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8,219,398 | 6 | 7 | 6. A computerized speech synthesizer according to claim 1 , wherein the text elements can each be selectively expressed by multiple prosodic values to represent the text elements in the prosodic speech signal with a desired one of multiple prosody styles. | 6. A computerized speech synthesizer according to claim 1 , wherein the text elements can each be selectively expressed by multiple prosodic values to represent the text elements in the prosodic speech signal with a desired one of multiple prosody styles. 7. A computerized speech synthesizer according to claim 6 comprising a differential phoneme database, the differential phoneme database comprising multiple phonetic modification parameters to change the prosody of individual acoustic phonemes in the phoneme database and enable the prosodic speech signal to be audibilized with different prosody styles. | 0.5 |
9,818,401 | 17 | 18 | 17. A computer-implemented method for recognizing and understanding spoken commands that include one or more proper name entities, comprising: receiving an utterance from a user; performing primary automatic speech recognition (ASR) processing upon said utterance with a primary automatic speech recognizer to output a dataset comprising at least a sequence of nominal transcribed words, optionally each with an associated nominal baseform, said sequence comprising the nominal primary transcription; performing understanding processing upon said dataset with a natural language understanding (NLU) processor to generate and augment the dataset with a nominal meaning for the utterance and to determine putative presence and type of one or more spoken proper name entities within said utterance, and wherein with each said spoken proper name entity is associated a contiguous sequence of nominal transcribed words within said dataset comprising a target span; performing one or more instances of secondary automatic speech recognition (ASR) processing upon an entirety of said utterance, in each instance said secondary automatic speech recognizer specialized to process a given putative type of proper name entity and associated target span to generate a nominal correct transcription and associated meaning for each said target span; substituting the nominal correct transcription and associated meaning obtained from each secondary recognition as appropriate within the dataset to revise the results of the primary automatic speech recognizer and natural language understanding processor; and outputting a complete and accurate transcription and meaning for the entire utterance. | 17. A computer-implemented method for recognizing and understanding spoken commands that include one or more proper name entities, comprising: receiving an utterance from a user; performing primary automatic speech recognition (ASR) processing upon said utterance with a primary automatic speech recognizer to output a dataset comprising at least a sequence of nominal transcribed words, optionally each with an associated nominal baseform, said sequence comprising the nominal primary transcription; performing understanding processing upon said dataset with a natural language understanding (NLU) processor to generate and augment the dataset with a nominal meaning for the utterance and to determine putative presence and type of one or more spoken proper name entities within said utterance, and wherein with each said spoken proper name entity is associated a contiguous sequence of nominal transcribed words within said dataset comprising a target span; performing one or more instances of secondary automatic speech recognition (ASR) processing upon an entirety of said utterance, in each instance said secondary automatic speech recognizer specialized to process a given putative type of proper name entity and associated target span to generate a nominal correct transcription and associated meaning for each said target span; substituting the nominal correct transcription and associated meaning obtained from each secondary recognition as appropriate within the dataset to revise the results of the primary automatic speech recognizer and natural language understanding processor; and outputting a complete and accurate transcription and meaning for the entire utterance. 18. The method of claim 17 , further comprising: specializing the secondary ASR recognizer by using an adaptation object of structure and content appropriate to the said putative span type, as determined by NLU processing. | 0.5 |
8,239,370 | 1 | 4 | 1. A computer-implemented method of determining search results based on a query, the method capable of being performed by a computing device that has access to one or more memory components, and the method comprising: receiving an initial query; inspecting an initial set of query-related information that is associated with the initial query, the initial set of query-related information stemming from an initial set of aggregated user-interaction data, wherein the initial set of aggregated user-interaction data reflects interactions by many users and how the many users have previously interacted with former search results that were presented in response to the initial query, which information includes prior metadata associated with the former search results; presenting an initial set of search results based on referencing the initial set of query-related information, wherein the initial set of search results includes a first listing of a plurality of items in a first order, which items include first descriptions; gathering current user-interaction data as a user interacts with the initial set of search results, the current user-interaction data including current metadata associated with at least a portion of the search results; updating the initial set of query-related information based on the current user-interaction data, thereby providing updated query-related information; and presenting a subsequent set of search results incident to receiving a subsequent query that is the same as the initial query, wherein the subsequent set of search results is based on referencing the updated query-related information, wherein presenting the subsequent set of search results includes presenting a second listing of a plurality of items in a different order from the first order, said different order based on the updated query-related information, and further wherein second descriptions are presented in connection with the second listing of the plurality of items, wherein the second descriptions are different from the first descriptions, and wherein the second descriptions are based on the updated query-related information. | 1. A computer-implemented method of determining search results based on a query, the method capable of being performed by a computing device that has access to one or more memory components, and the method comprising: receiving an initial query; inspecting an initial set of query-related information that is associated with the initial query, the initial set of query-related information stemming from an initial set of aggregated user-interaction data, wherein the initial set of aggregated user-interaction data reflects interactions by many users and how the many users have previously interacted with former search results that were presented in response to the initial query, which information includes prior metadata associated with the former search results; presenting an initial set of search results based on referencing the initial set of query-related information, wherein the initial set of search results includes a first listing of a plurality of items in a first order, which items include first descriptions; gathering current user-interaction data as a user interacts with the initial set of search results, the current user-interaction data including current metadata associated with at least a portion of the search results; updating the initial set of query-related information based on the current user-interaction data, thereby providing updated query-related information; and presenting a subsequent set of search results incident to receiving a subsequent query that is the same as the initial query, wherein the subsequent set of search results is based on referencing the updated query-related information, wherein presenting the subsequent set of search results includes presenting a second listing of a plurality of items in a different order from the first order, said different order based on the updated query-related information, and further wherein second descriptions are presented in connection with the second listing of the plurality of items, wherein the second descriptions are different from the first descriptions, and wherein the second descriptions are based on the updated query-related information. 4. The method of claim 1 , wherein gathering current user information includes monitoring user events as a user interacts with the initial set of search results, and wherein the user events include one or more of: click events, hover events, query abandonment, link avoidances, and dwell time. | 0.534921 |
9,185,119 | 14 | 16 | 14. The system of claim 9 , wherein the classification module assigns, based on the probability that the unknown file is of the same classification as the known file, the classification of the known file to the unknown file by: constructing a bipartite graph comprising a set of cluster nodes representing each client device cluster and a set of file nodes representing the known file and the unknown file, wherein cluster nodes are connected through edges to file nodes according to the occurrence of the file corresponding to the file node on the set of client devices represented by the cluster node; iteratively propagating the classification of the known file to the unknown file according to the probability that the unknown file is of the same classification as the known file. | 14. The system of claim 9 , wherein the classification module assigns, based on the probability that the unknown file is of the same classification as the known file, the classification of the known file to the unknown file by: constructing a bipartite graph comprising a set of cluster nodes representing each client device cluster and a set of file nodes representing the known file and the unknown file, wherein cluster nodes are connected through edges to file nodes according to the occurrence of the file corresponding to the file node on the set of client devices represented by the cluster node; iteratively propagating the classification of the known file to the unknown file according to the probability that the unknown file is of the same classification as the known file. 16. The system of claim 14 , wherein the classification module terminates iteratively propagating the classification when at least one of: the probability for the file node representing the unknown file converges within a threshold value; a predetermined number of iterations have been completed. | 0.850354 |
9,055,414 | 13 | 19 | 13. A computer readable storage device comprising instructions that when executed perform a method, comprising: configuring a text messaging pipeline to order two or more pipeline modules comprised in the text messaging pipeline, the ordering comprising ordering a non-translation module of the two or more pipeline modules in the text messaging pipeline and a translation module of the two or more pipeline modules in the text messaging pipeline such that execution of the non-translation module in relation to a text message is performed prior to execution of the translation module in relation to the text message based upon a determination that using the non-translation module prior to using the translation module results in a first use of resources and using the translation module prior to using the non-translation module results in a second use of resources, the first use of resources less than the second use of resources. | 13. A computer readable storage device comprising instructions that when executed perform a method, comprising: configuring a text messaging pipeline to order two or more pipeline modules comprised in the text messaging pipeline, the ordering comprising ordering a non-translation module of the two or more pipeline modules in the text messaging pipeline and a translation module of the two or more pipeline modules in the text messaging pipeline such that execution of the non-translation module in relation to a text message is performed prior to execution of the translation module in relation to the text message based upon a determination that using the non-translation module prior to using the translation module results in a first use of resources and using the translation module prior to using the non-translation module results in a second use of resources, the first use of resources less than the second use of resources. 19. The computer readable storage device of claim 13 , the method comprising: receiving a user request, through an online service website, to associate a scripting engine pipeline module with the text messaging pipeline; receiving a user defined script, uploaded through the online service website, that is to be executed by the scripting engine pipeline module upon a second text message; and configuring the text messaging pipeline to execute the scripting engine pipeline module upon the second text message. | 0.5 |
7,756,313 | 12 | 17 | 12. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for computer aided detection of anatomical abnormalities in medical images, said method comprising the steps of: providing a plurality of abnormality candidates and features of said abnormality candidates; and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(w T x+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more computationally complex features are used for each successive stage of said cascade of classifiers, wherein each stage of said cascade solves a linear program formulated using a training error rate ξ equivalent to max {0,1−y(w T x+b)} and an l 1 -norm equivalent to ∥w∥ t =Σ|w 1 | summed over all features, wherein said linear program is equivalent to the system represented by min u , v , ξ λ ∑ j = 1 d ( u j + v j ) + μ l + ∑ i ∈ C + ξ i + 1 - μ l - ∑ i ∈ C - ξ i , y ( ∑ j X ij ( u j - v j ) + b ) + ξ i ≥ 1 , such that ξ i ≥ 0 , i = 1 , … , l , u j , v j ≥ 0 , j = 1 , … , d , ( 1 ) wherein λ>0 is a regularization parameter, {x i , y i }, i=1, . . . , l denotes the abnormality candidates, y denotes a label indicating whether or not a candidate associated with a feature vector is a true positive, X denotes a feature matrix of d features wherein each row represents candidate feature vector x, and each column specifies a feature, l + is the number of positive candidates and l − the number of negative candidates, C + and C − contain, respectively, the sets of indices of positive candidates and negative candidates, 0≦μ≦1 is a tuning parameter for combining the false negative rate and false positive rate, and w j =u j −v j . | 12. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for computer aided detection of anatomical abnormalities in medical images, said method comprising the steps of: providing a plurality of abnormality candidates and features of said abnormality candidates; and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(w T x+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more computationally complex features are used for each successive stage of said cascade of classifiers, wherein each stage of said cascade solves a linear program formulated using a training error rate ξ equivalent to max {0,1−y(w T x+b)} and an l 1 -norm equivalent to ∥w∥ t =Σ|w 1 | summed over all features, wherein said linear program is equivalent to the system represented by min u , v , ξ λ ∑ j = 1 d ( u j + v j ) + μ l + ∑ i ∈ C + ξ i + 1 - μ l - ∑ i ∈ C - ξ i , y ( ∑ j X ij ( u j - v j ) + b ) + ξ i ≥ 1 , such that ξ i ≥ 0 , i = 1 , … , l , u j , v j ≥ 0 , j = 1 , … , d , ( 1 ) wherein λ>0 is a regularization parameter, {x i , y i }, i=1, . . . , l denotes the abnormality candidates, y denotes a label indicating whether or not a candidate associated with a feature vector is a true positive, X denotes a feature matrix of d features wherein each row represents candidate feature vector x, and each column specifies a feature, l + is the number of positive candidates and l − the number of negative candidates, C + and C − contain, respectively, the sets of indices of positive candidates and negative candidates, 0≦μ≦1 is a tuning parameter for combining the false negative rate and false positive rate, and w j =u j −v j . 17. The computer readable program storage device of claim 12 , wherein each classifier of said cascade of classifiers uses an accumulative set of features that includes those features used by preceding classifiers in said cascade. | 0.808013 |
9,652,473 | 14 | 19 | 14. A system comprising: one or more processors; and one or more computer-readable storage media storing computer-executable instructions that, responsive to execution by the one or more processors, cause the system to perform operations including: receiving social media data that includes topic information included in the social media data, sentiment values for at least some of the topic information, and location information correlated with the topic information; receiving user selection of a search term, a search term density value, and a sentiment density value for a particular sentiment value; comparing, for locations identified in the location information, a number of occurrences of the search term to a total volume of social media data received to determine a density of the search term at each of the locations; filtering the social media data to identify a set of locations that meet the user selected search term density value; filtering the set of locations to identify a subset of locations that meet the user selected sentiment density value for the particular sentiment value; and populating a map interface with indicia of the search term such that the search term is visually associated with the subset of locations, the indicia of the search term indicating one or more particular locations where the determined density of the particular sentiment value for the search term meets the user selected density value for the particular sentiment value. | 14. A system comprising: one or more processors; and one or more computer-readable storage media storing computer-executable instructions that, responsive to execution by the one or more processors, cause the system to perform operations including: receiving social media data that includes topic information included in the social media data, sentiment values for at least some of the topic information, and location information correlated with the topic information; receiving user selection of a search term, a search term density value, and a sentiment density value for a particular sentiment value; comparing, for locations identified in the location information, a number of occurrences of the search term to a total volume of social media data received to determine a density of the search term at each of the locations; filtering the social media data to identify a set of locations that meet the user selected search term density value; filtering the set of locations to identify a subset of locations that meet the user selected sentiment density value for the particular sentiment value; and populating a map interface with indicia of the search term such that the search term is visually associated with the subset of locations, the indicia of the search term indicating one or more particular locations where the determined density of the particular sentiment value for the search term meets the user selected density value for the particular sentiment value. 19. A system as recited in claim 14 , wherein said comparing and said populating occur in response to a user-initiated search of the social media data. | 0.665929 |
9,299,024 | 1 | 4 | 1. A computer system for scoring answers to questions in a question-answering system, comprising: a processor comprising an automated question answering (QA) system comprising: a tangible storage device operatively connected to said processor, said tangible storage device storing a corpus of data comprising natural language documents; and a user interface operatively connected to said processor, said user interface receiving a question into said automated QA system, said processor constructing title-oriented documents from said corpus of data, each title-oriented document comprising a title and a topical field, said topical field comprising a field name and field content associated with said topical field of a document in said corpus of data, said processor creating a relation instance by combining a field identifier for said topical field, a title concept identifier, and a corresponding field content concept identifier, said processor calculating a count for each said relation instance based on a number of occurrences of said title concept identifier and said field content concept identifier within a corresponding document in said corpus of data, said processor analyzing terms in said question, said analyzing identifying a question content identifier based on previously established question term categories, said processor comparing said question content identifier to said relation instance, said comparing identifying a question-matching relation instance, said processor generating an answer to said question by identifying said title concept identifier of each said question-matching relation instance as a candidate answer to said question, and said processor generating a score for said candidate answer by adding each said count within each said relation instance corresponding to said candidate answer. | 1. A computer system for scoring answers to questions in a question-answering system, comprising: a processor comprising an automated question answering (QA) system comprising: a tangible storage device operatively connected to said processor, said tangible storage device storing a corpus of data comprising natural language documents; and a user interface operatively connected to said processor, said user interface receiving a question into said automated QA system, said processor constructing title-oriented documents from said corpus of data, each title-oriented document comprising a title and a topical field, said topical field comprising a field name and field content associated with said topical field of a document in said corpus of data, said processor creating a relation instance by combining a field identifier for said topical field, a title concept identifier, and a corresponding field content concept identifier, said processor calculating a count for each said relation instance based on a number of occurrences of said title concept identifier and said field content concept identifier within a corresponding document in said corpus of data, said processor analyzing terms in said question, said analyzing identifying a question content identifier based on previously established question term categories, said processor comparing said question content identifier to said relation instance, said comparing identifying a question-matching relation instance, said processor generating an answer to said question by identifying said title concept identifier of each said question-matching relation instance as a candidate answer to said question, and said processor generating a score for said candidate answer by adding each said count within each said relation instance corresponding to said candidate answer. 4. The computer system of claim 1 , further comprising a named-entity extractor to locate and classify elements in said natural language document into predefined categories for said title and said field name, and said processor constructing said title-oriented documents from said corpus of data further comprising: for each said title-oriented document, identifying a term in said title that corresponds to said predefined categories to produce a title concept identifier; and for each said title-oriented document, synthesizing said topical field to produce a field concept identifier and a field content concept identifier from said predefined categories. | 0.5 |
10,154,812 | 16 | 18 | 16. A method for interpreting brain patterns and structures comprising: receiving at least one signal from at least one read modality, the signal representing at least one parameter or measurement of living brain or spinal cord tissue; detecting normal and deleterious signaling patterns in brain or spinal cord tissue by analyzing the received at least one signal. | 16. A method for interpreting brain patterns and structures comprising: receiving at least one signal from at least one read modality, the signal representing at least one parameter or measurement of living brain or spinal cord tissue; detecting normal and deleterious signaling patterns in brain or spinal cord tissue by analyzing the received at least one signal. 18. The method of claim 16 wherein the method is used for Pain Detection and Pain Management. | 0.819066 |
8,280,831 | 5 | 7 | 5. The computer-implementable method of claim 4 , wherein if a computer-generated knowledge statement having antecedent features that match the context features is found, the method further comprises the steps of: displaying the computer-generated knowledge statement consequent to the user via the display device; and receiving user feedback regarding the displayed computer-generated knowledge statement consequent. | 5. The computer-implementable method of claim 4 , wherein if a computer-generated knowledge statement having antecedent features that match the context features is found, the method further comprises the steps of: displaying the computer-generated knowledge statement consequent to the user via the display device; and receiving user feedback regarding the displayed computer-generated knowledge statement consequent. 7. The computer-implementable method of claim 5 , wherein if the user feedback indicates that the computer-generated knowledge statement consequent is incorrect, the method further comprises the step of creating, during runtime, a new rule by performing an analogical contextual transformation. | 0.561194 |
9,076,459 | 44 | 45 | 44. A speech recognition system, comprising: a sound classifier that includes at least one non-transitory processor-readable medium and at least one processor communicatively coupled to the at least one non-transitory processor-readable medium, and that analyzes a first segment of audio, determines at least two confidences among the following three confidences: a first confidence that the first segment of audio is speech; a second confidence that the first segment of audio is non-transient background noise; or a third confidence that the first segment of audio is transient background noise; generates a hypothesis for a second segment of audio that includes the first segment of audio; and adjusts a threshold at which the hypothesis is either rejected or accepted based at least in part on the at least two confidences. | 44. A speech recognition system, comprising: a sound classifier that includes at least one non-transitory processor-readable medium and at least one processor communicatively coupled to the at least one non-transitory processor-readable medium, and that analyzes a first segment of audio, determines at least two confidences among the following three confidences: a first confidence that the first segment of audio is speech; a second confidence that the first segment of audio is non-transient background noise; or a third confidence that the first segment of audio is transient background noise; generates a hypothesis for a second segment of audio that includes the first segment of audio; and adjusts a threshold at which the hypothesis is either rejected or accepted based at least in part on the at least two confidences. 45. The speech recognition system of claim 44 wherein, the sound classifier receives input from at least two microphones. | 0.5 |
7,729,655 | 1 | 2 | 1. A computer-implemented method for providing individualized essay writing instruction, the method comprising: receiving an essay in an electronic format using a computer; automatically determining with the computer a first value for each sentence in the essay that reflects the probability that the sentence is a member of a discourse element category, wherein the probability is based on the presence of each of a predetermined set of features in the sentence; utilizing the first value to determine with the computer whether each sentence in the essay should be assigned to a discourse element category; identifying with the computer any discourse elements in the essay; if the first values do not indicate the presence of a discourse element in the essay, indicating that the essay lacks sufficient clarity; generating feedback regarding the presence or absence of discourse elements in the essay; and transmitting the feedback for display to a user. | 1. A computer-implemented method for providing individualized essay writing instruction, the method comprising: receiving an essay in an electronic format using a computer; automatically determining with the computer a first value for each sentence in the essay that reflects the probability that the sentence is a member of a discourse element category, wherein the probability is based on the presence of each of a predetermined set of features in the sentence; utilizing the first value to determine with the computer whether each sentence in the essay should be assigned to a discourse element category; identifying with the computer any discourse elements in the essay; if the first values do not indicate the presence of a discourse element in the essay, indicating that the essay lacks sufficient clarity; generating feedback regarding the presence or absence of discourse elements in the essay; and transmitting the feedback for display to a user. 2. The method of claim 1 , further comprising: if the first values do not indicate the presence of a discourse element in the essay, indicating that the essay lacks sufficient clarity. | 0.5 |
8,738,363 | 1 | 2 | 1. A method for extraction of suggestions for improvement comprising: providing a structured terminology for a topic, the structured terminology including a set of semantic classes, each of a plurality of the semantic classes including a finite set of terms; providing a thesaurus of terms relating to suggestions of improvement; receiving a corpus of text documents, each document comprising a text string in a natural language; labeling text elements in the text strings which are instances of terms in the structured terminology with the corresponding semantic class; labeling text elements in the text strings which are instances of terms in the thesaurus; with a processor, applying a set of patterns to the labeled text strings to identify suggestions of improvement expressions, the patterns each defining a syntactic relation between text elements, the patterns including: for each of the semantic classes in the set, at least one pattern which specifies a syntactic relation in which one of the text elements in the relation is labeled as an instance of the semantic class, and wherein at least one of the patterns specifies a syntactic relation in which one of the text elements in the relation is labeled as an instance of one of the terms in the thesaurus; and outputting a set of suggestions for improvements based on the identified suggestions of improvement expressions. | 1. A method for extraction of suggestions for improvement comprising: providing a structured terminology for a topic, the structured terminology including a set of semantic classes, each of a plurality of the semantic classes including a finite set of terms; providing a thesaurus of terms relating to suggestions of improvement; receiving a corpus of text documents, each document comprising a text string in a natural language; labeling text elements in the text strings which are instances of terms in the structured terminology with the corresponding semantic class; labeling text elements in the text strings which are instances of terms in the thesaurus; with a processor, applying a set of patterns to the labeled text strings to identify suggestions of improvement expressions, the patterns each defining a syntactic relation between text elements, the patterns including: for each of the semantic classes in the set, at least one pattern which specifies a syntactic relation in which one of the text elements in the relation is labeled as an instance of the semantic class, and wherein at least one of the patterns specifies a syntactic relation in which one of the text elements in the relation is labeled as an instance of one of the terms in the thesaurus; and outputting a set of suggestions for improvements based on the identified suggestions of improvement expressions. 2. The method of claim 1 , further comprising natural language processing the text strings to extract syntactic relations between text elements in the text strings, the syntactic relations including the syntactic relations applied in the patterns. | 0.942371 |
7,949,525 | 1 | 4 | 1. A spoken language understanding system comprising: a processor; a first module configured to control the processor to classify a set of labeled candidate utterances based on a previously trained classifier and generate classification types for each candidate utterance, each classification type having a respective confidence score; a second module configured to control the processor to sort the candidate utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, wherein the analysis is based at least in part on a Kullback-Liebler divergence; and a third module configured to control the processor to recheck candidate utterances according to when the Kullback-Liebler divergence is greater than a threshold. | 1. A spoken language understanding system comprising: a processor; a first module configured to control the processor to classify a set of labeled candidate utterances based on a previously trained classifier and generate classification types for each candidate utterance, each classification type having a respective confidence score; a second module configured to control the processor to sort the candidate utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, wherein the analysis is based at least in part on a Kullback-Liebler divergence; and a third module configured to control the processor to recheck candidate utterances according to when the Kullback-Liebler divergence is greater than a threshold. 4. The system of claim 1 , wherein the analysis is based on a comparison of the generated classification type of the candidate utterance and the label of the candidate utterance, and the module rechecks the candidate utterances when the generated classification type does not match the label of the candidate utterance. | 0.610024 |
10,019,205 | 1 | 5 | 1. A distributed computing system for managing solutions for print device support requests, the distributed computing system comprising: a solution data store comprising one or more non-transitory computer-readable media sectors that store content representing a plurality of solutions to a plurality of print device issues; a solution processing system comprising a processor and programming instructions that are configured to cause the processor to: output a user interface, receive a support request via the user interface, wherein the support request comprises input data and one or more search terms that pertain to an issue with a print device, use the input data and the one or more search terms to generate a search query, query the solution data store using the search query to identify a list of possible solutions for the support request, wherein each possible solution is associated with a plurality of factors, wherein each factor has a weight value, output, via the user interface the list of possible solutions for the support request, receive a selection of one or more of the possible solutions, for each selection: receive, via the user interface, an indication of an exit status for the support request, wherein the exit status represents a resolution to the support request, generate a session record for the support request comprising the input data, the search terms, the possible solution associated with the selection and the exit status, correlate the possible solution associated with the selection with the input data and search terms, generate one or more factors based on the correlation, and use the one or more generated factors to update one or more fields of a solution index in the data store for the possible solution by, for each of the one or more factors: determining whether the factor is already present in the solution index, and in response to determining that the factor is not already present in the index, adding a field for the factor to the index and assign the factor a weight value. | 1. A distributed computing system for managing solutions for print device support requests, the distributed computing system comprising: a solution data store comprising one or more non-transitory computer-readable media sectors that store content representing a plurality of solutions to a plurality of print device issues; a solution processing system comprising a processor and programming instructions that are configured to cause the processor to: output a user interface, receive a support request via the user interface, wherein the support request comprises input data and one or more search terms that pertain to an issue with a print device, use the input data and the one or more search terms to generate a search query, query the solution data store using the search query to identify a list of possible solutions for the support request, wherein each possible solution is associated with a plurality of factors, wherein each factor has a weight value, output, via the user interface the list of possible solutions for the support request, receive a selection of one or more of the possible solutions, for each selection: receive, via the user interface, an indication of an exit status for the support request, wherein the exit status represents a resolution to the support request, generate a session record for the support request comprising the input data, the search terms, the possible solution associated with the selection and the exit status, correlate the possible solution associated with the selection with the input data and search terms, generate one or more factors based on the correlation, and use the one or more generated factors to update one or more fields of a solution index in the data store for the possible solution by, for each of the one or more factors: determining whether the factor is already present in the solution index, and in response to determining that the factor is not already present in the index, adding a field for the factor to the index and assign the factor a weight value. 5. The distributed computing system of claim 1 , wherein the programming instructions that are configured to cause the processor to correlate the possible solution associated with the selection with the input data and search terms comprise programming instructions that are configured to cause the processor to: correlate the input data of the session record with input data of each of a plurality of other session records, wherein each other session record is associated with the possible solution associated with the selection and reflects a session having the exit status, and correlate the search terms of the session record with the search terms of each of the other session records. | 0.630505 |
8,838,626 | 10 | 11 | 10. A system comprising: storage memory configured to store an XML document; one or more processors having a plurality of processor cores, coupled to the storage memory; an events partitioning module which, upon execution by the processor, causes the processor to partition the XML document into a plurality of XML chunks having a plurality of XML events contained therein, wherein partition includes determination of a type of event associated with individual XML events of the plurality of XML events and exclusion of character data contained within one or more XML events based, at least in part, on the type of event to prevent identification of character data contained within the one or more XML events from being identified as an XML event; a plurality of instances of an events parsing module which, upon execution by one or more of the plurality of processor cores, cause the one or more of the plurality of processor cores to perform, in parallel, events parsing of the plurality of XML chunks to produce respective sub-event streams and structure metadata, the structure metadata identifying unresolved items in the sub-event streams to avoid a parsing error based on the unresolved items, wherein the unresolved items include one or more of an identity of an unresolved start element, an identity of an unresolved end element, or an identity of an unresolved prefix; and a post-processing module which, when executed by at least one processor of the one or more processors, causes the at least one processor to perform post-processing on the sub-event streams to produce a result event stream, wherein to produce the result event stream includes resolution of an unresolved end element identified in the structure metadata with a preceding unresolved start element identified in the structure metadata or resolution of an unresolved prefix identified in the structure metadata with a namespace of a preceding start element to avert the need to reparse the XML chunks that produced the unresolved item. | 10. A system comprising: storage memory configured to store an XML document; one or more processors having a plurality of processor cores, coupled to the storage memory; an events partitioning module which, upon execution by the processor, causes the processor to partition the XML document into a plurality of XML chunks having a plurality of XML events contained therein, wherein partition includes determination of a type of event associated with individual XML events of the plurality of XML events and exclusion of character data contained within one or more XML events based, at least in part, on the type of event to prevent identification of character data contained within the one or more XML events from being identified as an XML event; a plurality of instances of an events parsing module which, upon execution by one or more of the plurality of processor cores, cause the one or more of the plurality of processor cores to perform, in parallel, events parsing of the plurality of XML chunks to produce respective sub-event streams and structure metadata, the structure metadata identifying unresolved items in the sub-event streams to avoid a parsing error based on the unresolved items, wherein the unresolved items include one or more of an identity of an unresolved start element, an identity of an unresolved end element, or an identity of an unresolved prefix; and a post-processing module which, when executed by at least one processor of the one or more processors, causes the at least one processor to perform post-processing on the sub-event streams to produce a result event stream, wherein to produce the result event stream includes resolution of an unresolved end element identified in the structure metadata with a preceding unresolved start element identified in the structure metadata or resolution of an unresolved prefix identified in the structure metadata with a namespace of a preceding start element to avert the need to reparse the XML chunks that produced the unresolved item. 11. The system of claim 10 , wherein the post-processing module, when executed by the at least one processor, causes the processor to: process the sub-event streams sequentially; and produce the result event stream through recombination of the plurality of XML chunks and linking of the processed sub-event streams. | 0.5 |
8,433,556 | 1 | 3 | 1. A method for aligning words in parallel segments, the method comprising: calculating a first probability distribution, utilizing a processor and a memory, according to a model estimate of word alignments within a first corpus comprising word-level unaligned parallel segments, the model estimate comprising an N-best list of one or more sub-models; modifying the model estimate according to the first probability distribution; discriminatively re-ranking one or more sub-models associated with the modified model estimate according to word-level annotated parallel segments; and calculating a second probability distribution of the word alignments within the first corpus according to the re-ranked sub-models associated with the modified model estimate; wherein discriminatively re-ranking one or more sub-models within the modified model estimate according to manual alignments further comprises: adding manual alignments to hypothesized alignments within the first corpus; comparing the manual alignments to the hypothesized alignments; and weighting the one or more sub-models according to the comparison; and wherein the comparing of the manual alignments to the hypothesized alignments comprises: comparing an updated weighting factor for each sub-model derived using the first corpus to randomly generated weighting factors; and selecting one of the updated weighting factor and the randomly generated weighting factor that generates a least amount of error. | 1. A method for aligning words in parallel segments, the method comprising: calculating a first probability distribution, utilizing a processor and a memory, according to a model estimate of word alignments within a first corpus comprising word-level unaligned parallel segments, the model estimate comprising an N-best list of one or more sub-models; modifying the model estimate according to the first probability distribution; discriminatively re-ranking one or more sub-models associated with the modified model estimate according to word-level annotated parallel segments; and calculating a second probability distribution of the word alignments within the first corpus according to the re-ranked sub-models associated with the modified model estimate; wherein discriminatively re-ranking one or more sub-models within the modified model estimate according to manual alignments further comprises: adding manual alignments to hypothesized alignments within the first corpus; comparing the manual alignments to the hypothesized alignments; and weighting the one or more sub-models according to the comparison; and wherein the comparing of the manual alignments to the hypothesized alignments comprises: comparing an updated weighting factor for each sub-model derived using the first corpus to randomly generated weighting factors; and selecting one of the updated weighting factor and the randomly generated weighting factor that generates a least amount of error. 3. The method recited in claim 1 , further comprising determining whether a first error associated with the re-ranked modified model estimate converges with a second error associated with the model estimate. | 0.808333 |
8,370,158 | 46 | 49 | 46. The server apparatus of claim 41 , wherein the at least one program is further configured to select advertising or other information that is contextually related to an attribute or type of the organization or entity for display on a remotely disposed electronic display device. | 46. The server apparatus of claim 41 , wherein the at least one program is further configured to select advertising or other information that is contextually related to an attribute or type of the organization or entity for display on a remotely disposed electronic display device. 49. The server apparatus of claim 46 , wherein the advertising or other information is configured to be displayed substantially contemporaneous with a display of the graphical or visual representation of that location. | 0.5 |
9,454,623 | 1 | 12 | 1. A method for social review of computer aided design (CAD) projects, comprising: executing a CAD application on the first computer; establishing a connection between an abstraction layer associated with the CAD application on the first computer and a separate social collaboration application on the first computer; in response to selection of interface elements in the CAD application by a first user, causing the separate social collaboration application to create a social review session between at least the first user of the first computer and a second user of a second computer, wherein the second computer lacks a copy of the CAD application but has a copy of a different application; using the social collaboration application on the first computer to synchronize the first user's current view of a CAD model in the CAD application on the first computer with a view seen by the second user in the different application on the second computer by receiving indications of the first user's current view of the CAD model from the CAD application on the first computer, at the abstraction layer on the first computer, and interfacing, by the abstraction layer on the first computer, with an application program interface (API) of the social collaboration application on the first computer to cause the social collaboration application on the first computer to transmit the indications of the first user's current view of the CAD model to the social collaboration application on the second computer; and using the social collaboration application on the first computer to exchange annotations on the first computer made by the first user on the current view of the CAD model with the second user of the different application on the second computer by receiving indications of the first user's annotations on the current view of the CAD model from the CAD application at the abstraction layer on the first computer, and interfacing, by the abstraction layer on the first computer, with the API of the social collaboration application on the first computer to cause the social collaboration application on the first computer to transmit the annotations to the social collaboration application on the second computer. | 1. A method for social review of computer aided design (CAD) projects, comprising: executing a CAD application on the first computer; establishing a connection between an abstraction layer associated with the CAD application on the first computer and a separate social collaboration application on the first computer; in response to selection of interface elements in the CAD application by a first user, causing the separate social collaboration application to create a social review session between at least the first user of the first computer and a second user of a second computer, wherein the second computer lacks a copy of the CAD application but has a copy of a different application; using the social collaboration application on the first computer to synchronize the first user's current view of a CAD model in the CAD application on the first computer with a view seen by the second user in the different application on the second computer by receiving indications of the first user's current view of the CAD model from the CAD application on the first computer, at the abstraction layer on the first computer, and interfacing, by the abstraction layer on the first computer, with an application program interface (API) of the social collaboration application on the first computer to cause the social collaboration application on the first computer to transmit the indications of the first user's current view of the CAD model to the social collaboration application on the second computer; and using the social collaboration application on the first computer to exchange annotations on the first computer made by the first user on the current view of the CAD model with the second user of the different application on the second computer by receiving indications of the first user's annotations on the current view of the CAD model from the CAD application at the abstraction layer on the first computer, and interfacing, by the abstraction layer on the first computer, with the API of the social collaboration application on the first computer to cause the social collaboration application on the first computer to transmit the annotations to the social collaboration application on the second computer. 12. The method of claim 1 , wherein the CAD application is an engineering project team collaboration application. | 0.91374 |
9,069,842 | 1 | 15 | 1. A method for accessing documents related to a subject from a document corpus, comprising: categorizing documents from the document corpus based on one or more subjects; creating a candidate list of word sequences, wherein respective ones of the word sequences comprise one or more elements derived from the document corpus; expanding the candidate list by adding one or more new word patterns, wherein each new pattern comprises a gapped sequence created by combining one or more elements derived from the document corpus with one of said word sequences; determining a predictive power with respect to the subject for respective ones of entries of the candidate list, wherein the entries comprise said word sequences and said new word patterns; pruning from the candidate list ones of said entries with the determined predictive power less than a predetermined threshold, wherein the predictive power comprises a measure of information gain, and wherein the pruning further comprises pruning from the candidate list ones of said entries with a frequency of occurrence less than a predetermined frequency threshold; accessing documents from the document corpus based on the pruned candidate list; updating the categorization of documents based on the accessing; and iteratively performing the expanding, the determining the predictive power, and the pruning, for increasing entry lengths until at least one of the entries is of a predetermined length. | 1. A method for accessing documents related to a subject from a document corpus, comprising: categorizing documents from the document corpus based on one or more subjects; creating a candidate list of word sequences, wherein respective ones of the word sequences comprise one or more elements derived from the document corpus; expanding the candidate list by adding one or more new word patterns, wherein each new pattern comprises a gapped sequence created by combining one or more elements derived from the document corpus with one of said word sequences; determining a predictive power with respect to the subject for respective ones of entries of the candidate list, wherein the entries comprise said word sequences and said new word patterns; pruning from the candidate list ones of said entries with the determined predictive power less than a predetermined threshold, wherein the predictive power comprises a measure of information gain, and wherein the pruning further comprises pruning from the candidate list ones of said entries with a frequency of occurrence less than a predetermined frequency threshold; accessing documents from the document corpus based on the pruned candidate list; updating the categorization of documents based on the accessing; and iteratively performing the expanding, the determining the predictive power, and the pruning, for increasing entry lengths until at least one of the entries is of a predetermined length. 15. The method of claim 1 , further comprising: pruning, from the candidate list, said entries having a frequency of occurrence in the document corpus which is less than a predetermined frequency threshold. | 0.752998 |
8,250,018 | 14 | 15 | 14. The computer program product claimed in claim 13 , wherein the inference engine code matches description-oriented keyterms in the query to determine if the query relates to a knowledge-based solution. | 14. The computer program product claimed in claim 13 , wherein the inference engine code matches description-oriented keyterms in the query to determine if the query relates to a knowledge-based solution. 15. The computer program product claimed in claim 14 , wherein the description-oriented keyterms relate to at least one of: “what is”; “what are”; “where is”; “where are”; “how many”; “how long”; “how to”; “how do I”; “tell about”; and “describe”. | 0.5 |
7,552,005 | 1 | 3 | 1. A method of analyzing a turbine engine to determine a normal engine condition or a faulty engine condition, said method comprising the steps of: acquiring at least one engine operating parameter; calculating at least one engine residual value from said at least one engine operating parameter; normalizing said at least one engine residual value to yield at least one normalized engine residual; mapping, via a clustering technique, said at least one normalized engine residual as at least one input vector into an engine condition space having a plurality of clusters, each of said plurality of clusters representing either a normal vector engine condition or a faulty vector engine condition; identifying a closest cluster within said engine condition space, said closest cluster being closer to said at least one input vector than any other of said plurality of clusters; and determining a normal engine condition for the engine undergoing analysis if said closest cluster represents a normal vector engine condition, and determining a faulty engine condition for the engine undergoing analysis if said closest cluster represents a faulty vector engine condition. | 1. A method of analyzing a turbine engine to determine a normal engine condition or a faulty engine condition, said method comprising the steps of: acquiring at least one engine operating parameter; calculating at least one engine residual value from said at least one engine operating parameter; normalizing said at least one engine residual value to yield at least one normalized engine residual; mapping, via a clustering technique, said at least one normalized engine residual as at least one input vector into an engine condition space having a plurality of clusters, each of said plurality of clusters representing either a normal vector engine condition or a faulty vector engine condition; identifying a closest cluster within said engine condition space, said closest cluster being closer to said at least one input vector than any other of said plurality of clusters; and determining a normal engine condition for the engine undergoing analysis if said closest cluster represents a normal vector engine condition, and determining a faulty engine condition for the engine undergoing analysis if said closest cluster represents a faulty vector engine condition. 3. The method of claim 1 wherein said step of acquiring at least one engine operating parameter comprises the step of collecting engine operating data in the field. | 0.680934 |
8,368,738 | 1 | 2 | 1. A computer-implemented communications system, comprising: an invite component for receiving an invitation for an invitee to participate in a communications session; a join component for checking to determine if the communications session has been created, and if so, automatically joining the invitee into the communications session in response to the invitation; and a microprocessor that executes computer-executable instructions associated with at least one of the invite component or the join component. | 1. A computer-implemented communications system, comprising: an invite component for receiving an invitation for an invitee to participate in a communications session; a join component for checking to determine if the communications session has been created, and if so, automatically joining the invitee into the communications session in response to the invitation; and a microprocessor that executes computer-executable instructions associated with at least one of the invite component or the join component. 2. The system of claim 1 , wherein the join component determines that the communications session has been created prior to the invitation and automatically joins the invitee using a communications mode associated with the session. | 0.665698 |
9,473,634 | 8 | 9 | 8. The system of claim 1 , wherein the first keyword is associated with the remote party requesting a telephone number associated with the remote party be placed in a do-not-call database. | 8. The system of claim 1 , wherein the first keyword is associated with the remote party requesting a telephone number associated with the remote party be placed in a do-not-call database. 9. The system of claim 8 , wherein the visual indication comprises text instructing the agent to confirm the telephone number associated with the remote party. | 0.5 |
8,239,350 | 16 | 25 | 16. A computer system for resolving ambiguities in date values associated with an attribute of an entity, the computer system comprising: one or more processors; memory; and one or more programs stored in the memory, the one or more programs comprising instructions to: obtain a first text string associated with an attribute of an entity, wherein the first text string is extracted from a first web document; determine if that the first text string conforms to one or more date formats; assign a first confidence value for each of the date formats for the first text string based on a first number of unknown variables that remain when interpreting the first text string using each of the date formats; obtain a second text string associated with the attribute of the entity, wherein the second text string is extracted from a second web document; determine if that the second text string conforms to one or more date formats; assign a second confidence value for each of the date formats for the second text string based on a second number of unknown variables that remain when interpreting the second text string using each of the date formats; determine a first date string expressed in a date format with a highest first confidence value for the first text string; determine a second date string expressed in a date format with a highest second confidence value for the second text string; and merge a first subset of the first date string and a second subset of the second date string to obtain a date value for the attribute. | 16. A computer system for resolving ambiguities in date values associated with an attribute of an entity, the computer system comprising: one or more processors; memory; and one or more programs stored in the memory, the one or more programs comprising instructions to: obtain a first text string associated with an attribute of an entity, wherein the first text string is extracted from a first web document; determine if that the first text string conforms to one or more date formats; assign a first confidence value for each of the date formats for the first text string based on a first number of unknown variables that remain when interpreting the first text string using each of the date formats; obtain a second text string associated with the attribute of the entity, wherein the second text string is extracted from a second web document; determine if that the second text string conforms to one or more date formats; assign a second confidence value for each of the date formats for the second text string based on a second number of unknown variables that remain when interpreting the second text string using each of the date formats; determine a first date string expressed in a date format with a highest first confidence value for the first text string; determine a second date string expressed in a date format with a highest second confidence value for the second text string; and merge a first subset of the first date string and a second subset of the second date string to obtain a date value for the attribute. 25. The system of claim 16 , wherein a respective text string is obtained from a respective date source, and wherein the system further comprises instructions to: repeat the instructions of claim 14 until a threshold number of date sources with identical date formats for the attribute are obtained. | 0.605541 |
8,099,430 | 17 | 20 | 17. A computer system managing comment information comprising: given one or more computers providing a main information source and one or more sources of comment information, the comment information being with respect to the main information, the main information being related to each comment information as parent-child, a keyword engine executed by a processor and responsive to the main information and the comment information, the keyword engine employing a semantic lexicon tool and: extracting initial keywords from the main information, the extracted initial keywords forming an initial taxonomy; extracting, including detecting, from the comment information words matching the initial taxonomy by being related to, but not duplicative of, the initial keywords extracted from the main information; and adding to a keyword listing the extracted initial keywords from the main information and the words extracted from the comment information, such that the keyword listing is formed of automatically generated keywords, each automatically generated keyword dynamically grouping related comment information and resulting in precise keywords that enhance retrieval of comment information; and a user interface executed by a computer coupled to receive the keyword listing from the keyword engine and displaying the keyword listing to a user in a manner enabling the user to manage the comment information based on the automatically generated keywords and dynamic groupings, and the user interface enabling subject matter of the main information to be made available to the user in the comment information. | 17. A computer system managing comment information comprising: given one or more computers providing a main information source and one or more sources of comment information, the comment information being with respect to the main information, the main information being related to each comment information as parent-child, a keyword engine executed by a processor and responsive to the main information and the comment information, the keyword engine employing a semantic lexicon tool and: extracting initial keywords from the main information, the extracted initial keywords forming an initial taxonomy; extracting, including detecting, from the comment information words matching the initial taxonomy by being related to, but not duplicative of, the initial keywords extracted from the main information; and adding to a keyword listing the extracted initial keywords from the main information and the words extracted from the comment information, such that the keyword listing is formed of automatically generated keywords, each automatically generated keyword dynamically grouping related comment information and resulting in precise keywords that enhance retrieval of comment information; and a user interface executed by a computer coupled to receive the keyword listing from the keyword engine and displaying the keyword listing to a user in a manner enabling the user to manage the comment information based on the automatically generated keywords and dynamic groupings, and the user interface enabling subject matter of the main information to be made available to the user in the comment information. 20. A system as claimed in claim 17 wherein the main information source is any of: a wiki, a blog, computer network printable content, written material in a computer, and text-like communications in a computer; and the comment information is any of computer implemented comments, responses, notes, and text-based messages. | 0.503086 |
8,739,129 | 1 | 12 | 1. One or more non-transitory computer-readable media storing computer-executable instructions executable by a processor, the media storing one or more instructions for: providing a graphical model including: a first entity associated with a first modeling domain, wherein the first modeling domain is one of a statechart domain, a time-based block diagram domain, a physical system domain, a data flow diagram domain, a unified modeling language domain, a discrete event modeling domain, or a compiled code domain, and a second entity associated with a second modeling domain, wherein the second modeling domain is of a different type than the first modeling domain and is one of the statechart domain, the time-based block diagram domain, the physical system domain, the data flow diagram domain, the unified modeling language domain, the discrete event modeling domain, or the compiled code domain; providing a programming interface to a debugger; transferring information associated with the first entity via the programming interface to the debugger after executing the first entity; generating a first domain-specific debugger view of the graphical model, the generated first domain-specific debugger view consistent with the first modeling domain, the generating first domain-specific debugger view being based on the transferring information associated with the first entity; transferring information associated with the second entity via the programming interface to the debugger after executing the second entity; generating a second domain-specific debugger view of the graphical model, the generated second domain-specific debugger view consistent with the second modeling domain, the generating the second domain-specific debugger being based on the transferring information associated with the second entity; displaying the first domain-specific debugger view on a display device, where the first domain-specific debugger view is displayed: after the first entity is executed, and using a first user interface element; automatically transitioning to an updated domain display, the transitioning occurring when the second entity is executing; and displaying the second domain-specific debugger view on the display device, where the second domain-specific debugger view is displayed: based on the transitioning, after the second entity is executed, and using a second user interface element. | 1. One or more non-transitory computer-readable media storing computer-executable instructions executable by a processor, the media storing one or more instructions for: providing a graphical model including: a first entity associated with a first modeling domain, wherein the first modeling domain is one of a statechart domain, a time-based block diagram domain, a physical system domain, a data flow diagram domain, a unified modeling language domain, a discrete event modeling domain, or a compiled code domain, and a second entity associated with a second modeling domain, wherein the second modeling domain is of a different type than the first modeling domain and is one of the statechart domain, the time-based block diagram domain, the physical system domain, the data flow diagram domain, the unified modeling language domain, the discrete event modeling domain, or the compiled code domain; providing a programming interface to a debugger; transferring information associated with the first entity via the programming interface to the debugger after executing the first entity; generating a first domain-specific debugger view of the graphical model, the generated first domain-specific debugger view consistent with the first modeling domain, the generating first domain-specific debugger view being based on the transferring information associated with the first entity; transferring information associated with the second entity via the programming interface to the debugger after executing the second entity; generating a second domain-specific debugger view of the graphical model, the generated second domain-specific debugger view consistent with the second modeling domain, the generating the second domain-specific debugger being based on the transferring information associated with the second entity; displaying the first domain-specific debugger view on a display device, where the first domain-specific debugger view is displayed: after the first entity is executed, and using a first user interface element; automatically transitioning to an updated domain display, the transitioning occurring when the second entity is executing; and displaying the second domain-specific debugger view on the display device, where the second domain-specific debugger view is displayed: based on the transitioning, after the second entity is executed, and using a second user interface element. 12. The non-transitory computer-readable media of claim 1 wherein at least one of the first domain-specific debugger view or the second domain-specific debugger view includes a display of a call chain of methods that are called during an execution of the graphical model. | 0.729 |
8,682,907 | 14 | 19 | 14. The system of claim 13 , wherein computing a score for the second term comprises: computing respective changes in co-occurrence frequency between corresponding elements of the first vector and the second vector; generating an order of co-occurring terms according to the corresponding computed changes in co-occurrence frequency; and computing a measure of importance of a top number of co-occurring terms in the order. | 14. The system of claim 13 , wherein computing a score for the second term comprises: computing respective changes in co-occurrence frequency between corresponding elements of the first vector and the second vector; generating an order of co-occurring terms according to the corresponding computed changes in co-occurrence frequency; and computing a measure of importance of a top number of co-occurring terms in the order. 19. The system of claim 14 , wherein the measure of importance of a term is an inverse document frequency of the term. | 0.870897 |
7,783,657 | 1 | 5 | 1. A search authoring tool for mapping queries to assets for use in an information retrieval system, the search authoring tool comprising: a memory adapted to store processor-executable instructions that map queries to assets, the processor-executable instructions comprising: a query-to-asset mappings database of approved mappings built from search author input and user mappings; a search authoring engine receiving operator input and generating a plurality of pending query-to-asset mappings based on the received operator input; a search evaluation engine comparing a performance of a first runtime classifier built from the approved mappings against a second runtime classifier built from the mappings and the plurality of pending query-to-asset mappings to determine whether using the plurality of pending query-to-asset mappings causes performance degradation of the information retrieval system, the search evaluation engine comparing performance of the first runtime classifier built from the approved mappings against performance of the second runtime classifier by comparing performance of the first runtime classifier against the performance of a plurality of second runtime classifiers built from the approved mappings and a plurality of different subsets of the pending query-to-asset mappings to identify specific subsets of the pending query-to-asset mappings that cause the performance degradation, the search evaluation engine marking for removal at least one of the plurality of pending query-to-asset mappings based on a determination that the at least one of the plurality of pending query-to-asset mappings is in an identified specific subset of the pending query-to-asset mappings that causes performance degradation; and a processor coupled to the memory and being a functional part of the search authoring tool and being activated by the search authoring engine and the search evaluation engine to facilitate mapping queries to assets for use in an information retrieval system. | 1. A search authoring tool for mapping queries to assets for use in an information retrieval system, the search authoring tool comprising: a memory adapted to store processor-executable instructions that map queries to assets, the processor-executable instructions comprising: a query-to-asset mappings database of approved mappings built from search author input and user mappings; a search authoring engine receiving operator input and generating a plurality of pending query-to-asset mappings based on the received operator input; a search evaluation engine comparing a performance of a first runtime classifier built from the approved mappings against a second runtime classifier built from the mappings and the plurality of pending query-to-asset mappings to determine whether using the plurality of pending query-to-asset mappings causes performance degradation of the information retrieval system, the search evaluation engine comparing performance of the first runtime classifier built from the approved mappings against performance of the second runtime classifier by comparing performance of the first runtime classifier against the performance of a plurality of second runtime classifiers built from the approved mappings and a plurality of different subsets of the pending query-to-asset mappings to identify specific subsets of the pending query-to-asset mappings that cause the performance degradation, the search evaluation engine marking for removal at least one of the plurality of pending query-to-asset mappings based on a determination that the at least one of the plurality of pending query-to-asset mappings is in an identified specific subset of the pending query-to-asset mappings that causes performance degradation; and a processor coupled to the memory and being a functional part of the search authoring tool and being activated by the search authoring engine and the search evaluation engine to facilitate mapping queries to assets for use in an information retrieval system. 5. The search authoring tool of claim 1 wherein the search evaluation tool is executable by the processor to label particular query-to-asset mappings of the approved mappings, the search authoring tool further comprising: a search trimming feature executable by the processor to remove the particular query-to-asset mappings from the query-to-asset mappings database based on the label. | 0.5 |
9,183,832 | 14 | 21 | 14. A display apparatus comprising: a display unit which displays a first display item; a text determination unit which extracts a first text from the first display item; a voice recognition unit which recognizes a voice input from a user; and a controller which: displays the first text so as to distinguish the first text from other texts, determines if the recognized voice input corresponds to the first text, and controls the display unit to select the first display item in response to a determination that the recognized voice input corresponds to the first text, wherein the voice input comprises a voice of the user speaking at least a word from the first text, and wherein the text determination unit extracts the first text comprising at least one word from the display item such that the first text does not share common words with a second text extracted from a second display item. | 14. A display apparatus comprising: a display unit which displays a first display item; a text determination unit which extracts a first text from the first display item; a voice recognition unit which recognizes a voice input from a user; and a controller which: displays the first text so as to distinguish the first text from other texts, determines if the recognized voice input corresponds to the first text, and controls the display unit to select the first display item in response to a determination that the recognized voice input corresponds to the first text, wherein the voice input comprises a voice of the user speaking at least a word from the first text, and wherein the text determination unit extracts the first text comprising at least one word from the display item such that the first text does not share common words with a second text extracted from a second display item. 21. The display apparatus of claim 14 , wherein the controller displays the recognized voice input in response to a determination that the recognized voice input does not match the first text. | 0.517588 |
8,391,618 | 1 | 9 | 1. A non-transitory computer-readable storage medium that stores program instructions computer-executable to implement: determining a plurality of semantic category scores for a digital image via application of a corresponding plurality of classifiers; automatically determining a semantic category profile for the image based on the plurality of semantic category scores, wherein the semantic category profile characterizes semantic content of the image, and is useable to perform semantic based operations with respect to the image; and performing a semantic based operation wherein the semantic based operation comprises: a search operation based on a semantic similarity measure for a plurality of semantic category profiles for a plurality of digital images, wherein the plurality of semantic category profiles includes the semantic category profile for the image; or a keyword operation based on a semantic similarity measure for a plurality of semantic category profiles for a plurality of digital images, wherein the plurality of semantic category profiles includes the semantic category profile for the image. | 1. A non-transitory computer-readable storage medium that stores program instructions computer-executable to implement: determining a plurality of semantic category scores for a digital image via application of a corresponding plurality of classifiers; automatically determining a semantic category profile for the image based on the plurality of semantic category scores, wherein the semantic category profile characterizes semantic content of the image, and is useable to perform semantic based operations with respect to the image; and performing a semantic based operation wherein the semantic based operation comprises: a search operation based on a semantic similarity measure for a plurality of semantic category profiles for a plurality of digital images, wherein the plurality of semantic category profiles includes the semantic category profile for the image; or a keyword operation based on a semantic similarity measure for a plurality of semantic category profiles for a plurality of digital images, wherein the plurality of semantic category profiles includes the semantic category profile for the image. 9. The non-transitory computer-readable storage medium of claim 1 , wherein the digital image is comprised in a digital image database, wherein the program instructions are further computer-executable to implement: receiving input specifying one or more keywords for a search query to a digital image database, wherein the digital image database comprises the plurality of digital images, each digital image having a respective semantic category profile; determining a first semantic category profile that corresponds to the one or more keywords by accessing a database of keywords associated with the plurality of semantic category profiles; determining a respective value of the semantic similarity measure for each digital image in the digital image database based on the first semantic category profile and the respective semantic category profiles of the digital images in the digital image database; and determining one or more digital images in the digital image database based on the values of the semantic similarity measure. | 0.5 |
9,170,989 | 1 | 2 | 1. A computerized method effecting entity interaction with a web page hosted at a first web site server to be viewable by others on a second web page hosted at a second web site server, the method comprising the actions of: embedding executable code within a first internet accessible web page hosted at the first web site server that when executed during the rendering of the first web page instantiates the display of a reader interaction function within the first web page which is incorporated and displayed within renderings of the first web page when accessed on the first web site server; receiving an actuation of the reader interaction function by an entity that is accessing the first web page; responding to receiving that actuation by sending data identifying the first web page to a remote second web site server, said data being stored in a database separate from said first web site server; displaying within a second internet accessible web page that is hosted by the second website server, information based on the data identifying the first web page thus resulting in the second web page providing an indication that the reader interaction function has been actuated for that first web page; and providing an option for the reader to manage his or her reader interactions with a plurality of web pages by publishing the reader interactions on a personal journal page. | 1. A computerized method effecting entity interaction with a web page hosted at a first web site server to be viewable by others on a second web page hosted at a second web site server, the method comprising the actions of: embedding executable code within a first internet accessible web page hosted at the first web site server that when executed during the rendering of the first web page instantiates the display of a reader interaction function within the first web page which is incorporated and displayed within renderings of the first web page when accessed on the first web site server; receiving an actuation of the reader interaction function by an entity that is accessing the first web page; responding to receiving that actuation by sending data identifying the first web page to a remote second web site server, said data being stored in a database separate from said first web site server; displaying within a second internet accessible web page that is hosted by the second website server, information based on the data identifying the first web page thus resulting in the second web page providing an indication that the reader interaction function has been actuated for that first web page; and providing an option for the reader to manage his or her reader interactions with a plurality of web pages by publishing the reader interactions on a personal journal page. 2. The method of claim 1 , wherein the action of receiving an actuation of the reader interaction function further comprises the first web site server receiving a comment provided by the entity; and the action of sending data comprises sending the comment. | 0.5 |
7,844,629 | 10 | 12 | 10. A system for querying a document containing hierarchical information that includes parent nodes and descendent nodes, comprising: a wrapper unit, implemented in a form of a processor, which locates a first parent node in the document by using a mapping specification, determines if the first parent node satisfies a query and fetches from the document nested descendent nodes relating to the first parent node in response to determining that the first parent node satisfies the query, wherein the wrapper unit parses only parent nodes in the document. | 10. A system for querying a document containing hierarchical information that includes parent nodes and descendent nodes, comprising: a wrapper unit, implemented in a form of a processor, which locates a first parent node in the document by using a mapping specification, determines if the first parent node satisfies a query and fetches from the document nested descendent nodes relating to the first parent node in response to determining that the first parent node satisfies the query, wherein the wrapper unit parses only parent nodes in the document. 12. The system of claim 10 , wherein upon fetching a last nested descendent node relating to the first parent node, the wrapper unit locates a second parent node in the document by using the mapping specification, stores the second parent node in the first relational storage area, fetches from the document nested descendent nodes relating to the second parent node and stores the fetched descendent nodes in the second relational storage area, wherein the nested descendent nodes are located by using the mapping specification. | 0.5 |
8,364,686 | 52 | 54 | 52. A system, comprising: one or more computer devices configured to: sample an input document to obtain a plurality of sampled blocks; compute a set of checksum values from the plurality of sampled blocks, where each checksum value, in the set of checksum values, corresponds to an address of a respective one of a plurality of bits of a fingerprint corresponding to the input document; set a particular bit, of the plurality of bits of the fingerprint, to a particular value to generate the fingerprint, the particular bit being set to the particular value based on a quantity of checksum values, in the set of checksum values, that corresponds to the address of the particular bit; and store, in a memory, the fingerprint as a representation of the input document. | 52. A system, comprising: one or more computer devices configured to: sample an input document to obtain a plurality of sampled blocks; compute a set of checksum values from the plurality of sampled blocks, where each checksum value, in the set of checksum values, corresponds to an address of a respective one of a plurality of bits of a fingerprint corresponding to the input document; set a particular bit, of the plurality of bits of the fingerprint, to a particular value to generate the fingerprint, the particular bit being set to the particular value based on a quantity of checksum values, in the set of checksum values, that corresponds to the address of the particular bit; and store, in a memory, the fingerprint as a representation of the input document. 54. The system of claim 52 , where the one or more computer devices are further configured to: compare the fingerprint and another fingerprint to determine whether the input document and another document, associated with the other fingerprint, are near-duplicate documents; and index and store information regarding only the input document or the other document when the input document and the other document are determined to be the near-duplicate documents based on the comparison of the fingerprint and the other fingerprint. | 0.592593 |
8,635,201 | 1 | 4 | 1. A method, performed by at least one computer, comprising acts of: (A) receiving a query from a device and location data indicating a location of the device, the location data having a level of specificity; (B) in response to the query being received, identifying at least one first search engine to which to submit a representation of the query and information indicating the location of the device; (C) determining whether the level of specificity of the location data received in (A) is sufficient for the at least one first search engine; (D) when the level of specificity of the location data is sufficient, instructing the device to issue the representation of the query to the at least one first search engine; and (E) when the level of specificity of the location data is not sufficient, instructing the device to send, to the at least one computer, location data at a greater level of specificity, wherein the act (C) comprises determining that the level of specificity of the location data received in (A) is not sufficient for the at least one first search engine, and wherein the method further comprises acts of: (F) receiving location data at the greater level of specificity; and (G) instructing the device to submit a representation of the query and information specifying the location of the device at the greater level of specificity to the at least one first search engine. | 1. A method, performed by at least one computer, comprising acts of: (A) receiving a query from a device and location data indicating a location of the device, the location data having a level of specificity; (B) in response to the query being received, identifying at least one first search engine to which to submit a representation of the query and information indicating the location of the device; (C) determining whether the level of specificity of the location data received in (A) is sufficient for the at least one first search engine; (D) when the level of specificity of the location data is sufficient, instructing the device to issue the representation of the query to the at least one first search engine; and (E) when the level of specificity of the location data is not sufficient, instructing the device to send, to the at least one computer, location data at a greater level of specificity, wherein the act (C) comprises determining that the level of specificity of the location data received in (A) is not sufficient for the at least one first search engine, and wherein the method further comprises acts of: (F) receiving location data at the greater level of specificity; and (G) instructing the device to submit a representation of the query and information specifying the location of the device at the greater level of specificity to the at least one first search engine. 4. The method of claim 1 , wherein the act (C) is performed based at least in part on predefined specificity requirements of the at least one first search engine. | 0.5 |
8,364,627 | 1 | 4 | 1. A computer implemented method of behavioral targeting comprising: receiving, at a computer, a plurality of granular events, wherein a granular event comprises an on-line activity between a user and an entity; preprocessing, using a computer, the received granular events to determine informational content of each granular event; generating, in a computer, preprocessed data comprising a plurality of input features by grouping the granular events into a plurality of clusters based on the informational content, wherein each cluster corresponds to a unique input feature from among the input features; and generating, in a computer, a predictive model based on a linear machine-learning model, the predictive model for determining, using the clusters and a linear combination of their corresponding input features, a likelihood of a predicted input action by a user. | 1. A computer implemented method of behavioral targeting comprising: receiving, at a computer, a plurality of granular events, wherein a granular event comprises an on-line activity between a user and an entity; preprocessing, using a computer, the received granular events to determine informational content of each granular event; generating, in a computer, preprocessed data comprising a plurality of input features by grouping the granular events into a plurality of clusters based on the informational content, wherein each cluster corresponds to a unique input feature from among the input features; and generating, in a computer, a predictive model based on a linear machine-learning model, the predictive model for determining, using the clusters and a linear combination of their corresponding input features, a likelihood of a predicted input action by a user. 4. The computer implemented method of claim 1 , wherein the online activity comprises a search and the method further comprises tracking a number of clicks on a search result. | 0.743402 |
8,249,856 | 19 | 22 | 19. A method for computer-assisted translation from a source language to a target language, the method comprising: accepting a plurality of rules, each rule associating a representation of a source sequence with a corresponding tree-based structure in the target language, wherein the tree-based structure including at least one target language token associated with a source token in the source sequence, and the tree-based structure is associated with a target language model score that depends on the tree-based structure and one or more target language tokens associated with the tree-based structure; and decoding, using a computer, an input sequence of source tokens according to the plurality of rules to generate a representation of one or more output sequences of target language tokens, including for each of multiple sub-sequences of the input sequence, generating a tree-based structure associated with the sub-sequence according to the plurality of rules, including determining a score associated with the generated tree-based structure that includes a language model component based on one or more language model scores of tree-based structures of the rules, and determining whether to discard the generated tree-based structure based on the determined score. | 19. A method for computer-assisted translation from a source language to a target language, the method comprising: accepting a plurality of rules, each rule associating a representation of a source sequence with a corresponding tree-based structure in the target language, wherein the tree-based structure including at least one target language token associated with a source token in the source sequence, and the tree-based structure is associated with a target language model score that depends on the tree-based structure and one or more target language tokens associated with the tree-based structure; and decoding, using a computer, an input sequence of source tokens according to the plurality of rules to generate a representation of one or more output sequences of target language tokens, including for each of multiple sub-sequences of the input sequence, generating a tree-based structure associated with the sub-sequence according to the plurality of rules, including determining a score associated with the generated tree-based structure that includes a language model component based on one or more language model scores of tree-based structures of the rules, and determining whether to discard the generated tree-based structure based on the determined score. 22. The method of claim 19 wherein decoding the input sequence further includes generating a full tree structure associated with the entirety of the input sequence using generated tree structures that were not determined to be discarded. | 0.662393 |
8,929,615 | 35 | 49 | 35. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to: extract one or more features from a photo; calculate an engagement metric for the photo based on the one or more extracted features, wherein the engagement metric represents the probability that one or more users will interact with the photo; and apply one or more policies to the photo based on the engagement metric. | 35. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to: extract one or more features from a photo; calculate an engagement metric for the photo based on the one or more extracted features, wherein the engagement metric represents the probability that one or more users will interact with the photo; and apply one or more policies to the photo based on the engagement metric. 49. The system of claim 35 , wherein the one or more features comprises the type of device that captured the photo. | 0.840278 |
8,954,412 | 19 | 20 | 19. A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer, the one or more programs comprising instructions for: defining a query, the query posing a question having an answer formed of terms from the electronic documents; creating one or more hypothetical facts in response to the query and the electronic documents, each hypothetical fact representing a possible answer to the query, wherein creating one or more hypothetical facts in response to the query comprises: parsing the query to filter out noise words and produce filtered terms; searching a repository of facts comprising attributes and values to identify attributes corresponding to the filtered terms; searching the electronic documents to identify terms that frequently appear near the filtered terms; and forming one or more hypothetical facts responsive to the attributes corresponding to the filtered terms and the terms that frequently appear near the filtered terms in the electronic documents; corroborating the one or more hypothetical facts using the electronic documents to identify a likely correct fact; and presenting the identified likely correct fact as the answer to the query. | 19. A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer, the one or more programs comprising instructions for: defining a query, the query posing a question having an answer formed of terms from the electronic documents; creating one or more hypothetical facts in response to the query and the electronic documents, each hypothetical fact representing a possible answer to the query, wherein creating one or more hypothetical facts in response to the query comprises: parsing the query to filter out noise words and produce filtered terms; searching a repository of facts comprising attributes and values to identify attributes corresponding to the filtered terms; searching the electronic documents to identify terms that frequently appear near the filtered terms; and forming one or more hypothetical facts responsive to the attributes corresponding to the filtered terms and the terms that frequently appear near the filtered terms in the electronic documents; corroborating the one or more hypothetical facts using the electronic documents to identify a likely correct fact; and presenting the identified likely correct fact as the answer to the query. 20. The computer readable storage medium of claim 19 , further comprising instructions for receiving a real-time query from a user of a web site. | 0.896724 |
9,558,163 | 1 | 10 | 1. A method for accelerated development of a mobile device specific webp age for at least one mobile computing device comprising: a) forming a plurality of data entry screen definitions for the mobile device specific webpage using an administrative processor and storing the plurality of data entry screen definitions in an administrative data storage; b) generating a list of mobile computing devices and specifications for displaying a created mobile device specific webpage and storing the list of mobile computing devices and specifications in the administrative data storage; c) identifying a specific mobile computing device for the mobile device specific webpage from the list of mobile computing devices and specifications; d) automatically generating a plurality of self-generating data entry screens for entering and storing predefined data for use on the mobile device specific webpage and storing the plurality of self-generating data entry screens in the administrative data storage; e) automatically generating hypertext for the mobile device specific webpage using the predefined data; f) bidirectionally controlling communication, data delivery and access permission, to and from one or more third party servers connected to a network to automatically collect, store and maintain data integrity of data processed for the one or more third party servers and maintain consistency of the predefined data collected from the one or more third party servers using the network, collecting the predefined data via the mobile device specific webpage from the one or more third party servers on the network, and updating the predefined data via the mobile device specific webpage to the one or more third party servers on the network simultaneously; g) simultaneously duplicating the predefined data to a hot spare environment and a cold spare environment while maintaining integrity of the predefined data preventing loss of the predefined data; h) enforcing a common stylistic look and feel for use on the mobile device specific webpage and maintaining consistency between additionally developed mobile device specific webpages for the specific mobile computing device using a plurality of common stylistic rules; i) merging the predefined data into a mobile device specific webpage document template automatically and generating the mobile device specific webpage for the specific mobile computing device while storing the generated mobile device specific webpage in the administrative data storage enabling a user or a non-administrative user to create the mobile device specific webpage filled with the predefined data with the plurality of self-generating data entry screen definitions using the plurality of common stylistic rules; and j) transmitting the generated mobile device specific webpage via the network for display on the specific mobile computing device. | 1. A method for accelerated development of a mobile device specific webp age for at least one mobile computing device comprising: a) forming a plurality of data entry screen definitions for the mobile device specific webpage using an administrative processor and storing the plurality of data entry screen definitions in an administrative data storage; b) generating a list of mobile computing devices and specifications for displaying a created mobile device specific webpage and storing the list of mobile computing devices and specifications in the administrative data storage; c) identifying a specific mobile computing device for the mobile device specific webpage from the list of mobile computing devices and specifications; d) automatically generating a plurality of self-generating data entry screens for entering and storing predefined data for use on the mobile device specific webpage and storing the plurality of self-generating data entry screens in the administrative data storage; e) automatically generating hypertext for the mobile device specific webpage using the predefined data; f) bidirectionally controlling communication, data delivery and access permission, to and from one or more third party servers connected to a network to automatically collect, store and maintain data integrity of data processed for the one or more third party servers and maintain consistency of the predefined data collected from the one or more third party servers using the network, collecting the predefined data via the mobile device specific webpage from the one or more third party servers on the network, and updating the predefined data via the mobile device specific webpage to the one or more third party servers on the network simultaneously; g) simultaneously duplicating the predefined data to a hot spare environment and a cold spare environment while maintaining integrity of the predefined data preventing loss of the predefined data; h) enforcing a common stylistic look and feel for use on the mobile device specific webpage and maintaining consistency between additionally developed mobile device specific webpages for the specific mobile computing device using a plurality of common stylistic rules; i) merging the predefined data into a mobile device specific webpage document template automatically and generating the mobile device specific webpage for the specific mobile computing device while storing the generated mobile device specific webpage in the administrative data storage enabling a user or a non-administrative user to create the mobile device specific webpage filled with the predefined data with the plurality of self-generating data entry screen definitions using the plurality of common stylistic rules; and j) transmitting the generated mobile device specific webpage via the network for display on the specific mobile computing device. 10. The method of claim 1 , comprising presenting a search screen configured with a look and feel identical to at least one of the plurality of self-generating data entry screens, by providing a static display of image feature and a sliding carousel display of images feature. | 0.872222 |
9,514,200 | 2 | 3 | 2. The method if claim 1 , wherein the first data table is stored in a first database and the second table is stored in a second database. | 2. The method if claim 1 , wherein the first data table is stored in a first database and the second table is stored in a second database. 3. The method if claim 2 , wherein the first database is of a first type and the second database is of a second type different from the first type. | 0.5 |
9,208,229 | 20 | 21 | 20. A non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of a back end computer system, the one or more programs and comprising instructions for: identifying a set of facts associated with an object, the set of facts having been previously extracted from multiple documents of a collection of documents, each fact comprising an attribute-value pair, including a fact attribute type and a fact value, and wherein the object is associated with an entity having a name fact attribute type; receiving a first document not included in the multiple documents from the collection of documents and a reference to the first document, said reference comprising user-viewable anchor text extracted from a second document from the collection of documents; determining that the user-viewable anchor text matches the name of the entity associated with the object; determining that one or both of the name of the entity associated with the object or the user-viewable anchor text appears in the first document; and responsive to determining that the user-viewable anchor text matches the name of the entity associated with the object and that one or both of the name of the entity associated with the object or the user-viewable anchor text appears in the first document, corroborating the set of facts using the first document; the corroborating comprising: identifying one or more facts in the first document, each identified fact having an attribute-value pair; and comparing a respective attribute-value pair of the set of facts to an identified attribute-value pair in the first document; and updating the set of facts in accordance with the corroborating, wherein the updating includes one or both of storing an attribute-value pair from the first document in the set of facts in association with the object or adjusting a status of an attribute-value pair of the set of facts. | 20. A non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of a back end computer system, the one or more programs and comprising instructions for: identifying a set of facts associated with an object, the set of facts having been previously extracted from multiple documents of a collection of documents, each fact comprising an attribute-value pair, including a fact attribute type and a fact value, and wherein the object is associated with an entity having a name fact attribute type; receiving a first document not included in the multiple documents from the collection of documents and a reference to the first document, said reference comprising user-viewable anchor text extracted from a second document from the collection of documents; determining that the user-viewable anchor text matches the name of the entity associated with the object; determining that one or both of the name of the entity associated with the object or the user-viewable anchor text appears in the first document; and responsive to determining that the user-viewable anchor text matches the name of the entity associated with the object and that one or both of the name of the entity associated with the object or the user-viewable anchor text appears in the first document, corroborating the set of facts using the first document; the corroborating comprising: identifying one or more facts in the first document, each identified fact having an attribute-value pair; and comparing a respective attribute-value pair of the set of facts to an identified attribute-value pair in the first document; and updating the set of facts in accordance with the corroborating, wherein the updating includes one or both of storing an attribute-value pair from the first document in the set of facts in association with the object or adjusting a status of an attribute-value pair of the set of facts. 21. The non-transitory computer readable storage medium of claim 20 , said computer-readable storage medium further comprising: program code for analyzing the anchor text to determine if the first document contains valid data. | 0.5 |
7,734,996 | 25 | 36 | 25. A documentation browsing program stored in a program memory characterized by causing a computer to execute the process of: generating correspondence between voices or images included in audio data or image data with a document included in document data; displaying the voices or the images included in said audio data or said image data and the document included in said document data associated with each other based on said correspondence; and updating said document data associated based on user editing instruction; displaying a displaying location of a document included in document data on a display screen in association with time information which indicates an elapsed time of voices or images; and outputting recalculation instruction information for instructing recalculation of relationship between a document and voices or images and recalculating said relationship when said document data is updated. | 25. A documentation browsing program stored in a program memory characterized by causing a computer to execute the process of: generating correspondence between voices or images included in audio data or image data with a document included in document data; displaying the voices or the images included in said audio data or said image data and the document included in said document data associated with each other based on said correspondence; and updating said document data associated based on user editing instruction; displaying a displaying location of a document included in document data on a display screen in association with time information which indicates an elapsed time of voices or images; and outputting recalculation instruction information for instructing recalculation of relationship between a document and voices or images and recalculating said relationship when said document data is updated. 36. The documentation browsing program according to claim 25 , causing a computer to execute the process of: detecting the case where voices or images and a document is not associated with each other as a mismatch state of the voices or the images and the document, and displaying that no section of documents associated with a section of voices or images exists or that no section of voices or images associated with a section of the document exists as a mismatch state when said mismatch is detected. | 0.576728 |
8,155,951 | 10 | 13 | 10. A computer assisted method for creating a semantic mapping table consisting of associations between a sentence from a corpus and sentential propositions in a knowledge base describing physical or tangible objects, comprising the steps of: a. reducing each said sentence to one or more said simple or complex sentences; and b. for each said simple or complex sentence using a computer processor to display candidate propositions which may represent the entire meaning of said simple or complex sentence to a domain expert; and c. said domain expert using a knowledge editor to associate one said candidate sentential proposition for each said simple or complex sentence; and, d. storing said associations using a computer processor in said mapping table, wherein a single sentential proposition represents the entire meaning of a simple or complex sentence. | 10. A computer assisted method for creating a semantic mapping table consisting of associations between a sentence from a corpus and sentential propositions in a knowledge base describing physical or tangible objects, comprising the steps of: a. reducing each said sentence to one or more said simple or complex sentences; and b. for each said simple or complex sentence using a computer processor to display candidate propositions which may represent the entire meaning of said simple or complex sentence to a domain expert; and c. said domain expert using a knowledge editor to associate one said candidate sentential proposition for each said simple or complex sentence; and, d. storing said associations using a computer processor in said mapping table, wherein a single sentential proposition represents the entire meaning of a simple or complex sentence. 13. The method of claim 10 wherein candidate sentential propositions that may represent the meaning of a selected sentence from the corpus are retrieved based on their string similarity to previously mapped sentences in the corpus. | 0.5 |
9,031,962 | 25 | 27 | 25. A method, comprising: receiving a search query; determining a character count of the search query; in response to receiving the search query, using a processing circuit to search for a match to the search query in an electronic collection of content items by searching subspace categories representing respective classes of the content items, the subspace categories having respective search query character count dependent relevance bias values representing a relevance of the respective classes of content items to search queries of a given character count; identifying, in the electronic collection of content items, a plurality of content items matching the search query including a first content item of a first class linked to a first subspace category of the subspace categories and a second content item of a second class linked to a second subspace category of the subspace categories, the first subspace category having a first search query character count dependent relevance bias value for the character count determined for the search query received and the second subspace category having a second search query character count dependent relevance bias value different than the first character count dependent relevance bias value for the character count determined for the search query received; determining an ordering of the first and second content items matching the search query based at least in part on the first search query character count dependent relevance bias value of the first subspace category and the second search query character count dependent relevance bias value of the second subspace category; and causing to be presented the plurality of content items in accordance with the ordering. | 25. A method, comprising: receiving a search query; determining a character count of the search query; in response to receiving the search query, using a processing circuit to search for a match to the search query in an electronic collection of content items by searching subspace categories representing respective classes of the content items, the subspace categories having respective search query character count dependent relevance bias values representing a relevance of the respective classes of content items to search queries of a given character count; identifying, in the electronic collection of content items, a plurality of content items matching the search query including a first content item of a first class linked to a first subspace category of the subspace categories and a second content item of a second class linked to a second subspace category of the subspace categories, the first subspace category having a first search query character count dependent relevance bias value for the character count determined for the search query received and the second subspace category having a second search query character count dependent relevance bias value different than the first character count dependent relevance bias value for the character count determined for the search query received; determining an ordering of the first and second content items matching the search query based at least in part on the first search query character count dependent relevance bias value of the first subspace category and the second search query character count dependent relevance bias value of the second subspace category; and causing to be presented the plurality of content items in accordance with the ordering. 27. The method of claim 25 , wherein the first subspace category comprises one or more terms associated with the first class of content items represented by the first subspace category is linked, and wherein the first search query character count dependent relevance bias value is a relevance bias value of the one or more terms. | 0.863145 |
9,626,610 | 1 | 2 | 1. A barcoded quality indicator operative to provide a machine-readable indication of exceedance of at least one threshold by one or more product quality affecting parameters, said barcoded quality indicator comprising: a first barcode being machine readable before actuation of said barcoded quality indicator; a second barcode not being machine readable before actuation of said barcoded quality indicator, said second barcode being machine readable following actuation of said barcoded quality indicator and prior to exceedance of said at least one threshold; and at least one third barcode not being machine readable before actuation of said barcoded quality indicator, said at least one third barcode not being machine readable before exceedance of said at least one threshold and being machine readable following exceedance of said at least one threshold. | 1. A barcoded quality indicator operative to provide a machine-readable indication of exceedance of at least one threshold by one or more product quality affecting parameters, said barcoded quality indicator comprising: a first barcode being machine readable before actuation of said barcoded quality indicator; a second barcode not being machine readable before actuation of said barcoded quality indicator, said second barcode being machine readable following actuation of said barcoded quality indicator and prior to exceedance of said at least one threshold; and at least one third barcode not being machine readable before actuation of said barcoded quality indicator, said at least one third barcode not being machine readable before exceedance of said at least one threshold and being machine readable following exceedance of said at least one threshold. 2. A barcoded quality indicator according to claim 1 and also comprising a pull strip, said pull strip being suitable to prevent the passage of solvents and coloring agents therethrough before removal thereof, and wherein removal of said pull strip actuates said barcoded quality indicator. | 0.5 |
8,706,713 | 1 | 4 | 1. A method of searching for desired information, the method comprising: receiving, from a user having user profile data associated therewith, a search request comprising a search argument; providing, to a first search engine, the received search argument for correlation with first contextual information in a database of network related information; providing, to a second search engine, the received search argument and the user profile data for correlation with a first advertisement in an advertisement database; providing the first contextual information and the first advertisement to the user as a first search result; updating the user profile data based on at least one of the first contextual information, the first advertisement, selection of the first advertisement by the user, and non-selection of the first advertisement by the user; receiving refinement information comprising a refined search argument from the user; providing, to the first search engine, the refined search argument for correlation with second contextual information in the database of network related information; providing, to the second search engine, the refined search argument and the updated user profile data for correlation with a second advertisement in the advertisement database; and providing the second contextual information and the second advertisement to the user. | 1. A method of searching for desired information, the method comprising: receiving, from a user having user profile data associated therewith, a search request comprising a search argument; providing, to a first search engine, the received search argument for correlation with first contextual information in a database of network related information; providing, to a second search engine, the received search argument and the user profile data for correlation with a first advertisement in an advertisement database; providing the first contextual information and the first advertisement to the user as a first search result; updating the user profile data based on at least one of the first contextual information, the first advertisement, selection of the first advertisement by the user, and non-selection of the first advertisement by the user; receiving refinement information comprising a refined search argument from the user; providing, to the first search engine, the refined search argument for correlation with second contextual information in the database of network related information; providing, to the second search engine, the refined search argument and the updated user profile data for correlation with a second advertisement in the advertisement database; and providing the second contextual information and the second advertisement to the user. 4. The method of claim 1 , wherein the user profile data comprises data determined from previous search activity of the user. | 0.695122 |
9,055,148 | 1 | 4 | 1. A computer implemented method for analyzing chat leakage, comprising: providing a processor configured for obtaining chat-related information from at least one chat session between a customer and an agent; said processor configured for identifying customer leakage information from said chat to another channel; said processor configured for building a model based on said chat-related information and said leakage information; said processor configured for applying said model to provide recommendations to said agent for said customer to improve the customer's experience and accordingly prevent or reduce leakage; said applying said model further comprising: when chat leakage is identified, analyzing said chat to determine factors that have contributed to said leakage; storing data pertaining to said leakage and said analysis results in a knowledge base; and using information and analysis thereof stored in said knowledge base to train agents and to make recommendations to agents and managers to improve the customer experience. | 1. A computer implemented method for analyzing chat leakage, comprising: providing a processor configured for obtaining chat-related information from at least one chat session between a customer and an agent; said processor configured for identifying customer leakage information from said chat to another channel; said processor configured for building a model based on said chat-related information and said leakage information; said processor configured for applying said model to provide recommendations to said agent for said customer to improve the customer's experience and accordingly prevent or reduce leakage; said applying said model further comprising: when chat leakage is identified, analyzing said chat to determine factors that have contributed to said leakage; storing data pertaining to said leakage and said analysis results in a knowledge base; and using information and analysis thereof stored in said knowledge base to train agents and to make recommendations to agents and managers to improve the customer experience. 4. The method of claim 1 , further comprising: said chat agent checking information related to the customer, said information comprising any of the customer's journey, the customer's communication history, the customer's interests, and other information associated with the customer. | 0.610193 |
8,269,734 | 17 | 19 | 17. The apparatus of claim 16 , further comprising instructions to reveal at least one visible representation of a user interface device, and further comprising instructions to activate the user interface device in response to the calculated stimulation level reaching or exceeding the stimulation trigger. | 17. The apparatus of claim 16 , further comprising instructions to reveal at least one visible representation of a user interface device, and further comprising instructions to activate the user interface device in response to the calculated stimulation level reaching or exceeding the stimulation trigger. 19. The apparatus of claim 17 , further comprising instructions to deactivate the representation of the user interface device in response to a reduction in the calculated stimulation level. | 0.525126 |
8,219,599 | 19 | 23 | 19. A computer program product for providing access to a knowledge base, the computer program product comprising computer program instructions stored in one or more non-transitory computer-readable media, the computer program instructions being configured when executed to cause a computing device to: receive a natural language question as input; transmit the natural language question to the knowledge base for translation to an internal query that represents an interpretation of the natural language question and has an internal format compatible with structured data of the knowledge base, the structured data representing first knowledge; and present a definitive natural language answer responsive to the natural language question received from the knowledge base and including second knowledge derived from the first knowledge in response to the internal query, the second knowledge not having been stored in the knowledge base prior to transmission of the natural language question to the knowledge base. | 19. A computer program product for providing access to a knowledge base, the computer program product comprising computer program instructions stored in one or more non-transitory computer-readable media, the computer program instructions being configured when executed to cause a computing device to: receive a natural language question as input; transmit the natural language question to the knowledge base for translation to an internal query that represents an interpretation of the natural language question and has an internal format compatible with structured data of the knowledge base, the structured data representing first knowledge; and present a definitive natural language answer responsive to the natural language question received from the knowledge base and including second knowledge derived from the first knowledge in response to the internal query, the second knowledge not having been stored in the knowledge base prior to transmission of the natural language question to the knowledge base. 23. The computer program product of claim 19 wherein the natural language question is received and the definitive natural language answer is presented using first and second web page interfaces, respectively. | 0.697674 |
8,539,348 | 3 | 5 | 3. The method of claim 1 , wherein at least some of the plurality of word frames further comprise a root portion preceding the contracted portion, the root portion comprising the characters of the at least first word associated with the word frame that precede the sequential plurality of characters, and further comprising as at least a portion of the identifying of the particular word frame, making a determination that a portion of the input that precedes any quantity of sequential selections of the particular input member corresponds with a root portion of the particular word frame except for at most one discrepancy therebetween. | 3. The method of claim 1 , wherein at least some of the plurality of word frames further comprise a root portion preceding the contracted portion, the root portion comprising the characters of the at least first word associated with the word frame that precede the sequential plurality of characters, and further comprising as at least a portion of the identifying of the particular word frame, making a determination that a portion of the input that precedes any quantity of sequential selections of the particular input member corresponds with a root portion of the particular word frame except for at most one discrepancy therebetween. 5. The method of claim 3 , further comprising identifying as the at most one discrepancy one of: the at least portion of the input comprising one input member selection in excess of the corresponding characters of the root portion of the particular word frame, and the root portion of the particular word frame comprising one character in excess of the corresponding input member selections of the at least portion of the input. | 0.5 |
9,641,195 | 1 | 9 | 1. A trellis coded modulator (TCM) for generating an encoded word from an input word, the TCM comprising: a single encoder, wherein the single encoder comprises: a first logic branch configured to generate a data portion of the encoded word; and a second logic branch, coupled in parallel with the first logic branch, and configured to generate a corresponding parity portion of the encoded word sequentially after the generation of the data portion of the encoded word, wherein the second logic branch comprises a first register configured to hold a final bit of the input word until after the generation of the parity portion of the encoded word. | 1. A trellis coded modulator (TCM) for generating an encoded word from an input word, the TCM comprising: a single encoder, wherein the single encoder comprises: a first logic branch configured to generate a data portion of the encoded word; and a second logic branch, coupled in parallel with the first logic branch, and configured to generate a corresponding parity portion of the encoded word sequentially after the generation of the data portion of the encoded word, wherein the second logic branch comprises a first register configured to hold a final bit of the input word until after the generation of the parity portion of the encoded word. 9. The TCM of claim 1 , wherein the second logic branch comprises: the first register having an input configured to receive the input word and an output; a first modulo 2 adder having a first input coupled to the output of the first register, a second input, a third input, a fourth input, a fifth input, and an output; a second register having an input coupled to the output of the first modulo 2 adder and an output coupled to the fifth input of the first modulo 2 adder; a second modulo 2 adder having a first input coupled to the output of the first modulo 2 adder, a second input coupled to the output of the second register, a third input, and an output configured to output the parity portion of the encoded word; a third register having an input coupled to the output of the second register, and an output coupled to the fourth input of the first modulo 2 adder; a fourth register having an input coupled to the output of the third register and an output coupled to the third input of the first modulo 2 adder; and a fifth register having an input coupled to the output of the fourth register, and an output coupled to the second input of the first modulo 2 adder and to the third input of the second modulo 2 adder. | 0.5 |
8,135,727 | 16 | 17 | 16. The system of claim 13 , wherein at least one selectable search parameter includes a metadata search attribute. | 16. The system of claim 13 , wherein at least one selectable search parameter includes a metadata search attribute. 17. The system of claim 16 , further comprising: receiving an activation of the metadata search attribute, wherein the activation causes a first list to be displayed, and wherein the first list includes a selection of metadata search attributes and an expand command. | 0.5 |
8,578,265 | 36 | 37 | 36. The non-transitory machine readable media of claim 26 , wherein the instructions are further structured to cause an apparatus to enable the first user to create a logical expression using the web-based visual editor. | 36. The non-transitory machine readable media of claim 26 , wherein the instructions are further structured to cause an apparatus to enable the first user to create a logical expression using the web-based visual editor. 37. The non-transitory machine readable media of claim 36 , wherein the logical expression includes a repeating loop. | 0.5 |
8,983,986 | 6 | 7 | 6. The method of claim 1 , further comprising associating a visual indicator with each of the web pages with a value that satisfies a predetermined threshold. | 6. The method of claim 1 , further comprising associating a visual indicator with each of the web pages with a value that satisfies a predetermined threshold. 7. The method of claim 6 , wherein the visual indicator comprises a clickable button. | 0.5 |
7,801,912 | 9 | 10 | 9. The system as recited in claim 5 , wherein each of the query nodes is configured to: receive a query request from a coordinator node; access the local query cache on the query node to determine if the query request can be satisfied from the local query cache; if the query request can be satisfied from the local query cache, return at least the entity identifiers from a set of one or more searchable data service objects from the local query cache that satisfy the query request to a client application that initiated the query request in accordance with the web service interface; if the query request cannot be satisfied from the local query cache, forward the query request to one or more of the storage nodes. | 9. The system as recited in claim 5 , wherein each of the query nodes is configured to: receive a query request from a coordinator node; access the local query cache on the query node to determine if the query request can be satisfied from the local query cache; if the query request can be satisfied from the local query cache, return at least the entity identifiers from a set of one or more searchable data service objects from the local query cache that satisfy the query request to a client application that initiated the query request in accordance with the web service interface; if the query request cannot be satisfied from the local query cache, forward the query request to one or more of the storage nodes. 10. The system as recited in claim 9 , wherein each of the one or more storage nodes is configured to: receive a query request from a query node; search a partition of a particular searchable index persistently stored by the storage node to locate a set of one or more searchable data service objects in the searchable index that satisfy the query request; and return at least the entity identifiers from the set of one or more searchable data service objects that satisfy the query request to a client application that initiated the query request in accordance with the web service interface. | 0.5 |
7,984,031 | 1 | 2 | 1. A query builder system, comprising: a processor coupled to a memory, the processor configured to execute the following computer-executable components stored in the memory: an interface component that receives input descriptive of a desired test; and a builder component that constructs an abstract test query representation from the input independent of any particular query language to facilitate application of the test query to a specific query language. | 1. A query builder system, comprising: a processor coupled to a memory, the processor configured to execute the following computer-executable components stored in the memory: an interface component that receives input descriptive of a desired test; and a builder component that constructs an abstract test query representation from the input independent of any particular query language to facilitate application of the test query to a specific query language. 2. The system of claim 1 , the input comprises a number of query operators and expressions. | 0.760526 |
7,548,934 | 1 | 2 | 1. A computer-implemented method of generating a list, comprising: receiving a candidate item comprising at least one of an artist name or a title of the candidate item; for each reference item of a plurality of reference items, comparing at least a portion of respective characters of the at least one of the artist name or the title of the candidate item to at least a portion of respective reference-item characters of at least one of a reference-item artist name or a reference-item title of the reference item to facilitate determining a respective matching score, relating to each of the at least one of the artist name or the title of the candidate item, for each reference item based at least in part on a respective number of matches between the at least a portion of the respective characters and the at least a portion of the respective reference-item characters for each reference item; identifying the candidate item based at least in part on a best reference item, the best reference item is a reference item of the plurality of reference items having a matching score that at least meets a predetermined threshold amount and has a highest matching score as compared to other reference items of the plurality of reference items; associating a plurality of metadata relating to the best reference item with the candidate item, wherein the associated plurality of metadata is a plurality of candidate item metadata relating to the candidate item, to facilitate comparing similarity between the candidate item and a seed item; retrieving a plurality of seed item metadata relating to the seed item identified by a received seed item identifier; comparing the plurality of seed item metadata to the plurality of candidate item metadata; computing a similarity score between the seed item and the candidate item based at least in part on a similarity of the plurality of seed item metadata to the plurality of candidate item metadata, wherein the similarity score is computed using a list-generation computer system; determining whether to add the candidate item to the list based at least in part on the similarity score; and generating the list, which is provided to a user. | 1. A computer-implemented method of generating a list, comprising: receiving a candidate item comprising at least one of an artist name or a title of the candidate item; for each reference item of a plurality of reference items, comparing at least a portion of respective characters of the at least one of the artist name or the title of the candidate item to at least a portion of respective reference-item characters of at least one of a reference-item artist name or a reference-item title of the reference item to facilitate determining a respective matching score, relating to each of the at least one of the artist name or the title of the candidate item, for each reference item based at least in part on a respective number of matches between the at least a portion of the respective characters and the at least a portion of the respective reference-item characters for each reference item; identifying the candidate item based at least in part on a best reference item, the best reference item is a reference item of the plurality of reference items having a matching score that at least meets a predetermined threshold amount and has a highest matching score as compared to other reference items of the plurality of reference items; associating a plurality of metadata relating to the best reference item with the candidate item, wherein the associated plurality of metadata is a plurality of candidate item metadata relating to the candidate item, to facilitate comparing similarity between the candidate item and a seed item; retrieving a plurality of seed item metadata relating to the seed item identified by a received seed item identifier; comparing the plurality of seed item metadata to the plurality of candidate item metadata; computing a similarity score between the seed item and the candidate item based at least in part on a similarity of the plurality of seed item metadata to the plurality of candidate item metadata, wherein the similarity score is computed using a list-generation computer system; determining whether to add the candidate item to the list based at least in part on the similarity score; and generating the list, which is provided to a user. 2. The computer-implemented method of claim 1 , further comprising: retrieving a plurality of candidate item metadata relating to a candidate item, wherein the plurality of candidate item metadata retrieved is an incomplete set. | 0.856423 |
9,098,532 | 14 | 16 | 14. An apparatus, comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: analyze an original image embedded in an electronic document to generate a data pattern for the image; perform a matching operation to identify one or more similar images in other electronic documents from one or more sources of electronic documents based on the generated data pattern; extract textual description information associated with the one or more similar images from data associated with the one or more similar images; generate an alternative text description for the original image based on the extracted textual description information associated with the one or more similar images; store the alternative text description for the original image in association with the original image; generate a confidence level value for the alternative text description, wherein the confidence level value identifies a level of confidence that the alternative text description accurately describes content of the original image; and store the confidence level value in association with the alternative text description, wherein the confidence level value is generated based on scoring each keyword in the alternative text description according to a frequency of occurrence of the keyword in textual description information associated with the one or more similar images and wherein the confidence level value is generated using the following relationship:
Confidence level value=( v/v+m ))* R +( m /( v+m ))* C where R is an average score for the keywords in the alternative text description, v is a number of keywords hi the alternative text description, m is a minimum number of keywords in the alternative text description required, and C is a mean confidence level value. | 14. An apparatus, comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: analyze an original image embedded in an electronic document to generate a data pattern for the image; perform a matching operation to identify one or more similar images in other electronic documents from one or more sources of electronic documents based on the generated data pattern; extract textual description information associated with the one or more similar images from data associated with the one or more similar images; generate an alternative text description for the original image based on the extracted textual description information associated with the one or more similar images; store the alternative text description for the original image in association with the original image; generate a confidence level value for the alternative text description, wherein the confidence level value identifies a level of confidence that the alternative text description accurately describes content of the original image; and store the confidence level value in association with the alternative text description, wherein the confidence level value is generated based on scoring each keyword in the alternative text description according to a frequency of occurrence of the keyword in textual description information associated with the one or more similar images and wherein the confidence level value is generated using the following relationship:
Confidence level value=( v/v+m ))* R +( m /( v+m ))* C where R is an average score for the keywords in the alternative text description, v is a number of keywords hi the alternative text description, m is a minimum number of keywords in the alternative text description required, and C is a mean confidence level value. 16. The apparatus of claim 14 , wherein the confidence level value is generated based on a comparison of the alternative text description to an existing text description associated with the original image. | 0.901631 |
7,533,013 | 27 | 29 | 27. A machine translation decoder comprising: a memory, the memory containing program instructions configured to be executed by a processor; a processor able to access and execute the program instructed stored in the memory; a decoding engine, configured to receive as input a text segment in a source language to be translated into a target language and to generate an initial translation as a current target language translation, comprising one or more modification operators to be applied to a current target language translation to generate one or more modified target language translations; a probability module in communication with the decoding engine configured to estimate a probability of correctness of the initial translation, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language, to estimate a probability of correctness of the one or more modified target language translations, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language, and to determine whether one or more of the modified target language translations represents an improved translation in comparison with the initial current target language translation by comparing the estimated probability of correctness of the initial translation with the estimated probability of correctness of the one or more modified target language translations; and a process loop configured to iteratively modify the current target language translation of a source language text based on the probability module estimate of the probability of correctness of the one or more modified target language translations and the probability module determination regarding whether one or more of the modified target language translations represent an improved translation, the process loop terminating upon occurrence of a termination condition. | 27. A machine translation decoder comprising: a memory, the memory containing program instructions configured to be executed by a processor; a processor able to access and execute the program instructed stored in the memory; a decoding engine, configured to receive as input a text segment in a source language to be translated into a target language and to generate an initial translation as a current target language translation, comprising one or more modification operators to be applied to a current target language translation to generate one or more modified target language translations; a probability module in communication with the decoding engine configured to estimate a probability of correctness of the initial translation, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language, to estimate a probability of correctness of the one or more modified target language translations, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language, and to determine whether one or more of the modified target language translations represents an improved translation in comparison with the initial current target language translation by comparing the estimated probability of correctness of the initial translation with the estimated probability of correctness of the one or more modified target language translations; and a process loop configured to iteratively modify the current target language translation of a source language text based on the probability module estimate of the probability of correctness of the one or more modified target language translations and the probability module determination regarding whether one or more of the modified target language translations represent an improved translation, the process loop terminating upon occurrence of a termination condition. 29. The decoder of claim 27 further comprising a module for determining a probability of correctness for a translation. | 0.949958 |
7,599,938 | 1 | 7 | 1. A news method, the method comprising the following steps: receiving at a first computer system a first submission, said first submission comprising first content, said first content comprising first account information, said first computer system comprising a first memory; storing said first account information in said first memory; receiving a second submission, said second submission comprising second content, said second content comprising first article information, said first article information comprising at least a first resource location or a first headline; receiving a plurality of additional submissions, said plurality of additional submissions comprising a third submission and a fourth submission, said third submission comprising third content, said fourth submission comprising fourth content, said fourth content comprising at least an indication of either approval or disapproval; storing at least some of said submissions in a first database system, said first database system comprising at least a first database; calculating a first content approval score, said step of calculating said first content approval score being performed at least partly according to a first time criterion and comprising a step of performing a first count, said step of performing said first count comprising a first step of counting at least some of said plurality of additional submissions; providing a first resource, said first resource comprising a plurality of items, said plurality of items comprising a first item, said first item comprising at least first indicia, said first indicia indicating a first value, said first value pertaining to said third content; performing a second count, said step of performing said second count comprising a second step of counting at least some of said plurality of additional submissions; providing first display information, said step of providing said first display information comprising a step of at least partly causing second indicia to appear in a second resource, said second indicia indicating a second value, said second value being based at least in part upon an outcome of said step of performing said second count; providing first submission-facilitation information, said step of providing said first submission-facilitation information comprising a step of at least partly causing a first submission-facilitation mechanism to appear in a third resource; providing fifth content; receiving a fifth submission, said fifth submission comprising sixth content, said sixth content comprising second article information, said second article information comprising at least a second resource location or a second headline; updating said fifth content, said step of updating said fifth content comprising a step of including said sixth content in said fifth content; associating a second item with a first probational status; associating a third item with said first probational status; determining a first measure of community approval; comparing said first measure of community approval to a first threshold; promoting said second item to a first elevated status, said step of promoting said second item to said first elevated status being performed at least partly according to an outcome of said step of comparing said first measure of community approval to said first threshold; determining a second measure of community approval; comparing said second measure of community approval to a second threshold; keeping said third item in association with said first probational status, said step of keeping said third item in association with said first probational status being performed at least partly according to an outcome of said step of comparing said second measure of community approval to said second threshold; promoting a fourth item to said first elevated status; and removing said second item from said first elevated status, wherein said first resource is different from said second resource and from said third resource. | 1. A news method, the method comprising the following steps: receiving at a first computer system a first submission, said first submission comprising first content, said first content comprising first account information, said first computer system comprising a first memory; storing said first account information in said first memory; receiving a second submission, said second submission comprising second content, said second content comprising first article information, said first article information comprising at least a first resource location or a first headline; receiving a plurality of additional submissions, said plurality of additional submissions comprising a third submission and a fourth submission, said third submission comprising third content, said fourth submission comprising fourth content, said fourth content comprising at least an indication of either approval or disapproval; storing at least some of said submissions in a first database system, said first database system comprising at least a first database; calculating a first content approval score, said step of calculating said first content approval score being performed at least partly according to a first time criterion and comprising a step of performing a first count, said step of performing said first count comprising a first step of counting at least some of said plurality of additional submissions; providing a first resource, said first resource comprising a plurality of items, said plurality of items comprising a first item, said first item comprising at least first indicia, said first indicia indicating a first value, said first value pertaining to said third content; performing a second count, said step of performing said second count comprising a second step of counting at least some of said plurality of additional submissions; providing first display information, said step of providing said first display information comprising a step of at least partly causing second indicia to appear in a second resource, said second indicia indicating a second value, said second value being based at least in part upon an outcome of said step of performing said second count; providing first submission-facilitation information, said step of providing said first submission-facilitation information comprising a step of at least partly causing a first submission-facilitation mechanism to appear in a third resource; providing fifth content; receiving a fifth submission, said fifth submission comprising sixth content, said sixth content comprising second article information, said second article information comprising at least a second resource location or a second headline; updating said fifth content, said step of updating said fifth content comprising a step of including said sixth content in said fifth content; associating a second item with a first probational status; associating a third item with said first probational status; determining a first measure of community approval; comparing said first measure of community approval to a first threshold; promoting said second item to a first elevated status, said step of promoting said second item to said first elevated status being performed at least partly according to an outcome of said step of comparing said first measure of community approval to said first threshold; determining a second measure of community approval; comparing said second measure of community approval to a second threshold; keeping said third item in association with said first probational status, said step of keeping said third item in association with said first probational status being performed at least partly according to an outcome of said step of comparing said second measure of community approval to said second threshold; promoting a fourth item to said first elevated status; and removing said second item from said first elevated status, wherein said first resource is different from said second resource and from said third resource. 7. The method in claim 1 additionally comprising the following steps: receiving a first URL; converting said first URL to a first character string, said first character string being different from said first URL; and causing current information pertaining to said first URL to display via a first toolbar. | 0.918011 |
8,145,632 | 12 | 13 | 12. The method of claim 2 , further comprising: for at least one of the resource identifiers, retrieving the corresponding document from the respective document source; identifying within the retrieved document no chunk that satisfies each of the search keywords; and displaying a link to search for chunks that satisfy any of the search keywords within the document. | 12. The method of claim 2 , further comprising: for at least one of the resource identifiers, retrieving the corresponding document from the respective document source; identifying within the retrieved document no chunk that satisfies each of the search keywords; and displaying a link to search for chunks that satisfy any of the search keywords within the document. 13. The method of claim 12 , further comprising: in response to a user selection of the link to search for chunks that satisfy any of the search keywords within the document, re-processing the document retrieved from the respective document source; identifying within the retrieved document one or more chunks that satisfy at least one of the search keywords; and displaying the identified chunks and a link to each of the identified chunks within the document. | 0.5 |
9,268,952 | 1 | 2 | 1. A method of generating results for a query to an encrypted database stored on a host, the method comprising: generating, with a first processor, encrypted indexes from records stored in the encrypted database, each encrypted index identifying the records of the encrypted database associated with a range of data for at least one field stored in the records of the encrypted database; generating, with the first processor, unencrypted index metadata associated with each encrypted index, each unencrypted index metadata indicating each field and the range of data within each field identified by the associated encrypted index; generating, with a second processor, a sub-query from the query for each field associated with the query, each sub-query being a portion of the query specific to the corresponding field; determining, with the first processor and the second processor, a subspace of search within the encrypted database based on sub-query results obtained by searching the unencrypted index metadata with each sub-query, the sub-query results indicating sub-query indexes; and searching, with the first processor, the subspace of the encrypted database associated with the sub-query indexes to generate the results of the query. | 1. A method of generating results for a query to an encrypted database stored on a host, the method comprising: generating, with a first processor, encrypted indexes from records stored in the encrypted database, each encrypted index identifying the records of the encrypted database associated with a range of data for at least one field stored in the records of the encrypted database; generating, with the first processor, unencrypted index metadata associated with each encrypted index, each unencrypted index metadata indicating each field and the range of data within each field identified by the associated encrypted index; generating, with a second processor, a sub-query from the query for each field associated with the query, each sub-query being a portion of the query specific to the corresponding field; determining, with the first processor and the second processor, a subspace of search within the encrypted database based on sub-query results obtained by searching the unencrypted index metadata with each sub-query, the sub-query results indicating sub-query indexes; and searching, with the first processor, the subspace of the encrypted database associated with the sub-query indexes to generate the results of the query. 2. The method according to claim 1 , wherein the generating the encrypted indexes and the unencrypted index metadata with the first processor is with a secure processor. | 0.700355 |
7,590,535 | 1 | 7 | 1. A computer-readable storage media, having computer-executable instructions encoded thereon, for implementing a method comprising: receiving handwritten data entered on a touch screen display associated with a computing device; passing the handwritten data to an ink processor component associated with the computing device; the ink processor component passing the handwritten data to a recognizer of the computing device; the recognizer returning alternates information and probability information to the ink processor component corresponding to one or more words recognized by the recognizer and which are stored in one or more buffers of the computing device that are associated with the ink processor component; receiving a request to provide alternates for the one or more words that has alternates and that is within a set of words containing at least two words included in the handwritten input and that have alternates, when no particular word that has alternates has been selected in the set; determining a current editing mode, in which the computing device is operating with respect to receiving input into the device; selecting a word in the set that has alternates as a selected word based on the current editing mode, and wherein the selecting of the word includes utilizing the probability information to determine whether any of the wards in the set will actually be selected as having alternates for purposes of providing alternates therefore and such that words that are determined to have a high actual probability of being correct are skipped over and refrained from being the selected word; and providing alternates for the selected word. | 1. A computer-readable storage media, having computer-executable instructions encoded thereon, for implementing a method comprising: receiving handwritten data entered on a touch screen display associated with a computing device; passing the handwritten data to an ink processor component associated with the computing device; the ink processor component passing the handwritten data to a recognizer of the computing device; the recognizer returning alternates information and probability information to the ink processor component corresponding to one or more words recognized by the recognizer and which are stored in one or more buffers of the computing device that are associated with the ink processor component; receiving a request to provide alternates for the one or more words that has alternates and that is within a set of words containing at least two words included in the handwritten input and that have alternates, when no particular word that has alternates has been selected in the set; determining a current editing mode, in which the computing device is operating with respect to receiving input into the device; selecting a word in the set that has alternates as a selected word based on the current editing mode, and wherein the selecting of the word includes utilizing the probability information to determine whether any of the wards in the set will actually be selected as having alternates for purposes of providing alternates therefore and such that words that are determined to have a high actual probability of being correct are skipped over and refrained from being the selected word; and providing alternates for the selected word. 7. The computer storage media of claim 1 , wherein the current editing mode comprises a selection mode, wherein the set is determined via a selection operation and the set contains at least two words that each has its own alternates associated therewith, and wherein selecting the word based on the current editing mode comprises selecting a first word in the set that has alternates associated therewith. | 0.5 |
8,734,158 | 1 | 2 | 1. A mathematical learning system comprised of a plurality of geometric shapes, wherein basic math operations can be calculated by visualizing and manipulating the plurality of geometric shapes; wherein the plurality of geometric shapes are comprised of physical, written or digitized substrates; and wherein the plurality of geometric shapes comprise: a large square having a value of “10” forming a perimeter of a 10 based shape, a small triangle having a value of “1” wherein 10 small triangles are capable of being positioned within the perimeter of the 10 based shape for a value of “10,” a large triangle having a value of “5” wherein 2 large triangles are capable of being positioned within the perimeter of the 10 based shape for a value of “10;” an outer 4 shape having a value of 4, wherein the outer 4 shape is capable of having two sides forming part of the perimeter of the 10 based shape, wherein 2 outer 4 shapes are capable of being positioned within the perimeter of the 10 based shape for a value of “8;” an inner 4 shape having a value of “4”, whereby the inner 4 shape never forms part of the perimeter of the 10 based shape; a small square having a value of “2”; whereby the small square never forms part of the perimeter of the 10 based shape” an inner 3 shape having a value of “3”; whereby the inner 3 shape is capable of either being placed inside the 10 based shape without forming part of the perimeter or being placed so as to form part of the perimeter of the 10 based shape; and an outer 3 shape having a value of “3” and having a geometry of 3 small triangles connected thereto at their respective corner points and having sides capable of forming part of the perimeter of the 10 based shape. | 1. A mathematical learning system comprised of a plurality of geometric shapes, wherein basic math operations can be calculated by visualizing and manipulating the plurality of geometric shapes; wherein the plurality of geometric shapes are comprised of physical, written or digitized substrates; and wherein the plurality of geometric shapes comprise: a large square having a value of “10” forming a perimeter of a 10 based shape, a small triangle having a value of “1” wherein 10 small triangles are capable of being positioned within the perimeter of the 10 based shape for a value of “10,” a large triangle having a value of “5” wherein 2 large triangles are capable of being positioned within the perimeter of the 10 based shape for a value of “10;” an outer 4 shape having a value of 4, wherein the outer 4 shape is capable of having two sides forming part of the perimeter of the 10 based shape, wherein 2 outer 4 shapes are capable of being positioned within the perimeter of the 10 based shape for a value of “8;” an inner 4 shape having a value of “4”, whereby the inner 4 shape never forms part of the perimeter of the 10 based shape; a small square having a value of “2”; whereby the small square never forms part of the perimeter of the 10 based shape” an inner 3 shape having a value of “3”; whereby the inner 3 shape is capable of either being placed inside the 10 based shape without forming part of the perimeter or being placed so as to form part of the perimeter of the 10 based shape; and an outer 3 shape having a value of “3” and having a geometry of 3 small triangles connected thereto at their respective corner points and having sides capable of forming part of the perimeter of the 10 based shape. 2. The mathematical learning system according to claim 1 , further comprising a longitudinal spacer having a null value. | 0.668508 |
9,754,101 | 1 | 2 | 1. A system comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: receiving a proposed password from a user; accessing a previous password of the user; decomposing the proposed password and the previous password into a first plurality of components and a second plurality of components, respectively; analyzing the first plurality of components and the second plurality of components in relation to the proposed password and the previous password, respectively, to discern a first set of formation rules used to form the proposed password from the first plurality of components and a second set of formation rules used to form the previous password from the second plurality of components; determining a similarity between the proposed password and the previous password based on a comparison between the first plurality of components and the second plurality of components and a comparison between the first set of formation rules and the second set of formation rules; and determining to accept the proposed password based on the similarity. | 1. A system comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: receiving a proposed password from a user; accessing a previous password of the user; decomposing the proposed password and the previous password into a first plurality of components and a second plurality of components, respectively; analyzing the first plurality of components and the second plurality of components in relation to the proposed password and the previous password, respectively, to discern a first set of formation rules used to form the proposed password from the first plurality of components and a second set of formation rules used to form the previous password from the second plurality of components; determining a similarity between the proposed password and the previous password based on a comparison between the first plurality of components and the second plurality of components and a comparison between the first set of formation rules and the second set of formation rules; and determining to accept the proposed password based on the similarity. 2. The system of claim 1 , wherein the determining the similarity comprises: determining a first similarity score based on the comparison between the first plurality of components and the second plurality of components; determining a second similarity score based on the comparison between the first set of formation rules and the second set of formation rules; and generating a total similarity score as a function of the first similarity score and the second similarity score. | 0.5 |
8,869,049 | 8 | 10 | 8. The method of claim 1 , wherein the design example is among a plurality of design examples in a plurality of layout groups, each layout group comprising one or more design examples with similar layouts, the method further comprising: receiving a user identification of a selected layout group; and providing user choices from the selected layout group. | 8. The method of claim 1 , wherein the design example is among a plurality of design examples in a plurality of layout groups, each layout group comprising one or more design examples with similar layouts, the method further comprising: receiving a user identification of a selected layout group; and providing user choices from the selected layout group. 10. The method of claim 8 , further comprising: providing for display a first scrollbar for a user to select one of a layout group level and a subgroup level; and providing for display a second scrollbar for the user to display UI components in a group corresponding to a level selected by the user through the first scrollbar. | 0.5 |
8,145,481 | 4 | 6 | 4. The method of claim 3 , further comprising: generating a confidence score to determine whether the generated word lattices are acceptable. | 4. The method of claim 3 , further comprising: generating a confidence score to determine whether the generated word lattices are acceptable. 6. The method of claim 4 , further comprising: saving at least one of the parameters of the background model and the parameters of the transducer model. | 0.788301 |
10,078,802 | 5 | 6 | 5. A system of discovering and analyzing structures of user groups in a microblog, which comprises a processor, characterized in that the processor is configured to perform the operations of: acquiring information on behavior data of microblog users of a target group; constructing a microblog user association network based on the information on behavior data of the microblog users of the target group; acquiring at least one maximal clique from the microblog user association network; acquiring at least one core clique based on the maximal clique; conducting behavior analysis on the user groups in the microblog based on the acquired maximal clique and/or the acquired core clique, wherein the operation of acquiring at least one maximal clique particularly comprises: acquiring all maximal cliques of the microblog user association network by utilizing a search-triangle based method operated with a certain pruning strategy, and wherein the operation of acquiring at least one core clique particularly comprises: based on the maximal cliques, analyzing a social relation circle of each microblog user and all other microblog users, and filtering out the core cliques of the microblog user association network based on an inclusion-consolidation strategy on the social relation circles, wherein if a social relation circle of one microblog user is not included by any of the other social relation circles, the social relation circle of the one microblog user is filtered out as one of the core cliques. | 5. A system of discovering and analyzing structures of user groups in a microblog, which comprises a processor, characterized in that the processor is configured to perform the operations of: acquiring information on behavior data of microblog users of a target group; constructing a microblog user association network based on the information on behavior data of the microblog users of the target group; acquiring at least one maximal clique from the microblog user association network; acquiring at least one core clique based on the maximal clique; conducting behavior analysis on the user groups in the microblog based on the acquired maximal clique and/or the acquired core clique, wherein the operation of acquiring at least one maximal clique particularly comprises: acquiring all maximal cliques of the microblog user association network by utilizing a search-triangle based method operated with a certain pruning strategy, and wherein the operation of acquiring at least one core clique particularly comprises: based on the maximal cliques, analyzing a social relation circle of each microblog user and all other microblog users, and filtering out the core cliques of the microblog user association network based on an inclusion-consolidation strategy on the social relation circles, wherein if a social relation circle of one microblog user is not included by any of the other social relation circles, the social relation circle of the one microblog user is filtered out as one of the core cliques. 6. The system of discovering and analyzing structures of user groups in a microblog according to claim 5 , characterized in that, the operation of acquiring information on behavior data of microblog users of a target group comprises: establishing association relationships among the microblog users by utilizing an association relationship evaluation model, based on the information on behavior data of the microblog users of the target group; and constructing the microblog user association network which is composed of a microblog user used as a node and an association relationship used as an edge, based on the established association relationships among the microblog users. | 0.5 |
9,779,168 | 8 | 14 | 8. A non-transitory storage storing instructions which, when processed by one or more processors, cause: at a local device, while a search query is being formulated by a user in a search box of one or more applications displayed at the local device, automatically displaying, separate from the search query a set of one or more selected quick-picks, wherein the set of one or more selected quick-picks is selected based, at least in part, on a current context associated with displayed content in the one or more applications executing on the local device, wherein the displayed content is different and distinct from the search box and the search query; receiving, at the local device, user input that selects a particular quick-pick from the set of one or more selected quick-picks; in response to receiving, at the local device, the user input, adding, in the search box displayed at the local device, the particular quick-pick as a separate term to the search query and allowing the search query including the separate term to continue to be formulated by the user in the search box displayed at the local device; displaying a second set of one or more quick-picks in response to the user input that selects the particular quick-pick from the set of one or more selected quick-picks, wherein the second set of one or more quick-picks includes at least one additional quick-pick that: (a) was not in the set of one or more selected quick-picks, and (b) was selected to be a quick-pick based, at least in part, on an association between the at least one additional quick-pick and the particular quick-pick; receiving user input that deselects the particular quick-pick from the set of one or more selected quick-picks; and in response to the user input that deselects the particular quick-pick from the set of one or more selected quick-picks, automatically removing the separate term from the search query. | 8. A non-transitory storage storing instructions which, when processed by one or more processors, cause: at a local device, while a search query is being formulated by a user in a search box of one or more applications displayed at the local device, automatically displaying, separate from the search query a set of one or more selected quick-picks, wherein the set of one or more selected quick-picks is selected based, at least in part, on a current context associated with displayed content in the one or more applications executing on the local device, wherein the displayed content is different and distinct from the search box and the search query; receiving, at the local device, user input that selects a particular quick-pick from the set of one or more selected quick-picks; in response to receiving, at the local device, the user input, adding, in the search box displayed at the local device, the particular quick-pick as a separate term to the search query and allowing the search query including the separate term to continue to be formulated by the user in the search box displayed at the local device; displaying a second set of one or more quick-picks in response to the user input that selects the particular quick-pick from the set of one or more selected quick-picks, wherein the second set of one or more quick-picks includes at least one additional quick-pick that: (a) was not in the set of one or more selected quick-picks, and (b) was selected to be a quick-pick based, at least in part, on an association between the at least one additional quick-pick and the particular quick-pick; receiving user input that deselects the particular quick-pick from the set of one or more selected quick-picks; and in response to the user input that deselects the particular quick-pick from the set of one or more selected quick-picks, automatically removing the separate term from the search query. 14. The non-transitory storage of claim 8 , wherein the particular quick-pick corresponds to a non-textual item and the adding the particular quick-pick as the separate term to the search query includes adding a search criteria item associated with the non-textual item to the search query. | 0.551084 |
9,378,187 | 4 | 7 | 4. The method of claim 1 wherein creating the presentation grammar for the structured document comprises: identifying a content type of the original document; selecting, in dependence upon the content type, a full presentation grammar from among a multiplicity of full presentation grammars; and filtering the full presentation grammar into the presentation grammar for the structured document in dependence upon the structural elements of the structured document. | 4. The method of claim 1 wherein creating the presentation grammar for the structured document comprises: identifying a content type of the original document; selecting, in dependence upon the content type, a full presentation grammar from among a multiplicity of full presentation grammars; and filtering the full presentation grammar into the presentation grammar for the structured document in dependence upon the structural elements of the structured document. 7. The method of claim 4 wherein filtering the full presentation grammar comprises writing from the full presentation grammar to the presentation grammar for the structured document each grammar element having a structural element identifier of a structural element that occurs in the structured document. | 0.542042 |
8,696,364 | 1 | 8 | 1. A method of training a user via an interactive electronic training system wherein the user views and hears a presentation and verbally articulates answers, the method comprising: providing via an interactive electronic training system terminal an informational presentation of information on which the user is to be tested and trained; providing via the interactive electronic training system terminal a training presentation of an interaction between two or more real or simulated people, of a person appearing to speak to the user, and/or a monologue by a person, wherein the training presentation includes audible articulated words and a visual presentation of at least one person speaking or appearing to speak; providing, via the interactive electronic training system terminal, user instructions via which the user is instructed to identify using verbal articulation one or more correct acts of a first type committed by at least one of the persons in the training presentation using an audible articulation, without providing a choice of answers from which the user can select; causing, at least in part, an indication to be stored by the interactive electronic training system, in computer readable memory as to whether the user correctly identified a first correct act performed by at least one of the persons in the training presentation; enabling, at least in part, a user instruction to be provided by the interactive electronic training system via which the user is instructed to verbally identify one or more errors committed by at least one of the persons in the training presentation, without providing a choice of answers from which the user can select; causing, at least in part, an indication to be stored by the interactive electronic training system in computer readable memory as to whether the user correctly identified a first error committed by at least one of the persons in the training presentation; providing, via the interactive electronic training system terminal, user instructions via which the user is instructed to verbally explain why it is important to correct the first error, without providing a choice of answers from which the user can select; causing, at least in part, an indication to be stored by the interactive electronic training system in computer readable memory as to whether the user correctly explained why it is important to correct the first error; causing, at least in part, the user to be asked, via the interactive electronic training system terminal, to recite, in the first person, correct language that should have been used by at least one of the persons in the training presentation so that the first error would not have occurred, without providing a choice of answers from which the user can select; presenting via the interactive electronic training system terminal a preprogrammed correct answer in the form of text, audio, animation, and/or video so that a scorer can compare the preprogrammed correct answer with language verbally provided by the user in response to the instruction to state correct language and enter a corresponding score substantially immediately after the user responded to the instruction to state correct language; causing, at least in part, an indication to be stored by the interactive electronic training system in computer readable memory as to whether the user recited the correct language; calculating at least one score by the interactive electronic training system based at least in part on one or more of the stored indications, including at least the indication as to whether the user correctly identified a first correct act performed by at least one of the persons in the training presentation; causing, by the interactive electronic training system, the at least one score to be visibly presented. | 1. A method of training a user via an interactive electronic training system wherein the user views and hears a presentation and verbally articulates answers, the method comprising: providing via an interactive electronic training system terminal an informational presentation of information on which the user is to be tested and trained; providing via the interactive electronic training system terminal a training presentation of an interaction between two or more real or simulated people, of a person appearing to speak to the user, and/or a monologue by a person, wherein the training presentation includes audible articulated words and a visual presentation of at least one person speaking or appearing to speak; providing, via the interactive electronic training system terminal, user instructions via which the user is instructed to identify using verbal articulation one or more correct acts of a first type committed by at least one of the persons in the training presentation using an audible articulation, without providing a choice of answers from which the user can select; causing, at least in part, an indication to be stored by the interactive electronic training system, in computer readable memory as to whether the user correctly identified a first correct act performed by at least one of the persons in the training presentation; enabling, at least in part, a user instruction to be provided by the interactive electronic training system via which the user is instructed to verbally identify one or more errors committed by at least one of the persons in the training presentation, without providing a choice of answers from which the user can select; causing, at least in part, an indication to be stored by the interactive electronic training system in computer readable memory as to whether the user correctly identified a first error committed by at least one of the persons in the training presentation; providing, via the interactive electronic training system terminal, user instructions via which the user is instructed to verbally explain why it is important to correct the first error, without providing a choice of answers from which the user can select; causing, at least in part, an indication to be stored by the interactive electronic training system in computer readable memory as to whether the user correctly explained why it is important to correct the first error; causing, at least in part, the user to be asked, via the interactive electronic training system terminal, to recite, in the first person, correct language that should have been used by at least one of the persons in the training presentation so that the first error would not have occurred, without providing a choice of answers from which the user can select; presenting via the interactive electronic training system terminal a preprogrammed correct answer in the form of text, audio, animation, and/or video so that a scorer can compare the preprogrammed correct answer with language verbally provided by the user in response to the instruction to state correct language and enter a corresponding score substantially immediately after the user responded to the instruction to state correct language; causing, at least in part, an indication to be stored by the interactive electronic training system in computer readable memory as to whether the user recited the correct language; calculating at least one score by the interactive electronic training system based at least in part on one or more of the stored indications, including at least the indication as to whether the user correctly identified a first correct act performed by at least one of the persons in the training presentation; causing, by the interactive electronic training system, the at least one score to be visibly presented. 8. The method as defined in claim 1 , wherein the user is queried to verbally explain why it is important to correct the first error to a person in physical and audible proximity with the user who also scores the explanation. | 0.873168 |
8,819,000 | 10 | 11 | 10. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving an original query including a first limitation that constrains a search; modifying the original query to obtain a modified query in which the first limitation that constrains the search has been omitted; obtaining, from a search engine system, first search results responsive to the modified query, wherein the first search results have an associated ranking determined by the search engine system, and wherein each of the first search results refers to a respective resource; identifying one or more common characteristics shared by two or more resources, each of the two or more resources corresponding to a different highly-ranked result of the first search results; generating a second modified query comprising the original query and a second limitation representing the one or more common characteristics, the second limitation requiring results responsive to the second modified query to reference a resource having the one or more common characteristics; obtaining second search results responsive to the second modified query, wherein each of the second search results refers to a resource having the one or more common characteristics; and providing the second search results in a response to the original query. | 10. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving an original query including a first limitation that constrains a search; modifying the original query to obtain a modified query in which the first limitation that constrains the search has been omitted; obtaining, from a search engine system, first search results responsive to the modified query, wherein the first search results have an associated ranking determined by the search engine system, and wherein each of the first search results refers to a respective resource; identifying one or more common characteristics shared by two or more resources, each of the two or more resources corresponding to a different highly-ranked result of the first search results; generating a second modified query comprising the original query and a second limitation representing the one or more common characteristics, the second limitation requiring results responsive to the second modified query to reference a resource having the one or more common characteristics; obtaining second search results responsive to the second modified query, wherein each of the second search results refers to a resource having the one or more common characteristics; and providing the second search results in a response to the original query. 11. The system of claim 10 , wherein the first limitation requires a search engine performing a search based on the original query to identify results that each reference a corresponding resource of a particular document type or a resource from a particular document collection. | 0.785162 |
8,521,526 | 15 | 19 | 15. The non-transitory computer storage medium of claim 14 , wherein the operations further comprise: determining a context associated with the spoken query term; and selecting a subset of the past search queries that include contexts which are similar to the context of the spoken query term, wherein determining, for each n-gram, a frequency with which the n-gram occurs in the past search queries further comprises determining, for each n-gram, a frequency with which the n-gram occurs in the past search queries that are members of the subset only. | 15. The non-transitory computer storage medium of claim 14 , wherein the operations further comprise: determining a context associated with the spoken query term; and selecting a subset of the past search queries that include contexts which are similar to the context of the spoken query term, wherein determining, for each n-gram, a frequency with which the n-gram occurs in the past search queries further comprises determining, for each n-gram, a frequency with which the n-gram occurs in the past search queries that are members of the subset only. 19. The non-transitory computer storage medium of claim 15 , wherein: determining a context associated with the spoken query term further comprise determining a location of the user when the spoken query term was spoken by the user; and selecting a subset of the past search queries that include contexts which are similar to the context of the spoken query term further comprises selecting a subset of the past search queries that were submitted by the user when the user was within a predetermined distance from the location, or when the user was in a same geographic region as the location. | 0.629838 |
9,977,775 | 4 | 13 | 4. The computer-readable storage medium of claim 1 , the data structure further comprising a fourth table comprised of entries each representing a different part of speech, each entry of the fourth table containing a word type ID identifying its word type, each entry of the second table further containing a word type ID identifying a word type to which its definition corresponds. | 4. The computer-readable storage medium of claim 1 , the data structure further comprising a fourth table comprised of entries each representing a different part of speech, each entry of the fourth table containing a word type ID identifying its word type, each entry of the second table further containing a word type ID identifying a word type to which its definition corresponds. 13. The computer-readable storage medium of claim 4 wherein a distinguished entity of the fourth table contains a word type ID indicating a service name word type. | 0.579897 |
9,336,790 | 8 | 10 | 8. A method of efficiently encoding a voiced frame, the method comprising: classifying a plurality of speech frames into a plurality of classes, and wherein at least for one of the classes, the following steps are included: having a Long-Term Prediction (LTP) excitation component; having a second excitation component; encoding an energy of the LTP excitation component by encoding a pitch gain; checking whether a pitch track or pitch lags within the voiced frame are stable from one subframe to a next subframe; checking whether the voiced frame is strongly voiced by checking whether pitch gains within the voiced frame are high; encoding the pitch lags or the pitch gains efficiently by a differential coding from one subframe to a next subframe when the voiced frame is strongly voiced and the pitch lags are stable; and forming an excitation by including the LTP excitation component and the second excitation component. | 8. A method of efficiently encoding a voiced frame, the method comprising: classifying a plurality of speech frames into a plurality of classes, and wherein at least for one of the classes, the following steps are included: having a Long-Term Prediction (LTP) excitation component; having a second excitation component; encoding an energy of the LTP excitation component by encoding a pitch gain; checking whether a pitch track or pitch lags within the voiced frame are stable from one subframe to a next subframe; checking whether the voiced frame is strongly voiced by checking whether pitch gains within the voiced frame are high; encoding the pitch lags or the pitch gains efficiently by a differential coding from one subframe to a next subframe when the voiced frame is strongly voiced and the pitch lags are stable; and forming an excitation by including the LTP excitation component and the second excitation component. 10. The method of claim 8 , comprising a Code-Excited Linear Prediction (CELP) methodology. | 0.735465 |
7,890,479 | 6 | 7 | 6. A computer program product comprising: a computer usable medium having computer usable program code for validating an XML fragment, said computer program product including: computer usable program code implementing a generic parser to perform a low level validation; computer usable program code implementing a schema validation parser to perform a high level validation; computer usable program code implementing a schema-loading module loading a fragment associated with a structured document from a schema in annotated automaton encoding format; computer usable program code implementing a scanner tokenizing said loaded fragment; computer usable program code implementing a run-time validation engine receiving output of said scanner and outputting a validation pass message based on an LR parsing technique as follows: obtaining a first token from said fragment of said structured document, determining whether said first token is of element type said fragment of said structured document that is to be validated against, and if so, obtaining next token from said fragment of said structured document, checking whether said next token signifies end of said fragment of said structured document, and if so, identifying a FOLLOW token from a type-mapping table for said element type; based on said FOLLOW token, performing a low level validation using said generic parser and performing a high level validation using said schema validation parser; and returning a validation pass if both validations of said generic parser and schema validation parser are successful and an annotated automaton encoding stack is empty; and if said next token does not signify end of said fragment of said structured document, continuing validation as in validating an entire structured document, and when successfully validated as in an entire structured document, returning to step (iii) until end of said structured document token is received and outputting a validation pass when said annotated automaton encoding stack is empty. | 6. A computer program product comprising: a computer usable medium having computer usable program code for validating an XML fragment, said computer program product including: computer usable program code implementing a generic parser to perform a low level validation; computer usable program code implementing a schema validation parser to perform a high level validation; computer usable program code implementing a schema-loading module loading a fragment associated with a structured document from a schema in annotated automaton encoding format; computer usable program code implementing a scanner tokenizing said loaded fragment; computer usable program code implementing a run-time validation engine receiving output of said scanner and outputting a validation pass message based on an LR parsing technique as follows: obtaining a first token from said fragment of said structured document, determining whether said first token is of element type said fragment of said structured document that is to be validated against, and if so, obtaining next token from said fragment of said structured document, checking whether said next token signifies end of said fragment of said structured document, and if so, identifying a FOLLOW token from a type-mapping table for said element type; based on said FOLLOW token, performing a low level validation using said generic parser and performing a high level validation using said schema validation parser; and returning a validation pass if both validations of said generic parser and schema validation parser are successful and an annotated automaton encoding stack is empty; and if said next token does not signify end of said fragment of said structured document, continuing validation as in validating an entire structured document, and when successfully validated as in an entire structured document, returning to step (iii) until end of said structured document token is received and outputting a validation pass when said annotated automaton encoding stack is empty. 7. The computer program product of claim 6 , wherein said structured document is an XML document. | 0.740642 |
9,852,225 | 6 | 7 | 6. A system comprising: accessing search queries that were previously submitted by users and a corresponding set of search results that were previously presented in search results pages for each of the search queries, each search result in the set of the search results including a link to a corresponding web page; identifying, for a given web page that was previously accessed through user interaction with links included in search results from the set of search results, a set of the search queries that were previously used to provide search results pages that included the links with which the user interactions occurred to access the given web page; determining, for each given search query in the set of search queries, a feature score based on a user dwell time at the given web page following user interaction with the links to the given web page that were provided in the search results pages for the given search query, including assigning a higher scores for higher dwell times at the given web page; storing, based on the determined features scores, one or more of the search queries from the identified set of search queries as keywords for the given web page, including storing the given query having a best feature score as a given keyword for the given web page; receiving a request for content to be displayed with the given web page; selecting content for display with the given web page based on the given keyword for the given web page matching terms used to distribute the content; and transmitting, via a network, the selected content for display with the given web page responsive to the request for content. | 6. A system comprising: accessing search queries that were previously submitted by users and a corresponding set of search results that were previously presented in search results pages for each of the search queries, each search result in the set of the search results including a link to a corresponding web page; identifying, for a given web page that was previously accessed through user interaction with links included in search results from the set of search results, a set of the search queries that were previously used to provide search results pages that included the links with which the user interactions occurred to access the given web page; determining, for each given search query in the set of search queries, a feature score based on a user dwell time at the given web page following user interaction with the links to the given web page that were provided in the search results pages for the given search query, including assigning a higher scores for higher dwell times at the given web page; storing, based on the determined features scores, one or more of the search queries from the identified set of search queries as keywords for the given web page, including storing the given query having a best feature score as a given keyword for the given web page; receiving a request for content to be displayed with the given web page; selecting content for display with the given web page based on the given keyword for the given web page matching terms used to distribute the content; and transmitting, via a network, the selected content for display with the given web page responsive to the request for content. 7. The system of claim 6 , wherein at least one search query in the set of search queries comprises one or more n-grams. | 0.5 |
7,734,468 | 1 | 9 | 1. A method of managing a dialog turn between a user and a spoken dialog system, the method comprising: storing weights assigned to varying dialog turn information based on statistical information in a weight database; generating first dialog turn information using dialog information analyzed from a speech uttered by the user; generating second dialog turn information using facial expression information analyzed from a face image of the user; synthesizing the first dialog turn information and the second dialog turn information and outputting the synthesized dialog turn information when the first dialog turn information is different than the second dialog turn information, and outputting one selected from the first dialog turn information and the second dialog turn information when the first dialog turn information is identical to the second dialog turn information; searching the weight database for weights corresponding to the synthesized dialog turn information, assigning a positive weight to each of the first and second dialog turn information based on the synthesized dialog turn information and selecting whichever one of the first and second dialog turn information has a greater positive weight; and determining a final dialog turn using the selected one dialog turn information, information on a status of the spoken dialog system, information on whether the user speech is input, and information on a no-answer time of the user, for controlling a dialog between the user and the spoken dialog system. | 1. A method of managing a dialog turn between a user and a spoken dialog system, the method comprising: storing weights assigned to varying dialog turn information based on statistical information in a weight database; generating first dialog turn information using dialog information analyzed from a speech uttered by the user; generating second dialog turn information using facial expression information analyzed from a face image of the user; synthesizing the first dialog turn information and the second dialog turn information and outputting the synthesized dialog turn information when the first dialog turn information is different than the second dialog turn information, and outputting one selected from the first dialog turn information and the second dialog turn information when the first dialog turn information is identical to the second dialog turn information; searching the weight database for weights corresponding to the synthesized dialog turn information, assigning a positive weight to each of the first and second dialog turn information based on the synthesized dialog turn information and selecting whichever one of the first and second dialog turn information has a greater positive weight; and determining a final dialog turn using the selected one dialog turn information, information on a status of the spoken dialog system, information on whether the user speech is input, and information on a no-answer time of the user, for controlling a dialog between the user and the spoken dialog system. 9. A computer-readable storage medium encoded with computer readable code to control a computer to implement the method of claim 1 . | 0.896714 |
8,838,587 | 1 | 8 | 1. A computer-implemented method comprising: receiving, at a computer system, a request to determine whether to assign a classification to a first query; selecting, by the computer system, a plurality of search entities that are associated with the first query based on respective user behavior data for the first query associated with each of the search entities; determining a first measure of how many of the plurality of search entities have been assigned the classification; determining that the first measure of how many of the plurality of search entities have been assigned the classification satisfies a classification threshold; in response to determining that the first measure of how many of the plurality of search entities have been assigned the classification satisfies the classification threshold, determining a second measure of how many of a top group of search entities from the plurality of search entities have been assigned the classification, each search entity in the top group of search entities being in a subset of the plurality of search entities having highest respective measures of relevance to the query based on the user behavior data; determining, by the computer system that the first measure of how many of the plurality of search entities have been assigned the classification is consistent with the second measure of how many of the top group of search entities have been assigned the classification; and in response to determining that the first measure of how many of the plurality of search entities have been assigned the classification is consistent with the second measure of how many of the top group of search entities have been assigned the classification, assigning the classification to the first query. | 1. A computer-implemented method comprising: receiving, at a computer system, a request to determine whether to assign a classification to a first query; selecting, by the computer system, a plurality of search entities that are associated with the first query based on respective user behavior data for the first query associated with each of the search entities; determining a first measure of how many of the plurality of search entities have been assigned the classification; determining that the first measure of how many of the plurality of search entities have been assigned the classification satisfies a classification threshold; in response to determining that the first measure of how many of the plurality of search entities have been assigned the classification satisfies the classification threshold, determining a second measure of how many of a top group of search entities from the plurality of search entities have been assigned the classification, each search entity in the top group of search entities being in a subset of the plurality of search entities having highest respective measures of relevance to the query based on the user behavior data; determining, by the computer system that the first measure of how many of the plurality of search entities have been assigned the classification is consistent with the second measure of how many of the top group of search entities have been assigned the classification; and in response to determining that the first measure of how many of the plurality of search entities have been assigned the classification is consistent with the second measure of how many of the top group of search entities have been assigned the classification, assigning the classification to the first query. 8. The method of claim 1 , wherein determining the first measure of how many of the plurality of search entities have been assigned the classification comprises: determining the first measure of how many of the plurality of search entities have been assigned the classification according to respective weights for the classification assigned to each of the plurality of search entities, the weights being based on the user behavior data associated with each of the plurality of search entities. | 0.674572 |
7,844,966 | 11 | 16 | 11. A system for generating a job flowchart, said system including: a network interface communicating with a non-transitory memory; said memory communicating with a second computer-based system for generating a job flowchart; and said second computer-based system, when executing a computer program, is configured to: receive a request, from a first computer-based system, for generating said job flowchart, wherein said request includes a parameter; retrieve job scheduling data from a third computer-based system based on said parameter, wherein said job scheduling data defines automated tasks to be performed by said third computer-based system; format said job scheduling data; transform said job scheduling data into a text file, wherein the job scheduling data is automatically transformed into a text file in response to predetermined intervals; assign a file extension to said text file based on a charting application; and transmit to said first computer-based system, said text file to facilitate opening said text file within said charting application, wherein said text file comprises said job flowchart. | 11. A system for generating a job flowchart, said system including: a network interface communicating with a non-transitory memory; said memory communicating with a second computer-based system for generating a job flowchart; and said second computer-based system, when executing a computer program, is configured to: receive a request, from a first computer-based system, for generating said job flowchart, wherein said request includes a parameter; retrieve job scheduling data from a third computer-based system based on said parameter, wherein said job scheduling data defines automated tasks to be performed by said third computer-based system; format said job scheduling data; transform said job scheduling data into a text file, wherein the job scheduling data is automatically transformed into a text file in response to predetermined intervals; assign a file extension to said text file based on a charting application; and transmit to said first computer-based system, said text file to facilitate opening said text file within said charting application, wherein said text file comprises said job flowchart. 16. The system of claim 11 , further configured to remove redundant dependencies from said job scheduling data. | 0.76971 |
10,078,487 | 1 | 4 | 1. A method of operating a digital assistant, comprising: at a device having one or more processors and memory: providing a list of notification items, the list including a plurality of notification items, wherein each respective one of the plurality of notification items is associated with a respective urgency value; prior to outputting a first notification item of the plurality of notification items: detecting an information item received from an external device; determining whether the information item is relevant to an urgency value of the first notification item of the plurality of notification items; and upon determining that the information item is relevant to the urgency value of the first notification item, adjusting the urgency value of the first notification item and incorporating content from the information item into the first notification item; determining whether the adjusted urgency value of the first notification item satisfies a predetermined threshold; and upon determining that the adjusted urgency value satisfies the predetermined threshold, providing the first notification item to a user, the first notification item including the incorporated content from the information item. | 1. A method of operating a digital assistant, comprising: at a device having one or more processors and memory: providing a list of notification items, the list including a plurality of notification items, wherein each respective one of the plurality of notification items is associated with a respective urgency value; prior to outputting a first notification item of the plurality of notification items: detecting an information item received from an external device; determining whether the information item is relevant to an urgency value of the first notification item of the plurality of notification items; and upon determining that the information item is relevant to the urgency value of the first notification item, adjusting the urgency value of the first notification item and incorporating content from the information item into the first notification item; determining whether the adjusted urgency value of the first notification item satisfies a predetermined threshold; and upon determining that the adjusted urgency value satisfies the predetermined threshold, providing the first notification item to a user, the first notification item including the incorporated content from the information item. 4. The method of claim 1 , further comprising establishing the predetermined threshold in accordance with a calendar item associated with a current time. | 0.778261 |
10,021,167 | 12 | 13 | 12. A computer system for accessing mobile, the system comprising: at least one memory to store executable instructions; and at least one processor communicatively coupled to the at least one memory, the at least one processor configured to execute the executable instructions to: from a device, receive a request for creating an analytical file corresponding to a document stored on a server; retrieve, from the device, a business intelligence archive resource (BIAR) file related to the document, wherein the BIAR file includes metadata related to the document and connection details of the server; connect to the server to retrieve the document and at least one of: values corresponding to the metadata, one or more annotations, and one or more operations related to the document from the server; integrate the retrieved document, retrieved BIAR file, and the retrieved at least one of the values of the corresponding metadata, the one or more annotations, and the one or more operations to create the analytical file corresponding to the document; and store the created analytical file locally on the device to enable accessing the document and the at least one of the values of the corresponding metadata, the one or more annotations, and the one or more operations related to the document, without being connected to the server. | 12. A computer system for accessing mobile, the system comprising: at least one memory to store executable instructions; and at least one processor communicatively coupled to the at least one memory, the at least one processor configured to execute the executable instructions to: from a device, receive a request for creating an analytical file corresponding to a document stored on a server; retrieve, from the device, a business intelligence archive resource (BIAR) file related to the document, wherein the BIAR file includes metadata related to the document and connection details of the server; connect to the server to retrieve the document and at least one of: values corresponding to the metadata, one or more annotations, and one or more operations related to the document from the server; integrate the retrieved document, retrieved BIAR file, and the retrieved at least one of the values of the corresponding metadata, the one or more annotations, and the one or more operations to create the analytical file corresponding to the document; and store the created analytical file locally on the device to enable accessing the document and the at least one of the values of the corresponding metadata, the one or more annotations, and the one or more operations related to the document, without being connected to the server. 13. The system of claim 12 , wherein the one or more annotations are stored as an image file and the image file has a unique image file identifier (ID). | 0.894298 |
8,793,757 | 10 | 11 | 10. The method of claim 8 , further comprises: providing at least one categorized group of user identity attributes; and determining a privacy preference for each category. | 10. The method of claim 8 , further comprises: providing at least one categorized group of user identity attributes; and determining a privacy preference for each category. 11. The method of claim 10 , further comprises: receiving from the environment the security policy having requirements specifying required attributes; identifying at least one category referencing the required attributes; and the evaluating further includes using the privacy preference of at least one identified category in the evaluation operation. | 0.5 |
7,899,657 | 32 | 41 | 32. A computer-based system for parameterizing a steady-state model of an in-situ hydrocarbon reservoir, the model having a plurality of model parameters for mapping model input to model output through a stored representation of said reservoir, the system comprising: a computer, comprising: a processor; and a memory medium coupled to the processor; an input coupled to the processor and the memory medium, wherein the input is operable to receive a training data set comprising a plurality of input values and a plurality of target output values, wherein the training data set is representative of production operations of said reservoir; and an output coupled to the processor and the memory medium; wherein the memory medium stores program instructions which are executable by the processor to: receive a next at least one input value of the plurality of input values and a next target output value of the plurality of target output values; parameterize the model with a predetermined algorithm using said next at least one input value and said next target output value, and one or more derivative constraints, wherein the one or more derivative constraints are imposed to constrain relationships between the at least one input value and a resulting model output value, wherein said parameterizing comprises using an optimizer to perform constrained optimization on the plurality of model parameters to satisfy an objective function subject to the derivative constraints; iteratively perform said receiving and said parameterizing using the optimizer to generate a parameterized model, wherein the model comprises a model function, wherein the one or more derivative constraints comprise upper and/or lower bounds on one or more model function derivatives, wherein one or more of the model function derivatives comprise one or more of: a first order derivative of the model function, wherein the first order derivative represents inter-well transmissibilities; a second order derivative of the model function, wherein the second order derivative of the model function represents curvature of the inter-well transmissibilities; and/or a third order derivative of the model function, wherein the third order derivative of the model function represents rate of curvature of the inter-well transmissibilities; and store the parameterized model in the memory medium, wherein the parameterized model is usable to analyze reservoir operations; and wherein the output is operable to provide the parameterized model and/or the resulting model output values to other systems or processes to manage the reservoir operations. | 32. A computer-based system for parameterizing a steady-state model of an in-situ hydrocarbon reservoir, the model having a plurality of model parameters for mapping model input to model output through a stored representation of said reservoir, the system comprising: a computer, comprising: a processor; and a memory medium coupled to the processor; an input coupled to the processor and the memory medium, wherein the input is operable to receive a training data set comprising a plurality of input values and a plurality of target output values, wherein the training data set is representative of production operations of said reservoir; and an output coupled to the processor and the memory medium; wherein the memory medium stores program instructions which are executable by the processor to: receive a next at least one input value of the plurality of input values and a next target output value of the plurality of target output values; parameterize the model with a predetermined algorithm using said next at least one input value and said next target output value, and one or more derivative constraints, wherein the one or more derivative constraints are imposed to constrain relationships between the at least one input value and a resulting model output value, wherein said parameterizing comprises using an optimizer to perform constrained optimization on the plurality of model parameters to satisfy an objective function subject to the derivative constraints; iteratively perform said receiving and said parameterizing using the optimizer to generate a parameterized model, wherein the model comprises a model function, wherein the one or more derivative constraints comprise upper and/or lower bounds on one or more model function derivatives, wherein one or more of the model function derivatives comprise one or more of: a first order derivative of the model function, wherein the first order derivative represents inter-well transmissibilities; a second order derivative of the model function, wherein the second order derivative of the model function represents curvature of the inter-well transmissibilities; and/or a third order derivative of the model function, wherein the third order derivative of the model function represents rate of curvature of the inter-well transmissibilities; and store the parameterized model in the memory medium, wherein the parameterized model is usable to analyze reservoir operations; and wherein the output is operable to provide the parameterized model and/or the resulting model output values to other systems or processes to manage the reservoir operations. 41. The system of claim 32 , wherein at least one of said upper and/or lower bounds comprises a function. | 0.866071 |
8,700,602 | 1 | 4 | 1. A computer-controlled method to monitor database performance, comprising: receiving at least one rule for query statement execution from a user through a user interface; gathering information from a remote, monitored database related to the monitored database performance during a predetermined time during operation of the database; collecting statistics corresponding to any queries executed during the predetermined time, the statistics consisting of data gathered from performance of the query; testing at least one rule against the statistics corresponding to each query; if the testing results in a rule violation, identifying the query that caused the rule violation; accessing a list of known rule-violating queries in a local repository to determine if the query causing the rule violation has been previously recorded and to determine if a correction for the query exists; and storing the query causing the rule violation in the repository. | 1. A computer-controlled method to monitor database performance, comprising: receiving at least one rule for query statement execution from a user through a user interface; gathering information from a remote, monitored database related to the monitored database performance during a predetermined time during operation of the database; collecting statistics corresponding to any queries executed during the predetermined time, the statistics consisting of data gathered from performance of the query; testing at least one rule against the statistics corresponding to each query; if the testing results in a rule violation, identifying the query that caused the rule violation; accessing a list of known rule-violating queries in a local repository to determine if the query causing the rule violation has been previously recorded and to determine if a correction for the query exists; and storing the query causing the rule violation in the repository. 4. The computer-controlled method of claim 1 , further comprising storing the information in a local repository. | 0.794118 |
9,679,002 | 5 | 9 | 5. The method of claim 3 , wherein: the searching is performed in at least one machine-readable data set, which is a numbered sequence of objects, a result of the searching is a bounded set of hits that represent occurrences of search object in said at least one data set; the loop termination condition in the future restoration operation is set prior to the loop, the future restoration operation is performed for each hit in said bounded set of hits, and applying said loop termination condition to end the loop, and obtaining a bounded set of future objects. | 5. The method of claim 3 , wherein: the searching is performed in at least one machine-readable data set, which is a numbered sequence of objects, a result of the searching is a bounded set of hits that represent occurrences of search object in said at least one data set; the loop termination condition in the future restoration operation is set prior to the loop, the future restoration operation is performed for each hit in said bounded set of hits, and applying said loop termination condition to end the loop, and obtaining a bounded set of future objects. 9. The method of claim 5 , further comprising analyzing object co-occurrence in the obtained bounded sets of past object simultaneously with an obtained set of future objects. | 0.5 |
8,055,661 | 1 | 6 | 1. A method of automatically generating an ontology instance, the method comprising: collecting documents corresponding to classes of an ontology; classifying the collected documents into unstructured documents and structured documents depending on whether information in the collected documents is in the form of a table; if the collected documents are unstructured documents, extracting first relationship information from the unstructured documents based on characteristics of entities, wherein the first relationship information represents relationships between entities in sentences of the unstructured document; if the collected documents are structured documents, extracting second relationship information from the structured documents based on characteristics of entities, wherein the second relationship information represents relationships between entities in a table of the structured documents; generating ontology instances from the first relationship information or the second relationship information; and mapping the generated ontology instances to corresponding classes of the ontology, wherein the extracting of the first relationship information is performed using a different process from that used to extract the second relationship information, wherein the extracting of the first relationship information from the unstructured documents includes: recognizing entity names from the unstructured documents; recognizing substitutes from the unstructured documents, wherein the substitutes are represented by pronouns in the sentences of the unstructured documents; and extracting the first relationship information from the recognized entity names and the recognized substitutes, and wherein the recognizing of the substitutes includes restoring the recognized substitutes into corresponding entity names. | 1. A method of automatically generating an ontology instance, the method comprising: collecting documents corresponding to classes of an ontology; classifying the collected documents into unstructured documents and structured documents depending on whether information in the collected documents is in the form of a table; if the collected documents are unstructured documents, extracting first relationship information from the unstructured documents based on characteristics of entities, wherein the first relationship information represents relationships between entities in sentences of the unstructured document; if the collected documents are structured documents, extracting second relationship information from the structured documents based on characteristics of entities, wherein the second relationship information represents relationships between entities in a table of the structured documents; generating ontology instances from the first relationship information or the second relationship information; and mapping the generated ontology instances to corresponding classes of the ontology, wherein the extracting of the first relationship information is performed using a different process from that used to extract the second relationship information, wherein the extracting of the first relationship information from the unstructured documents includes: recognizing entity names from the unstructured documents; recognizing substitutes from the unstructured documents, wherein the substitutes are represented by pronouns in the sentences of the unstructured documents; and extracting the first relationship information from the recognized entity names and the recognized substitutes, and wherein the recognizing of the substitutes includes restoring the recognized substitutes into corresponding entity names. 6. The method according to claim 1 , wherein the recognizing of the entity names comprises: parsing the sentences of the unstructured documents on a morpheme basis; generating a characteristic of each of the entity names from the parsed sentences using a characteristic dictionary; and recognizing the entity names using an entity name recognizing model. | 0.612691 |
5,515,455 | 12 | 13 | 12. The method according to claim 9 wherein the word is initially traced between the original feature point and the terminal feature point of each primitive. | 12. The method according to claim 9 wherein the word is initially traced between the original feature point and the terminal feature point of each primitive. 13. The method according to claim 12 wherein each primitive having one of the intersection feature points is traced in a first manner. | 0.5 |
7,783,639 | 1 | 12 | 1. A method performed by a device, the method comprising: identifying, by a processor of the device, a plurality of documents, where a first one of the identified documents is linked by a second one of the identified documents and the second document is one of a plurality of documents in an affiliated set of documents; calculating, by the processor, a first value for each document in the affiliated set of documents based on a ranking score of the document and a number of outbound links from the document; calculating, by the processor, a second value as a maximum of the first values for the documents in the affiliated set of documents; assigning, by the processor, a ranking score to the first document based the second value, where assigning the ranking score includes: determining whether the documents in the affiliated set of documents are weakly affiliated or strongly affiliated, and setting the amount that the second document contributes to the ranking score of the first document as a function that acts as a summation operator over the affiliated set of documents when the affiliated set is weakly affiliated and as a maximum operator over the affiliated set of documents when the affiliated set is strongly affiliated, where the function is defined as:
(CONTRIB(D 1 ) a +CONTRIB(D 2 ) a + . . . +CONTRIB(D k ) a ) 1/a , where CONTRIB for document D k represents an individual ranking score contribution for document k in the affiliated set, and a is defined as 1 ⅇ + ( 1 - ⅇ ) γ , where e is a constant and γ represents a continuous measure of the affiliation of the documents in the affiliated set; and storing, by the processor, the ranking score. | 1. A method performed by a device, the method comprising: identifying, by a processor of the device, a plurality of documents, where a first one of the identified documents is linked by a second one of the identified documents and the second document is one of a plurality of documents in an affiliated set of documents; calculating, by the processor, a first value for each document in the affiliated set of documents based on a ranking score of the document and a number of outbound links from the document; calculating, by the processor, a second value as a maximum of the first values for the documents in the affiliated set of documents; assigning, by the processor, a ranking score to the first document based the second value, where assigning the ranking score includes: determining whether the documents in the affiliated set of documents are weakly affiliated or strongly affiliated, and setting the amount that the second document contributes to the ranking score of the first document as a function that acts as a summation operator over the affiliated set of documents when the affiliated set is weakly affiliated and as a maximum operator over the affiliated set of documents when the affiliated set is strongly affiliated, where the function is defined as:
(CONTRIB(D 1 ) a +CONTRIB(D 2 ) a + . . . +CONTRIB(D k ) a ) 1/a , where CONTRIB for document D k represents an individual ranking score contribution for document k in the affiliated set, and a is defined as 1 ⅇ + ( 1 - ⅇ ) γ , where e is a constant and γ represents a continuous measure of the affiliation of the documents in the affiliated set; and storing, by the processor, the ranking score. 12. The method of claim 1 , wherein assigning the ranking score to a particular document is based on the formula: α + β ( ∑ i = 1 m SETCONTRIB ( S i , γ i ) ) , where α and β are predetermined constants, the sum is taken over m affiliated sets of documents that link to the particular document, and SETCONTRIB is the function (CONTRIB(D 1 ) a i +CONTRIB(D 2 ) a i + . . . +CONTRIB(D k ) a i ) 1/a i for each of the m sets of affiliated documents. | 0.5 |
8,234,561 | 54 | 63 | 54. A system comprising one or more processors: an input/output system; an auto-fill engine providing proposed values and corresponding likelihood assessments generated based on values entered in observed form fields using the input/output system, the likelihood assessments indicating relative probability of the proposed values being entered in one or more current form field objects in a current form instance; and a form presentation component displaying the current form instance using the input/output system such that one or more predicted values are displayed in connection with the one or more current form field objects, the one or more predicted values being selected from the proposed values based on the likelihood assessments, wherein the auto-fill engine provides the proposed values and the corresponding likelihood assessments based on a determination of semantic similarity among the one or more current form field objects and the observed form fields. | 54. A system comprising one or more processors: an input/output system; an auto-fill engine providing proposed values and corresponding likelihood assessments generated based on values entered in observed form fields using the input/output system, the likelihood assessments indicating relative probability of the proposed values being entered in one or more current form field objects in a current form instance; and a form presentation component displaying the current form instance using the input/output system such that one or more predicted values are displayed in connection with the one or more current form field objects, the one or more predicted values being selected from the proposed values based on the likelihood assessments, wherein the auto-fill engine provides the proposed values and the corresponding likelihood assessments based on a determination of semantic similarity among the one or more current form field objects and the observed form fields. 63. The system of claim 54 , wherein the form presentation component comprises a portion of a machine network browser. | 0.87605 |
8,521,764 | 8 | 12 | 8. A system comprising: one or more server devices to: identify entity identifiers, where each of the entity identifiers is associated with a document that was selected, from a plurality of documents, based on a search query including a same variation of an entity name; determine whether a total quantity of selections of the document associated with a particular entity identifier, of the entity identifiers, is greater than a total quantity of selections of each of the documents associated with other ones of the entity identifiers; and store, based on a result of the determining, the same variation of the entity name in a first memory that is used for rewriting search queries or a second memory that is used for suggesting rewritten search queries, where the same variation of the entity name is stored in the second memory when the total quantity of selections of the document associated with the particular entity identifier is greater than the total quantity of selections of each of the documents associated with the other ones of the entity identifiers, and where the same variation of the entity name is stored in the first memory when the total quantity of selections of the document associated with the particular entity identifier is substantially greater than the total quantity of selections of each of the documents associated with the other ones of the entity identifiers. | 8. A system comprising: one or more server devices to: identify entity identifiers, where each of the entity identifiers is associated with a document that was selected, from a plurality of documents, based on a search query including a same variation of an entity name; determine whether a total quantity of selections of the document associated with a particular entity identifier, of the entity identifiers, is greater than a total quantity of selections of each of the documents associated with other ones of the entity identifiers; and store, based on a result of the determining, the same variation of the entity name in a first memory that is used for rewriting search queries or a second memory that is used for suggesting rewritten search queries, where the same variation of the entity name is stored in the second memory when the total quantity of selections of the document associated with the particular entity identifier is greater than the total quantity of selections of each of the documents associated with the other ones of the entity identifiers, and where the same variation of the entity name is stored in the first memory when the total quantity of selections of the document associated with the particular entity identifier is substantially greater than the total quantity of selections of each of the documents associated with the other ones of the entity identifiers. 12. The system of claim 8 , where the one or more server devices are to: rewrite a search query using one of the first memory or the second memory, where, when rewriting the search query, the one or more server devices are further to: determine whether a term, included in the search query, matches a variation of a particular entity name stored in the first memory; rewrite, using the first memory, the search query into a rewritten search query that includes an entity identifier, in the first memory, associated with the variation of the particular entity name when the term matches the variation of the particular entity name in the first memory; and perform a search based on the rewritten search query instead of performing a search based on the search query. | 0.637441 |
4,881,197 | 20 | 34 | 20. In a data processing system for composing documents having multiple lines of alphanumeric data comprising: means for entering data and commands relating to a document to be composed; data processor means for processing said data and for executing commands; memory means for storing data and commands; a plurality of data presentation output means for presenting the document in a final form; means for selecting a first format for defining data presentation characteristics for at least a first portion of the document and for associating a first abstract format name with the first format upon entry of said first abstract format name via said means for entering; means for defining a first set of data presentation characteristics for a first data presentation output means and for associating said first set of characteristics with said first abstract format name; means for defining a second set of data presentation characteristics, which are independent from said first set of data presentation characteristics, for a second data presentation output means without affecting the data presentation characteristics defined for said first data presentation output means and for associating said second set of data presentation characteristics with said first abstract format name; means for associating with every line in the document an abstract format name which is linked to a data presentation characteristic defining format; means responsive to the entry of said first abstract format name for formatting the lines in the document on said first data presentation means to correspond with said first set of data presentation characteristics and for formatting the lines on said second data presentation output means to correspond with said second set of data presentation characteristics; and means for displaying the document during composition in a form substantially the same as the final form display. | 20. In a data processing system for composing documents having multiple lines of alphanumeric data comprising: means for entering data and commands relating to a document to be composed; data processor means for processing said data and for executing commands; memory means for storing data and commands; a plurality of data presentation output means for presenting the document in a final form; means for selecting a first format for defining data presentation characteristics for at least a first portion of the document and for associating a first abstract format name with the first format upon entry of said first abstract format name via said means for entering; means for defining a first set of data presentation characteristics for a first data presentation output means and for associating said first set of characteristics with said first abstract format name; means for defining a second set of data presentation characteristics, which are independent from said first set of data presentation characteristics, for a second data presentation output means without affecting the data presentation characteristics defined for said first data presentation output means and for associating said second set of data presentation characteristics with said first abstract format name; means for associating with every line in the document an abstract format name which is linked to a data presentation characteristic defining format; means responsive to the entry of said first abstract format name for formatting the lines in the document on said first data presentation means to correspond with said first set of data presentation characteristics and for formatting the lines on said second data presentation output means to correspond with said second set of data presentation characteristics; and means for displaying the document during composition in a form substantially the same as the final form display. 34. Apparatus according to claim 20, wherein the data processing system includes a CRT, and further including means for specifying further details for a selected abstract format name and for displaying on the CRT a detailed specification containing data insertion areas for the selected format, and means for inserting format specifying data into said areas for at least a plurality of output means including said CRT. | 0.692647 |
8,838,611 | 28 | 29 | 28. The document ranking method of claim 16 , further comprising: generating a community using content scores and contribution scores of a plurality of documents having words associated with a subject. | 28. The document ranking method of claim 16 , further comprising: generating a community using content scores and contribution scores of a plurality of documents having words associated with a subject. 29. The document ranking method of claim 28 , wherein the community generating further comprises: calculating a sum of document ranking scores of documents with respect to the words associated with the subject, the document ranking score of each document being determined using a content score and a contribution score of each of the documents, and constructing a sub-graph by retrieving neighboring documents of a document having a greatest document ranking score. | 0.5 |
9,626,703 | 15 | 21 | 15. A system for providing voice commerce, the system comprising: one or more physical processors programmed with computer program instructions which, when executed, cause the one or more physical processors to: receive a user input comprising a natural language utterance; provide the natural language utterance as an input to a speech recognition engine; obtain one or more words or phrases recognized from the natural language utterance as an output of the speech recognition engine; determine a context based at least on the one or more words or phrases; identify, without further user input after the receipt of the user input, a product or service to be purchased on behalf of a user based at least on the determined context; obtain payment information with which to pay for the product or service; obtain, without further user input after the receipt of the user input, shipping information with which to deliver the product or service, wherein the shipping information specifies a name or address of a recipient to which the product or service is to be delivered after the product or service is purchased; and complete, without further user input after the receipt of the user input, a purchase transaction for the product or service based on the payment information and shipping information. | 15. A system for providing voice commerce, the system comprising: one or more physical processors programmed with computer program instructions which, when executed, cause the one or more physical processors to: receive a user input comprising a natural language utterance; provide the natural language utterance as an input to a speech recognition engine; obtain one or more words or phrases recognized from the natural language utterance as an output of the speech recognition engine; determine a context based at least on the one or more words or phrases; identify, without further user input after the receipt of the user input, a product or service to be purchased on behalf of a user based at least on the determined context; obtain payment information with which to pay for the product or service; obtain, without further user input after the receipt of the user input, shipping information with which to deliver the product or service, wherein the shipping information specifies a name or address of a recipient to which the product or service is to be delivered after the product or service is purchased; and complete, without further user input after the receipt of the user input, a purchase transaction for the product or service based on the payment information and shipping information. 21. The system of claim 15 , wherein the one or more physical processors are further caused to: receive a previous user input prior to the receipt of the user input, wherein the previous user input is related to the product or service; and store context information associated with the user based on information related to the previous user input, wherein to identify the product or service as at least one product or service to be purchased on behalf of the user, the one or more physical processors are further caused to: determine, without further user input after the receipt of the user input, the product or service based on the natural language utterance and the context information related to the previous user input. | 0.5 |
7,930,719 | 14 | 23 | 14. A set top box device for enabling access to media programs with content distributed by a server in multiple languages comprising: a user interface for i) receiving a first user input to select a language for interacting with the set top box and ii) receiving a second user input to select a program for receiving from the server, wherein the program has a plurality of associated tracks having content in at least two different languages, wherein each track is associated with a single language, and wherein the first and the second selection opportunities are separate selection opportunities; a first data interface for i) receiving program guide data associated with the language selected by the user and ii) receiving from the server a transmission including the selected program; and a second data interface for i) sending the program guide data associated with the language selected by the user to a display and ii) in response to the second selection opportunity, sending only a track associated with the language selected by the first user input to the display. | 14. A set top box device for enabling access to media programs with content distributed by a server in multiple languages comprising: a user interface for i) receiving a first user input to select a language for interacting with the set top box and ii) receiving a second user input to select a program for receiving from the server, wherein the program has a plurality of associated tracks having content in at least two different languages, wherein each track is associated with a single language, and wherein the first and the second selection opportunities are separate selection opportunities; a first data interface for i) receiving program guide data associated with the language selected by the user and ii) receiving from the server a transmission including the selected program; and a second data interface for i) sending the program guide data associated with the language selected by the user to a display and ii) in response to the second selection opportunity, sending only a track associated with the language selected by the first user input to the display. 23. The device of claim 14 , wherein the user interface receives a third user input to select an alternate language associated with the selected program wherein the first data interface receives from the server a transmission including a track associated with the selected program having content in the alternate language, but not in the language selected by the first user input; and wherein the second data interface sends the track associated with the alternate language of the selected program to the display. | 0.5 |
7,912,904 | 1 | 2 | 1. A method for searching messages in a conversation-based message system, comprising: at a client computer, responding to receipt of a query from a requestor, the query having one or more query terms, including: transmitting the query over a network to a conversation management system; receiving from the conversation management system a list of conversations that match the one or more query terms, each of the conversations in the list having a respective conversation identifier, and wherein each conversation comprises one or more messages sharing a common set of characteristics that meet first predefined criteria and at least one conversation in the list of conversations comprises a plurality of messages; and presenting at least a portion of the list of conversations to the requestor, each conversation listed in the presented portion of the list being represented as a single item, the presented portion of the list including a plurality of items, each representing a distinct conversation, at least one of which comprises a plurality of messages, wherein each item representing a conversation having a plurality of messages has an associated icon indicating the number of electronic messages in the conversation. | 1. A method for searching messages in a conversation-based message system, comprising: at a client computer, responding to receipt of a query from a requestor, the query having one or more query terms, including: transmitting the query over a network to a conversation management system; receiving from the conversation management system a list of conversations that match the one or more query terms, each of the conversations in the list having a respective conversation identifier, and wherein each conversation comprises one or more messages sharing a common set of characteristics that meet first predefined criteria and at least one conversation in the list of conversations comprises a plurality of messages; and presenting at least a portion of the list of conversations to the requestor, each conversation listed in the presented portion of the list being represented as a single item, the presented portion of the list including a plurality of items, each representing a distinct conversation, at least one of which comprises a plurality of messages, wherein each item representing a conversation having a plurality of messages has an associated icon indicating the number of electronic messages in the conversation. 2. The method of claim 1 , wherein the single item for a respective conversation in the presented portion of the list of conversations is generated so as to include a text string having a highlighted instance of at least one of the one or more query terms. | 0.596215 |
8,107,731 | 5 | 7 | 5. A method for text conversion wherein a text is entered which includes at least one letter associated with a character to be transmitted, and one candidate matching the text entered is selected among a plurality of candidates of output texts to which the text entered is to be at least partially converted, the one candidate being output as an output text including the character to be transmitted, said method comprising: a first step of inputting a letter indicating a destination of transmission as information on the destination of transmission; a second step of storing an input text and the output text in association with either of the information on the destination of transmission and an attribute of the information on the destination of transmission; a third step of taking out, as a candidate for conversion for the text entered, at least one output text stored correlated with the input text relevant to the text entered and with either of the information on the destination of transmission and the attribute of the information on the destination of transmission stored and coincident with either of the information on the destination of transmission and the attribute of the information on the destination of transmission entered; and a fourth step of outputting at least one output text taken out in outputting a converted text matched to the text entered. | 5. A method for text conversion wherein a text is entered which includes at least one letter associated with a character to be transmitted, and one candidate matching the text entered is selected among a plurality of candidates of output texts to which the text entered is to be at least partially converted, the one candidate being output as an output text including the character to be transmitted, said method comprising: a first step of inputting a letter indicating a destination of transmission as information on the destination of transmission; a second step of storing an input text and the output text in association with either of the information on the destination of transmission and an attribute of the information on the destination of transmission; a third step of taking out, as a candidate for conversion for the text entered, at least one output text stored correlated with the input text relevant to the text entered and with either of the information on the destination of transmission and the attribute of the information on the destination of transmission stored and coincident with either of the information on the destination of transmission and the attribute of the information on the destination of transmission entered; and a fourth step of outputting at least one output text taken out in outputting a converted text matched to the text entered. 7. The method in accordance with claim 5 , wherein said fourth step outputs at least one output text taken out, based on conversion history information on conversion history. | 0.929669 |
4,624,012 | 11 | 14 | 11. Apparatus as set forth in claim 10, wherein said digital speech data from the source is subject to speech synthesization using a predetermined sampling period comprising a known number of task-accomplishing time increments; said speech parameter control means including sample rate control circuit means responsive to inputs from said voice character conversion controller means as determined by said voice character selection means thereof for adjusting the sampling period of said digital speech data from the source in a manner altering the digital speech formants contained therein to a preselected degree and providing adjusted sampling period signals as an output; and said speech synthesizer means being coupled to the output of said sample rate control circuit means for receiving said adjusted sampling period signals therefrom as the modified speech data from said speech data reconstructing means is being input thereto. | 11. Apparatus as set forth in claim 10, wherein said digital speech data from the source is subject to speech synthesization using a predetermined sampling period comprising a known number of task-accomplishing time increments; said speech parameter control means including sample rate control circuit means responsive to inputs from said voice character conversion controller means as determined by said voice character selection means thereof for adjusting the sampling period of said digital speech data from the source in a manner altering the digital speech formants contained therein to a preselected degree and providing adjusted sampling period signals as an output; and said speech synthesizer means being coupled to the output of said sample rate control circuit means for receiving said adjusted sampling period signals therefrom as the modified speech data from said speech data reconstructing means is being input thereto. 14. Apparatus as set forth in claim 11, wherein said sample rate control circuit means includes counter means operably connected to said voice character conversion controller means and being responsive thereto for establishing a preset count value, said counter means having a maximum count value at least equal to the preset count value, and clock means alternately enabling said speech synthesizer means and said counter means, said speech synthesizer means being idle during the time period said counter means is undergoing incremention from said preset count value to the maximum count value thereof. | 0.577031 |
9,548,051 | 9 | 13 | 9. A system comprising: a processor; and a computer-readable storage device having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: partitioning speech recognizer output into a plurality of independent clauses; and for an independent clause in the plurality of independent clauses: identifying an object; and recursively generating, via a processor, for each sub-independent clause within the independent clause, a semantic representation using the object. | 9. A system comprising: a processor; and a computer-readable storage device having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: partitioning speech recognizer output into a plurality of independent clauses; and for an independent clause in the plurality of independent clauses: identifying an object; and recursively generating, via a processor, for each sub-independent clause within the independent clause, a semantic representation using the object. 13. The system of claim 9 , wherein while recursively generating the semantic representation, additional objects are extracted from the independent clause. | 0.65859 |
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