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1. A method for calculating a similarity score of a query set comprising a query set of tokens and a first database set comprising a first database set of tokens, wherein the first database set is one of a plurality of database sets in a data collection set stored on a non-transitory computer readable medium, comprising the steps of: for each specific token in the query set, determining the number of database sets that contain the specific token; for each specific token in the query set, calculating an inverse document frequency (idf) weight, based at least in part on the number of database sets that contain the specific token and on the total number of database sets in the data collection set; calculating a normalized length of the first database set; calculating a normalized length of the query set; and, calculating a similarity score based at least in part on the normalized length of the first database set, the normalized length of the query set, and the idf weight of each of the tokens in the query set. | 1. A method for calculating a similarity score of a query set comprising a query set of tokens and a first database set comprising a first database set of tokens, wherein the first database set is one of a plurality of database sets in a data collection set stored on a non-transitory computer readable medium, comprising the steps of: for each specific token in the query set, determining the number of database sets that contain the specific token; for each specific token in the query set, calculating an inverse document frequency (idf) weight, based at least in part on the number of database sets that contain the specific token and on the total number of database sets in the data collection set; calculating a normalized length of the first database set; calculating a normalized length of the query set; and, calculating a similarity score based at least in part on the normalized length of the first database set, the normalized length of the query set, and the idf weight of each of the tokens in the query set. 3. The method of claim 1 further comprising the step of performing a shortest-first method. | 0.698269 |
7. An index creating computer for searching pieces of document data using a search keyword, the pieces of document data having a correlation with the search keyword, the index creating computer comprising: a CPU; and memory coupled to the CPU, wherein the memory stores a program that, when executed by the CPU, performs operations, the operations comprising: receiving the search keyword from a user terminal to search an index database stored in the index creating computer; calculating as first vectors respective probabilities that each of the pieces of document data belongs to clusters, wherein each of the first vectors corresponds to one of the pieces of document data; calculating, upon an entry of a search keyword, as a second vector, respective probabilities that the search keyword belongs to the clusters; calculating an inner product of each of the first vectors and the second vector, the calculated inner product being a first score of the corresponding piece of document data regarding the search keyword, wherein the inner product represents a scalar value; and acquiring a correlation value from a classification keyword set containing facet keywords and pieces of document data with the first score that is equal to or more than a predetermined threshold by multiplying a probability that a first word conceptually matching a facet keyword from the facet keywords occurs in the pieces of document data and a probability that a second word conceptually matching the search keyword occurs in the pieces of document data to generate a second score, and dividing a probability that both the first word and the second word occur in the pieces of document data by the second score, wherein the facet keywords represent a viewpoint of information using a plurality of attribute values as metadata and that are automatically selected by the index creating computer; and displaying a search result on the user terminal in descending order based on the correlation. | 7. An index creating computer for searching pieces of document data using a search keyword, the pieces of document data having a correlation with the search keyword, the index creating computer comprising: a CPU; and memory coupled to the CPU, wherein the memory stores a program that, when executed by the CPU, performs operations, the operations comprising: receiving the search keyword from a user terminal to search an index database stored in the index creating computer; calculating as first vectors respective probabilities that each of the pieces of document data belongs to clusters, wherein each of the first vectors corresponds to one of the pieces of document data; calculating, upon an entry of a search keyword, as a second vector, respective probabilities that the search keyword belongs to the clusters; calculating an inner product of each of the first vectors and the second vector, the calculated inner product being a first score of the corresponding piece of document data regarding the search keyword, wherein the inner product represents a scalar value; and acquiring a correlation value from a classification keyword set containing facet keywords and pieces of document data with the first score that is equal to or more than a predetermined threshold by multiplying a probability that a first word conceptually matching a facet keyword from the facet keywords occurs in the pieces of document data and a probability that a second word conceptually matching the search keyword occurs in the pieces of document data to generate a second score, and dividing a probability that both the first word and the second word occur in the pieces of document data by the second score, wherein the facet keywords represent a viewpoint of information using a plurality of attribute values as metadata and that are automatically selected by the index creating computer; and displaying a search result on the user terminal in descending order based on the correlation. 11. The index creating computer according to claim 7 , wherein the correlation value is acquired according to a correlation function corr total (s,t) described below: [ E β’ β’ 5 ] corr total β‘ ( s , t ) = 1 1 + a Β· [ corr regular β‘ ( s , t ) + a ] Β· corr concept β‘ ( s , t ) n Equation β’ β’ 5 where a and n are adjustable parameters, [ E β’ β’ 1 ] corr concept β‘ ( s , t ) = P concept β‘ ( s β t ) P concept β‘ ( s ) β’ P concept β‘ ( t ) Equation β’ β’ 1 [ E β’ β’ 2 ] P concept β‘ ( s ) = 1 N β’ β d = documents β’ β© s β’ ο d βͺ Equation β’ β’ 2 [ E β’ β’ 3 ] P concept β‘ ( t ) = 1 N β’ β d = documents β’ β© t β’ ο d βͺ Equation β’ β’ 3 [ E β’ β’ 4 ] P concept β‘ ( s β t ) = 1 N β’ β d = documents β’ β© s β’ ο d βͺ β’ β’ β© t β’ ο d βͺ Equation β’ β’ 4 where s is the facet keyword, t is the search keyword, d is the document data, N is a total number of pieces of document data, Ξ΄ s,d is one in a case where the facet keyword s is included in the document data d and zero in the other cases, Ξ΄ t,d is one in a case where the search keyword t is included in the document data d and zero in the other cases, [ E β’ β’ 10 ] corr concept β‘ ( s , t ) = P concept β‘ ( s β t ) P concept β‘ ( s ) β’ P concept β‘ ( t ) Equation β’ β’ 10 [ E β’ β’ 11 ] P concept β‘ ( s ) = 1 N β’ β d = documents β’ β© s β’ ο d βͺ Equation β’ β’ 11 [ E β’ β’ 12 ] P concept β‘ ( t ) = 1 N β’ β d = documents β’ β© t β’ ο d βͺ Equation β’ β’ 12 [ E β’ β’ 13 ] P concept β‘ ( s β t ) = 1 N β’ β d = documents β’ β© s β’ ο d βͺ β’ β’ β© t β’ ο d βͺ Equation β’ β’ 13 where s is the facet keyword, t is the search keyword, d is the document data, N is a total number of pieces of document data, <s|d> is Ξ£s i Γd i (Ξ£ is a sum regarding i=1, 2, . . . , k), <t|d> is Ξ£t i Γd i (Ξ£ is a sum regarding i=1, 2, . . . , k), and k is a total number of clusters and an integer. | 0.5 |
3. The method of claim 1 , wherein determining the respective convergence status of each of the plurality of processes comprises: determining a difference between two or more training iterations and comparing the difference to a predetermined threshold value; and determining that the process is unlikely to converge if the difference exceeds the threshold value, and otherwise, determining that the process is likely to converge if the difference is less than the threshold value. | 3. The method of claim 1 , wherein determining the respective convergence status of each of the plurality of processes comprises: determining a difference between two or more training iterations and comparing the difference to a predetermined threshold value; and determining that the process is unlikely to converge if the difference exceeds the threshold value, and otherwise, determining that the process is likely to converge if the difference is less than the threshold value. 5. The method of claim 3 , wherein the difference is an absolute numerical value change between two iterations and the predetermined threshold value is expressed as a numerical value. | 0.96173 |
17. One or more non-transitory computer-readable media as recited in claim 1 , wherein the storage location is a folder created in association with an account. | 17. One or more non-transitory computer-readable media as recited in claim 1 , wherein the storage location is a folder created in association with an account. 18. One or more non-transitory computer-readable media as recited in claim 17 , wherein the folder is created by a user of the account to store personal content related to tasks. | 0.965995 |
1. A method for classifying range profiles, comprising: gathering non-sensor based sources of information giving structural details of objects of interest; selecting, from the non-sensor based sources of information, features of the objects of interest that are configured to appear most prominent as peaks of backscatter in a sensor based observation of the objects of interest; generating, for each of the objects of interest, a probabilistic model representing, for one or more different orientations of the respective object of interest, possible sequences of distances between the features of the respective object of interest selected from the non-sensor based sources of information that are configured to appear as distinct peaks of backscatter in sensor based range data for the object, wherein the possible sequences of distances are derived from a first probabilistic representation of each of the features of the respective object of interest; classifying a given sensor based range profile by deriving an observed sequence of distances from the spacing of distinct peaks of backscatter in the given sensor based range profile and by calculating, for each of the probabilistic models, a probability that the respective probabilistic model represents the observed sequence of distances, wherein the object of interest represented by the probabilistic model that represents the observed sequence of distances with the greatest probability is associated with the given sensor based range profile; and generating classification results for at least one of the probabilistic models so as to predict the potential performance of a classifier. | 1. A method for classifying range profiles, comprising: gathering non-sensor based sources of information giving structural details of objects of interest; selecting, from the non-sensor based sources of information, features of the objects of interest that are configured to appear most prominent as peaks of backscatter in a sensor based observation of the objects of interest; generating, for each of the objects of interest, a probabilistic model representing, for one or more different orientations of the respective object of interest, possible sequences of distances between the features of the respective object of interest selected from the non-sensor based sources of information that are configured to appear as distinct peaks of backscatter in sensor based range data for the object, wherein the possible sequences of distances are derived from a first probabilistic representation of each of the features of the respective object of interest; classifying a given sensor based range profile by deriving an observed sequence of distances from the spacing of distinct peaks of backscatter in the given sensor based range profile and by calculating, for each of the probabilistic models, a probability that the respective probabilistic model represents the observed sequence of distances, wherein the object of interest represented by the probabilistic model that represents the observed sequence of distances with the greatest probability is associated with the given sensor based range profile; and generating classification results for at least one of the probabilistic models so as to predict the potential performance of a classifier. 3. The method according to claim 1 , wherein the probabilistic model for the object comprises a set of one or more Hidden Markov Models (HMM), each HMM defining, for a different orientation of the object, probabilities for the possible sequences of distances between the features of the respective object of interest selected from the non-sensor based sources of information. | 0.646645 |
28. The system of claim 23 , wherein the electronic catalog includes a search engine operable to search for content in the catalog. | 28. The system of claim 23 , wherein the electronic catalog includes a search engine operable to search for content in the catalog. 29. The system of claim 28 , wherein the search engine is operable to receive an exchange part number and identify a collaborative taxonomy instantiation associated with the exchange part number in the electronic catalog. | 0.909149 |
7. A computer system for identifying and presenting a plurality of recommended electronic books, comprising: one or more processors, one or more non-transitory computer-readable memories, one or more non-transitory computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: determining and storing a plurality of reading behaviors associated with a plurality of electronic books; receiving at least one electronic book search request associated with the plurality of electronic books; identifying the plurality of recommended electronic books based on the received at least one electronic book search request; identifying a plurality of topics associated with the identified plurality of recommended electronic books, wherein each topic associated with the identified plurality of topics are associated with at least one chapter and at least one section of the identified plurality of recommended books; scoring and ranking the identified plurality of topics based on the determined and stored plurality of reading behaviors associated with the identified plurality of topics; determining the plurality of recommendation levels for the identified plurality of recommended electronic books based on the scored and ranked identified plurality of topics, the determined and stored plurality of reading behaviors, and the received at least one electronic book search request; and generating and presenting a plurality of topic comparisons and a plurality of ranked combinations of recommendations for the identified plurality of recommended electronic books, wherein generating and presenting the plurality of topic comparisons comprises comparing a plurality of overlapping topics associated with the identified plurality of recommended electronic books, and wherein generating and presenting the plurality of ranked combinations of recommendations further comprises selecting one or more combinations of the identified plurality of recommended electronic books based on the scored and ranked identified plurality of topics, and ranking the selected one or more combinations per topic. | 7. A computer system for identifying and presenting a plurality of recommended electronic books, comprising: one or more processors, one or more non-transitory computer-readable memories, one or more non-transitory computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: determining and storing a plurality of reading behaviors associated with a plurality of electronic books; receiving at least one electronic book search request associated with the plurality of electronic books; identifying the plurality of recommended electronic books based on the received at least one electronic book search request; identifying a plurality of topics associated with the identified plurality of recommended electronic books, wherein each topic associated with the identified plurality of topics are associated with at least one chapter and at least one section of the identified plurality of recommended books; scoring and ranking the identified plurality of topics based on the determined and stored plurality of reading behaviors associated with the identified plurality of topics; determining the plurality of recommendation levels for the identified plurality of recommended electronic books based on the scored and ranked identified plurality of topics, the determined and stored plurality of reading behaviors, and the received at least one electronic book search request; and generating and presenting a plurality of topic comparisons and a plurality of ranked combinations of recommendations for the identified plurality of recommended electronic books, wherein generating and presenting the plurality of topic comparisons comprises comparing a plurality of overlapping topics associated with the identified plurality of recommended electronic books, and wherein generating and presenting the plurality of ranked combinations of recommendations further comprises selecting one or more combinations of the identified plurality of recommended electronic books based on the scored and ranked identified plurality of topics, and ranking the selected one or more combinations per topic. 8. The computer system of claim 7 , wherein the plurality of reading behaviors are selected from a group comprising an electronic book purchase history, a notification that the plurality of electronic books are repeatedly read, a notification that the plurality of electronic books are not repeatedly read, a notification that the plurality of electronic books are not read after a time based on the electronic book purchase history, a notification that the plurality electronic books are read with no omission of at least one page and at least one chapter, a notification that at least one chapter and at least one section associated with the plurality of electronic books are read, a notification that at least one topic associated with the plurality of electronic books is read, a notification that the plurality of electronic books are read with at least one different electronic book, a notification comprising at least one of a comments, a bookmark, and a highlight on the plurality of electronic books. | 0.5 |
1. A method implemented within a computer system comprising a central processing unit (CPU) and a random access memory (RAM), the method comprising: a. utilizing the CPU and RAM to obtain a history of online activities of a user; b. utilizing the CPU and RAM to receive user preference information from the user; c. utilizing the CPU and RAM to identify a plurality of online information resources linking to online resources viewed by the user, wherein each of the plurality of online information resources is associated with an online information source; d. utilizing the CPU and RAM to generate a plurality of relevance scores for each of the identified online information resource; and e. using the generated plurality of relevance scores to generate a ranked list of recommended online information sources, wherein a rank of each online information source is determined by aggregating at least some of the plurality of relevance scores of the identified online information resources according to the received user preference information, wherein: the ranked list comprises links to online sources that are not in the history of the online activities of the user, the online information resource is a blog post, the online resource viewed by the user is a visited web page, the online information source is a source blog web page, and the online source is a related web feed. | 1. A method implemented within a computer system comprising a central processing unit (CPU) and a random access memory (RAM), the method comprising: a. utilizing the CPU and RAM to obtain a history of online activities of a user; b. utilizing the CPU and RAM to receive user preference information from the user; c. utilizing the CPU and RAM to identify a plurality of online information resources linking to online resources viewed by the user, wherein each of the plurality of online information resources is associated with an online information source; d. utilizing the CPU and RAM to generate a plurality of relevance scores for each of the identified online information resource; and e. using the generated plurality of relevance scores to generate a ranked list of recommended online information sources, wherein a rank of each online information source is determined by aggregating at least some of the plurality of relevance scores of the identified online information resources according to the received user preference information, wherein: the ranked list comprises links to online sources that are not in the history of the online activities of the user, the online information resource is a blog post, the online resource viewed by the user is a visited web page, the online information source is a source blog web page, and the online source is a related web feed. 3. The method of claim 1 , wherein the history of online activities of the user is collected by intercepting requests of the user for Internet resources and transmitting the information on the requested online resources to a link analyzer. | 0.533984 |
1. A method of measuring performance parameters and reporting on at least one aspect of respective performance parameters of a plurality of agents, the method comprising at least the following: measuring in an agent training system at least a plurality of data, including data representing agent performance parameters for each of the plurality, of agents, wherein the plurality of data representing agent performance parameters includes data related to at least one of either sections or teams to which an agent is assigned or associated, personal development meetings (PDMs) during which the agent receives constructive criticism or other remedial instruction related to improving job performance, wherein the plurality of data is related to improving the job performance of at least one of the plurality of agents on an individual agent-basis or a group-level agent-basis; receiving from the agent training system the plurality of data from a first input representing respective performance parameters pertaining to the plurality of agents on the individual agent-basis or the group-level agent-basis working at least one call center; organizing the at least plurality of data related to improving the job performance received from the first input in a data store in at least one of a plurality of user-specified parameters; storing the at least plurality of data related to improving the job performance received from the first input in the data store arranged in at least one of the plurality of user-specified parameters for subsequent query and retrieval; receiving at least one query of the data store by a user pertaining to the improving job performance of at least one of the agents on the individual agent-basis or the group-level agent-basis; processing an output determined by the at least first input of at least the plurality of the data related to the improving job performance in response to the query and processing the output results in the plurality of data related to remedial instruction related to improving a job performance arranged in one of the plurality of user-specified parameters, including at least one of a productivity parameter, a conversion parameter, a schedule adherence parameter, and a number of calls parameter by a given agent; and presenting at least one report as determined by the output of the query to the at least one user. | 1. A method of measuring performance parameters and reporting on at least one aspect of respective performance parameters of a plurality of agents, the method comprising at least the following: measuring in an agent training system at least a plurality of data, including data representing agent performance parameters for each of the plurality, of agents, wherein the plurality of data representing agent performance parameters includes data related to at least one of either sections or teams to which an agent is assigned or associated, personal development meetings (PDMs) during which the agent receives constructive criticism or other remedial instruction related to improving job performance, wherein the plurality of data is related to improving the job performance of at least one of the plurality of agents on an individual agent-basis or a group-level agent-basis; receiving from the agent training system the plurality of data from a first input representing respective performance parameters pertaining to the plurality of agents on the individual agent-basis or the group-level agent-basis working at least one call center; organizing the at least plurality of data related to improving the job performance received from the first input in a data store in at least one of a plurality of user-specified parameters; storing the at least plurality of data related to improving the job performance received from the first input in the data store arranged in at least one of the plurality of user-specified parameters for subsequent query and retrieval; receiving at least one query of the data store by a user pertaining to the improving job performance of at least one of the agents on the individual agent-basis or the group-level agent-basis; processing an output determined by the at least first input of at least the plurality of the data related to the improving job performance in response to the query and processing the output results in the plurality of data related to remedial instruction related to improving a job performance arranged in one of the plurality of user-specified parameters, including at least one of a productivity parameter, a conversion parameter, a schedule adherence parameter, and a number of calls parameter by a given agent; and presenting at least one report as determined by the output of the query to the at least one user. 74. The method of claim 1 , wherein presenting at least one report of the query includes presenting at least one report related to at least one agent development system, displaying data for the plurality of agents, wherein at least one field of the at least one report is responsive to user input to display data specific to at least one selected agent. | 0.503297 |
1. A method for routing a facsimile, comprising: receiving or generating text of a facsimile in a computer-readable format; performing text analysis and fax analysis by parallel processing; wherein the text analysis comprises: analyzing portions of the text of the facsimile for at least one of a meaning and a context of the text to determine one or more other destinations, and determining one or more keywords in the text, wherein none of the one or more determined keywords contains any destination information selected from a group consisting of a physical address, an email address, a fax number, and a name of a desired recipient; and wherein the fax analysis comprises: analyzing a pattern of light and dark areas of a facsimile in the computer-readable format without optical character recognition; correlating the pattern to one or more forms without optical character recognition; routing the facsimile to one or more destinations based on the text analysis and the fax analysis. | 1. A method for routing a facsimile, comprising: receiving or generating text of a facsimile in a computer-readable format; performing text analysis and fax analysis by parallel processing; wherein the text analysis comprises: analyzing portions of the text of the facsimile for at least one of a meaning and a context of the text to determine one or more other destinations, and determining one or more keywords in the text, wherein none of the one or more determined keywords contains any destination information selected from a group consisting of a physical address, an email address, a fax number, and a name of a desired recipient; and wherein the fax analysis comprises: analyzing a pattern of light and dark areas of a facsimile in the computer-readable format without optical character recognition; correlating the pattern to one or more forms without optical character recognition; routing the facsimile to one or more destinations based on the text analysis and the fax analysis. 3. A method as recited in claim 1 , wherein the text analysis includes comparing one or more keywords in the text to at least one other element in the text. | 0.714008 |
1. A speech processing method, comprising: determining a set of available instructions; determining data structures corresponding to the available instructions; processing a natural language speech input representing at least one instruction with respect to the determined data structures; determining if the natural language speech input likely represents an instruction; determining a completeness and an ambiguity of the likely represented instruction with respect to the data structures, and if the likely represented instruction is too ambiguous or incomplete for proper execution, prompting for further speech input to reduce ambiguity or incompleteness; targeting a likely represented instruction which is sufficiently complete and unambiguous for proper execution to one of a plurality of respective applications; preserving a system state prior to at least partially executing the sufficiently complete and unambiguous instruction; executing the sufficiently complete and unambiguous instruction by the one of the plurality of applications; and restoring the preserved system state after execution of the sufficiently complete and unambiguous instruction. | 1. A speech processing method, comprising: determining a set of available instructions; determining data structures corresponding to the available instructions; processing a natural language speech input representing at least one instruction with respect to the determined data structures; determining if the natural language speech input likely represents an instruction; determining a completeness and an ambiguity of the likely represented instruction with respect to the data structures, and if the likely represented instruction is too ambiguous or incomplete for proper execution, prompting for further speech input to reduce ambiguity or incompleteness; targeting a likely represented instruction which is sufficiently complete and unambiguous for proper execution to one of a plurality of respective applications; preserving a system state prior to at least partially executing the sufficiently complete and unambiguous instruction; executing the sufficiently complete and unambiguous instruction by the one of the plurality of applications; and restoring the preserved system state after execution of the sufficiently complete and unambiguous instruction. 7. The method according to claim 1 , further comprising determining whether a portion of the natural language speech input represents unstructured language input not representing an instruction, and if not representing an instruction, suppressing the prompting. | 0.572359 |
6. A headset, comprising: a first microphone for receiving audio input; a memory; and a processor for executing instructions stored in the memory, the instructions comprising a sound classifier, wherein, when executing the sound classifier, the processor is configured for: receiving a plurality of frames of audio generated from the audio input received by the first microphone; analyzing each of the plurality of frames of audio; classifying a first number of the frames of audio as speech; classifying a second number of the frames of audio as non-transient background noise; classifying a third number of the frames of audio as transient noise events; and transmitting signals indicative of at least the classifications of the frames of audio to a speech recognition system. | 6. A headset, comprising: a first microphone for receiving audio input; a memory; and a processor for executing instructions stored in the memory, the instructions comprising a sound classifier, wherein, when executing the sound classifier, the processor is configured for: receiving a plurality of frames of audio generated from the audio input received by the first microphone; analyzing each of the plurality of frames of audio; classifying a first number of the frames of audio as speech; classifying a second number of the frames of audio as non-transient background noise; classifying a third number of the frames of audio as transient noise events; and transmitting signals indicative of at least the classifications of the frames of audio to a speech recognition system. 8. The headset of claim 6 , wherein transmitting signals indicative of at least the classifications of the frames of audio comprises wirelessly providing the signals with at least a logical relationship to respective data that represents audio of at least some of the frames of audio. | 0.778766 |
11. A method for decrypting user information encrypted on a storage device associated with an identity document of a user, the method comprising: collecting, at a server, user identity document data from the user; constructing, at the server, a token comprising the user identity document data; reading, at a mobile device comprising a reader, the user identity document data from the token by radio frequency identification communication, using the user identity document data read from the token to decrypt the user information stored on said storage device and reading, by radio frequency identification communication, a user identity document biometric facial image from said storage device using the user identity document data; capturing, at a camera, an image of the user's face; comparing, at a comparator, the captured image of the user's face with the user identity document biometric facial image read from the user identity document; and authenticating, at an authentication means, the user depending upon the result of the comparison. | 11. A method for decrypting user information encrypted on a storage device associated with an identity document of a user, the method comprising: collecting, at a server, user identity document data from the user; constructing, at the server, a token comprising the user identity document data; reading, at a mobile device comprising a reader, the user identity document data from the token by radio frequency identification communication, using the user identity document data read from the token to decrypt the user information stored on said storage device and reading, by radio frequency identification communication, a user identity document biometric facial image from said storage device using the user identity document data; capturing, at a camera, an image of the user's face; comparing, at a comparator, the captured image of the user's face with the user identity document biometric facial image read from the user identity document; and authenticating, at an authentication means, the user depending upon the result of the comparison. 12. The method according to claim 11 wherein the reader is a portable reader or scanner. | 0.596861 |
40. The system as described in claim 30 , wherein the system further comprises: one or more additional processors; one or more additional computer readable storage media; computer readable instructions stored on the one or more additional computer readable storage media which, when executed by the one or more processors, implement the cloud-based cluster. | 40. The system as described in claim 30 , wherein the system further comprises: one or more additional processors; one or more additional computer readable storage media; computer readable instructions stored on the one or more additional computer readable storage media which, when executed by the one or more processors, implement the cloud-based cluster. 43. The system as described in claim 40 , wherein the firewall is of the cloud-based cluster, and the firewall is configured to allow inbound communication based on an IP address associated with a cluster that performed said transmitting. | 0.932714 |
11. A method of determining a document score, which suggests a relevance of a document to a search query, the method comprising: receiving the search query; parsing the search query into a plurality of n-grams including a first n-gram, which includes a first weight quantifying an importance of the first n-gram to the search query; determining that the first weight satisfies a threshold weight criterion; identifying a first equivalent subject that is semantically similar to the first n-gram and that forms a subject group with the first n-gram, wherein the first equivalent subject is associated with an equivalent-subject score quantifying a confidence that the first equivalent subject and the first n-gram identify a same subject; determining a first-subject-group frequency comprised of both a first-subject frequency, which includes a number of times the first n-gram is found in the document, and a first-equivalent-subject frequency, which includes a number of times the first equivalent subject is found in the document; and calculating the document score of the document, wherein the document score is comprised of a first-subject-group score, and wherein the first-subject-group score is calculated by combining the first-equivalent-subject frequency with the equivalent-subject score, such that the first-equivalent-subject frequency is weighted based on a confidence that the first equivalent subject is semantically similar to the first n-gram. | 11. A method of determining a document score, which suggests a relevance of a document to a search query, the method comprising: receiving the search query; parsing the search query into a plurality of n-grams including a first n-gram, which includes a first weight quantifying an importance of the first n-gram to the search query; determining that the first weight satisfies a threshold weight criterion; identifying a first equivalent subject that is semantically similar to the first n-gram and that forms a subject group with the first n-gram, wherein the first equivalent subject is associated with an equivalent-subject score quantifying a confidence that the first equivalent subject and the first n-gram identify a same subject; determining a first-subject-group frequency comprised of both a first-subject frequency, which includes a number of times the first n-gram is found in the document, and a first-equivalent-subject frequency, which includes a number of times the first equivalent subject is found in the document; and calculating the document score of the document, wherein the document score is comprised of a first-subject-group score, and wherein the first-subject-group score is calculated by combining the first-equivalent-subject frequency with the equivalent-subject score, such that the first-equivalent-subject frequency is weighted based on a confidence that the first equivalent subject is semantically similar to the first n-gram. 12. The method of claim 11 , wherein the first-subject frequency and the first-equivalent-subject frequency are weighted based on respective locations within the document at which the first n-gram subject and first-equivalent subject are found. | 0.551713 |
1. A method for indexing information, comprising the steps of: processing a query having a phrase corresponding to concatenation of adjacent portions of the information; generating an index entry for the phrase; processing the query to identify one or more locations at which the phrase occurs within the information; and measuring an amount of time required to process the query to identify the one or more locations; wherein the index entry for the phrase is generated if the measured time exceeds a threshold. | 1. A method for indexing information, comprising the steps of: processing a query having a phrase corresponding to concatenation of adjacent portions of the information; generating an index entry for the phrase; processing the query to identify one or more locations at which the phrase occurs within the information; and measuring an amount of time required to process the query to identify the one or more locations; wherein the index entry for the phrase is generated if the measured time exceeds a threshold. 2. A method according to claim 1, wherein generating the index entry includes encoding the concatenation of adjacent portions of the information. | 0.796634 |
7. The method of claim 1 , further comprising: identifying a second substring from the textual representation that corresponds to a second attribute of the primary domain; and parsing the identified second substring to determine a second secondary domain representing a user intent for the second substring, wherein performing the task flow is further based on the second secondary domain. | 7. The method of claim 1 , further comprising: identifying a second substring from the textual representation that corresponds to a second attribute of the primary domain; and parsing the identified second substring to determine a second secondary domain representing a user intent for the second substring, wherein performing the task flow is further based on the second secondary domain. 10. The method of claim 7 , wherein parsing the identified second substring comprises: determining a confidence score for a plurality of interpretations of the second substring; and determining the second secondary domain representing a user intent for the second substring based on an interpretation of the plurality of interpretations of the second substring having the highest confidence score. | 0.736199 |
1. A computer-implemented process, comprising: receiving, in a computer memory: data identifying entities described in a document, wherein the document includes a plurality of tokens appearing in an order in the document, and data defining sentiment values assigned to the tokens in the document; and processing the data in the computer memory with a processor to assign a sentiment value to one of the identified entities in the document, by: applying a filter to a sequence of the sentiment values corresponding to a sequence of the tokens in the order the tokens appear in the document, the filter having a width defined by a number of tokens, the entity having a position in the sequence of tokens, the filter providing a combination of contributions of the sentiment values associated with the tokens surrounding the position of the entity in the document within the width of the filter. | 1. A computer-implemented process, comprising: receiving, in a computer memory: data identifying entities described in a document, wherein the document includes a plurality of tokens appearing in an order in the document, and data defining sentiment values assigned to the tokens in the document; and processing the data in the computer memory with a processor to assign a sentiment value to one of the identified entities in the document, by: applying a filter to a sequence of the sentiment values corresponding to a sequence of the tokens in the order the tokens appear in the document, the filter having a width defined by a number of tokens, the entity having a position in the sequence of tokens, the filter providing a combination of contributions of the sentiment values associated with the tokens surrounding the position of the entity in the document within the width of the filter. 9. The computer-implemented process of claim 1 , further comprising: displaying on a display the sentiment value of an entity in relation to a display of the document. | 0.643798 |
10. A method for cognitive recording and sharing of live events comprising: receiving, at a processing device, a biometric signal from an individual; obtaining a biometric signature of the individual based on a received biometric signal data; receive, at the processing device, from devices of one or more other individuals in proximity to the individual, a signal representing one or more of: a recognized emotional state of, a biometric signature of, and a determined precognition input of the one or more other individuals in proximity to the individual; determining, at said processing device, the individual's current emotional state based on the signature in combination with the signals received from the devices of said one or more other individuals in proximity to the individual; and triggering a recording device to record a live event responsive to determined emotional state. | 10. A method for cognitive recording and sharing of live events comprising: receiving, at a processing device, a biometric signal from an individual; obtaining a biometric signature of the individual based on a received biometric signal data; receive, at the processing device, from devices of one or more other individuals in proximity to the individual, a signal representing one or more of: a recognized emotional state of, a biometric signature of, and a determined precognition input of the one or more other individuals in proximity to the individual; determining, at said processing device, the individual's current emotional state based on the signature in combination with the signals received from the devices of said one or more other individuals in proximity to the individual; and triggering a recording device to record a live event responsive to determined emotional state. 13. The method as claimed in claim 10 , further comprising: correlating received biometric signals of the individual with a cognitive state or emotional state of said individual. | 0.726798 |
12. A computer-implemented method for performing an update to a semantic category list file, comprising: using a semantic category list tool determining whether to proceed with the update to the semantic category list file, wherein each semantic category in the semantic category list file is utilized to present a user with choices of actions that are executed based on a text and a type label of a string in an electronic document belonging to each semantic category, and wherein each referenced string in the electronic document is labeled with the type label associating the string with a semantic category; calling an update Universal Resource Locator (URL) of a web server to locate a semantic category update file; sending a lastcheckpoint value of the semantic category list file to the web server; determining whether a new update exists prior to performing the update by determining whether a checkpoint value of the semantic category update file is greater than the lastcheckpoint value of the semantic category list file, and, if so, then downloading a plurality of semantic category terms from the semantic category update file to replace a plurality of semantic category terms in the semantic category list file; if no update is available, leaving the semantic category list file unchanged; and storing the updated semantic category list file in a directory. | 12. A computer-implemented method for performing an update to a semantic category list file, comprising: using a semantic category list tool determining whether to proceed with the update to the semantic category list file, wherein each semantic category in the semantic category list file is utilized to present a user with choices of actions that are executed based on a text and a type label of a string in an electronic document belonging to each semantic category, and wherein each referenced string in the electronic document is labeled with the type label associating the string with a semantic category; calling an update Universal Resource Locator (URL) of a web server to locate a semantic category update file; sending a lastcheckpoint value of the semantic category list file to the web server; determining whether a new update exists prior to performing the update by determining whether a checkpoint value of the semantic category update file is greater than the lastcheckpoint value of the semantic category list file, and, if so, then downloading a plurality of semantic category terms from the semantic category update file to replace a plurality of semantic category terms in the semantic category list file; if no update is available, leaving the semantic category list file unchanged; and storing the updated semantic category list file in a directory. 13. The method of claim 12 further comprising updating the lastcheckpoint value in the semantic category list file that is equal to the checkpoint value of the semantic category update file. | 0.735251 |
1. A computer-implemented method for generating role-based notifications in a modular learning system, the method comprising: maintaining a database of activity items, each activity item associated with a set of user roles permitted to access the activity item by a plurality of kinds of users on a plurality of kinds of electronic user devices; monitoring activities of each of the plurality of the kinds of users; receiving notification generation requests from multiple ones of the plurality of user devices operated by a plurality of viewing users; determining, for each of the plurality of viewing users, a respective user role associated with the corresponding notification generation request; validating each of the user roles associated with the corresponding notification generation request by determining, for each of the user roles, a corresponding user role associated with each respective one of the plurality of viewing users to determine items to be displayed to the respective view user such that at least one of the items displayed to a first of the viewing users is not displayed to a second of the viewing users based on the respective roles of the first and second viewing users; for each of at least some of the viewing users, accessing the database of activity items and retrieving activity items that include the corresponding user role in the set of user roles permitted to access the activity item after validating the corresponding user role; for each of the at least some viewing users, generating role-based notifications for retrieved activity items for the corresponding user role such that different sets of role-based notifications are generated for the first and second viewing users; for each of the first and second viewing users, updating role-based notifications based on user role preferences of respective ones of the first and second viewing users; denying a request for role-based notification generation from one of the viewing users when a role of said viewing user is different from a role associated with the requested role-based notification; and causing to be displayed to respective ones of the user devices of the first and second viewing users, the corresponding items or updated role-based notifications that the respective first and second viewing users are permitted to view and are based on their respective user role preferences including at least one role-based notification for an activity related to the retrieved activity item conducted by an other one of the users on the modular learning system. | 1. A computer-implemented method for generating role-based notifications in a modular learning system, the method comprising: maintaining a database of activity items, each activity item associated with a set of user roles permitted to access the activity item by a plurality of kinds of users on a plurality of kinds of electronic user devices; monitoring activities of each of the plurality of the kinds of users; receiving notification generation requests from multiple ones of the plurality of user devices operated by a plurality of viewing users; determining, for each of the plurality of viewing users, a respective user role associated with the corresponding notification generation request; validating each of the user roles associated with the corresponding notification generation request by determining, for each of the user roles, a corresponding user role associated with each respective one of the plurality of viewing users to determine items to be displayed to the respective view user such that at least one of the items displayed to a first of the viewing users is not displayed to a second of the viewing users based on the respective roles of the first and second viewing users; for each of at least some of the viewing users, accessing the database of activity items and retrieving activity items that include the corresponding user role in the set of user roles permitted to access the activity item after validating the corresponding user role; for each of the at least some viewing users, generating role-based notifications for retrieved activity items for the corresponding user role such that different sets of role-based notifications are generated for the first and second viewing users; for each of the first and second viewing users, updating role-based notifications based on user role preferences of respective ones of the first and second viewing users; denying a request for role-based notification generation from one of the viewing users when a role of said viewing user is different from a role associated with the requested role-based notification; and causing to be displayed to respective ones of the user devices of the first and second viewing users, the corresponding items or updated role-based notifications that the respective first and second viewing users are permitted to view and are based on their respective user role preferences including at least one role-based notification for an activity related to the retrieved activity item conducted by an other one of the users on the modular learning system. 2. The computer-implemented method of claim 1 , wherein the database of activity items includes items reflecting performance of a learning application, items reflecting purchase of a learning application and items reflecting purchase of microlearning services and items reflecting each unique user activity performed on the modular learning system. | 0.526512 |
1. A method implemented by an information handling system that includes a processor and a memory accessible by the processor, the method comprising: analyzing, by the processor, a document that is being composed by a visually impaired user, wherein the analysis derives a sensitivity of the document; retrieving, from the memory, a vocal characteristic corresponding to the derived sensitivity based on one or more predefined settings; retrieving, from the memory, an additional vocal characteristic corresponding to an audience size of the document, wherein the audience size is based on a size of an intended audience selected from a group consisting of an email distribution list and an amount of recipients of a group posting; and audibly reading text from the document to the visually impaired user with a text to speech process utilizing both the retrieved vocal characteristic and the additional vocal characteristic. | 1. A method implemented by an information handling system that includes a processor and a memory accessible by the processor, the method comprising: analyzing, by the processor, a document that is being composed by a visually impaired user, wherein the analysis derives a sensitivity of the document; retrieving, from the memory, a vocal characteristic corresponding to the derived sensitivity based on one or more predefined settings; retrieving, from the memory, an additional vocal characteristic corresponding to an audience size of the document, wherein the audience size is based on a size of an intended audience selected from a group consisting of an email distribution list and an amount of recipients of a group posting; and audibly reading text from the document to the visually impaired user with a text to speech process utilizing both the retrieved vocal characteristic and the additional vocal characteristic. 2. The method of claim 1 wherein the vocal characteristic is a speaker gender and the additional vocal characteristic is a speaker volume. | 0.924155 |
1. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving geotagged audio signals that correspond to environmental audio recorded by multiple mobile devices in multiple geographic locations, receiving an audio signal that corresponds to an utterance recorded by a particular mobile device, determining a particular geographic location associated with the particular mobile device, generating a noise model for the particular geographic location using a subset of the geotagged audio signals, and performing noise compensation on the audio signal that corresponds to the utterance using the noise model that has been generated for the particular geographic location. | 1. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving geotagged audio signals that correspond to environmental audio recorded by multiple mobile devices in multiple geographic locations, receiving an audio signal that corresponds to an utterance recorded by a particular mobile device, determining a particular geographic location associated with the particular mobile device, generating a noise model for the particular geographic location using a subset of the geotagged audio signals, and performing noise compensation on the audio signal that corresponds to the utterance using the noise model that has been generated for the particular geographic location. 4. The system of claim 1 , wherein generating the noise model further comprises generating the noise model after receiving the audio signal that corresponds to the utterance. | 0.66614 |
10. A non-transitory computer-readable storage medium storing one or more instructions which, when executed by one or more processors, cause the one or more processors to perform steps comprising: receiving, by a server computer, a request from a client computer specifying particular content for a particular account, wherein the particular content is associated with an original audio language; in response to receiving the request, selecting, by the server computer, a preferred audio language and a preferred subtitle language for the particular content based on a particular record of a preference database that maps the original audio language and the particular account to the preferred audio language and the preferred subtitle language; wherein the preference database includes a plurality of records for the particular account, wherein each record of the plurality of records maps a different original audio language to a respective preferred audio language and a respective preferred subtitle language; wherein a first record of the plurality of records maps a first original audio language to a first combination of preferred audio language and preferred subtitle language and a second record of the plurality of records maps a second original audio language to a second combination of preferred audio language and preferred subtitle language, wherein the first original audio language is different than the second original audio language and the first combination is different than the second combination; providing, from the server computer to the client computer, asset identifying data for an asset associated with the particular content, the preferred audio language, and the preferred subtitle language; in response to receiving a message at the server computer from the client computer representing input which specifies a new audio language or a new subtitle language, providing different asset identifying data associated with the particular content and one or more of: the new audio language or the new subtitle language; receiving, by the server computer, one or more messages from the client computer that identify a presented audio language and a presented subtitle language that were presented to a particular user of the particular account in relation to the particular content; in response to a determination that one or more of: the presented audio language differs from the preferred audio language or that the presented subtitle language differs from the preferred subtitle language, the server computer updating the particular record in the preference database to specify that one or more of: the preferred audio language is the presented audio language or the preferred subtitle language is the presented subtitle language; receiving, by the server computer, a second request from the client computer specifying second particular content for the particular account, wherein the second particular content is associated with the original audio language; in response to receiving the request, selecting, by the server computer, a second preferred audio language and a second preferred subtitle language for the second particular content based on the particular record of the preference database for the particular account and the original language, wherein one or more of: the second preferred audio language is the presented audio language of the particular content or the second preferred subtitle language is the presented subtitle language for the particular content; providing, from the server computer to the client computer, second asset identifying data for a second asset associated with the second particular content, the second preferred audio language, and the second preferred subtitle language. | 10. A non-transitory computer-readable storage medium storing one or more instructions which, when executed by one or more processors, cause the one or more processors to perform steps comprising: receiving, by a server computer, a request from a client computer specifying particular content for a particular account, wherein the particular content is associated with an original audio language; in response to receiving the request, selecting, by the server computer, a preferred audio language and a preferred subtitle language for the particular content based on a particular record of a preference database that maps the original audio language and the particular account to the preferred audio language and the preferred subtitle language; wherein the preference database includes a plurality of records for the particular account, wherein each record of the plurality of records maps a different original audio language to a respective preferred audio language and a respective preferred subtitle language; wherein a first record of the plurality of records maps a first original audio language to a first combination of preferred audio language and preferred subtitle language and a second record of the plurality of records maps a second original audio language to a second combination of preferred audio language and preferred subtitle language, wherein the first original audio language is different than the second original audio language and the first combination is different than the second combination; providing, from the server computer to the client computer, asset identifying data for an asset associated with the particular content, the preferred audio language, and the preferred subtitle language; in response to receiving a message at the server computer from the client computer representing input which specifies a new audio language or a new subtitle language, providing different asset identifying data associated with the particular content and one or more of: the new audio language or the new subtitle language; receiving, by the server computer, one or more messages from the client computer that identify a presented audio language and a presented subtitle language that were presented to a particular user of the particular account in relation to the particular content; in response to a determination that one or more of: the presented audio language differs from the preferred audio language or that the presented subtitle language differs from the preferred subtitle language, the server computer updating the particular record in the preference database to specify that one or more of: the preferred audio language is the presented audio language or the preferred subtitle language is the presented subtitle language; receiving, by the server computer, a second request from the client computer specifying second particular content for the particular account, wherein the second particular content is associated with the original audio language; in response to receiving the request, selecting, by the server computer, a second preferred audio language and a second preferred subtitle language for the second particular content based on the particular record of the preference database for the particular account and the original language, wherein one or more of: the second preferred audio language is the presented audio language of the particular content or the second preferred subtitle language is the presented subtitle language for the particular content; providing, from the server computer to the client computer, second asset identifying data for a second asset associated with the second particular content, the second preferred audio language, and the second preferred subtitle language. 12. The non-transitory computer-readable storage medium of claim 10 , further comprising the server computer updating the particular record in response to a determination that the presented audio language was presented for more than a particular threshold period of time or a percentage of time, or a determination that the presented subtitle language was presented for more than a second particular threshold period of time or a second percentage of time. | 0.5 |
9. A computer program product for generating a help file, the computer program product comprising: a non-transitory computer readable medium; first program instructions for accessing a user model that models a software application related process, wherein the user model represents a network of related concepts, wherein the user model defines a plurality of objects and a plurality of linked relationships between the plurality of objects, wherein the user model provides a navigation model for a user to move between annotations and meta-model elements in a user interface, wherein the user model specifies requirements for the user interface, wherein the plurality of objects provide a plurality of menu lists as entry points to the context-specific help file and wherein the software application related process is any of an install software task, an uninstall software task, or a refresh installed software task; second program instructions for selecting a first object of the user model by the user in the user interface; third program instructions for receiving a text file corresponding with the first object; fourth program instructions for creating a first component of the context-specific help file from the text file; fifth program instructions for selecting select all additional objects of the user model not selected by the user and corresponding with the first component and to present at least one additional object to the user, the at least one additional object connected with the first object as defined by the user model; ninth program instructions for giving the at least one additional objects a new attribute with a stereotype of Β«help identifierΒ»; and sixth program instructions for creating at least one additional component of the context-specific help file corresponding with the at least one additional object, wherein the at least one additional component comprises at least a link to a respective text file of the at least one additional object connected to the first object. | 9. A computer program product for generating a help file, the computer program product comprising: a non-transitory computer readable medium; first program instructions for accessing a user model that models a software application related process, wherein the user model represents a network of related concepts, wherein the user model defines a plurality of objects and a plurality of linked relationships between the plurality of objects, wherein the user model provides a navigation model for a user to move between annotations and meta-model elements in a user interface, wherein the user model specifies requirements for the user interface, wherein the plurality of objects provide a plurality of menu lists as entry points to the context-specific help file and wherein the software application related process is any of an install software task, an uninstall software task, or a refresh installed software task; second program instructions for selecting a first object of the user model by the user in the user interface; third program instructions for receiving a text file corresponding with the first object; fourth program instructions for creating a first component of the context-specific help file from the text file; fifth program instructions for selecting select all additional objects of the user model not selected by the user and corresponding with the first component and to present at least one additional object to the user, the at least one additional object connected with the first object as defined by the user model; ninth program instructions for giving the at least one additional objects a new attribute with a stereotype of Β«help identifierΒ»; and sixth program instructions for creating at least one additional component of the context-specific help file corresponding with the at least one additional object, wherein the at least one additional component comprises at least a link to a respective text file of the at least one additional object connected to the first object. 14. The computer program of claim 9 , wherein the plurality of linked relationships comprise directional relationships and non-directional relationships. | 0.540806 |
60. The system of claim 57 , wherein the processor is further configured to perform the steps of: generating a log of nodes identified as being related to a plurality of search strings received over a period of time; determining co-occurrences among the logged nodes; and augmenting the ontology to include links among nodes based on frequency of the determined co-occurrences. | 60. The system of claim 57 , wherein the processor is further configured to perform the steps of: generating a log of nodes identified as being related to a plurality of search strings received over a period of time; determining co-occurrences among the logged nodes; and augmenting the ontology to include links among nodes based on frequency of the determined co-occurrences. 61. The system of claim 60 , wherein at least one relation among nodes comprises an indication that concepts represented by the nodes tend to occur together. | 0.92261 |
16. A system, comprising: a data processing apparatus; and a data store storing instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations comprising: identifying images that appear in a plurality of distinct book content items, the distinct book content items corresponding to published books and lacking explicit electronic links between each other; generating implicit links between two or more of the distinct book content items that each include a similar image, the links represented as weighted edges in a graph in which nodes represent corresponding book content items, each weighted edge representing one or more matches of image content in corresponding book content items as between different books for nodes that define a corresponding weighted edge; for particular distinct ones of the nodes, identifying images that match each other as between different book content items based on multiple descriptor points for each of the images, and assigning weightings to particular edges between the distinct ones of the nodes; and determining a rank score for each of the two or more distinct book content items based on the implicit links between the two or more distinct book content items, the rank score for each of the two or more distinct book content items being a value indicative of the importance of the distinct book content item relative to other distinct book content items. | 16. A system, comprising: a data processing apparatus; and a data store storing instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations comprising: identifying images that appear in a plurality of distinct book content items, the distinct book content items corresponding to published books and lacking explicit electronic links between each other; generating implicit links between two or more of the distinct book content items that each include a similar image, the links represented as weighted edges in a graph in which nodes represent corresponding book content items, each weighted edge representing one or more matches of image content in corresponding book content items as between different books for nodes that define a corresponding weighted edge; for particular distinct ones of the nodes, identifying images that match each other as between different book content items based on multiple descriptor points for each of the images, and assigning weightings to particular edges between the distinct ones of the nodes; and determining a rank score for each of the two or more distinct book content items based on the implicit links between the two or more distinct book content items, the rank score for each of the two or more distinct book content items being a value indicative of the importance of the distinct book content item relative to other distinct book content items. 17. The system of claim 16 , wherein generating implicit links comprises: identifying descriptor points from the images appearing in the plurality of distinct book content items, the descriptor points defining localized features of the images, the localized features being characteristics of a portion of the image; and generating an implicit link between two or more distinct book content items that each include an image having a matching descriptor point, each matching descriptor point being identified based on similarities between pairs of descriptor points. | 0.50764 |
6. A computer readable storage medium containing program instructions for displaying one or more visual cues on a display, the one or more cues each having one or more of a valid option, expected construct, and required syntax, wherein execution of program instructions by one or more processors of a computer causes the one or more processors to carry out the steps of: providing for displaying one or more visual cues on a display, the one or more cues each having a valid option, an expected construct, and a required syntax; providing for presenting one or more keywords to the display in relation to the one or more queries; providing for selectively engaging a content assistance mechanism and a list of valid options to create a query construct represented by one of the one or more functional template blocks for each of the one or more queries, wherein invoking a wizard is an option in the list of valid options; providing for highlighting at least one required template block in the display via a first form and highlighting at least one optional template block in the display via a second form; providing for a capability to receive a response to the one or more functional template blocks and determining compliance of the response received; and, wherein each functional template block includes at least one required template block and at least one optional template block associated with the one or more queries. | 6. A computer readable storage medium containing program instructions for displaying one or more visual cues on a display, the one or more cues each having one or more of a valid option, expected construct, and required syntax, wherein execution of program instructions by one or more processors of a computer causes the one or more processors to carry out the steps of: providing for displaying one or more visual cues on a display, the one or more cues each having a valid option, an expected construct, and a required syntax; providing for presenting one or more keywords to the display in relation to the one or more queries; providing for selectively engaging a content assistance mechanism and a list of valid options to create a query construct represented by one of the one or more functional template blocks for each of the one or more queries, wherein invoking a wizard is an option in the list of valid options; providing for highlighting at least one required template block in the display via a first form and highlighting at least one optional template block in the display via a second form; providing for a capability to receive a response to the one or more functional template blocks and determining compliance of the response received; and, wherein each functional template block includes at least one required template block and at least one optional template block associated with the one or more queries. 9. The computer readable storage medium of claim 6 , engaging a content assistance mechanism to create a query construct represented by a template block for each of the one or more queries. | 0.536364 |
5. A system for classifying a message, the system comprising: a module configured to receive a message to be classified, said message having a message identifier; a module configured to determine if said message identifier uniquely maps to a corresponding classification category; a module configured to label the message with the identified classification category if said message identifier maps directly to a corresponding classification category; a module configured to parse the message to be classified and identify a plurality of features from the parsed message if said message identifier does not map directly to a corresponding classification category; a module configured to compare at least one classification rule to the plurality of features if said message identifier does not map directly to a corresponding classification category; a module configured to rate each classification rule that matches to the plurality of features; a module configured to identify a classification category from said rating; and a module configured to label the message with the identified classification category. | 5. A system for classifying a message, the system comprising: a module configured to receive a message to be classified, said message having a message identifier; a module configured to determine if said message identifier uniquely maps to a corresponding classification category; a module configured to label the message with the identified classification category if said message identifier maps directly to a corresponding classification category; a module configured to parse the message to be classified and identify a plurality of features from the parsed message if said message identifier does not map directly to a corresponding classification category; a module configured to compare at least one classification rule to the plurality of features if said message identifier does not map directly to a corresponding classification category; a module configured to rate each classification rule that matches to the plurality of features; a module configured to identify a classification category from said rating; and a module configured to label the message with the identified classification category. 6. The system of claim 5 , further comprising: a module configured to compare a second classification rule to the plurality of features. | 0.79341 |
1. A computer implemented method for inferring a probability of an I th inference relating to a biological system, wherein I is an integer reflecting how many times a recursion process has been conducted, the computer implemented method comprising: receiving a I th query at a database, on a data processing system, regarding an I th fact related to the biological system, wherein the I th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process, wherein the I th inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum; establishing the I th fact as a frame of reference for the I th query, by a processing unit of the data processing system; mathematically refocusing the database such that the fact is modeled as a first center of an inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the fact; applying a I th set of rules to the I th query, by the processing unit, wherein the I th set of rules are determined for the I th query according to a J th set of rules, wherein J is equal to I-1, wherein the set of rules determine how the plurality of data are to be compared to the I th fact, wherein the I th set of rules is prioritized, and wherein the set of rules determine a search space of for the I th query including the associated metadata and associated key, wherein the J th set of rules is a rule set used in a previous iteration of the recursive process; executing the I th query, by the processing unit, to create the probability of the inference, wherein the probability of the inference is determined from comparing the I th search space according to the I th set of rules; automatically generating cohort data for the I th fact; and storing the probability of the I th inference and the cohort data for the I th fact by the processing unit in a memory element of the data processing system, wherein the I th inference and the cohort data are stored in the database at an atomic level; wherein the first inference relating to a biological system is selected from the group consisting of an interaction between the biological system and an environmental factor, monitoring the biological system, monitoring the environmental factor, a relationship between a biological pathway and a drug, a relationship between the biological pathway and a food, a relationship between the biological pathway and a substance interacting with the biological pathway, a relationship between the biological pathway and a gene, a relationship between the biological pathway and the environmental factor, and combinations thereof. | 1. A computer implemented method for inferring a probability of an I th inference relating to a biological system, wherein I is an integer reflecting how many times a recursion process has been conducted, the computer implemented method comprising: receiving a I th query at a database, on a data processing system, regarding an I th fact related to the biological system, wherein the I th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process, wherein the I th inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum; establishing the I th fact as a frame of reference for the I th query, by a processing unit of the data processing system; mathematically refocusing the database such that the fact is modeled as a first center of an inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the fact; applying a I th set of rules to the I th query, by the processing unit, wherein the I th set of rules are determined for the I th query according to a J th set of rules, wherein J is equal to I-1, wherein the set of rules determine how the plurality of data are to be compared to the I th fact, wherein the I th set of rules is prioritized, and wherein the set of rules determine a search space of for the I th query including the associated metadata and associated key, wherein the J th set of rules is a rule set used in a previous iteration of the recursive process; executing the I th query, by the processing unit, to create the probability of the inference, wherein the probability of the inference is determined from comparing the I th search space according to the I th set of rules; automatically generating cohort data for the I th fact; and storing the probability of the I th inference and the cohort data for the I th fact by the processing unit in a memory element of the data processing system, wherein the I th inference and the cohort data are stored in the database at an atomic level; wherein the first inference relating to a biological system is selected from the group consisting of an interaction between the biological system and an environmental factor, monitoring the biological system, monitoring the environmental factor, a relationship between a biological pathway and a drug, a relationship between the biological pathway and a food, a relationship between the biological pathway and a substance interacting with the biological pathway, a relationship between the biological pathway and a gene, a relationship between the biological pathway and the environmental factor, and combinations thereof. 9. The computer implemented method of claim 1 wherein the additional data is imported according to a technique selected from the group consisting of federation and extraction, transformation, and loading. | 0.776201 |
12. The computer storage medium of claim 8 , wherein identifying a related query for the same topic comprises: identifying resources that were requested, by the particular use device, through the first user interactions with the search results; identifying, for two or more of the resources, a keyword that is associated with each of at least two of the resources; and identifying the keyword as the related query for the same topic. | 12. The computer storage medium of claim 8 , wherein identifying a related query for the same topic comprises: identifying resources that were requested, by the particular use device, through the first user interactions with the search results; identifying, for two or more of the resources, a keyword that is associated with each of at least two of the resources; and identifying the keyword as the related query for the same topic. 13. The computer storage medium of claim 12 , wherein providing, for presentation on a search results page for the current query, the identified related query to the particular user device comprises providing, to the particular user device, data that cause presentation of the keyword and data that, in response to user interaction with the keyword, cause the particular user device to submit the keyword as a search query. | 0.872538 |
11. A non-transitory computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations for text input, the operations comprising: receiving a sequence of input characters corresponding to a user actuating multiple keys represented on an input; computing a character probability for a candidate word of multiple words in a dictionary, wherein the character probability for the candidate word is computed by combining character difference probabilities, and wherein each character difference probability is computed, for each selected character of multiple characters in the sequence of input characters, by applying a probability distribution that indicates, for the selected character, that a character in the candidate word was intended when the key corresponding to the selected character was actuated; and in response to receiving the sequence of input characters, selecting the candidate word, using a processor, based on the character probability for the candidate word. | 11. A non-transitory computer-readable storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations for text input, the operations comprising: receiving a sequence of input characters corresponding to a user actuating multiple keys represented on an input; computing a character probability for a candidate word of multiple words in a dictionary, wherein the character probability for the candidate word is computed by combining character difference probabilities, and wherein each character difference probability is computed, for each selected character of multiple characters in the sequence of input characters, by applying a probability distribution that indicates, for the selected character, that a character in the candidate word was intended when the key corresponding to the selected character was actuated; and in response to receiving the sequence of input characters, selecting the candidate word, using a processor, based on the character probability for the candidate word. 13. The computer-readable storage medium of claim 11 , wherein the operations further comprise obtaining a second probability for the candidate word, wherein the second probability for the candidate word indicates a likelihood, independent of the sequence of input characters, of occurrence of the candidate word; wherein selecting the candidate word is further based on the second probability for the candidate word; and wherein the likelihood indicated by the second probability for the candidate word is based at least in part on one or more of: a determination of a frequency with which the candidate word occurs, a sentence context into which the candidate word, when selected, will be used; or any combination thereof. | 0.5 |
8. A computer implemented multimedia method of capturing, storing, retrieving and disseminating personal and/or group legacy and history information comprising the steps of: a. providing a secure computer technology-based software platform for access by one or more authorized users over a computer network using one or more multimedia computer devices, the one or more computer devices capable of recording content in audio, video, photographic, and/or text format and/or combinations thereof; b. providing a graphical user interface for use by the one or more users with the one or more multimedia computer devices for interfacing with the platform over the network; c. interfacing the one or more computer devices with the platform; d. providing the ability within the platform for the one or more users to record new multimedia content from the one or more computer devices; e. providing the ability within the platform for the one or more users to access previously existing multimedia content available to the one or more computer devices; f. providing the ability for the one or more users to review or play back the new or previously existing content from the one or more computer devices from within the platform; g. providing the ability for the one or more users to edit the new or previously existing content from the one or more computer devices from within the platform; h. providing the ability for the one or more users to delete the new or previously existing content from the one or more computer devices; i. providing a platform server or platform cloud-based storage system for use by the one or more users for storing the recorded content within the platform; j. storing the recorded content onto the platform server or platform cloud-based storage; and k. providing the one or more users with the ability to retrieve the stored content from the platform server or platform cloud-based storage; wherein said graphical user interface further comprises a log-in process module for accessing a secure computer technology-based software platform for use by one or more authorized users over a computer network using one or more multimedia computer devices, the one or more computer devices capable of recording content in audio, video, photographic, and/or text format and/or combinations thereof; an auto question prompt module for prompting the user with a series of questions, one or more visual content images, audio files, music files, and/or one or more visual content videos stored within the system pertaining to topics of interest to prompt the user to provide answers to such questions to form part of the content; a tell a story process module for creating a recorded audio visual story capable of being played back by the one or more users by permitting the one or more users to access previously existing multimedia content available to the one or more computer devices, to review the new or previously existing content from the one or more computer devices from within the platform, to edit the new or previously existing content from the one or more computer devices from within the platform, and/or to delete the new or previously existing content from the one or more computer devices; wherein the step of editing the preexisting one or more visual content images and/or one or more visual content videos comprises selecting the desired visual content for display within the audio visual story, recording any desired audio content to accompany any of such selected visual content, such audio content being tied to such respective visual content so that such audio content becomes audible when such selected visual content is displayed during the playback of the recorded story; a save a story process module for permitting the one or more users to store the recorded content on the one or more computer devices, a platform server or a platform cloud-based storage system; and a share a story process module for permitting the one or more users to share the recorded content with others. | 8. A computer implemented multimedia method of capturing, storing, retrieving and disseminating personal and/or group legacy and history information comprising the steps of: a. providing a secure computer technology-based software platform for access by one or more authorized users over a computer network using one or more multimedia computer devices, the one or more computer devices capable of recording content in audio, video, photographic, and/or text format and/or combinations thereof; b. providing a graphical user interface for use by the one or more users with the one or more multimedia computer devices for interfacing with the platform over the network; c. interfacing the one or more computer devices with the platform; d. providing the ability within the platform for the one or more users to record new multimedia content from the one or more computer devices; e. providing the ability within the platform for the one or more users to access previously existing multimedia content available to the one or more computer devices; f. providing the ability for the one or more users to review or play back the new or previously existing content from the one or more computer devices from within the platform; g. providing the ability for the one or more users to edit the new or previously existing content from the one or more computer devices from within the platform; h. providing the ability for the one or more users to delete the new or previously existing content from the one or more computer devices; i. providing a platform server or platform cloud-based storage system for use by the one or more users for storing the recorded content within the platform; j. storing the recorded content onto the platform server or platform cloud-based storage; and k. providing the one or more users with the ability to retrieve the stored content from the platform server or platform cloud-based storage; wherein said graphical user interface further comprises a log-in process module for accessing a secure computer technology-based software platform for use by one or more authorized users over a computer network using one or more multimedia computer devices, the one or more computer devices capable of recording content in audio, video, photographic, and/or text format and/or combinations thereof; an auto question prompt module for prompting the user with a series of questions, one or more visual content images, audio files, music files, and/or one or more visual content videos stored within the system pertaining to topics of interest to prompt the user to provide answers to such questions to form part of the content; a tell a story process module for creating a recorded audio visual story capable of being played back by the one or more users by permitting the one or more users to access previously existing multimedia content available to the one or more computer devices, to review the new or previously existing content from the one or more computer devices from within the platform, to edit the new or previously existing content from the one or more computer devices from within the platform, and/or to delete the new or previously existing content from the one or more computer devices; wherein the step of editing the preexisting one or more visual content images and/or one or more visual content videos comprises selecting the desired visual content for display within the audio visual story, recording any desired audio content to accompany any of such selected visual content, such audio content being tied to such respective visual content so that such audio content becomes audible when such selected visual content is displayed during the playback of the recorded story; a save a story process module for permitting the one or more users to store the recorded content on the one or more computer devices, a platform server or a platform cloud-based storage system; and a share a story process module for permitting the one or more users to share the recorded content with others. 33. The computer implemented multimedia method of claim 8 wherein the audio editing comprises voice narration or commentary, singing a song or music. | 0.537197 |
16. The computer system implemented method of claim 15 , wherein the analytics model identifies user preferences for user experience options in a tax return preparation system that enables personalization of user experiences in the tax return preparation system to increase a likelihood of users performing one or more actions towards filing a tax return with the tax return preparation system. | 16. The computer system implemented method of claim 15 , wherein the analytics model identifies user preferences for user experience options in a tax return preparation system that enables personalization of user experiences in the tax return preparation system to increase a likelihood of users performing one or more actions towards filing a tax return with the tax return preparation system. 17. The computer system implemented method of claim 16 , wherein the one or more actions towards filing a tax return are selected from a group of actions consisting of: completing a sequence of questions; paying for a product within the tax return preparation system; completing a tax return preparation interview; completing a tax return preparation session; filing a tax return from within the tax return preparation system; entering personal information; entering credit card information into the tax return preparation system; transitioning from one user experience page to another user experience page in the tax return preparation system; logging into the tax return preparation system; and using the tax return preparation system for longer than a predetermined period of time. | 0.857524 |
1. In a general purpose computer, a method for automatically testing a business intelligence artifact in a business intelligence system, comprising: authoring a business intelligence artifact in the business intelligence system, wherein the business intelligence artifact produces output when the business intelligence artifact is executed in the business intelligence system, and wherein the business intelligence artifact is selected from the group consisting of: a report specification, an analysis cube, and a metadata model; creating at least one assertion to determine whether the business intelligence artifact is functioning properly; testing, with an automated agent interfaced with the business intelligence system, the business intelligence artifact to verify its proper functioning by determining whether the conditions of the at least one assertion are satisfied upon execution of the business intelligence artifact in the business intelligence system; reporting if the conditions of the at least one assertion are not satisfied upon execution of the business intelligence artifact in the business intelligence system; and recording one or more corrections made to the business intelligence artifact as a subsequent version of the business intelligence artifact. | 1. In a general purpose computer, a method for automatically testing a business intelligence artifact in a business intelligence system, comprising: authoring a business intelligence artifact in the business intelligence system, wherein the business intelligence artifact produces output when the business intelligence artifact is executed in the business intelligence system, and wherein the business intelligence artifact is selected from the group consisting of: a report specification, an analysis cube, and a metadata model; creating at least one assertion to determine whether the business intelligence artifact is functioning properly; testing, with an automated agent interfaced with the business intelligence system, the business intelligence artifact to verify its proper functioning by determining whether the conditions of the at least one assertion are satisfied upon execution of the business intelligence artifact in the business intelligence system; reporting if the conditions of the at least one assertion are not satisfied upon execution of the business intelligence artifact in the business intelligence system; and recording one or more corrections made to the business intelligence artifact as a subsequent version of the business intelligence artifact. 3. The method of claim 1 , further comprising executing the business intelligence artifact in the business intelligence system to generate a business intelligence output. | 0.594406 |
16. A method for performing multi-channel, selectable identity tagging data translation, comprising: receiving a word from a specified one of a plurality of controllable components, wherein the word comprises a shared address that does not distinguish between the controllable components and that contains no identifying information for the specified controllable component; generating a channel identifier (ID) corresponding to the specified controllable component, the channel ID uniquely identifying the specified controllable component; identifying a word type for the word; determining whether to tag the word with the channel ID based on the word type; and based on the determination, generating a processed word. | 16. A method for performing multi-channel, selectable identity tagging data translation, comprising: receiving a word from a specified one of a plurality of controllable components, wherein the word comprises a shared address that does not distinguish between the controllable components and that contains no identifying information for the specified controllable component; generating a channel identifier (ID) corresponding to the specified controllable component, the channel ID uniquely identifying the specified controllable component; identifying a word type for the word; determining whether to tag the word with the channel ID based on the word type; and based on the determination, generating a processed word. 19. The method of claim 16 , further comprising steering the processed word to one of a plurality of channel outputs based on the channel ID. | 0.812772 |
17. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: receiving encoded data from one or more browser applications, executing on one or more remote user computers, the encoded data generated by instrumentation within one or more instrumented web pages processed and rendered for display by the one or more browser applications; processing the received encoded data to produce a set of initially processed events, each of the set of initially processed events having an initial number of data entities; and further processing the set of initially processed events to generate a corresponding set of processed events, each processed event in the set of processed events having a data entity that represents a topic-assignment output assigned to the processed event, the further processing including: accessing a set of current distributions, the set of current distributions including: a regular-word distribution associated with a global topic; a seed-word distribution associated with the global topic; for each of a set of topics, a regular-word distribution associated with the topic; and for each of the set of topics, a seed-word distribution associated with the topic, the seed-word distribution associated with the topic including, for each seed word a plurality of seed words, a quantity indicating a number of observations where the seed word was included in an event and was associated with the topic; performing a set of iteration operations that include: for each word of a plurality of words in the set of initially processed events: determining, based on the set of current distributions, whether the word corresponds to a regular word or a seed word; determining, based on the set of current distributions, whether the word corresponds to a global topic or a discovered topic; and updating the set of current distributions based on, for each word of the plurality of words, the determination as to whether the word corresponds to a regular word or a seed word and the determination as to whether the word corresponds to a global topic or a discovered topic; and identifying based on one or more iterations of the set of iteration operations and for each initially processed event in the set of initially processed events, the topic-assignment output to be represented in a processed event in the set of processed events corresponding to the initially processed event. | 17. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: receiving encoded data from one or more browser applications, executing on one or more remote user computers, the encoded data generated by instrumentation within one or more instrumented web pages processed and rendered for display by the one or more browser applications; processing the received encoded data to produce a set of initially processed events, each of the set of initially processed events having an initial number of data entities; and further processing the set of initially processed events to generate a corresponding set of processed events, each processed event in the set of processed events having a data entity that represents a topic-assignment output assigned to the processed event, the further processing including: accessing a set of current distributions, the set of current distributions including: a regular-word distribution associated with a global topic; a seed-word distribution associated with the global topic; for each of a set of topics, a regular-word distribution associated with the topic; and for each of the set of topics, a seed-word distribution associated with the topic, the seed-word distribution associated with the topic including, for each seed word a plurality of seed words, a quantity indicating a number of observations where the seed word was included in an event and was associated with the topic; performing a set of iteration operations that include: for each word of a plurality of words in the set of initially processed events: determining, based on the set of current distributions, whether the word corresponds to a regular word or a seed word; determining, based on the set of current distributions, whether the word corresponds to a global topic or a discovered topic; and updating the set of current distributions based on, for each word of the plurality of words, the determination as to whether the word corresponds to a regular word or a seed word and the determination as to whether the word corresponds to a global topic or a discovered topic; and identifying based on one or more iterations of the set of iteration operations and for each initially processed event in the set of initially processed events, the topic-assignment output to be represented in a processed event in the set of processed events corresponding to the initially processed event. 18. The computer-program product of claim 17 , wherein the further processing further includes: for each iteration of at least one iteration: determining, after a most recent updating the set of current distributions, that another iteration is to be performed; and redefining, in response to determining that another iteration is to be performed, the set of current distributions to be the updated set of distributions and repeating the set of iteration operations; determining, after a most recent updating the set of current distributions, that another iteration is not to be performed; and wherein the topic-assignment output is identified in response to determining that another iteration is not to be performed, the topic-assignment output being based on the determination as to whether the word corresponds to a regular word or a seed word and the determination as to whether the word corresponds to a global topic or a discovered topic. | 0.626175 |
9. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: obtain question information identifying extracted features of an input question and a first source communication device that is a source of the input question; perform a clustering operation to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions; perform an operation based on results of the clustering of the input question with the one or more other questions, wherein the operation comprises automatically initiating a communication between the first source communication device and a second source communication device that is a source of another question in the cluster at least by one of automatically establishing a communication connection between the first source communication device and the second source communication device or automatically initiating a collaboration session, in a computer-implemented collaboration system, between the first source communication device and the second source communication device, wherein the input question is a question input to a Question and Answer (QA) system which processes the input question to generate an answer to the input question based on a corpus of information, and further generates one or more supporting evidence passages supporting the answer as being a correct answer for the input question; and generate, for the input question, one or more question (Q)-Answer (A)-evidence Passage (P) triplets, wherein performing the clustering operation comprises performing clustering on features of the one or more QAP triplets. | 9. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: obtain question information identifying extracted features of an input question and a first source communication device that is a source of the input question; perform a clustering operation to cluster the input question with one or more other questions of a cluster based on a similarity of the extracted features of the input question to features of the one or more other questions; perform an operation based on results of the clustering of the input question with the one or more other questions, wherein the operation comprises automatically initiating a communication between the first source communication device and a second source communication device that is a source of another question in the cluster at least by one of automatically establishing a communication connection between the first source communication device and the second source communication device or automatically initiating a collaboration session, in a computer-implemented collaboration system, between the first source communication device and the second source communication device, wherein the input question is a question input to a Question and Answer (QA) system which processes the input question to generate an answer to the input question based on a corpus of information, and further generates one or more supporting evidence passages supporting the answer as being a correct answer for the input question; and generate, for the input question, one or more question (Q)-Answer (A)-evidence Passage (P) triplets, wherein performing the clustering operation comprises performing clustering on features of the one or more QAP triplets. 13. The computer program product of claim 9 , wherein the first source communication device and second source communication device are communication devices associated with users, and wherein the operation further comprises at least one of: sending targeted advertising, by a third party source communication device, to the first source communication device and the second source communication device, providing information about the first source communication device and the second source communication device to the third party source communication device, performing, by a third party system, data mining of user information for the cluster, initiating a targeted processing of user information of the first source communication device or second source communication device to analyze other activities by the first source communication device or second source communication device, and identifying, by a government organization system, the first source communication device and the second source communication device as potentially engaged in illegal activity and targeting the first source communication device and second source communication device for further investigation of illegal activity. | 0.509414 |
1. A user-interface method of incrementally providing fully qualified links to a set of relevant search engines, the method comprising: identifying a set of search engines and associating each search engine of the set with at least one descriptive category to which the subject matter of the corresponding search engine relates; providing a database containing a collection of potential full queries, each potential full query associated in said database with at least one descriptive category; receiving a partial search query entered on a keypad by a user; after each keypress received from the user, inferring a set of potential full queries intended by the user, based at least in part on the partial search query; selecting a subset of the identified search engines that are relevant to the inferred full queries based on comparing the inferred full queries with the descriptive categories associated with the search engines, wherein the selecting the subset of relevant search engines is further based on the descriptive categories associated with the provided potential full queries; and for each of the selected search engines, providing a fully qualified link designed to directly launch a search for a relevant query string using the search engine. | 1. A user-interface method of incrementally providing fully qualified links to a set of relevant search engines, the method comprising: identifying a set of search engines and associating each search engine of the set with at least one descriptive category to which the subject matter of the corresponding search engine relates; providing a database containing a collection of potential full queries, each potential full query associated in said database with at least one descriptive category; receiving a partial search query entered on a keypad by a user; after each keypress received from the user, inferring a set of potential full queries intended by the user, based at least in part on the partial search query; selecting a subset of the identified search engines that are relevant to the inferred full queries based on comparing the inferred full queries with the descriptive categories associated with the search engines, wherein the selecting the subset of relevant search engines is further based on the descriptive categories associated with the provided potential full queries; and for each of the selected search engines, providing a fully qualified link designed to directly launch a search for a relevant query string using the search engine. 6. The method of claim 1 wherein the keypad includes overloaded keys in which each overloaded key corresponds to more than one alphanumeric character. | 0.571769 |
9. A computer program product, comprising modules embodied in a computer readable medium, for enabling translation of Standard Commands for Programmable Instrumentation (SCPI) protocol commands and queries to a programming language for controlling an instrument configured to use SCPI commands and queries, the computer program product comprising: a translation module configured to receive computer readable descriptions of SCPI commands and queries identified with respect to the instrument, and to generate programming language routines for translating the SCPI commands and queries to be compatible with the programming language; and a program library module configured to store the programming language routines, wherein the programming language routines are accessible to a control program written in the programming language for controlling at least one operation of the instrument. | 9. A computer program product, comprising modules embodied in a computer readable medium, for enabling translation of Standard Commands for Programmable Instrumentation (SCPI) protocol commands and queries to a programming language for controlling an instrument configured to use SCPI commands and queries, the computer program product comprising: a translation module configured to receive computer readable descriptions of SCPI commands and queries identified with respect to the instrument, and to generate programming language routines for translating the SCPI commands and queries to be compatible with the programming language; and a program library module configured to store the programming language routines, wherein the programming language routines are accessible to a control program written in the programming language for controlling at least one operation of the instrument. 12. The computer program product of claim 9 , wherein the translation module comprises: a syntax identification module configured to identify a syntax of each SCPI command and query based on corresponding information in the description file provided by the description module; a parameter input identification module configured to identify at least one input for each parameter of each syntax identified by the syntax identification module; and a string conversion module configured to convert each parameter input into a string having an appropriate format for the parameter of the command or query. | 0.603081 |
20. The non-transitory computer-readable medium of claim 17 , wherein: the similarity metric represents a difference between a position of an integer in the first sequence and a position of the integer in the second sequence, and the integer is included in the third sequence if the difference is within a first pre-determined distance. | 20. The non-transitory computer-readable medium of claim 17 , wherein: the similarity metric represents a difference between a position of an integer in the first sequence and a position of the integer in the second sequence, and the integer is included in the third sequence if the difference is within a first pre-determined distance. 21. The non-transitory computer-readable medium of claim 20 , wherein: the first predetermined distance is four. | 0.926282 |
1. A system, comprising: a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: based on an analysis of conversation data transmitted within an audio communication session between a first communication device of a network and a second communication device of the network, determining, during the audio communication session, goal data indicative of a goal associated with the first communication device and the second communication device; based on data received from a network data store coupled to the first communication device and the second communication device, determining solution data indicative of solutions to accomplish the goal; and in response to the determining the solution data and determining that first speech data associated with the conversation data is not being transmitted between the first communication device and the second communication device during the audio communication session, generating second speech data based on the solution data and adding a network device that is coupled to the first communication device and the second communication device via the network as an additional participant in the audio communication session, and simultaneously transmitting the second speech data from the network device to the first communication device and the second communication device, wherein, in addition to the conversation data transmitted between the first communication device and the second communication device during the audio communication session, the second speech data is transmitted to the first communication device and the second communication device during the audio communication session, and third speech data is transmitted between the first communication device and the second communication device after receiving the second speech data associated with the solution data. | 1. A system, comprising: a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: based on an analysis of conversation data transmitted within an audio communication session between a first communication device of a network and a second communication device of the network, determining, during the audio communication session, goal data indicative of a goal associated with the first communication device and the second communication device; based on data received from a network data store coupled to the first communication device and the second communication device, determining solution data indicative of solutions to accomplish the goal; and in response to the determining the solution data and determining that first speech data associated with the conversation data is not being transmitted between the first communication device and the second communication device during the audio communication session, generating second speech data based on the solution data and adding a network device that is coupled to the first communication device and the second communication device via the network as an additional participant in the audio communication session, and simultaneously transmitting the second speech data from the network device to the first communication device and the second communication device, wherein, in addition to the conversation data transmitted between the first communication device and the second communication device during the audio communication session, the second speech data is transmitted to the first communication device and the second communication device during the audio communication session, and third speech data is transmitted between the first communication device and the second communication device after receiving the second speech data associated with the solution data. 6. The system of claim 1 , wherein the solution data is determined based on location data representing a geographical location of the first communication device. | 0.597938 |
1. A method for efficiently identifying dynamic content of a webpage, the method comprising: (a) accessing, by a virtual browser of a plurality of virtual browsers executing on a device intermediary to a plurality of clients and a plurality of servers a first stored data file representing a first version of a web page and a first abstract syntax tree corresponding to the first stored data file, the abstract syntax tree comprising at least one static node, the static node including stored content; (b) identifying, by the virtual browser of the plurality of virtual browsers, non-matching dynamic content between the first stored data file and a second data file representing a second version of the web page without using a second abstract syntax tree corresponding to the second data file; and (c) replacing, by the virtual browser, the at least one static node corresponding to the non-matching dynamic content in the first abstract syntax tree with a token that identifies the portion of the abstract syntax tree containing the non-matching dynamic content. | 1. A method for efficiently identifying dynamic content of a webpage, the method comprising: (a) accessing, by a virtual browser of a plurality of virtual browsers executing on a device intermediary to a plurality of clients and a plurality of servers a first stored data file representing a first version of a web page and a first abstract syntax tree corresponding to the first stored data file, the abstract syntax tree comprising at least one static node, the static node including stored content; (b) identifying, by the virtual browser of the plurality of virtual browsers, non-matching dynamic content between the first stored data file and a second data file representing a second version of the web page without using a second abstract syntax tree corresponding to the second data file; and (c) replacing, by the virtual browser, the at least one static node corresponding to the non-matching dynamic content in the first abstract syntax tree with a token that identifies the portion of the abstract syntax tree containing the non-matching dynamic content. 2. The method of claim 1 , wherein step (b) further comprises receiving, by the device, the second version of the web page from a server. | 0.580721 |
13. An apparatus, comprising: a processor; and a memory to store instructions that, when executed by the processor, are operable to: receive an initial portion of a current user query, wherein the initial portion includes a particular string of text; train a model, said training comprising: continuously logging previous user queries; extracting a question from the continuously logged previous user queries; analyzing the extracted question to obtain at least one feature; assigning an intent to the extracted question; and adding an entry to the model and mapping the obtained feature for the extracted question to the assigned intent for the extracted question in the added entry of the model; guess an intent associated with the initial portion of the current user query while a remainder of the current user query is being received, wherein the guess using only the initial portion includes the following operations: extracting at least one feature from the initial portion; comparing, using the processing device, the extracted at least one feature to entries from the model; selecting, using the processing device, at least one entry from the model based on the comparison; and determining the intent for the initial portion of the current user query according to an intent included in the selected entry; wherein the intent guess obtained for the initial portion according to said extracting, said comparing, said selecting, and said determining does not include any portion of the particular text string; and respond to the initial portion of the current user query with the intent guess. | 13. An apparatus, comprising: a processor; and a memory to store instructions that, when executed by the processor, are operable to: receive an initial portion of a current user query, wherein the initial portion includes a particular string of text; train a model, said training comprising: continuously logging previous user queries; extracting a question from the continuously logged previous user queries; analyzing the extracted question to obtain at least one feature; assigning an intent to the extracted question; and adding an entry to the model and mapping the obtained feature for the extracted question to the assigned intent for the extracted question in the added entry of the model; guess an intent associated with the initial portion of the current user query while a remainder of the current user query is being received, wherein the guess using only the initial portion includes the following operations: extracting at least one feature from the initial portion; comparing, using the processing device, the extracted at least one feature to entries from the model; selecting, using the processing device, at least one entry from the model based on the comparison; and determining the intent for the initial portion of the current user query according to an intent included in the selected entry; wherein the intent guess obtained for the initial portion according to said extracting, said comparing, said selecting, and said determining does not include any portion of the particular text string; and respond to the initial portion of the current user query with the intent guess. 15. The apparatus of claim 13 , wherein the at least one feature includes at least one selected from the group comprising query tokens, query token stems, and concepts. | 0.624938 |
5. The security analysis tool of claim 2 , wherein the interface component is configured to render a display output having associated display objects and receive at least one input to facilitate operations with the analyzer component, and wherein the interface component is associated with at least one of an engine, an application, an editor tool, a web browser, or a web service. | 5. The security analysis tool of claim 2 , wherein the interface component is configured to render a display output having associated display objects and receive at least one input to facilitate operations with the analyzer component, and wherein the interface component is associated with at least one of an engine, an application, an editor tool, a web browser, or a web service. 7. The security analysis tool of claim 5 , wherein the at least one input comprises a user command from at least one of a mouse, a keyboard, speech input, a web site, a remote web service, a camera, or video input analyzer component. | 0.878219 |
2. The method of claim 1 , further comprising: f) calculating a figure of merit using the sample values in the gesture and sample values in one or more catalog gesture, wherein the figure of merit is a measure of how well the gesture matched the catalog gesture; g) determining whether an input gesture matches one of the one or more catalog gesture based on the figure of merit; and h) taking action if the input gesture matches the one of the one or more catalog gesture. | 2. The method of claim 1 , further comprising: f) calculating a figure of merit using the sample values in the gesture and sample values in one or more catalog gesture, wherein the figure of merit is a measure of how well the gesture matched the catalog gesture; g) determining whether an input gesture matches one of the one or more catalog gesture based on the figure of merit; and h) taking action if the input gesture matches the one of the one or more catalog gesture. 4. The method of claim 2 , wherein the method is applied to a video game. | 0.804617 |
1. A method for associating documents with searchable metadata, the method comprising: receiving as input at least one text document; and operating at least one programmed processor to perform acts of creating metadata to be associated with the at least one text document, the metadata comprising at least one text keyword, the creating comprising extracting a set of one or more data elements from text of the at least one text document, the set of one or more data elements comprising at least one keyword that appears in the text of the at least one text document; normalizing said set of data elements to create a set of normalized data elements, wherein the normalizing comprises, for a first keyword of the at least one keyword, determining at least one other keyword similar to the first keyword, the at least one other keyword not being a keyword appearing in the text of the at least one text document, and adding the at least one other keyword to the set of normalized data elements; identifying at least one previously-validated keyword that is associated as metadata with at least one previously-stored text document, the at least one previously-stored text document not being one of the at least one text document, the at least one previously-validated keyword not being in the set of normalized data elements; merging said set of normalized data elements with the at least one previously-validated keyword to form a preliminary set of data elements; presenting said preliminary set of data elements for review by a user; and receiving user input validating a validated set of data elements; and in response to the user input validating the validated set of data elements, storing the at least one text document and storing the validated set of data elements as the metadata, the metadata being associated with the at least one text document such that the at least one text document may be located through a search for any data element included in the validated set of data elements. | 1. A method for associating documents with searchable metadata, the method comprising: receiving as input at least one text document; and operating at least one programmed processor to perform acts of creating metadata to be associated with the at least one text document, the metadata comprising at least one text keyword, the creating comprising extracting a set of one or more data elements from text of the at least one text document, the set of one or more data elements comprising at least one keyword that appears in the text of the at least one text document; normalizing said set of data elements to create a set of normalized data elements, wherein the normalizing comprises, for a first keyword of the at least one keyword, determining at least one other keyword similar to the first keyword, the at least one other keyword not being a keyword appearing in the text of the at least one text document, and adding the at least one other keyword to the set of normalized data elements; identifying at least one previously-validated keyword that is associated as metadata with at least one previously-stored text document, the at least one previously-stored text document not being one of the at least one text document, the at least one previously-validated keyword not being in the set of normalized data elements; merging said set of normalized data elements with the at least one previously-validated keyword to form a preliminary set of data elements; presenting said preliminary set of data elements for review by a user; and receiving user input validating a validated set of data elements; and in response to the user input validating the validated set of data elements, storing the at least one text document and storing the validated set of data elements as the metadata, the metadata being associated with the at least one text document such that the at least one text document may be located through a search for any data element included in the validated set of data elements. 12. The method according to claim 1 , wherein the creating further comprises: identifying the at least one keyword by examining a set of one or more previously-stored documents to identify documents related to the at least one text document. | 0.572814 |
5. The method of claim 1 , wherein determining whether all identified components account for all of the structural, functional, operational, and conceptual terms includes determining whether the identified components account for all of the structural, functional, operational, and conceptual terms of a first data set. | 5. The method of claim 1 , wherein determining whether all identified components account for all of the structural, functional, operational, and conceptual terms includes determining whether the identified components account for all of the structural, functional, operational, and conceptual terms of a first data set. 7. The method of claim 5 , further comprising determining whether the one or more measured gaps account for all of the structural, functional, operational, and conceptual aspects of a gap between the first data set and a second data set. | 0.895279 |
7. A method for analyzing and reproducing text data, comprising: partitioning, with circuitry, according to a 4-shapes model, a collected dataset of an Arabic alphabet including sentences associated with the Arabic alphabet and Arabic typography, the 4-shapes model including a legative partition including isolated bigram representation and classified words that contain ligature representations of the collected dataset, an unlegative partition including single character shape representation of the collected data set, an isolated characters partition, and a passages and repeated phrases partition; identifying legative bigrams of character shapes within the partitioned dataset; generating, with the circuitry, a pangram based on the partitions of the 4-shapes model, the pangram including the occurrence of every character shape in the collected dataset and further including a lipogram condition set based on a desired digital output of the collected dataset, the lipogram condition omitting legative bigrams of predetermined Arabic character shapes; and outputting, with the circuitry, a digital representation of the pangram as synthesized text. | 7. A method for analyzing and reproducing text data, comprising: partitioning, with circuitry, according to a 4-shapes model, a collected dataset of an Arabic alphabet including sentences associated with the Arabic alphabet and Arabic typography, the 4-shapes model including a legative partition including isolated bigram representation and classified words that contain ligature representations of the collected dataset, an unlegative partition including single character shape representation of the collected data set, an isolated characters partition, and a passages and repeated phrases partition; identifying legative bigrams of character shapes within the partitioned dataset; generating, with the circuitry, a pangram based on the partitions of the 4-shapes model, the pangram including the occurrence of every character shape in the collected dataset and further including a lipogram condition set based on a desired digital output of the collected dataset, the lipogram condition omitting legative bigrams of predetermined Arabic character shapes; and outputting, with the circuitry, a digital representation of the pangram as synthesized text. 8. The method of claim 7 , further comprising: identifying, based on the lipogram condition, legative bigrams of character shapes that are not omni-ligatives, omni-ligatives being character shapes that are ligatable with every previous character. | 0.723485 |
7. 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: identifying, in a database of utterances, transcribed utterances and un-transcribed utterances; ordering transcription candidate utterances from the un-transcribed utterances based on confidence scores of the transcription candidate utterances, to yield a selectively sampled order; transcribing, via a processor, a top n utterances from the selectively sampled order, to yield additional transcribed utterances and remainder un-transcribed utterances, wherein the remainder un-transcribed utterances are the un-transcribed utterances without the additional transcribed utterances; receiving human-transcribed utterances, wherein the human-transcribed utterances are selected from the remainder un-transcribed utterances for human transcription based on the confidence scores; and adding the additional transcribed utterances and the human-transcribed utterances to the database of utterances. | 7. 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: identifying, in a database of utterances, transcribed utterances and un-transcribed utterances; ordering transcription candidate utterances from the un-transcribed utterances based on confidence scores of the transcription candidate utterances, to yield a selectively sampled order; transcribing, via a processor, a top n utterances from the selectively sampled order, to yield additional transcribed utterances and remainder un-transcribed utterances, wherein the remainder un-transcribed utterances are the un-transcribed utterances without the additional transcribed utterances; receiving human-transcribed utterances, wherein the human-transcribed utterances are selected from the remainder un-transcribed utterances for human transcription based on the confidence scores; and adding the additional transcribed utterances and the human-transcribed utterances to the database of utterances. 10. The system of claim 7 , the computer-readable storage medium having additional instruction stored which result in the operations further comprising: upon adding the additional transcribed utterances to the database of utterances, removing the additional utterances from the un-transcribed utterances. | 0.5 |
1. A method of providing a confidence-estimation-based inference, the method comprising: receiving a query concerning a patient from a user; accessing an electronic health record (EHR) for the patient, the EHR including a first component regarding the patient; querying the user, using a conversational interface, for a second component regarding the patient, the second component being in a natural language information form; receiving the second component regarding the patient in response to the query; calculating a first probability density function using the first component, and a second probability density function using the second component; combining the first and second probability density functions using a Gaussian mixture model; calculating at least one conditional probability table using the Gaussian mixture model; and providing the confidence-estimation-based inference based on the at least one conditional probability table. | 1. A method of providing a confidence-estimation-based inference, the method comprising: receiving a query concerning a patient from a user; accessing an electronic health record (EHR) for the patient, the EHR including a first component regarding the patient; querying the user, using a conversational interface, for a second component regarding the patient, the second component being in a natural language information form; receiving the second component regarding the patient in response to the query; calculating a first probability density function using the first component, and a second probability density function using the second component; combining the first and second probability density functions using a Gaussian mixture model; calculating at least one conditional probability table using the Gaussian mixture model; and providing the confidence-estimation-based inference based on the at least one conditional probability table. 8. The method of claim 1 further comprising: assigning a first weight to a source of the first component; assigning a second weight to a source of the second component, wherein the weights sum to one hundred percent; and mathematically combining the weight for the first source with the first probability density function and mathematically combining the weight for the second source with the second probability density function. | 0.820084 |
1. A system for facilitating the exchange of speech recognition and transcription among users, the system comprising: (a) at least one system transaction manager and at least one post processing manager, both using a uniform system protocol wherein the transaction manager is i) adapted to receive a speech information request from at least one user employing a first user legacy protocol and flag the information request as requiring post processing, and, ii) configured to route a response to the speech information request to one or more users employing a second user legacy protocol, the speech information request comprised of spoken text and commands, including spoken commands, wherein the response comprises at least a transcription of spoken text and the post processed information requested, and wherein the post processing manager is configured to i) receive structured transcription from a speech recognition and/or transcription engine, ii) operate upon the transcribed response, including spoken commands in accordance with the speech information request, and, iii) rout the requested response to a post processing application if specified in the speech information request; (b) at least one application service adapter configured to provide bi-directional communication conversion between the first user legacy protocol and the uniform system protocol and between the second user legacy protocol and the uniform system protocol and capable of bi-directional communication with the system transaction manager; and, (c) at least one speech recognition and/or transcription engine communicating with the system transaction manager, wherein the speech recognition and/or transcription engine is configured to receive the flagged speech information request containing spoken text and commands, including spoken commands, from the system transaction manager to generate a transcription in response to the speech information request and to route the response comprised of transcribed spoken text and commands, including transcribed spoken commands to the post processing manager. | 1. A system for facilitating the exchange of speech recognition and transcription among users, the system comprising: (a) at least one system transaction manager and at least one post processing manager, both using a uniform system protocol wherein the transaction manager is i) adapted to receive a speech information request from at least one user employing a first user legacy protocol and flag the information request as requiring post processing, and, ii) configured to route a response to the speech information request to one or more users employing a second user legacy protocol, the speech information request comprised of spoken text and commands, including spoken commands, wherein the response comprises at least a transcription of spoken text and the post processed information requested, and wherein the post processing manager is configured to i) receive structured transcription from a speech recognition and/or transcription engine, ii) operate upon the transcribed response, including spoken commands in accordance with the speech information request, and, iii) rout the requested response to a post processing application if specified in the speech information request; (b) at least one application service adapter configured to provide bi-directional communication conversion between the first user legacy protocol and the uniform system protocol and between the second user legacy protocol and the uniform system protocol and capable of bi-directional communication with the system transaction manager; and, (c) at least one speech recognition and/or transcription engine communicating with the system transaction manager, wherein the speech recognition and/or transcription engine is configured to receive the flagged speech information request containing spoken text and commands, including spoken commands, from the system transaction manager to generate a transcription in response to the speech information request and to route the response comprised of transcribed spoken text and commands, including transcribed spoken commands to the post processing manager. 4. The system of claim 1 wherein at least one embedded non-spoken command in the speech information request directs the post processing manager to flag the request for post processing. | 0.79807 |
21. Apparatus as in claim 20 wherein said assigning means includes means for representing the value p'(y.sub.k) for a given y.sub.k in a given phone machine by the maximum p(y.sub.k) value for the given y.sub.k in the given phone machine. | 21. Apparatus as in claim 20 wherein said assigning means includes means for representing the value p'(y.sub.k) for a given y.sub.k in a given phone machine by the maximum p(y.sub.k) value for the given y.sub.k in the given phone machine. 22. Apparatus as in claim 21 including means for setting the uniform value of the length probability to be equal to the maximum probability of any length generable by each particular phone machine. | 0.925413 |
10. One or more non-transitory computer-readable tangible media encoding software operable when executed to: access a corpus stored in one or more tangible media, the corpus comprising a plurality of terms; perform the following for each term of one or more terms of the plurality of terms to yield a plurality of parent-child relationships: identify one or more parent terms of the each term according to directional affinity, the plurality of terms comprising the one or more parent terms, the directional affinity being the number of co-occurrence contexts that include two terms, over the number of co-occurrence contexts that include one term; and establish one or more parent-child relationships from the one or more parent terms and the each term; and automatically generate a hierarchical graph from the plurality of parent-child relationships, wherein the automatically generating the hierarchical graph from the plurality of parent-child relationships comprises reducing the hierarchical graph by: identifying a parent-child relationship and a redundant parent-child relationship of the hierarchical graph; and removing the redundant parent-child relationship from the hierarchical graph. | 10. One or more non-transitory computer-readable tangible media encoding software operable when executed to: access a corpus stored in one or more tangible media, the corpus comprising a plurality of terms; perform the following for each term of one or more terms of the plurality of terms to yield a plurality of parent-child relationships: identify one or more parent terms of the each term according to directional affinity, the plurality of terms comprising the one or more parent terms, the directional affinity being the number of co-occurrence contexts that include two terms, over the number of co-occurrence contexts that include one term; and establish one or more parent-child relationships from the one or more parent terms and the each term; and automatically generate a hierarchical graph from the plurality of parent-child relationships, wherein the automatically generating the hierarchical graph from the plurality of parent-child relationships comprises reducing the hierarchical graph by: identifying a parent-child relationship and a redundant parent-child relationship of the hierarchical graph; and removing the redundant parent-child relationship from the hierarchical graph. 15. The computer-readable tangible media of claim 10 : the corpus comprising a plurality of search results organized into a plurality of clusters; a term of the one or more terms representing a topic of a cluster; and the hierarchical graph describing the plurality of parent-child relationships of the plurality of search results. | 0.69 |
1. A method for image management comprising steps performed by a processor, said method comprising: in the processor, accessing image data for an image containing objects; generating graphical representations of the objects based upon centroids of the objects; determining the centroids and the sizes of the graphical representations; determining the locations of the centroids of the graphical representations; determining morphologies of the graphical representations based upon the determined locations of the centroids, wherein the morphologies of the graphical representations comprise physical relationships of the graphical representations with respect to each other; and assigning human readable lexical representations of the locations of the centroids, the sizes, the colors and morphologies of the graphical representations. | 1. A method for image management comprising steps performed by a processor, said method comprising: in the processor, accessing image data for an image containing objects; generating graphical representations of the objects based upon centroids of the objects; determining the centroids and the sizes of the graphical representations; determining the locations of the centroids of the graphical representations; determining morphologies of the graphical representations based upon the determined locations of the centroids, wherein the morphologies of the graphical representations comprise physical relationships of the graphical representations with respect to each other; and assigning human readable lexical representations of the locations of the centroids, the sizes, the colors and morphologies of the graphical representations. 6. The method according to claim 1 , wherein assigning human readable lexical representations of the sizes of the graphical representations further comprises assigning a quantized set of lexical size designations to the regions. | 0.685036 |
8. The system of claim 1 including a CASE optimization tool for measuring component execution time and spatial requirements. | 8. The system of claim 1 including a CASE optimization tool for measuring component execution time and spatial requirements. 9. The system of claim 8 including a CASE automatic testing tool. | 0.969257 |
15. The method of claim 1 , further comprising: receiving a selection of at least one of the one or more predetermined search categories or at least one of the one or more predetermined subcategories on the initial search results page; generating a second search results page comprising: (1) a first display area useable for presenting a new updated set of search results relevant to the selected at least one of the one or more predetermined search categories or the at least one of the one or more predetermined subcategories; and (2) a second display area useable for presenting the portion of the one or more predetermined search categories and the one or more predetermined subcategories in their entirety. | 15. The method of claim 1 , further comprising: receiving a selection of at least one of the one or more predetermined search categories or at least one of the one or more predetermined subcategories on the initial search results page; generating a second search results page comprising: (1) a first display area useable for presenting a new updated set of search results relevant to the selected at least one of the one or more predetermined search categories or the at least one of the one or more predetermined subcategories; and (2) a second display area useable for presenting the portion of the one or more predetermined search categories and the one or more predetermined subcategories in their entirety. 16. The method of claim 15 , wherein the second display area on the second search results page is displayed in a separate area than the first display area on the second search results page. | 0.893823 |
10. The neural network of claim 9 , wherein: the at least one parameterized rule comprises a parameterized function of at least one parameter; and a characteristic of at least one of the plurality of nodes is configured to be adjusted based at least in part on the at least one parameter. | 10. The neural network of claim 9 , wherein: the at least one parameterized rule comprises a parameterized function of at least one parameter; and a characteristic of at least one of the plurality of nodes is configured to be adjusted based at least in part on the at least one parameter. 22. The neural network of claim 10 , wherein: the node output comprises a spiking signal; and generation of an output signal at the node is configured based at least in part on a state modification of a state of the node. | 0.861457 |
8. A computer system for enabling a user to execute one or more search queries to identify and rank relevant documents from a corpus of citationally-related documents, said computer system comprising: an input interface that enables said user to select a first set of identification information identifying one or more input documents from said corpus of citationally-related documents; a computer-accessible index stored in a computer-readable storage device, said computer-accessible index comprising identification information identifying each potential input document from said corpus of citationally-related documents and, for each said potential input document, identification information identifying a selected number of citationally-related potential output documents from said corpus of citationally-related documents, said computer-accessible index further comprising for each pair of citationally-related potential input document and potential output document a first numerical score configured to have a statistical correlation to whether a direct citation exists between said corresponding pair of citationally-related documents and wherein said first numerical score is calculated based at least in part on how many indirect citations exist between each said pair of citationally related documents and, for each indirect citation, how many citation links separate each said pair of citationally-related documents; a computer processor configured to execute instructions stored in a computer-readable storage device, said instructions configured to cause said computer processor to: use said first set of identification information to ascertain, from said computer-accessible index, a second set of identification information identifying, for each of said one or more input documents, a selected number of citationally-related output documents and, for each pair of citationally-related input document and output document, said first numerical score; and calculate, for each of said citationally-related output documents, a second numerical score configured to have a statistical correlation to whether a direct citation exists between any of said one or more input documents and each of said citationally-related output documents, and wherein said second numerical score is calculated based at least in part on said first numerical score; and an output interface to present search query results comprising a third set of identification information identifying one or more of said citationally-related output documents and wherein said search query results are sorted and displayed in accordance with said second numerical score. | 8. A computer system for enabling a user to execute one or more search queries to identify and rank relevant documents from a corpus of citationally-related documents, said computer system comprising: an input interface that enables said user to select a first set of identification information identifying one or more input documents from said corpus of citationally-related documents; a computer-accessible index stored in a computer-readable storage device, said computer-accessible index comprising identification information identifying each potential input document from said corpus of citationally-related documents and, for each said potential input document, identification information identifying a selected number of citationally-related potential output documents from said corpus of citationally-related documents, said computer-accessible index further comprising for each pair of citationally-related potential input document and potential output document a first numerical score configured to have a statistical correlation to whether a direct citation exists between said corresponding pair of citationally-related documents and wherein said first numerical score is calculated based at least in part on how many indirect citations exist between each said pair of citationally related documents and, for each indirect citation, how many citation links separate each said pair of citationally-related documents; a computer processor configured to execute instructions stored in a computer-readable storage device, said instructions configured to cause said computer processor to: use said first set of identification information to ascertain, from said computer-accessible index, a second set of identification information identifying, for each of said one or more input documents, a selected number of citationally-related output documents and, for each pair of citationally-related input document and output document, said first numerical score; and calculate, for each of said citationally-related output documents, a second numerical score configured to have a statistical correlation to whether a direct citation exists between any of said one or more input documents and each of said citationally-related output documents, and wherein said second numerical score is calculated based at least in part on said first numerical score; and an output interface to present search query results comprising a third set of identification information identifying one or more of said citationally-related output documents and wherein said search query results are sorted and displayed in accordance with said second numerical score. 10. The computer system of claim 8 wherein said instructions are configured to cause said computer processor to calculate said second numerical score for each of said citationally-related output documents by calculating the mathematical sum of said first numerical score for each corresponding pair of citationally-related input document and output document. | 0.524834 |
1. A method of generating an electronic document, the method comprising: photographing a document having a plurality of pages to generate moving picture data; detecting data of one page of the document by performing motion estimation on the moving picture data, performing document recognition on the data of the one page of the document, and storing the data of the one page of the document as first text data; detecting whether data of a next page is input by performing motion estimation on the moving picture data, and if the data of the next page is detected, performing document recognition on the data of the next page and storing the data of the next page as second text data; and storing the first text data and the second text data as one electronic document. | 1. A method of generating an electronic document, the method comprising: photographing a document having a plurality of pages to generate moving picture data; detecting data of one page of the document by performing motion estimation on the moving picture data, performing document recognition on the data of the one page of the document, and storing the data of the one page of the document as first text data; detecting whether data of a next page is input by performing motion estimation on the moving picture data, and if the data of the next page is detected, performing document recognition on the data of the next page and storing the data of the next page as second text data; and storing the first text data and the second text data as one electronic document. 7. The method of claim 1 , wherein in the photographing of the document, the document is photographed using an image data input device, and the moving picture data is generated from the photographed document. | 0.688259 |
17. The system of claim 16 , wherein the user-defined named memory variable is defined as a SECURE user-defined named memory variable, further comprising, determining whether a user associated with the first query satisfies one or more authorization privileges defined for the SECURE user-defined named memory variable; accessing the associated value for the SECURE user-defined named memory variable in response to the first query satisfying one or more authorization privileges and the first query referencing the SECURE user-defined named memory variable; and sending a result for the first query, the result based on the associated value. | 17. The system of claim 16 , wherein the user-defined named memory variable is defined as a SECURE user-defined named memory variable, further comprising, determining whether a user associated with the first query satisfies one or more authorization privileges defined for the SECURE user-defined named memory variable; accessing the associated value for the SECURE user-defined named memory variable in response to the first query satisfying one or more authorization privileges and the first query referencing the SECURE user-defined named memory variable; and sending a result for the first query, the result based on the associated value. 18. The system of claim 17 , wherein the authorization privileges are selected from the group comprising READ, WRITE, and WITH GRANT OPTION to grant privileges to others. | 0.913153 |
14. The tangible computer-readable medium of claim 12 , wherein the multi-dimensional data store is generated by calculating aggregated fact data from the fact data according to a multi-dimensional data aggregation process, and storing the aggregated fact data in the multi-dimensional data store. | 14. The tangible computer-readable medium of claim 12 , wherein the multi-dimensional data store is generated by calculating aggregated fact data from the fact data according to a multi-dimensional data aggregation process, and storing the aggregated fact data in the multi-dimensional data store. 15. The tangible computer-readable medium of claim 14 , wherein the aggregated fact data is calculated on demand and stored in the multi-dimensional data store. | 0.952213 |
1. A method comprising: by one or more computing devices, for each information item of a plurality of information items in a collection of information associated with a first user: constructing a vector space model; and adjusting the vector space model by an importance score of the information item, wherein the importance score indicates the level of importance the information item is to the first user; calculating a degree of similarity for every two information items in the collection of information; based on the degree of similarity calculated for every two information items in the collection of information, clustering the plurality of information items in the collection of information associated with the first user to generate a plurality of information topics using the adjusted vector space model of each information item; for each information topic of the plurality of information topics generated from the collection of information associated with the first user, determining an interest score, which indicates a level of interest the first user has in the information topic relative to other information topics within the plurality of information topics; for each information topic of the plurality of information topics, determining a second interest score, which indicates a level of interest a second user has in the information topic relative to other information topics within the plurality of information topics; determining that the first user shares a common interest in a plurality of common-interest information topics with the second user, wherein the determining that the interest is a shared common interest is based on the difference between the first user's level of interest in an information topic and the second user's level of interest in an information topic, as determined by the interest score and the second interest score respectively, being less than a threshold difference; determining a common-interest strength between the first user and the second user, wherein the common-interest strength is calculated based on: the number of common-interest information topics shared between the first user and the second user, a respective level of interest, as determined by the interest score and the second interest score, by each of the first user and the second users in the plurality of common-interest information topics, and a correlation and difference between the respective level of interest in the plurality of common-interest information topics by the first user and the second user, the common-interest strength measuring the strength of connection between the first user and the second user; and establishing a connection in a social network between the first and second users based on the common-interest strength. | 1. A method comprising: by one or more computing devices, for each information item of a plurality of information items in a collection of information associated with a first user: constructing a vector space model; and adjusting the vector space model by an importance score of the information item, wherein the importance score indicates the level of importance the information item is to the first user; calculating a degree of similarity for every two information items in the collection of information; based on the degree of similarity calculated for every two information items in the collection of information, clustering the plurality of information items in the collection of information associated with the first user to generate a plurality of information topics using the adjusted vector space model of each information item; for each information topic of the plurality of information topics generated from the collection of information associated with the first user, determining an interest score, which indicates a level of interest the first user has in the information topic relative to other information topics within the plurality of information topics; for each information topic of the plurality of information topics, determining a second interest score, which indicates a level of interest a second user has in the information topic relative to other information topics within the plurality of information topics; determining that the first user shares a common interest in a plurality of common-interest information topics with the second user, wherein the determining that the interest is a shared common interest is based on the difference between the first user's level of interest in an information topic and the second user's level of interest in an information topic, as determined by the interest score and the second interest score respectively, being less than a threshold difference; determining a common-interest strength between the first user and the second user, wherein the common-interest strength is calculated based on: the number of common-interest information topics shared between the first user and the second user, a respective level of interest, as determined by the interest score and the second interest score, by each of the first user and the second users in the plurality of common-interest information topics, and a correlation and difference between the respective level of interest in the plurality of common-interest information topics by the first user and the second user, the common-interest strength measuring the strength of connection between the first user and the second user; and establishing a connection in a social network between the first and second users based on the common-interest strength. 2. The method of claim 1 , further comprising for each information item of the plurality of information items, determining the importance score with respect to the first user. | 0.592249 |
8. A method for gathering Customer Relationship Management (CRM) information in a variable imaging (VI) application, the method comprising: providing at least one digital image included in a digital asset database; associating at least one or more keywords with each digital image in the digital asset database, the at least one or more keywords including marketing information associated with the digital image; receiving, from a requesting user, a template for a VI job stream; generating the VI job stream, including: merging the at least one digital image with variable information included in a database using the template; receiving the VI job stream by a VI production system; and after the variable digital image is merged in a VI document, for each digital image inserted into the VI job stream, extracting the at least one or more keywords previously associated with the digital image; generating a CRM output containing the at least one or more extracted keywords associated with a recipient of the VI document and marketing information is based on interests of the recipient; and delivering the CRM output to the requesting user. | 8. A method for gathering Customer Relationship Management (CRM) information in a variable imaging (VI) application, the method comprising: providing at least one digital image included in a digital asset database; associating at least one or more keywords with each digital image in the digital asset database, the at least one or more keywords including marketing information associated with the digital image; receiving, from a requesting user, a template for a VI job stream; generating the VI job stream, including: merging the at least one digital image with variable information included in a database using the template; receiving the VI job stream by a VI production system; and after the variable digital image is merged in a VI document, for each digital image inserted into the VI job stream, extracting the at least one or more keywords previously associated with the digital image; generating a CRM output containing the at least one or more extracted keywords associated with a recipient of the VI document and marketing information is based on interests of the recipient; and delivering the CRM output to the requesting user. 13. The method set forth in claim 8 , the step of processing the VI job stream further including: performing a raster image processing of the VI job stream to produce electronic output document images; reviewing the electronic output document images for accuracy; and performing finishing operations for the output document images. | 0.658142 |
1. A server/client system for processing data, the system comprising: a network comprising: a web server having information accessible remotely; a recognition server; a first client device adapted to receive information from the web server and having a visual interface browser to access information from the web server and a rendering device to visually indicate fields to be entered, the first client device configured to record input speech data associated with each of the fields upon an indication by a user of the first client device of which field subsequent input is intended for, and wherein the first client device is adapted to send the input speech data to the recognition server remote from the first client device; a second client device, remote from the first client device, having a microphone and a speaker and adapted to receive information from the web server, the second client device configured to record input speech data associated with each of a set of fields in response to prompts given to a user of the second client device, and wherein the second client device is adapted to send the input speech data to the same recognition server as used by the first client device, the recognition server being remote from the second client device, wherein the second client device comprises a telephone and a voice browser capable of rendering the information from the web server audibly; and wherein the recognition server is configured to receive the input speech data from both of the client devices separately, process the input speech data from each client device using an associated grammar, and return data indicative of what was recognized to at least one of the client device providing the input speech data and the web server; and wherein the recognition server is configured to receive data indicative of a prompt for the user to be used when the recognition results are indicative of no recognition of the input speech data from one of the client devices, convert the data indicative of the prompt to audible speech data when the recognition results are indicative of no recognition of the input speech data from said one of the client devices, and send the audible speech data to said one of the client devices over the wide area network. | 1. A server/client system for processing data, the system comprising: a network comprising: a web server having information accessible remotely; a recognition server; a first client device adapted to receive information from the web server and having a visual interface browser to access information from the web server and a rendering device to visually indicate fields to be entered, the first client device configured to record input speech data associated with each of the fields upon an indication by a user of the first client device of which field subsequent input is intended for, and wherein the first client device is adapted to send the input speech data to the recognition server remote from the first client device; a second client device, remote from the first client device, having a microphone and a speaker and adapted to receive information from the web server, the second client device configured to record input speech data associated with each of a set of fields in response to prompts given to a user of the second client device, and wherein the second client device is adapted to send the input speech data to the same recognition server as used by the first client device, the recognition server being remote from the second client device, wherein the second client device comprises a telephone and a voice browser capable of rendering the information from the web server audibly; and wherein the recognition server is configured to receive the input speech data from both of the client devices separately, process the input speech data from each client device using an associated grammar, and return data indicative of what was recognized to at least one of the client device providing the input speech data and the web server; and wherein the recognition server is configured to receive data indicative of a prompt for the user to be used when the recognition results are indicative of no recognition of the input speech data from one of the client devices, convert the data indicative of the prompt to audible speech data when the recognition results are indicative of no recognition of the input speech data from said one of the client devices, and send the audible speech data to said one of the client devices over the wide area network. 10. The system of claim 1 wherein the web server includes a server side plug-in module for dynamically generating markup language for each of the client devices. | 0.502157 |
9. The method of claim 8 , wherein the referencing information further comprises a link to at least one referencing page using the first anchor text to link to the at least one of the identified pages. | 9. The method of claim 8 , wherein the referencing information further comprises a link to at least one referencing page using the first anchor text to link to the at least one of the identified pages. 10. The method of claim 9 further comprising selecting the at least one referencing page based in part on a static rank of one or more referencing pages, the one or more referencing pages using the first anchor text to link to the at least one of the identified pages. | 0.923187 |
7. A program product comprising a non-transitory computer readable storage medium that stores code executable by a processor, the executable code comprising code to perform: representing state transition probabilities between a plurality of states that are statistical Markov model states and output probabilities associated with a plurality of previous states that are previous statistical Markov model states, wherein each of the plurality of states is represented by an integer state indicator i that indicates a current basic state, and a history indicator comprising a real number in the form h 1 h 2 . . . h n created as a concatenation of previous state indicators h, with more recent previous state indicators h having more numerical significance, to provide a signal history without excessive computational burden, the state transition probabilities between the plurality of previous states depend on a sequence of previous states of the plurality of previous states and the output probabilities associated with each of the plurality of states depend on the sequence of previous states; calculating a state transition probability for the plurality of states by minimizing an expression tr(Γ i T D T Γ i G)β2tr(DΓ i P Ξ±,i ) subject to constraints that 1 T Γ i Ο(h)β¦0 and Γ i Ο(h)β§0, wherein A |i is chosen so that A i Ο(h)βΔ(h), 1 T A i Ο(h)=1, A i Ο(h)β§0, βhΞ΅[0,1), 1 is a vector of all ones, D = [ I - 1 T ] , G=β« 0 1 Ο(h)Ο T (h)dh and P a,i =β« 1 1 Ο(h)b i (h) T dhβ« 1 1 Ο(h)b i (h) T dh, where G is a Grammian, P a,i is a cross correlation, Ο(h) is a basis function, A i is a state transition probability matrix, Γ i denotes a first sβ1 rows of A i , Δ(h) is a histogram-based estimate state transition probability, and b i (h) T =Δ 1i (h)βe s , e s =[0, 0, . . . 1] T ; and using the calculated state transition probability to generate an output signal for use in signal analysis that includes pattern recognition or pattern detection. | 7. A program product comprising a non-transitory computer readable storage medium that stores code executable by a processor, the executable code comprising code to perform: representing state transition probabilities between a plurality of states that are statistical Markov model states and output probabilities associated with a plurality of previous states that are previous statistical Markov model states, wherein each of the plurality of states is represented by an integer state indicator i that indicates a current basic state, and a history indicator comprising a real number in the form h 1 h 2 . . . h n created as a concatenation of previous state indicators h, with more recent previous state indicators h having more numerical significance, to provide a signal history without excessive computational burden, the state transition probabilities between the plurality of previous states depend on a sequence of previous states of the plurality of previous states and the output probabilities associated with each of the plurality of states depend on the sequence of previous states; calculating a state transition probability for the plurality of states by minimizing an expression tr(Γ i T D T Γ i G)β2tr(DΓ i P Ξ±,i ) subject to constraints that 1 T Γ i Ο(h)β¦0 and Γ i Ο(h)β§0, wherein A |i is chosen so that A i Ο(h)βΔ(h), 1 T A i Ο(h)=1, A i Ο(h)β§0, βhΞ΅[0,1), 1 is a vector of all ones, D = [ I - 1 T ] , G=β« 0 1 Ο(h)Ο T (h)dh and P a,i =β« 1 1 Ο(h)b i (h) T dhβ« 1 1 Ο(h)b i (h) T dh, where G is a Grammian, P a,i is a cross correlation, Ο(h) is a basis function, A i is a state transition probability matrix, Γ i denotes a first sβ1 rows of A i , Δ(h) is a histogram-based estimate state transition probability, and b i (h) T =Δ 1i (h)βe s , e s =[0, 0, . . . 1] T ; and using the calculated state transition probability to generate an output signal for use in signal analysis that includes pattern recognition or pattern detection. 10. The program product of claim 7 , wherein the output probability is calculated using a probability histogram comprising information from the sequence of previous states. | 0.531803 |
1. A computer program product embodied in a non-transitory computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of: ascertaining preferences of a plurality of users who are part of an internet based social interactive construct, wherein the plurality of users become a plurality of known users; determining the internet based social interactive graphical representation for the plurality of known users; and inferring the preferences of an unknown user present in the internet based social interactive graphical representation of the plurality of known users based on the interrelationships between the unknown user and the plurality of known users within the graphical representation. | 1. A computer program product embodied in a non-transitory computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of: ascertaining preferences of a plurality of users who are part of an internet based social interactive construct, wherein the plurality of users become a plurality of known users; determining the internet based social interactive graphical representation for the plurality of known users; and inferring the preferences of an unknown user present in the internet based social interactive graphical representation of the plurality of known users based on the interrelationships between the unknown user and the plurality of known users within the graphical representation. 2. The computer program product of claim 1 , wherein the Internet based social interactive graphical representation is a social network. | 0.554664 |
12. A non-transitory computer-readable storage medium having stored thereon data representing sequences of instructions that, when executed by a processor, cause the processor to perform operations comprising: receiving, with one or more hardware processing devices, a record access query for a database system, the query for all records for a certain one or more users a certain access level; searching, with one or more hardware processing devices, one or more sharing tables of entities in a computing environment for security descriptors, each security descriptor being associated with a set of one or more users having access to one or more records of a set of records at an access level, wherein each unique set of users having a same access level to one or more records in a set of records is associated with a same security descriptor in a sharing table for each of the one or more records, wherein the security descriptors identify each instance in which the access level is the same; identifying, with one or more hardware processing devices, security descriptors in the one or more sharing tables that relate to the certain one or more users with at least the certain access level; and searching, with one or more hardware processing devices, utilizing a single query the one or more records associated with each of the identified security descriptors according to the record access query. | 12. A non-transitory computer-readable storage medium having stored thereon data representing sequences of instructions that, when executed by a processor, cause the processor to perform operations comprising: receiving, with one or more hardware processing devices, a record access query for a database system, the query for all records for a certain one or more users a certain access level; searching, with one or more hardware processing devices, one or more sharing tables of entities in a computing environment for security descriptors, each security descriptor being associated with a set of one or more users having access to one or more records of a set of records at an access level, wherein each unique set of users having a same access level to one or more records in a set of records is associated with a same security descriptor in a sharing table for each of the one or more records, wherein the security descriptors identify each instance in which the access level is the same; identifying, with one or more hardware processing devices, security descriptors in the one or more sharing tables that relate to the certain one or more users with at least the certain access level; and searching, with one or more hardware processing devices, utilizing a single query the one or more records associated with each of the identified security descriptors according to the record access query. 15. The medium of claim 12 , wherein the search of one or more tables includes searching cached results from a previous search. | 0.560945 |
1. A method of capturing actions that are performed on at least one medical image of a patient during a medical imaging interpretation, the method being implemented using a computer system, the method comprising: (a) displaying a workflow template on a display of the computer system; (b) displaying the at least one medical image of the patient on said display; (c) automatically extracting data from an electronic medical record of the patient, or other data related to the patient, from a database, into said workflow template provided on said display; (d) capturing and storing one or more user actions as they are performed on the medical image of the patient, by an interpreting user during an entire medical imaging interpretation, using an auditing function of the computer system; (e) automatically generating user action information from the one or more captured actions, to prompt said user to perform certain actions; (f) storing the captured user actions and user action information, along with said data related to the patient, in said database, with the at least one medical image of the patient, as a new workflow sequence onto a new workflow template as a pre-defined protocol; (g) accessing said new workflow template having said pre-defined protocol from said database; (h) displaying to a new user, in said new workflow template, a recreation of the exact pre-defined protocol including said data related to the patient, stored by said previous interpreting user in said database, as a continuous replica of said previous interpreting user's actions and user action information stored in said workflow sequence, such that said new user may selectively review and modify clinically pertinent medical images and said data related to the patient, in a continuous manner that follows said stored workflow template as created by said previous interpreting user; and (i) repeating steps (b)-(f), such that a modified new workflow template is created and stored in said database: wherein each said modified new workflow template is a cumulative, refined, and dynamic workflow sequence of a series of said captured user actions and user action information, along with data related to each patient, with the at least one medical image of said patient, in order to provide best practice of said medical image interpretation for said user. | 1. A method of capturing actions that are performed on at least one medical image of a patient during a medical imaging interpretation, the method being implemented using a computer system, the method comprising: (a) displaying a workflow template on a display of the computer system; (b) displaying the at least one medical image of the patient on said display; (c) automatically extracting data from an electronic medical record of the patient, or other data related to the patient, from a database, into said workflow template provided on said display; (d) capturing and storing one or more user actions as they are performed on the medical image of the patient, by an interpreting user during an entire medical imaging interpretation, using an auditing function of the computer system; (e) automatically generating user action information from the one or more captured actions, to prompt said user to perform certain actions; (f) storing the captured user actions and user action information, along with said data related to the patient, in said database, with the at least one medical image of the patient, as a new workflow sequence onto a new workflow template as a pre-defined protocol; (g) accessing said new workflow template having said pre-defined protocol from said database; (h) displaying to a new user, in said new workflow template, a recreation of the exact pre-defined protocol including said data related to the patient, stored by said previous interpreting user in said database, as a continuous replica of said previous interpreting user's actions and user action information stored in said workflow sequence, such that said new user may selectively review and modify clinically pertinent medical images and said data related to the patient, in a continuous manner that follows said stored workflow template as created by said previous interpreting user; and (i) repeating steps (b)-(f), such that a modified new workflow template is created and stored in said database: wherein each said modified new workflow template is a cumulative, refined, and dynamic workflow sequence of a series of said captured user actions and user action information, along with data related to each patient, with the at least one medical image of said patient, in order to provide best practice of said medical image interpretation for said user. 4. The method according to claim 1 , wherein capturing one or more user actions include capturing individual steps that a user performs during said entire medical image interpretation. | 0.586183 |
10. A computer-implemented method for governing access of a query to a database on a computer system, the method comprising the steps of: (A) receiving an estimated query execution time for the query; (B) calculating a factor for the query, wherein the factor is one or more factors chosen from: a user factor, a query factor, a job priority factor, or a resource factor; (C) dynamically generating a tailored threshold that is unique to the query, where the tailored threshold is determined by applying the factor to a fixed threshold; (D) governing the query's access to the database based on the tailored threshold; and (E) wherein the method steps are implemented in a computer software program stored in computer memory and executed by a computer processor. | 10. A computer-implemented method for governing access of a query to a database on a computer system, the method comprising the steps of: (A) receiving an estimated query execution time for the query; (B) calculating a factor for the query, wherein the factor is one or more factors chosen from: a user factor, a query factor, a job priority factor, or a resource factor; (C) dynamically generating a tailored threshold that is unique to the query, where the tailored threshold is determined by applying the factor to a fixed threshold; (D) governing the query's access to the database based on the tailored threshold; and (E) wherein the method steps are implemented in a computer software program stored in computer memory and executed by a computer processor. 11. The method of claim 10 further comprising the step of: intelligently creating a user score for each user accessing the database that is used to calculate the user factor to determine the tailored threshold. | 0.512764 |
45. The apparatus of claim 44 , wherein the first advertising content and the second advertising content are caused to be presented substantially in an order corresponding to the order in which the first location and second location are to be encountered during said journey. | 45. The apparatus of claim 44 , wherein the first advertising content and the second advertising content are caused to be presented substantially in an order corresponding to the order in which the first location and second location are to be encountered during said journey. 46. The apparatus of claim 45 , wherein the first advertising content and the second advertising content are selected based at least in part on the respective available presentation times before the first location and second location are to be encountered during said journey. | 0.874206 |
1. A system for bug discovery using event reports, comprising: an interface configured to: receive symptom data extracted from event reports from a user system, wherein the symptom data is stored in a symptom database, and wherein the symptom data comprises one or more symptoms each with a corresponding symptom occurrence time; a processor configured to: determine an existence of one or more bugs of the user system based at least in part on a result of querying the symptom database using a bug definition, wherein the bug definition comprises a logic set operation on the symptom data, wherein the logic set operation comprises one of the following: AND, OR, XOR, MINUS, NOT, NAND, NOR or ADD. | 1. A system for bug discovery using event reports, comprising: an interface configured to: receive symptom data extracted from event reports from a user system, wherein the symptom data is stored in a symptom database, and wherein the symptom data comprises one or more symptoms each with a corresponding symptom occurrence time; a processor configured to: determine an existence of one or more bugs of the user system based at least in part on a result of querying the symptom database using a bug definition, wherein the bug definition comprises a logic set operation on the symptom data, wherein the logic set operation comprises one of the following: AND, OR, XOR, MINUS, NOT, NAND, NOR or ADD. 12. A system as in claim 1 , further comprising a processor configured to, in the event that the one or more bugs do exist, output a list of the one or more bugs. | 0.570431 |
18. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to perform operations to convert text to speech, comprising: generating a response text and a response intent based on user input; receiving response text and an intent representative of intended meaning of the response text that can be conveyed by non-lexical cues; determining, on the one or more computing devices, an insertion point of a non-lexical cue, in the response text, based on the intent; inserting by the one or more computing devices a non-lexical cue at the insertion point within the response text to generate augmented text; and providing the augmented text to a speech synthesizer to synthesize speech from the augmented text using speech units associated with the response text and the inserted response intent. | 18. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to perform operations to convert text to speech, comprising: generating a response text and a response intent based on user input; receiving response text and an intent representative of intended meaning of the response text that can be conveyed by non-lexical cues; determining, on the one or more computing devices, an insertion point of a non-lexical cue, in the response text, based on the intent; inserting by the one or more computing devices a non-lexical cue at the insertion point within the response text to generate augmented text; and providing the augmented text to a speech synthesizer to synthesize speech from the augmented text using speech units associated with the response text and the inserted response intent. 19. The computer-readable storage medium of claim 18 , further comprising deriving the intent by using a probabilistic model to determine a syntactic probability with respect to a sequence of words in the response text, wherein the intent includes the syntactic probability, and wherein the insertion point of the non-lexical cue is determined using the syntactic probability. | 0.5 |
1. A method comprising: receiving a first query that conforms to a first XML query language; determining that an operator included in the first query requires one or more node references, to one or more nodes, as operands of the operator instead of a value of any of the one or more nodes; wherein the operator included in the first query is one of a node comparison operator or an operator that requires context node traversal; and in response to determining that the operator included in the first query requires one or more node references as operands of the operator, generating, based on the first query, a second query (a) that conforms to a second XML query language that is different than the first XML query language and (b) that includes a particular operator not included in the first query; wherein execution of the particular operator included in the second query causes a reference, to a node in an XML document, to be returned instead of a value of the node; wherein the steps are performed by one or more computing devices. | 1. A method comprising: receiving a first query that conforms to a first XML query language; determining that an operator included in the first query requires one or more node references, to one or more nodes, as operands of the operator instead of a value of any of the one or more nodes; wherein the operator included in the first query is one of a node comparison operator or an operator that requires context node traversal; and in response to determining that the operator included in the first query requires one or more node references as operands of the operator, generating, based on the first query, a second query (a) that conforms to a second XML query language that is different than the first XML query language and (b) that includes a particular operator not included in the first query; wherein execution of the particular operator included in the second query causes a reference, to a node in an XML document, to be returned instead of a value of the node; wherein the steps are performed by one or more computing devices. 4. The method of claim 1 , further comprising: determining how XML data, that is targeted by the first query, can be accessed; generating, based on the second query and how the XML data can be accessed, a third query that indicates one or more data structures; and executing the third query, wherein the one or more data structures are accessed to execute the third query. | 0.569892 |
7. An apparatus, comprising: an interface for communicating database messages; a memory for storing database messages; a processor communicatively coupled the interface and the memory, the processor operable to: receive a database selection message from a database client; transmit an unconditional acknowledgement to the database client in response to the database selection message; access a database query from a queue of queries from the database client, the database query comprising a user identifier and a database identifier; determine whether a matching backend database connection exists, the matching backend database connection having a user identifier equivalent to the user identifier of the database query and a database identifier that is equivalent to the database identifier of the database query; if a matching backend database connection exists, forward the database query to the matching backend database connection; and determine, in response to determining that the matching backend database does not exist, whether a similar backend database connection exits, the similar backend database connection having a user identifier equivalent to the user identifier of the database query and a database identifier not equivalent to the database identifier of the database query, or having the user identifier not equivalent to the user identifier of the database query and the database identifier that is equivalent to the database identifier of the database query; if the similar backend database connection exists and the user identifier of the similar backend database connection is equivalent to the user identifier of the database query, transmit a request to a database server to switch the database identifier of the similar backend connection to the database identifier of the database query; and if the similar backend database connection exists and the database identifier of the similar backend database connection is equivalent to the database identifier of the database query, transmit a request to switch the user identifier of the similar backend database connection to the user identifier of the database query. | 7. An apparatus, comprising: an interface for communicating database messages; a memory for storing database messages; a processor communicatively coupled the interface and the memory, the processor operable to: receive a database selection message from a database client; transmit an unconditional acknowledgement to the database client in response to the database selection message; access a database query from a queue of queries from the database client, the database query comprising a user identifier and a database identifier; determine whether a matching backend database connection exists, the matching backend database connection having a user identifier equivalent to the user identifier of the database query and a database identifier that is equivalent to the database identifier of the database query; if a matching backend database connection exists, forward the database query to the matching backend database connection; and determine, in response to determining that the matching backend database does not exist, whether a similar backend database connection exits, the similar backend database connection having a user identifier equivalent to the user identifier of the database query and a database identifier not equivalent to the database identifier of the database query, or having the user identifier not equivalent to the user identifier of the database query and the database identifier that is equivalent to the database identifier of the database query; if the similar backend database connection exists and the user identifier of the similar backend database connection is equivalent to the user identifier of the database query, transmit a request to a database server to switch the database identifier of the similar backend connection to the database identifier of the database query; and if the similar backend database connection exists and the database identifier of the similar backend database connection is equivalent to the database identifier of the database query, transmit a request to switch the user identifier of the similar backend database connection to the user identifier of the database query. 9. The apparatus of claim 7 , wherein the request to the database server to switch the database identifier or switch the user identifier of the similar backend connection is transmitted when a user connection limit is not exceeded. | 0.510671 |
26. A mobile hand-held device, comprising: a processor; a wireless communications device, to facilitate wireless communication with a network that supports access to the Internet; a touch-sensitive display; and flash memory, operatively coupled to the processor, in which a plurality of instructions are stored comprising a plurality of software components including an HTML rendering engine, wherein the instructions, when executed by the mobile hand-held device, enable the mobile hand-held device to, receive an HTML document comprising HTML-based content including HTML code and cascading style sheet (CSS) code and content associated with the HTML document; produce scalable content by, processing the HTML-based content with the rendering engine to render an interpreted page layout, functionality, and design of the content associated with the HTML document in accordance with the HTML code and CSS code; logically grouping selected content into HTML objects, each HTML object including associated display content; defining a primary datum corresponding to the interpreted page layout; and, for each HTML object, defining an object datum corresponding to a layout location datum for the HTML object's associated display content; generating a vector from the primary datum to the object datum for the HTML object; and creating a reference that links the HTML object to the vector that is generated; and employ at least one of the scalable content or data derived therefrom to, render a view of the HTML document on the touch-sensitive display at a first zoom level under which the HTML document is displayed to fit across a width of the touch-sensitive display; and render views of the HTML document on the touch-sensitive display in response to associated user inputs to enable the HTML document to be viewed at various zoom levels and panned views while preserving the interpreted page layout, functionality, and design of the content associated with the HTML document at each zoom level and panned view including the first zoom level. | 26. A mobile hand-held device, comprising: a processor; a wireless communications device, to facilitate wireless communication with a network that supports access to the Internet; a touch-sensitive display; and flash memory, operatively coupled to the processor, in which a plurality of instructions are stored comprising a plurality of software components including an HTML rendering engine, wherein the instructions, when executed by the mobile hand-held device, enable the mobile hand-held device to, receive an HTML document comprising HTML-based content including HTML code and cascading style sheet (CSS) code and content associated with the HTML document; produce scalable content by, processing the HTML-based content with the rendering engine to render an interpreted page layout, functionality, and design of the content associated with the HTML document in accordance with the HTML code and CSS code; logically grouping selected content into HTML objects, each HTML object including associated display content; defining a primary datum corresponding to the interpreted page layout; and, for each HTML object, defining an object datum corresponding to a layout location datum for the HTML object's associated display content; generating a vector from the primary datum to the object datum for the HTML object; and creating a reference that links the HTML object to the vector that is generated; and employ at least one of the scalable content or data derived therefrom to, render a view of the HTML document on the touch-sensitive display at a first zoom level under which the HTML document is displayed to fit across a width of the touch-sensitive display; and render views of the HTML document on the touch-sensitive display in response to associated user inputs to enable the HTML document to be viewed at various zoom levels and panned views while preserving the interpreted page layout, functionality, and design of the content associated with the HTML document at each zoom level and panned view including the first zoom level. 29. The mobile hand-held device of claim 26 , wherein execution of the instructions further enables the user to zoom in on a user-selectable portion of a view of the HTML document in response to a user interface input made via the touch-sensitive display. | 0.683348 |
1. A computer-implemented method for assigning relevance scores to message elements, the method comprising: providing a structured collection of message elements stored in a data repository, the collection of message elements comprising: a plurality of messages elements, wherein each message element comprises a message content and metadata, the metadata including an author identity and a timestamp, and a plurality of oriented child-parent links each connecting a message element to an older message element called parent message element, so that each message element within the plurality of message elements is connected by a child-parent link to a parent message element within the plurality of message elements, parsing the message contents of the plurality of message elements to generate appreciative phrase marks assigned to the message elements, wherein an appreciative phrase mark is generated in response to detecting that the parsed message content of a later message element comprises a string of characters that matches an entry within a predefined dictionary of regard-expressing phrases and wherein the appreciative phrase mark is assigned to an earlier message element that is connected to the later message element by a sequence of one or more child-parent links, wherein the author identity of the later message element is different from the author identity of the earlier message element, parsing the metadata of the plurality of message elements to detect regard indicators assigned to the message elements, wherein a regard indicator assigned to a message element results from an action performed by a reader of the message element, wherein the regard indicators include the appreciative phrase marks, and computing relevance scores of the message elements as a function of the regard indicators assigned to the message elements; and generating a graph of authors comprising a collection of author nodes corresponding to the author identities comprised in the plurality of message elements and a collection of oriented regard links each associated to a respective appreciative phrase mark, wherein a regard link associated to an appreciative phrase mark originates from an author node corresponding to the author identity of the later message element comprising the regard-expressing phrase and points to an author node corresponding to the author identity of the earlier message element to which the appreciative phrase mark is assigned, and applying a link-based rank computation method to the graph of authors to determine link-based ranks of the author nodes, wherein the relevance score of a message element is computed as a function of the link-based rank of the author node corresponding to the author identity of the message element. | 1. A computer-implemented method for assigning relevance scores to message elements, the method comprising: providing a structured collection of message elements stored in a data repository, the collection of message elements comprising: a plurality of messages elements, wherein each message element comprises a message content and metadata, the metadata including an author identity and a timestamp, and a plurality of oriented child-parent links each connecting a message element to an older message element called parent message element, so that each message element within the plurality of message elements is connected by a child-parent link to a parent message element within the plurality of message elements, parsing the message contents of the plurality of message elements to generate appreciative phrase marks assigned to the message elements, wherein an appreciative phrase mark is generated in response to detecting that the parsed message content of a later message element comprises a string of characters that matches an entry within a predefined dictionary of regard-expressing phrases and wherein the appreciative phrase mark is assigned to an earlier message element that is connected to the later message element by a sequence of one or more child-parent links, wherein the author identity of the later message element is different from the author identity of the earlier message element, parsing the metadata of the plurality of message elements to detect regard indicators assigned to the message elements, wherein a regard indicator assigned to a message element results from an action performed by a reader of the message element, wherein the regard indicators include the appreciative phrase marks, and computing relevance scores of the message elements as a function of the regard indicators assigned to the message elements; and generating a graph of authors comprising a collection of author nodes corresponding to the author identities comprised in the plurality of message elements and a collection of oriented regard links each associated to a respective appreciative phrase mark, wherein a regard link associated to an appreciative phrase mark originates from an author node corresponding to the author identity of the later message element comprising the regard-expressing phrase and points to an author node corresponding to the author identity of the earlier message element to which the appreciative phrase mark is assigned, and applying a link-based rank computation method to the graph of authors to determine link-based ranks of the author nodes, wherein the relevance score of a message element is computed as a function of the link-based rank of the author node corresponding to the author identity of the message element. 2. The method in accordance with claim 1 , wherein the regard indicators assigned to a message element further include positive vote metadata resulting from a positive vote given by a reader of the message element. | 0.617742 |
5. A system, comprising: at least one computing device; and an item search application executable in the at least one computing device, wherein when executed the item search application causes the at least one computing device to at least: parse an unstructured search query to identify at least one category of a taxonomy of a collection of items generate a confidence score for each of the at least one category, each of the at least one category being associated with a confidence score, the confidence score being generated using a weighted combination of a plurality of factors, a first factor of the plurality of factors comprising a quality of text match of items in a respective category with the unstructured search query and a second factor of the plurality of factors comprising a number of refinements identified in the unstructured search query that are associated with the respective category, and data associated with the respective category comprising a description of the respective category, and the quality of text match being based at least in part on a frequency that at least a portion of the unstructured search query occurs within the description; identify at least one refinement from the unstructured search query by translating at least one keyword from the unstructured search query into at least one criterion for selecting items from the respective category of the at least one category, wherein the translation is configured to translate a plurality of synonyms into the at least one criterion; applying the at least one refinement to select a first pool of items from the respective category when the confidence score meets a threshold; applying the at least one refinement to select a second pool of items from the collection of items when no confidence score meets the threshold; and generate a network page listing at least a portion of the first or second pool of items that has been selected, the network page including a disambiguation tool when no confidence score meets the threshold, the disambiguation tool providing a user interface for selecting one category from the at least one category. | 5. A system, comprising: at least one computing device; and an item search application executable in the at least one computing device, wherein when executed the item search application causes the at least one computing device to at least: parse an unstructured search query to identify at least one category of a taxonomy of a collection of items generate a confidence score for each of the at least one category, each of the at least one category being associated with a confidence score, the confidence score being generated using a weighted combination of a plurality of factors, a first factor of the plurality of factors comprising a quality of text match of items in a respective category with the unstructured search query and a second factor of the plurality of factors comprising a number of refinements identified in the unstructured search query that are associated with the respective category, and data associated with the respective category comprising a description of the respective category, and the quality of text match being based at least in part on a frequency that at least a portion of the unstructured search query occurs within the description; identify at least one refinement from the unstructured search query by translating at least one keyword from the unstructured search query into at least one criterion for selecting items from the respective category of the at least one category, wherein the translation is configured to translate a plurality of synonyms into the at least one criterion; applying the at least one refinement to select a first pool of items from the respective category when the confidence score meets a threshold; applying the at least one refinement to select a second pool of items from the collection of items when no confidence score meets the threshold; and generate a network page listing at least a portion of the first or second pool of items that has been selected, the network page including a disambiguation tool when no confidence score meets the threshold, the disambiguation tool providing a user interface for selecting one category from the at least one category. 12. The system of claim 5 , wherein when executed the item search application further causes the at least one computing device to at least generate the confidence score for the respective category based at least in part on user profile data. | 0.608445 |
4. The method of claim 1 , wherein the web page search results include supplemental information related to the search query, and multiple users are able to modify the same portions of the supplemental information. | 4. The method of claim 1 , wherein the web page search results include supplemental information related to the search query, and multiple users are able to modify the same portions of the supplemental information. 7. The method of claim 4 , wherein the supplemental information includes a list of different concepts related to the search query. | 0.89959 |
1. A computer implemented method for optimizing results returned from interaction with a collection of information, the method comprising: establishing criteria associated with at least one operation on a collection of information, wherein the criteria is based, at least in part, on a comparison of a measurement of the distinctiveness of a set of results and a distinctiveness score threshold; establishing a rule that comprises the criteria and the at least one operation; determining the set of results from interaction with a collection of information, wherein the set of results comprises a plurality of documents retrieved from the collection of information, and wherein each of the plurality of documents further comprise a unit of storage of digital data; determining, by a computer system, a measurement of distinctiveness for the set of results based on a statistical distribution of at least one identifying characteristic, wherein the distinctiveness of the set of results is measured in relation to the collection of information, and wherein the determining the measurement of distinctiveness comprises: identifying the at least one identifying characteristic within an evaluated set, and determining a measure of distinctiveness of the evaluated set within the collection of information; modifying, by the computer system, the set of results according to the at least one operation by applying the rule to the set of results in response to determining that the set of results matches the criteria based, at least in part, on the comparison of the measurement of distinctiveness for the set of results and the distinctiveness score threshold; and outputting a modified result. | 1. A computer implemented method for optimizing results returned from interaction with a collection of information, the method comprising: establishing criteria associated with at least one operation on a collection of information, wherein the criteria is based, at least in part, on a comparison of a measurement of the distinctiveness of a set of results and a distinctiveness score threshold; establishing a rule that comprises the criteria and the at least one operation; determining the set of results from interaction with a collection of information, wherein the set of results comprises a plurality of documents retrieved from the collection of information, and wherein each of the plurality of documents further comprise a unit of storage of digital data; determining, by a computer system, a measurement of distinctiveness for the set of results based on a statistical distribution of at least one identifying characteristic, wherein the distinctiveness of the set of results is measured in relation to the collection of information, and wherein the determining the measurement of distinctiveness comprises: identifying the at least one identifying characteristic within an evaluated set, and determining a measure of distinctiveness of the evaluated set within the collection of information; modifying, by the computer system, the set of results according to the at least one operation by applying the rule to the set of results in response to determining that the set of results matches the criteria based, at least in part, on the comparison of the measurement of distinctiveness for the set of results and the distinctiveness score threshold; and outputting a modified result. 18. The method according to claim 1 , further comprising determining a measurement of distinctiveness for at least one set, wherein the measurement of distinctiveness is determined relative to a baseline measure. | 0.638956 |
15. A method, comprising: comparing, by a computer having a processor and a memory, a plurality of versions of a document in a data storage based on a plurality of document version curation policies, a first portion of the plurality of versions of the document created within a recent timeframe and a second portion of the plurality of versions of the document, different than the first portion of documents, created within an origin timeframe corresponding to creation of an original version of the document, the plurality of document version curation policies including a first document version curation policy related to the first portion of the plurality of versions of the document and a second document version curation policy related to the second portion of the plurality of versions of the document; executing the first document version curation policy on the first portion of the versions of the document, wherein the first document version curation policy includes deleting a predetermined first percentage of the first portion of the plurality of versions of the document; and executing the second document version curation policy on the second portion of the versions of the document, wherein the second document version curation policy includes deleting a predetermined second percentage of the second portion of the plurality of versions of the document. | 15. A method, comprising: comparing, by a computer having a processor and a memory, a plurality of versions of a document in a data storage based on a plurality of document version curation policies, a first portion of the plurality of versions of the document created within a recent timeframe and a second portion of the plurality of versions of the document, different than the first portion of documents, created within an origin timeframe corresponding to creation of an original version of the document, the plurality of document version curation policies including a first document version curation policy related to the first portion of the plurality of versions of the document and a second document version curation policy related to the second portion of the plurality of versions of the document; executing the first document version curation policy on the first portion of the versions of the document, wherein the first document version curation policy includes deleting a predetermined first percentage of the first portion of the plurality of versions of the document; and executing the second document version curation policy on the second portion of the versions of the document, wherein the second document version curation policy includes deleting a predetermined second percentage of the second portion of the plurality of versions of the document. 22. The method according to claim 15 , wherein at least one of the first document version curation policy and the second document version curation policy is based on comparing the differences between at least two versions of the document. | 0.590206 |
12. The method of claim 1 , wherein the identifying of the plurality of text elements of the GUI page comprises performing optical character recognition (OCR) of the GUI page to obtain text information of each text element of the GUI page and to obtain location information of each text element on the GUI page, and wherein the identifying of the plurality of user input objects of the GUI page comprises performing contour analysis of the GUI page to identify the presence, type, and location of each user input object of the GUI page. | 12. The method of claim 1 , wherein the identifying of the plurality of text elements of the GUI page comprises performing optical character recognition (OCR) of the GUI page to obtain text information of each text element of the GUI page and to obtain location information of each text element on the GUI page, and wherein the identifying of the plurality of user input objects of the GUI page comprises performing contour analysis of the GUI page to identify the presence, type, and location of each user input object of the GUI page. 13. The method of claim 12 , wherein the identification of the pluralities of text elements and user input objects of the GUI page comprises: automatically scrolling through the GUI page to obtain a plurality of images of different scrolled portions of the GUI page; and identifying text elements and user input objects in each image of the plurality of images of different scrolled portions of the GUI page; and wherein the identifying of the plurality of user input objects comprises: automatically expanding list or combo boxes of the GUI page and obtaining text information from the expanded list or combo boxes; and storing the obtained text information from each expanded list or combo box with the information on the presence, type, and location of the list or combo box. | 0.755579 |
3. The method of claim 2 , wherein one of the evaluation modules extracts lexical features from the strings of text and evaluates the lexical features with a classifier. | 3. The method of claim 2 , wherein one of the evaluation modules extracts lexical features from the strings of text and evaluates the lexical features with a classifier. 4. The method of claim 3 , wherein the classifier further provides at least one of the suggested alternative strings of text, based on at least one lexical feature found to be a relatively close match for at least one string of text with non-standard usage. | 0.880886 |
1. A process of organizing program performance data in semantic groups, the performance data including multiple samples, each of the multiple samples having at least one name and at least one associated cost, the process comprising the steps of: submitting a grouping expression to a performance analysis tool, the grouping expression specifying, in a transformation syntax language which supports pattern-matching, a pattern and a replacement for grouping multiple performance data samples, each of the performance data samples having a stack of names which represent nodes located in a directed acyclic graph (DAG) in a computer-readable memory, each of the nodes having an associated cost; and getting from the performance analysis tool a cost accounting created by execution of instructions by a processor in response to the submitting step, the cost accounting showing names of the performance data samples and associated attributed costs, all of the names being consistent with the grouping expression. | 1. A process of organizing program performance data in semantic groups, the performance data including multiple samples, each of the multiple samples having at least one name and at least one associated cost, the process comprising the steps of: submitting a grouping expression to a performance analysis tool, the grouping expression specifying, in a transformation syntax language which supports pattern-matching, a pattern and a replacement for grouping multiple performance data samples, each of the performance data samples having a stack of names which represent nodes located in a directed acyclic graph (DAG) in a computer-readable memory, each of the nodes having an associated cost; and getting from the performance analysis tool a cost accounting created by execution of instructions by a processor in response to the submitting step, the cost accounting showing names of the performance data samples and associated attributed costs, all of the names being consistent with the grouping expression. 4. The process of claim 1 , wherein the submitted grouping expression pattern matches at least one directory containing program code. | 0.668981 |
19. A computer program product for creating a voice response grammar in a voice response server, the computer program product comprising: a recording medium; means, recorded on the recording medium, for identifying presentation documents for a presentation, each presentation document having a presentation grammar; means, recorded on the recording medium, for storing each presentation grammar in a voice response grammar on a voice response server. | 19. A computer program product for creating a voice response grammar in a voice response server, the computer program product comprising: a recording medium; means, recorded on the recording medium, for identifying presentation documents for a presentation, each presentation document having a presentation grammar; means, recorded on the recording medium, for storing each presentation grammar in a voice response grammar on a voice response server. 25. The computer program product of claim 19 further comprising means, recorded on the recording medium, for creating a presentation document, including: means, recorded on the recording medium, for creating, in dependence upon an original document, a structured document comprising one or more structural elements; means, recorded on the recording medium, for classifying a structural element of the structured document according to a presentation attribute; and means, recorded on the recording medium, for creating a presentation grammar for the structured document, wherein the presentation grammar for the structured document includes grammar elements each of which includes an identifier for at least one structural element of the structured document. | 0.64066 |
1. A method in a mobile device for responding to a capture of text from a display of information that includes text, the method comprising: capturing, using an imaging component of the mobile device, an image of a display of information, wherein the image includes text; recognizing, using a text recognition component of the mobile device, at least a portion of the text within the captured image; identifying, via the mobile device, one or more actions associated with the captured text; presenting, via a display of the mobile device, a menu of user-selectable options associated with the identified one or more actions; and performing, at the mobile device, the identified one or more actions via a display and speakers of the mobile device, wherein when the action to be performed includes visual elements, performing the action using the display of the mobile device, and when the action to be performed includes audio elements, using the speakers of the mobile device. | 1. A method in a mobile device for responding to a capture of text from a display of information that includes text, the method comprising: capturing, using an imaging component of the mobile device, an image of a display of information, wherein the image includes text; recognizing, using a text recognition component of the mobile device, at least a portion of the text within the captured image; identifying, via the mobile device, one or more actions associated with the captured text; presenting, via a display of the mobile device, a menu of user-selectable options associated with the identified one or more actions; and performing, at the mobile device, the identified one or more actions via a display and speakers of the mobile device, wherein when the action to be performed includes visual elements, performing the action using the display of the mobile device, and when the action to be performed includes audio elements, using the speakers of the mobile device. 3. The method of claim 1 further comprising: identifying, at the mobile device, a digital counterpart of the display of information based on the captured text; and wherein identifying one or more actions associated with the captured text includes identifying one or more actions associated with the identified digital counterpart. | 0.623007 |
1. A method of assessing a document using a computer system, comprising: providing a document to the computer system, wherein the document comprises at least one information field; and for at least one non-signature information field of the document, comparing, using the computer system, handwriting in the non-signature information field to at least two handwriting profile representations from at least one non-signature information field of at least one other document, wherein writing in at least one of the information fields of the document comprises at least two examples of a type of handwritten information, and further comprising comparing at least two of the examples to assess whether two or more of the examples approximately match. | 1. A method of assessing a document using a computer system, comprising: providing a document to the computer system, wherein the document comprises at least one information field; and for at least one non-signature information field of the document, comparing, using the computer system, handwriting in the non-signature information field to at least two handwriting profile representations from at least one non-signature information field of at least one other document, wherein writing in at least one of the information fields of the document comprises at least two examples of a type of handwritten information, and further comprising comparing at least two of the examples to assess whether two or more of the examples approximately match. 15. The method of claim 1 , further comprising creating a mathematical representation of the handwriting in at least one non-signature information field. | 0.782585 |
1. A method of processing correlated keywords, the method comprising: providing a web page having a first input box and at least a second input box; receiving, from a user device associated with a user, a keyword inputted in the first input box; obtaining, by one or more servers associated with the web page, multiple related keywords that are related to the inputted keyword; receiving a user selection from the user of a related keyword of the multiple related keywords; providing a keyword list including the related keyword, wherein the keyword list is created using an embedded window object associated with the second input box; in response to a receipt of an additional user selection from the user of an additional related keyword of the multiple related keywords: determining whether the keyword list includes a duplication of the additional related keyword, and updating the keyword list by adding the additional related keyword into the keyword list in response to: a determination that a number of keywords existing in the keyword list is not greater than a predetermined value, and a determination that the keyword list does not already include the additional related keyword, wherein the updating the keyword list comprises: updating the second input box to include the related keyword; and adding the additional related keyword to the second input box before the user completes selections of any remaining additional related keywords; presenting to the user the updated keyword list; and enabling a search by the user using the updated keyword list. | 1. A method of processing correlated keywords, the method comprising: providing a web page having a first input box and at least a second input box; receiving, from a user device associated with a user, a keyword inputted in the first input box; obtaining, by one or more servers associated with the web page, multiple related keywords that are related to the inputted keyword; receiving a user selection from the user of a related keyword of the multiple related keywords; providing a keyword list including the related keyword, wherein the keyword list is created using an embedded window object associated with the second input box; in response to a receipt of an additional user selection from the user of an additional related keyword of the multiple related keywords: determining whether the keyword list includes a duplication of the additional related keyword, and updating the keyword list by adding the additional related keyword into the keyword list in response to: a determination that a number of keywords existing in the keyword list is not greater than a predetermined value, and a determination that the keyword list does not already include the additional related keyword, wherein the updating the keyword list comprises: updating the second input box to include the related keyword; and adding the additional related keyword to the second input box before the user completes selections of any remaining additional related keywords; presenting to the user the updated keyword list; and enabling a search by the user using the updated keyword list. 2. The method of claim 1 , wherein the embedded window object includes a predetermined height value of the keyword list and an offset that is relative to an input box that presents the keyword list. | 0.712428 |
19. A method of searching and merging search results across heterogeneous indices, comprising: identifying a query from a user; splitting the query into sub-queries; for each of the sub-queries, determining a user importance value for said each sub-query to quantify how important said each sub-query is to the user relative to how important the others of the sub-queries are to the user; executing each of the sub-queries by searching across heterogeneous indices, including structured, unstructured and semi-structured data sources, to obtain a search result for each of the sub-queries; using the user importance value determined for said each sub-query to assign a user relevance score to the search result obtained for each of the sub-queries; and combining the search results for the sub-queries based on the user relevance scores assigned to the search results to obtain merged search results; and wherein: the execution of each of the sub-queries includes identifying a multitude of search results for at least one of the sub-queries; the combining includes grouping said multitude of search results into a plurality of clusters, and computing a relevance score for each of said clusters, wherein each cluster represents a high level entity; the determining a user importance value for said each sub-query includes identifying one of the sub-queries as a primary focus, and identifying another of the sub-queries as providing context, computing a probability p(Q) of each of the sub-queries, and computing information content of each of the sub-queries as log p(Q); and the combining the search results further includes, once the information content of the sub-queries are computed, merging the search results. | 19. A method of searching and merging search results across heterogeneous indices, comprising: identifying a query from a user; splitting the query into sub-queries; for each of the sub-queries, determining a user importance value for said each sub-query to quantify how important said each sub-query is to the user relative to how important the others of the sub-queries are to the user; executing each of the sub-queries by searching across heterogeneous indices, including structured, unstructured and semi-structured data sources, to obtain a search result for each of the sub-queries; using the user importance value determined for said each sub-query to assign a user relevance score to the search result obtained for each of the sub-queries; and combining the search results for the sub-queries based on the user relevance scores assigned to the search results to obtain merged search results; and wherein: the execution of each of the sub-queries includes identifying a multitude of search results for at least one of the sub-queries; the combining includes grouping said multitude of search results into a plurality of clusters, and computing a relevance score for each of said clusters, wherein each cluster represents a high level entity; the determining a user importance value for said each sub-query includes identifying one of the sub-queries as a primary focus, and identifying another of the sub-queries as providing context, computing a probability p(Q) of each of the sub-queries, and computing information content of each of the sub-queries as log p(Q); and the combining the search results further includes, once the information content of the sub-queries are computed, merging the search results. 20. The method according to claim 19 , wherein the data sources contain entities with a hierarchical structure, which has a plurality of hierarchical levels, and the combining includes merging the search results at the same hierarchical level. | 0.654852 |
68. A computer-implemented method for analyzing potential patent infringement, comprising: receiving information regarding a patent; processing the information regarding the patent; identifying a claim of the patent; formulating a search query containing terms in the claim and a foreign language translation of at least one of the terms; automatically generating a natural language question for use in obtaining information from a chat room or an on-line bulletin board; transmitting the natural language question to the chat room or the on-line bulletin board; in response to the question, obtaining information regarding at least one of a product, products, a service, and services from the chat room or the on-line bulletin board; searching the information regarding at least one of a product, products, a service, and services using the query; generating claim chart information containing at least some of the information regarding the at least one of a product, products, a service, and services; and transmitting the claim chart information to a user communication device in order to display the claim chart information to a user. | 68. A computer-implemented method for analyzing potential patent infringement, comprising: receiving information regarding a patent; processing the information regarding the patent; identifying a claim of the patent; formulating a search query containing terms in the claim and a foreign language translation of at least one of the terms; automatically generating a natural language question for use in obtaining information from a chat room or an on-line bulletin board; transmitting the natural language question to the chat room or the on-line bulletin board; in response to the question, obtaining information regarding at least one of a product, products, a service, and services from the chat room or the on-line bulletin board; searching the information regarding at least one of a product, products, a service, and services using the query; generating claim chart information containing at least some of the information regarding the at least one of a product, products, a service, and services; and transmitting the claim chart information to a user communication device in order to display the claim chart information to a user. 83. The method of claim 68 , wherein: the information regarding at least one of a product, products, a service, and services is marked with at least one of a patent number and a patent pending notice. | 0.565456 |
6. The method of claim 1 , further comprising generating labeled image representations based on the first collection of buckets, on the second collection of buckets, and on labeled images, wherein labels that are associated with an image are associated with a respective labeled image representation of the image. | 6. The method of claim 1 , further comprising generating labeled image representations based on the first collection of buckets, on the second collection of buckets, and on labeled images, wherein labels that are associated with an image are associated with a respective labeled image representation of the image. 7. The method of claim 6 , wherein iteratively selecting the sub-collection of buckets from the first collection of buckets and from the second collection of buckets further includes iteratively selecting buckets that are most discriminative of the labels based on the labeled image representations, and wherein the method further comprises training respective classifiers for the labels based on the selected buckets that are most discriminative of the labels. | 0.827219 |
19. A method of creating a document layout using a computer processing system comprising the steps of: (a) scanning and digitizing a document to create computer understandable binary data corresponding to the image of said document; (b) creating a list of primitive objects from said binary data, said primitive objects being data structures representing a rectangular portion of said digitized document containing an image of a letter, a group of letters, or a picture; (c) building a list of rectangular text areas and picture areas, said text areas being formed by grouping primitive objects from said list of primitive objects that contain letters and groups of letters together and said picture areas being formed from said primitive objects in said list of primitive objects that contain picture images; (d) creating an ordered list of object areas by assigning reading order numbers to said text and picture areas according to a first criterion and ordering said list by said reading order numbers; (e) allowing a user to delete object areas from said ordered list of object areas so that said deleted object areas will not be present in the created document layout; (f) creating a plurality of document layouts by recursion, where each invocation of said recursion produces a document layout, each document layout including all of said object areas in said ordered list positioned in a document, by successively placing each of said object areas in said document, according to said order numbers, at one of a set of possible locations for said object area, each location being determined by a rule of placement, said recursion continuing until each of said object areas has been placed at every one of said possible locations in said set; and (g) selecting, according to a second criterion, a particular document layout from said plurality of document layouts. | 19. A method of creating a document layout using a computer processing system comprising the steps of: (a) scanning and digitizing a document to create computer understandable binary data corresponding to the image of said document; (b) creating a list of primitive objects from said binary data, said primitive objects being data structures representing a rectangular portion of said digitized document containing an image of a letter, a group of letters, or a picture; (c) building a list of rectangular text areas and picture areas, said text areas being formed by grouping primitive objects from said list of primitive objects that contain letters and groups of letters together and said picture areas being formed from said primitive objects in said list of primitive objects that contain picture images; (d) creating an ordered list of object areas by assigning reading order numbers to said text and picture areas according to a first criterion and ordering said list by said reading order numbers; (e) allowing a user to delete object areas from said ordered list of object areas so that said deleted object areas will not be present in the created document layout; (f) creating a plurality of document layouts by recursion, where each invocation of said recursion produces a document layout, each document layout including all of said object areas in said ordered list positioned in a document, by successively placing each of said object areas in said document, according to said order numbers, at one of a set of possible locations for said object area, each location being determined by a rule of placement, said recursion continuing until each of said object areas has been placed at every one of said possible locations in said set; and (g) selecting, according to a second criterion, a particular document layout from said plurality of document layouts. 20. The method of creating a document layout of claim 19 further comprising the step of creating and outputting a formatted document using said particular document layout. | 0.635135 |
8. The digital pictorial book system as claimed in claim 7 , wherein said informing means informs the user of information showing an image capturing method for capturing the image comprising the part by using said image capturing sensor in case the image showing the part is searched out in the main object by said featuring part searching means. | 8. The digital pictorial book system as claimed in claim 7 , wherein said informing means informs the user of information showing an image capturing method for capturing the image comprising the part by using said image capturing sensor in case the image showing the part is searched out in the main object by said featuring part searching means. 9. The digital pictorial book system as claimed in claim 8 , wherein the information showing the image capturing method comprises information showing the image capturing direction of said image capturing sensor. | 0.916626 |
1. In a digital medium classification environment, a method implemented by at least one computing device, the method comprising: aggregating, by the at least one computing device, patterns of neurons in a neural network by progressing through a sequence of layers of the neural network to classify an image as relating to a semantic class; communicating, by the at least one computing device, positive relevancy of the patterns formed by the neurons to the semantic class by progressing backwards through the sequence of layers of the neural network, wherein the communicating of the positive relevancy of the pattern between a plurality of layers from the sequence of layers is based on a probabilistic Winner-Take-All (WTA) approach; localizing, by the at least one computing device, the semantic class within the image based on the communicated positive relevancy of the aggregated patterns to the semantic class; and generating, by the at least one computing device, digital content based on localization of the semantic class within the image. | 1. In a digital medium classification environment, a method implemented by at least one computing device, the method comprising: aggregating, by the at least one computing device, patterns of neurons in a neural network by progressing through a sequence of layers of the neural network to classify an image as relating to a semantic class; communicating, by the at least one computing device, positive relevancy of the patterns formed by the neurons to the semantic class by progressing backwards through the sequence of layers of the neural network, wherein the communicating of the positive relevancy of the pattern between a plurality of layers from the sequence of layers is based on a probabilistic Winner-Take-All (WTA) approach; localizing, by the at least one computing device, the semantic class within the image based on the communicated positive relevancy of the aggregated patterns to the semantic class; and generating, by the at least one computing device, digital content based on localization of the semantic class within the image. 2. The method as described in claim 1 , wherein the semantic class identifies an object included in the image or emotional feeling expressed in the image. | 0.679654 |
1. A system comprising: a mobile device configured to perform operations comprising: receiving a speech input, the speech input including a speech data stream; while receiving the speech input, receiving a non-speech input and time information associated with the non-speech input, the time information operable to specify a sequence in which the speech input and non-speech input are assembled, wherein the time information includes a start streaming node and a stop streaming node; retrieving text data from the speech input using speech recognition; assembling the text data with the non-speech input based on the time information to produce output data, wherein the assembling comprises modifying the text data in accordance with the non-speech input; and providing the output data to a user through a user interface. | 1. A system comprising: a mobile device configured to perform operations comprising: receiving a speech input, the speech input including a speech data stream; while receiving the speech input, receiving a non-speech input and time information associated with the non-speech input, the time information operable to specify a sequence in which the speech input and non-speech input are assembled, wherein the time information includes a start streaming node and a stop streaming node; retrieving text data from the speech input using speech recognition; assembling the text data with the non-speech input based on the time information to produce output data, wherein the assembling comprises modifying the text data in accordance with the non-speech input; and providing the output data to a user through a user interface. 5. The system of claim 1 , wherein: the start streaming node is operable to indicate a time when the user selects to enable a speech input mode of a computing device, and the stop streaming node is operable to indicate a time when the user selects to disable a speech input mode of the computing device. | 0.5 |
9. A method according to claim 1, wherein said method is carried out through interactive operations. | 9. A method according to claim 1, wherein said method is carried out through interactive operations. 10. A method according to claim 9, wherein said interactive operations are carried out via a graphic terminal provided in place of said inputting means and outputting means. | 0.931872 |
2. The system of claim 1 , further configured to search the first database to identify the set of images, wherein the annotation criterion is in the set of annotations associated with the identified set of images. | 2. The system of claim 1 , further configured to search the first database to identify the set of images, wherein the annotation criterion is in the set of annotations associated with the identified set of images. 5. The system of claim 2 , further configured to add the given image to the pool of acceptable images also based on an image attribute selected by the user. | 0.943193 |
10. The system of claim 9 , wherein the identifying the candidate field includes evaluating a location of the candidate field relative to locations of other information on the second version of the web page with the first version of the web page containing the first unsuccessfully retrieved field. | 10. The system of claim 9 , wherein the identifying the candidate field includes evaluating a location of the candidate field relative to locations of other information on the second version of the web page with the first version of the web page containing the first unsuccessfully retrieved field. 11. The system of claim 10 , wherein the candidate field and the other information are both located in a same table on at least one of the second version of the web page and the first version of the one web page. | 0.904732 |
1. A method of translating an imperative language function into a hardware component, comprising the steps of: using a formal imperative function argument to represent at least one among a component input port and a component parameter; distinguishing between formal imperative function arguments intended as component parameters and formal imperative function arguments intended as component input ports within an instantiating framework; and translating imperative language functions to hardware description language for runtime operation. | 1. A method of translating an imperative language function into a hardware component, comprising the steps of: using a formal imperative function argument to represent at least one among a component input port and a component parameter; distinguishing between formal imperative function arguments intended as component parameters and formal imperative function arguments intended as component input ports within an instantiating framework; and translating imperative language functions to hardware description language for runtime operation. 2. The method of claim 1 , wherein the method further comprises the step of generating hardware description by providing a framework where imperative language functions can be translated into hardware components by being instantiated, combined and simulated. | 0.622248 |
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