program state buffer, the program state including a program counter; generating an edge of a control flow graph from a branch instruction associated with the program counter to a target address of the branch instruction in an abstract interpretation for an assignment instruction to a branch target variable of a program; the program allocating a particular branch target variable to each branch instruction having a plurality of branch targets configured to store branch target addresses of each of the plurality of branch targets, with a branch target address for one of the plurality of branch targets being loaded from the branch target variable upon branching; a branch address of a branch instruction having one branch target as well as the target address assigned by the assignment instruction to the branch target variable being determined as certain constant values determined by compiling the program; adding the target address assigned by the assignment instruction to a program counter of a new program state of the abstract interpretation, and setting a flag for the particular program counter associated with the branch instruction; and terminating a current abstract interpretation in the case that the abstract interpretation reaches to program state already subjected to the abstract interpretation, as indicated by a flag set for a program state. | 15. A program product for making a computer system execute an analysis of a control flow graph, the program product comprising a non-transitory computer readable media including a memory of the computer system, and having, recorded therein, a program for analyzing the control flow graph, the program comprising the program codes of: extracting a program state of the program from a program state buffer, the program state including a program counter; generating an edge of a control flow graph from a branch instruction associated with the program counter to a target address of the branch instruction in an abstract interpretation for an assignment instruction to a branch target variable of a program; the program allocating a particular branch target variable to each branch instruction having a plurality of branch targets configured to store branch target addresses of each of the plurality of branch targets, with a branch target address for one of the plurality of branch targets being loaded from the branch target variable upon branching; a branch address of a branch instruction having one branch target as well as the target address assigned by the assignment instruction to the branch target variable being determined as certain constant values determined by compiling the program; adding the target address assigned by the assignment instruction to a program counter of a new program state of the abstract interpretation, and setting a flag for the particular program counter associated with the branch instruction; and terminating a current abstract interpretation in the case that the abstract interpretation reaches to program state already subjected to the abstract interpretation, as indicated by a flag set for a program state. 17. The program product of claim 15 , further comprising the program codes of: copying an address of the program counter and a virtual address for performing the abstract interpretation to a memory; and deleting the copied program counter and the copied virtual address from the memory in a first-in-last-out scheme by completion of the abstract interpretation of the corresponding instruction. | 0.689764 |
6,032,116 | 18 | 19 | 18. A method of generating a robust distance measure in a speech recognition system comprising the steps of: generating P order line spectral pair frequencies for a speech input signal; determining a difference, for i=1 to N.sub.1, between the ith line spectral pair frequency and an ith line spectral frequency of a reference speech signal; shifting the difference, for i=1 to N.sub.1, by an ith frequency shifting factor to at least partially compensate for frequency shifting of the ith speech input signal line spectral pair frequency by acoustic noise; and utilizing the shifted difference to classify the speech input signal. | 18. A method of generating a robust distance measure in a speech recognition system comprising the steps of: generating P order line spectral pair frequencies for a speech input signal; determining a difference, for i=1 to N.sub.1, between the ith line spectral pair frequency and an ith line spectral frequency of a reference speech signal; shifting the difference, for i=1 to N.sub.1, by an ith frequency shifting factor to at least partially compensate for frequency shifting of the ith speech input signal line spectral pair frequency by acoustic noise; and utilizing the shifted difference to classify the speech input signal. 19. The method of claim 18 further comprising the step of: determining a difference, for i=N.sub.1 +1 to P, between ith speech input signal line spectral pair frequency and the ith reference speech signal line spectral pair frequency; and weighting of the difference, for i=N.sub.1 to P, by an ith frequency weighting factor. | 0.551105 |
9,558,415 | 1 | 16 | 1. A biometric authentication system comprising: a data storage system configured to maintain, for a group of multiple, different people, biometric data that includes sorted similarity scores, wherein each similarity score represents a similarity between a biometric image of at least a portion of a corresponding person from the group of multiple, different people and a reference image; and a biometric verification system that includes at least one processor, the biometric verification system being configured to: access data for a particular biometric image of at least a portion of a particular person; compute a particular similarity score that represents similarity between the particular biometric image and the reference image used in computing the similarity scores for each person in the group of multiple, different people; search, using the computed particular similarity score, the sorted similarity scores included in the biometric data for the group of multiple, different people to find a closest match to the computed particular similarity score in the sorted similarity scores; compute a difference between the computed particular similarity score and a similarity score of the closest match; determine whether the difference satisfies a threshold difference value; and output the closest match based on a determination that the difference satisfies the threshold difference value. | 1. A biometric authentication system comprising: a data storage system configured to maintain, for a group of multiple, different people, biometric data that includes sorted similarity scores, wherein each similarity score represents a similarity between a biometric image of at least a portion of a corresponding person from the group of multiple, different people and a reference image; and a biometric verification system that includes at least one processor, the biometric verification system being configured to: access data for a particular biometric image of at least a portion of a particular person; compute a particular similarity score that represents similarity between the particular biometric image and the reference image used in computing the similarity scores for each person in the group of multiple, different people; search, using the computed particular similarity score, the sorted similarity scores included in the biometric data for the group of multiple, different people to find a closest match to the computed particular similarity score in the sorted similarity scores; compute a difference between the computed particular similarity score and a similarity score of the closest match; determine whether the difference satisfies a threshold difference value; and output the closest match based on a determination that the difference satisfies the threshold difference value. 16. The biometric authentication system of claim 1 : wherein the biometric verification system is configured to access the data for the particular biometric image of at least the portion of the particular person by accessing data for multiple biometric images of the particular person, each of the multiple biometric images corresponding to a different portion of the particular person; wherein the biometric verification system is configured to compute the particular similarity score that represents similarity between the particular biometric image and the reference image by: computing multiple similarity scores, each of the multiple similarity scores representing similarity between one of the multiple biometric images and one of multiple reference images; and arranging the multiple similarity scores in a vector of similarity scores; and wherein the biometric verification system is configured to search the sorted similarity scores included in the biometric data using the computed particular similarity score by searching the sorted similarity scores included in the biometric data using the vector of similarity scores. | 0.5 |
7,991,615 | 11 | 13 | 11. A computer system comprising: a grapheme to phoneme model stored in storage of the computer; a recognizer coupled to the grapheme to phoneme model to recognize input speech as a corresponding grapheme sequence, the recognizer executed by a processor of the computer; and a retraining mechanism coupled to the recognizer that retrains the grapheme to phoneme model into a retrained grapheme to phoneme model based upon acoustic data and associated graphemes collected by a recognition system, the retraining mechanism executed by the processor of the computer. | 11. A computer system comprising: a grapheme to phoneme model stored in storage of the computer; a recognizer coupled to the grapheme to phoneme model to recognize input speech as a corresponding grapheme sequence, the recognizer executed by a processor of the computer; and a retraining mechanism coupled to the recognizer that retrains the grapheme to phoneme model into a retrained grapheme to phoneme model based upon acoustic data and associated graphemes collected by a recognition system, the retraining mechanism executed by the processor of the computer. 13. The computer of claim 11 wherein the retraining mechanism combines a pronunciation lexicon and acoustic information by interpolating grapheme to phoneme model parameters trained using a pronunciation lexicon with those trained using acoustic data, or by obtaining a phoneme sequence corresponding to an acoustic waveform sample and a grapheme sequence corresponding to the same acoustic waveform sample to obtain a grapheme-phoneme pair, and combining a substantial number of such grapheme-phoneme pairs with data in the pronunciation lexicon. | 0.5 |
8,892,550 | 11 | 13 | 11. The system as claimed in claim 10 , wherein the existing data content includes one or more documents, said processor further configured to: generate from said one or more documents, a topic name or a topic descriptor corresponding to units extracted from said one or more documents, said generated search queries including one or more: said topic name or words and phrases extracted from said topic descriptor. | 11. The system as claimed in claim 10 , wherein the existing data content includes one or more documents, said processor further configured to: generate from said one or more documents, a topic name or a topic descriptor corresponding to units extracted from said one or more documents, said generated search queries including one or more: said topic name or words and phrases extracted from said topic descriptor. 13. The system as claimed in claim 11 , wherein said processor is further configured to: receive a projected information need in the form of words, text strings, phrases or ranked lists of words or phrases not in the existing data content; wherein said generated search queries optionally include words and phrases extracted from said projected information need. | 0.5 |
8,924,421 | 18 | 23 | 18. A computer readable storage medium having stored therein instructions, which when executed by a computer system having a display, cause the computer system to: receive a first user request including a first set of search keywords; identify a first set of chunks within multiple documents, wherein each chunk includes terms matching the first set of search keywords; display at least a portion of the first set of chunks on the display, including highlighting the terms matching the first set of search keywords in the displayed portion in a first manner; receive a second user request to search among the documents for documents that satisfy a second set of search keywords; identify a second set of chunks within the documents, wherein each chunk includes terms matching the second set of search keywords; and display at least a portion of the second set of chunks on the display, including highlighting the terms matching the second set of search keywords in the displayed portion in a second manner that is different from the first manner. | 18. A computer readable storage medium having stored therein instructions, which when executed by a computer system having a display, cause the computer system to: receive a first user request including a first set of search keywords; identify a first set of chunks within multiple documents, wherein each chunk includes terms matching the first set of search keywords; display at least a portion of the first set of chunks on the display, including highlighting the terms matching the first set of search keywords in the displayed portion in a first manner; receive a second user request to search among the documents for documents that satisfy a second set of search keywords; identify a second set of chunks within the documents, wherein each chunk includes terms matching the second set of search keywords; and display at least a portion of the second set of chunks on the display, including highlighting the terms matching the second set of search keywords in the displayed portion in a second manner that is different from the first manner. 23. The computer readable storage medium of claim 18 , wherein the second set of chunks includes at least one chunk that is not included in the first set of chunks. | 0.797531 |
9,672,821 | 15 | 21 | 15. A method of training a speech recognition system (SRS) comprising: receiving at a first input, a plurality of raw microphone signals having a user voice signal based on a sample of user speech during a period of time and an echo signal based on a sample of sound produced by a speaker during the period of time; receiving at a second input, a plurality of types of echo information signals during the period of time, each type of echo information signal including information derived by an echo cancellation system from the echo signal; and training a speech recognition processor to recognize speech based on the raw microphone signals and the plurality of types of echo information signals. | 15. A method of training a speech recognition system (SRS) comprising: receiving at a first input, a plurality of raw microphone signals having a user voice signal based on a sample of user speech during a period of time and an echo signal based on a sample of sound produced by a speaker during the period of time; receiving at a second input, a plurality of types of echo information signals during the period of time, each type of echo information signal including information derived by an echo cancellation system from the echo signal; and training a speech recognition processor to recognize speech based on the raw microphone signals and the plurality of types of echo information signals. 21. The method of claim 15 , further comprising: a noise suppressor producing a plurality of different types of ambient noise suppression signals from the raw microphone signals, wherein the plurality of types of echo information signals include the plurality of different types of ambient noise suppression signals. | 0.771014 |
9,737,759 | 3 | 4 | 3. The method of claim 2 , wherein upon an exercise match not being found in said searching, then searching a user population history for previous matches, and selecting an exercise that has been most often tracked as said exercise to be tracked. | 3. The method of claim 2 , wherein upon an exercise match not being found in said searching, then searching a user population history for previous matches, and selecting an exercise that has been most often tracked as said exercise to be tracked. 4. The method of claim 3 , wherein upon an exercise match not being found in said user population history, processing said parsed text using a search string modifying algorithm to create a modified text string; performing a fuzzy search on an exercise database for exercise results relevant to said modified text string; scoring said exercise results to create an exercise text match score; and selecting as said exercise to be tracked an exercise having the top score. | 0.5 |
8,095,538 | 1 | 6 | 1. A method of encoding on a computer system for information retrieval an inverted list structure of annotation material, the method comprising: collecting a group of documents and storing the group of documents in a digital format; determining a group of external annotations referencing the group of documents; forming a snippet index by grouping the group of external annotations by unique annotation identifier; forming a snippet dictionary which, for each unique annotation identifier, indexes a corresponding position in the snippet index for the group of external annotations having that unique annotation identifier; computing a similarity score between a user query and document annotations utilizing a similarity function; and utilizing the similarity function to rank the relevant documents, wherein annotation relevance weightings are stored with the annotations in said snippet index, wherein the same annotation may be applied with high frequency to certain documents and wherein multiple annotations for a single document are grouped into a single annotation identifier with aggregated weight. | 1. A method of encoding on a computer system for information retrieval an inverted list structure of annotation material, the method comprising: collecting a group of documents and storing the group of documents in a digital format; determining a group of external annotations referencing the group of documents; forming a snippet index by grouping the group of external annotations by unique annotation identifier; forming a snippet dictionary which, for each unique annotation identifier, indexes a corresponding position in the snippet index for the group of external annotations having that unique annotation identifier; computing a similarity score between a user query and document annotations utilizing a similarity function; and utilizing the similarity function to rank the relevant documents, wherein annotation relevance weightings are stored with the annotations in said snippet index, wherein the same annotation may be applied with high frequency to certain documents and wherein multiple annotations for a single document are grouped into a single annotation identifier with aggregated weight. 6. A method as claimed in claim 1 further comprising: inputting a user search query; utilizing the snippet index to determine documents relevant to said query. | 0.863402 |
8,805,848 | 1 | 6 | 1. A computer program product for information retrieval from multiple documents, the computer program product comprising a tangible storage medium readable by a computer system and storing instructions for execution by the computer system for performing a method comprising: splitting each document into multiple snippets of words; indexing each snippet as a separate document; receiving an input search query including at least one sentence; processing the search query against the indexes of each of the multiple snippets by searching query terms over each of the multiple snippets to implicitly introduce term proximity information in the information retrieval; decomposing the search query into sub-queries; processing each sub-query against the indexes of each of the multiple snippets, sentence by sentence, using all words in each sentence of the sub-query to create an OR-Query of all non-stopwords in the sentence; returning a fit value for each OR-Query; and aggregating the fit values to provide a score for every document returned by the OR-Queries. | 1. A computer program product for information retrieval from multiple documents, the computer program product comprising a tangible storage medium readable by a computer system and storing instructions for execution by the computer system for performing a method comprising: splitting each document into multiple snippets of words; indexing each snippet as a separate document; receiving an input search query including at least one sentence; processing the search query against the indexes of each of the multiple snippets by searching query terms over each of the multiple snippets to implicitly introduce term proximity information in the information retrieval; decomposing the search query into sub-queries; processing each sub-query against the indexes of each of the multiple snippets, sentence by sentence, using all words in each sentence of the sub-query to create an OR-Query of all non-stopwords in the sentence; returning a fit value for each OR-Query; and aggregating the fit values to provide a score for every document returned by the OR-Queries. 6. The computer program product of claim 1 , wherein the method comprises: determining the frequency of each non-stopword in the documents; and removing from the search those words having a frequency that exceeds a threshold. | 0.613402 |
6,161,091 | 10 | 12 | 10. The speech recognition synthesis based encoding/decoding method according to claim 9, wherein said recognizing step includes dividing said input speech signal into analysis frames, acquiring a feature vector for each of the analysis frames, and computing a similarity between said feature vector for each of the analysis frames and a feature template vector previously prepared for each phonetic segment to determine a phonetic segment of said each synthesis frame which is used to recognize the character information. | 10. The speech recognition synthesis based encoding/decoding method according to claim 9, wherein said recognizing step includes dividing said input speech signal into analysis frames, acquiring a feature vector for each of the analysis frames, and computing a similarity between said feature vector for each of the analysis frames and a feature template vector previously prepared for each phonetic segment to determine a phonetic segment of said each synthesis frame which is used to recognize the character information. 12. The speech recognition synthesis based encoding/decoding method according to claim 10, further comprising the steps of determining if said input speech signal is a voiced speech or a unvoiced speech to detect a pitch period of said input speech signal when determined as a voiced speech, and detecting a duration of a phonetic segment recognized by said recognizing and detecting step. | 0.681669 |
8,402,018 | 3 | 4 | 3. The system of claim 1 , wherein a weighing method for property determines a property weight value according to discriminative power of a property and a predictability of a subject given the object, and vice versa, of a property. | 3. The system of claim 1 , wherein a weighing method for property determines a property weight value according to discriminative power of a property and a predictability of a subject given the object, and vice versa, of a property. 4. The system of claim 3 , wherein the property weight value is determined by the equation shown below:
w ( p ( d,r ))=α· l ( p ( d,r ))+β· Ml ( p ( d,r )) where w(p(d,r)) is a property weight value, I(p(d,r)) is a subject discriminative power of a property, MI(p(d,r)) is the predictability of the subject given the object, and vice versa, d is a domain, r is a range, and α and β are tuning parameters (0<α, β≦1). | 0.5 |
8,171,453 | 8 | 9 | 8. The method as recited in claim 4 , wherein the third region of the computer program source code is associated with particular type conversion restraints, such that the method further comprises: generating an error when type conversion constraints are violated within the third region of the computer program source code. | 8. The method as recited in claim 4 , wherein the third region of the computer program source code is associated with particular type conversion restraints, such that the method further comprises: generating an error when type conversion constraints are violated within the third region of the computer program source code. 9. The method as recited in claim 8 , wherein the particular type conversion restraints specifies whether implicit downcasts are permitted or not permitted. | 0.5 |
7,739,354 | 21 | 31 | 21. A computer implemented method for adding meta-data to a hypertext page using a proxy, said proxy comprising an intermediary between a client machine and one or more networks, the method comprising: intercepting an HTTP request from the client machine at the proxy, the HTTP request comprising a request for a resource, wherein the proxy is configured to accept requests from a plurality of client machines; extracting, at the proxy, a URL from the HTTP request and storing the URL in computer readable memory, the URL identifying the requested resource; forwarding the HTTP request to a network having the requested resource; receiving the requested resource at the proxy in the form of an HTTP response; extracting, at the proxy content out of the HTTP response; matching the received requested resource with the previously stored URL from an HTTP request forwarded in the forwarding step; analyzing, a plurality of templates using at least the previously stored URL; locating a template from the plurality of templates that corresponds to the received resource identified by the matching previously stored URL; embedding meta-data into the content using the template; and sending the content in the HTTP response to the client machine. | 21. A computer implemented method for adding meta-data to a hypertext page using a proxy, said proxy comprising an intermediary between a client machine and one or more networks, the method comprising: intercepting an HTTP request from the client machine at the proxy, the HTTP request comprising a request for a resource, wherein the proxy is configured to accept requests from a plurality of client machines; extracting, at the proxy, a URL from the HTTP request and storing the URL in computer readable memory, the URL identifying the requested resource; forwarding the HTTP request to a network having the requested resource; receiving the requested resource at the proxy in the form of an HTTP response; extracting, at the proxy content out of the HTTP response; matching the received requested resource with the previously stored URL from an HTTP request forwarded in the forwarding step; analyzing, a plurality of templates using at least the previously stored URL; locating a template from the plurality of templates that corresponds to the received resource identified by the matching previously stored URL; embedding meta-data into the content using the template; and sending the content in the HTTP response to the client machine. 31. The computer implemented method of claim 21 wherein said template assists a dictionary search, wherein said dictionary search comprises recognizing elements in said resource by a word or a phrase. | 0.713467 |
4,005,388 | 19 | 20 | 19. An interactive terminal as defined in claim 18, wherein said shift means incorporate shift level control keys, the number of shift level control keys at least equal to the number of information levels minus 1. | 19. An interactive terminal as defined in claim 18, wherein said shift means incorporate shift level control keys, the number of shift level control keys at least equal to the number of information levels minus 1. 20. An interactive terminal as defined in claim 19, further comprising a three-position scroll switch communicating with said memory for presenting on said display data held in said memory, having a normal position for displaying data as received, a scroll retrieval position for sequentially displaying data held in said memory, and a scroll position for holding data on the display. | 0.5 |
9,770,660 | 11 | 13 | 11. A system, comprising: a display; one or more communication ports configured to receive one or more attributes that correspond to participation of a physical sports player in a physical sports event, wherein the one or more attributes include at least one attribute related to an actual present location of the physical sports player during the physical sports event; one or more processors coupled to the display and to the one or more communication ports, and configured to start a simulation of the physical sports event in a virtual context, in which physical entities in the physical sports event are simulated from a situation similar to that of the physical sports event at a time of the start of the simulation, wherein the physical entities include the physical sports player, wherein the physical sports player is simulated by use of the one or more attributes as a stimulus for future behavior of the simulated physical sports player in the simulation of the physical sports event, and wherein the simulation of the physical sports player is based on a prediction of the future behavior of the physical sports player, wherein the prediction of the future behavior is made in response to the one or more attributes being presented, as the stimulus, on the display; one or more user input devices coupled to the one or more processors and configured to enable participatory user control of the simulated physical sports player in the simulation of the physical sports event; a first component coupled to the one or more processors and configured to enable control of an action by at least one of the simulated physical entities, other than the simulated physical sports player, in the simulation of the physical sports event; and a second component coupled to the one or more processors and the one or more user input devices, wherein the second component is configured to control the simulation of the physical sports event with the simulated physical sports player under the participatory user control in accordance with the predicted future behavior, and with the controlled action by the at least one of the simulated physical entities other than the simulated physical sports player. | 11. A system, comprising: a display; one or more communication ports configured to receive one or more attributes that correspond to participation of a physical sports player in a physical sports event, wherein the one or more attributes include at least one attribute related to an actual present location of the physical sports player during the physical sports event; one or more processors coupled to the display and to the one or more communication ports, and configured to start a simulation of the physical sports event in a virtual context, in which physical entities in the physical sports event are simulated from a situation similar to that of the physical sports event at a time of the start of the simulation, wherein the physical entities include the physical sports player, wherein the physical sports player is simulated by use of the one or more attributes as a stimulus for future behavior of the simulated physical sports player in the simulation of the physical sports event, and wherein the simulation of the physical sports player is based on a prediction of the future behavior of the physical sports player, wherein the prediction of the future behavior is made in response to the one or more attributes being presented, as the stimulus, on the display; one or more user input devices coupled to the one or more processors and configured to enable participatory user control of the simulated physical sports player in the simulation of the physical sports event; a first component coupled to the one or more processors and configured to enable control of an action by at least one of the simulated physical entities, other than the simulated physical sports player, in the simulation of the physical sports event; and a second component coupled to the one or more processors and the one or more user input devices, wherein the second component is configured to control the simulation of the physical sports event with the simulated physical sports player under the participatory user control in accordance with the predicted future behavior, and with the controlled action by the at least one of the simulated physical entities other than the simulated physical sports player. 13. The system of claim 11 , wherein the virtual context is simulated by a game console computer device. | 0.776824 |
9,116,918 | 8 | 10 | 8. A system for interpreting queries, the system comprising: a storage device for storing entity information, wherein the entity information is derived from metadata associated with a search domain; and a processor that is configured to: receive a search query in the search domain; determine a plurality of search terms based on the received search query; determine, for each of the plurality of search terms, whether a search term of the plurality of search terms corresponds to an entity name; in response to determining that a plurality of entity names corresponds to at least a portion of the plurality of search terms, determine a plurality of entity types associated with each of the plurality of corresponding entity names, wherein each entity type is associated with a category in which content associated with the entity name is included; determine an entity score associated with each of the plurality of entity names and corresponding entity types that indicates relatedness of an entity name to a corresponding entity type, based at least in part on a number of times media content associated with a particular entity name and entity type combination have been accessed; determine a remaining portion of the plurality of entity names by removing at least one of the plurality of entity types corresponding to a particular entity name based at least in part on the entity score associated with the particular entity name and the removed entity type; and perform a search in the search domain with the remaining portion of the plurality of entity names, wherein each entity name in the remaining portion of the plurality of entity names is search corresponding to the associated entity type. | 8. A system for interpreting queries, the system comprising: a storage device for storing entity information, wherein the entity information is derived from metadata associated with a search domain; and a processor that is configured to: receive a search query in the search domain; determine a plurality of search terms based on the received search query; determine, for each of the plurality of search terms, whether a search term of the plurality of search terms corresponds to an entity name; in response to determining that a plurality of entity names corresponds to at least a portion of the plurality of search terms, determine a plurality of entity types associated with each of the plurality of corresponding entity names, wherein each entity type is associated with a category in which content associated with the entity name is included; determine an entity score associated with each of the plurality of entity names and corresponding entity types that indicates relatedness of an entity name to a corresponding entity type, based at least in part on a number of times media content associated with a particular entity name and entity type combination have been accessed; determine a remaining portion of the plurality of entity names by removing at least one of the plurality of entity types corresponding to a particular entity name based at least in part on the entity score associated with the particular entity name and the removed entity type; and perform a search in the search domain with the remaining portion of the plurality of entity names, wherein each entity name in the remaining portion of the plurality of entity names is search corresponding to the associated entity type. 10. The system of claim 8 , wherein the entity score associated with the entity name is based at least in part on user feedback information. | 0.852941 |
8,787,695 | 3 | 4 | 3. The method of claim 2 further including: analyzing the edge trace image to identify spurious edge traces that are unlikely to correspond to the warped textual lines; removing any identified spurious edge traces from the edge trace image; and designating the remaining edge traces to be text line traces. | 3. The method of claim 2 further including: analyzing the edge trace image to identify spurious edge traces that are unlikely to correspond to the warped textual lines; removing any identified spurious edge traces from the edge trace image; and designating the remaining edge traces to be text line traces. 4. The method of claim 3 wherein the spurious edge traces are identified by determining a vertical separation distance between each edge pixel in the edge trace image and the nearest neighboring edge pixel in the vertical direction, and designating any edge pixels where the determined vertical separation distance falls outside of a predefined range to belong to a spurious edge trace. | 0.5 |
9,588,999 | 16 | 17 | 16. The computer system of claim 15 , wherein a database management system associated with the database registers usage of the one or more data elements in the one or more static SQL statements. | 16. The computer system of claim 15 , wherein a database management system associated with the database registers usage of the one or more data elements in the one or more static SQL statements. 17. The computer system of claim 16 , wherein the determining whether the first data elements of the one or more data elements on the list of data elements for deletion have been active in the one or more static SQL statements includes cross referencing the one or more data elements on the list of data elements for deletion with the one or more registered data elements. | 0.5 |
8,136,034 | 20 | 21 | 20. The method of claim 1 wherein said system compiles the identified one or more elements of text within said one or more scenes to provide a plurality of bookmarks that are different from one another as measured representations of the text. | 20. The method of claim 1 wherein said system compiles the identified one or more elements of text within said one or more scenes to provide a plurality of bookmarks that are different from one another as measured representations of the text. 21. The method of claim 20 further comprising: providing the system with a user-defined hierarchal ranking of said plurality of bookmarks; and comparing said plurality of bookmarks, according to said user-defined hierarchal ranking, with said data relative to one or more reference texts to identify similarities or differences between the text and said one or more reference texts. | 0.5 |
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