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8,296,130 | 4 | 5 | 4. The method of claim 1 , wherein the offensiveness threshold value is set by a service administrator; wherein the string of words is an input from a user to a service; and wherein the input from the user to the service is rejected if a candidate word in the string of words is identified as an offender word by having an offensiveness score exceeding the offensiveness threshold value set by the service administrator. | 4. The method of claim 1 , wherein the offensiveness threshold value is set by a service administrator; wherein the string of words is an input from a user to a service; and wherein the input from the user to the service is rejected if a candidate word in the string of words is identified as an offender word by having an offensiveness score exceeding the offensiveness threshold value set by the service administrator. 5. The method of claim 4 , wherein the service is a content review portal, and wherein the offensiveness threshold is set based on one of: grouping of the content in which content being reviewed resides; a particular content with which the offensiveness threshold is associated; and a third-party content rating for content. | 0.90625 |
9,444,939 | 13 | 18 | 13. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by a processor of a device, cause the processor to: receive, during a communication between a first person and a second person, a first plurality of voice signals from the first person and a second plurality of voice signals from the second person; temporarily store, based on intercepting the communication and in a first storage, at least a portion of the first plurality of voice signals and at least a portion of the second plurality of voice signals; detect, based on the first plurality of voice signals and the second plurality of voice signals, portions of utterances included in the first plurality of voice signals or the second plurality of voice signals; combine the portions of the utterances to generate multiple utterances; determine, based on the multiple utterances, that particular utterances, of the multiple utterances, include significances, the significances of the particular utterances corresponding to indications of whether the particular utterances are important in a given context, and providing information about a subsequent decision concerning how to respond, and the one or more instructions, that cause the processor to determine that the particular utterances include significances, cause the processor to: analyze information that associates a plurality of utterances with a corresponding significance; and determine, based on analyzing the information, that each of the particular utterances matches an utterance of the plurality of utterances; determine, based on the significances of the particular utterances, if a responsive action is appropriate, the responsive action being based on a nature of the communication, based on types of the particular utterances, and corresponding to a particular action to carry out, the responsive action being appropriate when a threshold number of the particular utterances is satisfied; remove, automatically and without user input, when the responsive action is not appropriate and prior to termination of the communication, the at least the portion of the first plurality of voice signals and the at least the portion of the second plurality of voice signals from the first storage; move, automatically and without the user input, when the responsive action is appropriate and prior to the termination of the communication, the at least the portion of the first plurality of voice signals and the at least the portion of the second plurality of voice signals from the first storage to a second storage, the second storage being different than the first storage; and process, when the responsive action is appropriate, the communication, the one or more instructions, that cause the processor to process the communication, cause the processor to deploy the responsive action, prior to the termination of the communication, by: displaying an indication of the responsive action to the second person, where the indication directs the second person to request that a third party monitor the communication substantially in real time, and redirecting the communication to the third party. | 13. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by a processor of a device, cause the processor to: receive, during a communication between a first person and a second person, a first plurality of voice signals from the first person and a second plurality of voice signals from the second person; temporarily store, based on intercepting the communication and in a first storage, at least a portion of the first plurality of voice signals and at least a portion of the second plurality of voice signals; detect, based on the first plurality of voice signals and the second plurality of voice signals, portions of utterances included in the first plurality of voice signals or the second plurality of voice signals; combine the portions of the utterances to generate multiple utterances; determine, based on the multiple utterances, that particular utterances, of the multiple utterances, include significances, the significances of the particular utterances corresponding to indications of whether the particular utterances are important in a given context, and providing information about a subsequent decision concerning how to respond, and the one or more instructions, that cause the processor to determine that the particular utterances include significances, cause the processor to: analyze information that associates a plurality of utterances with a corresponding significance; and determine, based on analyzing the information, that each of the particular utterances matches an utterance of the plurality of utterances; determine, based on the significances of the particular utterances, if a responsive action is appropriate, the responsive action being based on a nature of the communication, based on types of the particular utterances, and corresponding to a particular action to carry out, the responsive action being appropriate when a threshold number of the particular utterances is satisfied; remove, automatically and without user input, when the responsive action is not appropriate and prior to termination of the communication, the at least the portion of the first plurality of voice signals and the at least the portion of the second plurality of voice signals from the first storage; move, automatically and without the user input, when the responsive action is appropriate and prior to the termination of the communication, the at least the portion of the first plurality of voice signals and the at least the portion of the second plurality of voice signals from the first storage to a second storage, the second storage being different than the first storage; and process, when the responsive action is appropriate, the communication, the one or more instructions, that cause the processor to process the communication, cause the processor to deploy the responsive action, prior to the termination of the communication, by: displaying an indication of the responsive action to the second person, where the indication directs the second person to request that a third party monitor the communication substantially in real time, and redirecting the communication to the third party. 18. The non-transitory computer-readable medium of claim 13 , further comprising: one or more instructions which, when executed by the processor, cause the processor to: use statistical analysis techniques to analyze one or more portions of the first plurality of voice signals, and one or more portions of the second plurality of voice signals. | 0.620879 |
7,596,619 | 1 | 8 | 1. A content delivery network (CDN) for use by participating content providers, comprising: a domain name service managed by a CDN service provider (CDNSP) and authoritative only for given content domains associated with the participating content providers; and a set of content servers operated by the CDNSP; wherein, following an end-user request for a web page that is directed to a participating content provider domain, the domain name service uses a CDNSP-specific domain to identify an IP address associated with a CDN content server of the set of content servers operated by the CDNSP; wherein the CDN content server includes code (i) that determines whether a default markup language file associated with the web page exists on the CDN content server, (b) that is responsive to a determination that the default markup language file exists on the CDN content server for serving the default markup language file in response to the end-user request for the web page, (c) that is responsive to a determination that the default markup language file does not exist on the CDN content server for directing a request for the default markup language file to a second server, for receiving from the second server the default markup language file, for serving the default markup language file in response to the end-user request for the web page, and for caching the default markup language file for a given time and (d) that logs data associated with the default markup language file served from the CDN content server. | 1. A content delivery network (CDN) for use by participating content providers, comprising: a domain name service managed by a CDN service provider (CDNSP) and authoritative only for given content domains associated with the participating content providers; and a set of content servers operated by the CDNSP; wherein, following an end-user request for a web page that is directed to a participating content provider domain, the domain name service uses a CDNSP-specific domain to identify an IP address associated with a CDN content server of the set of content servers operated by the CDNSP; wherein the CDN content server includes code (i) that determines whether a default markup language file associated with the web page exists on the CDN content server, (b) that is responsive to a determination that the default markup language file exists on the CDN content server for serving the default markup language file in response to the end-user request for the web page, (c) that is responsive to a determination that the default markup language file does not exist on the CDN content server for directing a request for the default markup language file to a second server, for receiving from the second server the default markup language file, for serving the default markup language file in response to the end-user request for the web page, and for caching the default markup language file for a given time and (d) that logs data associated with the default markup language file served from the CDN content server. 8. The content delivery network as described in claim 1 wherein the data associated with the default markup language file served from the CDN content server comprises one of: cookie data, referrer data, user agent data, and content type data. | 0.653295 |
8,601,079 | 14 | 15 | 14. A system for classifying media content, comprising: at least one network device that manages a communications over a network; and one or more other network devices each having a processor configured to perform actions, the actions including: receiving, from a client device, a request to compose a message; providing, to a client device, a user interface that is configured to enable a user of the client device to compose the message; receiving, through the user interface, a request to attach a file to the message; displaying, to the user, a personalized hierarchical structure of tags (“PHST”) that is automatically generated from one or more automatic tags that are associated with one or more message file attachments and automatically modified including at least one custom generated tag automatically generated using the one or more custom tags received from the user for the at least one of the one or more message file attachments, where the at least one of the one or more message file attachments is associated with the one or more custom tags, wherein the PHST separately indicates the one or more automatic tags and the at least one custom generated tag; receiving, from the user, a tag selection of at least one tag from the PHST; and attaching one or more files that are associated with the tag selection to the message. | 14. A system for classifying media content, comprising: at least one network device that manages a communications over a network; and one or more other network devices each having a processor configured to perform actions, the actions including: receiving, from a client device, a request to compose a message; providing, to a client device, a user interface that is configured to enable a user of the client device to compose the message; receiving, through the user interface, a request to attach a file to the message; displaying, to the user, a personalized hierarchical structure of tags (“PHST”) that is automatically generated from one or more automatic tags that are associated with one or more message file attachments and automatically modified including at least one custom generated tag automatically generated using the one or more custom tags received from the user for the at least one of the one or more message file attachments, where the at least one of the one or more message file attachments is associated with the one or more custom tags, wherein the PHST separately indicates the one or more automatic tags and the at least one custom generated tag; receiving, from the user, a tag selection of at least one tag from the PHST; and attaching one or more files that are associated with the tag selection to the message. 15. The system of claim 14 , wherein the one or more other network devices enables further actions, the actions comprising: receiving a request to delete an association between a file and one or more tags within the PHST; and deleting the association between the file and the one or more tags. | 0.760229 |
10,108,812 | 5 | 7 | 5. The computer system of claim 4 , wherein the processing system is further configured to: in response to reception of the rejection command, generate and submit a cancel blockchain transaction to the blockchain, wherein the cancel blockchain transaction is to a blockchain address that is not associated with any of the plurality of intended recipients. | 5. The computer system of claim 4 , wherein the processing system is further configured to: in response to reception of the rejection command, generate and submit a cancel blockchain transaction to the blockchain, wherein the cancel blockchain transaction is to a blockchain address that is not associated with any of the plurality of intended recipients. 7. The computer system of claim 5 , wherein the blockchain address is not associated with any submitter, editor, or approver user of the computer system for securely releasing time-sensitive information. | 0.965048 |
8,972,958 | 29 | 30 | 29. The method of claim 1 , wherein the information regarding the capabilities of the reconfigurable processor that are being utilized by the custom instructions includes at least one of: variable type; external module identification; port identification; support for modules with multi-threaded execution with custom instructions; support for at least one of C style typedefs, structures, or unions; support for at least one of thread private, thread shared, global variables, or pipeline staging variables; support for custom instruction memory read and write operations; or support for modules to interact including at least one of remote module thread creation, message passing, or global variable access. | 29. The method of claim 1 , wherein the information regarding the capabilities of the reconfigurable processor that are being utilized by the custom instructions includes at least one of: variable type; external module identification; port identification; support for modules with multi-threaded execution with custom instructions; support for at least one of C style typedefs, structures, or unions; support for at least one of thread private, thread shared, global variables, or pipeline staging variables; support for custom instruction memory read and write operations; or support for modules to interact including at least one of remote module thread creation, message passing, or global variable access. 30. The method of claim 29 , wherein the custom instructions include individual case entries within switch statements. | 0.967689 |
8,918,359 | 1 | 4 | 1. A method, comprising: communicating a plurality of parameters from a client device, wherein the plurality of parameters are based, at least in part, on metadata information obtained from data mining one or more databases, at least one database including tags associated with objects, wherein the data mining includes applying one or more filters to the tags of the at least one database to obtain at least a portion of the metadata information, wherein the one or more filters are applied to one or more elements, respectively, of the tags; creating a rule associated with at least some of the parameters; and incorporating the rule into a security policy to be used by the client device and one or more additional client devices, wherein the security policy controls network communications involving the client device and the one or more additional client devices. | 1. A method, comprising: communicating a plurality of parameters from a client device, wherein the plurality of parameters are based, at least in part, on metadata information obtained from data mining one or more databases, at least one database including tags associated with objects, wherein the data mining includes applying one or more filters to the tags of the at least one database to obtain at least a portion of the metadata information, wherein the one or more filters are applied to one or more elements, respectively, of the tags; creating a rule associated with at least some of the parameters; and incorporating the rule into a security policy to be used by the client device and one or more additional client devices, wherein the security policy controls network communications involving the client device and the one or more additional client devices. 4. The method of claim 1 , wherein the rule includes an action to be performed on one or more objects identified by the plurality of parameters. | 0.82134 |
9,760,624 | 7 | 17 | 7. The computer-implemented method of claim 1 , wherein the language score for each language is calculated using an equation where each feature vector and each language are assigned a weight. | 7. The computer-implemented method of claim 1 , wherein the language score for each language is calculated using an equation where each feature vector and each language are assigned a weight. 17. The computer-implemented method of claim 7 , wherein each feature vector and each language is assigned a weight based on a newness of the active pages or time spent on the active pages. | 0.952821 |
8,626,507 | 6 | 7 | 6. A method for recognizing an utterance comprising: receiving an utterance comprising a plurality of modes; relating a first portion of the utterance comprising a first mode of the plurality of modes to a second portion of the utterance comprising a second mode of the plurality of modes, based on a first finite-state transducer; generating a second finite-state transducer, comprising a gesture and speech recognition model finite-state transducer, based on a first mode recognition lattice and the first finite-state transducer; and outputting a recognition result based on the utterance and the second finite-state transducer. | 6. A method for recognizing an utterance comprising: receiving an utterance comprising a plurality of modes; relating a first portion of the utterance comprising a first mode of the plurality of modes to a second portion of the utterance comprising a second mode of the plurality of modes, based on a first finite-state transducer; generating a second finite-state transducer, comprising a gesture and speech recognition model finite-state transducer, based on a first mode recognition lattice and the first finite-state transducer; and outputting a recognition result based on the utterance and the second finite-state transducer. 7. The method of claim 6 further comprising: generating the first mode recognition lattice and the first finite-state transducer, based on the first mode. | 0.618812 |
9,014,436 | 10 | 18 | 10. A system for identifying an individual, comprising: an operating interface via which a user requests identification of an individual via the system; a biometric identification information imager that images at least one first input biometric identifier for the individual in support of the identification request; a biometric information converter that converts the at least one first input biometric identifier for the individual to an individual identification text string; a data storage device that stores (1) a plurality of previously-stored text strings representing biometric identification information for a population of identifiable individuals, and (2) an established threshold criteria for comparing the individual identification text string to the plurality of previously-stored text strings; a text-based search engine that compares the individual identification text string to the plurality of previously-stored text strings representing the biometric identification information for members of the population of identifiable individuals; and an output device that executes an action based on results of the comparing when the comparing identifies whether the individual is a member of the population of identifiable individuals, the output device displaying results of the comparing that meet the established threshold criteria presented as a rank-ordered list derived from the population of identifiable individuals based on text strings from the plurality of stored text strings being determined to be substantially equivalent to the individual identification text string according to the established threshold criteria. | 10. A system for identifying an individual, comprising: an operating interface via which a user requests identification of an individual via the system; a biometric identification information imager that images at least one first input biometric identifier for the individual in support of the identification request; a biometric information converter that converts the at least one first input biometric identifier for the individual to an individual identification text string; a data storage device that stores (1) a plurality of previously-stored text strings representing biometric identification information for a population of identifiable individuals, and (2) an established threshold criteria for comparing the individual identification text string to the plurality of previously-stored text strings; a text-based search engine that compares the individual identification text string to the plurality of previously-stored text strings representing the biometric identification information for members of the population of identifiable individuals; and an output device that executes an action based on results of the comparing when the comparing identifies whether the individual is a member of the population of identifiable individuals, the output device displaying results of the comparing that meet the established threshold criteria presented as a rank-ordered list derived from the population of identifiable individuals based on text strings from the plurality of stored text strings being determined to be substantially equivalent to the individual identification text string according to the established threshold criteria. 18. The system of claim 10 , the individual identification text string and the plurality of stored text strings comprising combinations of at least one of numbers, letters and symbols that are arranged by the biometric information converter in a format other than forming human recognizable words. | 0.82931 |
9,966,063 | 8 | 11 | 8. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: selecting, based on a microphone type and a current location of a speaker, a user profile from a plurality of user profiles, wherein the user profile is associated with the speaker; and performing, via a processor, speech recognition on speech received from the speaker using the user profile. | 8. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: selecting, based on a microphone type and a current location of a speaker, a user profile from a plurality of user profiles, wherein the user profile is associated with the speaker; and performing, via a processor, speech recognition on speech received from the speaker using the user profile. 11. The system of claim 8 , wherein the selecting of the user profile is further based on a recipient profile associated with a recipient of the speech. | 0.768997 |
9,405,934 | 1 | 10 | 1. A method for hiding sensitive data in a plain text environment, comprising: recognizing a starting key entered by a user in the plain text environment, wherein the starting key indicates to a working system that text input subsequent to the starting key that is entered by the user is to be hidden according to a specified hiding method; receiving subsequent plain text, the working system carrying out the hiding method on the plain text so that input plain text is not displayed on a user display in the plain text environment at a time that the plain text is being entered by the user; recognizing an ending key, ending the hiding method and displaying on the user display subsequently input plain text being entered by the user in the plain text environment; and recognizing in the plain text a plain text character indication of a user group wherein a member of the user group is allowed to view the plain text subsequent to the starting key conditionally on a recognizing of the indication. | 1. A method for hiding sensitive data in a plain text environment, comprising: recognizing a starting key entered by a user in the plain text environment, wherein the starting key indicates to a working system that text input subsequent to the starting key that is entered by the user is to be hidden according to a specified hiding method; receiving subsequent plain text, the working system carrying out the hiding method on the plain text so that input plain text is not displayed on a user display in the plain text environment at a time that the plain text is being entered by the user; recognizing an ending key, ending the hiding method and displaying on the user display subsequently input plain text being entered by the user in the plain text environment; and recognizing in the plain text a plain text character indication of a user group wherein a member of the user group is allowed to view the plain text subsequent to the starting key conditionally on a recognizing of the indication. 10. The method as claimed in claim 1 , wherein the hiding method includes one or more of the following selected from the group consisting of encryption, encoding, and scrambling. | 0.899435 |
9,584,696 | 7 | 8 | 7. Image processing circuitry for modifying bits of an input pixel data word, comprising: an embedded data engine configured to output at least one bit of embedded data; decatenation circuitry that is coupled to the embedded data engine and that is configured to separate the input pixel data word into first and second subsets of bits; arithmetic circuitry that modifies bits of the first subset of bits based on whether the at least one bit of embedded data is the same as at least one corresponding bit of the second subset of bits; and concatenation circuitry that is coupled to the decatenation circuitry and that is configured to produce an output data word including the at least one bit of embedded data. | 7. Image processing circuitry for modifying bits of an input pixel data word, comprising: an embedded data engine configured to output at least one bit of embedded data; decatenation circuitry that is coupled to the embedded data engine and that is configured to separate the input pixel data word into first and second subsets of bits; arithmetic circuitry that modifies bits of the first subset of bits based on whether the at least one bit of embedded data is the same as at least one corresponding bit of the second subset of bits; and concatenation circuitry that is coupled to the decatenation circuitry and that is configured to produce an output data word including the at least one bit of embedded data. 8. The image processing circuitry defined in claim 7 , wherein the embedded data engine comprises: serial output circuitry; configuration data registers that are configured to provide register values to the serial output circuitry; a statistics engine that is configured to provide image statistics data to the serial output circuitry; and an interest points engine that is configured to provide coordinate values of interest points to the serial output circuitry. | 0.546875 |
8,838,640 | 1 | 5 | 1. A computer-implemented method of creating a user-specific playlist based on feedback received during playing of a song in a virtual environment, the method being implemented by a computer that includes one or more physical processors, the method comprising: receiving, by the computer, an instance of feedback about a song, from a user, during playing of the song in a virtual environment; determining, by the computer, a first situational context that corresponds to a first situation of an avatar of the user in the virtual environment at a time of receipt of the instance of feedback; populating, by the computer, a data structure with an entry for the instance of feedback, wherein the entry comprises, for the instance of feedback: (i) information identifying the song, and (ii) information corresponding to the first situational context, wherein the information corresponding to the first situational context comprises one or more indications of the mood of the user; determining, by the computer, a second situational context that corresponds to a second situation of the avatar of the user in the virtual environment; and generating, by the computer, a playlist specific to the user in the virtual environment based on the instance of feedback responsive to a determination that the second situational context corresponds to the first situational context. | 1. A computer-implemented method of creating a user-specific playlist based on feedback received during playing of a song in a virtual environment, the method being implemented by a computer that includes one or more physical processors, the method comprising: receiving, by the computer, an instance of feedback about a song, from a user, during playing of the song in a virtual environment; determining, by the computer, a first situational context that corresponds to a first situation of an avatar of the user in the virtual environment at a time of receipt of the instance of feedback; populating, by the computer, a data structure with an entry for the instance of feedback, wherein the entry comprises, for the instance of feedback: (i) information identifying the song, and (ii) information corresponding to the first situational context, wherein the information corresponding to the first situational context comprises one or more indications of the mood of the user; determining, by the computer, a second situational context that corresponds to a second situation of the avatar of the user in the virtual environment; and generating, by the computer, a playlist specific to the user in the virtual environment based on the instance of feedback responsive to a determination that the second situational context corresponds to the first situational context. 5. The computer-implemented method of claim 1 , wherein the instance of feedback comprises a rating of the song. | 0.898734 |
9,077,749 | 1 | 2 | 1. An apparatus for verifying an identity of at least one user to a text-based communication with at least a second user, comprising: a memory; and at least one hardware device, coupled to the memory, operative to: obtain a plurality of pair-wise characteristic features of at least one prior pair-wise text-based communication between said at least one user and said same second user; compare the plurality of obtained pair-wise characteristic features to corresponding pair-wise features of a current session of said pair-wise text-based communication between said at least one user and said same second user; and verify said identity of said at least one user based on a result of said comparison. | 1. An apparatus for verifying an identity of at least one user to a text-based communication with at least a second user, comprising: a memory; and at least one hardware device, coupled to the memory, operative to: obtain a plurality of pair-wise characteristic features of at least one prior pair-wise text-based communication between said at least one user and said same second user; compare the plurality of obtained pair-wise characteristic features to corresponding pair-wise features of a current session of said pair-wise text-based communication between said at least one user and said same second user; and verify said identity of said at least one user based on a result of said comparison. 2. The apparatus of claim 1 , wherein the plurality of pair-wise characteristic features are compared to said current session using the statistical properties of the words entered by the at least one user. | 0.648973 |
10,162,811 | 1 | 2 | 1. A computer-implemented method of identifying a language in a message, the method comprising: obtaining a text message generated by a user; removing non-language characters from the text message to generate a sanitized text message; detecting an alphabet and a script present in the sanitized text message, wherein (i) detecting the alphabet comprises performing an alphabet-based language detection test to determine a first set of scores, and wherein each score in the first set of scores represents a likelihood that the sanitized text message comprises the alphabet for one of a plurality of different languages, and (ii) detecting the script comprises performing a script-based language detection test to determine a second set of scores, and wherein each score in the second set of scores represents a likelihood that the sanitized text message comprises the script for one of the plurality of different languages; providing one or more combinations of the first and second sets of scores as input to one or more classifiers including a first classifier and a second classifier, wherein the first classifier was trained using outputs from a first combination of language detection tests and the second classifier was trained using outputs from a second combination of language detection tests; obtaining as output from at least one of the one or more classifiers a respective confidence score that the sanitized text message is in one of a plurality of different languages; and identifying the language in the sanitized text message based on the confidence score from at least one of the one or more classifiers. | 1. A computer-implemented method of identifying a language in a message, the method comprising: obtaining a text message generated by a user; removing non-language characters from the text message to generate a sanitized text message; detecting an alphabet and a script present in the sanitized text message, wherein (i) detecting the alphabet comprises performing an alphabet-based language detection test to determine a first set of scores, and wherein each score in the first set of scores represents a likelihood that the sanitized text message comprises the alphabet for one of a plurality of different languages, and (ii) detecting the script comprises performing a script-based language detection test to determine a second set of scores, and wherein each score in the second set of scores represents a likelihood that the sanitized text message comprises the script for one of the plurality of different languages; providing one or more combinations of the first and second sets of scores as input to one or more classifiers including a first classifier and a second classifier, wherein the first classifier was trained using outputs from a first combination of language detection tests and the second classifier was trained using outputs from a second combination of language detection tests; obtaining as output from at least one of the one or more classifiers a respective confidence score that the sanitized text message is in one of a plurality of different languages; and identifying the language in the sanitized text message based on the confidence score from at least one of the one or more classifiers. 2. The method of claim 1 , wherein the non-language characters comprise at least one of an emoji, a punctuation mark, an extra space, a carriage return, and a numerical character. | 0.867407 |
7,823,054 | 17 | 18 | 17. A data processing system, comprising: a processor; and a memory for storing instructions, which when executed from the memory, cause the processor to perform operations, including accessing a first document presentation other than a default initial document presentation based on an address displayed within an address field of a window, wherein the first document presentation is displayed within a display area of the window, recording a first location of the first document presentation, accessing and displaying in the display area of the window a sequence of intermediate document presentations originated from the first document presentation, displaying a first snapback button associated with the address field, conducting a search via a search facility based on one or more search keywords entered in a search field of the window, displaying a search result in the display area of the window, in response to a first input received via the first snapback button, without having to select from a menu of document presentations, directly retrieving and displaying the first document presentation from the first location without having to access the intermediate document presentations again, and displaying a second snapback button associated with the search field, wherein the second snapback button is used to directly redisplay the search result in the display area of the window. | 17. A data processing system, comprising: a processor; and a memory for storing instructions, which when executed from the memory, cause the processor to perform operations, including accessing a first document presentation other than a default initial document presentation based on an address displayed within an address field of a window, wherein the first document presentation is displayed within a display area of the window, recording a first location of the first document presentation, accessing and displaying in the display area of the window a sequence of intermediate document presentations originated from the first document presentation, displaying a first snapback button associated with the address field, conducting a search via a search facility based on one or more search keywords entered in a search field of the window, displaying a search result in the display area of the window, in response to a first input received via the first snapback button, without having to select from a menu of document presentations, directly retrieving and displaying the first document presentation from the first location without having to access the intermediate document presentations again, and displaying a second snapback button associated with the search field, wherein the second snapback button is used to directly redisplay the search result in the display area of the window. 18. The system of claim 17 , wherein the operations further comprise: accessing and displaying a second intermediate document presentation in the display area of the window from the redisplayed first document presentation; and redisplaying the first snapback button within the first predetermined proximity of the address field. | 0.581633 |
7,664,849 | 1 | 7 | 1. A method for graphically defining an alert condition for a signal waveform in a policy-based automation system, wherein the signal waveform corresponds to a metric for an object monitored by the policy-based automation system, the method comprising: pictorially displaying on a display device a portion of the signal waveform including one or more impulses for which the alert condition is to be defined, wherein the portion of the signal waveform is displayed on a graph in an alert definition graphical user interface (GUI) on the display device; displaying a plurality of alert parameter user interface elements with the portion of the signal waveform displayed on the graph in the alert definition GUI, wherein each alert parameter user interface element represents a different one of a plurality of alert parameters for the signal waveform, wherein a position of each alert parameter user interface element relative to the displayed portion of the signal waveform in the alert definition GUI corresponds to a particular value for the respective alert parameter, and wherein the position of at least one of the plurality of alert parameter user interface elements relative to the displayed portion of the signal waveform in the alert definition GUI specifies a state change of the signal waveform at which the alert condition is raised to alert the policy-based automation system that the metric for the object monitored by the policy-based automation system indicates a condition of the object to which the policy-based automation system is to respond; displaying a plurality of alert parameter control user interface elements in the alert definition GUI, wherein each of the plurality of alert parameter control user interface elements corresponds to a different one of the plurality of alert parameter user interface elements, wherein each of the plurality of alert parameter control user interface elements is configured to receive user input to manipulate the position of a corresponding one of the plurality of alert parameter user interface elements relative to the displayed portion of the signal waveform in the alert definition GUI; receiving user input to at least one of the plurality of alert parameter control user interface elements to manipulate the positions of corresponding ones of the plurality of alert parameter user interface elements relative to the displayed portion of the signal waveform in the alert definition GUI, wherein manipulating the position of an alert parameter user interface element causes a corresponding change in the value of the associated alert parameter; and generating an alert definition for the policy-based automation system that specifies the alert condition from the values of the plurality of alert parameters associated with the plurality of alert parameter user interface elements; wherein the policy-based automation system is a computing environment management system. | 1. A method for graphically defining an alert condition for a signal waveform in a policy-based automation system, wherein the signal waveform corresponds to a metric for an object monitored by the policy-based automation system, the method comprising: pictorially displaying on a display device a portion of the signal waveform including one or more impulses for which the alert condition is to be defined, wherein the portion of the signal waveform is displayed on a graph in an alert definition graphical user interface (GUI) on the display device; displaying a plurality of alert parameter user interface elements with the portion of the signal waveform displayed on the graph in the alert definition GUI, wherein each alert parameter user interface element represents a different one of a plurality of alert parameters for the signal waveform, wherein a position of each alert parameter user interface element relative to the displayed portion of the signal waveform in the alert definition GUI corresponds to a particular value for the respective alert parameter, and wherein the position of at least one of the plurality of alert parameter user interface elements relative to the displayed portion of the signal waveform in the alert definition GUI specifies a state change of the signal waveform at which the alert condition is raised to alert the policy-based automation system that the metric for the object monitored by the policy-based automation system indicates a condition of the object to which the policy-based automation system is to respond; displaying a plurality of alert parameter control user interface elements in the alert definition GUI, wherein each of the plurality of alert parameter control user interface elements corresponds to a different one of the plurality of alert parameter user interface elements, wherein each of the plurality of alert parameter control user interface elements is configured to receive user input to manipulate the position of a corresponding one of the plurality of alert parameter user interface elements relative to the displayed portion of the signal waveform in the alert definition GUI; receiving user input to at least one of the plurality of alert parameter control user interface elements to manipulate the positions of corresponding ones of the plurality of alert parameter user interface elements relative to the displayed portion of the signal waveform in the alert definition GUI, wherein manipulating the position of an alert parameter user interface element causes a corresponding change in the value of the associated alert parameter; and generating an alert definition for the policy-based automation system that specifies the alert condition from the values of the plurality of alert parameters associated with the plurality of alert parameter user interface elements; wherein the policy-based automation system is a computing environment management system. 7. The method as recited in claim 1 , further comprising displaying a GUI configuration user interface element the alert definition GUI, wherein the GUI configuration user interface element is configured to receive user input to add or remove alert parameter control user interface elements or alert parameter user interface elements to the alert definition GUI. | 0.819721 |
8,463,610 | 18 | 19 | 18. The ASIC of claim 15 wherein each of the hash functions for the plurality of hash tables is a Cyclic Redundancy Check (CRC) function. | 18. The ASIC of claim 15 wherein each of the hash functions for the plurality of hash tables is a Cyclic Redundancy Check (CRC) function. 19. The ASIC of claim 18 wherein the language model engine stores one or more lookup tables in internal memory to assist in computations required for the CRC function for each of the hash functions of the plurality of hash tables. | 0.944552 |
9,342,609 | 1 | 9 | 1. A computer-implemented method comprising: receiving a search query requesting a search of a collection of custom content resources, wherein resources in the collection of custom content resources are resources exposed to a search engine by a user; obtaining a custom content search result that the search engine has identified in response to the search query using a custom search index generated from the collection of custom content resources; obtaining an indication of relative importance for a custom content resource identified by the custom content search result, the indication of relative importance being an indication of the importance of the custom content resource relative to other resources in the collection of resources and being assigned by the user that exposed the custom content resource to the search engine; determining a score for the custom content search result based on the indication of relative importance for the custom content resource; and ranking the custom content search result with one or more other custom content search results that were identified by the search engine in response to the received query, using the determined score. | 1. A computer-implemented method comprising: receiving a search query requesting a search of a collection of custom content resources, wherein resources in the collection of custom content resources are resources exposed to a search engine by a user; obtaining a custom content search result that the search engine has identified in response to the search query using a custom search index generated from the collection of custom content resources; obtaining an indication of relative importance for a custom content resource identified by the custom content search result, the indication of relative importance being an indication of the importance of the custom content resource relative to other resources in the collection of resources and being assigned by the user that exposed the custom content resource to the search engine; determining a score for the custom content search result based on the indication of relative importance for the custom content resource; and ranking the custom content search result with one or more other custom content search results that were identified by the search engine in response to the received query, using the determined score. 9. The method of claim 1 , wherein the custom content resources include documents, emails, files, collections of files, videos, or images. | 0.866279 |
4,453,217 | 11 | 12 | 11. The invention set forth in claim 3 wherein said identifying step includes the step of terminating said subdividing and said examining step when a preset number of character mismatches occur. | 11. The invention set forth in claim 3 wherein said identifying step includes the step of terminating said subdividing and said examining step when a preset number of character mismatches occur. 12. The invention set forth in claim 11 wherein said terminating step includes the distribution of character mismatches between said presented character string and said directory character string. | 0.890747 |
9,390,196 | 5 | 6 | 5. A system comprising: a processor, a computer readable memory, and a computer readable storage medium; first program instructions to receive a seed term from a user; second program instructions to receive a first expansion signal from the user; third program instructions to construct an ontological graph that includes nodes representing the seed term plus other terms that are located in accordance with instructions derived from the first expansion signal, wherein the seed term and the other terms share a common trait; and fourth program instructions to display terms from the ontological graph as string literals in a dictionary, wherein the dictionary contains related other terms at a resolution level that is controlled by the first expansion signal from the user and the seed term, wherein the first expansion signal causes additional nodes to be identified for the ontological graph, wherein the dictionary is an original dictionary that contains the seed term, wherein the first expansion signal further causes terms represented by the additional nodes to populate an expanded dictionary, and wherein the expanded dictionary is expanded from the original dictionary in which the seed term was located; and wherein the first, second, third, and fourth program instructions are stored on the computer readable storage medium and executed by the processor via the computer readable memory. | 5. A system comprising: a processor, a computer readable memory, and a computer readable storage medium; first program instructions to receive a seed term from a user; second program instructions to receive a first expansion signal from the user; third program instructions to construct an ontological graph that includes nodes representing the seed term plus other terms that are located in accordance with instructions derived from the first expansion signal, wherein the seed term and the other terms share a common trait; and fourth program instructions to display terms from the ontological graph as string literals in a dictionary, wherein the dictionary contains related other terms at a resolution level that is controlled by the first expansion signal from the user and the seed term, wherein the first expansion signal causes additional nodes to be identified for the ontological graph, wherein the dictionary is an original dictionary that contains the seed term, wherein the first expansion signal further causes terms represented by the additional nodes to populate an expanded dictionary, and wherein the expanded dictionary is expanded from the original dictionary in which the seed term was located; and wherein the first, second, third, and fourth program instructions are stored on the computer readable storage medium and executed by the processor via the computer readable memory. 6. The system of claim 5 , further comprising: fifth program instructions to receive a second expansion signal from the user; and sixth program instructions to expand the dictionary to include related terms for at least one of said terms from the ontological graph, wherein the related terms describe said at least one of said terms from the ontological graph, and wherein the dictionary is expanded to include the related terms in accordance with instructions derived from the second expansion signal; and wherein the fifth and sixth program instructions are stored on the computer readable storage medium and executed by the processor via the computer readable memory. | 0.500745 |
8,249,804 | 10 | 13 | 10. The method of claim 9 , wherein the consulting a Global City List with spatial index further comprises determining whether the matching city names are within a second predetermined geographic distance from the navigation device when no matching city names are found within the first predetermined geographic distance. | 10. The method of claim 9 , wherein the consulting a Global City List with spatial index further comprises determining whether the matching city names are within a second predetermined geographic distance from the navigation device when no matching city names are found within the first predetermined geographic distance. 13. The method of claim 10 , wherein the second predetermined geographic distance is greater than the first predetermined geographic distance. | 0.942464 |
9,996,592 | 12 | 13 | 12. The method of claim 10 , wherein the storing the query relationship data structures in the query relationship data structure (RELSTRUCT) repository includes storing the query relationship data structures in the query relationship data structure (RELSTRUCT) repository in a hierarchical manner, in which at least some of the query relationship data structures are connected, in one or both of a parent or child relationship, to at least one other query relationship data structure. | 12. The method of claim 10 , wherein the storing the query relationship data structures in the query relationship data structure (RELSTRUCT) repository includes storing the query relationship data structures in the query relationship data structure (RELSTRUCT) repository in a hierarchical manner, in which at least some of the query relationship data structures are connected, in one or both of a parent or child relationship, to at least one other query relationship data structure. 13. The method of claim 12 , wherein each query relationship data structure is applicable against the data source to obtain corresponding query results therefrom, and uses query relationship parts from query relationship data structures connected thereto in a child relationship, if any, to access the data source. | 0.923302 |
9,602,310 | 1 | 20 | 1. A message correlation system comprising: a message manager and processor that determine whether there are two or more outstanding queries to which a response from a user to any of the outstanding queries that includes no context would render the response uncorrelatable to one of the outstanding queries; and a keyword management module that correlates a keyword associated with a response to an outstanding query, wherein, when there is a conflict where the keyword is used in at least two of the two or more outstanding queries or the keyword is not used in the two or more outstanding queries, a supplemental query is sent to the user requesting clarification. | 1. A message correlation system comprising: a message manager and processor that determine whether there are two or more outstanding queries to which a response from a user to any of the outstanding queries that includes no context would render the response uncorrelatable to one of the outstanding queries; and a keyword management module that correlates a keyword associated with a response to an outstanding query, wherein, when there is a conflict where the keyword is used in at least two of the two or more outstanding queries or the keyword is not used in the two or more outstanding queries, a supplemental query is sent to the user requesting clarification. 20. The system of claim 1 , wherein the conflict is where the keyword is not used in the two or more outstanding queries. | 0.726244 |
7,606,718 | 19 | 24 | 19. A system for processing an interaction with a person, comprising a processor, one or more analyst user interface devices in communication with the processor, and a memory in communication with the processor, the memory storing programming instructions executable by the processor to: receive data representing an utterance from the person; automatically present the utterance in perceptible form to two or more intent analysts at substantially the same time, each through a respective one of the analyst user interface devices; accept intent input from each of the two or more intent analysts through the respective analyst user interface device, where the intent input characterizes the intent analyst's interpretation of the person's intent expressed in the utterance, but does not directly indicate to the system any information that is to be communicated to the person; when the two or more intent analysts provide intent input that characterizes different intents: automatically present the utterance in perceptible form to at least one additional intent analyst through at least one additional analyst user interface device; and accept further intent input from the at least one additional intent analyst through the at least one additional analyst user interface device, where the further intent input characterizes the at least one additional intent analyst's interpretation of the person's intent expressed in the utterance, and where the further intent input does not directly indicate to the system any information that is to be communicated to the person; and automatically communicate a message to the person, in perceptible form and in substantially real time relative to the receiving function, the message being selected as a function of the intent input and the further intent input. | 19. A system for processing an interaction with a person, comprising a processor, one or more analyst user interface devices in communication with the processor, and a memory in communication with the processor, the memory storing programming instructions executable by the processor to: receive data representing an utterance from the person; automatically present the utterance in perceptible form to two or more intent analysts at substantially the same time, each through a respective one of the analyst user interface devices; accept intent input from each of the two or more intent analysts through the respective analyst user interface device, where the intent input characterizes the intent analyst's interpretation of the person's intent expressed in the utterance, but does not directly indicate to the system any information that is to be communicated to the person; when the two or more intent analysts provide intent input that characterizes different intents: automatically present the utterance in perceptible form to at least one additional intent analyst through at least one additional analyst user interface device; and accept further intent input from the at least one additional intent analyst through the at least one additional analyst user interface device, where the further intent input characterizes the at least one additional intent analyst's interpretation of the person's intent expressed in the utterance, and where the further intent input does not directly indicate to the system any information that is to be communicated to the person; and automatically communicate a message to the person, in perceptible form and in substantially real time relative to the receiving function, the message being selected as a function of the intent input and the further intent input. 24. The system of claim 19 , wherein the programming instructions are further executable by the processor to portion a multi-utterance transaction with the person into discrete, logical units so that no intent analyst is ever exposed to more than one element of a given set of customer data. | 0.560423 |
10,067,939 | 3 | 4 | 3. The machine translation method of claim 1 , wherein the encoder for the first language is further trained to encode a first training sentence included in parallel corpus data in the first language and the second language to output training language-independent information; and the training language-independent information corresponds to information output by encoding a second training sentence included in the parallel corpus data in an encoder for the second language, the second training sentence corresponding to the first training sentence. | 3. The machine translation method of claim 1 , wherein the encoder for the first language is further trained to encode a first training sentence included in parallel corpus data in the first language and the second language to output training language-independent information; and the training language-independent information corresponds to information output by encoding a second training sentence included in the parallel corpus data in an encoder for the second language, the second training sentence corresponding to the first training sentence. 4. The machine translation method of claim 3 , wherein the decoder for the second language is trained to output the second training sentence in response to an input of the training language-independent information from the encoder for the first language. | 0.922655 |
10,042,896 | 9 | 10 | 9. An apparatus for providing a search recommendation, the apparatus comprising: a first search term obtaining unit that obtains a first search term, the first search term input by a user; a keyword matching unit that matches the first search term with at least one keyword in a list to select the at least one keyword from the list, a respective keyword in the list corresponding to at least one search recommendation; a first search recommendation providing unit that obtains one or more search recommendations corresponding to the at least one keyword and provides the one or more search recommendations corresponding to the at least one keyword to the user as one or more search recommendations of the first search term; a historical behavior obtaining unit that obtains a historical user behavior relating to the respective keyword from a log record; a document obtaining unit that obtaining a document generated by the historical user behavior; a text segmenting unit that applies a text segmentation of the document to obtain one or more candidate recommendations; and a filtering unit that filters the one or more candidate recommendations according to importance degree characteristics of the one or more candidate recommendations and improves a probability that the at least one search recommendation fits user requirements and reduces a number of user searches, search time, and search traffic by using a candidate recommendation passing the filtering as a search recommendation corresponding to the respective keyword. | 9. An apparatus for providing a search recommendation, the apparatus comprising: a first search term obtaining unit that obtains a first search term, the first search term input by a user; a keyword matching unit that matches the first search term with at least one keyword in a list to select the at least one keyword from the list, a respective keyword in the list corresponding to at least one search recommendation; a first search recommendation providing unit that obtains one or more search recommendations corresponding to the at least one keyword and provides the one or more search recommendations corresponding to the at least one keyword to the user as one or more search recommendations of the first search term; a historical behavior obtaining unit that obtains a historical user behavior relating to the respective keyword from a log record; a document obtaining unit that obtaining a document generated by the historical user behavior; a text segmenting unit that applies a text segmentation of the document to obtain one or more candidate recommendations; and a filtering unit that filters the one or more candidate recommendations according to importance degree characteristics of the one or more candidate recommendations and improves a probability that the at least one search recommendation fits user requirements and reduces a number of user searches, search time, and search traffic by using a candidate recommendation passing the filtering as a search recommendation corresponding to the respective keyword. 10. The apparatus of claim 9 , wherein the user behavior obtaining unit comprises: a search term obtaining sub-unit that obtains one or more search terms used by one or more historical users from the log record; a clustering sub-unit that clusters the one or more search terms used by the one or more historical users; a cluster determining sub-unit that determines a cluster corresponding to the respective keyword; and a user behavior determining unit that determines a user behavior relating to a search term included in the cluster corresponding to the respective keyword according to the log record and uses the user behavior as the historical user behavior relating to the respective keyword. | 0.500715 |
9,535,953 | 10 | 11 | 10. A non-transitory computer readable medium having computer executable instructions comprising: a compiler of a database management system to: generate a cache key based on a first received query; generate a first query plan based on the first received query; generate the cache key based on a second received query, the second received query having a different parameter than the first received query; generate a second query plan, different than the first query plan based on an annotated query plan stored in a query repository table, wherein the annotated query plan is stored in memory before the generating of the cache key based on the first received query; and access the query repository table to change at least one of heuristic and logic functions of a query optimizer for generating query plans, the changing of the at least one of the heuristic and logic functions of the query optimizer being based on runtime statistics stored in the query repository table. | 10. A non-transitory computer readable medium having computer executable instructions comprising: a compiler of a database management system to: generate a cache key based on a first received query; generate a first query plan based on the first received query; generate the cache key based on a second received query, the second received query having a different parameter than the first received query; generate a second query plan, different than the first query plan based on an annotated query plan stored in a query repository table, wherein the annotated query plan is stored in memory before the generating of the cache key based on the first received query; and access the query repository table to change at least one of heuristic and logic functions of a query optimizer for generating query plans, the changing of the at least one of the heuristic and logic functions of the query optimizer being based on runtime statistics stored in the query repository table. 11. The non-transitory computer readable medium of claim 10 , wherein the annotated query plan comprises text that defines a structured query language (SQL) relational operator that describes the shape of a query tree. | 0.903454 |
4,839,822 | 3 | 10 | 3. A method for providing to a user a suggested treatment for a patient having physical trauma, comprising the steps of: creating at least one knowledge base containing rules that relate different types of physical trauma characteristics and patient characteistics to different types of treatments for physical trauma; eliciting information from said user concerning characteristics of said patient including the type of physical trauma substained by said patient, to thereby develop at least one database containing patient and trauma characteristics; applying said knowledge base and database to a computer; and using said computer to infer the apropriate treatment for said patient from said rules and said database, by forward chaining, in which a sequence of rules relating to a particular treatment is established, and backward chaining, in which said sequence of rules es reversed and said rules are tested based on said trauma and patient characteristics, to determine the desirability of said particular treatment. | 3. A method for providing to a user a suggested treatment for a patient having physical trauma, comprising the steps of: creating at least one knowledge base containing rules that relate different types of physical trauma characteristics and patient characteistics to different types of treatments for physical trauma; eliciting information from said user concerning characteristics of said patient including the type of physical trauma substained by said patient, to thereby develop at least one database containing patient and trauma characteristics; applying said knowledge base and database to a computer; and using said computer to infer the apropriate treatment for said patient from said rules and said database, by forward chaining, in which a sequence of rules relating to a particular treatment is established, and backward chaining, in which said sequence of rules es reversed and said rules are tested based on said trauma and patient characteristics, to determine the desirability of said particular treatment. 10. The method of claim 3, wherein said step of using the computer to infer the appropriate treatment further includes the steps of assingning a value to each of a plurality of possible treatments, and adjusting said values according to a treatment hierarchy, wherein values assigned to specific treatments are more heavily weighted than values assigned to general treatments. | 0.664286 |
9,082,310 | 34 | 42 | 34. A non-transitory computer-readable medium comprising computer-readable instructions tangibly stored on the non-transitory computer-readable medium, wherein the instructions are executable by at least one computer processor to execute a method for use with a system, the non-transitory computer-readable medium comprising: instructions to select, by the at least one computer processor, a first question instance including first text and a first region definition designed to identify a region of a data set likely to contain information that may be used to provide an answer to the question represented by the first text, the region definition identifying a region aligned with a tagged element in the data set; instructions to automatically identify, by the at least one computer processor, before providing output to a user representing the first question instance and before providing output to the user representing the first region of the data set, the first region of the data set, based on the first region definition; instructions to provide output, by the at least one computer processor, to a user representing the first question instance; and instructions to provide output, by the at least one computer processor, to the user, the output representing the first region of the data set before receiving an answer to the first question instance. | 34. A non-transitory computer-readable medium comprising computer-readable instructions tangibly stored on the non-transitory computer-readable medium, wherein the instructions are executable by at least one computer processor to execute a method for use with a system, the non-transitory computer-readable medium comprising: instructions to select, by the at least one computer processor, a first question instance including first text and a first region definition designed to identify a region of a data set likely to contain information that may be used to provide an answer to the question represented by the first text, the region definition identifying a region aligned with a tagged element in the data set; instructions to automatically identify, by the at least one computer processor, before providing output to a user representing the first question instance and before providing output to the user representing the first region of the data set, the first region of the data set, based on the first region definition; instructions to provide output, by the at least one computer processor, to a user representing the first question instance; and instructions to provide output, by the at least one computer processor, to the user, the output representing the first region of the data set before receiving an answer to the first question instance. 42. The non-transitory computer-readable medium of claim 34 , wherein the instructions to identify) comprise instructions to identify a plurality of portions of the data set based on the first region definition, wherein the first region includes the plurality of portions. | 0.858333 |
9,507,876 | 12 | 13 | 12. The method of claim 1 , wherein the method further comprises sending, to the client device of the first user and responsive to a selection of a query-filter element, a sub-filter page comprising a query field. | 12. The method of claim 1 , wherein the method further comprises sending, to the client device of the first user and responsive to a selection of a query-filter element, a sub-filter page comprising a query field. 13. The method of claim 12 , further comprising: receiving, from the client device of the first user, and text string inputted into the query field; and identifying one or more second nodes matching the text string. | 0.944185 |
7,549,119 | 3 | 4 | 3. A computer implemented method according to claim 1 wherein processing step (d) further comprises the steps of: (d1) checking a first character of a first of said plurality of strings of words against a predefined alias character list, wherein each of said alias characters is a predefined character mapping where more than one character in an ordered sequence is mapped to a single character; (d2) when a match is found in said step (d1) for said first character of a first word of said first plurality of strings of words, building a temporary alias list for said first character; (d3) comparing said first character against a first character of all said previously identified undesirable terms in said secondary database of undesirable terms for a match; (d4) when a match is found, checking to see if said first character is at an end of said first word; (d5) when said first character is not at said end of said first word, calling, by said recursive comparison subroutine, said recursive comparison subroutine recursively; (d6) moving to a next character in said first word of said first plurality of strings of words; (d7) repeating steps (d1) through (d6) for said next character; (d8) repeating step (d7) for each remaining character in said first word of said first plurality of strings of words; (d9) repeating steps (d1) through (d8) for each next word in said first of said plurality of strings of words; and (d10) repeating steps (d1) through (d9) for the remaining plurality of strings of words. | 3. A computer implemented method according to claim 1 wherein processing step (d) further comprises the steps of: (d1) checking a first character of a first of said plurality of strings of words against a predefined alias character list, wherein each of said alias characters is a predefined character mapping where more than one character in an ordered sequence is mapped to a single character; (d2) when a match is found in said step (d1) for said first character of a first word of said first plurality of strings of words, building a temporary alias list for said first character; (d3) comparing said first character against a first character of all said previously identified undesirable terms in said secondary database of undesirable terms for a match; (d4) when a match is found, checking to see if said first character is at an end of said first word; (d5) when said first character is not at said end of said first word, calling, by said recursive comparison subroutine, said recursive comparison subroutine recursively; (d6) moving to a next character in said first word of said first plurality of strings of words; (d7) repeating steps (d1) through (d6) for said next character; (d8) repeating step (d7) for each remaining character in said first word of said first plurality of strings of words; (d9) repeating steps (d1) through (d8) for each next word in said first of said plurality of strings of words; and (d10) repeating steps (d1) through (d9) for the remaining plurality of strings of words. 4. A computer implemented method according to claim 3 wherein building step (d2) further comprises the steps of: (d2a) when a match is not found for said first character of a first word of said first plurality of strings of words, determining if there are any of said alias characters left for said first character of said first word; (d2b) when said determining step (d2a) result is yes, replacing said first character of said first word with a next alias character in said ordered sequence and passing control to said comparing step (d3) for continued processing; (d2c) when said determining step (d2a) result is no, determining if said recursive comparison subroutine is pointing to said first character of said first word of said first plurality of strings of words on its way back up from said recursion; (d2d) when said determining step (d2c) result is no, stepping back up a level recursively to a previous character position and passing control to said determining step (d2a) for continued processing; (d2e) when said determining step (d2c) result is yes, returning any said at least one undesirable terms found to said blocking step (e). | 0.799229 |
8,176,048 | 7 | 10 | 7. A method for transforming unstructured text into structured data using a domain-specific ontology, the method comprising: inputting the unstructured text into an information extraction module (IEM); embedding the unstructured text in a domain-specific text archive; retrieving data from a plurality of different knowledge sources, including the domain-specific text archive, via the IEM using the unstructured text; processing the unstructured text using the IEM based on the retrieved data to thereby generate a plurality of nodes in the domain-specific ontology, wherein each of the nodes represents a corresponding single concept as a cluster of synonyms for the unstructured text; transforming the unstructured text into the structured data via the plurality of nodes, including classifying the unstructured text to predetermined corresponding objects of interest; quantifying all sub-phrases of the classified unstructured text by relative informativeness as a normalized value between 0 and 1 using an informativeness function; and eliminating all quantified sub-phrases from the domain-specific ontology having a normalized value that is less than a calibrated threshold. | 7. A method for transforming unstructured text into structured data using a domain-specific ontology, the method comprising: inputting the unstructured text into an information extraction module (IEM); embedding the unstructured text in a domain-specific text archive; retrieving data from a plurality of different knowledge sources, including the domain-specific text archive, via the IEM using the unstructured text; processing the unstructured text using the IEM based on the retrieved data to thereby generate a plurality of nodes in the domain-specific ontology, wherein each of the nodes represents a corresponding single concept as a cluster of synonyms for the unstructured text; transforming the unstructured text into the structured data via the plurality of nodes, including classifying the unstructured text to predetermined corresponding objects of interest; quantifying all sub-phrases of the classified unstructured text by relative informativeness as a normalized value between 0 and 1 using an informativeness function; and eliminating all quantified sub-phrases from the domain-specific ontology having a normalized value that is less than a calibrated threshold. 10. The method of claim 7 , further comprising: automatically classifying phrases near the unstructured text; and using the classified phrases to infer a classification of the unstructured text. | 0.842532 |
7,730,059 | 24 | 25 | 24. A computer readable storage medium containing program instructions, tangibly stored thereon, for displaying results of a search query, the program instructions comprising instructions for: receiving a query; obtaining documents that satisfy the query using a search index, the search index storing information relating to a plurality of documents, the information including metadata describing a hierarchical relationship among the plurality of documents; constructing a facet hierarchy based on the metadata describing the hierarchical relationship associated with the documents that satisfy the query; determining whether a pre-defined cube structure corresponds to the facet hierarchy; and responsive to the pre-defined cube structure not corresponding to the facet hierarchy, modifying the pre-defined cube structure to correspond with the facet hierarchy and outputting for display a multi-dimensional search interface based on the modified cube structure. | 24. A computer readable storage medium containing program instructions, tangibly stored thereon, for displaying results of a search query, the program instructions comprising instructions for: receiving a query; obtaining documents that satisfy the query using a search index, the search index storing information relating to a plurality of documents, the information including metadata describing a hierarchical relationship among the plurality of documents; constructing a facet hierarchy based on the metadata describing the hierarchical relationship associated with the documents that satisfy the query; determining whether a pre-defined cube structure corresponds to the facet hierarchy; and responsive to the pre-defined cube structure not corresponding to the facet hierarchy, modifying the pre-defined cube structure to correspond with the facet hierarchy and outputting for display a multi-dimensional search interface based on the modified cube structure. 25. The computer readable storage medium of claim 24 , further comprising instructions for outputting the multi-dimensional search interface based on the pre-defined cube structure responsive to pre-defined cube structure corresponding to the facet hierarchy. | 0.501923 |
7,814,107 | 20 | 21 | 20. A tangible computer-readable medium bearing computer-executable instructions which, when executed on a computing device, cause the computing device to carry out operations for determining the likelihood of two documents describing similar subject matter, the operations comprising: obtaining a set of tokens for each of a first and a second document, wherein each token in the set of tokens represents a series of characters found in its corresponding document; obtaining a set of matched token pairs, each matched token pair comprising a first token from the set of tokens corresponding to the first document and a second token matching the first token, the second token from the set of tokens corresponding to the second document, each matched token pair having a token similarity score; determining a similarity score for the first and second documents according to the token similarity scores of the token pairs in the set of matched token pairs by dividing the sum of the similarity scores of the token pairs in the set of matched token pairs by the sum of the number of token pairs in the set of matched token pairs, the number of tokens from the first document less the number of token pairs in the set of matched token pairs, and the number of tokens from the second document less the number of token pairs in the set of matched token pairs; and providing the determined similarity score as the likelihood of the first and second documents describing similar subject matter. | 20. A tangible computer-readable medium bearing computer-executable instructions which, when executed on a computing device, cause the computing device to carry out operations for determining the likelihood of two documents describing similar subject matter, the operations comprising: obtaining a set of tokens for each of a first and a second document, wherein each token in the set of tokens represents a series of characters found in its corresponding document; obtaining a set of matched token pairs, each matched token pair comprising a first token from the set of tokens corresponding to the first document and a second token matching the first token, the second token from the set of tokens corresponding to the second document, each matched token pair having a token similarity score; determining a similarity score for the first and second documents according to the token similarity scores of the token pairs in the set of matched token pairs by dividing the sum of the similarity scores of the token pairs in the set of matched token pairs by the sum of the number of token pairs in the set of matched token pairs, the number of tokens from the first document less the number of token pairs in the set of matched token pairs, and the number of tokens from the second document less the number of token pairs in the set of matched token pairs; and providing the determined similarity score as the likelihood of the first and second documents describing similar subject matter. 21. The computer-readable medium of claim 20 , wherein each token is associated with a type. | 0.953109 |
7,895,068 | 22 | 23 | 22. A method in a transitive trust network for providing a framework for at least two entities to establish relationships between one another, the transitive trust network including at least one computer, the method comprising: a) receiving, by an associated computer, at a second entity a contact identifying a first entity; b) checking a list of trusted entities, associated with the second entity, by the associated computer; c) in response to the checking, determining, by the associated computer, that the first entity is not a trusted entity; d) in response to determining that the first entity is not a trusted entity and where a proxy parameter is indicative that trusted entities are permitted to forward requests to other trusted entities, by the second entity at least a third entity of the trusted entities associated with the second entity, querying another computer and specifying, by the associated computer, a predetermined degree of separation, the predetermined degree of separation being dependent on an activity trust level of a relationship the first entity is seeking to establish with the second entity; e) checking a list of trusted entities, associated with the third entity, by the third entity to determine if the first entity is a trusted entity; f) continuing querying and checking, if the first entity is not a trusted entity, until the associated computer determines that a maximum separation of the predetermined degree of separation is reached or until the associated computer determines that the first entity is known to a respective trusted entity; and g) causing, by the associated computer, the relationship between the first and second entities to be established when the associated computer determines that the first entity is known by at least one respective entity of the trusted entities, the relationship being based on information from one of the least one respective entity, the information being indicative of a level of trust about the first entity. | 22. A method in a transitive trust network for providing a framework for at least two entities to establish relationships between one another, the transitive trust network including at least one computer, the method comprising: a) receiving, by an associated computer, at a second entity a contact identifying a first entity; b) checking a list of trusted entities, associated with the second entity, by the associated computer; c) in response to the checking, determining, by the associated computer, that the first entity is not a trusted entity; d) in response to determining that the first entity is not a trusted entity and where a proxy parameter is indicative that trusted entities are permitted to forward requests to other trusted entities, by the second entity at least a third entity of the trusted entities associated with the second entity, querying another computer and specifying, by the associated computer, a predetermined degree of separation, the predetermined degree of separation being dependent on an activity trust level of a relationship the first entity is seeking to establish with the second entity; e) checking a list of trusted entities, associated with the third entity, by the third entity to determine if the first entity is a trusted entity; f) continuing querying and checking, if the first entity is not a trusted entity, until the associated computer determines that a maximum separation of the predetermined degree of separation is reached or until the associated computer determines that the first entity is known to a respective trusted entity; and g) causing, by the associated computer, the relationship between the first and second entities to be established when the associated computer determines that the first entity is known by at least one respective entity of the trusted entities, the relationship being based on information from one of the least one respective entity, the information being indicative of a level of trust about the first entity. 23. The method according to claim 22 , the method further comprising: h) providing a capability domain and activity trust level database for each of the entities, the database having a plurality of levels of trust and a plurality of entity roles. | 0.539326 |
9,667,587 | 1 | 3 | 1. A text message processing system comprising: a text message processing server configured to, by at least one processor: receive a first text message from a party, the first text message including an identification of information being sought by the party and instructions to manipulate the identified information; assign a unique transaction ID (TX ID) to the first text message and store the TX ID in a transaction database; based on a particular type of transaction as determined from the received first text message, predict an expected next message; if the expected next message is not received within a set period of time, void the transaction; parse the first text message to identify the information being sought by the party; generate a request for the identified information and manipulate the identified information in accordance with the instructions, the request being in a format other than a text message format; submit the request to an information server having access to the identified information; obtain the identified information as manipulated from the information server, the obtained information being in a format other than a text message format; include the obtained information in a second text message; and send the second text message back to the party. | 1. A text message processing system comprising: a text message processing server configured to, by at least one processor: receive a first text message from a party, the first text message including an identification of information being sought by the party and instructions to manipulate the identified information; assign a unique transaction ID (TX ID) to the first text message and store the TX ID in a transaction database; based on a particular type of transaction as determined from the received first text message, predict an expected next message; if the expected next message is not received within a set period of time, void the transaction; parse the first text message to identify the information being sought by the party; generate a request for the identified information and manipulate the identified information in accordance with the instructions, the request being in a format other than a text message format; submit the request to an information server having access to the identified information; obtain the identified information as manipulated from the information server, the obtained information being in a format other than a text message format; include the obtained information in a second text message; and send the second text message back to the party. 3. The system of claim 1 , further comprising: a party information database storing information relating to the first party; wherein the transaction database is configured to store messages exchanged between the party and the text message processing server. | 0.501938 |
9,336,297 | 15 | 16 | 15. A non-transitory computer readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: receiving a user search content, wherein the user search content corresponds to a user needs expression having a user subject and a user feature; determining a user syntactic parse tree corresponding to the user needs expression; determining a plurality of contents through an online search engine server using the user search content, wherein the plurality of contents includes a plurality of sentences having a plurality of sentiment expressions; creating a syntactic parse tree of each of the plurality of sentences; identifying a plurality of sentiments in the plurality of sentiment expressions, wherein the plurality of sentiment corresponds to a plurality of polarities; determining a plurality of needs expressions corresponding to the plurality of sentiments, wherein the plurality of needs expressions includes a plurality of subject; determining a plurality of features corresponding to the plurality of subjects; creating a sub-tree corresponding to each of the plurality of needs expressions; aligning phrases between the user syntactic parse tree and the syntactic parse tree; determining a generalization score between the user search content and the first sentence using the phrases aligned between the user syntactic parse tree and the syntactic parse tree, wherein the generalization score is determined using scoring weights assigned to aligned nouns, aligned verbs, and other aligned parts of speech; and providing search results based on the generalization score and a plurality of generalization scores for the user syntactic parse tree and a plurality of syntactic parse trees. | 15. A non-transitory computer readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: receiving a user search content, wherein the user search content corresponds to a user needs expression having a user subject and a user feature; determining a user syntactic parse tree corresponding to the user needs expression; determining a plurality of contents through an online search engine server using the user search content, wherein the plurality of contents includes a plurality of sentences having a plurality of sentiment expressions; creating a syntactic parse tree of each of the plurality of sentences; identifying a plurality of sentiments in the plurality of sentiment expressions, wherein the plurality of sentiment corresponds to a plurality of polarities; determining a plurality of needs expressions corresponding to the plurality of sentiments, wherein the plurality of needs expressions includes a plurality of subject; determining a plurality of features corresponding to the plurality of subjects; creating a sub-tree corresponding to each of the plurality of needs expressions; aligning phrases between the user syntactic parse tree and the syntactic parse tree; determining a generalization score between the user search content and the first sentence using the phrases aligned between the user syntactic parse tree and the syntactic parse tree, wherein the generalization score is determined using scoring weights assigned to aligned nouns, aligned verbs, and other aligned parts of speech; and providing search results based on the generalization score and a plurality of generalization scores for the user syntactic parse tree and a plurality of syntactic parse trees. 16. The non-transitory computer readable medium of claim 15 , wherein the operations further comprise: normalizing the plurality of needs expressions by identifying the plurality of subjects and removing excess wording. | 0.82224 |
8,352,927 | 12 | 13 | 12. A computer program product comprising a computer-readable storage medium encoded with computer readable code for controlling a processor to interactively teach a modeling language to a user, by carrying out the steps of: within a wizard for specifying language-specific constructs in a respective implementation language, during wizard workflow: (a) prompting a user to enter user specifications of a language-specific construct, the user specifications being with respect to the respective implementation language, wherein the wizard displays a plurality of user selectable options for defining the language-specific construct; (b) mapping from the implementation language of the language-specific constructs to a modeling language, and producing in terms of the modeling language a depiction of expected code in the implementation language in accordance with the user specifications, wherein the expected code is code that would be generated in the implementation language according to which options are selected by the user in the wizard; (c) interactively teaching the modeling language to the user by (i) coupling the mapping to wizard workflow, said coupling enabling modeling language depictions to be displayed in real-time relative to wizard workflow and (ii) interactively illustrating in real-time a relationship between parts of the expected code and modeling language terms; and (d) including in the interactive illustrating, in real-time, displaying the produced modeling language depiction and corresponding wizard prompts responsible for producing the expected code in response to user input; the steps of (c) and (d) interactively illustrating to the user relationships between parts of the expected code and modeling language terms. | 12. A computer program product comprising a computer-readable storage medium encoded with computer readable code for controlling a processor to interactively teach a modeling language to a user, by carrying out the steps of: within a wizard for specifying language-specific constructs in a respective implementation language, during wizard workflow: (a) prompting a user to enter user specifications of a language-specific construct, the user specifications being with respect to the respective implementation language, wherein the wizard displays a plurality of user selectable options for defining the language-specific construct; (b) mapping from the implementation language of the language-specific constructs to a modeling language, and producing in terms of the modeling language a depiction of expected code in the implementation language in accordance with the user specifications, wherein the expected code is code that would be generated in the implementation language according to which options are selected by the user in the wizard; (c) interactively teaching the modeling language to the user by (i) coupling the mapping to wizard workflow, said coupling enabling modeling language depictions to be displayed in real-time relative to wizard workflow and (ii) interactively illustrating in real-time a relationship between parts of the expected code and modeling language terms; and (d) including in the interactive illustrating, in real-time, displaying the produced modeling language depiction and corresponding wizard prompts responsible for producing the expected code in response to user input; the steps of (c) and (d) interactively illustrating to the user relationships between parts of the expected code and modeling language terms. 13. A computer program product as claimed in claim 12 wherein the modeling language is UML. | 0.854633 |
8,676,827 | 1 | 3 | 1. A computer-implemented method for expansion of rare queries to improve advertisement results, the method comprising: receiving a query from a user by a search engine; determining that the query does not match an entry in an ad query lookup table stored in data storage of the search engine; retrieving one or more expanded queries located within a query feature index whose features relate to one or more features of the received query, wherein the query feature index is stored in a database of the data storage and comprises a plurality of expanded queries; wherein retrieving comprises: representing the features as vectors; weighting the vectors of the received query based on a number of times corresponding respective features occur in the query and on an inverse document frequency for corresponding respective features in an ad corpus, to more heavily weight the rare queries; and using a vector space-based retrieval approach for retrieving the expanded queries; generating, in real time and by the search engine, an ad query comprising an expanded version of the received query based on features of the retrieved expanded queries; and selecting one or more advertisements based on the generated ad query, wherein the one or more advertisements are displayed to the user in response to the query received from the user. | 1. A computer-implemented method for expansion of rare queries to improve advertisement results, the method comprising: receiving a query from a user by a search engine; determining that the query does not match an entry in an ad query lookup table stored in data storage of the search engine; retrieving one or more expanded queries located within a query feature index whose features relate to one or more features of the received query, wherein the query feature index is stored in a database of the data storage and comprises a plurality of expanded queries; wherein retrieving comprises: representing the features as vectors; weighting the vectors of the received query based on a number of times corresponding respective features occur in the query and on an inverse document frequency for corresponding respective features in an ad corpus, to more heavily weight the rare queries; and using a vector space-based retrieval approach for retrieving the expanded queries; generating, in real time and by the search engine, an ad query comprising an expanded version of the received query based on features of the retrieved expanded queries; and selecting one or more advertisements based on the generated ad query, wherein the one or more advertisements are displayed to the user in response to the query received from the user. 3. The method of claim 1 , wherein features of the received and expanded queries comprise one or more of unigrams, phrases, and semantic classes. | 0.930622 |
8,074,199 | 1 | 10 | 1. A computer-implemented system for programming a unified messaging (UM) application, comprising: a user interface; a programming environment operating on at least one computing device and accessed via the user interface for composing in an eXtensible Markup Language (XML) a UM finite state machine (FSM) comprising menu states defined by a plurality of user prompts and transitions between user prompts, each transition defined by a particular user response to a prompt; a UM software component including an external software component called by the UM FSM; an XML feature utilized by the programming environment to create a valid menu state based upon the UM software component, wherein the XML feature includes a function wrapper that is used by the programming environment to validate the external software component during a compilation phase when the external software component is present and generate a binary UM FSM, and to generate an error when the external software component is absent during the compilation phase; and a verification tool invoked during execution of the binary UM FSM that confirms that a version of the external software component present during a compilation phase is the same as a version of the external software component available at execution. | 1. A computer-implemented system for programming a unified messaging (UM) application, comprising: a user interface; a programming environment operating on at least one computing device and accessed via the user interface for composing in an eXtensible Markup Language (XML) a UM finite state machine (FSM) comprising menu states defined by a plurality of user prompts and transitions between user prompts, each transition defined by a particular user response to a prompt; a UM software component including an external software component called by the UM FSM; an XML feature utilized by the programming environment to create a valid menu state based upon the UM software component, wherein the XML feature includes a function wrapper that is used by the programming environment to validate the external software component during a compilation phase when the external software component is present and generate a binary UM FSM, and to generate an error when the external software component is absent during the compilation phase; and a verification tool invoked during execution of the binary UM FSM that confirms that a version of the external software component present during a compilation phase is the same as a version of the external software component available at execution. 10. The computer-implemented system of claim 1 , further comprising an automated speech recognition menu for defining a semantic event as one of a plurality of user responses semantically equivalent to the user prompts, the transitions dependent upon a particular semantic event. | 0.501786 |
9,411,559 | 12 | 13 | 12. The device of claim 11 , where the one or more processors are further to: link, based on one or more of the graphical symbols, the textual code with one or more state blocks of the graphical hierarchy, process the chart to generate output code, and execute the output code to generate output code results. | 12. The device of claim 11 , where the one or more processors are further to: link, based on one or more of the graphical symbols, the textual code with one or more state blocks of the graphical hierarchy, process the chart to generate output code, and execute the output code to generate output code results. 13. The device of claim 12 , where the one or more processors are further to: store the output code in storage associated with the device, or embed the output code in a physical device separate from the device. | 0.957797 |
9,626,959 | 1 | 8 | 1. A method of processing natural language command, the method being implemented by a computer system that comprises one or more physical processors executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, by the computer system, a natural language command from a user; generating, by the computer system, a first interpretation of the natural language command based on one or more recognized words of the natural language command; performing, by the computer system, a first action specified by the natural language command based on the first interpretation; accessing, by the computer system, a personalized cognitive model to proactively select a second interpretation of the natural language command responsive to an indication from the user that the first interpretation is not correct; and proactively performing, by the computer system, a second action specified by the natural language command based on the second interpretation. | 1. A method of processing natural language command, the method being implemented by a computer system that comprises one or more physical processors executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, by the computer system, a natural language command from a user; generating, by the computer system, a first interpretation of the natural language command based on one or more recognized words of the natural language command; performing, by the computer system, a first action specified by the natural language command based on the first interpretation; accessing, by the computer system, a personalized cognitive model to proactively select a second interpretation of the natural language command responsive to an indication from the user that the first interpretation is not correct; and proactively performing, by the computer system, a second action specified by the natural language command based on the second interpretation. 8. The method of claim 1 , wherein the second action includes an action that is predicted to be requested or taken by the user after the receipt of the natural language command. | 0.898041 |
9,514,113 | 8 | 11 | 8. A hardware computer-readable storage medium including program instructions executable by one or more processors that, when executed, cause the one or more processors to perform operations, the operations comprising: accessing, at one or more computing devices, a document; identifying a plurality of sentences of the document, each sentence identified based on punctuation in the document; and executing, for each of the sentences of the document and using the one or more computing devices, a document modification operation that includes: generating a ranking score for each of a plurality of passages from external documents, wherein the ranking score is based at least on a degree of semantic similarity of each of the plurality of passages from the external documents with respect to the sentence of the document, modifying the sentence to include a footnote link for the sentence in the document, the footnote link including a link to the external document having a highest ranked passage therein if the ranking score of the highest ranked passage with respect to the sentence exceeds a threshold value, and skipping modification of the sentence if the ranking score of the highest ranked passage with respect to the sentence does not exceed the threshold value. | 8. A hardware computer-readable storage medium including program instructions executable by one or more processors that, when executed, cause the one or more processors to perform operations, the operations comprising: accessing, at one or more computing devices, a document; identifying a plurality of sentences of the document, each sentence identified based on punctuation in the document; and executing, for each of the sentences of the document and using the one or more computing devices, a document modification operation that includes: generating a ranking score for each of a plurality of passages from external documents, wherein the ranking score is based at least on a degree of semantic similarity of each of the plurality of passages from the external documents with respect to the sentence of the document, modifying the sentence to include a footnote link for the sentence in the document, the footnote link including a link to the external document having a highest ranked passage therein if the ranking score of the highest ranked passage with respect to the sentence exceeds a threshold value, and skipping modification of the sentence if the ranking score of the highest ranked passage with respect to the sentence does not exceed the threshold value. 11. The hardware computer-readable storage medium of claim 8 , wherein the plurality of passages from external documents are identified using a subset of words from the sentence of the document as an input for a search function. | 0.787313 |
7,571,145 | 10 | 23 | 10. The method of claim 1 , further comprising: based on one or more scores that have been assigned to one or more submissions submitted by a second submitter, classifying the second submitter with a particular classification by storing, on a volatile or non-volatile computer-readable storage medium, data that indicates that the second submitter is associated with the particular classification; and based on the second submitter being classified with the particular classification, presenting, to the second submitter, through a display device, one or more questions to which the second submitter can submit answers. | 10. The method of claim 1 , further comprising: based on one or more scores that have been assigned to one or more submissions submitted by a second submitter, classifying the second submitter with a particular classification by storing, on a volatile or non-volatile computer-readable storage medium, data that indicates that the second submitter is associated with the particular classification; and based on the second submitter being classified with the particular classification, presenting, to the second submitter, through a display device, one or more questions to which the second submitter can submit answers. 23. A volatile or non-volatile computer-readable storage medium that stores instructions which, when executed by one or more processors, cause the one or more processors to perform the method of claim 10 . | 0.929698 |
8,285,755 | 11 | 14 | 11. A computer program product comprising a computer usable medium having computer usable program code for programmatically partially updating a row in a database, said computer program product including: computer usable program code for creating an instance of a prepared statement; computer usable program code for binding a plurality of fields in a designated row in said prepared statement; computer usable program code for setting values only for a subset of said fields in said prepared statement; and, computer usable program code for executing said prepared statement. | 11. A computer program product comprising a computer usable medium having computer usable program code for programmatically partially updating a row in a database, said computer program product including: computer usable program code for creating an instance of a prepared statement; computer usable program code for binding a plurality of fields in a designated row in said prepared statement; computer usable program code for setting values only for a subset of said fields in said prepared statement; and, computer usable program code for executing said prepared statement. 14. The computer program product of claim 11 , wherein said computer usable program code for binding a plurality of fields in a designated row in said prepared statement further comprises computer usable program code for comparing a version field for said designated row. | 0.501838 |
7,650,348 | 24 | 28 | 24. A processor-implemented system for building a custom word list for use in text operations on an electronic device, comprising: a first data store for storing a collection of text items associated with a user of the electronic device; a scanning module configured to scan the collection of text items to identify words in the text items; a weighting module configured to assign a weighting to each identified word; a second data store for storing each identified word and its corresponding weighting; and a module configured to determine a source of each text item in the collection of text items; wherein the weighting module calculates the weighting for each identified word based on the source of the text item in which the word was identified; wherein the text item sources include a user text item source and an external text item source, wherein text items from the user text item source are assigned a higher weighting than text items from the external text item source. | 24. A processor-implemented system for building a custom word list for use in text operations on an electronic device, comprising: a first data store for storing a collection of text items associated with a user of the electronic device; a scanning module configured to scan the collection of text items to identify words in the text items; a weighting module configured to assign a weighting to each identified word; a second data store for storing each identified word and its corresponding weighting; and a module configured to determine a source of each text item in the collection of text items; wherein the weighting module calculates the weighting for each identified word based on the source of the text item in which the word was identified; wherein the text item sources include a user text item source and an external text item source, wherein text items from the user text item source are assigned a higher weighting than text items from the external text item source. 28. The system of claim 24 , wherein the first data store, the scanning module, the weighting module, and the second data store are implemented at a computer system, further comprising a word list loader at the electronic device configured to receive the identified words and their corresponding weightings from the second data store, and to store the identified words and their corresponding weightings at the electronic device. | 0.501163 |
10,097,580 | 1 | 11 | 1. A computer implemented method, comprising: obtaining a first hyperlink associated with a first web resource accessible via a client terminal; converting at least one portion of the first hyperlink into a query comprising at least one search term derived, at least in part, from the at least one portion of the first hyperlink; submitting the query to at least one search engine configured to search for information via the internet; receiving, from the at least one search engine, search results associated with the query, the search results including a plurality of second hyperlinks; determining whether to replace the first hyperlink with a replacement hyperlink selected from at least a subset of the plurality of second hyperlinks based, at least in part, on a result of an analysis of similarity of the first hyperlink compared to each second hyperlink of the at least a subset of the plurality of second hyperlinks; and causing the client terminal to access either the first web resource associated with the first hyperlink or a second web resource associated with the replacement hyperlink based on the determination. | 1. A computer implemented method, comprising: obtaining a first hyperlink associated with a first web resource accessible via a client terminal; converting at least one portion of the first hyperlink into a query comprising at least one search term derived, at least in part, from the at least one portion of the first hyperlink; submitting the query to at least one search engine configured to search for information via the internet; receiving, from the at least one search engine, search results associated with the query, the search results including a plurality of second hyperlinks; determining whether to replace the first hyperlink with a replacement hyperlink selected from at least a subset of the plurality of second hyperlinks based, at least in part, on a result of an analysis of similarity of the first hyperlink compared to each second hyperlink of the at least a subset of the plurality of second hyperlinks; and causing the client terminal to access either the first web resource associated with the first hyperlink or a second web resource associated with the replacement hyperlink based on the determination. 11. The computer implemented method of claim 1 , wherein the analysis produces a similarity score for the each of the at least one of a plurality of second hyperlinks to identify the replacement hyperlink having a highest similarity score, the similarity score is calculated by analyzing a respective one of the at least one second hyperlink compared to the first hyperlink. | 0.686242 |
8,584,226 | 1 | 26 | 1. A method for a geographic country of origin filter of information transmitting on a network: a) a network object on which the method is embodied, which extracts a network address from Internet traffic routed or collected by said network object; and performs at least one data lookup operation to obtain country of origin geographic information pertaining to said Internet network address; b) configuring said geographic country of origin filter by: Sending or receiving information used to generate a set of persistent geographic country of origin associations comprising a plurality of Internet address blocks; Performing at least one data processing operating to associate a geographic country of origin location pertaining to each block; and Generating at least one geographic country of origin security assertion wherein a device action is defined for at least one geographic country of origin association wherein the device action is triggered for any Internet address belonging to a defined network address block having an estimated country of origin geographic location, wherein the device action either: Allows Internet traffic to be sent or received from said Internet address to the desired destination; Disallows Internet traffic to be sent or received from said Internet address to the desired destination; or Mows Internet traffic to be sent or received from said Internet address to an undesired destination determined by said geographic filter; c) Optimizing said geographic country of origin information pertaining to Internet network addresses in accordance with at least one algorithm, wherein an algorithm is applied to the plurality of geographic country of origin associations between IP address blocks and geographic country of origin locations. | 1. A method for a geographic country of origin filter of information transmitting on a network: a) a network object on which the method is embodied, which extracts a network address from Internet traffic routed or collected by said network object; and performs at least one data lookup operation to obtain country of origin geographic information pertaining to said Internet network address; b) configuring said geographic country of origin filter by: Sending or receiving information used to generate a set of persistent geographic country of origin associations comprising a plurality of Internet address blocks; Performing at least one data processing operating to associate a geographic country of origin location pertaining to each block; and Generating at least one geographic country of origin security assertion wherein a device action is defined for at least one geographic country of origin association wherein the device action is triggered for any Internet address belonging to a defined network address block having an estimated country of origin geographic location, wherein the device action either: Allows Internet traffic to be sent or received from said Internet address to the desired destination; Disallows Internet traffic to be sent or received from said Internet address to the desired destination; or Mows Internet traffic to be sent or received from said Internet address to an undesired destination determined by said geographic filter; c) Optimizing said geographic country of origin information pertaining to Internet network addresses in accordance with at least one algorithm, wherein an algorithm is applied to the plurality of geographic country of origin associations between IP address blocks and geographic country of origin locations. 26. The method of claim 1 , wherein said optimizing is compression into as large continuous blocks as possible. | 0.836765 |
8,676,866 | 5 | 6 | 5. The method of claim 1 , wherein the plurality of constraints define the removal of exclusive edges from the conflict-free merged graph to provide an MHS, exclusive edges being identified based on leaf nodes of the conflict-free merged graph. | 5. The method of claim 1 , wherein the plurality of constraints define the removal of exclusive edges from the conflict-free merged graph to provide an MHS, exclusive edges being identified based on leaf nodes of the conflict-free merged graph. 6. The method of claim 5 , wherein the plurality of constraints comprise a maximum occurrence constraint, the maximum occurrence constraint providing that a child node can have only one parent node. | 0.937697 |
9,477,646 | 1 | 9 | 1. A computer-implemented method of drawing an arbitrary graphics object in a web page comprising: creating, by a web browser, a file in a non-transitory computer readable storage medium, including coded markup language that specifies a drawing space as an extent within the web page and coded procedural language that specifies a drawing command to draw the arbitrary graphics object in the drawing space in the web page; creating the drawing space within the web page using the coded markup language; and drawing, by the web browser, the arbitrary graphics object into the drawing space within the web page using the coded procedural language. | 1. A computer-implemented method of drawing an arbitrary graphics object in a web page comprising: creating, by a web browser, a file in a non-transitory computer readable storage medium, including coded markup language that specifies a drawing space as an extent within the web page and coded procedural language that specifies a drawing command to draw the arbitrary graphics object in the drawing space in the web page; creating the drawing space within the web page using the coded markup language; and drawing, by the web browser, the arbitrary graphics object into the drawing space within the web page using the coded procedural language. 9. The method of claim 1 , wherein the drawing space comprises a three-dimensional drawing space. | 0.928042 |
5,379,366 | 11 | 40 | 11. The method for representing information in a computer system according to claim 9, wherein each record of said database stores a plurality of relationships, said relationships being comprised of a characterization and a value, the characterization of said relationship being a URN of a second record which defines the nature of the relationship, and the value of said relationship being a complex data type comprised of internal, external and mixed values which define the object of a relationship, internal values storing only URNs of other records and, external values storing values other than the URNs of other records such as character strings, integers, and real numbers, and mixed values storing a combination of values typical of internal and external values. | 11. The method for representing information in a computer system according to claim 9, wherein each record of said database stores a plurality of relationships, said relationships being comprised of a characterization and a value, the characterization of said relationship being a URN of a second record which defines the nature of the relationship, and the value of said relationship being a complex data type comprised of internal, external and mixed values which define the object of a relationship, internal values storing only URNs of other records and, external values storing values other than the URNs of other records such as character strings, integers, and real numbers, and mixed values storing a combination of values typical of internal and external values. 40. The method for representing information in a computer system according to claim 11, further comprising the step of: establishing in said computer system a descriptive database for describing an active concept record designated by a user, comprised of a plurality of records each of which stores a single relationship having an associated URN for said active concept record, and an associated URN for a source record in which said relationship is stored, the URN for said active concept being the URN of the record in said knowledge representation database for which the description in the descriptive database is assembled, and the URN for the source record being the URN of that record in the knowledge representation database in which said relationship is stored. | 0.948978 |
8,307,279 | 13 | 27 | 13. A method, comprising: receiving a structured document defining a plurality of display elements, the plurality of display elements including a resizable container element and a scalable element defined to be located at least partially within the resizable container element; executing a rendering function that calculates a display position for each of the plurality of display elements; producing rendered content, the rendered content based at least in part on the display position for each of the plurality of display elements; outputting a viewable area of the rendered content; receiving a scaling input; redefining the size of the scalable element according to the scaling input; and selectively redefining the size of the resizable container element based on the display position of the resizable container element with respect to the viewable area of the rendered content by determining whether the resizable container element will be located within the viewable area if resized to completely contain the scalable element, resizing the resizable container element if it will be located within the viewable area, and maintaining the size of the resizable container element if it will not be located within the viewable area. | 13. A method, comprising: receiving a structured document defining a plurality of display elements, the plurality of display elements including a resizable container element and a scalable element defined to be located at least partially within the resizable container element; executing a rendering function that calculates a display position for each of the plurality of display elements; producing rendered content, the rendered content based at least in part on the display position for each of the plurality of display elements; outputting a viewable area of the rendered content; receiving a scaling input; redefining the size of the scalable element according to the scaling input; and selectively redefining the size of the resizable container element based on the display position of the resizable container element with respect to the viewable area of the rendered content by determining whether the resizable container element will be located within the viewable area if resized to completely contain the scalable element, resizing the resizable container element if it will be located within the viewable area, and maintaining the size of the resizable container element if it will not be located within the viewable area. 27. The method of claim 13 , wherein the scaling input includes a percentage change in the distance between two touch points that are sensed by a display screen. | 0.910951 |
8,095,539 | 7 | 8 | 7. A computer-implemented method comprising: (A) identifying a first plurality of documents associated with a user; (B) identifying, by a processor, a plurality of search strings based on a plurality of class keywords associated with a plurality of nodes in a taxonomy, the plurality of nodes being associated with a first plurality of classes, comprising: (B)(1) traversing a branch in the taxonomy linking the root node of the taxonomy to node N; and (B)(2) for each node in the branch, selecting a class keyword associated with node N and adding the selected class keyword to one of the plurality of search strings; (C) identifying a second plurality of classes, in the taxonomy, associated with the first plurality of documents by performing a plurality of searches on the first plurality of documents using the identified plurality of search strings; (D) associating the second plurality of classes with the user; and (E) storing the association of the second plurality of classes with the user. | 7. A computer-implemented method comprising: (A) identifying a first plurality of documents associated with a user; (B) identifying, by a processor, a plurality of search strings based on a plurality of class keywords associated with a plurality of nodes in a taxonomy, the plurality of nodes being associated with a first plurality of classes, comprising: (B)(1) traversing a branch in the taxonomy linking the root node of the taxonomy to node N; and (B)(2) for each node in the branch, selecting a class keyword associated with node N and adding the selected class keyword to one of the plurality of search strings; (C) identifying a second plurality of classes, in the taxonomy, associated with the first plurality of documents by performing a plurality of searches on the first plurality of documents using the identified plurality of search strings; (D) associating the second plurality of classes with the user; and (E) storing the association of the second plurality of classes with the user. 8. The method of claim 7 , wherein (B) further comprises delimiting each of the plurality of class keywords in the one of the plurality of search strings to mark each of the plurality of class keywords in the one of the plurality of search strings as an atomic entity. | 0.737769 |
7,747,937 | 11 | 13 | 11. A method for processing a collection of web bookmarks, comprising the steps of: receiving at least one bookmark notice, said notice comprising a reference to a web resource, and a natural language description of said web resource; parsing said natural language description to obtain at least one sequence of at least one content word; associating a topic category with said sequence; recursively computing generic topics using non-empty subsequences of content words from the sequence associated with the tonic category, wherein the generic topics are used to automatically construct a taxonomy of topics for classifying web resources; associating said web resource with said topic category; presenting the association of said web resource with said topic category; and presenting the association of said topic category with said taxonomy of topics. | 11. A method for processing a collection of web bookmarks, comprising the steps of: receiving at least one bookmark notice, said notice comprising a reference to a web resource, and a natural language description of said web resource; parsing said natural language description to obtain at least one sequence of at least one content word; associating a topic category with said sequence; recursively computing generic topics using non-empty subsequences of content words from the sequence associated with the tonic category, wherein the generic topics are used to automatically construct a taxonomy of topics for classifying web resources; associating said web resource with said topic category; presenting the association of said web resource with said topic category; and presenting the association of said topic category with said taxonomy of topics. 13. The method of claim 11 , further comprising the steps of: associating a date with each of said at least one bookmark notice; and presenting a plurality of said at least one bookmark notice in any of forward and reverse chronological order, according to said associated date. | 0.872243 |
10,133,814 | 11 | 14 | 11. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing an explanatory electronic document, the operations comprising: receiving, by the one or more processors, input from a user, the input comprising data that is at least partially representative of a subject; performing, by the one or more processors, semantic context association based on the user input, one or more computer-readable ontologies, and a computer-readable knowledge graph to provide a target subject profile, the target subject profile comprising two or more associations describing the subject at respective degrees of specificity, at least one association comprising concepts from the knowledge graph that are more general than respective entities provided in the input; providing, by the one or more processors, a set of peer user profiles based on a user profile and a superset of peer user profiles using semantic user profile association between the user profile and each peer user profile in the superset of peer user profiles; retrieving, by the one or more processors, one or more peer subject profiles from computer-readable memory, each peer subject profile being associated with a peer user profile in the set of peer user profiles, and comprising one or more associations, each association describing a past subject experienced by a peer user; filtering, by the one or more processors, at least one association from a peer subject profile based on a comparison with a respective association in the target subject profile and data provided in a knowledge graph; providing, by the one or more processors, at least one explanatory text string associated with the subject based on at least one remaining association in the peer subject profile; and providing, by the one or more processors, the explanatory electronic document comprising the at least one explanatory text string. | 11. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing an explanatory electronic document, the operations comprising: receiving, by the one or more processors, input from a user, the input comprising data that is at least partially representative of a subject; performing, by the one or more processors, semantic context association based on the user input, one or more computer-readable ontologies, and a computer-readable knowledge graph to provide a target subject profile, the target subject profile comprising two or more associations describing the subject at respective degrees of specificity, at least one association comprising concepts from the knowledge graph that are more general than respective entities provided in the input; providing, by the one or more processors, a set of peer user profiles based on a user profile and a superset of peer user profiles using semantic user profile association between the user profile and each peer user profile in the superset of peer user profiles; retrieving, by the one or more processors, one or more peer subject profiles from computer-readable memory, each peer subject profile being associated with a peer user profile in the set of peer user profiles, and comprising one or more associations, each association describing a past subject experienced by a peer user; filtering, by the one or more processors, at least one association from a peer subject profile based on a comparison with a respective association in the target subject profile and data provided in a knowledge graph; providing, by the one or more processors, at least one explanatory text string associated with the subject based on at least one remaining association in the peer subject profile; and providing, by the one or more processors, the explanatory electronic document comprising the at least one explanatory text string. 14. The computer-readable storage medium of claim 11 , wherein providing the at least one explanatory text string comprises determining that a subject associated with the at least one remaining association is of the same type and of a different value than a respective subject of a peer subject profile, and in response, retrieving the at least one explanatory text string from computer-readable memory. | 0.516787 |
8,145,482 | 1 | 6 | 1. A method for enhancing the analysis of at least one test word extracted from a test audio source, the method operating within an environment having an acoustic environment, the method comprising the steps of: a first receiving step for receiving on a computing platform at least one training word extracted from a training audio source; a first key phrase extraction step for extracting a training key phrase from the at least one training word according to a linguistic rule; a first feature extraction step for extracting at least one first feature from each of the at least one training word from the environment, or from the acoustic environment; a second receiving step for receiving tagging information relating to a significance level or an importance level of the training key phrase; a key phrase model generation step for generating a key phrase training model based on the training key phrase and the at least one first feature, and the tagging; a third receiving step for receiving at least one test word extracted from a test audio source; a second key phrase extraction step for extracting a test key phrase from the at least one test word according to the linguistic rule; a second feature extraction step for extracting at least one second feature from each of the at least one test key phrase, from the environment, or from the acoustic environment; and applying the key phrase training model on the test key phrase and the at least one second feature, thus obtaining an importance indication for the test key phrase. | 1. A method for enhancing the analysis of at least one test word extracted from a test audio source, the method operating within an environment having an acoustic environment, the method comprising the steps of: a first receiving step for receiving on a computing platform at least one training word extracted from a training audio source; a first key phrase extraction step for extracting a training key phrase from the at least one training word according to a linguistic rule; a first feature extraction step for extracting at least one first feature from each of the at least one training word from the environment, or from the acoustic environment; a second receiving step for receiving tagging information relating to a significance level or an importance level of the training key phrase; a key phrase model generation step for generating a key phrase training model based on the training key phrase and the at least one first feature, and the tagging; a third receiving step for receiving at least one test word extracted from a test audio source; a second key phrase extraction step for extracting a test key phrase from the at least one test word according to the linguistic rule; a second feature extraction step for extracting at least one second feature from each of the at least one test key phrase, from the environment, or from the acoustic environment; and applying the key phrase training model on the test key phrase and the at least one second feature, thus obtaining an importance indication for the test key phrase. 6. The method of claim 1 wherein the first feature extraction step or the second feature extraction step comprise extracting at least one item selected from the group consisting of: number of tokens in the test key phrase or in the training key phrase; number of characters of a word in the test key phrase or in the training key phrase; test key phrase or training key phrase frequency within the test audio source or training audio source; total text length; word stems of words comprised in the test key phrase or in the training key phrase; phonemes comprised in a word in the test key phrase or in the training key phrase; adjacent words to the test key phrase or to the training key phrase; average speech-to-text certainty of words in the test key phrase or in the training key phrase; relative position of a first instance of the test key phrase or the training key phrase within the extracted text; speaker side; part of speech of a word of the test key phrase or the training key phrase; part of speech of adjacent words to a word of the test key phrase or the training key phrase; emotion degree within a word of the test key phrase or the training key phrase; and overlap with talkover or laughter indications. | 0.500409 |
9,870,468 | 12 | 19 | 12. A computer-implemented system for segregating data and code in a dynamic language, wherein an environment and the segregated data and code operating in the environment are controlled using a common dynamic language, the system comprising: a non-transitory computer-readable medium, encoded with instructions in the common dynamic language, which when executed by one or more processors, causes the system to: implement the environment in the common dynamic language, the environment including a framework, the framework including a plurality of properties; identify a visible framework property that is visible to applications; identify an invisible framework property that is not visible to the applications; and implement a first application in a first sandbox within the environment, wherein the first application is implemented in the common dynamic language, wherein the first application is unable to access the invisible framework property, and wherein the first application is able to access the visible framework property. | 12. A computer-implemented system for segregating data and code in a dynamic language, wherein an environment and the segregated data and code operating in the environment are controlled using a common dynamic language, the system comprising: a non-transitory computer-readable medium, encoded with instructions in the common dynamic language, which when executed by one or more processors, causes the system to: implement the environment in the common dynamic language, the environment including a framework, the framework including a plurality of properties; identify a visible framework property that is visible to applications; identify an invisible framework property that is not visible to the applications; and implement a first application in a first sandbox within the environment, wherein the first application is implemented in the common dynamic language, wherein the first application is unable to access the invisible framework property, and wherein the first application is able to access the visible framework property. 19. The system of claim 12 , wherein the computer-readable medium is further encoded with instructions, which when executed by the one or more processors, causes the system to: receive, for each of a plurality of framework properties, an identification of whether the framework property is visible or invisible to applications operating in sandboxes; wherein the framework is implemented according to the received identifications. | 0.603321 |
8,538,754 | 2 | 4 | 2. The method of claim 1 , wherein retrieving the set of presentable suggestions includes selecting a second set of suggestions from a first set of suggestions that can potentially change the passage. | 2. The method of claim 1 , wherein retrieving the set of presentable suggestions includes selecting a second set of suggestions from a first set of suggestions that can potentially change the passage. 4. The method of claim 2 , wherein the highlighting is based on the second set of suggestions. | 0.948578 |
8,533,236 | 1 | 8 | 1. A computer-implemented method comprising: obtaining, by a computer system, information that identifies interactions among users of a social network; generating, by the computer system, a graph that is based at least in part on the obtained information and comprises i) nodes that represent the users of the social network and ii) edges that connect the nodes and that represent relationships between the users; assigning, to at least a portion of the nodes in the graph and for one or more labels, initial label values that indicate levels of interest of users associated with the portion of the nodes in content associated with the one or more labels; determining, for the nodes in the graph, label values for the one or more labels based on iterative propagation of the initial label values among the nodes using the edges of the graph, wherein iterative propagation comprises, for a particular node from the nodes in the graph, determining particular label values for the particular node at each of a plurality of iterations by combining, at each of the plurality of iterations, neighboring label values for neighboring nodes that are connected to the particular node by a portion of the edges of the graph; and identifying, by the computer system for a particular label from the one or more labels, one or more users to provide with particular content that is associated with the particular label, wherein the one or more users are identified based on the determined label values for the particular label. | 1. A computer-implemented method comprising: obtaining, by a computer system, information that identifies interactions among users of a social network; generating, by the computer system, a graph that is based at least in part on the obtained information and comprises i) nodes that represent the users of the social network and ii) edges that connect the nodes and that represent relationships between the users; assigning, to at least a portion of the nodes in the graph and for one or more labels, initial label values that indicate levels of interest of users associated with the portion of the nodes in content associated with the one or more labels; determining, for the nodes in the graph, label values for the one or more labels based on iterative propagation of the initial label values among the nodes using the edges of the graph, wherein iterative propagation comprises, for a particular node from the nodes in the graph, determining particular label values for the particular node at each of a plurality of iterations by combining, at each of the plurality of iterations, neighboring label values for neighboring nodes that are connected to the particular node by a portion of the edges of the graph; and identifying, by the computer system for a particular label from the one or more labels, one or more users to provide with particular content that is associated with the particular label, wherein the one or more users are identified based on the determined label values for the particular label. 8. The computer-implemented method of claim 1 , wherein combining the neighboring label values comprises adding and normalizing the neighboring label values. | 0.879231 |
7,734,287 | 17 | 18 | 17. The method of claim 16 , further comprising the step of displaying communications received by the wireless ground station at a user terminal configured with a graphical display. | 17. The method of claim 16 , further comprising the step of displaying communications received by the wireless ground station at a user terminal configured with a graphical display. 18. The method of claim 17 , wherein said user terminal comprises a user terminal interface and wherein said wireless ground station comprises a ground station transmitter. | 0.947689 |
9,240,181 | 15 | 16 | 15. An apparatus comprising: an input/output interface configured to receive an audio stream; a processor coupled to the input/output interface and configured to: segment the audio stream into a plurality of time segments using speaker segmentation and recognition (SSR), each of the plurality of time segments corresponding to a name label, to produce an SSR transcript; transcribe the audio stream into a plurality of word regions using automatic speech recognition (ASR), each of the plurality of word regions having an associated accuracy confidence, to produce an ASR transcript; identify a plurality of low confidence regions from the plurality of word regions, each of the low confidence regions having an associated accuracy confidence below a threshold; identify at least one likely name region from the ASR transcript using named entity recognition (NER) rules, wherein the NER rules analyze word regions to identify the at least one likely name region, and the NER rules associate each of the at least one likely name regions with a name label from the SSR transcript corresponding to one of a current, previous, or next time segment; filter the at least one likely name region with the plurality of low confidence regions to determine at least one low confidence name region; select all of the likely low confidence name regions associated with a selected name label, the selected name label being selected from the name labels in the SSR transcript; create a phoneme transcript from the audio stream for each of the selected likely name regions using a phoneme decoder; and correlate the selected name label with all of the phoneme transcripts for the selected likely name regions. | 15. An apparatus comprising: an input/output interface configured to receive an audio stream; a processor coupled to the input/output interface and configured to: segment the audio stream into a plurality of time segments using speaker segmentation and recognition (SSR), each of the plurality of time segments corresponding to a name label, to produce an SSR transcript; transcribe the audio stream into a plurality of word regions using automatic speech recognition (ASR), each of the plurality of word regions having an associated accuracy confidence, to produce an ASR transcript; identify a plurality of low confidence regions from the plurality of word regions, each of the low confidence regions having an associated accuracy confidence below a threshold; identify at least one likely name region from the ASR transcript using named entity recognition (NER) rules, wherein the NER rules analyze word regions to identify the at least one likely name region, and the NER rules associate each of the at least one likely name regions with a name label from the SSR transcript corresponding to one of a current, previous, or next time segment; filter the at least one likely name region with the plurality of low confidence regions to determine at least one low confidence name region; select all of the likely low confidence name regions associated with a selected name label, the selected name label being selected from the name labels in the SSR transcript; create a phoneme transcript from the audio stream for each of the selected likely name regions using a phoneme decoder; and correlate the selected name label with all of the phoneme transcripts for the selected likely name regions. 16. The apparatus of claim 15 , wherein the processor is further configured to: create a phoneme string of the selected name label using a grapheme-to-phoneme (G2P) tool; match at least a portion of the phoneme string of the selected name label with at least a portion of the phoneme transcript for each of the selected likely name regions. | 0.612756 |
7,827,029 | 27 | 28 | 27. The system of claim 26 , in which the meaning structure is determined based on a parsing grammar. | 27. The system of claim 26 , in which the meaning structure is determined based on a parsing grammar. 28. The system of claim 27 , in which the parsing grammar is determined based on at least one of: a characteristic of the passage and user preference. | 0.951204 |
7,529,731 | 16 | 23 | 16. A computer program product comprising: a computer usable data carrier having computer readable instructions embodied therein for causing a computer to perform a method, said method comprising: generating a plurality of candidate phrases responsive to a first open-site network search for a category term; determining an external score responsive to a second open-site network search for at least one of said plurality of candidate phrases; determining at least one targeted-site expected to be associated with each of said plurality of candidate phrases; determining an internal score for said at least one of said plurality of candidate phrases responsive to at least one targeted-site network search of said at least one targeted-site for said category term, said determining at least one targeted-site comprising: constructing a site query for each one of said plurality of candidate phrases responsive to the one of said plurality of candidate phrases and receiving at least one site query result responsive to the site query; filtering the at least one site query result by discarding each site query result for which the one of said plurality of candidate phrases is not contained in at least one of a site address and a site name of the site query result; extracting at least one probable web host name from the remaining of the at least one site query result; constructing a second mode query responsive to said category term and said at least one probable web host and submitting at least one targeted-site network search using said second mode query; receiving at least one third search result responsive to said at least one targeted-site network search using said second mode query; repeating said constructing a site query, said discarding, said extracting at least one probable web host name, said constructing a second mode query, and said receiving at least one third search result for each remaining of said plurality of candidate phrases; and determining said internal score responsive to said at least one third search result; determining a final score for said at least one of said plurality of candidate phrases responsive to said internal score and said external score; and presenting said at least one of said plurality of candidate phrases. | 16. A computer program product comprising: a computer usable data carrier having computer readable instructions embodied therein for causing a computer to perform a method, said method comprising: generating a plurality of candidate phrases responsive to a first open-site network search for a category term; determining an external score responsive to a second open-site network search for at least one of said plurality of candidate phrases; determining at least one targeted-site expected to be associated with each of said plurality of candidate phrases; determining an internal score for said at least one of said plurality of candidate phrases responsive to at least one targeted-site network search of said at least one targeted-site for said category term, said determining at least one targeted-site comprising: constructing a site query for each one of said plurality of candidate phrases responsive to the one of said plurality of candidate phrases and receiving at least one site query result responsive to the site query; filtering the at least one site query result by discarding each site query result for which the one of said plurality of candidate phrases is not contained in at least one of a site address and a site name of the site query result; extracting at least one probable web host name from the remaining of the at least one site query result; constructing a second mode query responsive to said category term and said at least one probable web host and submitting at least one targeted-site network search using said second mode query; receiving at least one third search result responsive to said at least one targeted-site network search using said second mode query; repeating said constructing a site query, said discarding, said extracting at least one probable web host name, said constructing a second mode query, and said receiving at least one third search result for each remaining of said plurality of candidate phrases; and determining said internal score responsive to said at least one third search result; determining a final score for said at least one of said plurality of candidate phrases responsive to said internal score and said external score; and presenting said at least one of said plurality of candidate phrases. 23. The computer program product of claim 16 , wherein said category term is a generic term for a product or service and said plurality of candidate phrases contain one or more brands of said product or service. | 0.969849 |
9,158,757 | 18 | 20 | 18. A system for displaying a list of candidate words corresponding to a continuous stroke, comprising: means for receiving a portion of a first part of a continuous stroke which has been input into a virtual keyboard with an integrated correction display, wherein the first part of the continuous stroke corresponds to locations, on the virtual keyboard, of multiple letters of the beginning of a word in sequence, without indicating a last letter of the word; and wherein the portion of the first part of the continuous stroke corresponds to one or more initial letters that comprise less than all the letters of the word; means for determining, while the first part of the continuous stroke is being received and before the last letter corresponding to the first part of the continuous stroke has been indicated, a matching set of word patterns that at least partially match the first part of the continuous stroke, wherein the determining is performed, at least in part, by comparing the received portion of the first part of the continuous stroke with an initial portion of multiple known word patterns which correspond to multiple known words; means for generating, from the matching set of word patterns, multiple known words which correspond to the received portion of the first part of the continuous stroke; means for displaying, in response to a trigger event, at least one of the multiple known words on the integrated correction display, integrated with the virtual keyboard, before the continuous stroke has been completed, wherein the trigger event is based on a comparison between speed information concerning a current rate of movement of the continuous stroke and a threshold speed value. | 18. A system for displaying a list of candidate words corresponding to a continuous stroke, comprising: means for receiving a portion of a first part of a continuous stroke which has been input into a virtual keyboard with an integrated correction display, wherein the first part of the continuous stroke corresponds to locations, on the virtual keyboard, of multiple letters of the beginning of a word in sequence, without indicating a last letter of the word; and wherein the portion of the first part of the continuous stroke corresponds to one or more initial letters that comprise less than all the letters of the word; means for determining, while the first part of the continuous stroke is being received and before the last letter corresponding to the first part of the continuous stroke has been indicated, a matching set of word patterns that at least partially match the first part of the continuous stroke, wherein the determining is performed, at least in part, by comparing the received portion of the first part of the continuous stroke with an initial portion of multiple known word patterns which correspond to multiple known words; means for generating, from the matching set of word patterns, multiple known words which correspond to the received portion of the first part of the continuous stroke; means for displaying, in response to a trigger event, at least one of the multiple known words on the integrated correction display, integrated with the virtual keyboard, before the continuous stroke has been completed, wherein the trigger event is based on a comparison between speed information concerning a current rate of movement of the continuous stroke and a threshold speed value. 20. The system of claim 18 : wherein the continuous stroke is associated with an active application; and wherein the system further comprises means for deleting a selected one of the multiple known words from the associated active application or correction display. | 0.935017 |
7,490,092 | 25 | 53 | 25. A method of indexing and searching timed media files, as recited in claim 1 , wherein said calculation includes the processing of language contained within the timed media file. | 25. A method of indexing and searching timed media files, as recited in claim 1 , wherein said calculation includes the processing of language contained within the timed media file. 53. A method of indexing and searching timed media files, as recited in claim 25 , wherein a part of speech is determined for at least one information representation. | 0.927889 |
8,160,306 | 1 | 9 | 1. A method of analyzing a patent document, said method comprising the steps of: providing a patent document, wherein said patent document includes text data and image data comprised of a figure; providing element name data for said patent document comprising at least one element name and at least one reference numeral associated with said at least one element name; analyzing said image data of said patent document; identifying a figure reference numeral within said figure of said image data; identifying a location of said figure reference numeral within said figure; identifying an element name within said element name data that is associated with said figure reference numeral within said image data; adding said element name to said image data adjacent to said figure reference numeral; and creating a final image file that includes said image data and said element name. | 1. A method of analyzing a patent document, said method comprising the steps of: providing a patent document, wherein said patent document includes text data and image data comprised of a figure; providing element name data for said patent document comprising at least one element name and at least one reference numeral associated with said at least one element name; analyzing said image data of said patent document; identifying a figure reference numeral within said figure of said image data; identifying a location of said figure reference numeral within said figure; identifying an element name within said element name data that is associated with said figure reference numeral within said image data; adding said element name to said image data adjacent to said figure reference numeral; and creating a final image file that includes said image data and said element name. 9. The method of analyzing a patent document of claim 1 , wherein said step of adding said element name includes identifying an area adjacent said reference numeral that has less than a maximum percentage of non-white pixels. | 0.551793 |
9,706,403 | 1 | 15 | 1. A method for providing a mobile device-based community corrections supervision system, comprising: receiving, by a mobile device of an enrollee and from a remote server, a first alert requiring the enrollee to perform a first check-in, wherein the enrollee is enrolled within the mobile device-based community corrections supervision system; receiving a first acknowledgement of the first alert from the enrollee; presenting, in response to receiving the first acknowledgement, a displayed text to the enrollee via a screen on the mobile device of the enrollee; requesting, after the presenting, the enrollee to recite the displayed text; recording, using a camera and a microphone on the mobile device of the enrollee, the enrollee reciting the displayed text to obtain a recordation; and transmitting the recordation to the remote server for monitoring by a supervision case manager assigned to the enrollee. | 1. A method for providing a mobile device-based community corrections supervision system, comprising: receiving, by a mobile device of an enrollee and from a remote server, a first alert requiring the enrollee to perform a first check-in, wherein the enrollee is enrolled within the mobile device-based community corrections supervision system; receiving a first acknowledgement of the first alert from the enrollee; presenting, in response to receiving the first acknowledgement, a displayed text to the enrollee via a screen on the mobile device of the enrollee; requesting, after the presenting, the enrollee to recite the displayed text; recording, using a camera and a microphone on the mobile device of the enrollee, the enrollee reciting the displayed text to obtain a recordation; and transmitting the recordation to the remote server for monitoring by a supervision case manager assigned to the enrollee. 15. The method of claim 1 , wherein the enrollee is enrolled in the mobile device-based community corrections supervision system for parole-purposes. | 0.84919 |
8,874,553 | 6 | 7 | 6. The method of claim 4 , wherein the reference corpus is a taxonomy and the article is a node in the taxonomy. | 6. The method of claim 4 , wherein the reference corpus is a taxonomy and the article is a node in the taxonomy. 7. The method of claim 6 , further comprising generating a lineage vector by combining concept vectors for any descendent articles of the article corresponding to the homonym concept in the taxonomy; and wherein combining the first and second word vectors to generate the concept vector for the homonym concept further comprises combining the first and second vectors and the lineage vector. | 0.899949 |
9,600,919 | 8 | 9 | 8. The method of claim 1 , wherein generating the narrative comprises at least one of: receiving a source document associated with the entity from the selected data source; parsing the source document; classifying the parsed contents of the source document; defining at least one text section by identifying text section boundaries in the source document; scoring the at least one identified text section in the document based on content or position of the at least one identified text section; or selecting at least one of the at least one scored text sections to be added to a narration, the selecting based on a score of the at least one of the at least one scored text sections. | 8. The method of claim 1 , wherein generating the narrative comprises at least one of: receiving a source document associated with the entity from the selected data source; parsing the source document; classifying the parsed contents of the source document; defining at least one text section by identifying text section boundaries in the source document; scoring the at least one identified text section in the document based on content or position of the at least one identified text section; or selecting at least one of the at least one scored text sections to be added to a narration, the selecting based on a score of the at least one of the at least one scored text sections. 9. The method of claim 8 , wherein the classification of the parsed contents of the source document is based on metadata or title keyword matching. | 0.953628 |
8,341,178 | 20 | 26 | 20. A computer-readable storage medium storing instructions, wherein the instructions include instructions which, when executed by one or more processors, cause the one or more processors to perform steps of: selecting, from a workload set, a set of targeted database query language statements for performance analysis, wherein the workload set comprises database query language statements; executing, on a first database system, the set of targeted database query language statements; wherein executing the set of targeted database query language statements on the first database system comprises gathering a first set of performance data about the execution of each database query language statement of said targeted database query language statements on the first database system; executing, on a second database system, the set of targeted database query language statements, wherein the database of the second database system is a modified version of the database of the first database system; wherein executing the set of targeted database query language statements on the second database system comprises gathering a second set of performance data about the execution of said each database query language statement of said targeted database query language statements on the second database system; wherein each of the first set of performance data and second set of performance data comprises statistics based on at least one of the following: (a) CPU time consumed to execute said each database query language statement of said set of targeted database query language statements, (b) buffer reads incurred to execute said each database query language statement of said set of targeted database query language statements, and (c) disk reads incurred to execute said each database query language statement of said set of targeted database query language statements; comparing the first set of performance data with the second set of performance data; and generating information that indicates a result of the comparison, wherein the information that indicates a result of the comparison includes a difference in a total performance metric for executing the set of targeted database query language statements between the first database system and the second database system; and wherein the information that indicates a result of the comparison includes, for each database query language statement in the set of targeted database query language statements, a difference in a performance metric for executing said each database query language statement between the first database system and the second database system. | 20. A computer-readable storage medium storing instructions, wherein the instructions include instructions which, when executed by one or more processors, cause the one or more processors to perform steps of: selecting, from a workload set, a set of targeted database query language statements for performance analysis, wherein the workload set comprises database query language statements; executing, on a first database system, the set of targeted database query language statements; wherein executing the set of targeted database query language statements on the first database system comprises gathering a first set of performance data about the execution of each database query language statement of said targeted database query language statements on the first database system; executing, on a second database system, the set of targeted database query language statements, wherein the database of the second database system is a modified version of the database of the first database system; wherein executing the set of targeted database query language statements on the second database system comprises gathering a second set of performance data about the execution of said each database query language statement of said targeted database query language statements on the second database system; wherein each of the first set of performance data and second set of performance data comprises statistics based on at least one of the following: (a) CPU time consumed to execute said each database query language statement of said set of targeted database query language statements, (b) buffer reads incurred to execute said each database query language statement of said set of targeted database query language statements, and (c) disk reads incurred to execute said each database query language statement of said set of targeted database query language statements; comparing the first set of performance data with the second set of performance data; and generating information that indicates a result of the comparison, wherein the information that indicates a result of the comparison includes a difference in a total performance metric for executing the set of targeted database query language statements between the first database system and the second database system; and wherein the information that indicates a result of the comparison includes, for each database query language statement in the set of targeted database query language statements, a difference in a performance metric for executing said each database query language statement between the first database system and the second database system. 26. The computer-readable storage medium of claim 20 , wherein the set of targeted database query language statements comprises database query language statements that originate from a specific application. | 0.794411 |
9,508,346 | 12 | 14 | 12. The method of claim 5 , further comprising: evaluating the at least one confusion network on an utterance by utterance basis to determine an utterance by utterance conformity between a respective audio file and the language model; and evaluating the at least one confusion network on an overall basis to determine an overall conformity between a respective audio file and the language model. | 12. The method of claim 5 , further comprising: evaluating the at least one confusion network on an utterance by utterance basis to determine an utterance by utterance conformity between a respective audio file and the language model; and evaluating the at least one confusion network on an overall basis to determine an overall conformity between a respective audio file and the language model. 14. The method of claim 12 , wherein selecting at least one best transcription from the plurality of audio file transcriptions further comprises filtering the plurality of audio file transcriptions based upon the overall transcription quality scores for each audio file transcription to select a set of high quality audio file transcriptions from the plurality of audio file transcriptions. | 0.860913 |
10,165,307 | 1 | 6 | 1. A computer-implemented method, comprising computer-executable instructions that, when executed by a hardware processor, cause the hardware processor to perform acts of: accessing information related to an event; identifying a named entity and an associated entity attribute from the information related to the event, the associated entity attribute including an entity type and an entity popularity parameter, wherein the entity type includes at least one of an organization, person, or location, and the entity popularity parameter defines at least a popularity of the named entity in the information related to the event; accessing trending information based on the named entity and the associated entity attribute; collecting, by using the trending information, training data for identification of one or more entities in a video related to the event, wherein the training data is image data of at least one entity likely associated with the event; training a model using the training data, to learn features of the one or more entities, while presenting the video; performing recognition processing of the one or more entities in the video to identify the one or more entities while the video is being presented, wherein the recognition processing is performed using the trained model; performing a search to retrieve content relevant to the one or more entities identified by the recognition processing; and presenting, in real-time during presenting of the video, the content relevant to the one or more entities identified. | 1. A computer-implemented method, comprising computer-executable instructions that, when executed by a hardware processor, cause the hardware processor to perform acts of: accessing information related to an event; identifying a named entity and an associated entity attribute from the information related to the event, the associated entity attribute including an entity type and an entity popularity parameter, wherein the entity type includes at least one of an organization, person, or location, and the entity popularity parameter defines at least a popularity of the named entity in the information related to the event; accessing trending information based on the named entity and the associated entity attribute; collecting, by using the trending information, training data for identification of one or more entities in a video related to the event, wherein the training data is image data of at least one entity likely associated with the event; training a model using the training data, to learn features of the one or more entities, while presenting the video; performing recognition processing of the one or more entities in the video to identify the one or more entities while the video is being presented, wherein the recognition processing is performed using the trained model; performing a search to retrieve content relevant to the one or more entities identified by the recognition processing; and presenting, in real-time during presenting of the video, the content relevant to the one or more entities identified. 6. The method of claim 1 , wherein the accessing of the information accesses the information from social media networks and according to a predetermined time window relative to the event. | 0.684122 |
8,275,614 | 10 | 16 | 10. A support method for supporting generation of text from speech data, comprising the steps of: calculating a confirmed utterance rate, using a processor, which is an utterance rate of a confirmed part having already-confirmed text in the speech data; obtaining a plurality of candidate character strings which are a speech recognition result of an unconfirmed part having unconfirmed text in the speech data; and selecting one of the plurality of candidate character strings having the utterance time closest to the utterance time of the unconfirmed part in the speech data according to the utterance time consumed to utter the candidate character string at the confirmed utterance rate; further comprising the step of: calculating a candidate utterance rate for each of the plurality of candidate character strings, wherein the candidate utterance rate is an utterance time consumed to utter the candidate character string at the confirmed utterance rate on the basis of the confirmed utterance rate and a number of moras or syllables in the candidate character string; wherein the step of calculating a confirmed utterance rate calculates, as the confirmed utterance rate, the number of moras or syllables uttered per unit time in the confirmed part having already-confirmed text in the speech data; and wherein the selecting step selects one of the plurality of candidate character strings having the utterance time closest to the utterance time of the unconfirmed part in the speech data according to the utterance time calculated by the candidate time calculator; Wherein the step of calculating a candidate utterance rate comprises the steps of: generating a phoneme string of the candidate character string; calculating a correction factor based on a phoneme string of the candidate character string; and calculating, as an utterance time consumed to utter the candidate character string at the confirmed utterance rate, a value obtained by a calculation where the number of moras in the candidate character string is multiplied by the correction factor and then the obtained value is divided by the confirmation utterance rate. | 10. A support method for supporting generation of text from speech data, comprising the steps of: calculating a confirmed utterance rate, using a processor, which is an utterance rate of a confirmed part having already-confirmed text in the speech data; obtaining a plurality of candidate character strings which are a speech recognition result of an unconfirmed part having unconfirmed text in the speech data; and selecting one of the plurality of candidate character strings having the utterance time closest to the utterance time of the unconfirmed part in the speech data according to the utterance time consumed to utter the candidate character string at the confirmed utterance rate; further comprising the step of: calculating a candidate utterance rate for each of the plurality of candidate character strings, wherein the candidate utterance rate is an utterance time consumed to utter the candidate character string at the confirmed utterance rate on the basis of the confirmed utterance rate and a number of moras or syllables in the candidate character string; wherein the step of calculating a confirmed utterance rate calculates, as the confirmed utterance rate, the number of moras or syllables uttered per unit time in the confirmed part having already-confirmed text in the speech data; and wherein the selecting step selects one of the plurality of candidate character strings having the utterance time closest to the utterance time of the unconfirmed part in the speech data according to the utterance time calculated by the candidate time calculator; Wherein the step of calculating a candidate utterance rate comprises the steps of: generating a phoneme string of the candidate character string; calculating a correction factor based on a phoneme string of the candidate character string; and calculating, as an utterance time consumed to utter the candidate character string at the confirmed utterance rate, a value obtained by a calculation where the number of moras in the candidate character string is multiplied by the correction factor and then the obtained value is divided by the confirmation utterance rate. 16. The support method according to claim 10 , wherein the selecting step selects, from the plurality of candidate character strings, a candidate character string included in a part where text is already confirmed. | 0.9144 |
8,520,982 | 13 | 14 | 13. A computer-implemented method comprising: identifying a first advertisement; identifying reference advertisements, wherein each reference advertisement is associated with at least one keyword; comparing image data that specify optical content depicted by the first advertisement with image data that specify optical content depicted by each reference advertisement; determining, by one or more processors, a set of one or more of the reference advertisements having optical content that matches the optical content of the first advertisement based on the comparison; determining a predetermined number of highest scoring reference advertisements from the set to associate with the first advertisement; associating keywords from the predetermined number of highest scoring reference advertisements with the first advertisement; and using the keywords from the predetermined number of highest scoring reference advertisements to deliver the first advertisement in response to a received advertisement request that includes at least one of the keywords. | 13. A computer-implemented method comprising: identifying a first advertisement; identifying reference advertisements, wherein each reference advertisement is associated with at least one keyword; comparing image data that specify optical content depicted by the first advertisement with image data that specify optical content depicted by each reference advertisement; determining, by one or more processors, a set of one or more of the reference advertisements having optical content that matches the optical content of the first advertisement based on the comparison; determining a predetermined number of highest scoring reference advertisements from the set to associate with the first advertisement; associating keywords from the predetermined number of highest scoring reference advertisements with the first advertisement; and using the keywords from the predetermined number of highest scoring reference advertisements to deliver the first advertisement in response to a received advertisement request that includes at least one of the keywords. 14. The method of claim 13 , wherein comparing image data that specify optical content depicted by the first advertisement with image data specifying optical content depicted by each reference advertisement comprises: comparing textual content of the first advertisement with textual content of each reference advertisement. | 0.69606 |
7,937,409 | 23 | 28 | 23. The computer program product of claim 19 , wherein the physical markup representation is a paginated representation including pages each having a respective physical width and a respective physical height. | 23. The computer program product of claim 19 , wherein the physical markup representation is a paginated representation including pages each having a respective physical width and a respective physical height. 28. The computer program product of claim 23 , wherein the instructions for causing a computer to conform the physical markup representation comprise instructions for causing a computer to: scale the height of the physical markup representation by the scaling factor. | 0.906118 |
8,761,988 | 1 | 2 | 1. A method for operating a powertrain system including a multi-mode transmission configured to transfer torque among an engine, torque machines, and a driveline, the method comprising: executing a search to determine a preferred engine operating point for operating the powertrain system in a transmission range in response to an output torque request, said search comprising: for each of a plurality of candidate engine speeds within an input speed range and each of a plurality of candidate torque normalization ratios wherein the candidate torque normalization ratios for the candidate engine speed include a normalized engine torque at 0.0 corresponding to a minimum permissible engine torque for the candidate engine speed and a normalized engine torque at 1.0 corresponding to a maximum permissible engine torque for the candidate engine speed: employing the candidate torque normalization ratio to determine a candidate engine torque from a normalized torque search space, and determining a candidate power cost associated with operating the powertrain system at the candidate engine torque; determining a preferred engine speed comprising the candidate engine speed corresponding to the one of the candidate engine torques associated with a minimum of the candidate power costs; and controlling engine operation responsive to the preferred engine speed. | 1. A method for operating a powertrain system including a multi-mode transmission configured to transfer torque among an engine, torque machines, and a driveline, the method comprising: executing a search to determine a preferred engine operating point for operating the powertrain system in a transmission range in response to an output torque request, said search comprising: for each of a plurality of candidate engine speeds within an input speed range and each of a plurality of candidate torque normalization ratios wherein the candidate torque normalization ratios for the candidate engine speed include a normalized engine torque at 0.0 corresponding to a minimum permissible engine torque for the candidate engine speed and a normalized engine torque at 1.0 corresponding to a maximum permissible engine torque for the candidate engine speed: employing the candidate torque normalization ratio to determine a candidate engine torque from a normalized torque search space, and determining a candidate power cost associated with operating the powertrain system at the candidate engine torque; determining a preferred engine speed comprising the candidate engine speed corresponding to the one of the candidate engine torques associated with a minimum of the candidate power costs; and controlling engine operation responsive to the preferred engine speed. 2. The method of claim 1 , wherein employing the candidate torque normalization ratio to determine the candidate engine torque from the normalized torque search space comprises: determining the minimum and maximum permissible engine torques subject to constraints at the candidate engine speed; and employing the candidate torque normalization ratio to determine the candidate engine torque based upon said minimum and maximum permissible engine torques. | 0.62969 |
7,792,667 | 17 | 18 | 17. The method according to claim 16 , further comprising: retrieving a sentence from the document, the sentence containing the sequence of words, if the sequence of words is a significant phrase; and searching an abstract of the document to determine whether the sentence is included in the abstract. | 17. The method according to claim 16 , further comprising: retrieving a sentence from the document, the sentence containing the sequence of words, if the sequence of words is a significant phrase; and searching an abstract of the document to determine whether the sentence is included in the abstract. 18. The method according to claim 17 , further comprising including the sentence in the abstract, if the sentence is not included in the abstract. | 0.942063 |
7,739,257 | 1 | 6 | 1. A method of retrieving documents from a database comprising the steps of: a. semantically editing a document to create at least one searchable compound word that contains information contextually relevant to the contents of the document; b. associating the at least one compound word with the document thereby to produce an enhanced document; c. storing the enhanced document in an enhanced document database; d. providing a semantic query editor that is operable to receive a query input by a searcher, and using said query editor being operable to convert the query into at least one query searchable compound words, that contains information contextually relevant to the query; e. providing a search means to search the enhanced document database, searching the enhanced document database to match the at least one query searchable compound word with compound words associated with a document and thereby locate specific documents in the database containing the at least one compound search word; and f. presenting the specific documents to the searcher, wherein there is provided a semantic rule engine that is operable to generate and store rules each of which includes at least one compound word derived from at least one of the enhanced documents, and the method comprises the further steps, prior to step (f), of semantic searching a selected enhanced document to generate at least one searchable compound word associated with the selected enhanced document, searching the rules to find at least one rule specifying the at least one searchable compound word and at least one additional compound word to generate a set of candidate rules as rules which are possibly relevant to the selected enhanced document, and processing the set of candidate rules and adding to the selected enhanced document additional compound words specified in at least one of the rules in the set of candidate rules where the respective rule is satisfied for the selected enhanced document. | 1. A method of retrieving documents from a database comprising the steps of: a. semantically editing a document to create at least one searchable compound word that contains information contextually relevant to the contents of the document; b. associating the at least one compound word with the document thereby to produce an enhanced document; c. storing the enhanced document in an enhanced document database; d. providing a semantic query editor that is operable to receive a query input by a searcher, and using said query editor being operable to convert the query into at least one query searchable compound words, that contains information contextually relevant to the query; e. providing a search means to search the enhanced document database, searching the enhanced document database to match the at least one query searchable compound word with compound words associated with a document and thereby locate specific documents in the database containing the at least one compound search word; and f. presenting the specific documents to the searcher, wherein there is provided a semantic rule engine that is operable to generate and store rules each of which includes at least one compound word derived from at least one of the enhanced documents, and the method comprises the further steps, prior to step (f), of semantic searching a selected enhanced document to generate at least one searchable compound word associated with the selected enhanced document, searching the rules to find at least one rule specifying the at least one searchable compound word and at least one additional compound word to generate a set of candidate rules as rules which are possibly relevant to the selected enhanced document, and processing the set of candidate rules and adding to the selected enhanced document additional compound words specified in at least one of the rules in the set of candidate rules where the respective rule is satisfied for the selected enhanced document. 6. The method according to claim 1 comprising the step of the semantic rule engine storing the rules in a ripple down rule tree. | 0.938164 |
8,326,686 | 1 | 5 | 1. A computer-implemented method comprising: a) accepting, for each of at least one advertiser, by an advertising system including one or more computers, information from at least one advertiser document, wherein the at least one advertiser document defines an inventory of at least one of products and services offered on an online Website of the at least one advertiser; b) generating ads for the at least one advertiser, by the advertising system, each of the generated ads including i) a creative, and ii) offer information, using the accepted information from the at least one advertiser document; c) generating, by the advertising system, an index mapping information extracted from the at least one advertiser document to one of (A) advertiser document identifiers for the at least one advertiser document on which the extracted information is found, and (B) ad identifiers for ads generated from the at least one advertiser document on which the extracted information is found; d) accepting, by the advertising system, additional information, wherein the additional information is one of (A) search query information and (B) document relevance information; e) determining, by the advertising system, one or more ads relevant to the additional information using the index generated and the additional information; and f) serving at least one of the determined one or more relevant ads for rendering on a client device, wherein the offer information is expressed procedurally. | 1. A computer-implemented method comprising: a) accepting, for each of at least one advertiser, by an advertising system including one or more computers, information from at least one advertiser document, wherein the at least one advertiser document defines an inventory of at least one of products and services offered on an online Website of the at least one advertiser; b) generating ads for the at least one advertiser, by the advertising system, each of the generated ads including i) a creative, and ii) offer information, using the accepted information from the at least one advertiser document; c) generating, by the advertising system, an index mapping information extracted from the at least one advertiser document to one of (A) advertiser document identifiers for the at least one advertiser document on which the extracted information is found, and (B) ad identifiers for ads generated from the at least one advertiser document on which the extracted information is found; d) accepting, by the advertising system, additional information, wherein the additional information is one of (A) search query information and (B) document relevance information; e) determining, by the advertising system, one or more ads relevant to the additional information using the index generated and the additional information; and f) serving at least one of the determined one or more relevant ads for rendering on a client device, wherein the offer information is expressed procedurally. 5. The computer-implemented method of claim 1 , wherein the offer information is one of (A) an offer per ad impression, (B) a maximum offer per ad impression, (C) an offer per ad selection, (D) a maximum offer per ad selection, (E) an offer per ad conversion and (F) a maximum offer per ad conversion. | 0.818675 |
9,633,013 | 1 | 8 | 1. A computer-implemented method comprising: receiving a sequence of symbols that have been optically captured from a rendered document; determining that the sequence of symbols includes a particular symbol, word, or phrase that has been mapped to one or more actions; selecting an action from the one or more actions; and transmitting an instruction to a document management system to perform the selected action. | 1. A computer-implemented method comprising: receiving a sequence of symbols that have been optically captured from a rendered document; determining that the sequence of symbols includes a particular symbol, word, or phrase that has been mapped to one or more actions; selecting an action from the one or more actions; and transmitting an instruction to a document management system to perform the selected action. 8. The computer-implemented method of claim 1 , further comprising: receiving information associated with a location of a capture device used to optically capture the sequence of symbols from the rendered document; wherein selecting the action from the one or more actions is based at least in part on the location of the capture device. | 0.704904 |
8,838,992 | 1 | 8 | 1. A computer-implemented method of identifying normal scripts, the method comprising: receiving a machine learning model and a feature set in a client computer, the machine learning model being trained using sample scripts that are known to be normal and sample scripts that are known to be potentially malicious and takes into account lexical and semantic characteristics of the sample scripts that are known to be normal and the sample scripts that are known to be potentially malicious; receiving a target script along with a web page in the client computer, the target script and the web page being received from a server computer over a computer network; extracting from the target script features that are included in the feature set; inputting the extracted features of the target script into the machine learning model to receive a classification of the target script from the machine learning model; and detecting that the target script is a normal script and not a potentially malicious script based on the classification of the target script. | 1. A computer-implemented method of identifying normal scripts, the method comprising: receiving a machine learning model and a feature set in a client computer, the machine learning model being trained using sample scripts that are known to be normal and sample scripts that are known to be potentially malicious and takes into account lexical and semantic characteristics of the sample scripts that are known to be normal and the sample scripts that are known to be potentially malicious; receiving a target script along with a web page in the client computer, the target script and the web page being received from a server computer over a computer network; extracting from the target script features that are included in the feature set; inputting the extracted features of the target script into the machine learning model to receive a classification of the target script from the machine learning model; and detecting that the target script is a normal script and not a potentially malicious script based on the classification of the target script. 8. The method of claim 1 further comprising: receiving another target script in the client computer; detecting that the other target script is a potentially malicious script based on a classification of the other target script by the machine learning model; and in response to detecting that the other target script is a potentially malicious script, initiating further examination of the other target script by an anti-malware running in the client computer. | 0.501087 |
8,972,387 | 16 | 22 | 16. A computer program product for an entity in an entity resolution system, 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: receiving search input made up of attributes having attribute types; performing a resolution search using the search input to generate a first search result set comprising one or more entities and zero or more generic attributes, wherein the resolution search determines that the search input and each of the one or more entities has a similarity score exceeding a first threshold, and wherein each of the generic attributes has a frequency of occurrence exceeding a second threshold; in response to determining that the resolution search generated less than two generic attributes, returning the first search result set; and in response to determining that the resolution search generated at least two generic attributes, for each generic group that includes a subset of the search input and includes at least two generic attribute types from the attribute types of attributes in the search input, performing a query search to identify additional entities; combining the identified additional entities with the entities in the first search result set to generate a second search result set; and returning the second search result set. | 16. A computer program product for an entity in an entity resolution system, 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: receiving search input made up of attributes having attribute types; performing a resolution search using the search input to generate a first search result set comprising one or more entities and zero or more generic attributes, wherein the resolution search determines that the search input and each of the one or more entities has a similarity score exceeding a first threshold, and wherein each of the generic attributes has a frequency of occurrence exceeding a second threshold; in response to determining that the resolution search generated less than two generic attributes, returning the first search result set; and in response to determining that the resolution search generated at least two generic attributes, for each generic group that includes a subset of the search input and includes at least two generic attribute types from the attribute types of attributes in the search input, performing a query search to identify additional entities; combining the identified additional entities with the entities in the first search result set to generate a second search result set; and returning the second search result set. 22. The computer program product of claim 16 , wherein the computer readable program code, when executed by the processor of the computer, is configured to perform: removing generic candidate keys; retrieving one or more candidate entities for non-generic candidate keys; performing similarity scoring of the candidate entities against the search input to determine a similarity score for each of the candidate entities; and for each of the candidate entities, in response to a similarity score of that candidate entity exceeding the first threshold, including that candidate entity as an entity in the first search result set. | 0.500796 |
8,831,365 | 16 | 17 | 16. A non-transitory computer readable medium having stored therein instructions, that when executed by a computing device, cause the computing device to perform functions comprising: receiving, at a computing device, text captured from a rendered document during a text capture operation; receiving, at the computing device, supplemental information relating to circumstances under which the text capture operation was performed, the supplemental information comprising information indicating a geographical location at which the text capture operation occurs, the information indicating a geographical location at which the text capture operation occurs comprising information indicating a location as being indoors or outdoors; and determining, by the computing device and based on the captured text and the supplemental information, an action to be performed; wherein a determination of the location as being indoors or outdoors is based on light entering a sensor of an optical capture device. | 16. A non-transitory computer readable medium having stored therein instructions, that when executed by a computing device, cause the computing device to perform functions comprising: receiving, at a computing device, text captured from a rendered document during a text capture operation; receiving, at the computing device, supplemental information relating to circumstances under which the text capture operation was performed, the supplemental information comprising information indicating a geographical location at which the text capture operation occurs, the information indicating a geographical location at which the text capture operation occurs comprising information indicating a location as being indoors or outdoors; and determining, by the computing device and based on the captured text and the supplemental information, an action to be performed; wherein a determination of the location as being indoors or outdoors is based on light entering a sensor of an optical capture device. 17. The non-transitory computer readable medium of claim 16 , wherein the stored instructions, when executed by the computing device, further cause performance of the determined action. | 0.750674 |
8,055,672 | 5 | 8 | 5. An apparatus comprising: at least one processor; a memory coupled to the at least one processor; a database table residing in the memory; a database relationship document residing in the memory that specifies at least one relationship in the database; and a graphical query interface residing in the memory and executed by the at least one processor, the graphical query interface comprising: a first window that displays a graphical representation of at least one relationship in the database specified in the database relationship document when building a query in a series of steps, the graphical representation comprising at least one item in the database that the user may select; and a second window that displays filtered information to the user at each step in building the query according to all user selections on the graphical representation in all previous steps in building the query. | 5. An apparatus comprising: at least one processor; a memory coupled to the at least one processor; a database table residing in the memory; a database relationship document residing in the memory that specifies at least one relationship in the database; and a graphical query interface residing in the memory and executed by the at least one processor, the graphical query interface comprising: a first window that displays a graphical representation of at least one relationship in the database specified in the database relationship document when building a query in a series of steps, the graphical representation comprising at least one item in the database that the user may select; and a second window that displays filtered information to the user at each step in building the query according to all user selections on the graphical representation in all previous steps in building the query. 8. The apparatus of claim 5 wherein the database relationship document comprises an XML document. | 0.753807 |
8,601,023 | 1 | 2 | 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. 2. The computer implemented method of claim 1 , further comprising: based upon the usage patterns, automatically determining the topic of the on-line asset. | 0.867121 |
8,577,898 | 13 | 14 | 13. The method of claim 9 , wherein providing the quotation score includes a training phase and a testing phase, and the training phase is performed offline on the at least one training document in order to index the at least one training document. | 13. The method of claim 9 , wherein providing the quotation score includes a training phase and a testing phase, and the training phase is performed offline on the at least one training document in order to index the at least one training document. 14. The method of claim 13 , wherein the testing phase is performed online on the writing in order to extract all of the sentences of the writing, and the extracted sentences are compared to the index of the at least one training document to provide the quotation score. | 0.950513 |
6,137,041 | 9 | 11 | 9. The computer-readable recording medium storing a music score reading program comprising: sign recognizing function of recognizing all signs and notes of a music score; notation estimating function of estimating a drum notation in a drum part of the music score based on information obtained by said sign recognizing function; musical instrument allocating function of allocating actual tone generating musical instruments to the recognized signs of the drum part according to the drum notation estimated by said notation estimating function, such that the music score is converted into a readable music score data format. | 9. The computer-readable recording medium storing a music score reading program comprising: sign recognizing function of recognizing all signs and notes of a music score; notation estimating function of estimating a drum notation in a drum part of the music score based on information obtained by said sign recognizing function; musical instrument allocating function of allocating actual tone generating musical instruments to the recognized signs of the drum part according to the drum notation estimated by said notation estimating function, such that the music score is converted into a readable music score data format. 11. A computer-readable recording medium storing a music score reading program according to claim 9 wherein said notation estimating function estimates the drum notation based on information obtained by a sign recognizing function and including at least a staff position of a drum head, a kind of a drum head, a hi-hat open sign relating to a drum note, a hi-hat close sign relating to a drum note, an accent sign relating to a drum note, a stem of a drum note, a flag of a drum note, a character string for designation of a drum tone of a note, a tone length determined by the flag of the drum note, and another tone length determined by a head kind based on existence of the tone length of the drum note derived by said flag. | 0.778489 |
7,702,599 | 1 | 3 | 1. A cognitive memory system configured for receiving input data or input patterns from one or more sensors, storing said input data or input patterns wherever storage space is available, and retrieving said input data or input patterns upon receipt of an input prompt or query pattern, wherein said cognitive memory system comprises: (a) a memory input line that carries said input data and input patterns throughout said cognitive memory system for recording wherever storage space is available; (b) a prompt line that carries said input prompt or query pattern throughout said cognitive memory system; (c) one or more memory segments, each memory segment having: (i) one or more memory folders, each memory folder configured for storing said input data or input patterns from said one or more sensors streaming in a sequence over time, and a scanner for continuously scanning the stored contents of said one or more folders and generating patterns; (ii) a trainable auto-associative neural network whose training input patterns are obtained from said scanner generated patterns and whose sensing input patterns are said input query patterns obtained from said prompt line, said trainable auto-associative neural network further including: a training algorithm for training said auto-associative neural network to reproduce said training input patterns at its output; (iii) a first comparator configured for pixel-by-pixel subtracting said sensing input patterns from the auto-associative neural network output to form first error patterns, (iv) first means for computing the magnitude or mean square of the first error patterns, (v) a first threshold device that closes a first switch when a sensing input pattern is identified as a hit query pattern when the magnitude or mean square of its first error pattern is below the first threshold level of said first threshold device, said first switch configured to connect a prompt memory buffer to said prompt line to store said hit query pattern in said prompt memory buffer; (vi) a second comparator configured for pixel-by-pixel subtraction of said scanner generated patterns received from said scanner from said hit query pattern stored in said prompt memory buffer and generating differences, the generated differences being second error patterns; (vii) second means for computing the magnitude or mean square of the second error patterns, (viii) a second threshold device that closes a second switch when there is a second hit when the magnitude or mean square of a second error pattern is below the second threshold level of said second threshold device, said second switch configured to connect the memory output line of said memory segment to deliver as output the contents of the memory folder containing the hit pattern associated with said second hit. | 1. A cognitive memory system configured for receiving input data or input patterns from one or more sensors, storing said input data or input patterns wherever storage space is available, and retrieving said input data or input patterns upon receipt of an input prompt or query pattern, wherein said cognitive memory system comprises: (a) a memory input line that carries said input data and input patterns throughout said cognitive memory system for recording wherever storage space is available; (b) a prompt line that carries said input prompt or query pattern throughout said cognitive memory system; (c) one or more memory segments, each memory segment having: (i) one or more memory folders, each memory folder configured for storing said input data or input patterns from said one or more sensors streaming in a sequence over time, and a scanner for continuously scanning the stored contents of said one or more folders and generating patterns; (ii) a trainable auto-associative neural network whose training input patterns are obtained from said scanner generated patterns and whose sensing input patterns are said input query patterns obtained from said prompt line, said trainable auto-associative neural network further including: a training algorithm for training said auto-associative neural network to reproduce said training input patterns at its output; (iii) a first comparator configured for pixel-by-pixel subtracting said sensing input patterns from the auto-associative neural network output to form first error patterns, (iv) first means for computing the magnitude or mean square of the first error patterns, (v) a first threshold device that closes a first switch when a sensing input pattern is identified as a hit query pattern when the magnitude or mean square of its first error pattern is below the first threshold level of said first threshold device, said first switch configured to connect a prompt memory buffer to said prompt line to store said hit query pattern in said prompt memory buffer; (vi) a second comparator configured for pixel-by-pixel subtraction of said scanner generated patterns received from said scanner from said hit query pattern stored in said prompt memory buffer and generating differences, the generated differences being second error patterns; (vii) second means for computing the magnitude or mean square of the second error patterns, (viii) a second threshold device that closes a second switch when there is a second hit when the magnitude or mean square of a second error pattern is below the second threshold level of said second threshold device, said second switch configured to connect the memory output line of said memory segment to deliver as output the contents of the memory folder containing the hit pattern associated with said second hit. 3. The cognitive memory system of claim 1 , wherein input prompt or input query patterns are obtained from said contents of the memory folder containing said hit pattern associated with said second hit. | 0.941585 |
7,620,912 | 1 | 2 | 1. A system for associating behaviors with objects, wherein a script for said behaviors is automatically generated and is accessible by a client from a host, said system comprising: a source object; a graphical behavior tool rendered as a part of said source object for establishing a behavior relationship between said source object and a target by using a pointer to perform a gesture with said graphical behavior tool; and an automated script generator for generating computer readable script defining said behavior relationship. | 1. A system for associating behaviors with objects, wherein a script for said behaviors is automatically generated and is accessible by a client from a host, said system comprising: a source object; a graphical behavior tool rendered as a part of said source object for establishing a behavior relationship between said source object and a target by using a pointer to perform a gesture with said graphical behavior tool; and an automated script generator for generating computer readable script defining said behavior relationship. 2. The system of claim 1 wherein said gesture comprises: dragging said graphical behavior tool from said source object to said target. | 0.862705 |
9,477,446 | 11 | 16 | 11. A non-transitory computer-readable medium for building an integrated system using a formal language, the non-transitory computer-readable medium having computer instructions stored thereon that are executable by a processing device to perform or control performance of steps comprising: designing one or more models for one or more software components to be included in the integrated system, wherein the one or more models describe one or more requirements for the one or more software components; assigning one or more contracts to the one or more models, wherein the one or more contracts are written in the formal language and the one or more contracts include one or more low-level contracts and one or more high-level contracts; integrating the one or more models based on the composition of the one or more contracts to form an integrated model, wherein the integrated model includes each requirement for the one or more software components which is described by the one or more models which form the integrated model; and analyzing the one or more contracts and the integrated model to determine whether the one or more contracts include each requirement described by the integrated model, wherein the one or more low-level contracts are designed relative to the one or more high-level contracts so that analysis of the one or more low-level contracts alone indicates whether an error will be present in the one or more high-level contracts. | 11. A non-transitory computer-readable medium for building an integrated system using a formal language, the non-transitory computer-readable medium having computer instructions stored thereon that are executable by a processing device to perform or control performance of steps comprising: designing one or more models for one or more software components to be included in the integrated system, wherein the one or more models describe one or more requirements for the one or more software components; assigning one or more contracts to the one or more models, wherein the one or more contracts are written in the formal language and the one or more contracts include one or more low-level contracts and one or more high-level contracts; integrating the one or more models based on the composition of the one or more contracts to form an integrated model, wherein the integrated model includes each requirement for the one or more software components which is described by the one or more models which form the integrated model; and analyzing the one or more contracts and the integrated model to determine whether the one or more contracts include each requirement described by the integrated model, wherein the one or more low-level contracts are designed relative to the one or more high-level contracts so that analysis of the one or more low-level contracts alone indicates whether an error will be present in the one or more high-level contracts. 16. The non-transitory computer-readable medium of claim 11 , wherein the integrated system includes an embedded system included in a vehicle. | 0.782209 |
9,177,022 | 17 | 20 | 17. A system for processing queries, comprising: a network connection that is coupled to tenants of the multi-tenant service; a processor and a computer-readable medium; an operating environment stored on the computer-readable medium and executing on the processor; and a pipeline manager operating under the control of the operating environment and operative to: receiving user input creating a pipeline configuration for executing a query from an input device; receiving a first query; obtaining a context and conditions of the first query; determining rules based on the context and the conditions of the first query by utilizing the pipeline configuration, wherein the rules are triggered in response to receiving the first query; applying the rules to the first query to determine additional queries to execute; executing the additional queries; receiving supplemental results from execution of each the additional queries; and mixing the received results with any core results to form mixed results. | 17. A system for processing queries, comprising: a network connection that is coupled to tenants of the multi-tenant service; a processor and a computer-readable medium; an operating environment stored on the computer-readable medium and executing on the processor; and a pipeline manager operating under the control of the operating environment and operative to: receiving user input creating a pipeline configuration for executing a query from an input device; receiving a first query; obtaining a context and conditions of the first query; determining rules based on the context and the conditions of the first query by utilizing the pipeline configuration, wherein the rules are triggered in response to receiving the first query; applying the rules to the first query to determine additional queries to execute; executing the additional queries; receiving supplemental results from execution of each the additional queries; and mixing the received results with any core results to form mixed results. 20. The system of claim 17 , wherein executing the additional queries comprises: performing a federated search and executing parallel queries that are created in response to applying the rules to the first query. | 0.502347 |
7,783,984 | 1 | 10 | 1. A method of interfacing, in a web-enabled console application, to communicate between a warehouse management system and a portable device, the method comprising: receiving, from the warehouse management system, graphical user interface (GUI) screen data comprising visual content; generating voice extensible markup language (XML) data based on the visual content by a computer system, the voice XML data including at least one portion which is representative of voice information; transmitting the generated voice XML data to the portable device; reading a radio frequency identification (RFID) tag information with the portable device; transmitting the information to the web-enabled console application; and modifying the RFID tag information responsive to an instruction transmitted via the web enabled console application to the portable device. | 1. A method of interfacing, in a web-enabled console application, to communicate between a warehouse management system and a portable device, the method comprising: receiving, from the warehouse management system, graphical user interface (GUI) screen data comprising visual content; generating voice extensible markup language (XML) data based on the visual content by a computer system, the voice XML data including at least one portion which is representative of voice information; transmitting the generated voice XML data to the portable device; reading a radio frequency identification (RFID) tag information with the portable device; transmitting the information to the web-enabled console application; and modifying the RFID tag information responsive to an instruction transmitted via the web enabled console application to the portable device. 10. The method of claim 1 , wherein generating voice XML data comprising: the voice XML data at runtime. | 0.755869 |
8,190,609 | 5 | 6 | 5. The computer-implemented method of claim 1 wherein determining, for each product-query pair, whether the query of the product-query pair is associated with the product of the product-query pair further comprises: multiplying the number of times that the product of the product-query pair was selected from results for the query of the product-query pair by a weighted version of a number of product offers maintained by the computer for the product of the product-query pair to determine an intermediate value; computing a ratio between the intermediate value and a number of times that the query of the product-query pair was received to determine an association value; determining whether the association value meets or exceeds a threshold; and in response to the association value meeting or exceeding the threshold, determining that the query of the product-query pair is associated with the product of the product-query pair. | 5. The computer-implemented method of claim 1 wherein determining, for each product-query pair, whether the query of the product-query pair is associated with the product of the product-query pair further comprises: multiplying the number of times that the product of the product-query pair was selected from results for the query of the product-query pair by a weighted version of a number of product offers maintained by the computer for the product of the product-query pair to determine an intermediate value; computing a ratio between the intermediate value and a number of times that the query of the product-query pair was received to determine an association value; determining whether the association value meets or exceeds a threshold; and in response to the association value meeting or exceeding the threshold, determining that the query of the product-query pair is associated with the product of the product-query pair. 6. The computer-implemented method of claim 5 , wherein the weighted version of the number of product offers maintained by the computer for the product of the product-query pair comprises a square root of the number of product offers maintained by the computer for the product of the product-query pair. | 0.930727 |
10,009,297 | 1 | 2 | 1. A method of displaying related content on a user interface, the method comprising: analyzing, by one or more processors, an initial electronic document to identify a first mention in the initial electronic document; generating, by one or more processors, a first mention descriptor of the first mention in the initial electronic document according to a context of the initial electronic document; appending, by one or more processors, the first mention descriptor to the initial electronic document; mapping, by one or more processors, the first mention descriptor from the initial electronic document to a disambiguation identifier from a knowledge base that contains a version of the first mention, wherein the disambiguation identifier is an entity within the knowledge base; associating, by one or more processors, the disambiguation identifier with the initial electronic document; locating, by one or more processors, the disambiguation identifier on an entity metadata visualization panel, wherein the disambiguation identifier was appended to the entity metadata visualization panel after mapping the entity metadata visualization panel to the disambiguation identifier from the knowledge base, and wherein the entity metadata visualization panel was mapped to the disambiguation identifier by mapping a second mention descriptor for a second mention in the entity metadata visualization panel to the disambiguation identifier; retrieving, by one or more processors, the entity metadata visualization panel; and displaying, on the user interface, the entity metadata visualization panel as related content that is related to the first mention in the initial electronic document. | 1. A method of displaying related content on a user interface, the method comprising: analyzing, by one or more processors, an initial electronic document to identify a first mention in the initial electronic document; generating, by one or more processors, a first mention descriptor of the first mention in the initial electronic document according to a context of the initial electronic document; appending, by one or more processors, the first mention descriptor to the initial electronic document; mapping, by one or more processors, the first mention descriptor from the initial electronic document to a disambiguation identifier from a knowledge base that contains a version of the first mention, wherein the disambiguation identifier is an entity within the knowledge base; associating, by one or more processors, the disambiguation identifier with the initial electronic document; locating, by one or more processors, the disambiguation identifier on an entity metadata visualization panel, wherein the disambiguation identifier was appended to the entity metadata visualization panel after mapping the entity metadata visualization panel to the disambiguation identifier from the knowledge base, and wherein the entity metadata visualization panel was mapped to the disambiguation identifier by mapping a second mention descriptor for a second mention in the entity metadata visualization panel to the disambiguation identifier; retrieving, by one or more processors, the entity metadata visualization panel; and displaying, on the user interface, the entity metadata visualization panel as related content that is related to the first mention in the initial electronic document. 2. The method of claim 1 , wherein the initial electronic document is from a social media service, and wherein the method further comprises: analyzing a frequency of the initial electronic document being viewed within predetermined time periods; generating a trending graph of the frequency of the initial electronic document being viewed within the predetermined time periods; and displaying, on the user interface, the trending graph. | 0.646104 |
8,606,807 | 17 | 18 | 17. The non-transitory computer readable medium of claim 15 , wherein the operations further comprise displaying in a fourth display area of the main window one or more related tags that are related to at least one tag displayed in the third display area. | 17. The non-transitory computer readable medium of claim 15 , wherein the operations further comprise displaying in a fourth display area of the main window one or more related tags that are related to at least one tag displayed in the third display area. 18. The non-transitory computer readable medium of claim 17 , wherein the operations further comprise, in response to selecting or unselecting a related tag displayed in the fourth display area, dynamically updating resources displayed in the first display area, such that only resources that are tagged by the selected tags and the related tags are displayed in the first display area. | 0.890403 |
7,689,588 | 1 | 6 | 1. A method executed in a computer system for comparing the similarity of a target string to at least one string in a set of strings, the target string and each string in the set of strings being a sequence of symbols from an alphabet of symbols, the method comprising the steps of: (a) Generating a multi-level Trie associated with the set of strings, the Trie having a null level and a null node thereon, and a plurality of data levels having a plurality of data nodes thereon, each data node having a symbol of the alphabet associated therewith; (b) From the null node, traversing a plurality of levels of the Trie commencing with the first data level adjacent the null node, and thereafter descending level by level within the Trie; (c) For each data level of the Trie traversed, making a first selection of a set of data nodes from that level, which first selection step includes the steps of: i. In the case of the first data level of the Trie, selecting all of the data nodes in that level of the Trie; and ii. In the case of at least one other data levels of the Trie, for each such level A. Making a second selection of all of the children of the nodes of the first selection of the level immediately previously traversed; B. For each of the data nodes selected in step A, calculating a heuristic measurement between the sequence of symbols in the target string and the sequence formed by the symbols on the path from the null node to that selected data node; and C. For at least a first symbol represented, selecting a first cluster of nodes representing that first symbol that have the most optimal calculated heuristic measurement associated therewith, up to a bounded number of nodes and identifying the terminal nodes in the first cluster of nodes; (d) Continuing to traverse the Trie, level by level, repeating step (c) in respect of each level traversed, until step (c) has been completed on that level of the Trie for which there are no children of the selected nodes; and (e) In respect of each terminal node identified during the traversal of the plurality of levels of the Trie, comparing the calculated heuristic measurement associated therewith and reporting at least one string from the set of strings associated with the optimal calculated heuristic measurement. | 1. A method executed in a computer system for comparing the similarity of a target string to at least one string in a set of strings, the target string and each string in the set of strings being a sequence of symbols from an alphabet of symbols, the method comprising the steps of: (a) Generating a multi-level Trie associated with the set of strings, the Trie having a null level and a null node thereon, and a plurality of data levels having a plurality of data nodes thereon, each data node having a symbol of the alphabet associated therewith; (b) From the null node, traversing a plurality of levels of the Trie commencing with the first data level adjacent the null node, and thereafter descending level by level within the Trie; (c) For each data level of the Trie traversed, making a first selection of a set of data nodes from that level, which first selection step includes the steps of: i. In the case of the first data level of the Trie, selecting all of the data nodes in that level of the Trie; and ii. In the case of at least one other data levels of the Trie, for each such level A. Making a second selection of all of the children of the nodes of the first selection of the level immediately previously traversed; B. For each of the data nodes selected in step A, calculating a heuristic measurement between the sequence of symbols in the target string and the sequence formed by the symbols on the path from the null node to that selected data node; and C. For at least a first symbol represented, selecting a first cluster of nodes representing that first symbol that have the most optimal calculated heuristic measurement associated therewith, up to a bounded number of nodes and identifying the terminal nodes in the first cluster of nodes; (d) Continuing to traverse the Trie, level by level, repeating step (c) in respect of each level traversed, until step (c) has been completed on that level of the Trie for which there are no children of the selected nodes; and (e) In respect of each terminal node identified during the traversal of the plurality of levels of the Trie, comparing the calculated heuristic measurement associated therewith and reporting at least one string from the set of strings associated with the optimal calculated heuristic measurement. 6. A method according to claim 1 wherein representations of the nodes in each cluster are maintained in a priority queue in which the position of the representations of the nodes in the priority queue is determined by the value of the heuristic measurement associated with the said nodes. | 0.581395 |
7,693,940 | 1 | 4 | 1. A method for conversation detection in email systems, comprising: providing a computer system, wherein the system comprises distinct program modules embodied on a computer-readable medium, and wherein the distinct program modules comprise a conversation detector; executing the conversation detector to group email messages into first groups, each of the first groups having a common core subject to thereby define a conversation by applying a similarity function a first time based on a similarity of the email messages' attributes, the similarity function including: a similarity between the email messages' participants; at least one of a similarity between the email messages' subjects and a similarity between the email messages' contents; applying the similarity function a second time to group the email messages of the first groups into respective second groups; and defining the second groups as sub-conversations. | 1. A method for conversation detection in email systems, comprising: providing a computer system, wherein the system comprises distinct program modules embodied on a computer-readable medium, and wherein the distinct program modules comprise a conversation detector; executing the conversation detector to group email messages into first groups, each of the first groups having a common core subject to thereby define a conversation by applying a similarity function a first time based on a similarity of the email messages' attributes, the similarity function including: a similarity between the email messages' participants; at least one of a similarity between the email messages' subjects and a similarity between the email messages' contents; applying the similarity function a second time to group the email messages of the first groups into respective second groups; and defining the second groups as sub-conversations. 4. The method as claimed in claim 1 , wherein the similarity function includes weightings for the contributions of the email messages' attributes. | 0.907712 |
7,925,663 | 1 | 14 | 1. A method of searching an electronic filing system, the method including the steps of: obtaining a handwritten search query in the form of digital ink; obtaining a text search query including a text search term; performing a search of at least one database for instances of the text search term; performing a search of the at least one database for handwritten annotations that are less than a given physical distance from at least one of the instances of the text search term; comparing the handwritten search query and the handwritten annotations in order to determine matches between the handwritten search query and the handwritten annotations; and providing results of the comparing step as output. | 1. A method of searching an electronic filing system, the method including the steps of: obtaining a handwritten search query in the form of digital ink; obtaining a text search query including a text search term; performing a search of at least one database for instances of the text search term; performing a search of the at least one database for handwritten annotations that are less than a given physical distance from at least one of the instances of the text search term; comparing the handwritten search query and the handwritten annotations in order to determine matches between the handwritten search query and the handwritten annotations; and providing results of the comparing step as output. 14. The method as claimed in claim 1 , wherein the at least one database is a digital image database containing one or more of pictures, drawings, images, and figures. | 0.88911 |
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