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9,183,311 | 15 | 18 | 15. A non-transitory computer-readable storage medium comprising: one or more instructions which, when executed by at least one processor, cause the at least one processor to: identify, based on a search query, search results, each search result, of the search results, being associated with a particular score; determine a particular country for the search results by: analyzing, at a first time, interface characteristics to identify the particular country, determining, based on analyzing the interface characteristics, that the particular country is not identified, analyzing, at a second time based on determining that analyzing the interface characteristics does not identify the particular country, Internet Protocol (IP) characteristics to identify the particular country, the second time occurring after the first time; determine, based on determining the particular country for the search results, if respective particular scores for a set of the search results are numerically adjustable; adjust, based on determining if the respective particular scores for the set of the search results are numerically adjustable, an ordering of at least one of the search results among other ones of the search results to create a list of search results when the at least one of the search results is associated with the particular country, a search result, of the set of search results, being ordered based on a shifting factor when the particular score, associated with the search result, is numerically adjustable and being ordered based on a weighting factor when the particular score is not numerically adjustable; and provide for presentation, based on the adjusted ordering, the list of search results. | 15. A non-transitory computer-readable storage medium comprising: one or more instructions which, when executed by at least one processor, cause the at least one processor to: identify, based on a search query, search results, each search result, of the search results, being associated with a particular score; determine a particular country for the search results by: analyzing, at a first time, interface characteristics to identify the particular country, determining, based on analyzing the interface characteristics, that the particular country is not identified, analyzing, at a second time based on determining that analyzing the interface characteristics does not identify the particular country, Internet Protocol (IP) characteristics to identify the particular country, the second time occurring after the first time; determine, based on determining the particular country for the search results, if respective particular scores for a set of the search results are numerically adjustable; adjust, based on determining if the respective particular scores for the set of the search results are numerically adjustable, an ordering of at least one of the search results among other ones of the search results to create a list of search results when the at least one of the search results is associated with the particular country, a search result, of the set of search results, being ordered based on a shifting factor when the particular score, associated with the search result, is numerically adjustable and being ordered based on a weighting factor when the particular score is not numerically adjustable; and provide for presentation, based on the adjusted ordering, the list of search results. 18. The medium of claim 15 , where at least one instruction, of the one or more instructions, to cause the at least one processor to analyze the IP characteristics includes: one or more instructions to cause the at least one processor to determine country information based on an IP address associated with an interface; and one or more instructions to cause the at least one processor to analyze the IP characteristics by analyzing the determined country information. | 0.548263 |
9,087,519 | 1 | 11 | 1. A computer-implemented method of scoring speech, comprising: receiving a speech sample, wherein the speech sample is based upon speaking from a script; aligning, using a processing system, the speech sample with the script; extracting, using the processing system, an event recognition metric of the speech sample; detecting, using the processing system, locations of prosodic events in the speech sample based on the event recognition metric; comparing, using the processing system, the locations of the detected prosodic events with locations of model prosodic events, wherein the locations of model prosodic events identify expected locations of prosodic events of a fluent, native speaker speaking the script, and wherein the comparing comprises comparing a first data structure for the model prosodic events and a second data structure for the detected prosodic events, the first data structure and the second data structure including binary data per syllable representing whether or not a syllable exhibits a stress and whether or not the syllable exhibits a tone change, said comparing including comparing per syllable the binary data representing stress and the binary data representing tone change for the model prosodic events and the detected prosodic events; calculating, using the processing system, a prosodic event metric based on the comparison; and scoring, using the processing system, the speech sample using a scoring model based upon the prosodic event metric. | 1. A computer-implemented method of scoring speech, comprising: receiving a speech sample, wherein the speech sample is based upon speaking from a script; aligning, using a processing system, the speech sample with the script; extracting, using the processing system, an event recognition metric of the speech sample; detecting, using the processing system, locations of prosodic events in the speech sample based on the event recognition metric; comparing, using the processing system, the locations of the detected prosodic events with locations of model prosodic events, wherein the locations of model prosodic events identify expected locations of prosodic events of a fluent, native speaker speaking the script, and wherein the comparing comprises comparing a first data structure for the model prosodic events and a second data structure for the detected prosodic events, the first data structure and the second data structure including binary data per syllable representing whether or not a syllable exhibits a stress and whether or not the syllable exhibits a tone change, said comparing including comparing per syllable the binary data representing stress and the binary data representing tone change for the model prosodic events and the detected prosodic events; calculating, using the processing system, a prosodic event metric based on the comparison; and scoring, using the processing system, the speech sample using a scoring model based upon the prosodic event metric. 11. The method of claim 1 , wherein event recognition metrics include measurements of power, pitch, silences in the speech sample, or dictionary stressing information of words recognized by an automated speech recognition system. | 0.72476 |
8,787,494 | 10 | 11 | 10. The circuit of claim 9 , wherein the first predistorter model comprises a set of polynomial basis functions. | 10. The circuit of claim 9 , wherein the first predistorter model comprises a set of polynomial basis functions. 11. The circuit of claim 10 , wherein the set of polynomial basis functions is a set of orthogonal basis functions. | 0.676966 |
8,860,732 | 16 | 17 | 16. One or more computer-readable storage memories comprising program instructions, the program instructions computer-executable to implement operations comprising: receiving input defining a trajectory of an animated character as a goal constraint during execution of an interactive application; receiving input representing one or more external forces acting on the animated character, a root of the animated character being unactuated by the one or more external forces; determining a reference pose for the animated character in a current animation frame in reaction to the one or more external forces, said determining comprising performing a quasi-physical simulation, the quasi-physical simulation being expressed as a linear system enforcing the goal constraint for the horizontal components of a center of mass of the animated character by performing operations comprising: determining joint torques to minimize an addition of a non-physical force at the root of the animated character, the joint torques being determined based on a torque objective; determining the non-physical force to apply to the root of the animated character to enforce the goal constraint, the determining the non-physical force not being constrained by the laws of physics simulated in the quasi-physical simulation and the goal constraint being expressed as a linear function of joint accelerations; adding the determined non-physical force to the root of the animated character; and displaying the animated character in its determined pose. | 16. One or more computer-readable storage memories comprising program instructions, the program instructions computer-executable to implement operations comprising: receiving input defining a trajectory of an animated character as a goal constraint during execution of an interactive application; receiving input representing one or more external forces acting on the animated character, a root of the animated character being unactuated by the one or more external forces; determining a reference pose for the animated character in a current animation frame in reaction to the one or more external forces, said determining comprising performing a quasi-physical simulation, the quasi-physical simulation being expressed as a linear system enforcing the goal constraint for the horizontal components of a center of mass of the animated character by performing operations comprising: determining joint torques to minimize an addition of a non-physical force at the root of the animated character, the joint torques being determined based on a torque objective; determining the non-physical force to apply to the root of the animated character to enforce the goal constraint, the determining the non-physical force not being constrained by the laws of physics simulated in the quasi-physical simulation and the goal constraint being expressed as a linear function of joint accelerations; adding the determined non-physical force to the root of the animated character; and displaying the animated character in its determined pose. 17. The computer-readable storage memories of claim 16 , wherein the quasi-physical simulation is dependent on one or more of: a pose objective that tracks a reference motion for the animated character, or an end-effector objective that tracks the movement of one or more joints of the animated character that are in contact with the environment. | 0.548303 |
9,626,352 | 7 | 12 | 7. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: analyzing a plurality of posts included in one or more threads of a threaded discussion, wherein the analyzing further comprises: identifying a term in a parent post of the threaded discussion; detecting that an anaphor in a child post of the threaded discussion references the identified term; and resolving the anaphor found in the child post with the identified term; storing the parent post with the identified term and the child post with the resolved anaphor in the memory; and ingesting the parent post with the identified term and the child post with the resolved anaphor into a corpus utilized by a question answering (QA) system. | 7. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: analyzing a plurality of posts included in one or more threads of a threaded discussion, wherein the analyzing further comprises: identifying a term in a parent post of the threaded discussion; detecting that an anaphor in a child post of the threaded discussion references the identified term; and resolving the anaphor found in the child post with the identified term; storing the parent post with the identified term and the child post with the resolved anaphor in the memory; and ingesting the parent post with the identified term and the child post with the resolved anaphor into a corpus utilized by a question answering (QA) system. 12. The computer program product of claim 7 wherein the actions further comprise: analyzing each of a plurality of posts in the online forum, wherein the plurality of posts include the child post and the parent post; identify any referential types corresponding to a plurality of words included in each of the posts; identify any anaphora types corresponding to a plurality of words included in each of the posts; associating each of a plurality of child posts with at least one parent post as a relationship; resolving the anaphora types included in the child posts with at least one of the referential types included in the respective associated parent posts; and building a thread tree corresponding to the threaded discussion, wherein the thread tree includes the plurality of posts, the relationships between posts, and the resolved anaphora types. | 0.50522 |
7,644,075 | 1 | 7 | 1. A method in a computing device with a processor and a memory for assessing keyword usage, the method comprising: providing frequency of keywords during various periods; calculating by the computing device a frequency impulse score for a keyword, the frequency impulse score indicating amount of recent change in frequency of the keyword according to the following equation: I n = ( f n - ∑ i = 1 n - 1 f i * α i ) / f n where I n represents the frequency impulse score for period n, f n represents the frequency of the keyword during the period n divided by the total frequency of all keywords during the period n, and α i represents a decay factor during period i according to the following equation:
α i =2 i-n ; calculating by the computing device a frequency weight for the keyword, the frequency weight indicating a recent measure of the frequency of the keyword according to the following equation: W n = log ( ∑ i = 1 n freq i * α i ) where W n represents the frequency weight for period n and freq i represents the frequency during period i; and combining the frequency impulse score and frequency weight of the keyword to give the recent usage score for the keyword for a recent period according to the following equation:
U n =I n *W n where U n represents the recent usage score for period n. | 1. A method in a computing device with a processor and a memory for assessing keyword usage, the method comprising: providing frequency of keywords during various periods; calculating by the computing device a frequency impulse score for a keyword, the frequency impulse score indicating amount of recent change in frequency of the keyword according to the following equation: I n = ( f n - ∑ i = 1 n - 1 f i * α i ) / f n where I n represents the frequency impulse score for period n, f n represents the frequency of the keyword during the period n divided by the total frequency of all keywords during the period n, and α i represents a decay factor during period i according to the following equation:
α i =2 i-n ; calculating by the computing device a frequency weight for the keyword, the frequency weight indicating a recent measure of the frequency of the keyword according to the following equation: W n = log ( ∑ i = 1 n freq i * α i ) where W n represents the frequency weight for period n and freq i represents the frequency during period i; and combining the frequency impulse score and frequency weight of the keyword to give the recent usage score for the keyword for a recent period according to the following equation:
U n =I n *W n where U n represents the recent usage score for period n. 7. The method of claim 1 including selecting keywords for placement of advertisements based at least in part on the recent usage scores of the keywords. | 0.751634 |
9,305,227 | 1 | 2 | 1. A computer-implemented method comprising: generating an image at a mobile computing device using a camera; determining features corresponding to a first word and a second word of text in the image; generating, on the mobile computing device, mobile optical character recognition (OCR) data including mobile OCR results associated with the first word by performing OCR on the features associated with the first word; determining an OCR latency of the mobile computing device; determining an OCR accuracy of the mobile computing device; sending the image to a remote device to perform remote OCR on the image; causing the mobile OCR results to be displayed on the mobile computing device, including causing a first textual output associated with the first word to be displayed; receiving remote OCR data, wherein the remote OCR data includes remote OCR results from the remote device associated with the second word; determining differences between the mobile OCR data and the remote OCR data; generating hybrid OCR results based on the differences by merging the mobile OCR data and the remote OCR data, including generating hybrid OCR results that include a second textual output associated with the second word and wherein the generating occurs based on at least one of the OCR latency being less than a threshold amount of time or the OCR accuracy being less than an accuracy threshold; and causing the hybrid OCR results to be displayed on the mobile computing device, including causing the first textual output to be displayed with the second textual output. | 1. A computer-implemented method comprising: generating an image at a mobile computing device using a camera; determining features corresponding to a first word and a second word of text in the image; generating, on the mobile computing device, mobile optical character recognition (OCR) data including mobile OCR results associated with the first word by performing OCR on the features associated with the first word; determining an OCR latency of the mobile computing device; determining an OCR accuracy of the mobile computing device; sending the image to a remote device to perform remote OCR on the image; causing the mobile OCR results to be displayed on the mobile computing device, including causing a first textual output associated with the first word to be displayed; receiving remote OCR data, wherein the remote OCR data includes remote OCR results from the remote device associated with the second word; determining differences between the mobile OCR data and the remote OCR data; generating hybrid OCR results based on the differences by merging the mobile OCR data and the remote OCR data, including generating hybrid OCR results that include a second textual output associated with the second word and wherein the generating occurs based on at least one of the OCR latency being less than a threshold amount of time or the OCR accuracy being less than an accuracy threshold; and causing the hybrid OCR results to be displayed on the mobile computing device, including causing the first textual output to be displayed with the second textual output. 2. The computer-implemented method of claim 1 , wherein merging the mobile OCR data and the remote OCR data comprises adding the second textual output from the remote OCR results to the first textual output from the mobile OCR results to create a textual list of outputs to be displayed. | 0.790205 |
9,542,933 | 1 | 22 | 1. A method, comprising: buffering audio samples received from a microphone, performing speech recognition on the buffered audio samples using a locally-stored vocabulary as a reference, and when the speech recognition identifies a recognized speech element, outputting a wake up signal indicating a match and outputting buffered audio signals corresponding to the recognized speech element, wherein at least one of the buffering, the performing, and the outputting is performed at least in part by a processor. | 1. A method, comprising: buffering audio samples received from a microphone, performing speech recognition on the buffered audio samples using a locally-stored vocabulary as a reference, and when the speech recognition identifies a recognized speech element, outputting a wake up signal indicating a match and outputting buffered audio signals corresponding to the recognized speech element, wherein at least one of the buffering, the performing, and the outputting is performed at least in part by a processor. 22. The method of claim 1 , wherein the performing the speech recognition on the buffered audio samples includes identifying a target word or target phrase from the buffered audio samples, and wherein the recognized speech element includes the target word or target phrase. | 0.651786 |
10,021,395 | 1 | 5 | 1. A method of compressing visual descriptors from at least one image by exploiting redundancy of natural image descriptors, comprising: extracting the visual descriptors from at least one image, said visual descriptors describing key points in images; creating model parameters of a generative probabilistic model from the extracted visual descriptors in a maximum likelihood sense; quantizing and encoding said model parameters; quantizing said extracted visual descriptors; and, applying a model-based arithmetic encoding to said quantized extracted visual descriptors using said encoded model parameters exploiting redundancy of the visual descriptors within the at least one image for compression of the visual descriptors. | 1. A method of compressing visual descriptors from at least one image by exploiting redundancy of natural image descriptors, comprising: extracting the visual descriptors from at least one image, said visual descriptors describing key points in images; creating model parameters of a generative probabilistic model from the extracted visual descriptors in a maximum likelihood sense; quantizing and encoding said model parameters; quantizing said extracted visual descriptors; and, applying a model-based arithmetic encoding to said quantized extracted visual descriptors using said encoded model parameters exploiting redundancy of the visual descriptors within the at least one image for compression of the visual descriptors. 5. The method of claim 1 , wherein the generative probabilistic model is a Gaussian mixture model. | 0.916096 |
9,082,310 | 34 | 49 | 34. A non-transitory computer-readable medium comprising computer-readable instructions tangibly stored on the non-transitory computer-readable medium, wherein the instructions are executable by at least one computer processor to execute a method for use with a system, the non-transitory computer-readable medium comprising: instructions to select, by the at least one computer processor, a first question instance including first text and a first region definition designed to identify a region of a data set likely to contain information that may be used to provide an answer to the question represented by the first text, the region definition identifying a region aligned with a tagged element in the data set; instructions to automatically identify, by the at least one computer processor, before providing output to a user representing the first question instance and before providing output to the user representing the first region of the data set, the first region of the data set, based on the first region definition; instructions to provide output, by the at least one computer processor, to a user representing the first question instance; and instructions to provide output, by the at least one computer processor, to the user, the output representing the first region of the data set before receiving an answer to the first question instance. | 34. A non-transitory computer-readable medium comprising computer-readable instructions tangibly stored on the non-transitory computer-readable medium, wherein the instructions are executable by at least one computer processor to execute a method for use with a system, the non-transitory computer-readable medium comprising: instructions to select, by the at least one computer processor, a first question instance including first text and a first region definition designed to identify a region of a data set likely to contain information that may be used to provide an answer to the question represented by the first text, the region definition identifying a region aligned with a tagged element in the data set; instructions to automatically identify, by the at least one computer processor, before providing output to a user representing the first question instance and before providing output to the user representing the first region of the data set, the first region of the data set, based on the first region definition; instructions to provide output, by the at least one computer processor, to a user representing the first question instance; and instructions to provide output, by the at least one computer processor, to the user, the output representing the first region of the data set before receiving an answer to the first question instance. 49. The non-transitory computer-readable medium of claim 34 , wherein the instructions to provide output representing the first region comprises instructions to provide output to the user representing a summary of the first region of the data set. | 0.690476 |
7,945,581 | 1 | 15 | 1. A system for processing at least one query to at least one electronic database, the system comprising: a query server for receiving a query and compiling at least one executable from query language based source code, the at least one executable having at least a first portion and a second portion, the first portion configured to generate a portion of initial query results and the second portion configured to execute one or more operations on a set of initial query results; a first type of processing matrix adapted to perform a database operation on hierarchical data; a second type of processing matrix adapted to perform an indexing database operation; a third type of processing matrix adapted to perform a database operation on large amounts of data; a query agent being adapted to select a type of processing matrix, from at least the first type of processing matrix, the second type of processing matrix, and the third type of processing matrix, to process the query based on at least one database operation to be performed to process the query, wherein each type of processing matrix comprises a master node and a plurality of slave nodes; the master node of the selected type of processing matrix being adapted to receive the at least one executable and comprising: at least one storage device; and a processor; and the plurality of slave nodes of the selected type of processing matrix operably connected to the master node, each of the plurality of slave nodes receiving at least the same first portion of the at least one executable from the master node, each slave node comprising: disk storage for receiving and storing a distinct portion of the database wherein the entire database is distributed among the plurality of slave nodes in distinct portions; and a processor for executing the first portion of the at least one executable on the distinct portion of the database to generate a portion of initial query results; wherein the plurality of slave nodes executes the first portion of the at least one executable substantially in parallel to generate the set of initial query results; wherein the second portion of the at least one executable is executed on the initial query results to generate resultant query results, which are stored at the master node; and wherein the plurality of slave nodes are used to process the query. | 1. A system for processing at least one query to at least one electronic database, the system comprising: a query server for receiving a query and compiling at least one executable from query language based source code, the at least one executable having at least a first portion and a second portion, the first portion configured to generate a portion of initial query results and the second portion configured to execute one or more operations on a set of initial query results; a first type of processing matrix adapted to perform a database operation on hierarchical data; a second type of processing matrix adapted to perform an indexing database operation; a third type of processing matrix adapted to perform a database operation on large amounts of data; a query agent being adapted to select a type of processing matrix, from at least the first type of processing matrix, the second type of processing matrix, and the third type of processing matrix, to process the query based on at least one database operation to be performed to process the query, wherein each type of processing matrix comprises a master node and a plurality of slave nodes; the master node of the selected type of processing matrix being adapted to receive the at least one executable and comprising: at least one storage device; and a processor; and the plurality of slave nodes of the selected type of processing matrix operably connected to the master node, each of the plurality of slave nodes receiving at least the same first portion of the at least one executable from the master node, each slave node comprising: disk storage for receiving and storing a distinct portion of the database wherein the entire database is distributed among the plurality of slave nodes in distinct portions; and a processor for executing the first portion of the at least one executable on the distinct portion of the database to generate a portion of initial query results; wherein the plurality of slave nodes executes the first portion of the at least one executable substantially in parallel to generate the set of initial query results; wherein the second portion of the at least one executable is executed on the initial query results to generate resultant query results, which are stored at the master node; and wherein the plurality of slave nodes are used to process the query. 15. The system of claim 1 , wherein the at least one executable includes at least one dynamic link library (DLL). | 0.850529 |
7,945,596 | 2 | 4 | 2. The method of claim 1 further comprising loading all data associated with the set of properties of the entity when programming against said first or second entity view class. | 2. The method of claim 1 further comprising loading all data associated with the set of properties of the entity when programming against said first or second entity view class. 4. The method of claim 2 further comprising adding additional properties to an existing entity definition, whether original or previously customized; and creating a new entity view class that contains one or more of the additional properties. | 0.6703 |
9,170,826 | 1 | 4 | 1. A computer implemented method comprising: obtaining a first textual expression contained within an application, wherein the first textual expression is expressed in a first language; generating a unique key from a hash of the first textual expression, the unique key being generated based on a location of where the first textual expression is used within the application and a type of object of the application using the first textual expression; determining a language code representative of a second language, the language code being associated with the unique key; determining, based on the generated unique key and the determined language code, a second textual expression in the second language representative of a translation from the first language into the second language indicated by the language code; and providing, based on the location and the type of object, the second textual expression to the application to replace the first textual expression in a view presented to a user. | 1. A computer implemented method comprising: obtaining a first textual expression contained within an application, wherein the first textual expression is expressed in a first language; generating a unique key from a hash of the first textual expression, the unique key being generated based on a location of where the first textual expression is used within the application and a type of object of the application using the first textual expression; determining a language code representative of a second language, the language code being associated with the unique key; determining, based on the generated unique key and the determined language code, a second textual expression in the second language representative of a translation from the first language into the second language indicated by the language code; and providing, based on the location and the type of object, the second textual expression to the application to replace the first textual expression in a view presented to a user. 4. The method according to claim 1 , wherein the determining the second textual expression further comprises: accessing a table including the generated unique key and the determined language code mapped to the second language expression. | 0.508299 |
8,671,364 | 1 | 3 | 1. An apparatus, comprising: a logic device; and an information visualization application operative on the logic device, the information visualization application comprising a multivariable presentation component arranged to generate a multivariable decomposition visualization to present hierarchical information for a response variable and multiple reporting variables defined for the response variable in a single user interface view, the multivariable decomposition visualization comprising multiple graphical user interface (GUI) elements each representing a reporting variable value of multiple reporting variables for multiple hierarchical levels, with a GUI element of a reporting variable value of a reporting variable of a hierarchical level selectable for decomposition into multiple GUI elements representing reporting variable values of a different reporting variable for a different hierarchical level, the selectable GUI element of the hierarchical level positioned adjacent to the decomposed GUI elements of the different hierarchical level when the selectable GUI element is selected for decomposition. | 1. An apparatus, comprising: a logic device; and an information visualization application operative on the logic device, the information visualization application comprising a multivariable presentation component arranged to generate a multivariable decomposition visualization to present hierarchical information for a response variable and multiple reporting variables defined for the response variable in a single user interface view, the multivariable decomposition visualization comprising multiple graphical user interface (GUI) elements each representing a reporting variable value of multiple reporting variables for multiple hierarchical levels, with a GUI element of a reporting variable value of a reporting variable of a hierarchical level selectable for decomposition into multiple GUI elements representing reporting variable values of a different reporting variable for a different hierarchical level, the selectable GUI element of the hierarchical level positioned adjacent to the decomposed GUI elements of the different hierarchical level when the selectable GUI element is selected for decomposition. 3. The apparatus of claim 1 , the multivariable presentation component operative to generate the multivariable decomposition visualization with each GUI element having a response variable value associated with the reporting variable value. | 0.814729 |
9,626,438 | 16 | 17 | 16. A non-transitory, computer-readable medium storing instructions that, when executed, cause a computing device to: receive search data from a server hosting a first website, the search data including first information regarding content accessed by a user subsequent to a search, the first information indicative of a first content format accessed by the user, the first content format corresponding to a first search result of a plurality of search results provided to the user from the search, and the plurality of search results including content in a second content format that is different from the first content format, wherein the user does not access the second content format; identify a category related to at least a portion of the search data, the identifying based at least on formats of content on target websites; determine, based on the search data, a topic for first content associated with the identified category; determine a score indicative of a level of popularity for the topic, wherein determining the score is based on the search data, information regarding different formats of content offered on the first website, the format of content accessed by the user on the first website, and the user not accessing the second content format; and present the topic and the score to a content provider for producing the first content for the target websites, the content provider to select a format for the first content based on the score and on user interaction with the plurality of search results. | 16. A non-transitory, computer-readable medium storing instructions that, when executed, cause a computing device to: receive search data from a server hosting a first website, the search data including first information regarding content accessed by a user subsequent to a search, the first information indicative of a first content format accessed by the user, the first content format corresponding to a first search result of a plurality of search results provided to the user from the search, and the plurality of search results including content in a second content format that is different from the first content format, wherein the user does not access the second content format; identify a category related to at least a portion of the search data, the identifying based at least on formats of content on target websites; determine, based on the search data, a topic for first content associated with the identified category; determine a score indicative of a level of popularity for the topic, wherein determining the score is based on the search data, information regarding different formats of content offered on the first website, the format of content accessed by the user on the first website, and the user not accessing the second content format; and present the topic and the score to a content provider for producing the first content for the target websites, the content provider to select a format for the first content based on the score and on user interaction with the plurality of search results. 17. The computer-readable medium of claim 16 , wherein determining the score includes determining a first score for content for the topic having a first format, and determining a second score for content for the topic having a second format, and wherein the instructions further cause the computing device to transmit, over a network, the first score and the second score to a client device of the content provider. | 0.5 |
7,882,121 | 1 | 7 | 1. A method for testing a component of a database application, the method comprising: populating, as specified by a user, a column in a database with test data that falls within a certain range specified by the user; specifying, by the user, a desired cardinality constraint suitable for testing the component, the component operating on a computing device, wherein the component is a software component; specifying, by the user, a parametric pattern query that includes a parameter, wherein the parametric pattern query is compatible with the database, and wherein the parametric pattern query is configured to restrict cardinality when evaluated against the database; selecting a candidate value; evaluating, by the computing device via the component, the parametric pattern query against the database with the parameter set to the candidate value; calculating, by the computing device, a cardinality error as a difference between a returned cardinality and the desired cardinality constraint, wherein the returned cardinality results from the evaluating; and adjusting the candidate value based on the calculated cardinality error and then repeating the evaluating the parametric pattern query against the database with the parameter set to the adjusted candidate value and the calculating the cardinality error until the calculated cardinality error is within an allowable limit. | 1. A method for testing a component of a database application, the method comprising: populating, as specified by a user, a column in a database with test data that falls within a certain range specified by the user; specifying, by the user, a desired cardinality constraint suitable for testing the component, the component operating on a computing device, wherein the component is a software component; specifying, by the user, a parametric pattern query that includes a parameter, wherein the parametric pattern query is compatible with the database, and wherein the parametric pattern query is configured to restrict cardinality when evaluated against the database; selecting a candidate value; evaluating, by the computing device via the component, the parametric pattern query against the database with the parameter set to the candidate value; calculating, by the computing device, a cardinality error as a difference between a returned cardinality and the desired cardinality constraint, wherein the returned cardinality results from the evaluating; and adjusting the candidate value based on the calculated cardinality error and then repeating the evaluating the parametric pattern query against the database with the parameter set to the adjusted candidate value and the calculating the cardinality error until the calculated cardinality error is within an allowable limit. 7. The method of claim 1 , wherein the parametric pattern query comprises a plurality of subqueries and wherein a distinct cardinality constraint is specified for each subquery. | 0.854441 |
8,843,362 | 11 | 12 | 11. A computerized process comprising: receiving at a computer processor data relating to a composition of a target group; receiving at the computer processor logged communications of the target group; extracting with the computer processor textual information from the logged communications; analyzing with the computer processor the textual information using statistical and linguistic sentiment analysis techniques; generating one or more vectors from the analysis of the textual information; combining one or more assessments from multiple vectors; identifying with the computer processor an individual or sub-group from the target group as a function of the analysis of the textual information; and causing to be displayed on a user interface or transmitting to another processor the identified individual or sub-group of the target group and causing to be displayed on the user interface or transmitting to another processor a sentiment assessment of the identified individual or sub-group as a function of the statistical and linguistic sentiment analysis; wherein the logged communications are a database of recorded audio voices; and wherein the system comprises a processor configured to analyze the one or more recorded audio voices for audio voice characteristics; and wherein the one or more processors are further configured to analyze the sentiment of the individual or sub-group over a period of time. | 11. A computerized process comprising: receiving at a computer processor data relating to a composition of a target group; receiving at the computer processor logged communications of the target group; extracting with the computer processor textual information from the logged communications; analyzing with the computer processor the textual information using statistical and linguistic sentiment analysis techniques; generating one or more vectors from the analysis of the textual information; combining one or more assessments from multiple vectors; identifying with the computer processor an individual or sub-group from the target group as a function of the analysis of the textual information; and causing to be displayed on a user interface or transmitting to another processor the identified individual or sub-group of the target group and causing to be displayed on the user interface or transmitting to another processor a sentiment assessment of the identified individual or sub-group as a function of the statistical and linguistic sentiment analysis; wherein the logged communications are a database of recorded audio voices; and wherein the system comprises a processor configured to analyze the one or more recorded audio voices for audio voice characteristics; and wherein the one or more processors are further configured to analyze the sentiment of the individual or sub-group over a period of time. 12. The computerized process of claim 11 , wherein the data relating to a composition of a target group comprises one or more of a one to many relationship or a many to many relationship. | 0.884994 |
4,742,516 | 1 | 4 | 1. A method for transmitting information over a system having at least one broadcasting station and a plurality of receivers, comprising the steps of: composing information packets each of which includes a text portion including a sequence of words and symbols, a classification code signifying the type of text being transmitted and a distinction code indicating which of a plurality of repetitions a particular message is annexed to for each text portion; converting the information packets into digital signals; transmitting the digital signals representing said information packets at least once; receiving the digital signal information packets at said receivers, each of which as preprogrammed therein at least one classification code, the at least one pre-programmed classification code at a particular receiver being changed, added as a new classification code or deleted by a user for signifying the type of text the user wishes to receive at said particular receiver; selecting received information packets with a classification code corresponding to the pre-programmed classification code at each receiver; selecting the information packets with a distinction code other than the distinction codes corresponding to already accumulated text portions so that duplication information is not received; accumulating the selected information packets; and conveying to a user information corresponding to the text portions of the accumulated information packets by at least one of visually displaying said information and enunciating said information over a speaker at each receiver. | 1. A method for transmitting information over a system having at least one broadcasting station and a plurality of receivers, comprising the steps of: composing information packets each of which includes a text portion including a sequence of words and symbols, a classification code signifying the type of text being transmitted and a distinction code indicating which of a plurality of repetitions a particular message is annexed to for each text portion; converting the information packets into digital signals; transmitting the digital signals representing said information packets at least once; receiving the digital signal information packets at said receivers, each of which as preprogrammed therein at least one classification code, the at least one pre-programmed classification code at a particular receiver being changed, added as a new classification code or deleted by a user for signifying the type of text the user wishes to receive at said particular receiver; selecting received information packets with a classification code corresponding to the pre-programmed classification code at each receiver; selecting the information packets with a distinction code other than the distinction codes corresponding to already accumulated text portions so that duplication information is not received; accumulating the selected information packets; and conveying to a user information corresponding to the text portions of the accumulated information packets by at least one of visually displaying said information and enunciating said information over a speaker at each receiver. 4. A method according to claim 1 wherein said step composing comprises the step of composing information packets wherein the classification code comprises a symbol signifying a classification code and a sequence of a predetermined number of numerals. | 0.680307 |
8,359,543 | 4 | 6 | 4. The method of claim 1 , further comprising: generating a plurality of letter combinations based on the determined text characters; selecting a plurality of potential words from the plurality of letter combinations based on a validation dictionary; weighting each potential word of the plurality of potential words based on a frequency of use and previous text words; and providing potential words based on the weights. | 4. The method of claim 1 , further comprising: generating a plurality of letter combinations based on the determined text characters; selecting a plurality of potential words from the plurality of letter combinations based on a validation dictionary; weighting each potential word of the plurality of potential words based on a frequency of use and previous text words; and providing potential words based on the weights. 6. The method of claim 4 , further comprising: displaying the provided potential words; and generating the text word based on a user selection of a potential word from the displayed potential words. | 0.5 |
9,785,707 | 13 | 17 | 13. A system for processing audio text files generated from audio files comprising: an electronic text transformer associated with a processor linguistically analyzing the audio text files within a context of multiple lexicons to form edited text results, said multiple lexicons comprising more than one of common words, proper names, client specific lexicons, application specific lexicons, industry specific lexicons and third party lexicons, said transformer creating electronic reflection files corresponding to the audio text files from the edited text results; an electronic reflection repository in communication with the text transformer storing the electronic reflection files therein; a processing tool processing, in response to a user request, the reflection files by forming a search index from the reflection files and searching the search index, or text analyzing the reflection files using the search index and said processing tool searching metadata associated with the reflection files or text analyzing metadata associated with the reflection files; and a user device displaying on a display a first reflection file from the reflection files or a first audio text file from the audio text files in response to processing and, searching, in response to a user request, metadata associated with the reflection files or text analyzing the metadata associated with the reflection files, said user device displaying a search bar for inputting search terms into the processing tool. | 13. A system for processing audio text files generated from audio files comprising: an electronic text transformer associated with a processor linguistically analyzing the audio text files within a context of multiple lexicons to form edited text results, said multiple lexicons comprising more than one of common words, proper names, client specific lexicons, application specific lexicons, industry specific lexicons and third party lexicons, said transformer creating electronic reflection files corresponding to the audio text files from the edited text results; an electronic reflection repository in communication with the text transformer storing the electronic reflection files therein; a processing tool processing, in response to a user request, the reflection files by forming a search index from the reflection files and searching the search index, or text analyzing the reflection files using the search index and said processing tool searching metadata associated with the reflection files or text analyzing metadata associated with the reflection files; and a user device displaying on a display a first reflection file from the reflection files or a first audio text file from the audio text files in response to processing and, searching, in response to a user request, metadata associated with the reflection files or text analyzing the metadata associated with the reflection files, said user device displaying a search bar for inputting search terms into the processing tool. 17. A system as recited in claim 13 wherein the user device displays a plurality of search results in response to searching. | 0.725664 |
8,683,328 | 9 | 10 | 9. A system configured to enable entry and modification of multimedia elements in electronic communication, comprising: a database including a set of mnemonic names, associated with a set of multimedia objects, that includes a first mnemonic name associated with a first multimedia object; and one or more processors in operative communication with the database, the one or more processors configured to: receive a sequence of control inputs that designates content of an electronic communication to be sent from a first user to a second user, identify, in the received sequence of control inputs, an occurrence of the first mnemonic name, set a theme for multimedia objects that applies to a plurality of multimedia objects and presents a variant of each of the plurality of multimedia objects consistent with the theme; determine a replacement of the identified occurrence of the first mnemonic name with the first multimedia object in the electronic communication based on a capability of a receiving device, replace, in the electronic communication, the first mnemonic name with the first multimedia object present in the received sequence of control inputs, send the electronic communication including the first mnemonic name replaced with the first multimedia object to the second user. | 9. A system configured to enable entry and modification of multimedia elements in electronic communication, comprising: a database including a set of mnemonic names, associated with a set of multimedia objects, that includes a first mnemonic name associated with a first multimedia object; and one or more processors in operative communication with the database, the one or more processors configured to: receive a sequence of control inputs that designates content of an electronic communication to be sent from a first user to a second user, identify, in the received sequence of control inputs, an occurrence of the first mnemonic name, set a theme for multimedia objects that applies to a plurality of multimedia objects and presents a variant of each of the plurality of multimedia objects consistent with the theme; determine a replacement of the identified occurrence of the first mnemonic name with the first multimedia object in the electronic communication based on a capability of a receiving device, replace, in the electronic communication, the first mnemonic name with the first multimedia object present in the received sequence of control inputs, send the electronic communication including the first mnemonic name replaced with the first multimedia object to the second user. 10. The system of claim 9 , wherein the first multimedia object has at least one editable property, and wherein the one or more processors are further configured to edit the at least one editable property in the electronic communication responsive to receiving corresponding input from the first user. | 0.501656 |
8,996,922 | 2 | 3 | 2. The method of claim 1 , further comprising: eliminating the quantifier through instantiating the quantifier with a symbolic variable; and determining whether the set of constraints is satisfiable based on the instantiation of the quantifier with the symbolic variable. | 2. The method of claim 1 , further comprising: eliminating the quantifier through instantiating the quantifier with a symbolic variable; and determining whether the set of constraints is satisfiable based on the instantiation of the quantifier with the symbolic variable. 3. The method of claim 2 , wherein the quantifier is an “exists” quantifier. | 0.848606 |
9,684,699 | 18 | 19 | 18. The non-transitory computer-readable medium of claim 1 , wherein before executing the code, the computer-readable instructions further cause the computing device to: identify relationship information from the loaded model that is associated with the described business rule; determine that the identified relationship information defines an assigned filter; and write assigned filters code to the replication code file based on the identified relationship information. | 18. The non-transitory computer-readable medium of claim 1 , wherein before executing the code, the computer-readable instructions further cause the computing device to: identify relationship information from the loaded model that is associated with the described business rule; determine that the identified relationship information defines an assigned filter; and write assigned filters code to the replication code file based on the identified relationship information. 19. The non-transitory computer-readable medium of claim 18 , wherein before executing the code, the computer-readable instructions further cause the computing device to: write assigned filters folder code to the replication code file based on the identified relationship information before writing the assigned filters code. | 0.632353 |
8,782,726 | 5 | 6 | 5. The method of claim 1 , wherein the action comprises presenting a user associated with the user electronic device with additional information associated with the electronic media work. | 5. The method of claim 1 , wherein the action comprises presenting a user associated with the user electronic device with additional information associated with the electronic media work. 6. The method of claim 5 , wherein the additional information is related to one or more products or services. | 0.5 |
9,705,917 | 13 | 14 | 13. A computer readable storage device storing a computer program which, when executed by one or more computer processors, causes the one or more computer processors to perform operations comprising: receiving, by a computing device processor, an electronic document including a plurality of content items, a rule, a first digest, and a digital signature of a document author, wherein the rule identifies a subset of the content items that are invariant to operations authorized by the rule; generating a second digest for the electronic document by digesting the subset of content items that are invariant to operations authorized by the rule, wherein content items that are modifiable pursuant to the rule are not considered in generating the second digest; invalidating the digital signature of the document author in response to detecting a discrepancy between the first and second digests and restricting access to the electronic document in response to detecting the invalidated digital signature. | 13. A computer readable storage device storing a computer program which, when executed by one or more computer processors, causes the one or more computer processors to perform operations comprising: receiving, by a computing device processor, an electronic document including a plurality of content items, a rule, a first digest, and a digital signature of a document author, wherein the rule identifies a subset of the content items that are invariant to operations authorized by the rule; generating a second digest for the electronic document by digesting the subset of content items that are invariant to operations authorized by the rule, wherein content items that are modifiable pursuant to the rule are not considered in generating the second digest; invalidating the digital signature of the document author in response to detecting a discrepancy between the first and second digests and restricting access to the electronic document in response to detecting the invalidated digital signature. 14. The computer readable storage device of claim 13 , wherein the first digest is generated before the second digest. | 0.842246 |
7,900,143 | 19 | 20 | 19. The article of claim 11 , wherein further storing instructions that enable the processor-based system to convert each converted character into an encoding and index into a font file using the encoding to obtain the character. | 19. The article of claim 11 , wherein further storing instructions that enable the processor-based system to convert each converted character into an encoding and index into a font file using the encoding to obtain the character. 20. The article of claim 19 , further storing instructions that if executed enable the processor-based system to use the encoding to access the font file for the characters of the second type arranged in a row and column format. | 0.5 |
8,150,842 | 1 | 2 | 1. A computer implemented online-content management method, comprising: receiving at one or more first computers a plurality of online content items authored by a plurality of authors for online publication; and for each online content item, determining a reputation score for an author of the online content item, where the reputation score is based at least in part on: (a) scores of online content items authored by the author, the scores provided by one or more reviewers other than the author; and (b) an authentication score for the author, the authentication score being a function of determinations made to identify that the author is who the author purports to be; and in response to a query for online content received from a second computer, generating a set of search results including an online content item from the plurality of online content items; and determining a ranking of the online content item in the set based at least in part on a reputation score of the author. | 1. A computer implemented online-content management method, comprising: receiving at one or more first computers a plurality of online content items authored by a plurality of authors for online publication; and for each online content item, determining a reputation score for an author of the online content item, where the reputation score is based at least in part on: (a) scores of online content items authored by the author, the scores provided by one or more reviewers other than the author; and (b) an authentication score for the author, the authentication score being a function of determinations made to identify that the author is who the author purports to be; and in response to a query for online content received from a second computer, generating a set of search results including an online content item from the plurality of online content items; and determining a ranking of the online content item in the set based at least in part on a reputation score of the author. 2. The method of claim 1 , wherein the reputation score is further based on a level of frame of the author. | 0.887841 |
7,478,342 | 17 | 21 | 17. A machine-readable medium tangibly embodying a software product comprising instructions operable to cause one or more data processing apparatus to perform operations comprising: receiving commands resulting from interaction with controls of a software interface dialog; distinguishing between a first command that is held upon receipt, in at least one circumstance, to be performed later, and a second command that is passed through the dialog to be performed upon receipt; and retaining both the first command and the second command in a command sequence managed by a cancel control of the dialog; wherein the dialog includes a preview control operable to specify whether effects of interaction with the controls are to be previewed as interaction with the controls occurs, the first command is subject to the preview control, the second command is not subject to the preview control, the first and second commands being additional to one or more commands affecting the preview control, and the distinguishing comprises performing the second command but not the first command when the preview control specifies the effects of interaction are not to be previewed. | 17. A machine-readable medium tangibly embodying a software product comprising instructions operable to cause one or more data processing apparatus to perform operations comprising: receiving commands resulting from interaction with controls of a software interface dialog; distinguishing between a first command that is held upon receipt, in at least one circumstance, to be performed later, and a second command that is passed through the dialog to be performed upon receipt; and retaining both the first command and the second command in a command sequence managed by a cancel control of the dialog; wherein the dialog includes a preview control operable to specify whether effects of interaction with the controls are to be previewed as interaction with the controls occurs, the first command is subject to the preview control, the second command is not subject to the preview control, the first and second commands being additional to one or more commands affecting the preview control, and the distinguishing comprises performing the second command but not the first command when the preview control specifies the effects of interaction are not to be previewed. 21. The machine-readable medium of claim 17 , wherein the first command is received before the second command, and the distinguishing comprises: performing the first command upon receipt when the preview control specifies the effects of interaction are to be previewed; undoing the first command in conjunction with receipt of the second command; performing the second command after undoing the first command; and redoing the first command after performing the second command; wherein the second command precedes the first command in the command sequence. | 0.5 |
4,689,743 | 12 | 15 | 12. A method as claimed in claim 9 wherein, after said searching step, the method includes the step of testing a highest ranked character code for an exact match with said incoming character code, and in the event of an exact match, by-passing said selecting step. | 12. A method as claimed in claim 9 wherein, after said searching step, the method includes the step of testing a highest ranked character code for an exact match with said incoming character code, and in the event of an exact match, by-passing said selecting step. 15. A method as claimed in claim 12 for use in association with a series of incoming character codes wherein each step in said method is carried out by physical apparatus and the method further comprises the steps of initializing all said apparatus, whereby to accept a next incoming character code, and repeating all previous steps until the entire said series has been validated. | 0.5 |
9,483,736 | 6 | 7 | 6. The method of claim 1 , further comprising determining the system action based on applying one or more rules. | 6. The method of claim 1 , further comprising determining the system action based on applying one or more rules. 7. The method of claim 6 , wherein the one or more rules are predefined rules customizable by the user. | 0.5 |
9,405,794 | 1 | 2 | 1. A method, comprising: identifying, by a computing device, characteristics of a relational database; generating, by the computing device, tokens from the characteristics of the relational database; receiving, by the computing device, a search request containing a search term entered into a field of a user interface; identifying, by the computing device, a set of the tokens associated with the search term; automatically generating, by the computing device, a structured query based on the set of the tokens associated with the search term; using, by the computing device, the structured query to retrieve data in the relational database; classifying the tokens as non-numeric attributes and numeric measures; identifying a first one of the tokens associated with the search term; identifying the first one of the tokens as one of the non-numeric attributes; identifying a first table associated with the first one of the tokens containing the non-numeric attributes; searching for a second table linked to the first table containing numeric measures; displaying a second one of the tokens associated with the second table; and displaying the numeric measures from the second table in response to identifying the first one of the tokens as one of the non-numeric attributes. | 1. A method, comprising: identifying, by a computing device, characteristics of a relational database; generating, by the computing device, tokens from the characteristics of the relational database; receiving, by the computing device, a search request containing a search term entered into a field of a user interface; identifying, by the computing device, a set of the tokens associated with the search term; automatically generating, by the computing device, a structured query based on the set of the tokens associated with the search term; using, by the computing device, the structured query to retrieve data in the relational database; classifying the tokens as non-numeric attributes and numeric measures; identifying a first one of the tokens associated with the search term; identifying the first one of the tokens as one of the non-numeric attributes; identifying a first table associated with the first one of the tokens containing the non-numeric attributes; searching for a second table linked to the first table containing numeric measures; displaying a second one of the tokens associated with the second table; and displaying the numeric measures from the second table in response to identifying the first one of the tokens as one of the non-numeric attributes. 2. The method of claim 1 , further comprising: identifying a first partial set of characters for the search term; identifying the tokens associated with the first partial set of characters; and displaying the tokens associated with the first partial set of characters. | 0.5 |
9,824,322 | 12 | 17 | 12. A computer system, comprising one or more processors and a memory, operatively interconnected to one another and configured to implement steps for analyzing a control-flow in a business process, comprising: invoking a representation of the business process as an acyclic workflow graph containing a plurality of types of nodes and edges linking nodes of the graph; labeling edges of the graph such that a label assigned to a first edge comprises a plurality of edge identifiers identifying respective edges, each of the edges identified being an outgoing edge of a split node in the graph, whereby executing any one of the identified edges ensures that the first edge will be executed; and checking the labels for a deadlock using a processor, while labeling the edges of the graph, wherein a deadlock is found if a condition for relaxed soundness is true. | 12. A computer system, comprising one or more processors and a memory, operatively interconnected to one another and configured to implement steps for analyzing a control-flow in a business process, comprising: invoking a representation of the business process as an acyclic workflow graph containing a plurality of types of nodes and edges linking nodes of the graph; labeling edges of the graph such that a label assigned to a first edge comprises a plurality of edge identifiers identifying respective edges, each of the edges identified being an outgoing edge of a split node in the graph, whereby executing any one of the identified edges ensures that the first edge will be executed; and checking the labels for a deadlock using a processor, while labeling the edges of the graph, wherein a deadlock is found if a condition for relaxed soundness is true. 17. The computer system of claim 12 , further comprising a step of if a deadlock is detected at the step of checking, returning to a user a characterization indicative of the detected deadlock via a graphical user interface (GUI). | 0.567669 |
8,529,263 | 13 | 18 | 13. A data processing system comprising a non-transitory computer readable medium having stored thereon computer-executable instructions, the computer executable instructions causing a processor of a data processing system to execute a method of constructing at least a part of a fiber-based garment by a user, the system comprising a plurality of instruction sets comprising: an instruction set for receiving electronic instructional content from a plurality of instructional manuals, the instructional content comprising a plurality of instructional parts; an instruction set for selecting a plurality of instructional parts to enable construction of at least part of a fiber-based garment; an instruction set for editing one or more of the plurality of instructional parts to create at least one edited instructional part; an instruction set for assembling the at least one edited instructional part with the plurality of instructional parts to create an integrated assembly instruction set; an instruction set for presenting the integrated assembly instruction set to the user; an instruction set for tracking the progress of the user with respect to the integrated assembly instruction set; an instruction set for presenting at least one set of companion instructions to the user to execute, the companion instructions presented based upon user progress with respect to at least one of: i) a row of instructions, and ii) the plurality of instruction parts; and a module for constructing at least a part of a fiber-based garment by the user. | 13. A data processing system comprising a non-transitory computer readable medium having stored thereon computer-executable instructions, the computer executable instructions causing a processor of a data processing system to execute a method of constructing at least a part of a fiber-based garment by a user, the system comprising a plurality of instruction sets comprising: an instruction set for receiving electronic instructional content from a plurality of instructional manuals, the instructional content comprising a plurality of instructional parts; an instruction set for selecting a plurality of instructional parts to enable construction of at least part of a fiber-based garment; an instruction set for editing one or more of the plurality of instructional parts to create at least one edited instructional part; an instruction set for assembling the at least one edited instructional part with the plurality of instructional parts to create an integrated assembly instruction set; an instruction set for presenting the integrated assembly instruction set to the user; an instruction set for tracking the progress of the user with respect to the integrated assembly instruction set; an instruction set for presenting at least one set of companion instructions to the user to execute, the companion instructions presented based upon user progress with respect to at least one of: i) a row of instructions, and ii) the plurality of instruction parts; and a module for constructing at least a part of a fiber-based garment by the user. 18. The system of claim 13 , wherein the integrated assembly set is presented to the user by a display screen device. | 0.680328 |
7,865,465 | 1 | 6 | 1. A model edit control system for controlling editing of a data model, the model edit control system comprising: a computer; and a computer readable medium storing instructions, wherein the instructions, when executed by the computer, cause the computer to implement; a model repository manager, wherein the model repository manager is configured to copy a stored model stored in a repository to multiple users for executing actions on multiple model copies in parallel; an action log manager, wherein the action log manager is configured to create a model action log for tracking actions executed on the stored model and to create a current action log for each model copy to record actions executed on the model copy; and a model merger manager, wherein the model merger manager is configured to merge the model copies into the stored model in the repository by playing actions in the current action log against the stored model, wherein the model merger manager comprises: an action player, wherein the action player is configured to play the executed actions in the model copies against the stored model in the repository based on the current action log of each model copy; and a conflict handler, wherein the conflict handler is configured to handle conflict between the stored model in the repository and the model copies based on the action log of each model copy, wherein the conflict handler comprises: a conflict information handler, wherein the conflict information handler is configured to provide information of at least one conflict identified responsive to playing the executed actions to the user of the model copy; and an option handler, wherein the option handler is configured to present options of resolving the at least one conflict and receiving a selected option. | 1. A model edit control system for controlling editing of a data model, the model edit control system comprising: a computer; and a computer readable medium storing instructions, wherein the instructions, when executed by the computer, cause the computer to implement; a model repository manager, wherein the model repository manager is configured to copy a stored model stored in a repository to multiple users for executing actions on multiple model copies in parallel; an action log manager, wherein the action log manager is configured to create a model action log for tracking actions executed on the stored model and to create a current action log for each model copy to record actions executed on the model copy; and a model merger manager, wherein the model merger manager is configured to merge the model copies into the stored model in the repository by playing actions in the current action log against the stored model, wherein the model merger manager comprises: an action player, wherein the action player is configured to play the executed actions in the model copies against the stored model in the repository based on the current action log of each model copy; and a conflict handler, wherein the conflict handler is configured to handle conflict between the stored model in the repository and the model copies based on the action log of each model copy, wherein the conflict handler comprises: a conflict information handler, wherein the conflict information handler is configured to provide information of at least one conflict identified responsive to playing the executed actions to the user of the model copy; and an option handler, wherein the option handler is configured to present options of resolving the at least one conflict and receiving a selected option. 6. The model edit control system as claimed in claim 1 , wherein the action player comprises: an action merger, wherein the action merger is configured to merge actions played and accepted into the stored model. | 0.827332 |
8,726,256 | 36 | 37 | 36. The computer-implemented method of claim 34 , wherein converting the automaton into machine code comprises converting the automaton into an image configured to program a parallel machine. | 36. The computer-implemented method of claim 34 , wherein converting the automaton into machine code comprises converting the automaton into an image configured to program a parallel machine. 37. The computer-implemented method of claim 36 , further comprising: publishing the image. | 0.5 |
9,330,668 | 5 | 6 | 5. A computer-implemented method of processing voice applications in a client-server computing system comprising a server device and a client device, said method comprising performing on said client device: communicating, to said server device, data, from said client device, said data being indicative of a computing capability of said client device, said data comprising: a measure of memory available on said client device, a battery power of said client device, a processing capability of said client device, and information identifying one or more resources available on said client device; receiving a script from said server device, said script being selected dependent on said data indicative of said computing capability of said client device; parsing said script and determining a set of instructions allocating a division of voice-based dialog processing or speech processing tasks to be performed between said client device and said server device; determining, based on said script, voice-based dialog processing or speech processing tasks to be performed by said client device according to said set of instructions and an order to perform said voice-based dialog processing or speech processing tasks; executing said set of instructions of said voice-based dialog processing or said speech processing tasks on said client device based on said division of said voice-based dialog processing or said speech processing tasks; and synchronizing execution of said voice-based dialog processing or said speech processing tasks between said client device and said server device. | 5. A computer-implemented method of processing voice applications in a client-server computing system comprising a server device and a client device, said method comprising performing on said client device: communicating, to said server device, data, from said client device, said data being indicative of a computing capability of said client device, said data comprising: a measure of memory available on said client device, a battery power of said client device, a processing capability of said client device, and information identifying one or more resources available on said client device; receiving a script from said server device, said script being selected dependent on said data indicative of said computing capability of said client device; parsing said script and determining a set of instructions allocating a division of voice-based dialog processing or speech processing tasks to be performed between said client device and said server device; determining, based on said script, voice-based dialog processing or speech processing tasks to be performed by said client device according to said set of instructions and an order to perform said voice-based dialog processing or speech processing tasks; executing said set of instructions of said voice-based dialog processing or said speech processing tasks on said client device based on said division of said voice-based dialog processing or said speech processing tasks; and synchronizing execution of said voice-based dialog processing or said speech processing tasks between said client device and said server device. 6. The method according to claim 5 , further comprising: sending said data indicative of computing capability from said client device to said server device. | 0.787466 |
9,135,653 | 145 | 146 | 145. The method of claim 118 comprising at a first time, serving personalized content to the recipient based on the first value associated with the first edge. | 145. The method of claim 118 comprising at a first time, serving personalized content to the recipient based on the first value associated with the first edge. 146. The method of claim 145 comprising at a second time, serving personalized content to the recipient based on the second value associated with the first edge. | 0.5 |
8,115,869 | 3 | 10 | 3. The method of claim 2 , wherein the extracting of key information includes extracting words from the second word buffer based on a frequency of words appearing in the first word buffer. | 3. The method of claim 2 , wherein the extracting of key information includes extracting words from the second word buffer based on a frequency of words appearing in the first word buffer. 10. The method of claim 3 wherein selecting a set of extraction rules based on the genre further includes selecting extraction rules by mapping from content to a set of rules from a number of rules in a rules library that includes rules for extracting various keywords. | 0.507326 |
8,341,610 | 12 | 13 | 12. A system as in claim 11 , wherein the authoring step further comprises: means for editing the questions and states; and means for modifying cell values relating each of said questions with each of said states, and wherein said probabilities of occurrence and said answer probabilities are updated by said testing. | 12. A system as in claim 11 , wherein the authoring step further comprises: means for editing the questions and states; and means for modifying cell values relating each of said questions with each of said states, and wherein said probabilities of occurrence and said answer probabilities are updated by said testing. 13. A system as in claim 12 , wherein the questions have associated costs expressed in some unit of measure, and wherein the flowchart presents the questions in an optimally informative order. | 0.5 |
7,996,367 | 20 | 21 | 20. The method of claim 18 wherein the individual that returned the document is sent an automatic message to ask if a modification was made to the document as an automatic process if a change is identified in the document away from a signature block that is consistent with added text and/or pages to the original document. | 20. The method of claim 18 wherein the individual that returned the document is sent an automatic message to ask if a modification was made to the document as an automatic process if a change is identified in the document away from a signature block that is consistent with added text and/or pages to the original document. 21. The method of claim 20 wherein the document is rerouted to previous signatories to confirm a modification made to the document. | 0.5 |
7,580,908 | 16 | 17 | 16. An interactive system, comprising the following components: a processor; a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions to implement the system including: a first component that analyzes a sequential human communications related to an underlying communicative intention, the component concurrently employing a utility based Bayesian decision model utilizing at least two of the communications to determine an action with a highest utility to facilitate achieving the intention; and a second component that performs the action when the underlying communicative intention is above a confidence threshold. | 16. An interactive system, comprising the following components: a processor; a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions to implement the system including: a first component that analyzes a sequential human communications related to an underlying communicative intention, the component concurrently employing a utility based Bayesian decision model utilizing at least two of the communications to determine an action with a highest utility to facilitate achieving the intention; and a second component that performs the action when the underlying communicative intention is above a confidence threshold. 17. The system of claim 16 , wherein the first component includes speech, gestures and other modalities related to an underlying communicative intention, the component concurrently employing at least two of the communications modalities in determining an action to facilitate achieving the intention. | 0.5 |
8,705,863 | 15 | 16 | 15. The method of claim 14 wherein the at least three design document views further includes an overview schematic document view corresponding to a design layout of the automated system, wherein the overview schematic document view includes design data identifying each device of the plurality of devices and a location associated with each device. | 15. The method of claim 14 wherein the at least three design document views further includes an overview schematic document view corresponding to a design layout of the automated system, wherein the overview schematic document view includes design data identifying each device of the plurality of devices and a location associated with each device. 16. The method of claim 15 further comprising receiving an indication selecting the overview schematic document view as a fourth design document view, comparing text in the third design document view identifying a device of the plurality of devices to text in the fourth design document view and, when a substantial match is determined, identifying the location of the device associated with the control point of the control module. | 0.5 |
7,613,602 | 1 | 4 | 1. A structured document processing apparatus comprising: an acquisition unit configured to acquire a structured document; a storage unit configured to store a structure model tree which indicates a typical structure of the acquired structured document; a parsing unit configured to parse the acquired structured document; an updating unit configured to update the structure model tree to match a structure of the parsed structured document therewith; a division unit configured to divide the acquired structured document into a plurality of lexical items; a calculation unit configured to calculate frequency-of-occurrence information indicating locations of each of the lexical items in the acquired structured document; a broadening unit configured to broaden a range until a lexical item having not less than a frequency of occurrence is present within the range; and an assignment unit configured to assign a lexical identifier of a lexical item which has a highest frequency of occurrence within the broadened range as a relevant lexical identifier of the lexical item. | 1. A structured document processing apparatus comprising: an acquisition unit configured to acquire a structured document; a storage unit configured to store a structure model tree which indicates a typical structure of the acquired structured document; a parsing unit configured to parse the acquired structured document; an updating unit configured to update the structure model tree to match a structure of the parsed structured document therewith; a division unit configured to divide the acquired structured document into a plurality of lexical items; a calculation unit configured to calculate frequency-of-occurrence information indicating locations of each of the lexical items in the acquired structured document; a broadening unit configured to broaden a range until a lexical item having not less than a frequency of occurrence is present within the range; and an assignment unit configured to assign a lexical identifier of a lexical item which has a highest frequency of occurrence within the broadened range as a relevant lexical identifier of the lexical item. 4. The apparatus according to claim 1 , further comprising: a storage unit configured to store respective relevant lexical Identifiers of the lexical hams; an assignment unit configured to assign structure model tree identifiers to nodes of the structure model tree; an assignment unit configured to assign a structure model tree identifier of a node where the relevant lexical identifier of a lexical item occurs most frequently a relevant structure model tree identifier; and a storage unit configured to store respective relevant structure model tree identifiers of the lexical items. | 0.5 |
8,700,597 | 14 | 15 | 14. The system of claim 13 , wherein the user interface also includes: a definitions section that defines subsets of data by (i) displaying a subset assignment operator, (ii) receiving a word or phrase identifying the defined subset, (iii) appending the word or phrase identifying the subset to the subset assignment operator, (iv) appending a selection preposition to the word or phrase identifying the subset, wherein the selection preposition is a word or phrase indicative of selecting a data set from a plurality of data sets of a data source, (v) displaying a list including the plurality of data sets, (vi) receiving input selecting the data set from the list, (vi) appending a word or phrase identifying the selected data set to the selection preposition, (viii) displaying a parameter assignment operator adjacent to the words or phrases identifying the defined subset and the selected data set, (ix) receiving input identifying a parameter for the selected data set, (x) appending a word or phrase identifying the parameter of the data set to the parameter assignment operator, wherein the parameter is usable for specifying the defined subset, (xi) receiving input identifying a value or range of values for the parameter usable for identifying data elements from the data set to be included in the defined subset, and (xii) storing a definition of the subset based on the input received to the user interface, the definition comprising the word or phrase identifying the defined subset, the word or phrase identifying the selected dataset, and the value or range of values for the parameter. | 14. The system of claim 13 , wherein the user interface also includes: a definitions section that defines subsets of data by (i) displaying a subset assignment operator, (ii) receiving a word or phrase identifying the defined subset, (iii) appending the word or phrase identifying the subset to the subset assignment operator, (iv) appending a selection preposition to the word or phrase identifying the subset, wherein the selection preposition is a word or phrase indicative of selecting a data set from a plurality of data sets of a data source, (v) displaying a list including the plurality of data sets, (vi) receiving input selecting the data set from the list, (vi) appending a word or phrase identifying the selected data set to the selection preposition, (viii) displaying a parameter assignment operator adjacent to the words or phrases identifying the defined subset and the selected data set, (ix) receiving input identifying a parameter for the selected data set, (x) appending a word or phrase identifying the parameter of the data set to the parameter assignment operator, wherein the parameter is usable for specifying the defined subset, (xi) receiving input identifying a value or range of values for the parameter usable for identifying data elements from the data set to be included in the defined subset, and (xii) storing a definition of the subset based on the input received to the user interface, the definition comprising the word or phrase identifying the defined subset, the word or phrase identifying the selected dataset, and the value or range of values for the parameter. 15. The system of claim 14 , wherein the user interface also includes a composite collections section configured for receiving input identifying a composite subset of data, wherein the composite subset of data comprises at least two subsets of data defined via the definitions section. | 0.5 |
8,880,390 | 14 | 17 | 14. A computer program embedded in a non-transitory computer-readable storage medium, when executed by one or more processors, for providing internet content, the computer program comprising: program instructions for receiving a plurality of features by a classifier that is operable to determine a probability of an availability of news for a sentence, wherein at least one of the features when found in the sentence increases a probability of the availability of news for the sentence, the sentence including one or more noun phrases and ending in a full stop; program instructions for determining by the classifier which sentences in a document are candidate sentences for having available news; program instructions for finding, for each candidate sentence, an associated news article when the associated news article exceeds a threshold of relevance to the candidate sentence, wherein finding the associated news article further includes: program instructions for performing, for each candidate sentence, a search on the candidate sentence to find potential news articles; program instructions for evaluating each potential news article against the candidate sentence; and program instructions for selecting a best potential news article based on the evaluation to be the associated news article when a score of the best potential news article exceeds a threshold score; program instructions for adding links in the document to the found associated news articles; and program instructions for displaying the document with the added links. | 14. A computer program embedded in a non-transitory computer-readable storage medium, when executed by one or more processors, for providing internet content, the computer program comprising: program instructions for receiving a plurality of features by a classifier that is operable to determine a probability of an availability of news for a sentence, wherein at least one of the features when found in the sentence increases a probability of the availability of news for the sentence, the sentence including one or more noun phrases and ending in a full stop; program instructions for determining by the classifier which sentences in a document are candidate sentences for having available news; program instructions for finding, for each candidate sentence, an associated news article when the associated news article exceeds a threshold of relevance to the candidate sentence, wherein finding the associated news article further includes: program instructions for performing, for each candidate sentence, a search on the candidate sentence to find potential news articles; program instructions for evaluating each potential news article against the candidate sentence; and program instructions for selecting a best potential news article based on the evaluation to be the associated news article when a score of the best potential news article exceeds a threshold score; program instructions for adding links in the document to the found associated news articles; and program instructions for displaying the document with the added links. 17. The computer program as recited in claim 14 , wherein adding links in the document further includes: program instructions for adding a link in each candidate sentence with the associated news article to the associated news article, the link being added to a plurality of words in the candidate sentence, the plurality of words including a verb. | 0.512605 |
8,543,649 | 16 | 17 | 16. A processing system, comprising: a processor; and a memory coupled with the processor and storing instructions which, when executed by the processor, cause the processing system to perform a process that includes: intercepting emails sent by a first entity while the emails are being transmitted on a network; automatically constructing a knowledge profile of the first entity based on a plurality of sets of text from the intercepted emails; storing the knowledge profile as part of a knowledge base stored in a machine-accessible storage facility; in response to preparation of a particular email prepared by a second entity and intended for at least one other entity, identifying, prior to sending the particular email by the second entity, one or more suggested potential recipients as meeting one or more criteria based at least in part on a comparison of the knowledge profile of the one or more suggested potential recipients and text contained in the particular email prepared by the second entity; outputting a recommendation to include at least a first entity as a recipient of the particular email prepared by the second entity, the first entity meeting the one or more criteria and being one of the one or more suggested potential recipients; and providing a matching metric indicative of a relative strength of the recommendation for the one or more suggested potential recipients, the matching metric comprises a sum of confidence level values associated with the one or more suggested potential recipients. | 16. A processing system, comprising: a processor; and a memory coupled with the processor and storing instructions which, when executed by the processor, cause the processing system to perform a process that includes: intercepting emails sent by a first entity while the emails are being transmitted on a network; automatically constructing a knowledge profile of the first entity based on a plurality of sets of text from the intercepted emails; storing the knowledge profile as part of a knowledge base stored in a machine-accessible storage facility; in response to preparation of a particular email prepared by a second entity and intended for at least one other entity, identifying, prior to sending the particular email by the second entity, one or more suggested potential recipients as meeting one or more criteria based at least in part on a comparison of the knowledge profile of the one or more suggested potential recipients and text contained in the particular email prepared by the second entity; outputting a recommendation to include at least a first entity as a recipient of the particular email prepared by the second entity, the first entity meeting the one or more criteria and being one of the one or more suggested potential recipients; and providing a matching metric indicative of a relative strength of the recommendation for the one or more suggested potential recipients, the matching metric comprises a sum of confidence level values associated with the one or more suggested potential recipients. 17. The processing system as recited in claim 16 , the memory further storing instructions which, when executed by the processor, cause the processing system to: in response to identifying that the first entity meets the one or more criteria, indicate to the second entity that the first entity meets the one or more criteria. | 0.594527 |
8,767,825 | 6 | 9 | 6. The system of claim 1 , wherein the video rate-distortion modeling engine comprises: a video encoder configured to encode a plurality of videos selected from among the plurality of different videos of the video corpus in multiple video formats; a rate-distortion manager configured to obtain a plurality of rate-distortion data from the encoded videos, the rate-distortion manager coupled to the video encoder; a rate-distortion estimation module configured to estimate the rate-distortion model based on the obtained plurality of rate-distortion data, the rate-distortion estimation module coupled to the rate-distortion manager; and a scaling model estimation module configured to estimate the scaling model based on the obtained plurality of rate-distortion data, the scaling model estimation module coupled to the rate-distortion estimation module and the rat-distortion manager. | 6. The system of claim 1 , wherein the video rate-distortion modeling engine comprises: a video encoder configured to encode a plurality of videos selected from among the plurality of different videos of the video corpus in multiple video formats; a rate-distortion manager configured to obtain a plurality of rate-distortion data from the encoded videos, the rate-distortion manager coupled to the video encoder; a rate-distortion estimation module configured to estimate the rate-distortion model based on the obtained plurality of rate-distortion data, the rate-distortion estimation module coupled to the rate-distortion manager; and a scaling model estimation module configured to estimate the scaling model based on the obtained plurality of rate-distortion data, the scaling model estimation module coupled to the rate-distortion estimation module and the rat-distortion manager. 9. The system of claim 6 , wherein the scaling estimation module is further configured to scale the rate-distortion measurement with at least one of a resolution ratio and a frame rate ratio. | 0.527228 |
7,636,656 | 19 | 20 | 19. The computer-accessible medium as recited in claim 15 , wherein in said localization process, after said search of the localization database for translations of the extracted localizable text strings, in response to the translation kit not including any localizable text strings to be translated, the localization mechanism is further configured to write the recorded translations for the localizable text strings into localized versions of the localizable files at locations from which the corresponding localizable text strings were extracted, such that the localized versions match an original file structure of the localizable files. | 19. The computer-accessible medium as recited in claim 15 , wherein in said localization process, after said search of the localization database for translations of the extracted localizable text strings, in response to the translation kit not including any localizable text strings to be translated, the localization mechanism is further configured to write the recorded translations for the localizable text strings into localized versions of the localizable files at locations from which the corresponding localizable text strings were extracted, such that the localized versions match an original file structure of the localizable files. 20. The computer-accessible medium as recited in claim 19 , wherein the localization mechanism is further configured to store the localized versions of the localizable files in file system locations in accordance with a file organization scheme for the product, wherein the localized versions of the localizable files in the file system locations have the original file structure and are ready for a final build into a localized version of the software product such that all translations in the localized versions relative to the localizable files are obtained by the program instructions performing said extracting, said searching, said writing and said storing. | 0.5 |
6,141,002 | 23 | 31 | 23. A set top box for receiving from a broadcast center an application program and a glyph, wherein the application program includes a character for display on a television, wherein the glyph describes a graphic representation of the character, the set top box comprising: a receiver configured to receive said application program and said glyph from the broadcast center; an operating environment configured to accept one or more glyph sets; a processor configured to operably receive said application program from said receiver and execute said application program; a hash table configured to store information regarding said one or more glyph sets; and a rendering engine for rendering said character for display on the television, wherein said rendering engine is operable to determine an index using the character, use the index to retrieve information from the hash table, and use the information from the hash table to render said character for display on the television. | 23. A set top box for receiving from a broadcast center an application program and a glyph, wherein the application program includes a character for display on a television, wherein the glyph describes a graphic representation of the character, the set top box comprising: a receiver configured to receive said application program and said glyph from the broadcast center; an operating environment configured to accept one or more glyph sets; a processor configured to operably receive said application program from said receiver and execute said application program; a hash table configured to store information regarding said one or more glyph sets; and a rendering engine for rendering said character for display on the television, wherein said rendering engine is operable to determine an index using the character, use the index to retrieve information from the hash table, and use the information from the hash table to render said character for display on the television. 31. The set top box as recited in claim 23, further comprising an encoding engine configured to receive said character from said application program, and to invoke said rendering engine. | 0.5 |
9,244,536 | 30 | 31 | 30. The device of claim 29 , wherein the plurality of possible single touch user inputs that will result in selection of the suggested replacement character string includes inputs that are not associated with a virtual punctuation mark key. | 30. The device of claim 29 , wherein the plurality of possible single touch user inputs that will result in selection of the suggested replacement character string includes inputs that are not associated with a virtual punctuation mark key. 31. The device of claim 30 , wherein the plurality of possible single touch user inputs that are not associated with a virtual punctuation mark key and that will result in selection of the suggested replacement character string comprises a single touch input through the space bar of the virtual keyboard. | 0.535061 |
8,706,715 | 12 | 14 | 12. A non-transitory computer-readable medium containing program code executable by a processor in a computer to improve a query in a multi-tenant database system, the program code including instructions to: receiving at a network interface of a server in a multi-tenant database system an original query transmitted to the multi-tenant database system by a user associated with a tenant, wherein the original query is associated with data accessible only by the tenant, and wherein the multi-tenant database system includes at least a first index and a second index, wherein the first index is a standard index and wherein the second index is a custom index to provide a private sharing paradigm within the multi-tenant database system that allows groups defined within one or more particular tenants to share information only among members of that group; retrieving, using a processor of the server, metadata associated with the data accessible only by the tenant in the multi-tenant database system, wherein at least a portion of the data accessible only by the tenant is stored in a common table within the multi-tenant database system; scanning a first index column to identify a first set of rows, wherein the first index column is selected based on the original query; scanning a second index column to identify a second set of rows, wherein the second index column is based on the original query; determining a third set of rows corresponding to an intersection of the first set of rows and the second set of rows; determining, using the processor, a tenant-selective query syntax, wherein determining comprises analyzing at least one of metadata generated from information about the tenant or metadata generated from the data accessible by the tenant; and generating, using the processor, an improved query using the query syntax, wherein the improved query is based upon the original query and the third set of rows. | 12. A non-transitory computer-readable medium containing program code executable by a processor in a computer to improve a query in a multi-tenant database system, the program code including instructions to: receiving at a network interface of a server in a multi-tenant database system an original query transmitted to the multi-tenant database system by a user associated with a tenant, wherein the original query is associated with data accessible only by the tenant, and wherein the multi-tenant database system includes at least a first index and a second index, wherein the first index is a standard index and wherein the second index is a custom index to provide a private sharing paradigm within the multi-tenant database system that allows groups defined within one or more particular tenants to share information only among members of that group; retrieving, using a processor of the server, metadata associated with the data accessible only by the tenant in the multi-tenant database system, wherein at least a portion of the data accessible only by the tenant is stored in a common table within the multi-tenant database system; scanning a first index column to identify a first set of rows, wherein the first index column is selected based on the original query; scanning a second index column to identify a second set of rows, wherein the second index column is based on the original query; determining a third set of rows corresponding to an intersection of the first set of rows and the second set of rows; determining, using the processor, a tenant-selective query syntax, wherein determining comprises analyzing at least one of metadata generated from information about the tenant or metadata generated from the data accessible by the tenant; and generating, using the processor, an improved query using the query syntax, wherein the improved query is based upon the original query and the third set of rows. 14. The computer-readable medium of claim 12 , wherein the program code includes further instructions to: calculating selectivity for one or more columns of data accessible by the tenant; and wherein determining, using a processor of the server, comprises analyzing the selectivity of the one or more columns of data accessible by the tenant. | 0.645228 |
10,157,593 | 11 | 17 | 11. A system rendering application content for display on a computer-enabled display surface comprising: a first content description memory for storing a content description containing platform-agnostic commands received from an application generating application content, the commands describing how to draw the application content; and a processor executing the application and a rendering engine, wherein the rendering engine receives a drawing request from the application to display specific application content on a computer-enabled display surface and determines if the specific application content to be displayed is found within the content description; when the specific application content to be displayed is found within the content description, rendering the content description into platform-specific commands that render display components representative of the specific application content for display on the computer-enabled display surface; and when the specific application content to be displayed is not found within the content description, calling the application for a rasterized image representative of the application content to be displayed for display on the computer-enabled display surface. | 11. A system rendering application content for display on a computer-enabled display surface comprising: a first content description memory for storing a content description containing platform-agnostic commands received from an application generating application content, the commands describing how to draw the application content; and a processor executing the application and a rendering engine, wherein the rendering engine receives a drawing request from the application to display specific application content on a computer-enabled display surface and determines if the specific application content to be displayed is found within the content description; when the specific application content to be displayed is found within the content description, rendering the content description into platform-specific commands that render display components representative of the specific application content for display on the computer-enabled display surface; and when the specific application content to be displayed is not found within the content description, calling the application for a rasterized image representative of the application content to be displayed for display on the computer-enabled display surface. 17. The system of claim 11 wherein the rendering engine is in communication with a compositor responsible for compositing a final image for display on the computer-enabled display surface. | 0.654412 |
9,373,327 | 16 | 17 | 16. The computer readable storage medium of claim 13 , wherein refining is based upon, at least in part, one or more features vectors. | 16. The computer readable storage medium of claim 13 , wherein refining is based upon, at least in part, one or more features vectors. 17. The computer readable storage medium of claim 16 , wherein refining includes, at least in part, calculating a score for one or more potential refinement sources. | 0.5 |
8,463,769 | 12 | 16 | 12. A system, comprising: at least one computing device; and logic executed on the at least one computing device, the logic further comprising: logic that associates at least one search term with an item in an electronic repository based at least upon behavior of interactions of at least one user with the at least one computing device; logic that determines whether a search of the electronic repository surfaces the item; logic that calculates a plurality of weight values representing a degree of association between the at least one search term and the item; logic that identifies at least one search term associated with a weight value that exceeds a threshold; logic that identifies the at least one search term as a missing search phrase if a search of the electronic repository does not associate the at least one search term with the item; and logic that performs remedial measures if the search does not surface the item by adding the at least one search term to at least one attribute associated with the item in the electronic repository without user intervention. | 12. A system, comprising: at least one computing device; and logic executed on the at least one computing device, the logic further comprising: logic that associates at least one search term with an item in an electronic repository based at least upon behavior of interactions of at least one user with the at least one computing device; logic that determines whether a search of the electronic repository surfaces the item; logic that calculates a plurality of weight values representing a degree of association between the at least one search term and the item; logic that identifies at least one search term associated with a weight value that exceeds a threshold; logic that identifies the at least one search term as a missing search phrase if a search of the electronic repository does not associate the at least one search term with the item; and logic that performs remedial measures if the search does not surface the item by adding the at least one search term to at least one attribute associated with the item in the electronic repository without user intervention. 16. The system of claim 12 , wherein the logic that determines whether the search of the electronic repository does not associate the at least one search term with the item further comprises logic that performs a natural language search of the electronic repository with the at least one search term and determines whether the natural language search associates the at least one search term with the item. | 0.5 |
9,311,599 | 13 | 15 | 13. The method of claim 11 , further comprising determining a frequency value associated with the classification based at least in part on keywords associated with the classification made by the predictive model. | 13. The method of claim 11 , further comprising determining a frequency value associated with the classification based at least in part on keywords associated with the classification made by the predictive model. 15. The method of claim 13 , further comprising determining the reward based at least in part on the frequency value and the confidence value associated with the classification. | 0.79274 |
10,026,393 | 9 | 11 | 9. The system of claim 1 , further comprising a database of recorded audio pieces the speech synthesizer can use and concatenate together to synthesize speech, wherein the database of recorded audio pieces includes audio pieces of non-lexical cues. | 9. The system of claim 1 , further comprising a database of recorded audio pieces the speech synthesizer can use and concatenate together to synthesize speech, wherein the database of recorded audio pieces includes audio pieces of non-lexical cues. 11. The system of claim 9 , wherein the audio pieces of non-lexical cues in the database include at least one of a non-verbal disfluency, a breathing noise, and a phonological gesture. | 0.743733 |
8,630,856 | 25 | 26 | 25. The hardware of claim 18 , further comprising the step of receiving a top-n list of possible meanings for said language input, wherein said determining of possible meanings further comprises selecting at least two possible meanings contained within said top-n list. | 25. The hardware of claim 18 , further comprising the step of receiving a top-n list of possible meanings for said language input, wherein said determining of possible meanings further comprises selecting at least two possible meanings contained within said top-n list. 26. The hardware of claim 25 , wherein said possible meanings of said top-n list are each associated with a probability that said possible meaning is a correct interpretation of said language input, said selecting step further comprises the step of selecting said two possible meanings having the highest probabilities. | 0.5 |
9,507,854 | 8 | 11 | 8. A method for generating answers to questions, comprising: receiving an input query; extracting specified information about the input query; obtaining, from an unstructured data source, a plurality of candidate answers to the input query; producing a first score for each of the candidate answers; sending the input query to a model selection module; using the model selection module to use said extracted specified information about the input query to select one of a plurality of scoring models; sending each of the candidate answers to the selected one of the scoring models; using the selected one of the scoring models for weighting the first scores for the candidate answers to determine an answer score for each of the candidate answers; and generating at least one answer to the input query based on the answer scores. | 8. A method for generating answers to questions, comprising: receiving an input query; extracting specified information about the input query; obtaining, from an unstructured data source, a plurality of candidate answers to the input query; producing a first score for each of the candidate answers; sending the input query to a model selection module; using the model selection module to use said extracted specified information about the input query to select one of a plurality of scoring models; sending each of the candidate answers to the selected one of the scoring models; using the selected one of the scoring models for weighting the first scores for the candidate answers to determine an answer score for each of the candidate answers; and generating at least one answer to the input query based on the answer scores. 11. The method according to claim 8 , wherein the extracted information identifies a grammatical complexity of the input query. | 0.691748 |
9,405,791 | 1 | 2 | 1. A computing device implemented method comprising: obtaining, by the device and via a browser toolbar implemented by the device, a version identifier corresponding to first configuration file, stored locally by the device, that includes configuration options relating to a language translation service provided by the browser toolbar, the browser toolbar being provided in a web browser application; requesting, by the device, via the browser toolbar, and without intervention by a user of the device, a version identifier corresponding to a second configuration file, stored at an update server; determining, by the device, via the browser toolbar, and based on the version identifiers corresponding to the first configuration file and the second configuration file, whether the second configuration file stored at the update server is different than the first configuration file, the first configuration file and the second configuration file comprising data stored as a key-value pair; downloading, by the device, the second configuration file, from the update server, when the second configuration file is different than the first configuration file; parsing, by the device and via the browser toolbar, the second configuration file to obtain updated configuration options relating to the language translation service provided by the browser toolbar; and updating, by the device and via the browser toolbar, operation of the language translation service based on the obtained options. | 1. A computing device implemented method comprising: obtaining, by the device and via a browser toolbar implemented by the device, a version identifier corresponding to first configuration file, stored locally by the device, that includes configuration options relating to a language translation service provided by the browser toolbar, the browser toolbar being provided in a web browser application; requesting, by the device, via the browser toolbar, and without intervention by a user of the device, a version identifier corresponding to a second configuration file, stored at an update server; determining, by the device, via the browser toolbar, and based on the version identifiers corresponding to the first configuration file and the second configuration file, whether the second configuration file stored at the update server is different than the first configuration file, the first configuration file and the second configuration file comprising data stored as a key-value pair; downloading, by the device, the second configuration file, from the update server, when the second configuration file is different than the first configuration file; parsing, by the device and via the browser toolbar, the second configuration file to obtain updated configuration options relating to the language translation service provided by the browser toolbar; and updating, by the device and via the browser toolbar, operation of the language translation service based on the obtained options. 2. The method of claim 1 , further comprising: overwriting, when the second configuration file is downloaded, the first configuration file with the second configuration file. | 0.631356 |
9,128,945 | 21 | 22 | 21. The non-transitory computer storage medium of claim 18 , wherein the instructions, when executed by data processing apparatus, cause the data processing apparatus to perform further operations comprising: classifying the synthetic query as a synthetic query in the augmentation query data store; and adjusting a performance ranking of the synthetic query relative to a performance ranking of a non-synthetic query in response to the classification. | 21. The non-transitory computer storage medium of claim 18 , wherein the instructions, when executed by data processing apparatus, cause the data processing apparatus to perform further operations comprising: classifying the synthetic query as a synthetic query in the augmentation query data store; and adjusting a performance ranking of the synthetic query relative to a performance ranking of a non-synthetic query in response to the classification. 22. The non-transitory computer storage medium of claim 21 , wherein adjusting a performance ranking of the synthetic query comprises reducing the performance ranking of the synthetic query for input queries designated as being directed to general content and increasing the performance ranking of the synthetic query for input queries designated as being directed to specific content specifying a specific entity or a specific location. | 0.5 |
7,487,448 | 1 | 8 | 1. A system comprising: one or more computer-readable storage media; software instructions resident on the media which, when executed, are capable of representing a document with a markup representation comprising: a first element which logically binds an ordered sequence of pages together into a single multi-page document; and one or more second elements each of which is a child of the first element and refers to a source of content for a single page of the document, said elements being mappable to an associated object class and collectively defining a fixed payload, wherein the fixed payload has a fixed number of pages and a layout that is predetermined and wherein layout calculations do not have to be performed on a consuming device where content of the document can be rendered, and wherein the fixed payload has multiple fixed payload parts at least two of which are connected, each connected fixed payload part having an associated discoverable relationship part containing one or more relationships for which that associated connected fixed payload part is a source, individual relationships representing a connection and making the connection discoverable without parsing content of the fixed payload parts associated with the connection, the multiple fixed payload parts comprising: a root part; one or more a fixed page parts, each referenced by one of the one or more second elements and containing fixed page markup describing properties and additional elements associated with rendering document content; an image part representing one or more images within the document; and a font part describing one or more fonts used in the document. | 1. A system comprising: one or more computer-readable storage media; software instructions resident on the media which, when executed, are capable of representing a document with a markup representation comprising: a first element which logically binds an ordered sequence of pages together into a single multi-page document; and one or more second elements each of which is a child of the first element and refers to a source of content for a single page of the document, said elements being mappable to an associated object class and collectively defining a fixed payload, wherein the fixed payload has a fixed number of pages and a layout that is predetermined and wherein layout calculations do not have to be performed on a consuming device where content of the document can be rendered, and wherein the fixed payload has multiple fixed payload parts at least two of which are connected, each connected fixed payload part having an associated discoverable relationship part containing one or more relationships for which that associated connected fixed payload part is a source, individual relationships representing a connection and making the connection discoverable without parsing content of the fixed payload parts associated with the connection, the multiple fixed payload parts comprising: a root part; one or more a fixed page parts, each referenced by one of the one or more second elements and containing fixed page markup describing properties and additional elements associated with rendering document content; an image part representing one or more images within the document; and a font part describing one or more fonts used in the document. 8. The system of claim 1 , wherein at least one of said properties is expressed via a resource dictionary reference, and wherein a referenced resource dictionary holds one or more complex properties that are referenced by said resource dictionary reference. | 0.5 |
8,676,805 | 1 | 5 | 1. A method of relational analysis of data, comprising: receiving a data set {{F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m }, comprising a plurality data objects having a plurality of types of data associated with different latent classes, the plurality of data objects being interrelated, at least a first portion of the plurality of data objects having respective data object attributes {Θ (j) } j=1 m , at least a second portion of the plurality of data objects having homogeneous relations {Γ (j) } j=1 m between the respective data object and data objects having the same type, and at least a third portion of the plurality of data objects having heterogeneous relations { (ij) } i,j=1 m between the respective data object and data objects of different types; providing a mixed membership model {Λ (j) } j=1 m , representing the plurality of data objects, comprising, for each respective data object, relationships with other data objects based on the latent classes, and a latent indicator {C (j) } j=1 m having respective latent class membership parameters generated based on a multinomial distribution; automatically optimizing the mixed membership model {Λ (j) } j=1 m by maximizing a likelihood function {tilde over (Ω)}={{{tilde over (Λ)} (j) } j=1 m ,{{tilde over (Θ)} (j) } j=1 m ,{{tilde over (Γ)} (j) } j=1 m ,{ (ij) } i,j=1 m }}, which is initialized, and the posterior Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}) computed using a Monte Carlo approach, to estimate unknown parameters of a joint probability distribution matrix over the latent indicators {C (j) } j=1 m of the plurality of data objects, and observations of the data object attributes {Θ (j) } j=1 m , the homogeneous relations {Γ (j) } j=1 m between the respective data object and data objects having the same type, and the heterogeneous relations { (ij) } i,j=1 m between the respective data object and data objects of different types; and representing in a memory the optimized mixed membership model {Λ (j) } j=1 m . | 1. A method of relational analysis of data, comprising: receiving a data set {{F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m }, comprising a plurality data objects having a plurality of types of data associated with different latent classes, the plurality of data objects being interrelated, at least a first portion of the plurality of data objects having respective data object attributes {Θ (j) } j=1 m , at least a second portion of the plurality of data objects having homogeneous relations {Γ (j) } j=1 m between the respective data object and data objects having the same type, and at least a third portion of the plurality of data objects having heterogeneous relations { (ij) } i,j=1 m between the respective data object and data objects of different types; providing a mixed membership model {Λ (j) } j=1 m , representing the plurality of data objects, comprising, for each respective data object, relationships with other data objects based on the latent classes, and a latent indicator {C (j) } j=1 m having respective latent class membership parameters generated based on a multinomial distribution; automatically optimizing the mixed membership model {Λ (j) } j=1 m by maximizing a likelihood function {tilde over (Ω)}={{{tilde over (Λ)} (j) } j=1 m ,{{tilde over (Θ)} (j) } j=1 m ,{{tilde over (Γ)} (j) } j=1 m ,{ (ij) } i,j=1 m }}, which is initialized, and the posterior Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}) computed using a Monte Carlo approach, to estimate unknown parameters of a joint probability distribution matrix over the latent indicators {C (j) } j=1 m of the plurality of data objects, and observations of the data object attributes {Θ (j) } j=1 m , the homogeneous relations {Γ (j) } j=1 m between the respective data object and data objects having the same type, and the heterogeneous relations { (ij) } i,j=1 m between the respective data object and data objects of different types; and representing in a memory the optimized mixed membership model {Λ (j) } j=1 m . 5. The method according to claim 1 , wherein: the posterior Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}) is computed using the Gibbs sampler. | 0.642276 |
6,141,011 | 1 | 3 | 1. In a computing device having a limited set of input keys, a method for assisting a user with entering user input, the method comprising: providing a terse set of physical input keys comprising navigation keys, a select key, and an edit key; at a different location on said computing device than where said physical input keys are located, displaying a user interface that requires input of information from the user; receiving user input at the navigation keys for moving a screen cursor to different regions of the user interface; with the screen cursor positioned at a particular region of the user interface, receiving user input at the edit key for invoking a context-sensitive input system; determining a set of appropriate user input entries for the device for the particular region of the user interface where the screen cursor is currently positioned; displaying at the particular region a control based on at least some of said set of appropriate user input entries; receiving user input at the navigation keys for positioning the screen cursor at a desired entry from said set of appropriate user input entries; and receiving user input at the select key for inputting the desired entry as user input for the device. | 1. In a computing device having a limited set of input keys, a method for assisting a user with entering user input, the method comprising: providing a terse set of physical input keys comprising navigation keys, a select key, and an edit key; at a different location on said computing device than where said physical input keys are located, displaying a user interface that requires input of information from the user; receiving user input at the navigation keys for moving a screen cursor to different regions of the user interface; with the screen cursor positioned at a particular region of the user interface, receiving user input at the edit key for invoking a context-sensitive input system; determining a set of appropriate user input entries for the device for the particular region of the user interface where the screen cursor is currently positioned; displaying at the particular region a control based on at least some of said set of appropriate user input entries; receiving user input at the navigation keys for positioning the screen cursor at a desired entry from said set of appropriate user input entries; and receiving user input at the select key for inputting the desired entry as user input for the device. 3. The method of claim 1, wherein said input of information is inputted at least in part using a text input control. | 0.701031 |
8,533,593 | 10 | 11 | 10. The computer-readable storage medium of claim 8 , wherein the step of identifying comprises: for each data object in the plurality of data objects, calculating an elapsed time between the event associated with each data object and the next subsequent event generated by the application; and identifying the first time and the second time based on one or more timestamps included in the plurality of data objects, wherein the one or more timestamps are selected based on the elapsed times calculated for each data object in the plurality of data objects. | 10. The computer-readable storage medium of claim 8 , wherein the step of identifying comprises: for each data object in the plurality of data objects, calculating an elapsed time between the event associated with each data object and the next subsequent event generated by the application; and identifying the first time and the second time based on one or more timestamps included in the plurality of data objects, wherein the one or more timestamps are selected based on the elapsed times calculated for each data object in the plurality of data objects. 11. The computer-readable storage medium of claim 10 , wherein the one or more timestamps are selected from the data objects corresponding to the longest elapsed times. | 0.5 |
9,742,912 | 1 | 3 | 1. A computer implemented method, comprising: receiving, at an interactive voice response (IVR) system, a natural language query associated with a customer during a customer interaction; identifying, by the IVR system, a customer intent based on key features in the natural language query along with any of past interactions of said customer with the IVR system, customer relations management (CRM) system attributes, and customer segment attributes, said identifying including: converting speech associated with a natural language response of said customer into text, the natural language response received during the customer interaction; determining a predicted identity of the customer based on a gender of the customer and an age group of the customer; accessing a customer relations management (CRM) system to obtain the CRM attributes associated with the customer and the customer segment attributes, wherein the CRM attributes include the past interactions of the customer with the IVR system, wherein the customer segment attributes include attributes associated with the gender and the age group identified based on the natural language response; extracting at least one key feature from the text, wherein the at least one key feature is an identified keyword based on a statistical model; and computing a probability score of at least one intent associated with the at least one key feature, said computing the probability score comprising finding a best match, by a machine learning algorithm, between a first sequence of customer intents immediately preceding the customer intent and a second sequence of customer intents preceding the first sequence of customer intents, wherein the first sequence of customer intents and the second sequence of customer intents are ordered by a time and a date, wherein if the probability score of the at least one intent is greater than a threshold a journey of the IVR system of the customer is optimized and if the probability score of the at least one intent is less than the threshold a standard journey of the IVR system is offered to the customer. | 1. A computer implemented method, comprising: receiving, at an interactive voice response (IVR) system, a natural language query associated with a customer during a customer interaction; identifying, by the IVR system, a customer intent based on key features in the natural language query along with any of past interactions of said customer with the IVR system, customer relations management (CRM) system attributes, and customer segment attributes, said identifying including: converting speech associated with a natural language response of said customer into text, the natural language response received during the customer interaction; determining a predicted identity of the customer based on a gender of the customer and an age group of the customer; accessing a customer relations management (CRM) system to obtain the CRM attributes associated with the customer and the customer segment attributes, wherein the CRM attributes include the past interactions of the customer with the IVR system, wherein the customer segment attributes include attributes associated with the gender and the age group identified based on the natural language response; extracting at least one key feature from the text, wherein the at least one key feature is an identified keyword based on a statistical model; and computing a probability score of at least one intent associated with the at least one key feature, said computing the probability score comprising finding a best match, by a machine learning algorithm, between a first sequence of customer intents immediately preceding the customer intent and a second sequence of customer intents preceding the first sequence of customer intents, wherein the first sequence of customer intents and the second sequence of customer intents are ordered by a time and a date, wherein if the probability score of the at least one intent is greater than a threshold a journey of the IVR system of the customer is optimized and if the probability score of the at least one intent is less than the threshold a standard journey of the IVR system is offered to the customer. 3. The method of claim 1 , wherein said speech is converted into the text based on a statistical language mode. | 0.813758 |
9,703,393 | 12 | 14 | 12. A vehicle including a device for inputting and identifying a character string, the device including: a keyboard implemented user interface located inside the vehicle to detect and to input a plurality of keystrokes into the keyboard implemented user interface in succession to form a character string including a plurality of alpha-numeric characters; a controller of the vehicle to carry out a confidence analysis, wherein comparisons are carried out between each keystroke of the plurality of keystrokes with reference data for alpha-numeric characters stored in a database, wherein a confidence value is assigned which indicates a level of confidence that an input keystroke is meant to correspond to one of the plurality of alpha-numeric characters represented on the keyboard implemented user interface, wherein the confidence value is 0, 1, or a value therebetween, wherein the confidence analysis generates a confidence value for each alpha-numeric character represented on keys of the keyboard implemented user interface at a touch location or adjacent to the touched location on the keyboard implemented user interface, wherein each of the alpha-numeric characters for which confidence values are generated are identified as potentially relevant alpha-numeric characters, wherein the controller forms alpha-numeric character combinations based on the identified, potentially relevant alpha-numeric characters assigned to each keystroke; wherein overall confidence measures for the alpha-numeric character combinations are determined based on the confidence values assigned to the potentially relevant alpha-numeric characters included in the alpha-numeric character combinations; and a display coupled to the controller, by which a subset of the formed alpha-numeric character combinations is hierarchically output onto the display as a function of the overall confidence measures, wherein the formed alpha-numeric character combinations with higher overall confidence measures are listed before alpha-numeric character combinations with lower overall confidence measures, and wherein the subset of output characters combinations includes only formed alpha-numeric character combinations having an overall confidence value above a specified adaptable threshold value. | 12. A vehicle including a device for inputting and identifying a character string, the device including: a keyboard implemented user interface located inside the vehicle to detect and to input a plurality of keystrokes into the keyboard implemented user interface in succession to form a character string including a plurality of alpha-numeric characters; a controller of the vehicle to carry out a confidence analysis, wherein comparisons are carried out between each keystroke of the plurality of keystrokes with reference data for alpha-numeric characters stored in a database, wherein a confidence value is assigned which indicates a level of confidence that an input keystroke is meant to correspond to one of the plurality of alpha-numeric characters represented on the keyboard implemented user interface, wherein the confidence value is 0, 1, or a value therebetween, wherein the confidence analysis generates a confidence value for each alpha-numeric character represented on keys of the keyboard implemented user interface at a touch location or adjacent to the touched location on the keyboard implemented user interface, wherein each of the alpha-numeric characters for which confidence values are generated are identified as potentially relevant alpha-numeric characters, wherein the controller forms alpha-numeric character combinations based on the identified, potentially relevant alpha-numeric characters assigned to each keystroke; wherein overall confidence measures for the alpha-numeric character combinations are determined based on the confidence values assigned to the potentially relevant alpha-numeric characters included in the alpha-numeric character combinations; and a display coupled to the controller, by which a subset of the formed alpha-numeric character combinations is hierarchically output onto the display as a function of the overall confidence measures, wherein the formed alpha-numeric character combinations with higher overall confidence measures are listed before alpha-numeric character combinations with lower overall confidence measures, and wherein the subset of output characters combinations includes only formed alpha-numeric character combinations having an overall confidence value above a specified adaptable threshold value. 14. The vehicle of claim 12 , wherein the specified, adaptable threshold value is adjusted after each time the user interface detects a user input. | 0.5 |
8,626,804 | 7 | 8 | 7. The method of claim 1 , wherein the accessed web resource comprises a software product and wherein a special browser plugin or helper object is associated with the software product such that the special browser plugin or helper object is started automatically when the software product is used. | 7. The method of claim 1 , wherein the accessed web resource comprises a software product and wherein a special browser plugin or helper object is associated with the software product such that the special browser plugin or helper object is started automatically when the software product is used. 8. The method of claim 7 , wherein the browser plugin or helper object is selected from a group consisting of: a special component, a shared object, a dynamic library, a driver or other software extension. | 0.646552 |
7,764,202 | 1 | 4 | 1. A computer implemented method of compressing data, comprising: receiving an input stream of characters; parsing the input stream into a plurality of strings each of which include one or more of the characters, wherein each parsed string is a longest match to a string entry in a data structure; and transforming the input stream into an output stream that includes a first portion having literal values of the characters and a separate and distinct second portion having index values corresponding to string entries in the data structure that match parsed strings from the input stream. | 1. A computer implemented method of compressing data, comprising: receiving an input stream of characters; parsing the input stream into a plurality of strings each of which include one or more of the characters, wherein each parsed string is a longest match to a string entry in a data structure; and transforming the input stream into an output stream that includes a first portion having literal values of the characters and a separate and distinct second portion having index values corresponding to string entries in the data structure that match parsed strings from the input stream. 4. The method of claim 1 , wherein the first portion is appended to one end of the second portion in the output stream. | 0.846649 |
7,516,125 | 8 | 10 | 8. A method for finding phrases in a corpus of documents using a data processor, wherein the words in the corpus of documents include a set of stopwords, comprising: storing an index structure on a medium readable by the data processor, the index structure mapping entries in the index structure to documents in the corpus, the index structure including entries representing words found in the corpus of documents associated with locations of the words in the documents, and entries representing marks which identify a characteristic of corresponding marked words associated with locations of the marked words in the documents, and wherein one or more entries representing marks include fewer, if any, than all of the characters of the corresponding marked words; modifying an input phrase query provided to the data processor to form a modified query by adding a mark corresponding to a word in a subject phrase; and executing the modified query using said index structure and the data processor; wherein at least one entry representing a mark in the index structure comprises a token representing a type of mark coalesced with a prefix of a corresponding marked word, the prefix comprising one or more leading characters of the corresponding marked word. | 8. A method for finding phrases in a corpus of documents using a data processor, wherein the words in the corpus of documents include a set of stopwords, comprising: storing an index structure on a medium readable by the data processor, the index structure mapping entries in the index structure to documents in the corpus, the index structure including entries representing words found in the corpus of documents associated with locations of the words in the documents, and entries representing marks which identify a characteristic of corresponding marked words associated with locations of the marked words in the documents, and wherein one or more entries representing marks include fewer, if any, than all of the characters of the corresponding marked words; modifying an input phrase query provided to the data processor to form a modified query by adding a mark corresponding to a word in a subject phrase; and executing the modified query using said index structure and the data processor; wherein at least one entry representing a mark in the index structure comprises a token representing a type of mark coalesced with a prefix of a corresponding marked word, the prefix comprising one or more leading characters of the corresponding marked word. 10. The method of claim 8 , wherein the prefix comprises N leading characters of the marked word, and N is 1 . | 0.813559 |
7,698,342 | 1 | 6 | 1. A method for completing a form, comprising: storing data associated with a plurality of forms into a data structure comprising a plurality of nodes corresponding to a plurality of fields in the plurality of forms; receiving a search term from a user and obtaining a related word based on the search term to generate a search criteria; searching, using a processor of a computer, the plurality of forms by comparing the search criteria to each of the plurality of nodes in the data structure to generate a match; identifying, using the processor of the computer, a field within the form of the plurality of forms based on the match and the data structure; and presenting the field within the form to the user for completing the form. | 1. A method for completing a form, comprising: storing data associated with a plurality of forms into a data structure comprising a plurality of nodes corresponding to a plurality of fields in the plurality of forms; receiving a search term from a user and obtaining a related word based on the search term to generate a search criteria; searching, using a processor of a computer, the plurality of forms by comparing the search criteria to each of the plurality of nodes in the data structure to generate a match; identifying, using the processor of the computer, a field within the form of the plurality of forms based on the match and the data structure; and presenting the field within the form to the user for completing the form. 6. The method of claim 1 , wherein the related word is defined by at least one user. | 0.903226 |
7,751,533 | 15 | 16 | 15. A device comprising: computer code configured to select a template for a message, wherein the template comprises a dynamic field; to select a context data item from context data associated with an executing application; and to automatically insert the selected context data item in the dynamic field to facilitate the creation of a message; a memory, wherein the memory stores the computer code; and a processor coupled to the memory, wherein the processor executes the computer code. | 15. A device comprising: computer code configured to select a template for a message, wherein the template comprises a dynamic field; to select a context data item from context data associated with an executing application; and to automatically insert the selected context data item in the dynamic field to facilitate the creation of a message; a memory, wherein the memory stores the computer code; and a processor coupled to the memory, wherein the processor executes the computer code. 16. The device of claim 15 , further comprising computer code configured to determine if a field is a dynamic field. | 0.5 |
9,384,223 | 1 | 7 | 1. An apparatus for converting MLOAD and TPUMP operations, the apparatus comprising: a memory; at least one processor; and a module stored in the memory, wherein said module comprising computer instruction code executable by the at least one processor, and structured to cause said at least one processor to: electronically receive an input production parameter, wherein the input production parameter is associated with a load utility and defines a library of parameters, wherein the input production parameter comprises one or more records, wherein the library of parameters defines a first syntax, wherein the library of parameters comprise a Log table, a Work table, a Uniqueness Violation (UV) table, and/or an Error table; count the number of records in the input production parameter; compare the counted number of records to a predetermined threshold value; identify the load utility to be used to load the records into a database based on the comparison between the counted number of records and the predetermined threshold value, wherein identifying further comprises (i) determining that the number of records is greater than the predetermined threshold, and loading the input production parameter into a database using a multiload load utility (MLOAD); or (ii) determining that the number of records is less than the predetermined threshold, and loading the input production parameter into the database using a Teradata load utility (TPUMP), whereby larger volumes of records are processed during the MLOAD load utility and smaller volumes of records are processed using the TPUMP utility, and whereby less computer resources are used to implement TPUMP than MLOAD thereby providing for conservation of computer resources; load the input production parameter into the database, wherein loading further comprises switching the loading of the input production parameter into the database between the MLOAD load utility and the TPUMP utility based on at least comparing the counted number of records to the predetermined threshold value; convert the first syntax of the library of parameters to a second syntax, wherein the second syntax is associated with the identified load utility, wherein converting the first syntax comprises generating at least one script and computer code of the library of the parameters using the second syntax, wherein converting further comprises: identifying one or more unassociated parameters, wherein the one or more unassociated parameters are not associated with the identified load utility; automatically editing the one or more unassociated parameters to comply with the second syntax, wherein automatically editing further comprises automatically adding a statement and a parameter into the library of parameters associated with the identified load utility; and updating the library of parameters based on at least automatically editing the one or more unassociated parameters, wherein updating further comprises overwriting the library of parameters; validate the second syntax of the library of parameters, wherein validating the second syntax of the library of parameters comprises using a compiler; and write an output parameter to a memory location based on positive validation of the second syntax of the library of parameters. | 1. An apparatus for converting MLOAD and TPUMP operations, the apparatus comprising: a memory; at least one processor; and a module stored in the memory, wherein said module comprising computer instruction code executable by the at least one processor, and structured to cause said at least one processor to: electronically receive an input production parameter, wherein the input production parameter is associated with a load utility and defines a library of parameters, wherein the input production parameter comprises one or more records, wherein the library of parameters defines a first syntax, wherein the library of parameters comprise a Log table, a Work table, a Uniqueness Violation (UV) table, and/or an Error table; count the number of records in the input production parameter; compare the counted number of records to a predetermined threshold value; identify the load utility to be used to load the records into a database based on the comparison between the counted number of records and the predetermined threshold value, wherein identifying further comprises (i) determining that the number of records is greater than the predetermined threshold, and loading the input production parameter into a database using a multiload load utility (MLOAD); or (ii) determining that the number of records is less than the predetermined threshold, and loading the input production parameter into the database using a Teradata load utility (TPUMP), whereby larger volumes of records are processed during the MLOAD load utility and smaller volumes of records are processed using the TPUMP utility, and whereby less computer resources are used to implement TPUMP than MLOAD thereby providing for conservation of computer resources; load the input production parameter into the database, wherein loading further comprises switching the loading of the input production parameter into the database between the MLOAD load utility and the TPUMP utility based on at least comparing the counted number of records to the predetermined threshold value; convert the first syntax of the library of parameters to a second syntax, wherein the second syntax is associated with the identified load utility, wherein converting the first syntax comprises generating at least one script and computer code of the library of the parameters using the second syntax, wherein converting further comprises: identifying one or more unassociated parameters, wherein the one or more unassociated parameters are not associated with the identified load utility; automatically editing the one or more unassociated parameters to comply with the second syntax, wherein automatically editing further comprises automatically adding a statement and a parameter into the library of parameters associated with the identified load utility; and updating the library of parameters based on at least automatically editing the one or more unassociated parameters, wherein updating further comprises overwriting the library of parameters; validate the second syntax of the library of parameters, wherein validating the second syntax of the library of parameters comprises using a compiler; and write an output parameter to a memory location based on positive validation of the second syntax of the library of parameters. 7. The apparatus of claim 1 , wherein validating the second syntax of the library of parameters comprises comparing the second syntax to a table stored in a second memory location that contains syntax associated with the load utility. | 0.5 |
8,745,045 | 3 | 4 | 3. The processor-implemented method of claim 2 , wherein the input further includes an advanced search option, the advanced search option being selected from a plurality of pre-defined advanced search options. | 3. The processor-implemented method of claim 2 , wherein the input further includes an advanced search option, the advanced search option being selected from a plurality of pre-defined advanced search options. 4. The processor-implemented method of claim 3 , wherein the advanced search option is a log-in passwords search. | 0.578358 |
7,729,920 | 2 | 3 | 2. An automated system, comprising: a command processing system that processes input commands; and a command execution system that executes an input command based on command processing results provided by the command processing system, wherein the command processing system evaluates consequences of executing input commands and take preventative actions for execution of input commands that could result in undesirable consequences, and wherein the command processing system, comprises: an automatic recognition system for recognizing input patterns associated with the input command; a command interpretation system to interpret input commands based on recognition results generated by the automatic recognition system; a consequence evaluation system to determine a potential consequence of executing input commands based on interpretation results generated by the command interpretation system and determine a likelihood that the potential consequences can occur; and a feedback system to perform preventative actions for executing input commands when consequence evaluation results of an input command indicate at the likelihood of the potential consequence is greater than a threshold, wherein the potential consequence is an undesirable consequence of executing the input command. | 2. An automated system, comprising: a command processing system that processes input commands; and a command execution system that executes an input command based on command processing results provided by the command processing system, wherein the command processing system evaluates consequences of executing input commands and take preventative actions for execution of input commands that could result in undesirable consequences, and wherein the command processing system, comprises: an automatic recognition system for recognizing input patterns associated with the input command; a command interpretation system to interpret input commands based on recognition results generated by the automatic recognition system; a consequence evaluation system to determine a potential consequence of executing input commands based on interpretation results generated by the command interpretation system and determine a likelihood that the potential consequences can occur; and a feedback system to perform preventative actions for executing input commands when consequence evaluation results of an input command indicate at the likelihood of the potential consequence is greater than a threshold, wherein the potential consequence is an undesirable consequence of executing the input command. 3. The system of claim 2 , wherein the command processing system further comprises an ambiguity evaluation system to determine a degree of ambiguity of recognition results or interpretation results of an input command. | 0.513393 |
9,236,047 | 4 | 5 | 4. The method of claim 3 , wherein the received at least one text entry is associated with an electronic document. | 4. The method of claim 3 , wherein the received at least one text entry is associated with an electronic document. 5. The method of claim 4 , further comprising: receiving a selection of the displayed at least one text chunk from the user; and inserting the displayed at least one text chunk into the electronic document. | 0.5 |
8,892,423 | 1 | 32 | 1. A computer-implemented method for generating examples for electronic dictionaries to serve as an aid to translation between languages performed by one or more processors, the method comprising: creating an electronic dictionary example by: acquiring at least one dictionary entry comprising a headword W j in a source language and at least one translation T j1 , T j2 , . . . T jn for the headword W j in a target language; generating a first set comprising possible forms for the headword W j in the source language and a second set comprising possible forms for each translation T j1 , T j2 , . . . T jn in the target language; searching a corpus of translations, where the corpus of translations is a preexisting corpus of translation sentence pairs, each translation sentence pair comprising a first sentence in the source language and a second sentence in the target language, where the first sentence is a translation of the second sentence, and the searching includes searching at least one first sentence in the source language included in the corpus of translations and searching at least one second sentence in the target language in the corpus of translations; identifying in the corpus of translations at least one translation sentence pair, from either the searching of the at least one first sentence in the source language or the searching of the at least one second sentence in the target language, that consists of the first sentence that incorporates the headword W j , or one of its generated forms, and the second sentence that incorporates the translation T jn or one of its generated forms; and providing the at least one translation sentence pair to a user. | 1. A computer-implemented method for generating examples for electronic dictionaries to serve as an aid to translation between languages performed by one or more processors, the method comprising: creating an electronic dictionary example by: acquiring at least one dictionary entry comprising a headword W j in a source language and at least one translation T j1 , T j2 , . . . T jn for the headword W j in a target language; generating a first set comprising possible forms for the headword W j in the source language and a second set comprising possible forms for each translation T j1 , T j2 , . . . T jn in the target language; searching a corpus of translations, where the corpus of translations is a preexisting corpus of translation sentence pairs, each translation sentence pair comprising a first sentence in the source language and a second sentence in the target language, where the first sentence is a translation of the second sentence, and the searching includes searching at least one first sentence in the source language included in the corpus of translations and searching at least one second sentence in the target language in the corpus of translations; identifying in the corpus of translations at least one translation sentence pair, from either the searching of the at least one first sentence in the source language or the searching of the at least one second sentence in the target language, that consists of the first sentence that incorporates the headword W j , or one of its generated forms, and the second sentence that incorporates the translation T jn or one of its generated forms; and providing the at least one translation sentence pair to a user. 32. The system of claim 1 , wherein each translation sentence pair is selected based on at least one of semantic class information, semantemes, and pragmatic descriptions. | 0.745536 |
9,607,267 | 6 | 7 | 6. The method of claim 1 where each of the first set of probabilities is θ ek =p(k|e), calculated as follows for each of the plurality of aspects k and each of the plurality of entities e: θ ek = p ( k | e ) = p ( e , k ) p ( e ) ∝ ∑ u ɛ U p ( u ) p ( k | u ) p ( e | k ) where p(u) is the probability that a user u makes an endorsement. | 6. The method of claim 1 where each of the first set of probabilities is θ ek =p(k|e), calculated as follows for each of the plurality of aspects k and each of the plurality of entities e: θ ek = p ( k | e ) = p ( e , k ) p ( e ) ∝ ∑ u ɛ U p ( u ) p ( k | u ) p ( e | k ) where p(u) is the probability that a user u makes an endorsement. 7. The method of claim 6 where the term-aspect distribution is determined by said sampling the value for an aspect z i that generated the term W at a term-slot t i using the aspect assignments of all other term-slots z−i as follows: P ( z i = k | t i = w , t - i , z - i ) ∝ n k | e , - i + α d e , - i + α K × n t | k , = i + β n . | k , - i + β υ where n k|e,-i is the number of times one of the plurality of aspects k is observed for one of the plurality of entities e, n t|k,-i is the number of times one of the plurality of terms t is sampled from k, |d e,-i | is the number of term occurrences associated with e, and n •|k,-i s the total number of entities generated from aspect k, α and β are Dirichlet smoothing parameters. | 0.5 |
8,407,604 | 1 | 3 | 1. A method comprising: defining, via a computing device, text to be included within a first text message; assigning, via the computing device, one or more tags to the first text message, wherein the one or more tags are chosen from a plurality of available tags, and wherein the plurality of available tags are defined before the text to be included within the first text message is defined; and transmitting, via the computing device, the first text message, including the text and the one or more tags, to one or more recipients; wherein the plurality of tags includes one or more of a handraise tag, a chat tag, a minute tag, and a scribe tag, and wherein at least one of: the handraise tag is assigned to a message when a user indicates an intention to focus the attention of participants of an online discussion on the message; the chat tag is assigned to a message when a user indicates an intention to discuss the message in greater detail; the minute tag is assigned to a message when a user indicates that the message should be memorialized within minutes associated with the online discussion; the scribe tag is assigned when the user indicates that the message should be supplemented for ease of use by a user with a disability. | 1. A method comprising: defining, via a computing device, text to be included within a first text message; assigning, via the computing device, one or more tags to the first text message, wherein the one or more tags are chosen from a plurality of available tags, and wherein the plurality of available tags are defined before the text to be included within the first text message is defined; and transmitting, via the computing device, the first text message, including the text and the one or more tags, to one or more recipients; wherein the plurality of tags includes one or more of a handraise tag, a chat tag, a minute tag, and a scribe tag, and wherein at least one of: the handraise tag is assigned to a message when a user indicates an intention to focus the attention of participants of an online discussion on the message; the chat tag is assigned to a message when a user indicates an intention to discuss the message in greater detail; the minute tag is assigned to a message when a user indicates that the message should be memorialized within minutes associated with the online discussion; the scribe tag is assigned when the user indicates that the message should be supplemented for ease of use by a user with a disability. 3. The method of claim 1 further comprising: displaying a plurality of text messages, and wherein the plurality of text messages includes the first text message. | 0.800743 |
8,850,372 | 8 | 10 | 8. The computer-implemented method of claim 3 , wherein performing the localization abstraction process comprises: initializing the localization abstraction process with every design component replaced by a cutpoint; performing satisfiability-based (SAT-based) bounded model checking (BMC) for a first timestep on the abstracted design; identifying and analyzing spurious counterexamples; refining the localization abstraction by adding sufficient design components, to the abstracted design to eliminate the spurious counterexamples, wherein the design components added are selected based on an analysis of assigned priorities for the design components; in response to completion of elimination of spurious counterexamples at a specific timestep, performing proof-based abstraction (PBA) up to that specific timestep to eliminate unnecessary logic from the abstracted design; and incrementing a timestep and iterating through processes including the performing SAT-based BMC, the identifying and analyzing, the refining, and the performing PBA processes up to the configured resource limit, wherein counterexample based abstraction (CBA) and proof-based abstraction (PBA) are interleaved utilizing the assigned priorities of the design components to complete the refining. | 8. The computer-implemented method of claim 3 , wherein performing the localization abstraction process comprises: initializing the localization abstraction process with every design component replaced by a cutpoint; performing satisfiability-based (SAT-based) bounded model checking (BMC) for a first timestep on the abstracted design; identifying and analyzing spurious counterexamples; refining the localization abstraction by adding sufficient design components, to the abstracted design to eliminate the spurious counterexamples, wherein the design components added are selected based on an analysis of assigned priorities for the design components; in response to completion of elimination of spurious counterexamples at a specific timestep, performing proof-based abstraction (PBA) up to that specific timestep to eliminate unnecessary logic from the abstracted design; and incrementing a timestep and iterating through processes including the performing SAT-based BMC, the identifying and analyzing, the refining, and the performing PBA processes up to the configured resource limit, wherein counterexample based abstraction (CBA) and proof-based abstraction (PBA) are interleaved utilizing the assigned priorities of the design components to complete the refining. 10. The computer-implemented method of claim 8 , wherein refining the localization abstraction comprises: in response to encountering a spurious counterexample within the abstracted design: identifying a set of cutpoints to refine by executing one or more of a counterexample-based algorithm, a proof-based algorithm, and a hybrid algorithm; selecting, from the identified set of cutpoints, a subset of cutpoints that correspond to design components that have a highest priority among the design components replaced with cutpoints within the identified set of cutpoints; and replacing the subset of cutpoints corresponding to the design components having the highest priority with their corresponding design components to generate a refined abstraction. | 0.5 |
5,537,485 | 15 | 20 | 15. A method of computerized detection of clustered microcalcifications in a digital mammogram derived from an original radiographic image, comprising: filtering said digital mammogram to produce a filtered image from which signals meeting predetermined criteria are removed; performing a first cluster filtering on said filtered image to produce a cluster image of single-pixel signals identifying locations of prospective problematic microcalcifications; extracting predetermined features from signals at locations in the digital mammogram identified by the single-pixel signals in said cluster image; performing at least one predetermined thresholding test based on the extracted features and based on said predetermined thresholding test, determining whether the signals from which the features were extracted correspond to a false-positive microcalcification cluster; eliminating from said cluster image those single-pixel signals corresponding to locations of false-positive microcalcification clusters; performing a second cluster filtering on the cluster image remaining after said eliminating step and outputting a further cluster image comprised of single-pixel values; and indicating in said original radiographic image locations of clustered microcalcifications based on the locations of pixels having a predetermined value in said further cluster image. | 15. A method of computerized detection of clustered microcalcifications in a digital mammogram derived from an original radiographic image, comprising: filtering said digital mammogram to produce a filtered image from which signals meeting predetermined criteria are removed; performing a first cluster filtering on said filtered image to produce a cluster image of single-pixel signals identifying locations of prospective problematic microcalcifications; extracting predetermined features from signals at locations in the digital mammogram identified by the single-pixel signals in said cluster image; performing at least one predetermined thresholding test based on the extracted features and based on said predetermined thresholding test, determining whether the signals from which the features were extracted correspond to a false-positive microcalcification cluster; eliminating from said cluster image those single-pixel signals corresponding to locations of false-positive microcalcification clusters; performing a second cluster filtering on the cluster image remaining after said eliminating step and outputting a further cluster image comprised of single-pixel values; and indicating in said original radiographic image locations of clustered microcalcifications based on the locations of pixels having a predetermined value in said further cluster image. 20. The method according to claim 15, wherein said first cluster filtering step comprises: a step of area-point transformation in which signals representing possible microcalcifications are reduced to respective single pixels in a first binary image; a step of counting the number of pixels exceeding a predetermined value in a kernel of predetermined size scanned across said first binary image and copying the contents of the said binary image that lay within the kernel to the corresponding locations in a second binary image whenever the contents of the kernel meet a predetermined grouping criteria based on said counting; and outputting said second binary image comprised of the pixels remaining after the counting and copying step. | 0.768507 |
7,849,148 | 5 | 29 | 5. The method of claim 4 , and further comprising, utilizing the at least one window, displaying, in response to a first user interaction, the first additional information associated with the first message. | 5. The method of claim 4 , and further comprising, utilizing the at least one window, displaying, in response to a first user interaction, the first additional information associated with the first message. 29. The method of claim 5 , wherein the first user interaction includes interactions with one of the message summaries. | 0.75813 |
9,466,286 | 15 | 18 | 15. A method implemented at least in part by an electronic device that is configured to transition from a first state to a second state in response to a received audio signal having a similarity to a representation of a defined word or phrase, the method comprising: receiving, at the electronic device, the audio signal; determining an occurrence of an event while the electronic device is in the first state; modifying a similarity acceptance criterion for a period of time, based at least in part on the occurrence of the event; determining a similarity score between the audio signal and a representation of a defined word or phrase; and transitioning the electronic device from the first state to the second state based at least in part on a determination that the similarity score satisfies the modified similarity acceptance criterion. | 15. A method implemented at least in part by an electronic device that is configured to transition from a first state to a second state in response to a received audio signal having a similarity to a representation of a defined word or phrase, the method comprising: receiving, at the electronic device, the audio signal; determining an occurrence of an event while the electronic device is in the first state; modifying a similarity acceptance criterion for a period of time, based at least in part on the occurrence of the event; determining a similarity score between the audio signal and a representation of a defined word or phrase; and transitioning the electronic device from the first state to the second state based at least in part on a determination that the similarity score satisfies the modified similarity acceptance criterion. 18. The method as recited in claim 15 , wherein the event comprises determining that a user is within a proximity of the electronic device. | 0.809066 |
10,152,526 | 8 | 9 | 8. A computer program product for generating and utilizing synthetic context-based objects, the computer program product comprising: a non-transitory computer readable storage medium; first program instructions to define a context object, wherein the context object comports with at least one constraint that defines a scope and bound of the context object; second program instructions to associate a non-contextual data object with the context object to define a synthetic context-based object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, wherein the context object provides a descriptor of the non-contextual data object, wherein the descriptor is not part of the non-contextual data object, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object; third program instructions to associate the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is relevant to the context of the synthetic context-based object, wherein the specific subject-matter for said at least one specific data store in a data structure overlaps a subject-matter of another data store in the data structure, and wherein the synthetic context-based object is mapped to multiple data stores such that there is a one-to-many relationship between the synthetic context-based object and the multiple data stores; fourth program instructions to receive, from a requester, a request for data from said at least one specific data store that is associated with the synthetic context-based object; and fifth program instructions to return, to the requester, data from said at least one specific data store that is associated with the synthetic context-based object; and wherein the first, second, third, fourth, and fifth program instructions are stored on the non-transitory computer readable storage medium. | 8. A computer program product for generating and utilizing synthetic context-based objects, the computer program product comprising: a non-transitory computer readable storage medium; first program instructions to define a context object, wherein the context object comports with at least one constraint that defines a scope and bound of the context object; second program instructions to associate a non-contextual data object with the context object to define a synthetic context-based object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, wherein the context object provides a descriptor of the non-contextual data object, wherein the descriptor is not part of the non-contextual data object, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object; third program instructions to associate the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is relevant to the context of the synthetic context-based object, wherein the specific subject-matter for said at least one specific data store in a data structure overlaps a subject-matter of another data store in the data structure, and wherein the synthetic context-based object is mapped to multiple data stores such that there is a one-to-many relationship between the synthetic context-based object and the multiple data stores; fourth program instructions to receive, from a requester, a request for data from said at least one specific data store that is associated with the synthetic context-based object; and fifth program instructions to return, to the requester, data from said at least one specific data store that is associated with the synthetic context-based object; and wherein the first, second, third, fourth, and fifth program instructions are stored on the non-transitory computer readable storage medium. 9. The computer program product of claim 8 , further comprising: sixth program instructions to data mine a data structure for the non-contextual data object and the context object, wherein said data mining locates said at least one specific data store that comprises data contained in the non-contextual data object and the context object; and wherein the sixth program instructions are stored on the non-transitory computer readable storage medium. | 0.590328 |
9,361,880 | 10 | 11 | 10. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, result in the processor performing operations comprising: for each dialect in a plurality of dialects identified within a speech utterance, selecting a corresponding dialect grammar, to yield a plurality of dialect grammars; blending the plurality of dialect grammars, to yield a blended dialect grammar; and recognizing speech utterances using the blended dialect grammar. | 10. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, result in the processor performing operations comprising: for each dialect in a plurality of dialects identified within a speech utterance, selecting a corresponding dialect grammar, to yield a plurality of dialect grammars; blending the plurality of dialect grammars, to yield a blended dialect grammar; and recognizing speech utterances using the blended dialect grammar. 11. The system of claim 10 , wherein selection of the corresponding dialect grammar is based on a similarity of the dialect to the corresponding dialect grammar, wherein the similarity is based on phoneme usage in the dialect and the corresponding dialect grammar. | 0.514706 |
9,070,435 | 9 | 16 | 9. A pre-computation based ternary content addressable memory (TCAM), comprising: a counter configured to generate a count of a number of ones or zeros in a search word; a primary TCAM configured to store a data word; and a secondary TCAM configured to store a pre-computation word representing a range inclusive of a lower and upper bound of a number of ones or zeros possible in a portion of the data word, wherein the secondary TCAM is configured to disable pre-charging of a match line associated with the data word if the count of the number of ones or zeroes in a portion of the search word does not fall within the range represented by the pre-computation word. | 9. A pre-computation based ternary content addressable memory (TCAM), comprising: a counter configured to generate a count of a number of ones or zeros in a search word; a primary TCAM configured to store a data word; and a secondary TCAM configured to store a pre-computation word representing a range inclusive of a lower and upper bound of a number of ones or zeros possible in a portion of the data word, wherein the secondary TCAM is configured to disable pre-charging of a match line associated with the data word if the count of the number of ones or zeroes in a portion of the search word does not fall within the range represented by the pre-computation word. 16. The pre-computation based TCAM of claim 9 , wherein the secondary TCAM is configured to disable pre-charging of match lines associated with a plurality of data words stored in the primary TCAM if the count does not fall within the range represented by the pre-computation word. | 0.5 |
9,575,980 | 1 | 3 | 1. A method of operating an information management system, comprising: obtaining a first collection of source files; for each source file in the first collection: parsing, by a processor of a computing device, the respective source file to extract one or more tags, comparing, by the processor, the one or more tags to tags in at least one dictionary to determine one or more matching tags, wherein the at least one dictionary comprises a hierarchical listing of tags, and associating, by the processor, with the respective source file, the one or more matching tags; generating, by the processor, a first virtual relational network comprising the source files in the first collection, wherein the first virtual relational network comprises: one or more nodes, wherein each node of the one or more nodes represents a particular matching tag of the one or more matching tags associated with a particular source file of the source files in the first collection, and one or more links, wherein each link of the one or more links represents a connection between a pair of nodes, wherein each node of the pair of nodes is associated with a same tag; comparing, by the processor, the first virtual relational network to a second virtual relational network to identify at least one of (a) nodes common to the first and second virtual relational networks, and (b) links common to the first and second virtual relational networks, wherein the second virtual relational network is created from a second collection of source files different from the first collection of source files, and the second virtual relational network is created using one or more dictionaries of the at least one dictionary; and displaying a graphical representation of at least part of the first and second virtual relational networks. | 1. A method of operating an information management system, comprising: obtaining a first collection of source files; for each source file in the first collection: parsing, by a processor of a computing device, the respective source file to extract one or more tags, comparing, by the processor, the one or more tags to tags in at least one dictionary to determine one or more matching tags, wherein the at least one dictionary comprises a hierarchical listing of tags, and associating, by the processor, with the respective source file, the one or more matching tags; generating, by the processor, a first virtual relational network comprising the source files in the first collection, wherein the first virtual relational network comprises: one or more nodes, wherein each node of the one or more nodes represents a particular matching tag of the one or more matching tags associated with a particular source file of the source files in the first collection, and one or more links, wherein each link of the one or more links represents a connection between a pair of nodes, wherein each node of the pair of nodes is associated with a same tag; comparing, by the processor, the first virtual relational network to a second virtual relational network to identify at least one of (a) nodes common to the first and second virtual relational networks, and (b) links common to the first and second virtual relational networks, wherein the second virtual relational network is created from a second collection of source files different from the first collection of source files, and the second virtual relational network is created using one or more dictionaries of the at least one dictionary; and displaying a graphical representation of at least part of the first and second virtual relational networks. 3. The method of claim 1 wherein tags comprise representations of embedded objects. | 0.866559 |
8,352,485 | 13 | 16 | 13. A non-transitory computer readable storage medium having stored therein instructions, which, when executed by a computer system, cause the computer system with a display to: while a document displayed on the display is being browsed: display a first portion of the document on the display; receive a user-specified text string that includes multiple search keywords; and upon receiving the user-specified text string that includes multiple search keywords: compare the multiple search keywords with a plurality of candidate chunks within the document, wherein each candidate chunk corresponds to a predefined semantically-based unit of text in the document; identify, among the plurality of candidate chunks, one or more chunks matching the multiple search keywords; and display, in addition to the first portion of the document, a list of the one or more chunks matching the multiple search keywords in the document on the display, wherein terms in a respective chunk that satisfy the search keywords are either ordered differently from the search keywords in the user-specified text string or separated from one another by at least one term that does not satisfy any of the search keywords. | 13. A non-transitory computer readable storage medium having stored therein instructions, which, when executed by a computer system, cause the computer system with a display to: while a document displayed on the display is being browsed: display a first portion of the document on the display; receive a user-specified text string that includes multiple search keywords; and upon receiving the user-specified text string that includes multiple search keywords: compare the multiple search keywords with a plurality of candidate chunks within the document, wherein each candidate chunk corresponds to a predefined semantically-based unit of text in the document; identify, among the plurality of candidate chunks, one or more chunks matching the multiple search keywords; and display, in addition to the first portion of the document, a list of the one or more chunks matching the multiple search keywords in the document on the display, wherein terms in a respective chunk that satisfy the search keywords are either ordered differently from the search keywords in the user-specified text string or separated from one another by at least one term that does not satisfy any of the search keywords. 16. The computer readable storage medium of claim 13 , wherein the instructions cause the computer system to: detect a user-selection of a second chunk from the list of one or more chunks; and upon detecting the user-selection of the second chunk from the list of one or more chunks, display, in addition to the list of one or more chunks in the document, a third portion of the document, the third portion including the second chunk. | 0.551653 |
8,600,746 | 1 | 8 | 1. A computer-implemented method comprising: receiving audio data that encodes an utterance of a user; determining that the user has been classified as a novice user of a speech recognizer, comprising determining that an amount of training data that has been collected for the user does not satisfy a threshold; in response to determining that the user has been classified as a novice user of a speech recognizer, selecting a speech recognizer setting that is used by the speech recognizer in generating a transcription of the utterance, wherein the selected speech recognizer setting is different than a default speech recognizer setting that is used by the speech recognizer in generating transcriptions of utterances of users that are not classified as novice users, and wherein the selected speech recognizer setting results in increased speech recognition accuracy for the utterance in comparison with the default setting, and wherein the selected speech recognizer setting results in increased speech recognition latency for the utterance in comparison with the default setting; and obtaining a transcription of the utterance that is generated by the speech recognizer using the selected speech recognizer setting. | 1. A computer-implemented method comprising: receiving audio data that encodes an utterance of a user; determining that the user has been classified as a novice user of a speech recognizer, comprising determining that an amount of training data that has been collected for the user does not satisfy a threshold; in response to determining that the user has been classified as a novice user of a speech recognizer, selecting a speech recognizer setting that is used by the speech recognizer in generating a transcription of the utterance, wherein the selected speech recognizer setting is different than a default speech recognizer setting that is used by the speech recognizer in generating transcriptions of utterances of users that are not classified as novice users, and wherein the selected speech recognizer setting results in increased speech recognition accuracy for the utterance in comparison with the default setting, and wherein the selected speech recognizer setting results in increased speech recognition latency for the utterance in comparison with the default setting; and obtaining a transcription of the utterance that is generated by the speech recognizer using the selected speech recognizer setting. 8. The method of claim 1 , wherein selecting a speech recognizer setting comprises adjusting an endpoint parameter. | 0.839385 |
9,747,347 | 8 | 9 | 8. A computer program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: automatically retrieve extended content from other users that are a predetermined number of hops away from a user in an extended network of the user; obtain engaging content for the user from a network service to increase the user's engagement with a content stream by identifying the engaging content having a score over a predefined threshold for likelihood of user engagement based upon one or more of quality of posts, trends of posts, and strength of a relationship between the user and a poster, the obtaining engaging content including generating a message with an action button for the user to take an action in a social network; combine the engaging content and the extended content to create a combined list of content; rank the combined content by relevance to the user; and provide one or more of the ranked content. | 8. A computer program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: automatically retrieve extended content from other users that are a predetermined number of hops away from a user in an extended network of the user; obtain engaging content for the user from a network service to increase the user's engagement with a content stream by identifying the engaging content having a score over a predefined threshold for likelihood of user engagement based upon one or more of quality of posts, trends of posts, and strength of a relationship between the user and a poster, the obtaining engaging content including generating a message with an action button for the user to take an action in a social network; combine the engaging content and the extended content to create a combined list of content; rank the combined content by relevance to the user; and provide one or more of the ranked content. 9. The computer program product of claim 8 , wherein the combined content is ranked by a prediction of engagement by the user. | 0.881132 |
7,882,100 | 2 | 5 | 2. The method of claim 1 , wherein said transforming step includes determining a portion of the left deep nested loop join tree requiring transformation into a bushy tree shape for generating a semantically correct query execution plan. | 2. The method of claim 1 , wherein said transforming step includes determining a portion of the left deep nested loop join tree requiring transformation into a bushy tree shape for generating a semantically correct query execution plan. 5. The method of claim 2 , wherein said portion of the left deep nested loop join tree requiring transformation includes a left deep semi-join subtree. | 0.522152 |
8,380,502 | 19 | 23 | 19. A non-transitory computer-readable medium comprising: one or more instructions that, when executed by at least one processor, cause the at least one processor to receive a voice search query from a user; one or more instructions that, when executed by at least one processor, cause the at least one processor to derive a plurality of recognition hypotheses from the voice search query; one or more instructions that, when executed by at least one processor, cause the at least one processor to determine a plurality of scores associated with the plurality of recognition hypotheses, the plurality of scores being based on a comparison of the plurality of recognition hypotheses to previously received search queries; one or more instructions that, when executed by at least one processor, cause the at least one processor to discard at least one of the plurality of recognition hypotheses, that is associated with at least one first score, of the plurality of scores, that is less than a threshold value; one or more instructions that, when executed by at least one processor, cause the at least one processor to construct a first query using at least one first non-discarded recognition hypothesis, of the plurality of recognition hypotheses, where the at least one first non-discarded recognition hypothesis is associated with at least one second score, of the plurality of scores, that at least meets the threshold value, the one or more instructions to construct the first query, being further including: one or more instructions form an initial query based on the at least one first non-discarded recognition hypothesis, one or more instructions identify a plurality of stop words in the initial query, and one or more instructions prune, from the initial query, one or more of the plurality of stop words to form the first query, the first query satisfying a length threshold; one or more instructions that, when executed by at least one processor, cause the at least one processor to forward the first query to a search system; one or more instructions that, when executed by at least one processor, cause the at least one processor to receive, from the search system, results associated with the first query; and one or more instructions that, when executed by at least one processor, cause the at least one processor to provide, to the user, the first results. | 19. A non-transitory computer-readable medium comprising: one or more instructions that, when executed by at least one processor, cause the at least one processor to receive a voice search query from a user; one or more instructions that, when executed by at least one processor, cause the at least one processor to derive a plurality of recognition hypotheses from the voice search query; one or more instructions that, when executed by at least one processor, cause the at least one processor to determine a plurality of scores associated with the plurality of recognition hypotheses, the plurality of scores being based on a comparison of the plurality of recognition hypotheses to previously received search queries; one or more instructions that, when executed by at least one processor, cause the at least one processor to discard at least one of the plurality of recognition hypotheses, that is associated with at least one first score, of the plurality of scores, that is less than a threshold value; one or more instructions that, when executed by at least one processor, cause the at least one processor to construct a first query using at least one first non-discarded recognition hypothesis, of the plurality of recognition hypotheses, where the at least one first non-discarded recognition hypothesis is associated with at least one second score, of the plurality of scores, that at least meets the threshold value, the one or more instructions to construct the first query, being further including: one or more instructions form an initial query based on the at least one first non-discarded recognition hypothesis, one or more instructions identify a plurality of stop words in the initial query, and one or more instructions prune, from the initial query, one or more of the plurality of stop words to form the first query, the first query satisfying a length threshold; one or more instructions that, when executed by at least one processor, cause the at least one processor to forward the first query to a search system; one or more instructions that, when executed by at least one processor, cause the at least one processor to receive, from the search system, results associated with the first query; and one or more instructions that, when executed by at least one processor, cause the at least one processor to provide, to the user, the first results. 23. The non-transitory computer-readable medium of claim 19 , further comprising one or more instructions to identify a language model, of a plurality of language models, based on at least one characteristic associated with the user, and where the plurality of recognition hypotheses are derived using the identified language model. | 0.71912 |
8,386,438 | 8 | 14 | 8. A computer system comprising a processor; and a memory storing program instructions for restoring data from an archived copy of a database file, the database file being a single monolithic database file, including program instructions executable by the processor to: receive a request to restore an item from the archived copy of the database file, wherein the item comprises a file and associated context information describing the file in a first intranet portal application environment; retrieve a schema from the archived copy of the database file, wherein the schema represents a data structure of the database file that includes the item and allows access or restoration of selected items of the archived copy of the database file without use of a database; generate an item file and associated metadata file from the retrieved schema, wherein the item file comprises a copy of the file and is independent of the context information, and wherein the metadata file is based on the context information; store the item file and the associated metadata file in a file system; and restore the item to a second intranet portal application via the file system. | 8. A computer system comprising a processor; and a memory storing program instructions for restoring data from an archived copy of a database file, the database file being a single monolithic database file, including program instructions executable by the processor to: receive a request to restore an item from the archived copy of the database file, wherein the item comprises a file and associated context information describing the file in a first intranet portal application environment; retrieve a schema from the archived copy of the database file, wherein the schema represents a data structure of the database file that includes the item and allows access or restoration of selected items of the archived copy of the database file without use of a database; generate an item file and associated metadata file from the retrieved schema, wherein the item file comprises a copy of the file and is independent of the context information, and wherein the metadata file is based on the context information; store the item file and the associated metadata file in a file system; and restore the item to a second intranet portal application via the file system. 14. The computer system of claim 8 , wherein the item comprises an electronic document. | 0.954403 |
7,882,498 | 4 | 5 | 4. The method of claim 3 , wherein determining the dependency of the statements comprises: determining data and control dependency of the statements, wherein statements in the producer group do not depend on statements in any consumer group, and statements in one determined consumer group do not have data or control dependences with respect to other consumer groups. | 4. The method of claim 3 , wherein determining the dependency of the statements comprises: determining data and control dependency of the statements, wherein statements in the producer group do not depend on statements in any consumer group, and statements in one determined consumer group do not have data or control dependences with respect to other consumer groups. 5. The method of claim 4 , wherein the group of statements that are processed by the producer thread comprises one group including statements that are part of a recurrence and statements on which the recurrence depends. | 0.5 |
7,917,363 | 7 | 8 | 7. A process as claimed in claim 3 , wherein said probability of confusing one phrase with another is derived using a list of branching decisions within a context-free grammar. | 7. A process as claimed in claim 3 , wherein said probability of confusing one phrase with another is derived using a list of branching decisions within a context-free grammar. 8. A process as claimed in claim 7 , wherein said branching decisions are determined by generating from said grammar inactive edges corresponding to a parse tree, and active edges including only terminal symbols. | 0.5 |
9,875,121 | 4 | 5 | 4. The non-transitory machine readable medium of claim 1 , wherein the data abstractor performs create, retrieve, update, and delete (CRUD) functions that connect the JS objects to the data storages. | 4. The non-transitory machine readable medium of claim 1 , wherein the data abstractor performs create, retrieve, update, and delete (CRUD) functions that connect the JS objects to the data storages. 5. The non-transitory machine readable medium of claim 4 , wherein through the CRUD functions, the data abstractor allows the JS object to create a new data record in a data storage, to read a data record in the data storage, to write to a data record in the data storage, and to delete a data record in the data storage. | 0.5 |
5,579,224 | 4 | 6 | 4. The method of claim 3, wherein the selectively extracting step extracts each section of the second language document which contains a corresponding word of each first language character string according the first and second language correspondence dictionary, and selectively extracts each corresponding second language character string according to a level of correspondence between a section of the first language document from which each first language character string is extracted and each section of the corresponding second language document to which each corresponding second language character string belongs. | 4. The method of claim 3, wherein the selectively extracting step extracts each section of the second language document which contains a corresponding word of each first language character string according the first and second language correspondence dictionary, and selectively extracts each corresponding second language character string according to a level of correspondence between a section of the first language document from which each first language character string is extracted and each section of the corresponding second language document to which each corresponding second language character string belongs. 6. The method of claim 4, wherein the selectively extracting step extracts each corresponding second language character string from each section of the corresponding second language document which has corresponding words for constituent words of each first language character string which are arranged in an identical manner as the constituent words of each first language character string are arranged in the first language. | 0.5 |
7,831,442 | 27 | 36 | 27. A system for consolidating edits that facilitate alteration of information of an insurance claim, said system comprising: a processor operable to execute software having instructions to: access an edit list, the edit list comprising a plurality of edits applicable to insurance claims, each of the plurality of edits comprising a directive of an insurance provider to correct or reject an insurance claim under specified circumstances, each edit of the plurality of edits having associated therewith an insurance-provider identifier representing the insurance provider; compare words of at least two edits of the edit list having a same insurance-provider identifier to determine similarity between the directive contained in each of the at least two edits for a specific insurance provider; wherein the comparison comprises computation of a discrete value indicative of a degree of similarity between the at least two edits of the edit list, the discrete value being based on a number of matched words in the at least two edits; reduce a number of the plurality of edits applicable to insurance claims for the specific insurance provider, the reduction comprising consolidating the at least two edits having the same insurance-provider identifier into a new edit based on a comparison of the discrete value to at least a predetermined threshold; and apply the reduced number of the plurality of edits to one or more insurance claims for the specific insurance provider, the application comprising assessment of validity for the one or more insurance claims. | 27. A system for consolidating edits that facilitate alteration of information of an insurance claim, said system comprising: a processor operable to execute software having instructions to: access an edit list, the edit list comprising a plurality of edits applicable to insurance claims, each of the plurality of edits comprising a directive of an insurance provider to correct or reject an insurance claim under specified circumstances, each edit of the plurality of edits having associated therewith an insurance-provider identifier representing the insurance provider; compare words of at least two edits of the edit list having a same insurance-provider identifier to determine similarity between the directive contained in each of the at least two edits for a specific insurance provider; wherein the comparison comprises computation of a discrete value indicative of a degree of similarity between the at least two edits of the edit list, the discrete value being based on a number of matched words in the at least two edits; reduce a number of the plurality of edits applicable to insurance claims for the specific insurance provider, the reduction comprising consolidating the at least two edits having the same insurance-provider identifier into a new edit based on a comparison of the discrete value to at least a predetermined threshold; and apply the reduced number of the plurality of edits to one or more insurance claims for the specific insurance provider, the application comprising assessment of validity for the one or more insurance claims. 36. The system according to claim 27 , wherein the software further includes instructions to: utilize a scale to convert the discrete value to a symbolic description of the similarity of the at least two edits. | 0.589844 |
7,870,087 | 31 | 32 | 31. A system operable to solve database queries, the system comprising: at least one processor operable to execute instructions; and at least one computer-readable medium storing instructions that cause the at least one processor to determine a query graph representative of a query, determine an association graph based on the query graph and based on a database graph, the database graph representative of at least a portion of information stored in a database of information that does not represent three-dimensional geometry, and determine a clique of the association graph. | 31. A system operable to solve database queries, the system comprising: at least one processor operable to execute instructions; and at least one computer-readable medium storing instructions that cause the at least one processor to determine a query graph representative of a query, determine an association graph based on the query graph and based on a database graph, the database graph representative of at least a portion of information stored in a database of information that does not represent three-dimensional geometry, and determine a clique of the association graph. 32. The system of claim 31 wherein the at least one processor includes an analog processor operable to evolve to a final state representative of the clique of the association graph. | 0.5 |
6,163,852 | 2 | 4 | 2. The apparatus of claim 1, wherein the first memory register includes a plurality of separately-clocked data words, and wherein the controller is configured to sequentially clock the stream of data into successive words in the plurality of separately-clocked data words. | 2. The apparatus of claim 1, wherein the first memory register includes a plurality of separately-clocked data words, and wherein the controller is configured to sequentially clock the stream of data into successive words in the plurality of separately-clocked data words. 4. The method of claim 2, wherein the plurality of separately-clocked data words are coupled to a plurality of word disable signals from the controller that work in concert with the data clock signal to provide the sequential clocking, the plurality of word disable signals being generated by the controller in response to changes in the data clock signal. | 0.5 |
9,910,909 | 1 | 8 | 1. A computer implemented method for performing analysis on data generated by user interactions, comprising: providing a processor executing instructions for receiving text information from at least one interaction between a user and an agent; said processor text mining said interaction information to extract personal information relating to said user automatically; said processor using a topic model to extract lines from said text information to reduce a number of dimensions required to represent the text, wherein all information of interest is highly pronounced, and wherein a resulting lower dimensional representation of the text allows significantly faster computations; said processor extracting said lines of text as anchored text lines that are indicative of personal information that is present in the anchored text lines; said processor identifying said anchored text lines by checking for specific keywords which are present when said user is mentioning personal information during said interaction; said processor applying a statistical technique to said anchored text lines to discover information present in the anchored text lines; said processor using a k nearest neighbor algorithm to discover said information present in the anchored text lines; said processor representing said text in a topic space with a score along each axis to indicate an extent to which said text contains personal information about said user; upon receiving a new data point, which optionally comprises an anchored text line from said text, said processor picking the k closest points to said new data point, determining a predominant class among classes in the k closest points, and assigning said predominant class to said new data point; said processor assigning a user corresponding to said text to at least one class based on said identified personal information; and said processor classifying said user based upon said extracted lines of text. | 1. A computer implemented method for performing analysis on data generated by user interactions, comprising: providing a processor executing instructions for receiving text information from at least one interaction between a user and an agent; said processor text mining said interaction information to extract personal information relating to said user automatically; said processor using a topic model to extract lines from said text information to reduce a number of dimensions required to represent the text, wherein all information of interest is highly pronounced, and wherein a resulting lower dimensional representation of the text allows significantly faster computations; said processor extracting said lines of text as anchored text lines that are indicative of personal information that is present in the anchored text lines; said processor identifying said anchored text lines by checking for specific keywords which are present when said user is mentioning personal information during said interaction; said processor applying a statistical technique to said anchored text lines to discover information present in the anchored text lines; said processor using a k nearest neighbor algorithm to discover said information present in the anchored text lines; said processor representing said text in a topic space with a score along each axis to indicate an extent to which said text contains personal information about said user; upon receiving a new data point, which optionally comprises an anchored text line from said text, said processor picking the k closest points to said new data point, determining a predominant class among classes in the k closest points, and assigning said predominant class to said new data point; said processor assigning a user corresponding to said text to at least one class based on said identified personal information; and said processor classifying said user based upon said extracted lines of text. 8. The method of claim 1 , further comprising: said processor associating an identification with said user. | 0.85462 |
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