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61. At least one non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed, perform a method comprising: transcribing audio data comprising audio of one or more clinical personnel speaking while performing a surgical procedure, the audio data comprising audio of a first clinician speaking to one or more other clinical personnel while performing the surgical procedure; analyzing the transcribed audio data, including the transcribed audio of the first clinician speaking to the one or more other clinical personnel while performing the surgical procedure, at least in part by automatically extracting one or more clinical facts from the transcribed audio data, to identify relevant information for documenting the surgical procedure, wherein analyzing the transcribed audio data comprises identifying within the transcribed audio data a present-tense narration by the first clinician stating to the other clinical personnel that the first clinician is currently performing a particular step of the surgical procedure; automatically generating a text report including the relevant information documenting the surgical procedure, wherein automatically generating the text report comprises automatically transforming the present-tense narration into a non-present-tense portion in the report, stating that the particular step of the surgical procedure was performed; and outputting the automatically generated text report for review via a user interface on an audio and/or visual display device. | 61. At least one non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed, perform a method comprising: transcribing audio data comprising audio of one or more clinical personnel speaking while performing a surgical procedure, the audio data comprising audio of a first clinician speaking to one or more other clinical personnel while performing the surgical procedure; analyzing the transcribed audio data, including the transcribed audio of the first clinician speaking to the one or more other clinical personnel while performing the surgical procedure, at least in part by automatically extracting one or more clinical facts from the transcribed audio data, to identify relevant information for documenting the surgical procedure, wherein analyzing the transcribed audio data comprises identifying within the transcribed audio data a present-tense narration by the first clinician stating to the other clinical personnel that the first clinician is currently performing a particular step of the surgical procedure; automatically generating a text report including the relevant information documenting the surgical procedure, wherein automatically generating the text report comprises automatically transforming the present-tense narration into a non-present-tense portion in the report, stating that the particular step of the surgical procedure was performed; and outputting the automatically generated text report for review via a user interface on an audio and/or visual display device. 87. The at least one non-transitory computer-readable storage medium of claim 61 , wherein the method further comprises allowing a human user to make edits to the text report. | 0.670187 |
17. A pattern recognition system as described in claim 10, wherein the means for collecting and combining includes an error correcting decoding means. | 17. A pattern recognition system as described in claim 10, wherein the means for collecting and combining includes an error correcting decoding means. 18. A pattern recognition system as described in claim 17, wherein the error correcting decoding means implements a BCH decoding system. | 0.979305 |
1. A method comprising: receiving, by a wearable computing device, input data from one or more input source devices; determining that the received input data includes both a first data pattern representing an explicit command and a second data pattern representing an implicit search request, wherein the first data pattern representing the explicit command comprises the first data pattern indicating that the wearable computing device should carry out a particular operation, and wherein the second data pattern representing the implicit search request comprises the second data pattern indicating that the wearable computing device should provide search results based on particular content even though the input data is without an explicit indication to provide the search results based on the particular content; and in response to determining that the received input data includes both the first data pattern representing the explicit command and the second data pattern representing the implicit search request, the wearable computing device prioritizing the explicit command over the implicit search request by carrying out the particular operation. | 1. A method comprising: receiving, by a wearable computing device, input data from one or more input source devices; determining that the received input data includes both a first data pattern representing an explicit command and a second data pattern representing an implicit search request, wherein the first data pattern representing the explicit command comprises the first data pattern indicating that the wearable computing device should carry out a particular operation, and wherein the second data pattern representing the implicit search request comprises the second data pattern indicating that the wearable computing device should provide search results based on particular content even though the input data is without an explicit indication to provide the search results based on the particular content; and in response to determining that the received input data includes both the first data pattern representing the explicit command and the second data pattern representing the implicit search request, the wearable computing device prioritizing the explicit command over the implicit search request by carrying out the particular operation. 4. The method of claim 1 , wherein the wearable computing device comprises one or more output components, wherein the one or more output components comprise an audio output and/or a video output, and wherein the wearable computing device provides, via at least one of the output components, one or more of an output based on the search results and an output based on the particular operation. | 0.529032 |
1. A method for automatically generating an IVR application flow from recorded audio files of calls to or from an IVR system, the method comprising: creating a statistical semantic model for IVR prompts by extracting speech segments from audio recordings of an IVR portion of a first set of calls, converting the extracted speech segments to text, clustering the speech segments based on semantic meaning, displaying the clusters in a user interface, enabling a user to label each of the clusters with an IVR state, and building a statistical semantic model based on the labeled clusters; identifying an IVR state sequence for each of a second set of calls from recorded audio files of said calls, wherein, for each call in the second set, such identification comprises: extracting speech segments from an audio recording of the IVR portion of the call, wherein the extracted speech segments correspond to one or more IVR prompts in the call, converting the extracted speech segments to text, classifying each segment with an IVR state to obtain an IVR state sequence for the call, wherein each segment is automatically classified using a semantic classifier that classifies the segment with one of a plurality of predefined IVR states based on the text in the segment and the statistical semantic model for IVR prompts; identifying the N most common IVR state sequences among the second set of calls, wherein N is greater than or equal to one; and displaying an application flow for the IVR application using only the N most common IVR state sequences. | 1. A method for automatically generating an IVR application flow from recorded audio files of calls to or from an IVR system, the method comprising: creating a statistical semantic model for IVR prompts by extracting speech segments from audio recordings of an IVR portion of a first set of calls, converting the extracted speech segments to text, clustering the speech segments based on semantic meaning, displaying the clusters in a user interface, enabling a user to label each of the clusters with an IVR state, and building a statistical semantic model based on the labeled clusters; identifying an IVR state sequence for each of a second set of calls from recorded audio files of said calls, wherein, for each call in the second set, such identification comprises: extracting speech segments from an audio recording of the IVR portion of the call, wherein the extracted speech segments correspond to one or more IVR prompts in the call, converting the extracted speech segments to text, classifying each segment with an IVR state to obtain an IVR state sequence for the call, wherein each segment is automatically classified using a semantic classifier that classifies the segment with one of a plurality of predefined IVR states based on the text in the segment and the statistical semantic model for IVR prompts; identifying the N most common IVR state sequences among the second set of calls, wherein N is greater than or equal to one; and displaying an application flow for the IVR application using only the N most common IVR state sequences. 5. The method of claim 1 , wherein clustering the speech segments includes assigning weights to individual words in the speech segments and wherein transcripts with similar words are clustered together while weighing down frequently-used words. | 0.618744 |
29. A computer program product that includes a non-transitory computer readable storage medium, the computer readable medium comprising a plurality of computer instructions which, when executed by a processor, cause the processor to execute performing a process for testing a user interface to a software application, the process comprising: extending a hardware verification language by defining one or more custom libraries such that the extended hardware verification language can be used to interface with the user interface to the software application in addition to hardware designs, wherein the hardware verification language is different from a programming language used to create the user interface to the software application, and is a programming language specifically designed for verification of hardware designs, and wherein the extended hardware verification language is extended by providing an API (applications programming interface) corresponding to the e language; generating a test for the user interface to the software application written in the extended hardware verification language; using the test written in the extended hardware verification language to drive one or more elements of the user interface to the software application; collecting data resulting from driving the user interface to the software application using the test; analyzing the data from driving the user interface to the software application; and displaying analysis results or storing the analysis results in a computer readable medium. | 29. A computer program product that includes a non-transitory computer readable storage medium, the computer readable medium comprising a plurality of computer instructions which, when executed by a processor, cause the processor to execute performing a process for testing a user interface to a software application, the process comprising: extending a hardware verification language by defining one or more custom libraries such that the extended hardware verification language can be used to interface with the user interface to the software application in addition to hardware designs, wherein the hardware verification language is different from a programming language used to create the user interface to the software application, and is a programming language specifically designed for verification of hardware designs, and wherein the extended hardware verification language is extended by providing an API (applications programming interface) corresponding to the e language; generating a test for the user interface to the software application written in the extended hardware verification language; using the test written in the extended hardware verification language to drive one or more elements of the user interface to the software application; collecting data resulting from driving the user interface to the software application using the test; analyzing the data from driving the user interface to the software application; and displaying analysis results or storing the analysis results in a computer readable medium. 33. The computer program product of claim 29 further comprising coverage analysis. | 0.519741 |
2. The method as described in claim 1 , further comprising: establishing the list of candidate product words comprises: for at least one product information entry contained in a database: performing a coarse granularity segmentation by the largest semantic units; and extracting a third core product word contained in segmented results; determining whether the third core product word has been extracted from the segmented results; in the event that the third core product word has been extracted from the segmented results, performing a fine granularity segmentation by the smallest semantic units: determining whether at least two of the words obtained are product words; in the event that at least two of the words obtained are product words; using the first product word as a key product word; and using the last product word as a candidate product word of the key product word; computing correlations of at least one key product word and at least one candidate product word; determining whether the correlation of the at least one key product word and the at least one candidate product word meets a threshold value; selecting a candidate product word having a correlation that meets the threshold value; and for the same key product word, generating the list of candidate product words based on the selected candidate product word. | 2. The method as described in claim 1 , further comprising: establishing the list of candidate product words comprises: for at least one product information entry contained in a database: performing a coarse granularity segmentation by the largest semantic units; and extracting a third core product word contained in segmented results; determining whether the third core product word has been extracted from the segmented results; in the event that the third core product word has been extracted from the segmented results, performing a fine granularity segmentation by the smallest semantic units: determining whether at least two of the words obtained are product words; in the event that at least two of the words obtained are product words; using the first product word as a key product word; and using the last product word as a candidate product word of the key product word; computing correlations of at least one key product word and at least one candidate product word; determining whether the correlation of the at least one key product word and the at least one candidate product word meets a threshold value; selecting a candidate product word having a correlation that meets the threshold value; and for the same key product word, generating the list of candidate product words based on the selected candidate product word. 3. The method as described in claim 2 , wherein the computing of the correlations of the at least one key product word and the at least one candidate product word, the determining whether the correlation of the at least one key product word and the at least one candidate product word meets the threshold value, and the selecting of the candidate product word having the correlation that meets the threshold value comprises: for the at least one key product word and the at least one candidate product word: vectorizing the at least one key product word based on a click through rate for a category of the at least one key product word to obtain a vector; and vectorizing the at least one candidate product word based on a click through rate for a category of the at least one candidate product word to obtain a vector; computing angle values between the vectors corresponding to the key product words and the vectors corresponding to the candidate product words; computing correlations between the angle values; determining whether at least one candidate product word having a correlation meets a threshold value based on an angle value; and selecting a candidate product word having the correlation that meets the threshold value based on the angle value. | 0.779638 |
27. The method of claim 15 , further comprising: receive a third spoken user input; and in response to receiving the third spoken user input, undo the task. | 27. The method of claim 15 , further comprising: receive a third spoken user input; and in response to receiving the third spoken user input, undo the task. 28. The method of claim 27 , further comprising: receive a fourth spoken user input; and in response to receiving the fourth spoken user input, redo the task. | 0.959755 |
5. The method according to claim 3 , wherein acquiring the rhythm feature comprises: acquiring input text data corresponding to the input speech data; aligning the input text data with the input speech data; and determining the phrase boundary location based on alignment of the input text data with the input speech data. | 5. The method according to claim 3 , wherein acquiring the rhythm feature comprises: acquiring input text data corresponding to the input speech data; aligning the input text data with the input speech data; and determining the phrase boundary location based on alignment of the input text data with the input speech data. 6. The method according to claim 5 , wherein acquiring the standard rhythm feature comprises: matching the input language structure with the standard language structure of standard speech; and selecting a standard phrase boundary location for the input language structure as the standard rhythm feature based on a plurality of occurrence probabilities of phrase boundary locations wherein individual occurrence probabilities of phrase boundary locations in the plurality of occurrence probabilities of phrase boundary locations correspond to individual words in the input speech data. | 0.836851 |
24. The method according to claim 20 , further comprising: storing in the at least one of the at least two caches, for the document, at least one of the document key identifier associated with the document, the prioritization identifier assigned to the printer-readable format, and the printer-readable format corresponding to the document and the document key identifier. | 24. The method according to claim 20 , further comprising: storing in the at least one of the at least two caches, for the document, at least one of the document key identifier associated with the document, the prioritization identifier assigned to the printer-readable format, and the printer-readable format corresponding to the document and the document key identifier. 27. The method according to claim 24 , wherein the at least two caches comprises a first cache and a second cache, and wherein if a priority threshold for the prioritization identifier has been met for the printer-readable format, and wherein the printer-readable format is stored in the first cache, the method further comprises: at least one of copying and moving the printer-readable format to the second cache; and wherein if a priority threshold for the prioritization identifier has not been met for the printer-readable format, and wherein the printer-readable format is stored in one of the at least two caches, the method further comprises: removing the printer-readable format from the one of the at least two caches. | 0.846024 |
14. A processor-implemented service for searching for all occurrences of a queried date in a plurality of electronic documents, the service comprising: receiving the electronic documents; invoking an autonomic hardware configuration utility, wherein the electronic documents are made available to the autonomic hardware configuration utility for automatically searching for all occurrences of the queried date by: processing each of the electronic documents via extended regular expression matching to generate a canonicalized format of each occurrence of any date in any date format in the document; packaging the canonicalized format of each occurrence to support one or more types of date querying; processing a reconstruction of a span and location of one of the occurrences of the queried date after removal of html tags from crawled html content of one of the electronic documents; and outputting all occurrences of the queried date using the packaged canonicalized format. | 14. A processor-implemented service for searching for all occurrences of a queried date in a plurality of electronic documents, the service comprising: receiving the electronic documents; invoking an autonomic hardware configuration utility, wherein the electronic documents are made available to the autonomic hardware configuration utility for automatically searching for all occurrences of the queried date by: processing each of the electronic documents via extended regular expression matching to generate a canonicalized format of each occurrence of any date in any date format in the document; packaging the canonicalized format of each occurrence to support one or more types of date querying; processing a reconstruction of a span and location of one of the occurrences of the queried date after removal of html tags from crawled html content of one of the electronic documents; and outputting all occurrences of the queried date using the packaged canonicalized format. 21. The service of claim 14 wherein packaging comprises structuring the canonicalized format of each occurrence to support range date querying. | 0.837747 |
5. In a computer speech processing apparatus having a processing unit and a memory unit, a computer implemented method for generating pronunciations that include one or more pronunciations of an input orthographic word in a first language for a non-native speaker of the first language who is a native speaker of a second language, the method comprising: a) comparing the input word to one or more words in a partial database of words and corresponding pronunciations to determine if the input word matches any words in the partial database, wherein each word in the partial database has an associated phoneme language and pronunciation language, entering a matching word and pronunciation into a Grammar and Dictionary (GnD) for the first language when the pronunciation language and phoneme language are both the first language, entering the entering a matching word and pronunciation into a Grammar and Dictionary (GnD) for the second language when the pronunciation language is the first language and phoneme language is the second language, and b) generating a pronunciation for the input word when the input word does not match any word in the partial database and entering the input word and generated pronunciation into the GnD for the first language when the language of origin of the input word and the pronunciation language of the input word is the first language and entering the input word and generated pronunciation into the GnD for the second language when the language of origin of the input word is the first language and the pronunciation language of the input word is the second language, wherein generating the pronunciation includes converting each grapheme of the input word to a corresponding phoneme of the first language when the language of origin of the input word and the pronunciation language of the input word is the first language, and converting each grapheme of the input word to a corresponding phoneme of the first language and mapping each corresponding phoneme of the first language to one or more phonemes of the second language when the language of origin of the input word is the first language and the pronunciation language of the input word is the second language. | 5. In a computer speech processing apparatus having a processing unit and a memory unit, a computer implemented method for generating pronunciations that include one or more pronunciations of an input orthographic word in a first language for a non-native speaker of the first language who is a native speaker of a second language, the method comprising: a) comparing the input word to one or more words in a partial database of words and corresponding pronunciations to determine if the input word matches any words in the partial database, wherein each word in the partial database has an associated phoneme language and pronunciation language, entering a matching word and pronunciation into a Grammar and Dictionary (GnD) for the first language when the pronunciation language and phoneme language are both the first language, entering the entering a matching word and pronunciation into a Grammar and Dictionary (GnD) for the second language when the pronunciation language is the first language and phoneme language is the second language, and b) generating a pronunciation for the input word when the input word does not match any word in the partial database and entering the input word and generated pronunciation into the GnD for the first language when the language of origin of the input word and the pronunciation language of the input word is the first language and entering the input word and generated pronunciation into the GnD for the second language when the language of origin of the input word is the first language and the pronunciation language of the input word is the second language, wherein generating the pronunciation includes converting each grapheme of the input word to a corresponding phoneme of the first language when the language of origin of the input word and the pronunciation language of the input word is the first language, and converting each grapheme of the input word to a corresponding phoneme of the first language and mapping each corresponding phoneme of the first language to one or more phonemes of the second language when the language of origin of the input word is the first language and the pronunciation language of the input word is the second language. 6. The method of claim 5 , further comprising: c) converting a sound signal produced by a speaker into a group of input phonemes; d) comparing the group of phonemes to one or more entries in the database; and e) executing a command corresponding to an entry that matches the group of input phonemes. | 0.732143 |
14. The system of claim 9 , wherein the one or more altitude attributes related to the content are determined by the altitude module. | 14. The system of claim 9 , wherein the one or more altitude attributes related to the content are determined by the altitude module. 16. The system of claim 14 , wherein the altitude module is further configured to: determine the one or more altitude attributes related to the content based on address information related to the content. | 0.924101 |
15. A system for searching, comprising: a processor; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to: identify one or more matching objects matching one or more terms of a search request in a search index; execute one or more return path methods associated with each of the one or more matching objects; wherein each of the one or more return path methods traverses an object tree and returns a parent object, wherein the parent object is added to a list of search result parent objects, wherein the data associated with the parent object is stored along with modification dates, wherein the search request further comprises an evaluation date; and wherein the parent object is stored with one or more effective dates, wherein each effective date comprises a past, present or future date; and wherein an effective state of the parent object is determined at the evaluation date by comparing the evaluation date to one or more of the effective dates; rank the list of search result parent objects according to a ranking function; and provide the ranked list of search result parent objects. | 15. A system for searching, comprising: a processor; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to: identify one or more matching objects matching one or more terms of a search request in a search index; execute one or more return path methods associated with each of the one or more matching objects; wherein each of the one or more return path methods traverses an object tree and returns a parent object, wherein the parent object is added to a list of search result parent objects, wherein the data associated with the parent object is stored along with modification dates, wherein the search request further comprises an evaluation date; and wherein the parent object is stored with one or more effective dates, wherein each effective date comprises a past, present or future date; and wherein an effective state of the parent object is determined at the evaluation date by comparing the evaluation date to one or more of the effective dates; rank the list of search result parent objects according to a ranking function; and provide the ranked list of search result parent objects. 19. A system as in claim 15 , wherein the search index is based on the data stored in the network of objects comprising the object tree. | 0.589474 |
1. A method of automatically deploying an information technology (IT) system instance having hardware and software components, the method comprising the steps of: based on components consisting of system context diagrams, component models, operational models, data flow diagrams, and use cases describing functional requirements of the IT system instance, generating, by the computer, an application model of the software components; based on the application model and the use cases, generating, by the computer, an infrastructure model of infrastructure components; based on the application model and the infrastructure model, generating, by the computer, a computer file in a markup language, wherein the computer file includes a design of the IT system instance having the hardware and software components, and further includes first instructions for accessing first assets stored in an infrastructure runtime source library and second instructions for accessing second assets stored in a software source library, and wherein the first assets specify the hardware components and the second assets specify the software components; and exporting, by the computer, the computer file in the markup language to a deployment tool, wherein a result of the exporting the computer file to the deployment tool is an automatic deployment of the IT system instance based on carrying out the first and second instructions included in the computer file to access the first and second assets stored in the infrastructure runtime source library and the software source library, respectively, wherein the step of generating the application model, the step of generating the infrastructure model based on the application model, the step of generating the computer file in the markup language, the step of exporting the computer file to the deployment tool, and the automatic deployment of the IT system instance resulting from the step of exporting the computer file to the deployment tool are steps performed automatically in succession without a human-performed step being performed between any of the steps. | 1. A method of automatically deploying an information technology (IT) system instance having hardware and software components, the method comprising the steps of: based on components consisting of system context diagrams, component models, operational models, data flow diagrams, and use cases describing functional requirements of the IT system instance, generating, by the computer, an application model of the software components; based on the application model and the use cases, generating, by the computer, an infrastructure model of infrastructure components; based on the application model and the infrastructure model, generating, by the computer, a computer file in a markup language, wherein the computer file includes a design of the IT system instance having the hardware and software components, and further includes first instructions for accessing first assets stored in an infrastructure runtime source library and second instructions for accessing second assets stored in a software source library, and wherein the first assets specify the hardware components and the second assets specify the software components; and exporting, by the computer, the computer file in the markup language to a deployment tool, wherein a result of the exporting the computer file to the deployment tool is an automatic deployment of the IT system instance based on carrying out the first and second instructions included in the computer file to access the first and second assets stored in the infrastructure runtime source library and the software source library, respectively, wherein the step of generating the application model, the step of generating the infrastructure model based on the application model, the step of generating the computer file in the markup language, the step of exporting the computer file to the deployment tool, and the automatic deployment of the IT system instance resulting from the step of exporting the computer file to the deployment tool are steps performed automatically in succession without a human-performed step being performed between any of the steps. 4. The method of claim 1 , wherein the step of generating the application model includes representing the software components in Unified Modeling Language (UML), wherein the step of generating the infrastructure model includes representing the hardware components in the UML, wherein the step of generating the computer file in the markup language includes generating the computer file in Extensible Markup Language (XML) based on the UML providing a direct mapping of the infrastructure model to XML via XML Metadata Interchange (XMI). | 0.653325 |
1. A method for estimating a filter factor for access path optimization in a database, comprising: extracting from a statement segment for database query a relation condition which defines a relationship between a variable and a first table; obtaining a first statistics information according to the relation condition and statistics information of the first table, wherein the first statistics information comprises first data value information relating to possible values of the variable under the relation condition, and a first probability information relating to an occurrence probability of data values in the first data value information; extracting from the statement segment a filter condition which defines a relationship between the variable and a second table; obtaining a second statistics information according to the filter condition and statistics information of the second table, wherein the second statistics information comprises second data value information relating to the possible values of the variable under the filter condition, and a second probability information relating to an occurrence probability of the data values in the second data value information; and according to the first statistics information and the second statistics information, estimating the filter factor of the filter condition for access path optimization in the database query relating to the filter condition, said estimating including: determining possible values of a filtering result, the determining including: taking a predetermined part of the data values in an intersection of the first data value information and the second data value information as the possible values of the filtering result, wherein the predetermined part is selected in one of the following ways: selecting a predetermined number of data values with the largest occurrence probabilities in the intersection, and selecting the data values whose occurrence probabilities in the intersection exceed a predetermined threshold. | 1. A method for estimating a filter factor for access path optimization in a database, comprising: extracting from a statement segment for database query a relation condition which defines a relationship between a variable and a first table; obtaining a first statistics information according to the relation condition and statistics information of the first table, wherein the first statistics information comprises first data value information relating to possible values of the variable under the relation condition, and a first probability information relating to an occurrence probability of data values in the first data value information; extracting from the statement segment a filter condition which defines a relationship between the variable and a second table; obtaining a second statistics information according to the filter condition and statistics information of the second table, wherein the second statistics information comprises second data value information relating to the possible values of the variable under the filter condition, and a second probability information relating to an occurrence probability of the data values in the second data value information; and according to the first statistics information and the second statistics information, estimating the filter factor of the filter condition for access path optimization in the database query relating to the filter condition, said estimating including: determining possible values of a filtering result, the determining including: taking a predetermined part of the data values in an intersection of the first data value information and the second data value information as the possible values of the filtering result, wherein the predetermined part is selected in one of the following ways: selecting a predetermined number of data values with the largest occurrence probabilities in the intersection, and selecting the data values whose occurrence probabilities in the intersection exceed a predetermined threshold. 2. The method according to claim 1 , wherein the at least one of first statistics information and the second statistics information is expressed in at least one of the following forms: set, vector, matrix, polynomial and array. | 0.627377 |
32. A non-transitory computer readable storage medium as defined in claim 31 , wherein the input interface sends a request to the message service to prompt the message service to generate the size information. | 32. A non-transitory computer readable storage medium as defined in claim 31 , wherein the input interface sends a request to the message service to prompt the message service to generate the size information. 34. A non-transitory computer readable storage medium as defined in claim 32 , wherein the request includes log in credentials to allow the message service to authenticate the request. | 0.91256 |
1. A mobile discovery method for recognizing and/or identifying media and physical objects, the method employing a mobile device equipped with plural sensors, including an optical sensor, the mobile device being operated in an ambient environment illuminated by a solid state lamp fixture that is separate from the mobile device, the method including the acts: in a first circumstance, a processor of said device selecting a first recognition agent, from among plural available recognition agents, and launching said first recognition agent, the first recognition agent performing a recognition process selected from the list: image watermark recognition, pattern matching, object recognition, facial recognition, barcode recognition, sign language recognition, optical character recognition, audio watermark recognition, speech recognition, and music recognition; and in a second circumstance, the processor selecting a second recognition agent different than the first recognition agent, and launching said second recognition agent, the second recognition agent performing a second, different, recognition process selected from said list; wherein the method includes the optical sensor of the mobile device sensing an encoded optical signal emitted by said solid state lamp fixture, and the mobile device decoding said optical signal sensed by the optical sensor to extract plural-bit payload data, wherein said selecting of the first recognition agent from among the plural available recognition agents depends at least in part on said plural-bit payload data decoded by the mobile device from the encoded optical signal emitted from the lamp fixture. | 1. A mobile discovery method for recognizing and/or identifying media and physical objects, the method employing a mobile device equipped with plural sensors, including an optical sensor, the mobile device being operated in an ambient environment illuminated by a solid state lamp fixture that is separate from the mobile device, the method including the acts: in a first circumstance, a processor of said device selecting a first recognition agent, from among plural available recognition agents, and launching said first recognition agent, the first recognition agent performing a recognition process selected from the list: image watermark recognition, pattern matching, object recognition, facial recognition, barcode recognition, sign language recognition, optical character recognition, audio watermark recognition, speech recognition, and music recognition; and in a second circumstance, the processor selecting a second recognition agent different than the first recognition agent, and launching said second recognition agent, the second recognition agent performing a second, different, recognition process selected from said list; wherein the method includes the optical sensor of the mobile device sensing an encoded optical signal emitted by said solid state lamp fixture, and the mobile device decoding said optical signal sensed by the optical sensor to extract plural-bit payload data, wherein said selecting of the first recognition agent from among the plural available recognition agents depends at least in part on said plural-bit payload data decoded by the mobile device from the encoded optical signal emitted from the lamp fixture. 6. The method of claim 1 that includes, in the first circumstance, and in dependence on the plural-bit data decoded from the optical signal, selecting a first recognition agent that performs image watermark recognition, and launching said first recognition agent. | 0.515687 |
1. A system for ranking data, comprising: at least one processor; at least one computer-readable storage medium connected to the processor; a data store on the medium; entries stored on the data store; a series of instructions on the medium and executable by the processor, including; a reception component that receives a query; a first calculation component that calculates a qualitative semantic similarity score of a semantic part of the query by associating the semantic part with text from data entries in a data store; a second calculation component that calculates a general quantitative score of by at least calculating a distance score for the data entries based on a geographic location part of the query; a third calculation component that adds the general qualitative semantic similarity score and the general quantitative score to obtain a vector score for the at least one data entry, the general qualitative semantic similarity score and the quantitative score each having a range with a minimum value of the general qualitative score being more than a maximum value of the general quantitative semantic similarity score such that the general quantitative score is never overruled by the general qualitative similarity semantic score; and a ranking component that ranks the at least one data entry among other data entries using the vector score. | 1. A system for ranking data, comprising: at least one processor; at least one computer-readable storage medium connected to the processor; a data store on the medium; entries stored on the data store; a series of instructions on the medium and executable by the processor, including; a reception component that receives a query; a first calculation component that calculates a qualitative semantic similarity score of a semantic part of the query by associating the semantic part with text from data entries in a data store; a second calculation component that calculates a general quantitative score of by at least calculating a distance score for the data entries based on a geographic location part of the query; a third calculation component that adds the general qualitative semantic similarity score and the general quantitative score to obtain a vector score for the at least one data entry, the general qualitative semantic similarity score and the quantitative score each having a range with a minimum value of the general qualitative score being more than a maximum value of the general quantitative semantic similarity score such that the general quantitative score is never overruled by the general qualitative similarity semantic score; and a ranking component that ranks the at least one data entry among other data entries using the vector score. 3. The system of claim 1 , wherein the general quantitative score comprises a semantic similarity score, a distance score, and a rating score. | 0.502935 |
7. A system for processing voice mail messages, the system comprising: means for receiving and storing one or more voice mail messages; means for determining an identity of a speaker of each of the one or more voice mail messages (1) by comparing speech signals from each of the voice mail messages with a group of speaker models and (2) based on an analysis of the content of each of the one or more voice mail messages; means for tagging each of the one or more voice mail messages with the respective determined identity; means for, when the determined identity of the speaker of a voice mail message cannot be determined to a threshold certainty, tagging the voice mail message as unknown, receiving an indicated identity of the speaker from a voice mail subscriber and creating a new storage folder for voice mail messages from the speaker corresponding to the received indicated identity; and means for adapting a previously created speaker model associated with the speaker based on speech signals in the voice mail message associated with the speaker. | 7. A system for processing voice mail messages, the system comprising: means for receiving and storing one or more voice mail messages; means for determining an identity of a speaker of each of the one or more voice mail messages (1) by comparing speech signals from each of the voice mail messages with a group of speaker models and (2) based on an analysis of the content of each of the one or more voice mail messages; means for tagging each of the one or more voice mail messages with the respective determined identity; means for, when the determined identity of the speaker of a voice mail message cannot be determined to a threshold certainty, tagging the voice mail message as unknown, receiving an indicated identity of the speaker from a voice mail subscriber and creating a new storage folder for voice mail messages from the speaker corresponding to the received indicated identity; and means for adapting a previously created speaker model associated with the speaker based on speech signals in the voice mail message associated with the speaker. 11. The system of claim 7 , wherein the means for determining an identity of a speaker of each of the one or more voice mail messages further comprises means for using automatic number identification to assist in determining the identity of the speaker. | 0.699764 |
29. The method according to claim 17 , wherein in the step of updating the signature frequency table, the frequency of signatures if an observation period is not elapsed and increases the frequency of a signature after initializing the frequency of a signature if an observation period is elapsed when a currently analyzing signature is present in the signature frequency table, and increases the frequency of signatures after generating and initializing an new entry for a signature when the currently analyzing signature is not present in the signature frequency table. | 29. The method according to claim 17 , wherein in the step of updating the signature frequency table, the frequency of signatures if an observation period is not elapsed and increases the frequency of a signature after initializing the frequency of a signature if an observation period is elapsed when a currently analyzing signature is present in the signature frequency table, and increases the frequency of signatures after generating and initializing an new entry for a signature when the currently analyzing signature is not present in the signature frequency table. 31. The method according to claim 29 , wherein the setup value are decided as a function of frequency quantized to the number of entries deleted or replaced from the signature frequency table, or to an average value of substring frequency values in a substring frequency table or an entropy value interrupting. | 0.775591 |
1. A mobile device comprising: at least one sensor; at least one display; a tangible, non-transitory computer readable memory storing software instructions; and at least one hardware processor coupled with the memory and the sensor, and configurable, upon execution of the software instructions, to: capture, via the at least one sensor, a digital representation a scene; obtain access to contextually relevant key frame bundles based on a context derived from the digital representation, wherein the contextually relevant key frame bundles include recognition features associated with modeled features of at least one known object; recognize at least one scene object in the scene as the at least one known object according to at least one recognition algorithm and as a function of the recognition features and the digital representation; obtain access to augmented reality (AR) content associated with the at least one known object, wherein the AR content comprises an object mask generated from an object model of the at least one known object; anchor the AR content to an anchor point on the at least one scene object as a function of the modeled features corresponding to the recognition features used to recognize the at least one scene object; and render the AR content on the display in relation to the anchor point on the at least one scene object. | 1. A mobile device comprising: at least one sensor; at least one display; a tangible, non-transitory computer readable memory storing software instructions; and at least one hardware processor coupled with the memory and the sensor, and configurable, upon execution of the software instructions, to: capture, via the at least one sensor, a digital representation a scene; obtain access to contextually relevant key frame bundles based on a context derived from the digital representation, wherein the contextually relevant key frame bundles include recognition features associated with modeled features of at least one known object; recognize at least one scene object in the scene as the at least one known object according to at least one recognition algorithm and as a function of the recognition features and the digital representation; obtain access to augmented reality (AR) content associated with the at least one known object, wherein the AR content comprises an object mask generated from an object model of the at least one known object; anchor the AR content to an anchor point on the at least one scene object as a function of the modeled features corresponding to the recognition features used to recognize the at least one scene object; and render the AR content on the display in relation to the anchor point on the at least one scene object. 6. The mobile device of claim 1 , wherein the digital representation comprises at least one of image data, video data, audio data, location data, biometric data, tactile data, time data, temperature data, and accelerometer data. | 0.518957 |
1. A method of assessing fraud in a document using a computer system, comprising: providing a document to the computer system, wherein the document comprises at least one information field; and for at least one of the information fields of the document, comparing handwriting in the information field to at least one handwriting profile representation from at least two information fields of at least one other document, wherein at least one handwriting profile representation has been stored in a computer system; assessing fraud in the document using at least one comparison, wherein evidence of fraud comprises a failure of at least a portion of the handwriting in at least one of the information fields of the document to approximately match at least one handwriting profile representation. | 1. A method of assessing fraud in a document using a computer system, comprising: providing a document to the computer system, wherein the document comprises at least one information field; and for at least one of the information fields of the document, comparing handwriting in the information field to at least one handwriting profile representation from at least two information fields of at least one other document, wherein at least one handwriting profile representation has been stored in a computer system; assessing fraud in the document using at least one comparison, wherein evidence of fraud comprises a failure of at least a portion of the handwriting in at least one of the information fields of the document to approximately match at least one handwriting profile representation. 7. The method of claim 1 , wherein at least one handwriting profile representation is obtained from a valid document. | 0.916074 |
8. The method of claim 1 , wherein the analyzing step comprises the steps of: detecting audio variation of the sports video; analyzing the audio variation of the sports video; and determining the semantic event according to the audio variation. | 8. The method of claim 1 , wherein the analyzing step comprises the steps of: detecting audio variation of the sports video; analyzing the audio variation of the sports video; and determining the semantic event according to the audio variation. 10. The method of claim 8 , wherein the step of analyzing the audio variation of the sports video comprises analyzing a specific term by a sports broadcaster or commentator in the sports video. | 0.882583 |
18. A computer-readable storage medium containing a program which, when executed, performs a process of managing access by multiple users to a limited subset of data, the process comprising: executing a query against one or more databases; generating, by a computer processor executing the program, a plurality of persistent data objects, wherein generating comprises, for each persistent data object: receiving a query result comprising a dataset obtained from one or more databases in response to execution of a query against the one or more databases; attaching security information to the query result in order to restrict access to the query result, and wherein the security information includes security settings for a plurality of potential requesting entities, the security settings defining access rights of each potential requesting entity to at least a portion of the query result; and storing the query result and the attached security information as a rowset in a persistent data object in a data repository; whereby each respective persistent data object is independently accessible on the basis of the respective security information contained within the respective persistent data object; and granting access to the query result by the multiple users, whereby the access by a particular user is dependent on one or more attributes of the particular user and the security information attached to the query result. | 18. A computer-readable storage medium containing a program which, when executed, performs a process of managing access by multiple users to a limited subset of data, the process comprising: executing a query against one or more databases; generating, by a computer processor executing the program, a plurality of persistent data objects, wherein generating comprises, for each persistent data object: receiving a query result comprising a dataset obtained from one or more databases in response to execution of a query against the one or more databases; attaching security information to the query result in order to restrict access to the query result, and wherein the security information includes security settings for a plurality of potential requesting entities, the security settings defining access rights of each potential requesting entity to at least a portion of the query result; and storing the query result and the attached security information as a rowset in a persistent data object in a data repository; whereby each respective persistent data object is independently accessible on the basis of the respective security information contained within the respective persistent data object; and granting access to the query result by the multiple users, whereby the access by a particular user is dependent on one or more attributes of the particular user and the security information attached to the query result. 22. The computer-readable medium of claim 18 , wherein access to the limited subset of data by a user is restricted by presenting the user with only a limited portion of the limited subset of data obtained by executing a query, received from the user, against the limited subset of data, based on the security information attached to the limited subset of data and one or more attributes of the user. | 0.682814 |
8. A non-transitory computer readable medium having stored thereon instructions that when executed by a processor will cause the processor to: identify one or more schema objects of each of a first XML schema and a second XML schema; compare one of the identified schema objects of the first XML schema with one of the identified schema objects of the second XML schema; if a difference is detected between the compared schema objects, evaluate a risk of the detected difference affecting operation of a composite application incorporating the first schema or the second schema; and issue a notification of the evaluated risk of the detected difference; wherein evaluating the risk comprises evaluating the risk in accordance with a predetermined rule. | 8. A non-transitory computer readable medium having stored thereon instructions that when executed by a processor will cause the processor to: identify one or more schema objects of each of a first XML schema and a second XML schema; compare one of the identified schema objects of the first XML schema with one of the identified schema objects of the second XML schema; if a difference is detected between the compared schema objects, evaluate a risk of the detected difference affecting operation of a composite application incorporating the first schema or the second schema; and issue a notification of the evaluated risk of the detected difference; wherein evaluating the risk comprises evaluating the risk in accordance with a predetermined rule. 15. The non-transitory computer readable medium of claim 8 , wherein the instructions to evaluate the risk comprise instructions to determine an effect of the detected difference on a type from which the one of the compared objects inherits a property, or on a type that inherits a property from one of the compared objects. | 0.535354 |
1. An apparatus for retrieving a webpage associated with a domain-specific keyword, comprising: a memory; and a processor communicatively coupled to the memory, the processor being configured to: receive an instruction from a client to request a first resource defined by a first URL, wherein the first URL includes a keyword in the form of a fragment identifier, wherein the keyword is specified by the client; send a first HTTP request to a first web server associated with a domain specified by the first URL; receive a first HTTP response from the first web server, wherein the first HTTP response communicates a client-executable program; and execute the client-executable program upon receipt without further client input, wherein executing the client-executable program comprises: determining a second URL associated with the first URL, the second URL being associated with a target page and being established by a third party not associated with the domain. | 1. An apparatus for retrieving a webpage associated with a domain-specific keyword, comprising: a memory; and a processor communicatively coupled to the memory, the processor being configured to: receive an instruction from a client to request a first resource defined by a first URL, wherein the first URL includes a keyword in the form of a fragment identifier, wherein the keyword is specified by the client; send a first HTTP request to a first web server associated with a domain specified by the first URL; receive a first HTTP response from the first web server, wherein the first HTTP response communicates a client-executable program; and execute the client-executable program upon receipt without further client input, wherein executing the client-executable program comprises: determining a second URL associated with the first URL, the second URL being associated with a target page and being established by a third party not associated with the domain. 4. The apparatus of claim 1 , wherein the second URL is determined based on data included in a brand-driven keyword registry database, the data mapping the keyword to a particular URL based on the domain. | 0.67649 |
490. The system of claim 466 , further comprising: means for receiving the job description; means for storing the job description in the resume database; and means for sending a portion of the a result set to a recruiter, wherein the result set includes at least one matching resume from the resume database, each said at least one matching resume satisfying the job description. | 490. The system of claim 466 , further comprising: means for receiving the job description; means for storing the job description in the resume database; and means for sending a portion of the a result set to a recruiter, wherein the result set includes at least one matching resume from the resume database, each said at least one matching resume satisfying the job description. 495. The system of claim 490 , wherein the job description is a clone of an existing job description stored in the resume database. | 0.921206 |
9. The method of claim 1 , wherein a subcomponent of the response generation module adjusts a translation of a response description into a sequence of at least one of words and acoustic icons. | 9. The method of claim 1 , wherein a subcomponent of the response generation module adjusts a translation of a response description into a sequence of at least one of words and acoustic icons. 10. The method of claim 9 , wherein the subcomponent of the response generation module is configured to adjust the translation of the response description into the sequence of at least one of the words and acoustic icons with respect to an amount of response to be provided to a user of the spoken language dialog system. | 0.88704 |
13. A system comprising: at least one planner for the development of a set of plans; a hypothesis generator in communication with said at least one planner; a database in communication with said hypothesis generator and said at least one planner; at least one analytic in communication with said hypotheses generator and said database; and at least one sensor in communication with one of said at least one analytic, and wherein at least one of said hypotheses generator and said at least one planner converts each plan in the set of plans into a hypothesis, each plan in the set of plans including a sequence of action operators, each action operator maps a state into another state, the hypothesis comprising an explanation of observations. | 13. A system comprising: at least one planner for the development of a set of plans; a hypothesis generator in communication with said at least one planner; a database in communication with said hypothesis generator and said at least one planner; at least one analytic in communication with said hypotheses generator and said database; and at least one sensor in communication with one of said at least one analytic, and wherein at least one of said hypotheses generator and said at least one planner converts each plan in the set of plans into a hypothesis, each plan in the set of plans including a sequence of action operators, each action operator maps a state into another state, the hypothesis comprising an explanation of observations. 14. The system according to claim 13 , wherein said at least one analytic converts the data received from said at least one sensor into observations that are at least one of stored in said database and communicated with said hypotheses generator. | 0.666667 |
16. The method of claim 15 , further comprising: assigning a first importance weight to the sample based on the outcome of the classification of the sample by the first weak learner and a count of samples used to update the classifier; for each of a second plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining an updated error rate of the weak learner based on the first importance weight; selecting a second weak learner from the second plurality based on the updated error rate of the second weak learner; and updating the classifier based on the second weak learner. | 16. The method of claim 15 , further comprising: assigning a first importance weight to the sample based on the outcome of the classification of the sample by the first weak learner and a count of samples used to update the classifier; for each of a second plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining an updated error rate of the weak learner based on the first importance weight; selecting a second weak learner from the second plurality based on the updated error rate of the second weak learner; and updating the classifier based on the second weak learner. 17. The method of claim 16 , wherein the first importance weight is determined based on a ratio between the count of positive samples and a count of all samples used to update the classifier. | 0.88785 |
1. A computer-implemented method, comprising: receiving a request for a verification phrase for verifying an identity of a user; in response to receiving the request for the verification phrase for verifying the identity of the user, identifying subwords to be included in the verification phrase; in response to identifying the subwords to be included in the verification phrase, obtaining a candidate phrase that includes at least some of the identified subwords as the verification phrase; and providing the verification phrase as a response to the request for the verification phrase for verifying the identity of the user. | 1. A computer-implemented method, comprising: receiving a request for a verification phrase for verifying an identity of a user; in response to receiving the request for the verification phrase for verifying the identity of the user, identifying subwords to be included in the verification phrase; in response to identifying the subwords to be included in the verification phrase, obtaining a candidate phrase that includes at least some of the identified subwords as the verification phrase; and providing the verification phrase as a response to the request for the verification phrase for verifying the identity of the user. 5. The method of claim 1 , comprising: obtaining acoustic data representing the user speaking the verification phrase; determining that the obtained acoustic data matches stored acoustic data for the user; and in response to determining that the obtained acoustic data matches stored acoustic data for the user, classifying the user as the user. | 0.71976 |
29. The system of claim 27 , wherein the at least one processor also automatically categorizes content. | 29. The system of claim 27 , wherein the at least one processor also automatically categorizes content. 30. The system of claim 29 , wherein the at least one processor also identifies a categorization for the content based on a comparison of the content to other content. | 0.928521 |
1. A method, comprising: estimating respective pitch values for an audio signal; identifying candidate harmonic segments of the audio signal from the estimated pitch values; determining respective levels of harmonic content in the candidate harmonic segments; and generating an associated classification record for each of the candidate harmonic segments based on a harmonic content predicate defining at least one condition on the harmonic content levels. | 1. A method, comprising: estimating respective pitch values for an audio signal; identifying candidate harmonic segments of the audio signal from the estimated pitch values; determining respective levels of harmonic content in the candidate harmonic segments; and generating an associated classification record for each of the candidate harmonic segments based on a harmonic content predicate defining at least one condition on the harmonic content levels. 3. The method of claim 1 , wherein the identifying comprises identifying the candidate harmonic segments based on a candidate segment predicate defining at least one condition on the estimated pitch values. | 0.776637 |
1. A activity recognition robot device comprising: a memory storing known activity data objects, wherein each known activity data object represents a known activity and includes similarity scoring techniques and clustered temporal features; and an activity recognition device coupled with the memory having a processor, wherein, upon execution of software instructions stored on a non-transitory computer readable medium, the processor is configurable to: generate a plurality of temporal features from a digital representation of an observed action involving at least one recognized object using at least one feature detection algorithm; establish an observed activity data object comprising one or more observed temporal feature clusters generated from the plurality of temporal features; calculate a similarity activity score for the observed activity data object relative to at least one of the known activity data objects as a function of the similarity scoring techniques that are contextually relevant to the activity recognition device, the clustered temporal features, and the observed temporal feature clusters; access an activity recognition results set as a function of the similarity activity score; and cause the robot to take action based on the activity recognition results set. | 1. A activity recognition robot device comprising: a memory storing known activity data objects, wherein each known activity data object represents a known activity and includes similarity scoring techniques and clustered temporal features; and an activity recognition device coupled with the memory having a processor, wherein, upon execution of software instructions stored on a non-transitory computer readable medium, the processor is configurable to: generate a plurality of temporal features from a digital representation of an observed action involving at least one recognized object using at least one feature detection algorithm; establish an observed activity data object comprising one or more observed temporal feature clusters generated from the plurality of temporal features; calculate a similarity activity score for the observed activity data object relative to at least one of the known activity data objects as a function of the similarity scoring techniques that are contextually relevant to the activity recognition device, the clustered temporal features, and the observed temporal feature clusters; access an activity recognition results set as a function of the similarity activity score; and cause the robot to take action based on the activity recognition results set. 9. The robot device of claim 1 , wherein the activity recognition device is further configured to receive the known activity data objects based on a contextual query submitted to an activity database. | 0.62352 |
1. A method of identifying type-ahead candidates, comprising the steps of: analyzing past non-threaded emails or documents for words or phrases, wherein the emails or documents are in files and folders associated with an application; generating a database of the words or phrases and corresponding addressee information within the past non-threaded emails or documents having matching subject matter; receiving in a current email or document of the application one or more characters to provide a basis to determine a match; analyzing the current email or document for current words or phrases; determining matches between the current words or phrases within the current email or document and one or more words or phrases in the database; identifying, based on the determined matches, one or more of the corresponding addressee information of the past non-threaded emails or documents; matching the identified corresponding addressee information with the one or more received characters to identify one or more candidate words; presenting the one or more candidate words to a user on a personal computer as type-ahead choices; providing the one or more candidate words based on the results of the matching step; and presenting and including a selected one of the one or more candidate words in the current email or document, wherein the analyzing the past non-threaded emails or documents comprises: creating the database, which is a running log of messages sent, a record of to whom the messages were sent, messages received, and a record of from whom the messages were received, the database comprising a correlation table comprising: corresponding addressee information for the messages sent and the messages received that are considered matching in subject matter; and one or more uncommon words that appear within the messages sent and the messages received; and wherein the method further comprises: assigning a first weight to the one or more candidate words based on the match with the corresponding addressee information, assigning a second weight to the one or more candidate words based on a frequency match, assigning a third weight to the one or more candidate words based on a time based time proximity match, assigning a fourth weight to the one or more candidate words based on inclusive references, and assigning a fifth weight to the one or more candidate words based on associative matches; creating a preferences profile that defines: (i) preferred contacts or names, (ii) preferred words, and (iii) preferred phrases for use in prioritizing the one or more candidate words; and assigning a sixth weight to the one or more candidate words based on the preferences profile, and wherein analyzing the current email or document comprises: analyzing the past non-threaded emails or documents for context by identifying matching keywords or phrases between the past non-threaded emails or documents and the current email or document; associating the corresponding addressee information for the matched past non-threaded emails or documents with the identified matching keywords or phrases; and creating a context index of the corresponding addressee information for use by the matching step, wherein: the corresponding addressee information are contacts listed as senders or receivers of the matched past non-threaded emails or documents; and the presenting step includes presenting the one or more candidate words based at least in part on the assigned weights. | 1. A method of identifying type-ahead candidates, comprising the steps of: analyzing past non-threaded emails or documents for words or phrases, wherein the emails or documents are in files and folders associated with an application; generating a database of the words or phrases and corresponding addressee information within the past non-threaded emails or documents having matching subject matter; receiving in a current email or document of the application one or more characters to provide a basis to determine a match; analyzing the current email or document for current words or phrases; determining matches between the current words or phrases within the current email or document and one or more words or phrases in the database; identifying, based on the determined matches, one or more of the corresponding addressee information of the past non-threaded emails or documents; matching the identified corresponding addressee information with the one or more received characters to identify one or more candidate words; presenting the one or more candidate words to a user on a personal computer as type-ahead choices; providing the one or more candidate words based on the results of the matching step; and presenting and including a selected one of the one or more candidate words in the current email or document, wherein the analyzing the past non-threaded emails or documents comprises: creating the database, which is a running log of messages sent, a record of to whom the messages were sent, messages received, and a record of from whom the messages were received, the database comprising a correlation table comprising: corresponding addressee information for the messages sent and the messages received that are considered matching in subject matter; and one or more uncommon words that appear within the messages sent and the messages received; and wherein the method further comprises: assigning a first weight to the one or more candidate words based on the match with the corresponding addressee information, assigning a second weight to the one or more candidate words based on a frequency match, assigning a third weight to the one or more candidate words based on a time based time proximity match, assigning a fourth weight to the one or more candidate words based on inclusive references, and assigning a fifth weight to the one or more candidate words based on associative matches; creating a preferences profile that defines: (i) preferred contacts or names, (ii) preferred words, and (iii) preferred phrases for use in prioritizing the one or more candidate words; and assigning a sixth weight to the one or more candidate words based on the preferences profile, and wherein analyzing the current email or document comprises: analyzing the past non-threaded emails or documents for context by identifying matching keywords or phrases between the past non-threaded emails or documents and the current email or document; associating the corresponding addressee information for the matched past non-threaded emails or documents with the identified matching keywords or phrases; and creating a context index of the corresponding addressee information for use by the matching step, wherein: the corresponding addressee information are contacts listed as senders or receivers of the matched past non-threaded emails or documents; and the presenting step includes presenting the one or more candidate words based at least in part on the assigned weights. 10. The method of claim 1 , wherein the current email or document is in response to or related to one or more previous communications, and further comprising the steps of: creating a list of contacts from the one or more previous communications; creating a list of any contacts in a body of the one or more previous communications; and creating a list of contacts included in any past non-threaded emails or documents involving any contacts associated in any address field in any of the one or more previous communications. | 0.509764 |
14. A method of filling out an invention disclosure form, the method comprising: receiving dictation from an inventor over a network, the dictation being related to a concept at least in part attributable to the inventor; receiving oral requests from the inventor for a search to locate files over the network related to the concept; converting the dictation to text with a computer having a processor; retrieving, with the computer, the files over the network; using the computer to place the text into an invention disclosure form; attaching the files to the invention disclosure form; and transmitting, with the computer, the invention disclosure form and attached files to a recipient. | 14. A method of filling out an invention disclosure form, the method comprising: receiving dictation from an inventor over a network, the dictation being related to a concept at least in part attributable to the inventor; receiving oral requests from the inventor for a search to locate files over the network related to the concept; converting the dictation to text with a computer having a processor; retrieving, with the computer, the files over the network; using the computer to place the text into an invention disclosure form; attaching the files to the invention disclosure form; and transmitting, with the computer, the invention disclosure form and attached files to a recipient. 17. The method of claim 14 wherein receiving oral requests from the inventor for files over the network includes requesting copies of patents over the network. | 0.552956 |
17. A mobile station comprising: a wireless-communication interface; a processor; and data storage having stored therein instructions executable by the processor for carrying out functions including: making a first determination that the mobile station is moving at greater than a threshold rate; making a second determination that, while the mobile station is moving at greater than the threshold rate, outgoing text messaging from the mobile station reflects more than a threshold degradation in typing proficiency; and in response to making the first and second determinations, causing the mobile station to perform at least one action selected from the group consisting of (i) disabling visible presentation of received text messages and (ii) disabling manual keying of text messages. | 17. A mobile station comprising: a wireless-communication interface; a processor; and data storage having stored therein instructions executable by the processor for carrying out functions including: making a first determination that the mobile station is moving at greater than a threshold rate; making a second determination that, while the mobile station is moving at greater than the threshold rate, outgoing text messaging from the mobile station reflects more than a threshold degradation in typing proficiency; and in response to making the first and second determinations, causing the mobile station to perform at least one action selected from the group consisting of (i) disabling visible presentation of received text messages and (ii) disabling manual keying of text messages. 19. The mobile station of claim 17 , wherein the at least one action comprises disabling manual keying of text messages, and wherein the functions further comprise, in response to making the first and second determinations, causing the mobile station to enable audible input of text messages. | 0.515968 |
16. A computer program product for summarizing a first unit of text data with relation to an existing document collection, the computer program product including a computer-readable medium encoded with computer program instructions, wherein the computer program instructions, when executed by a processor, cause the processor to perform predetermined operations comprising: computing a term weight that is representative of the relevance of a term to a second unit of text data with relation to the existing document collection; performing one of a domain driven text summarization, an example type query driven text summarization, and a term type query driven text summarization on a selected document; recomposing a vector representation of at least one of the first and second units of text data in a predetermined multidimensional subspace by projecting the vector representation back into an original term space of the existing document collection when performing domain driven text summarization or example type query driven text summarization; computing a relationship of the term to the terms in a query using a term-term matrix associated with the original term space when performing term type query driven text summarization; comparing the computed term weight to a predetermined threshold; and returning a relevant term based at least in part on a result of the comparison. | 16. A computer program product for summarizing a first unit of text data with relation to an existing document collection, the computer program product including a computer-readable medium encoded with computer program instructions, wherein the computer program instructions, when executed by a processor, cause the processor to perform predetermined operations comprising: computing a term weight that is representative of the relevance of a term to a second unit of text data with relation to the existing document collection; performing one of a domain driven text summarization, an example type query driven text summarization, and a term type query driven text summarization on a selected document; recomposing a vector representation of at least one of the first and second units of text data in a predetermined multidimensional subspace by projecting the vector representation back into an original term space of the existing document collection when performing domain driven text summarization or example type query driven text summarization; computing a relationship of the term to the terms in a query using a term-term matrix associated with the original term space when performing term type query driven text summarization; comparing the computed term weight to a predetermined threshold; and returning a relevant term based at least in part on a result of the comparison. 20. The computer program product of claim 16 , wherein the second unit of text data is the same as the first unit of text data. | 0.716903 |
15. A non-transitory computer-readable medium comprising software, the software when executed by one or more processing units operable to perform operations comprising: accessing text comprising a plurality of words; tagging each of the plurality of words with one of a plurality of parts of speech (POS) tags; creating a plurality of tokens, each token comprising one of the plurality of words and its associated POS tag; clustering one or more of the created tokens into a chunk of tokens, the one or more tokens clustered into the chunk of tokens based on the POS tags of the one or more tokens; and forming a phrase based on the chunk of tokens, the phrase comprising the words of the one or more tokens clustered into the chunk of tokens. | 15. A non-transitory computer-readable medium comprising software, the software when executed by one or more processing units operable to perform operations comprising: accessing text comprising a plurality of words; tagging each of the plurality of words with one of a plurality of parts of speech (POS) tags; creating a plurality of tokens, each token comprising one of the plurality of words and its associated POS tag; clustering one or more of the created tokens into a chunk of tokens, the one or more tokens clustered into the chunk of tokens based on the POS tags of the one or more tokens; and forming a phrase based on the chunk of tokens, the phrase comprising the words of the one or more tokens clustered into the chunk of tokens. 20. The non-transitory computer-readable medium of claim 15 , wherein clustering one or more of the created tokens into the chunk of tokens comprises clustering tokens having the same POS tags into the chunk of tokens. | 0.708015 |
7. A method for optimizing the description of contents in a layout document, the method comprising: parsing the content of an original layout document to obtain text graphic unit data; identifying text properties of each character of the text graphic unit data, and classifying characters of the text graphic unit data according to the text properties, to save characters with the same text properties to the same text node along with the same text properties; for characters saved in each text node, saving characters on the same line or column, coordinates of an initial character on the same line or column, average character spacing of the same line or column to a text content node to obtain optimized contents in the layout document, wherein the text content node is a text content node under the text node and corresponding to the same line or column. | 7. A method for optimizing the description of contents in a layout document, the method comprising: parsing the content of an original layout document to obtain text graphic unit data; identifying text properties of each character of the text graphic unit data, and classifying characters of the text graphic unit data according to the text properties, to save characters with the same text properties to the same text node along with the same text properties; for characters saved in each text node, saving characters on the same line or column, coordinates of an initial character on the same line or column, average character spacing of the same line or column to a text content node to obtain optimized contents in the layout document, wherein the text content node is a text content node under the text node and corresponding to the same line or column. 10. The method of claim 7 , characterized in that wherein the step of creating the text content node comprises: buffering characters belonging to the same line or column for characters of each text node, calculating an average character spacing and actual character spacing among characters on the same line or column; adding characters successively determined and having a difference between the actual character spacing and the average character spacing that is less than or equal to a predetermined value into the text content node, and saving the average character spacing and coordinates of the first character added to the text content node to the text content node; adding characters having a difference between the actual character spacing and the average character spacing that is larger than the predetermined value to a newly created text content node corresponding to the same line or column. | 0.551372 |
1. A method, comprising: creating a user profile which includes interests of a user to succinctly provide a basis that defines user preferred categories; defining the user preferred categories associated with a plurality of taxonomies prior to conducting a search of a database; storing the defined user preferred categories such that the user preferred categories can be used subsequently in conducting a fast path search, wherein the user preferred categories are recorded and automatically added to a list associated with the user; selecting a fast path searching operation that allows the search of the database to be conducted as the fast path search in only the defined user preferred categories; conducting the fast path search of the database located on a server using a displayed control to search the user preferred categories, the search being within a plurality of the user preferred categories based upon search criteria by comparing the search criteria to content information within each of the user preferred categories; displaying on a workstation search results associated with each of the user preferred categories which have matching criteria based on the conducted fast path search; displaying the user preferred categories in an expanded manner to show sub categories of the user preferred categories and to show the user preferred categories in a hierarchical relationship; during the displaying of the user preferred categories, displaying the plurality of taxonomies associated with the results; using the server to define one or more common categories found within multiple searches as the user preferred categories; and using the server to provide a prompt, which allows one or more previously conducted multiple searches to be added to the user preferred categories. | 1. A method, comprising: creating a user profile which includes interests of a user to succinctly provide a basis that defines user preferred categories; defining the user preferred categories associated with a plurality of taxonomies prior to conducting a search of a database; storing the defined user preferred categories such that the user preferred categories can be used subsequently in conducting a fast path search, wherein the user preferred categories are recorded and automatically added to a list associated with the user; selecting a fast path searching operation that allows the search of the database to be conducted as the fast path search in only the defined user preferred categories; conducting the fast path search of the database located on a server using a displayed control to search the user preferred categories, the search being within a plurality of the user preferred categories based upon search criteria by comparing the search criteria to content information within each of the user preferred categories; displaying on a workstation search results associated with each of the user preferred categories which have matching criteria based on the conducted fast path search; displaying the user preferred categories in an expanded manner to show sub categories of the user preferred categories and to show the user preferred categories in a hierarchical relationship; during the displaying of the user preferred categories, displaying the plurality of taxonomies associated with the results; using the server to define one or more common categories found within multiple searches as the user preferred categories; and using the server to provide a prompt, which allows one or more previously conducted multiple searches to be added to the user preferred categories. 12. The method of claim 1 , further comprising allowing a user to navigate to a combination of categories within the plurality of different taxonomies. | 0.67931 |
19. A computer program product comprising a non-transitory computer readable medium storing computer instructions for adaptive indexing of multimedia content, the computer instructions comprising: instructions for identifying a keyword associated with a first feature and a second feature found in a data portion of a multimedia item, the feature being of a first media type and the second feature being of a second media type, and the first media type being associated with a first weight and the second media type associated with a second weight; instructions for determining a relevance for the keyword by calculating a relevance value using said first weight and said second weight; instructions for updating the first weight associated with the first media type based on a search result selected by a user from a set of search results that were identified to the user in response to a query from the user to produce an updated first weight, wherein the selected search result indicates a multimedia item having a feature being of the first media type and another feature being of the second media type, each of the features being associated with the keyword; instructions for updating the second weight based on a particular set of content items being identified by the selected search result to produce an updated second weight; and instructions for updating the relevance of the keyword using the updated first weight, and the updated second weight. | 19. A computer program product comprising a non-transitory computer readable medium storing computer instructions for adaptive indexing of multimedia content, the computer instructions comprising: instructions for identifying a keyword associated with a first feature and a second feature found in a data portion of a multimedia item, the feature being of a first media type and the second feature being of a second media type, and the first media type being associated with a first weight and the second media type associated with a second weight; instructions for determining a relevance for the keyword by calculating a relevance value using said first weight and said second weight; instructions for updating the first weight associated with the first media type based on a search result selected by a user from a set of search results that were identified to the user in response to a query from the user to produce an updated first weight, wherein the selected search result indicates a multimedia item having a feature being of the first media type and another feature being of the second media type, each of the features being associated with the keyword; instructions for updating the second weight based on a particular set of content items being identified by the selected search result to produce an updated second weight; and instructions for updating the relevance of the keyword using the updated first weight, and the updated second weight. 21. The computer program product claim 19 , wherein the first weight associated with the first media type is associated with a specific set of one or more users or devices and not with any other set of users or devices. | 0.665105 |
22. A system for converting a textual passage to a synthesized image sequence, comprising: processing electronics configured to determine a reading speed of a user, to determine a first textual passage of a text source currently being read by the user, to predict a second textual passage of the text source that will be read by the user based on the reading speed of the user and an amount of text between the first textual passage and the second textual passage, and to generate a synthesized image sequence associated with the second textual passage. | 22. A system for converting a textual passage to a synthesized image sequence, comprising: processing electronics configured to determine a reading speed of a user, to determine a first textual passage of a text source currently being read by the user, to predict a second textual passage of the text source that will be read by the user based on the reading speed of the user and an amount of text between the first textual passage and the second textual passage, and to generate a synthesized image sequence associated with the second textual passage. 24. The system of claim 22 , wherein the amount of text between the first textual passage and the second textual passage comprises a number of paragraphs. | 0.554012 |
5. The computer readable storage medium of claim 3 wherein each said group of portlets share dynamic context names in common. | 5. The computer readable storage medium of claim 3 wherein each said group of portlets share dynamic context names in common. 6. The computer readable storage medium of claim 5 further comprising communicating changes in dynamic context values of a master portlet to slave portlets of said master portlet. | 0.947307 |
10. A computer software product, including a non-transitory computer-readable storage medium in which computer program instructions are stored, which instructions, when executed by a computer, cause the computer to perform the steps of: identifying properties of a target individual, wherein a correlation processor extracts user identifiers from retrieved data items, and correlates the user identifiers from different web sites; building an initial social circle of the target individual by crawling a plurality of web sites from different social media providers to identify direct and indirect associations between users of the social media providers and the target individual, wherein the target individual has no direct connection with at least one of the social media providers; deriving references to the target individual from the direct and indirect associations; and compiling a dossier on the target individual from the references to the target individual. | 10. A computer software product, including a non-transitory computer-readable storage medium in which computer program instructions are stored, which instructions, when executed by a computer, cause the computer to perform the steps of: identifying properties of a target individual, wherein a correlation processor extracts user identifiers from retrieved data items, and correlates the user identifiers from different web sites; building an initial social circle of the target individual by crawling a plurality of web sites from different social media providers to identify direct and indirect associations between users of the social media providers and the target individual, wherein the target individual has no direct connection with at least one of the social media providers; deriving references to the target individual from the direct and indirect associations; and compiling a dossier on the target individual from the references to the target individual. 15. The computer software product according to claim 10 , wherein compiling a dossier comprises correlating the references according to similarities and commonalities therebetween. | 0.621363 |
7. A computer program product for filtering notifications to a user from a device based on an enhanced white list comprising a static white list set by the user and a temporary white list, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer comprising at least one processor, one or more memories and one or more computer readable storage media to perform a method comprising: monitoring, by the computer, applications and activities on the device which generate notifications to the user to create and maintain a temporary white list to be used with a static white list for notifications to the user comprising the program instructions: monitoring, by the computer, the applications and activities on the device which generate notifications and determining which of the monitored applications and activities on the device are not present on the static white list; searching for and analyzing, by the computer, activities of the device and user interaction with the device to extract at least keywords and context associated with the activities and user interaction of the device; determining, by the computer, whether the keywords and context extracted are associated with a dependency list between applications of the device and context; and if the keywords and context extracted are present on the dependency list, adding, by the computer, the application and activity on the device as an expiring entry on the temporary white list; receiving, by the computer, a notification from an application of the device for the user; and if the application is on the enhanced white list, allowing the notification from the application to audibly sound to the user through the device. | 7. A computer program product for filtering notifications to a user from a device based on an enhanced white list comprising a static white list set by the user and a temporary white list, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer comprising at least one processor, one or more memories and one or more computer readable storage media to perform a method comprising: monitoring, by the computer, applications and activities on the device which generate notifications to the user to create and maintain a temporary white list to be used with a static white list for notifications to the user comprising the program instructions: monitoring, by the computer, the applications and activities on the device which generate notifications and determining which of the monitored applications and activities on the device are not present on the static white list; searching for and analyzing, by the computer, activities of the device and user interaction with the device to extract at least keywords and context associated with the activities and user interaction of the device; determining, by the computer, whether the keywords and context extracted are associated with a dependency list between applications of the device and context; and if the keywords and context extracted are present on the dependency list, adding, by the computer, the application and activity on the device as an expiring entry on the temporary white list; receiving, by the computer, a notification from an application of the device for the user; and if the application is on the enhanced white list, allowing the notification from the application to audibly sound to the user through the device. 11. The computer program product of claim 7 , wherein a topic is derived from the extracted keywords and the expiring entry on the temporary white list is based on the topic. | 0.712351 |
12. A computer system for managing message communications, comprising: a storage media configured to store message objects including emails, chats, SMS, and other type of messages and software modules; a user interface configured to display message contents, and information about message senders or recipients; and one or more processors configured to access a memory module and the storage media, coupled with the user interface, and further configured to invoke a software module on a server or client computing device to (a) receive a first message, wherein the first message is addressed to or received by multiple recipients, (b) register a user action to reply to the first message, (c) display an message composition or editing interface in response to the user action, (d) detect whether the user is replying only to the sender of the first message as a first operation mode, or to multiple recipients of the first message as a second operation mode, and (e) display a notification, wherein the notification indicates whether the user is replying in the first operation mode or in the second operation mode. | 12. A computer system for managing message communications, comprising: a storage media configured to store message objects including emails, chats, SMS, and other type of messages and software modules; a user interface configured to display message contents, and information about message senders or recipients; and one or more processors configured to access a memory module and the storage media, coupled with the user interface, and further configured to invoke a software module on a server or client computing device to (a) receive a first message, wherein the first message is addressed to or received by multiple recipients, (b) register a user action to reply to the first message, (c) display an message composition or editing interface in response to the user action, (d) detect whether the user is replying only to the sender of the first message as a first operation mode, or to multiple recipients of the first message as a second operation mode, and (e) display a notification, wherein the notification indicates whether the user is replying in the first operation mode or in the second operation mode. 16. The system of claim 12 , wherein a format of displaying the notification comprises a textual message, an icon, a sound, or a vibration, or a color code or a highlighting method that distinguishes the first operation mode from the second operation mode, or a user interface object that allows the user to change the current operation mode without leaving the current composing window, or bears a label that reminds the user of the context of the current operation mode, or attaches a notification to the message to indicate the current operation mode based on user indication. | 0.564567 |
1. A computer-implemented method comprising: processing a plurality of data frames, each data frame of the plurality of data frames comprising one or more body point locations of each of a plurality of collaborating users that are interfacing with an application at each of a plurality of time intervals; defining a spatial volume for each of the plurality of collaborating users based on the plurality of processed data frames; detecting a gesture performed by a first collaborating user of the plurality of collaborating users based on the plurality of processed data frames; determining the gesture to be an input gesture based on the gesture being performed by the first collaborating user in a first spatial volume; interpreting, by a machine having a memory and at least one processor, the input gesture based on a context of the first spatial volume, the context of the first spatial volume comprising an intersection volume between the first spatial volume and a second spatial volume for a second collaborating user; and providing an input command to the application based on the interpreted input gesture, the input command being different for the gesture being within the intersection volume than for the gesture being outside of the intersection volume. | 1. A computer-implemented method comprising: processing a plurality of data frames, each data frame of the plurality of data frames comprising one or more body point locations of each of a plurality of collaborating users that are interfacing with an application at each of a plurality of time intervals; defining a spatial volume for each of the plurality of collaborating users based on the plurality of processed data frames; detecting a gesture performed by a first collaborating user of the plurality of collaborating users based on the plurality of processed data frames; determining the gesture to be an input gesture based on the gesture being performed by the first collaborating user in a first spatial volume; interpreting, by a machine having a memory and at least one processor, the input gesture based on a context of the first spatial volume, the context of the first spatial volume comprising an intersection volume between the first spatial volume and a second spatial volume for a second collaborating user; and providing an input command to the application based on the interpreted input gesture, the input command being different for the gesture being within the intersection volume than for the gesture being outside of the intersection volume. 16. The computer-implemented method of claim 1 , further comprising providing a forward feedback signal based on the input gesture the forward feedback signal being configured to convey where the input gesture is in the spatial volume, the forward feedback signal comprising one or more of a sound and a touch feedback. | 0.500347 |
1. A method of processing speech, the method comprising: receiving a spoken utterance of a plurality of uttered characters; determining an identified character sequence by determining corresponding identified characters for individual ones of the plurality of uttered characters; selecting a plurality of known character sequences that potentially correspond to the identified character sequence; and for each selected known character sequence, scoring such known character sequence, using a processor, based at least in part on a weighting of individual characters that comprise the known character sequence, wherein scoring the known character sequence comprises: responsive to determining that a first character of the known character sequence matches a second identified character of the identified character sequence, selecting a value that corresponds to the first character of the known character sequence; and adding the selected value to a cumulative score associated with the known character sequence. | 1. A method of processing speech, the method comprising: receiving a spoken utterance of a plurality of uttered characters; determining an identified character sequence by determining corresponding identified characters for individual ones of the plurality of uttered characters; selecting a plurality of known character sequences that potentially correspond to the identified character sequence; and for each selected known character sequence, scoring such known character sequence, using a processor, based at least in part on a weighting of individual characters that comprise the known character sequence, wherein scoring the known character sequence comprises: responsive to determining that a first character of the known character sequence matches a second identified character of the identified character sequence, selecting a value that corresponds to the first character of the known character sequence; and adding the selected value to a cumulative score associated with the known character sequence. 3. The method of claim 1 , wherein the selected value is pre-assigned to the first character in a character set. | 0.805842 |
1. A method for identifying content corresponding to a language, the method comprising: storing a first association between a first phoneme sequence and a first language and a second association between a second phoneme sequence and a second language; generating a phoneme sequence based on first verbal input received from a first user; determining which one of the first and second phoneme sequences, associated respectively with the first and second languages, corresponds to the generated phoneme sequence; automatically determining, with voice recognition circuitry, that a language spoken by the first user corresponds to the first language based on determining that the first phoneme sequence corresponds to the generated phoneme sequence; measuring a first duration of the received first verbal input representing how often the first language is spoken in the household; storing the first duration in a database that associates the first duration with the first language; measuring a second duration of second verbal input received subsequent to the first verbal input representing how often the second language is spoken in the household, wherein the second verbal input corresponds to the second language; storing a second duration in the database that associates the second duration with the second language; comparing the first duration to the second duration; in response to determining that the first duration is greater than the second duration, determining that the first language is spoken more often than the second language in the household; searching a database of content sources to identify a content source associated with a language field value that corresponds to the first language based on determining that the first language is spoken more often than the second language in the household, wherein the language field in the database identifies the language that the associated content source transmits content to a plurality of users; and generating for display a representation of the identified content source to the first user. | 1. A method for identifying content corresponding to a language, the method comprising: storing a first association between a first phoneme sequence and a first language and a second association between a second phoneme sequence and a second language; generating a phoneme sequence based on first verbal input received from a first user; determining which one of the first and second phoneme sequences, associated respectively with the first and second languages, corresponds to the generated phoneme sequence; automatically determining, with voice recognition circuitry, that a language spoken by the first user corresponds to the first language based on determining that the first phoneme sequence corresponds to the generated phoneme sequence; measuring a first duration of the received first verbal input representing how often the first language is spoken in the household; storing the first duration in a database that associates the first duration with the first language; measuring a second duration of second verbal input received subsequent to the first verbal input representing how often the second language is spoken in the household, wherein the second verbal input corresponds to the second language; storing a second duration in the database that associates the second duration with the second language; comparing the first duration to the second duration; in response to determining that the first duration is greater than the second duration, determining that the first language is spoken more often than the second language in the household; searching a database of content sources to identify a content source associated with a language field value that corresponds to the first language based on determining that the first language is spoken more often than the second language in the household, wherein the language field in the database identifies the language that the associated content source transmits content to a plurality of users; and generating for display a representation of the identified content source to the first user. 3. The method of claim 1 further comprising: receiving a user input from the first user requesting access to a given content source; generating a display of content provided by the given content source; identifying a language field associated with the given content source; and determining whether the language spoken by the first user corresponds to a value of the language field associated with the given content source; wherein the generated display of the representation of the identified content source is provided to a display device if the language spoken by the first user does not correspond to the value of the language field associated with the given content source. | 0.513793 |
2. The method of claim 1 wherein the step of tokenizing the phrases includes tokenizing pauses. | 2. The method of claim 1 wherein the step of tokenizing the phrases includes tokenizing pauses. 7. The method of claim 2 wherein the step of ranking each of the plurality of documents based on the spoken query includes representing the spoken query by a vector of weighted N-grams. | 0.890201 |
1. A computer implemented method of presenting logical structures on a computer display screen, comprising the steps of: providing a logical description of a plurality of objects that defines relationships between the plurality of objects; parsing the logical description to identify the relationships between the plurality of objects; extracting a plurality of delimiters which define the relationships between the plurality of objects; and displaying the relationships with nested windows on the computer display as defined by the delimiters where the relationships having a higher order of precedence are arranged in windows on top of other windows that represent lower order of precedence. | 1. A computer implemented method of presenting logical structures on a computer display screen, comprising the steps of: providing a logical description of a plurality of objects that defines relationships between the plurality of objects; parsing the logical description to identify the relationships between the plurality of objects; extracting a plurality of delimiters which define the relationships between the plurality of objects; and displaying the relationships with nested windows on the computer display as defined by the delimiters where the relationships having a higher order of precedence are arranged in windows on top of other windows that represent lower order of precedence. 4. The method of claim 1 wherein the objects comprise a plurality of variable expressions. | 0.557692 |
14. A system for managing interactions with a person, comprising non-transitory computer storage media storing programming instructions executable by at least one processor to: receive data representing an utterance from a person; automatically present the utterance in perceptible form to an intent analyst through an analyst user interface; accept intent input from intent analyst through the analyst user interface, where the intent input characterizes the intent analyst's interpretation of the person's intent expressed in the utterance; provide the data and the intent input to a training subsystem; accept from the training subsystem training information generated responsive to the data and the intent input; and send the training information to a real-time recognition processor in order to improve performance thereof. | 14. A system for managing interactions with a person, comprising non-transitory computer storage media storing programming instructions executable by at least one processor to: receive data representing an utterance from a person; automatically present the utterance in perceptible form to an intent analyst through an analyst user interface; accept intent input from intent analyst through the analyst user interface, where the intent input characterizes the intent analyst's interpretation of the person's intent expressed in the utterance; provide the data and the intent input to a training subsystem; accept from the training subsystem training information generated responsive to the data and the intent input; and send the training information to a real-time recognition processor in order to improve performance thereof. 15. The system of claim 14 , wherein the instructions further comprise instructions to accept intent input from a second intent analyst, wherein said instructions to provide are responsive to intent input from the intent analyst matching intent input from the second intent analyst. | 0.672852 |
8. A musical score playing method comprising: calculating a prescribed measure time from the meter of a musical composition, by one or more processors; calculating a measure playing time from sound emission timings and note values of notes and rests within a measure, by said one or more processors; comparing the calculated prescribed measure time and measure playing time, by said one or more processors; and when the prescribed measure time and the measure playing time are not matched, determining by said one or more processors; that a tuplet is present within the measure, storing a note sequence within the measure, grouping the notes within the measure according to each beat, and when a playing time of the grouped notes and a reference time of a single beat that is calculated from the note sequence are not matched, changing, by said one or more processors, the note values of the grouped notes to correct the sound emission timings and note values of the notes and rests. | 8. A musical score playing method comprising: calculating a prescribed measure time from the meter of a musical composition, by one or more processors; calculating a measure playing time from sound emission timings and note values of notes and rests within a measure, by said one or more processors; comparing the calculated prescribed measure time and measure playing time, by said one or more processors; and when the prescribed measure time and the measure playing time are not matched, determining by said one or more processors; that a tuplet is present within the measure, storing a note sequence within the measure, grouping the notes within the measure according to each beat, and when a playing time of the grouped notes and a reference time of a single beat that is calculated from the note sequence are not matched, changing, by said one or more processors, the note values of the grouped notes to correct the sound emission timings and note values of the notes and rests. 10. The musical score playing method according to claim 8 , wherein said changing further comprises changing the note values of the grouped notes when the playing time of the grouped notes is shorter than the reference time of one beat and the number of sixteenth notes in the group is 3, or the number of thirty-second notes is 5 or 7. | 0.658578 |
31. A server system for processing query information, comprising: one or more processors; and memory to store data and one or more programs to be executed by the one or more processors, the one or more programs including instructions for: prior to a user of a client device signaling completion of a search query: receiving from a search requestor a partial search query, the search requestor located remotely from the server system; predicting from the partial search query a set of predicted complete queries relevant to the partial search query, where the predicted complete queries comprise previously submitted complete queries submitted by a community of users, wherein the partial search query and the set of predicted complete queries are in a first language; subsequent to the predicting, obtaining translations of at least a subset of the set of predicted complete queries, wherein the translations are in a second language different from the first language, and the subset comprises multiple predicted complete queries, wherein the first and second languages are predicted based, at least in part, on the partial search query; and conveying both the set of predicted complete queries and the corresponding translations to the search requestor for concurrent display. | 31. A server system for processing query information, comprising: one or more processors; and memory to store data and one or more programs to be executed by the one or more processors, the one or more programs including instructions for: prior to a user of a client device signaling completion of a search query: receiving from a search requestor a partial search query, the search requestor located remotely from the server system; predicting from the partial search query a set of predicted complete queries relevant to the partial search query, where the predicted complete queries comprise previously submitted complete queries submitted by a community of users, wherein the partial search query and the set of predicted complete queries are in a first language; subsequent to the predicting, obtaining translations of at least a subset of the set of predicted complete queries, wherein the translations are in a second language different from the first language, and the subset comprises multiple predicted complete queries, wherein the first and second languages are predicted based, at least in part, on the partial search query; and conveying both the set of predicted complete queries and the corresponding translations to the search requestor for concurrent display. 35. The server system of claim 31 , wherein the subset of predicted complete queries includes multiple predicted complete queries distinct from the partial search query. | 0.582647 |
1. A computerized method for electronic document classification, the method comprising: providing training documents sorted into a plurality of classes; using a processor to perform linear programming including selecting input values which maximize an output value, given specific constraints on the input values, wherein the output value maximized is a difference between: a. a first estimated probability that a document instance will be correctly classified, by a given classifier corresponding to given input values, as belonging to its own class, and b. a second estimated probability that the document instance will be classified, by the given classifier, as belonging to a class other than its own class; and classifying electronic document instances into the plurality of classes, using at least one preferred classifier corresponding to the input values selected by said linear programming including storing an indication of said classifying in computer memory, wherein some electronic document instances are classified as belonging to none of the plurality of classes. | 1. A computerized method for electronic document classification, the method comprising: providing training documents sorted into a plurality of classes; using a processor to perform linear programming including selecting input values which maximize an output value, given specific constraints on the input values, wherein the output value maximized is a difference between: a. a first estimated probability that a document instance will be correctly classified, by a given classifier corresponding to given input values, as belonging to its own class, and b. a second estimated probability that the document instance will be classified, by the given classifier, as belonging to a class other than its own class; and classifying electronic document instances into the plurality of classes, using at least one preferred classifier corresponding to the input values selected by said linear programming including storing an indication of said classifying in computer memory, wherein some electronic document instances are classified as belonging to none of the plurality of classes. 21. A method according to claim 1 , wherein at least one estimated probability that an electronic document belongs to a particular class of documents, or to no known class thereof is computed by: finding K documents that are the nearest neighbors to the current document instance; computing an average distance to said K documents; and using said average distance to compute an estimated probability to be in any of C classes and an estimated probability to belong to none of the C classes. | 0.556119 |
15. A non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions comprising: one or more instructions to receive history data for a document, the history data comprising an appearance date for each of a plurality of links to document; one or more instructions to determine, from the history data, that there has been a decrease in a rate or quantity of appearances of new links that point to the document over time, and then classify the document as stale, the one or more instructions to determine that there has been a decrease in the rate or quantity of appearances of new links that point to the document over time, including: one or more instructions to compare an oldest appearance date, of the appearance dates of the links to the document, to an oldest appearance date of the appearance dates of a group of newest links to the document, each link in the group of newest links to the document having an appearance date that is within a percentage of most recent appearance dates of the links to the document; one or more instructions to decrease, based on classifying the document as stale, an initial score for the document, resulting in an altered score; and one or more instructions to rank the document with regard to at least one other document based on the altered score. | 15. A non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions comprising: one or more instructions to receive history data for a document, the history data comprising an appearance date for each of a plurality of links to document; one or more instructions to determine, from the history data, that there has been a decrease in a rate or quantity of appearances of new links that point to the document over time, and then classify the document as stale, the one or more instructions to determine that there has been a decrease in the rate or quantity of appearances of new links that point to the document over time, including: one or more instructions to compare an oldest appearance date, of the appearance dates of the links to the document, to an oldest appearance date of the appearance dates of a group of newest links to the document, each link in the group of newest links to the document having an appearance date that is within a percentage of most recent appearance dates of the links to the document; one or more instructions to decrease, based on classifying the document as stale, an initial score for the document, resulting in an altered score; and one or more instructions to rank the document with regard to at least one other document based on the altered score. 18. The non-transitory computer-readable storage medium of claim 15 , where the one or more instructions to determine that there has been a decrease in the rate or quantity of appearances of new links that point to the document over time further include one or more instructions to compare a quantity of appearances of new links to the document during a recent pre-defined time period to a total quantity of appearances of new links to the document. | 0.628688 |
15. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for constructing a model that generates text, the method comprising: representing a concept as a cluster node; representing a word as a terminal node; assigning a weight to a link between two nodes; and training the model based on a set of documents, comprising: for each cluster node, computing a probabilistic cost of a corresponding concept existing in a document but not triggering any words. | 15. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for constructing a model that generates text, the method comprising: representing a concept as a cluster node; representing a word as a terminal node; assigning a weight to a link between two nodes; and training the model based on a set of documents, comprising: for each cluster node, computing a probabilistic cost of a corresponding concept existing in a document but not triggering any words. 17. The computer-readable storage medium of claim 15 , wherein training the model based on the documents further comprises: for a given link, producing a weight function for each document; and optimizing a product of all the weight functions for the given link. | 0.643862 |
17. The method of claim 11 , wherein the method further comprises the step of: when the reputation score is better than a second predefined threshold, performing a second specified action associated with responding to messages that are not unsolicited, wherein the first predefined threshold is different from the second predefined threshold. | 17. The method of claim 11 , wherein the method further comprises the step of: when the reputation score is better than a second predefined threshold, performing a second specified action associated with responding to messages that are not unsolicited, wherein the first predefined threshold is different from the second predefined threshold. 20. The method of claim 17 , wherein the step of performing the second specified action comprises performing one or more filter operations on the message. | 0.905063 |
1. A system including computer executable instructions on a computer storage media and the computer executable instructions being executed by a processing unit to implement one or more components comprising: a node generator configured to receive a parsed natural language sentence or phrase and recover phrasal and constituent nodes and grammatical tags for the phrasal and constituent nodes of the parsed natural language sentence or phrase and the node generator uses a head analysis component to analyze the phrasal and constituent nodes and grammatical tags of the parsed natural language sentence or phrase to generate hierarchical and dependent nodes of a language neutral representation of the parsed natural language sentence or phrase; and a node dependency generator configured to receive the hierarchical and dependent nodes and the grammatical tags of the parsed natural language sentence and create an iterative dependency structure including a preliminary dependency structure including one or more unlabeled dependencies to one or more semantic heads and a secondary dependency structure including semantic or grammatical labels replacing the one or more unlabeled dependencies to generate an unordered hierarchical dependency structure for the hierarchical and dependent nodes and the semantic or grammatical labels representing a language neutral relation between the hierarchical and dependent nodes different from the grammatical tags of the parsed natural language sentence or phrase using a semantic relation between the hierarchical and dependent nodes derived from the grammatical tags of the parsed natural language sentence or phrase. | 1. A system including computer executable instructions on a computer storage media and the computer executable instructions being executed by a processing unit to implement one or more components comprising: a node generator configured to receive a parsed natural language sentence or phrase and recover phrasal and constituent nodes and grammatical tags for the phrasal and constituent nodes of the parsed natural language sentence or phrase and the node generator uses a head analysis component to analyze the phrasal and constituent nodes and grammatical tags of the parsed natural language sentence or phrase to generate hierarchical and dependent nodes of a language neutral representation of the parsed natural language sentence or phrase; and a node dependency generator configured to receive the hierarchical and dependent nodes and the grammatical tags of the parsed natural language sentence and create an iterative dependency structure including a preliminary dependency structure including one or more unlabeled dependencies to one or more semantic heads and a secondary dependency structure including semantic or grammatical labels replacing the one or more unlabeled dependencies to generate an unordered hierarchical dependency structure for the hierarchical and dependent nodes and the semantic or grammatical labels representing a language neutral relation between the hierarchical and dependent nodes different from the grammatical tags of the parsed natural language sentence or phrase using a semantic relation between the hierarchical and dependent nodes derived from the grammatical tags of the parsed natural language sentence or phrase. 3. The system of claim 1 wherein the node generator creates a head node for each of the phrasal nodes of the parsed natural language sentence or phrase. | 0.584178 |
8. A system for printing a document having a printed copy detection pattern, comprising: a computing device; an intermediate electronic device operatively coupled to said computing device through a first communications channel, said intermediate electronic device storing a cryptographic key; a printing device operatively coupled to said intermediate electronic device through a second communications channel; wherein said computing device is adapted to generate printer control commands and send said printer control commands to said intermediate electronic device over said first communications channel, said printer control commands including: (i) commands for printing based on document data, and (ii) an identification of a determined portion of the document data that is to be used in generating said printed copy detection pattern; and wherein said intermediate electronic device is adapted to: (i) generate copy detection pattern data using said determined portion of the document data and said cryptographic key, (ii) generate modified printer control commands, said modified printer control commands including commands for printing a first document portion based on the document data and a second document portion including said printed copy detection pattern based on said copy detection pattern data, and (iii) send said modified printer control commands to said printing device over said second communications channel for printing said first document portion and said second document portion. | 8. A system for printing a document having a printed copy detection pattern, comprising: a computing device; an intermediate electronic device operatively coupled to said computing device through a first communications channel, said intermediate electronic device storing a cryptographic key; a printing device operatively coupled to said intermediate electronic device through a second communications channel; wherein said computing device is adapted to generate printer control commands and send said printer control commands to said intermediate electronic device over said first communications channel, said printer control commands including: (i) commands for printing based on document data, and (ii) an identification of a determined portion of the document data that is to be used in generating said printed copy detection pattern; and wherein said intermediate electronic device is adapted to: (i) generate copy detection pattern data using said determined portion of the document data and said cryptographic key, (ii) generate modified printer control commands, said modified printer control commands including commands for printing a first document portion based on the document data and a second document portion including said printed copy detection pattern based on said copy detection pattern data, and (iii) send said modified printer control commands to said printing device over said second communications channel for printing said first document portion and said second document portion. 12. The system according to claim 8 , wherein said determined portion of the document data is image data and wherein said copy detection pattern data comprises said image data encrypted using said cryptographic key. | 0.502966 |
7. The method of claim 6 , further comprising: configuring, by a computer, said databases with a semantic web enabled layer. | 7. The method of claim 6 , further comprising: configuring, by a computer, said databases with a semantic web enabled layer. 8. The method of claim 7 , wherein said semantic web enabled layer comprises a SPARQL endpoint. | 0.964229 |
1. A method of clustering devices in an Internet of Things (‘IoT’), the method comprising: receiving, by a device clustering module, a characteristic set for a device, wherein the characteristic set specifies one or more device attributes and an attribute value for each device attribute; clustering, by the device clustering module, the device into an attribute level cluster based on the one or more device attributes specified in the characteristic set for the device; and clustering, by the device clustering module, the device into a value level cluster based on the attribute value for each device attribute, wherein the value level cluster is a subset of the attribute level cluster. | 1. A method of clustering devices in an Internet of Things (‘IoT’), the method comprising: receiving, by a device clustering module, a characteristic set for a device, wherein the characteristic set specifies one or more device attributes and an attribute value for each device attribute; clustering, by the device clustering module, the device into an attribute level cluster based on the one or more device attributes specified in the characteristic set for the device; and clustering, by the device clustering module, the device into a value level cluster based on the attribute value for each device attribute, wherein the value level cluster is a subset of the attribute level cluster. 2. The method of claim 1 wherein the characteristic set is specified as an extensible markup language (‘XML’) structured document. | 0.842062 |
1. A method implemented by a computer for culture mapping and intelligence, comprising: selecting, by the computer, at least one data source containing unstructured words and phrases from a plurality of networked computers in response to a user query relative to a topic of interest; electronically receiving data using the computer from the data source via a computer network; generating a list of monitored user accounts using the computer based on one or more selected criteria; querying the data source to identify user account profile data, wherein the profile data comprises unstructured data containing words and phrases from the monitored user accounts; processing the unstructured profile data and performing semiotic analysis of the words and phrases to associate a behavior archetype based on the profile data; and mapping the user account to a grid relative to first and second behavior continuums of behavior archetypes representing a relationship of user to self along a first axis and a relationship of user to society along a second axis based on the semiotic analysis of the words and phrases. | 1. A method implemented by a computer for culture mapping and intelligence, comprising: selecting, by the computer, at least one data source containing unstructured words and phrases from a plurality of networked computers in response to a user query relative to a topic of interest; electronically receiving data using the computer from the data source via a computer network; generating a list of monitored user accounts using the computer based on one or more selected criteria; querying the data source to identify user account profile data, wherein the profile data comprises unstructured data containing words and phrases from the monitored user accounts; processing the unstructured profile data and performing semiotic analysis of the words and phrases to associate a behavior archetype based on the profile data; and mapping the user account to a grid relative to first and second behavior continuums of behavior archetypes representing a relationship of user to self along a first axis and a relationship of user to society along a second axis based on the semiotic analysis of the words and phrases. 2. The method of claim 1 further comprising applying at least one weighting factor to the monitored user accounts. | 0.568711 |
13. A method for speech recognition, comprising: inserting an artificial ambiguity about one or more homonyms for the word into the grammar of a speech recognition engine for the word to create an ambiguity between the word and the one or more homonyms; receiving a voice command containing at least one word; and identifying a correct interpretation for the at least one word based on the at least one word in the voice command and the grammar for the word that includes the inserted information about the one or more homonyms. | 13. A method for speech recognition, comprising: inserting an artificial ambiguity about one or more homonyms for the word into the grammar of a speech recognition engine for the word to create an ambiguity between the word and the one or more homonyms; receiving a voice command containing at least one word; and identifying a correct interpretation for the at least one word based on the at least one word in the voice command and the grammar for the word that includes the inserted information about the one or more homonyms. 14. The method of claim 13 , wherein identifying a correct interpretation further comprises triggering a disambiguation unit due to the ambiguity between the one or more homonyms and generating a voice prompt to a user with information about each of the one or more homonyms to identify the correct interpretation for the received word. | 0.78622 |
9. A communication method, comprising: providing a communications platform capable of handling multiple types of communications with multiple users, comprising: a browser for a communications network, the browser configured to interact with and organize information about the multiple communication types and the multiple users; a server for handling communications between the platform and user devices that are external to the platform; a database configured to store user data comprising a preferred communication mode and contact protocol for a given user; a mining engine configured to analyze the communication data flowing through and present in the platform; a speech engine for converting text to speech, for converting speech to text, or both; and a network interface; connecting a communications network to the network interface of the communications platform; user the browser to provide an interface between the communication platform and the communications network, the interface configured for the platform to interact with various types of user devices and communication modes; connecting first and second user devices from first and second users to the communications network at the same time using each user's preferred contact protocol; determining a communication mode for the first user and the second user based on the user data in the database, wherein the first user employs a first communication mode with first mode components and the second user employs a second communication mode with second mode components different than the first communication mode; allowing the first user to communicate using a first communication mode while at substantially the same time allowing the second user to communicate using a second communication mode; and synchronizing the communication between the first user and the second user by matching the mode components from the first communication mode to the mode components of the second communication mode to minimize the time needed by both the first and second user for the communication; and using the mining engine to optimize operation of the platform. | 9. A communication method, comprising: providing a communications platform capable of handling multiple types of communications with multiple users, comprising: a browser for a communications network, the browser configured to interact with and organize information about the multiple communication types and the multiple users; a server for handling communications between the platform and user devices that are external to the platform; a database configured to store user data comprising a preferred communication mode and contact protocol for a given user; a mining engine configured to analyze the communication data flowing through and present in the platform; a speech engine for converting text to speech, for converting speech to text, or both; and a network interface; connecting a communications network to the network interface of the communications platform; user the browser to provide an interface between the communication platform and the communications network, the interface configured for the platform to interact with various types of user devices and communication modes; connecting first and second user devices from first and second users to the communications network at the same time using each user's preferred contact protocol; determining a communication mode for the first user and the second user based on the user data in the database, wherein the first user employs a first communication mode with first mode components and the second user employs a second communication mode with second mode components different than the first communication mode; allowing the first user to communicate using a first communication mode while at substantially the same time allowing the second user to communicate using a second communication mode; and synchronizing the communication between the first user and the second user by matching the mode components from the first communication mode to the mode components of the second communication mode to minimize the time needed by both the first and second user for the communication; and using the mining engine to optimize operation of the platform. 12. The method of claim 9 , wherein the mode components include the type of user device, the software operating on the user device, and the communication channel for the user and wherein the communication mode is determined using these three mode components. | 0.506385 |
8. The method of claim 1 , further comprising purging both the program wide memory window and the most recent memory window are purged when the currently accessed multimedia data has completed playing. | 8. The method of claim 1 , further comprising purging both the program wide memory window and the most recent memory window are purged when the currently accessed multimedia data has completed playing. 10. The method of claim 8 , wherein it is determined that the currently accessed multimedia data has completed playing by comparing a current time to an ending time identified for the currently accessed multimedia data in an electronic program guide entry for the currently accessed multimedia data. | 0.851234 |
35. A computer-readable medium containing a data structure for use by a computer system to identify patterns, each pattern having a set of characteristics, the data structure comprising: for each possible pair of characteristics, a stored probability value that is generated from a total count of that pair of characteristics that occur between each possible pair of sample patterns and from a match count of that pair of characteristics that occur between each possible pair of sample patterns that represent the same pattern, wherein each stored probability value can be retrieved from the data structure by using an indication of the pair of characteristics for that stored probability value, so that the stored probability values can be used to identify patterns. | 35. A computer-readable medium containing a data structure for use by a computer system to identify patterns, each pattern having a set of characteristics, the data structure comprising: for each possible pair of characteristics, a stored probability value that is generated from a total count of that pair of characteristics that occur between each possible pair of sample patterns and from a match count of that pair of characteristics that occur between each possible pair of sample patterns that represent the same pattern, wherein each stored probability value can be retrieved from the data structure by using an indication of the pair of characteristics for that stored probability value, so that the stored probability values can be used to identify patterns. 36. The computer-readable medium of claim 35 wherein each pattern comprises handwritten strokes. | 0.889378 |
1. A method for creating presenting keywords used for a review of an item, the method comprising: obtaining a plurality of user-created keywords, wherein each user-created keyword is created by a user in a review; receiving votes to indicate users' ratings of effectiveness of the user-created keywords used in reviews; transferring at least a portion of the plurality of user-created keywords for display to a user, wherein a digital processor is used to display the user-created keywords on a display screen in association with a category along with a rating for each keyword, wherein the rating is derived from the votes, wherein the rating includes a number indicating the amount of times that the keyword has been used in other reviews about the item; and accepting a signal from a user input device operated by the user to select at least one of the plurality of user-created keywords for use in a new review about the item being created by the user. | 1. A method for creating presenting keywords used for a review of an item, the method comprising: obtaining a plurality of user-created keywords, wherein each user-created keyword is created by a user in a review; receiving votes to indicate users' ratings of effectiveness of the user-created keywords used in reviews; transferring at least a portion of the plurality of user-created keywords for display to a user, wherein a digital processor is used to display the user-created keywords on a display screen in association with a category along with a rating for each keyword, wherein the rating is derived from the votes, wherein the rating includes a number indicating the amount of times that the keyword has been used in other reviews about the item; and accepting a signal from a user input device operated by the user to select at least one of the plurality of user-created keywords for use in a new review about the item being created by the user. 10. The method of claim 1 , further comprising: replacing a particular user-created keyword. | 0.595861 |
1. A method for creating a hash index on-demand and reusing the hash index for queries in a database, comprising: determining, by at least one processor, during query optimization that a first database query has a query execution plan comprising a sub-query which executes N times a correlated predicate having an operator being one of equal and not equal to a base column; comparing, by the at least one processor, based on the correlated predicate, a cost of creating a hash index and probing the hash index N times to a cost of fully scanning the base column N times; creating on-demand, by the at least one processor, a hash index based on the comparing; and executing, by the at least one processor, a second database query using the hash index, wherein the executing eliminates fully scanning the base column N times. | 1. A method for creating a hash index on-demand and reusing the hash index for queries in a database, comprising: determining, by at least one processor, during query optimization that a first database query has a query execution plan comprising a sub-query which executes N times a correlated predicate having an operator being one of equal and not equal to a base column; comparing, by the at least one processor, based on the correlated predicate, a cost of creating a hash index and probing the hash index N times to a cost of fully scanning the base column N times; creating on-demand, by the at least one processor, a hash index based on the comparing; and executing, by the at least one processor, a second database query using the hash index, wherein the executing eliminates fully scanning the base column N times. 6. The method of claim 1 , wherein the first database query comprises one of a correlated sub-query and a nested loop push down join query. | 0.640571 |
1. A method comprising: receiving a query having one or more terms, the query associated with a user locale, the user locale indicating a locale associated with a user that submitted the query; determining, using one or more processors, a degree of implicit local relevance for the query with respect to each of one or more locales, where the degree of implicit local relevance is determined using one or more terms of the query that do not explicitly identify a locale and where the degree of implicit local relevance for the query identifies a combined degree to which each of the one or more terms of the query is relevant to the one or more respective locales; receiving one or more search results for the query, each search result having a respective score and a respective result locale; and modifying the score of a respective search result using the degree of implicit local relevance for the query, the user locale, and the respective result locale of the respective search result. | 1. A method comprising: receiving a query having one or more terms, the query associated with a user locale, the user locale indicating a locale associated with a user that submitted the query; determining, using one or more processors, a degree of implicit local relevance for the query with respect to each of one or more locales, where the degree of implicit local relevance is determined using one or more terms of the query that do not explicitly identify a locale and where the degree of implicit local relevance for the query identifies a combined degree to which each of the one or more terms of the query is relevant to the one or more respective locales; receiving one or more search results for the query, each search result having a respective score and a respective result locale; and modifying the score of a respective search result using the degree of implicit local relevance for the query, the user locale, and the respective result locale of the respective search result. 8. The method of claim 1 , where the degree of implicit local relevance for the query is with respect to a combination of the user locale and a language of a user interface from which the query was received. | 0.567015 |
3. Device according to claim 1 , wherein said processing means (PM) is arranged, in case where each network element is defined by an ontology representation comprising at least one element property, characterizing said network element, and at least one class specificity, characterizing at least partly a class to which belongs said network element, for determining a parameter value while taking into account weights associated to the properties and/or the class specificities of the ontology representation of said failing network element and said other network element(s). | 3. Device according to claim 1 , wherein said processing means (PM) is arranged, in case where each network element is defined by an ontology representation comprising at least one element property, characterizing said network element, and at least one class specificity, characterizing at least partly a class to which belongs said network element, for determining a parameter value while taking into account weights associated to the properties and/or the class specificities of the ontology representation of said failing network element and said other network element(s). 4. Device according to claim 3 , wherein said processing means (PM) is arranged for determining a parameter value from a ratio between a first sum of the weights associated to the class specificities that are common to said failing network element and an other network element and the weights associated to the element properties that are common to said failing network element and said other network element, and a second sum of the weights associated to the class specificities of said failing network element and the weights associated to the element properties of said failing network element. | 0.754804 |
28. The computer-readable storage medium of claim 27 wherein the ranking is based on the hierarchical structure. | 28. The computer-readable storage medium of claim 27 wherein the ranking is based on the hierarchical structure. 31. The computer-readable storage medium of claim 28 wherein the ranking is based on an extent of depth of the hierarchical structure in each semantic solution. | 0.946072 |
1. A method performed by one or more processing devices, the method comprising: identifying websites with which users of a social networking service have established an affiliation; filtering the identified websites by removing websites in which a count of users who have established an affiliation with the website exceeds a threshold, the filtering producing a plurality of filtered websites; producing a list of valid affiliations for each filtered website, comprising: generating a validity score for each of the established affiliations with the respective filtered website, determining whether each of the established affiliations with the respective filtered website is valid based on the corresponding validity score, and ranking the users who established valid affiliations with the respective filtered website based on the corresponding validity scores; and providing the list of the valid affiliations ordered by the ranking. | 1. A method performed by one or more processing devices, the method comprising: identifying websites with which users of a social networking service have established an affiliation; filtering the identified websites by removing websites in which a count of users who have established an affiliation with the website exceeds a threshold, the filtering producing a plurality of filtered websites; producing a list of valid affiliations for each filtered website, comprising: generating a validity score for each of the established affiliations with the respective filtered website, determining whether each of the established affiliations with the respective filtered website is valid based on the corresponding validity score, and ranking the users who established valid affiliations with the respective filtered website based on the corresponding validity scores; and providing the list of the valid affiliations ordered by the ranking. 6. The method of claim 1 , wherein generating the validity score comprises determining a number of times a respective user links to the respective filtered website on posts generated by the respective user. | 0.579951 |
1. A method, comprising: determining, by a processor, whether at least one agent has followed at least one script relating to an automatic speech recognition component's ability to analyze at least one voice interaction between the at least one agent and at least one client; and dispositioning at least one interaction, wherein the at least one agent reads the at least one script to the at least one client, based on a comparison of a duration of the at least one interaction to an expected duration parameter associated with the at least one interaction, wherein the dispositioning comprises indicating the at least one interaction as potentially fraudulent if the duration is outside of an expected duration. | 1. A method, comprising: determining, by a processor, whether at least one agent has followed at least one script relating to an automatic speech recognition component's ability to analyze at least one voice interaction between the at least one agent and at least one client; and dispositioning at least one interaction, wherein the at least one agent reads the at least one script to the at least one client, based on a comparison of a duration of the at least one interaction to an expected duration parameter associated with the at least one interaction, wherein the dispositioning comprises indicating the at least one interaction as potentially fraudulent if the duration is outside of an expected duration. 15. The method of claim 1 , wherein determining whether the at least one agent has followed the at least one script includes, at least in part, dispositioning at least one interaction, wherein the at least one agent reads at least one script to the at least one client, based at least in part on a comparison of data representing the duration of the at least one interaction to data representing an expected duration parameter associated with the at least one interaction. | 0.693418 |
1. A method for training acoustic models for automatic speech recognition comprising: building a dialect recognition system configured to identify at least one dialect of a standard form language in input data by distinguishing phones of the standard form language and the at least one dialect, the building the dialect recognition system further comprising generating a phone decoder for building an acoustic training data set; applying the dialect recognition system with at least one processor to identify portions of the acoustic training data set that conform to the at least one dialect based on distinguished phones of the at least one dialect in the training data set; and performing automatic speech recognition using at least one dialect language model trained based on the portions of the acoustic training data set that are identified as conforming to the at least one dialect. | 1. A method for training acoustic models for automatic speech recognition comprising: building a dialect recognition system configured to identify at least one dialect of a standard form language in input data by distinguishing phones of the standard form language and the at least one dialect, the building the dialect recognition system further comprising generating a phone decoder for building an acoustic training data set; applying the dialect recognition system with at least one processor to identify portions of the acoustic training data set that conform to the at least one dialect based on distinguished phones of the at least one dialect in the training data set; and performing automatic speech recognition using at least one dialect language model trained based on the portions of the acoustic training data set that are identified as conforming to the at least one dialect. 9. The method of claim 1 , wherein the building comprises building a support vector machine classifier based on kernel values determined for different pairs of dialects of the standard form language. | 0.555204 |
3. The method of claim 1 , wherein editing the copies of the sentences includes providing a user interface that displays the copies of the sentences to a human editor and provides an input device to receive input from the editor. | 3. The method of claim 1 , wherein editing the copies of the sentences includes providing a user interface that displays the copies of the sentences to a human editor and provides an input device to receive input from the editor. 5. The method of claim 3 , wherein the step of providing the user interface includes providing an indication of suggested editorial corrections to the editor. | 0.903409 |
3. The system of claim 2 , the operations further comprising determining an aggregate penalty for the paragraph layout, the aggregate penalty being a function of at least the justification penalties assessed against a plurality of lines within the paragraph layout. | 3. The system of claim 2 , the operations further comprising determining an aggregate penalty for the paragraph layout, the aggregate penalty being a function of at least the justification penalties assessed against a plurality of lines within the paragraph layout. 4. The system of claim 3 , wherein the aggregate penalty is also a function of a penalty associated with at least one additional format parameter. | 0.87787 |
7. A non-transitory computer-readable storage medium encoded with a plurality of instructions that, when executed by at least one processor, perform a method for use with a voicemail transcription system that processes a voicemail message and generates a textual representation of at least a portion of the voicemail message, the method comprising: determining based, at least in part, on the textual representation of the at least a portion of the voicemail message, at least one emotion expressed in the voicemail message, wherein the determining comprises applying at least one emotion classifier to the textual representation of the at least a portion of the voicemail message; storing preference information for a user of a client device configured to receive voicemail transcriptions, wherein the preference information describes preferences for how the user of the client device wants emotion information associated with the received voicemail transcriptions to be conveyed to the user on the client device; and providing on the client device, in accordance with the stored preference information, an indication of the determined at least one emotion prior to displaying the textual representation of the at least a portion of the voicemail message on the client device, wherein providing an indication of the determined at least one emotion comprises displaying on the client device at least one graphical symbol representing the determined at least one emotion with a truncated version of the textual representation. | 7. A non-transitory computer-readable storage medium encoded with a plurality of instructions that, when executed by at least one processor, perform a method for use with a voicemail transcription system that processes a voicemail message and generates a textual representation of at least a portion of the voicemail message, the method comprising: determining based, at least in part, on the textual representation of the at least a portion of the voicemail message, at least one emotion expressed in the voicemail message, wherein the determining comprises applying at least one emotion classifier to the textual representation of the at least a portion of the voicemail message; storing preference information for a user of a client device configured to receive voicemail transcriptions, wherein the preference information describes preferences for how the user of the client device wants emotion information associated with the received voicemail transcriptions to be conveyed to the user on the client device; and providing on the client device, in accordance with the stored preference information, an indication of the determined at least one emotion prior to displaying the textual representation of the at least a portion of the voicemail message on the client device, wherein providing an indication of the determined at least one emotion comprises displaying on the client device at least one graphical symbol representing the determined at least one emotion with a truncated version of the textual representation. 8. The non-transitory computer-readable storage medium of claim 7 , wherein the determining the at least one emotion further comprises determining the at least one emotion based, at least in part, on an analysis of audio of the voicemail message. | 0.544729 |
1. A method for monitoring changes to data comprising: receiving an indication that a data bearing object expects to undergo a change, wherein the data bearing object includes a programmatic grouping of data and the change includes a modification, an addition, or a removal of associated data; taking, at least in part in response to receiving said indication, a snapshot of said data bearing object, wherein the snapshot includes a copy of a state of at least a portion of the data of the data bearing object; and identifying said change to said data bearing object by comparing (1) said data bearing object after said change has been made to (2) said snapshot of said object. | 1. A method for monitoring changes to data comprising: receiving an indication that a data bearing object expects to undergo a change, wherein the data bearing object includes a programmatic grouping of data and the change includes a modification, an addition, or a removal of associated data; taking, at least in part in response to receiving said indication, a snapshot of said data bearing object, wherein the snapshot includes a copy of a state of at least a portion of the data of the data bearing object; and identifying said change to said data bearing object by comparing (1) said data bearing object after said change has been made to (2) said snapshot of said object. 2. A method as recited in claim 1 , wherein said indication is sent in response to an event that initiates execution of application code. | 0.612257 |
88. An apparatus for recognizing an environmental sound, comprising: means for storing a plurality of sound models representing environmental sounds and a plurality of labels, wherein each of the plurality of labels identifies at least one of the plurality of sound models; means for capturing an input environmental sound; means for generating an input sound model from the input environmental sound; means for determining similarity values between the input sound model and the plurality of sound models to identify one or more sound models from the means for storing that are similar to the input sound model; means for selecting a first label from one or more labels, of the plurality of labels, associated with the one or more sound models; means for associating the first label with the input environmental sound based on a confidence level of the first label; means for transmitting the input sound model to a server if the confidence level is less than a confidence threshold; and means for receiving a second label identifying the input environmental sound from the server. | 88. An apparatus for recognizing an environmental sound, comprising: means for storing a plurality of sound models representing environmental sounds and a plurality of labels, wherein each of the plurality of labels identifies at least one of the plurality of sound models; means for capturing an input environmental sound; means for generating an input sound model from the input environmental sound; means for determining similarity values between the input sound model and the plurality of sound models to identify one or more sound models from the means for storing that are similar to the input sound model; means for selecting a first label from one or more labels, of the plurality of labels, associated with the one or more sound models; means for associating the first label with the input environmental sound based on a confidence level of the first label; means for transmitting the input sound model to a server if the confidence level is less than a confidence threshold; and means for receiving a second label identifying the input environmental sound from the server. 98. The apparatus of claim 88 , further comprising means for receiving a second label identifying the input environmental sound from a server, wherein the means for receiving is configured to receive location information associated with the input sound model, and wherein the means for storing is configured to store information corresponding to at least one of a location or a time associated with each of the plurality of sound models. | 0.582477 |
9. A system, comprising: at least one processor; and at least one storage medium storing instructions which, when executed by the at least one processor, perform acts of: issuing a first command, via a reusable dialog component, to a data access service to retrieve data from at least one back-end data source, the format of the first command being independent of the at least one back-end data source; retrieving the data, via the data access service, from the at least one back-end data source by using a second command that is selected based, at least in part, on the first command and the at least one back-end data source; storing the data, via the data access service, in a data structure in a data structure format that is independent of the at least one back-end data source; building a grammar based on the data structure using, at least in part, a dynamic grammar builder; and loading the grammar into a first voice application in a manner enabling its use by the reusable dialog component, the reusable dialog component defining an interaction sequence between the first voice application and a user of the first voice application, the reusable dialog component being adapted for use in at least one voice application other than the first voice application. | 9. A system, comprising: at least one processor; and at least one storage medium storing instructions which, when executed by the at least one processor, perform acts of: issuing a first command, via a reusable dialog component, to a data access service to retrieve data from at least one back-end data source, the format of the first command being independent of the at least one back-end data source; retrieving the data, via the data access service, from the at least one back-end data source by using a second command that is selected based, at least in part, on the first command and the at least one back-end data source; storing the data, via the data access service, in a data structure in a data structure format that is independent of the at least one back-end data source; building a grammar based on the data structure using, at least in part, a dynamic grammar builder; and loading the grammar into a first voice application in a manner enabling its use by the reusable dialog component, the reusable dialog component defining an interaction sequence between the first voice application and a user of the first voice application, the reusable dialog component being adapted for use in at least one voice application other than the first voice application. 10. The system of claim 9 , wherein storing the data in the data structure comprises the data access service populating a data graph based on the data. | 0.68735 |
2. The image tagging apparatus according to claim 1 , wherein the tag scores obtained by the linear combination is: TS c =Σ t=1, . . . , M w t TS t ; where, TS t is the first score, M is the number of the multiple modalities, and w t is a linear weight, the linear weight satisfying conditions below: (1) all the linear weights are greater than or equal to 0; (2) a L2-norm of the linear weight is minimal; and (3) TS c and TS a in the linear combination are as close as possible, TS a being the second score. | 2. The image tagging apparatus according to claim 1 , wherein the tag scores obtained by the linear combination is: TS c =Σ t=1, . . . , M w t TS t ; where, TS t is the first score, M is the number of the multiple modalities, and w t is a linear weight, the linear weight satisfying conditions below: (1) all the linear weights are greater than or equal to 0; (2) a L2-norm of the linear weight is minimal; and (3) TS c and TS a in the linear combination are as close as possible, TS a being the second score. 4. The image tagging apparatus according to claim 2 , wherein the processor further executes the instructions to: update the second score TS a according to the tag score TS c obtained by the linear combination; and solve the linear weight w according to the updated second score TS a , so as to update the tag score TS c obtained by the linear combination. | 0.807692 |
8. A system comprising: a data store for storing data; and one or more processors configured to interact with the data store, the one or more processors being further configured to perform operations comprising: receiving a series of queries provided from a user device, the series of queries comprising two or more queries; determining a query pattern of the series of queries based on one or more entities and one or more aspects associated with the two or more queries, the determining comprising: for each query in the series of queries: determining a set of entities comprising one or more entities and described in the query, and determining a set of aspects comprising one or more aspects and described in the query; comparing sets of entities across queries in the series of queries; comparing sets of aspects across queries in the series of queries; determining that at least one of a set of aspects are consistent in each of the queries or a set of entities are consistent in each of the queries; and determining that the at least one of a set of aspects that are consistent in each of the queries or a set of entities that are consistent in each of the queries is a context of the queries, and that the context defines the query pattern; determining, at least partially based on the context defining the query pattern, that a teachable moment interface is to be displayed with search results on the user device; and transmitting content to be displayed in the teachable moment interface on a user device, the content including instructions to a user that instructs the user that the user need not include the content that defines the query pattern in queries that are subsequent to the series of queries. | 8. A system comprising: a data store for storing data; and one or more processors configured to interact with the data store, the one or more processors being further configured to perform operations comprising: receiving a series of queries provided from a user device, the series of queries comprising two or more queries; determining a query pattern of the series of queries based on one or more entities and one or more aspects associated with the two or more queries, the determining comprising: for each query in the series of queries: determining a set of entities comprising one or more entities and described in the query, and determining a set of aspects comprising one or more aspects and described in the query; comparing sets of entities across queries in the series of queries; comparing sets of aspects across queries in the series of queries; determining that at least one of a set of aspects are consistent in each of the queries or a set of entities are consistent in each of the queries; and determining that the at least one of a set of aspects that are consistent in each of the queries or a set of entities that are consistent in each of the queries is a context of the queries, and that the context defines the query pattern; determining, at least partially based on the context defining the query pattern, that a teachable moment interface is to be displayed with search results on the user device; and transmitting content to be displayed in the teachable moment interface on a user device, the content including instructions to a user that instructs the user that the user need not include the content that defines the query pattern in queries that are subsequent to the series of queries. 14. The system of claim 8 , wherein the content is specific to one or more of the query pattern and terms of the two or more queries in the series of queries. | 0.541088 |
15. The article of manufacture as claimed in claim 10 wherein the one or more computer programs when executed causing the machine to generate a revenue per click (RPC) value for a keyword cluster. | 15. The article of manufacture as claimed in claim 10 wherein the one or more computer programs when executed causing the machine to generate a revenue per click (RPC) value for a keyword cluster. 16. The article of manufacture as claimed in claim 15 wherein the one or more computer programs when executed causing the machine to incorporate value for confirmed registered user (VCRU) data into the generated RPC value. | 0.870796 |
1. A spine stabilization component comprising: a deflectable post which includes a shaft associated with a compliant member and further associated with a tubular shield; the shaft having a mount at a proximal end; a tubular shield surrounding the compliant member; the mount extending beyond the tubular shield; the compliant member positioned around the shaft between the shaft and the shield such that the mount may deflect relative to the shield; and a fastening mechanism adapted to secure the shield to a bone screw so that the deflectable post is one of substantially parallel and substantially coaxial with the bone screw and the mount is exposed and adapted for connection of a spinal rod. | 1. A spine stabilization component comprising: a deflectable post which includes a shaft associated with a compliant member and further associated with a tubular shield; the shaft having a mount at a proximal end; a tubular shield surrounding the compliant member; the mount extending beyond the tubular shield; the compliant member positioned around the shaft between the shaft and the shield such that the mount may deflect relative to the shield; and a fastening mechanism adapted to secure the shield to a bone screw so that the deflectable post is one of substantially parallel and substantially coaxial with the bone screw and the mount is exposed and adapted for connection of a spinal rod. 7. The spine stabilization component of claim 1 , in combination with a bone screw having a bore in a proximal end thereof, said bore being configured such that the tubular shield can fit therein. | 0.805506 |
8. A computer-implemented graphical user interface and narrative content generation system for generating narrative content for a potential real-world event, comprising: a narrative framework database storing sets of phrase patterns; an integrated development environment (IDE) system, coupled to the narrative framework database and further coupled to a communications network, for generating a narrative framework using phrase patterns from the narrative framework database; a content generation system, coupled to the IDE system and the narrative framework database, for applying real-world event information to narrative frameworks to generate narrative content; a computing device comprising a display unit, the computing device coupled to the IDE system through the communications network and operable to: generate a display for the display unit, the display comprising first and second phrase patterns received from the IDE system and the narrative framework database; generate an error indication for the display, indicating that the first phrase pattern fails to satisfy a criterion relative to the second phrase pattern; receive a third phrase pattern from the IDE system for presentation on the display; receive an input from a user, indicating that the third phrase pattern is to be used instead of the second phrase pattern for the generation of the narrative framework; generate for display on the display unit, a representation of the narrative framework received from the IDE system, the narrative framework comprising the first phrase pattern and the third phrase pattern and not the second phrase pattern; generate for display on the display unit, a content preview received from the IDE system and the content generation system for the potential real-world event, the content preview comprising the populating of the first phrase pattern and the third phrase pattern with sample information for the potential real-world event. | 8. A computer-implemented graphical user interface and narrative content generation system for generating narrative content for a potential real-world event, comprising: a narrative framework database storing sets of phrase patterns; an integrated development environment (IDE) system, coupled to the narrative framework database and further coupled to a communications network, for generating a narrative framework using phrase patterns from the narrative framework database; a content generation system, coupled to the IDE system and the narrative framework database, for applying real-world event information to narrative frameworks to generate narrative content; a computing device comprising a display unit, the computing device coupled to the IDE system through the communications network and operable to: generate a display for the display unit, the display comprising first and second phrase patterns received from the IDE system and the narrative framework database; generate an error indication for the display, indicating that the first phrase pattern fails to satisfy a criterion relative to the second phrase pattern; receive a third phrase pattern from the IDE system for presentation on the display; receive an input from a user, indicating that the third phrase pattern is to be used instead of the second phrase pattern for the generation of the narrative framework; generate for display on the display unit, a representation of the narrative framework received from the IDE system, the narrative framework comprising the first phrase pattern and the third phrase pattern and not the second phrase pattern; generate for display on the display unit, a content preview received from the IDE system and the content generation system for the potential real-world event, the content preview comprising the populating of the first phrase pattern and the third phrase pattern with sample information for the potential real-world event. 13. The system of claim 8 , wherein the content preview received from the IDE system and the content generation system is based at least in part on a tone associated with the potential real-world event. | 0.641489 |
2. The method of claim 1 , wherein ranking the search results comprises: calculating one or more weight values of the one or more agents; and determining the ranking of the one or more search results based on the confidence values and the weight values. | 2. The method of claim 1 , wherein ranking the search results comprises: calculating one or more weight values of the one or more agents; and determining the ranking of the one or more search results based on the confidence values and the weight values. 3. The method of claim 2 , wherein calculating the confidence values comprises: assigning a low discrepancy value to each agent; and subtracting the low discrepancy value from one. | 0.898631 |
15. An apparatus for detecting script language viruses, comprising: a script language processor, wherein the script language processor prepares language description data corresponding to at least one script language; a detection data processor, wherein the detection data processor prepares detection data for viral code corresponding to a script language virus and wherein the detection data processor generates viral code detection data by analyzing a plurality of samples of polymorphic script language viral code; and a detection engine, wherein the detection engine converts a data stream to a stream of tokens using lexical analysis, wherein the tokens correspond to respective language constructs, wherein the detection engine lexically analyzes the stream of tokens using the language description data and the detection data to identify the script language virus. | 15. An apparatus for detecting script language viruses, comprising: a script language processor, wherein the script language processor prepares language description data corresponding to at least one script language; a detection data processor, wherein the detection data processor prepares detection data for viral code corresponding to a script language virus and wherein the detection data processor generates viral code detection data by analyzing a plurality of samples of polymorphic script language viral code; and a detection engine, wherein the detection engine converts a data stream to a stream of tokens using lexical analysis, wherein the tokens correspond to respective language constructs, wherein the detection engine lexically analyzes the stream of tokens using the language description data and the detection data to identify the script language virus. 16. The apparatus of claim 15 , wherein the language description data correspond to Dynamic Finite Automata data. | 0.580383 |
5. The computer-implemented method of claim 1 , wherein initiating the response includes using a graphical user interface that begins displaying graphics corresponding to the spoken utterance. | 5. The computer-implemented method of claim 1 , wherein initiating the response includes using a graphical user interface that begins displaying graphics corresponding to the spoken utterance. 6. The computer-implemented method of claim 5 , wherein displaying the graphics includes using a sequence of graphics that appears as a commencement of search results. | 0.937918 |
18. A tangible, non-transitory, computer-readable medium, comprising code configured to direct a processor to: generate a classification score for an instance of an unlabeled case; generate a desirability factor for the unlabeled case, based, at least in part, on the classification score, the desirability factor corresponding to a level of desirability of selecting the unlabeled case as the next case for which to obtain training data; and select the unlabeled case as the next case for which to obtain input based, at least in part, on the desirability factor. | 18. A tangible, non-transitory, computer-readable medium, comprising code configured to direct a processor to: generate a classification score for an instance of an unlabeled case; generate a desirability factor for the unlabeled case, based, at least in part, on the classification score, the desirability factor corresponding to a level of desirability of selecting the unlabeled case as the next case for which to obtain training data; and select the unlabeled case as the next case for which to obtain input based, at least in part, on the desirability factor. 19. The tangible, non-transitory, computer-readable medium of claim 18 , comprising code configured to direct the processor to generate a case-centric instance feature, wherein the classifications scores are generated based, at least in part, on the case-centric instance feature. | 0.714706 |
15. A computer readable storage medium for storing a computer program including instructions when executed causing a computer to: bootstrap the collection of data to generate a feature lexicon, a language lexicon, and grammar configuration files for use by an information access process in processing one or more queries for the collection of data, wherein the bootstrap comprises instructions for causing the computer to: extract text from a collection of data, the extracted text corresponding to keys and values; generate the feature lexicon from the extracted text, the feature lexicon comprising a vocabulary of words and their definition in the collection of data; generate the language lexicon from the extracted text, the language lexicon identifying words of interest in the collection of data; and generate the grammar configuration files corresponding to the extracted text, the grammar configuration files comprising rules that identify patterns in the collection of data; and process the queries for the collection of data in the information access process using the feature lexicon, the language lexicon, and the grammar configuration files, wherein each query is normalized using the language lexicon, the normalized query is parsed into fragments that are annotated using the feature lexicon, and the fragments are converted into groupings of the keys and values using the grammar configuration files, in order to answer the query. | 15. A computer readable storage medium for storing a computer program including instructions when executed causing a computer to: bootstrap the collection of data to generate a feature lexicon, a language lexicon, and grammar configuration files for use by an information access process in processing one or more queries for the collection of data, wherein the bootstrap comprises instructions for causing the computer to: extract text from a collection of data, the extracted text corresponding to keys and values; generate the feature lexicon from the extracted text, the feature lexicon comprising a vocabulary of words and their definition in the collection of data; generate the language lexicon from the extracted text, the language lexicon identifying words of interest in the collection of data; and generate the grammar configuration files corresponding to the extracted text, the grammar configuration files comprising rules that identify patterns in the collection of data; and process the queries for the collection of data in the information access process using the feature lexicon, the language lexicon, and the grammar configuration files, wherein each query is normalized using the language lexicon, the normalized query is parsed into fragments that are annotated using the feature lexicon, and the fragments are converted into groupings of the keys and values using the grammar configuration files, in order to answer the query. 17. The computer readable storage medium of claim 15 further comprising instructions for causing the computer to: receive a query; and answer the query in conjunction with the feature lexicon, the language lexicon and the grammar configuration files. | 0.534788 |
9. A system according to claim 8, wherein the character group consists of the last character therein, the Type of the next-to-last character, and the Type of the character immediately preceding the next-to-last character. | 9. A system according to claim 8, wherein the character group consists of the last character therein, the Type of the next-to-last character, and the Type of the character immediately preceding the next-to-last character. 11. A system according to claim 9, wherein a first Type substantially includes the 26 capital letters A-Z, a second Type substantially includes the 26 lower case letters a-z, and a third Type substantially includes the 10 decimal numbers 0-9. | 0.950123 |
1. A method comprising: using one or more computers, obtaining and storing a first set of information comprising a set of emotional states with which online elements may be associated; using one or more computers, obtaining and storing a second set of information comprising information relating to a set of online elements; using one or more computers, based at least in part on the second set of information, assigning each of the set of online elements to at least one associated emotional state, of the set of emotional states; using one or more computers, obtaining and storing a third set of information comprising information relating to online activity of a user in association with at least one online element of the set of online elements, and comprising an emotional state to which the at least one online element of the set of online elements is assigned; using one or more computers, based at least in part on the third set of information, classifying the user into at least one emotional state of the set of emotional states; presenting the user with an online advertisement based at least in part on the at least one emotional state, of the set of emotional states, into which the user is classified; and based at least in part on at least one direct online activity of the user and at least one indirect online activity of the user, predicting an emotional state that the user is likely to be in at a particular time at which, or during a particular period of time during which, the online advertisement is anticipated to be served, wherein the at least one direct online activity of the user includes one or more of usage, usage frequency, extent of personalization, sharing of emoticons, and sharing of emoticlips, and wherein the at least one indirect online activity of the user includes one or more of visits to specific user post, user review or blog entry domains or Web sites, and engagement with specific user post, user review or blog entry domains or Web sites. | 1. A method comprising: using one or more computers, obtaining and storing a first set of information comprising a set of emotional states with which online elements may be associated; using one or more computers, obtaining and storing a second set of information comprising information relating to a set of online elements; using one or more computers, based at least in part on the second set of information, assigning each of the set of online elements to at least one associated emotional state, of the set of emotional states; using one or more computers, obtaining and storing a third set of information comprising information relating to online activity of a user in association with at least one online element of the set of online elements, and comprising an emotional state to which the at least one online element of the set of online elements is assigned; using one or more computers, based at least in part on the third set of information, classifying the user into at least one emotional state of the set of emotional states; presenting the user with an online advertisement based at least in part on the at least one emotional state, of the set of emotional states, into which the user is classified; and based at least in part on at least one direct online activity of the user and at least one indirect online activity of the user, predicting an emotional state that the user is likely to be in at a particular time at which, or during a particular period of time during which, the online advertisement is anticipated to be served, wherein the at least one direct online activity of the user includes one or more of usage, usage frequency, extent of personalization, sharing of emoticons, and sharing of emoticlips, and wherein the at least one indirect online activity of the user includes one or more of visits to specific user post, user review or blog entry domains or Web sites, and engagement with specific user post, user review or blog entry domains or Web sites. 8. The method of claim 1 , comprising obtaining and storing a second set of information comprising information relating to a set of online elements, wherein online elements comprise emoticlips. | 0.739247 |
4. A digital data processor as in claim 1 , further comprising circuitry, coupled to an output of said code page, to combine an addressed instruction word read out of said code page with a corresponding instruction word extension that is also read out of said code page. | 4. A digital data processor as in claim 1 , further comprising circuitry, coupled to an output of said code page, to combine an addressed instruction word read out of said code page with a corresponding instruction word extension that is also read out of said code page. 6. A digital data processor as in claim 4 , where said combining circuitry comprises an instruction decode stage of an instruction pipeline. | 0.932081 |
2. The method of claim 1 , wherein the test script is a first test script, the method further comprising identifying a second test script stored in the first database, the first test script and the second test script associated with a test battery. | 2. The method of claim 1 , wherein the test script is a first test script, the method further comprising identifying a second test script stored in the first database, the first test script and the second test script associated with a test battery. 3. The method of claim 2 , wherein the first test script and the second test script are associated with the test battery by a common annotation included within the first test script and the second test script. | 0.905914 |
18. A server, comprising: one or more computing devices; and a non-transitory computer readable medium containing computer executable instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising: receiving text captured by a capture device from a paper form of a published document, the document having a publisher, identifying the published document and the publisher from the captured text; retrieving from a repository of digital documents, a digital form of the published document, identifying a first product, the first product being related to the captured text or to the digital document; providing a purchase option to purchase the first product from a seller server, receiving a request to purchase the first product from the capture device and providing the request to the seller server; receiving a purchase notice confirming the purchase from the seller server, associating the purchase with the document and publisher; providing confirmation of the purchase to the capture device; and providing a purchase and source notice to the publisher, enabling the publisher or an author of the document to obtain compensation for the purchase. | 18. A server, comprising: one or more computing devices; and a non-transitory computer readable medium containing computer executable instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising: receiving text captured by a capture device from a paper form of a published document, the document having a publisher, identifying the published document and the publisher from the captured text; retrieving from a repository of digital documents, a digital form of the published document, identifying a first product, the first product being related to the captured text or to the digital document; providing a purchase option to purchase the first product from a seller server, receiving a request to purchase the first product from the capture device and providing the request to the seller server; receiving a purchase notice confirming the purchase from the seller server, associating the purchase with the document and publisher; providing confirmation of the purchase to the capture device; and providing a purchase and source notice to the publisher, enabling the publisher or an author of the document to obtain compensation for the purchase. 21. The server of claim 18 , wherein providing the purchase option to purchase the first product comprises providing a context menu of actions comprising the purchase option to purchase the first product. | 0.604849 |
1. A next search keyword presentation apparatus, comprising: an input unit for inputting a search keyword; a search control unit which sends the search keyword to a search system and receives a search result from the search system in the form of a plurality of documents; a display unit which displays the documents of the search result; a text body extraction unit which extracts a text body from the plurality of search result documents, wherein the text body extraction unit removes a portion from the plurality of search result documents deemed unrelated to the text body; an analysis unit which carries out a semantic attribute analysis of words contained within the text body; a storage unit which stores the search keyword as user history data uniquely provided for each user; a creation unit which creates document representative information based on the semantic attributes of each word and the history data; and a cluster representative keyword extraction unit which clusters document characteristic information and extracts cluster representative keywords; wherein the display unit displays the cluster representative keywords as the next search keyword candidate in response to inputting the search keyword. | 1. A next search keyword presentation apparatus, comprising: an input unit for inputting a search keyword; a search control unit which sends the search keyword to a search system and receives a search result from the search system in the form of a plurality of documents; a display unit which displays the documents of the search result; a text body extraction unit which extracts a text body from the plurality of search result documents, wherein the text body extraction unit removes a portion from the plurality of search result documents deemed unrelated to the text body; an analysis unit which carries out a semantic attribute analysis of words contained within the text body; a storage unit which stores the search keyword as user history data uniquely provided for each user; a creation unit which creates document representative information based on the semantic attributes of each word and the history data; and a cluster representative keyword extraction unit which clusters document characteristic information and extracts cluster representative keywords; wherein the display unit displays the cluster representative keywords as the next search keyword candidate in response to inputting the search keyword. 10. The apparatus according to claim 1 , wherein the cluster representative keyword extraction unit extracts two clusters with a largest distance between them by calculating the distances between the keywords of individual clusters. | 0.525909 |
15. A communication device operable by a first user, the device comprising: a transceiver; a user interface configured for communicating with a user of the communication device; and a processor operatively coupled to the transceiver and to the user interface and configured for receiving, via the user interface and from a first user, a first search query, for receiving, from a second user distinct from the first user, a second search query, and for performing a search, the search based, at least in part, on a logical combination of the first and second search queries; wherein the search is based on aspects of situational awareness consisting of: a location of the first user when specifying the first search query, a location of the second user when specifying the second search query, a time-of-day when the first search query is specified, a time-of-day when the second search query is specified, image data captured when the first search query is specified, image data captured when the second search query is specified, sound data captured when the first search query is specified, sound data captured when the second search query is specified, a presence of another member of a group that includes the first user and the second user when the first search query is specified, and a presence of another member of a group that includes the first user and the second user when the second search query is specified. | 15. A communication device operable by a first user, the device comprising: a transceiver; a user interface configured for communicating with a user of the communication device; and a processor operatively coupled to the transceiver and to the user interface and configured for receiving, via the user interface and from a first user, a first search query, for receiving, from a second user distinct from the first user, a second search query, and for performing a search, the search based, at least in part, on a logical combination of the first and second search queries; wherein the search is based on aspects of situational awareness consisting of: a location of the first user when specifying the first search query, a location of the second user when specifying the second search query, a time-of-day when the first search query is specified, a time-of-day when the second search query is specified, image data captured when the first search query is specified, image data captured when the second search query is specified, sound data captured when the first search query is specified, sound data captured when the second search query is specified, a presence of another member of a group that includes the first user and the second user when the first search query is specified, and a presence of another member of a group that includes the first user and the second user when the second search query is specified. 17. The communication device of claim 15 wherein the processor is further configured for: performing a first search, the first search based, at least in part, on the first search query, the first search returning first search results; wherein the second search query is based, at least in part, on an element selected from the group consisting of: the first search query and at least a portion of the first search results. | 0.5 |
31. A method as in claim 28 , wherein a step for creating a control file further comprises: i) by using a user interface for a spreadsheet loader, selecting the business object in an object tree section of the user interface, wherein the selection of the business object opens a list of attributes for the selected business object in an attributes tab section of the user interface; ii) dragging and dropping the attributes for the selected business object into columns of a spreadsheet data area of the user interface, the spreadsheet data area reflecting a spreadsheet dataset that is to be loaded by the spreadsheet loader; iii) using the drop and dragged attributes, mapping the columns into which the attributes were dragged to the business objects of the dragged attributes; iv) generating a control file reflecting the mapping step a)(iii). | 31. A method as in claim 28 , wherein a step for creating a control file further comprises: i) by using a user interface for a spreadsheet loader, selecting the business object in an object tree section of the user interface, wherein the selection of the business object opens a list of attributes for the selected business object in an attributes tab section of the user interface; ii) dragging and dropping the attributes for the selected business object into columns of a spreadsheet data area of the user interface, the spreadsheet data area reflecting a spreadsheet dataset that is to be loaded by the spreadsheet loader; iii) using the drop and dragged attributes, mapping the columns into which the attributes were dragged to the business objects of the dragged attributes; iv) generating a control file reflecting the mapping step a)(iii). 49. A method as in claim 31 , wherein the attributes are defined on the database. | 0.803248 |
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