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8,565,486 | 1 | 3 | 1. A classification system comprising: a memory device; a processor communicatively connected to said memory device; and an input communicatively connected to said processor, wherein said input is configured to receive data comprising at least one object that is to be classified as one of an object of interest (OOI) and a nuisance of interest (NOI) based upon at least one non-Boolean attribute of said object; wherein said at least one object that is to be classified is at least one of a headlight from an oncoming vehicle and a tail light of a leading vehicle; wherein said OOI is said at least one object to be classified and said NOI is all other detected objects; wherein said processor is configured as a Bayesian classifier to classify said object based upon said non-Boolean attribute using a non-linear probability function; wherein said processor is further configured to determine a number of times said value of said non-Boolean attribute is within a threshold range for said OOI classification; and wherein said processor is further configured to determine a number of times said value of said non-Boolean attribute is within a threshold range for said NOI classification. | 1. A classification system comprising: a memory device; a processor communicatively connected to said memory device; and an input communicatively connected to said processor, wherein said input is configured to receive data comprising at least one object that is to be classified as one of an object of interest (OOI) and a nuisance of interest (NOI) based upon at least one non-Boolean attribute of said object; wherein said at least one object that is to be classified is at least one of a headlight from an oncoming vehicle and a tail light of a leading vehicle; wherein said OOI is said at least one object to be classified and said NOI is all other detected objects; wherein said processor is configured as a Bayesian classifier to classify said object based upon said non-Boolean attribute using a non-linear probability function; wherein said processor is further configured to determine a number of times said value of said non-Boolean attribute is within a threshold range for said OOI classification; and wherein said processor is further configured to determine a number of times said value of said non-Boolean attribute is within a threshold range for said NOI classification. 3. The classification system of claim 1 , wherein said processor is further configured to compute a likelihood that said non-Boolean attribute occurs in an OOI based upon a value of said non-Boolean attribute. | 0.537611 |
9,489,381 | 14 | 16 | 14. A system for comparing a first document and a second document, comprising: at least one processor configured to execute: an asserter configuration module configured to receive, from a user, a request including custom rules defining at least one expected difference that indicates a positional change between at least one actual element of a first document and at least one expected element of a second document, the first document having a first structure defining a first hierarchical arrangement of elements within the first document that includes the at least one actual element positioned within the first structure, the second document having a second structure defining a second hierarchical arrangement of elements within the second document that includes the at least one expected element positioned within the second structure, the at least one expected difference including an expected structural difference between the first structure and the second structure, and the asserter configuration module configured to update the custom rules based at least in part on the received request; a document traversing module for traversing the first structure in the first document and the second structure in the second document to identify one or more potential matches between elements in the first and second documents, the document traversing module being configured to determine when differences between the elements are significant as defined by the custom rules, wherein significant differences are indicated by variations between the first and second document other than the at least one expected difference; and a custom error handler module for storing, for each of at least a subset of the one or more potential matches, information for any differences between matching elements that are determined to be significant, the custom error handler being configurable by a user to allow the user to specify how the differences are stored. | 14. A system for comparing a first document and a second document, comprising: at least one processor configured to execute: an asserter configuration module configured to receive, from a user, a request including custom rules defining at least one expected difference that indicates a positional change between at least one actual element of a first document and at least one expected element of a second document, the first document having a first structure defining a first hierarchical arrangement of elements within the first document that includes the at least one actual element positioned within the first structure, the second document having a second structure defining a second hierarchical arrangement of elements within the second document that includes the at least one expected element positioned within the second structure, the at least one expected difference including an expected structural difference between the first structure and the second structure, and the asserter configuration module configured to update the custom rules based at least in part on the received request; a document traversing module for traversing the first structure in the first document and the second structure in the second document to identify one or more potential matches between elements in the first and second documents, the document traversing module being configured to determine when differences between the elements are significant as defined by the custom rules, wherein significant differences are indicated by variations between the first and second document other than the at least one expected difference; and a custom error handler module for storing, for each of at least a subset of the one or more potential matches, information for any differences between matching elements that are determined to be significant, the custom error handler being configurable by a user to allow the user to specify how the differences are stored. 16. The system of claim 14 , wherein the custom error handling module is further able to collect statistics for the significant differences between a pair of elements in a potential match at a respective location over multiple instances of comparison between the first document and the second document. | 0.5 |
8,234,274 | 1 | 2 | 1. A method for characterizing a corpus of documents each having one or more links, comprising: forming a Bayesian network using the documents; determining a Bayesian network structure using the one or more links; generating a content link model where the model is a generative probabilistic model of the corpus along with citation information among documents, each document represented as a mixture over latent topics, and each relationship among documents is modeled by another generative process with a topic distribution of each document being a mixture of distributions associated with related documents; using a citation-topic (CT) model with a generative process for each word w in the document d in the corpus, with document probabilities Ξ, topic distribution matrix Ξ and word probabilities matrix Ξ¨, including: choosing a related document c from p (c|d,Ξ), a multinomial probability conditioned on the document d; choosing a topic z from the topic distribution of the document c, p(z|c,Ξ); choosing a word w which follows the multinomial distribution p(w|z,Ξ¨) conditioned on the topic z; and determining one or more topics in the corpus and topic distribution for each document wherein the content link model captures direct and indirect relationships represented by the links. | 1. A method for characterizing a corpus of documents each having one or more links, comprising: forming a Bayesian network using the documents; determining a Bayesian network structure using the one or more links; generating a content link model where the model is a generative probabilistic model of the corpus along with citation information among documents, each document represented as a mixture over latent topics, and each relationship among documents is modeled by another generative process with a topic distribution of each document being a mixture of distributions associated with related documents; using a citation-topic (CT) model with a generative process for each word w in the document d in the corpus, with document probabilities Ξ, topic distribution matrix Ξ and word probabilities matrix Ξ¨, including: choosing a related document c from p (c|d,Ξ), a multinomial probability conditioned on the document d; choosing a topic z from the topic distribution of the document c, p(z|c,Ξ); choosing a word w which follows the multinomial distribution p(w|z,Ξ¨) conditioned on the topic z; and determining one or more topics in the corpus and topic distribution for each document wherein the content link model captures direct and indirect relationships represented by the links. 2. The method of claim 1 , comprising applying a Bayesian inference to extract document topics. | 0.954371 |
7,895,198 | 4 | 6 | 4. The method of claim 1 , wherein said defining the query error as a difference between an optimal DCG function and an approximated version of a DCG comprises: defining the query error as max Ο β’ β i β U q β’ D β‘ ( Ο β‘ ( i ) ) β’ G β‘ ( y i ) - β j = 1 ND q β’ D β‘ ( j ) β’ β i β U q β’ G β‘ ( y i ) β’ h ij β i β U q β’ h ij where D(j)=a discount function, NDq=a number of documents in the set of documents for the query, Uq=the set of documents for the query, Ο=a permutation from Uq to 1-NDq, y i =a training relevance for document i, G(y i )=a mapping function for y i , and h ij =a continuous version of an indicator function where document i is ranked in a position j by the relevance function. | 4. The method of claim 1 , wherein said defining the query error as a difference between an optimal DCG function and an approximated version of a DCG comprises: defining the query error as max Ο β’ β i β U q β’ D β‘ ( Ο β‘ ( i ) ) β’ G β‘ ( y i ) - β j = 1 ND q β’ D β‘ ( j ) β’ β i β U q β’ G β‘ ( y i ) β’ h ij β i β U q β’ h ij where D(j)=a discount function, NDq=a number of documents in the set of documents for the query, Uq=the set of documents for the query, Ο=a permutation from Uq to 1-NDq, y i =a training relevance for document i, G(y i )=a mapping function for y i , and h ij =a continuous version of an indicator function where document i is ranked in a position j by the relevance function. 6. The method of claim 4 , further comprising: testing the relevance function having the determined values for the one or more coefficients. | 0.847162 |
9,990,441 | 22 | 23 | 22. The system of claim 20 , wherein the processors are further operable when executing the instructions to generate the index of keyword phrases through an extraction of keyword phrases from a set of posts authored by one or more second users of the online social network. | 22. The system of claim 20 , wherein the processors are further operable when executing the instructions to generate the index of keyword phrases through an extraction of keyword phrases from a set of posts authored by one or more second users of the online social network. 23. The system of claim 22 , wherein the set of posts comprises posts the first user has viewed. | 0.8125 |
8,954,374 | 1 | 10 | 1. A method for creating and enabling access to a community-augmented map, wherein the method comprises: uploading user-generated content, wherein said user-generated content comprises content pertaining to one or more locations on a map and content pertaining to a relationship between the one or more locations and one or more additional locations on a map, and wherein said user-generated content comprises multiple modalities; processing the user-generated content and storing the user-generated content in an intelligent knowledgebase; applying one or more existing items of user-generated content from the intelligent knowledgebase to the uploaded user-generated content to infer one or more characteristics of one or more locations on the map based on the content pertaining to the relationship between the one or more locations in the uploaded user-generated content and the one or more additional locations on a map, wherein said one or more inferred characteristics are inferred from one or more human-originated non-numeric relationships, derived via one or more human community members, of the one or more locations relative to one or more surrounding locations; retrieving the one or more inferred characteristics of the one or more locations on the map from the intelligent knowledgebase; augmenting metadata on the map based on the one or more inferred characteristics, wherein said augmenting comprises (i) providing real-time information about one or more locations on the map, (ii) identifying one or more traversable routes between two or more locations on the map, and (iii) super-imposing user-generated content pertaining to one or more locations on the one or more traversable routes; and outputting one of the one or more traversable routes in response to a query comprising a source location name and a destination location name, wherein said output traversable route comprises (i) a sequential list of multiple location names that represents a path traversal from the source location to the destination location and (ii) at least one inferred characteristic, from the one or more inferred characteristics, associated with each of the multiple location names in the sequential list. | 1. A method for creating and enabling access to a community-augmented map, wherein the method comprises: uploading user-generated content, wherein said user-generated content comprises content pertaining to one or more locations on a map and content pertaining to a relationship between the one or more locations and one or more additional locations on a map, and wherein said user-generated content comprises multiple modalities; processing the user-generated content and storing the user-generated content in an intelligent knowledgebase; applying one or more existing items of user-generated content from the intelligent knowledgebase to the uploaded user-generated content to infer one or more characteristics of one or more locations on the map based on the content pertaining to the relationship between the one or more locations in the uploaded user-generated content and the one or more additional locations on a map, wherein said one or more inferred characteristics are inferred from one or more human-originated non-numeric relationships, derived via one or more human community members, of the one or more locations relative to one or more surrounding locations; retrieving the one or more inferred characteristics of the one or more locations on the map from the intelligent knowledgebase; augmenting metadata on the map based on the one or more inferred characteristics, wherein said augmenting comprises (i) providing real-time information about one or more locations on the map, (ii) identifying one or more traversable routes between two or more locations on the map, and (iii) super-imposing user-generated content pertaining to one or more locations on the one or more traversable routes; and outputting one of the one or more traversable routes in response to a query comprising a source location name and a destination location name, wherein said output traversable route comprises (i) a sequential list of multiple location names that represents a path traversal from the source location to the destination location and (ii) at least one inferred characteristic, from the one or more inferred characteristics, associated with each of the multiple location names in the sequential list. 10. The method of claim 1 , further comprising facilitating one or more input modalities and one or more output modalities. | 0.908889 |
7,904,401 | 9 | 10 | 9. The apparatus of claim 1 wherein the reasoning engine generates the simplified ontology and compresses the original ontology with the simplified ontology, wherein the simplified ontology tracks changes in the original ontology, such that updates to said original ontology do not require re-generation of the simplified ontology; and wherein the reasoning engine reasons over the simplified ontology instead of the original ontology for executing the query. | 9. The apparatus of claim 1 wherein the reasoning engine generates the simplified ontology and compresses the original ontology with the simplified ontology, wherein the simplified ontology tracks changes in the original ontology, such that updates to said original ontology do not require re-generation of the simplified ontology; and wherein the reasoning engine reasons over the simplified ontology instead of the original ontology for executing the query. 10. The apparatus of claim 9 wherein the reasoning engine, responsive to receiving a query, determines a response to the query in conjunction with the simplified ontology. | 0.5 |
9,170,782 | 8 | 13 | 8. A method comprising: generating, by a source code editor, a display of editor suggestions based on program source code to be edited, the display of editor suggestions comprising a collection of entries; triggering, by the source code editor, execution of extension code as a result of the display of editor suggestions, wherein the extension code is distinct from the source code to be edited and is loaded from an external source into the source code editor, determining, by executing the extension code, that the display of editor suggestions is to be modified, and changing the display of editor suggestions upon determining by the extension code that the editor display is to be modified; and displaying, by the source code editor, the changed editor display. | 8. A method comprising: generating, by a source code editor, a display of editor suggestions based on program source code to be edited, the display of editor suggestions comprising a collection of entries; triggering, by the source code editor, execution of extension code as a result of the display of editor suggestions, wherein the extension code is distinct from the source code to be edited and is loaded from an external source into the source code editor, determining, by executing the extension code, that the display of editor suggestions is to be modified, and changing the display of editor suggestions upon determining by the extension code that the editor display is to be modified; and displaying, by the source code editor, the changed editor display. 13. The method of claim 8 , further comprising: receiving the extension code in a file comprising the source code. | 0.598592 |
9,215,212 | 11 | 19 | 11. A system of generating a representation of a plurality of learned rules from a learning engine of an application firewall based on a history of uniform resource locator (URL) communications with a web server, comprising: a learning engine of an application firewall, determining a plurality of learned rules based on a history of URL communications with a web server, each of the plurality of learned rules assigned a URL string, each URL string comprising a path to a resource; and a visualizer executing on a device, categorizing a subset of the plurality of learned rules under a first check type of a plurality of check types, generating a first tree representation of URL strings of the subset of learned rules, each node of the first tree representation corresponding to a segment of the URL strings identified based on application of a first delimiter to the URL strings to segment the URL strings into a first plurality of segments, each of the first plurality of URL strings comprising multiple segments identified based on application of the first selected delimiter, and generating, responsive to changing the first delimiter to a second selected delimiter for the same URL strings via the visualizer responsive to a user operating the visualizer, a second tree representation of the same URL strings of the subset of learned rules change, each node of the second tree corresponding to a segment of the URL strings identified based on application of the second selected delimiter to the URL strings to segment the URL strings into a second plurality of segments, the change allowing a visual comparison of hierarchical distributions of the first plurality of segments and the second plurality of segments between the first tree and the second tree, and distributions of the subset of learned rules corresponding to the first plurality of segments and the second plurality of segments. | 11. A system of generating a representation of a plurality of learned rules from a learning engine of an application firewall based on a history of uniform resource locator (URL) communications with a web server, comprising: a learning engine of an application firewall, determining a plurality of learned rules based on a history of URL communications with a web server, each of the plurality of learned rules assigned a URL string, each URL string comprising a path to a resource; and a visualizer executing on a device, categorizing a subset of the plurality of learned rules under a first check type of a plurality of check types, generating a first tree representation of URL strings of the subset of learned rules, each node of the first tree representation corresponding to a segment of the URL strings identified based on application of a first delimiter to the URL strings to segment the URL strings into a first plurality of segments, each of the first plurality of URL strings comprising multiple segments identified based on application of the first selected delimiter, and generating, responsive to changing the first delimiter to a second selected delimiter for the same URL strings via the visualizer responsive to a user operating the visualizer, a second tree representation of the same URL strings of the subset of learned rules change, each node of the second tree corresponding to a segment of the URL strings identified based on application of the second selected delimiter to the URL strings to segment the URL strings into a second plurality of segments, the change allowing a visual comparison of hierarchical distributions of the first plurality of segments and the second plurality of segments between the first tree and the second tree, and distributions of the subset of learned rules corresponding to the first plurality of segments and the second plurality of segments. 19. The system of claim 11 , wherein the visualizer determines, for the first tree representation, a regular expression representative of URL strings of learned rules associated with a node. | 0.723837 |
9,544,650 | 16 | 17 | 16. The system of claim 11 , wherein the hardware processor is further configured to: receive additional captioning content corresponding to the channel; update the captioning content associated with the channel by adding the additional captioning content and removing a portion of the captioning content; and determine whether the at least one of the plurality of keywords associated with the plurality of news documents is included in the updated captioning content. | 16. The system of claim 11 , wherein the hardware processor is further configured to: receive additional captioning content corresponding to the channel; update the captioning content associated with the channel by adding the additional captioning content and removing a portion of the captioning content; and determine whether the at least one of the plurality of keywords associated with the plurality of news documents is included in the updated captioning content. 17. The system of claim 16 , wherein the hardware processor is further configured to: rank the plurality of keywords based on the frequency, wherein the frequency is at least one of: a term frequency and an inverse document frequency; search for the ranked plurality of keywords in the updated captioning content; determine that at least a portion of the ranked plurality of keywords are found in the selected news document; determine that the selected news document matches the updated captioning content based on the ranked plurality of keywords found in the news document; and retrieve a news item that corresponds to the selected news document. | 0.5 |
9,367,766 | 10 | 16 | 10. A system comprising: a processor; a memory communicatively coupled to the processor and bearing instructions that, upon execution by the processor, cause the system to at least: segment a portion of an image into regions; select candidate text regions from the regions based on likelihoods of comprising text components; generate text lines between pairs of candidate text regions; assign a set of the text lines to a set of the candidate text regions, wherein the assignment is based on features of the candidate text regions, and wherein the assignment reduces a total number of the text lines; and generate a bounding box for a portion of text in the image based on the set of the text lines, the bounding box facilitating recognition of text in the image. | 10. A system comprising: a processor; a memory communicatively coupled to the processor and bearing instructions that, upon execution by the processor, cause the system to at least: segment a portion of an image into regions; select candidate text regions from the regions based on likelihoods of comprising text components; generate text lines between pairs of candidate text regions; assign a set of the text lines to a set of the candidate text regions, wherein the assignment is based on features of the candidate text regions, and wherein the assignment reduces a total number of the text lines; and generate a bounding box for a portion of text in the image based on the set of the text lines, the bounding box facilitating recognition of text in the image. 16. The system of claim 10 , wherein assigning a set of text lines to a set of candidate text regions comprises generate a model for assigning the set of the text lines to the set of the candidate text regions, wherein the model is configured to: select a first text line; measure first differences between features of the first text line and features of a text component; select a second text line; measure second differences between features of the second line and features of the text component; assign the first text line to the text component based on a determination that the first differences are smaller than the second differences; and remove the second text line. | 0.5 |
9,378,738 | 8 | 14 | 8. A system for determining turn order between a user and an interactive turn-taking spoken dialog system based on a result, the system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: receiving speech; and while continuing to receive the speech: identifying a starting point associated with the speech; identifying content of the speech received so far, to yield identified content; predicting a stability of the identified content; and identifying an end point associated with the speech, wherein the end point is a pinch node in a content lattice; and returning, a result based on the stability between the starting point and the end point. | 8. A system for determining turn order between a user and an interactive turn-taking spoken dialog system based on a result, the system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: receiving speech; and while continuing to receive the speech: identifying a starting point associated with the speech; identifying content of the speech received so far, to yield identified content; predicting a stability of the identified content; and identifying an end point associated with the speech, wherein the end point is a pinch node in a content lattice; and returning, a result based on the stability between the starting point and the end point. 14. The system of claim 8 , wherein the result comprises partial speech recognition. | 0.708333 |
8,266,594 | 1 | 4 | 1. A method for correcting semantic errors in code in an integrated development environment, said method comprising: inputting, using a code editor, code being developed in an integrated development environment; identifying, in a syntax tree constructed for said code inputted, one or more nodes containing semantic errors pertaining to use of a third-party library; choosing a node of said one or more nodes identified in said syntax tree constructed containing said semantic errors; displaying multiple suggestions for correcting said semantic errors identified for said node chosen, wherein each of said multiple suggestions include one or more executable code snippets associated with one or more collaboration records located for said node chosen, and wherein said one or more executable code snippets are submitted by peer developers to correct said semantic errors identified for said node chosen; displaying an executable code snippet configuration interface for configuring said one or more executable code snippets, wherein the executable code snippet configuration interface comprises: an input parameter active field for identifying input parameters that are required, by said node chosen, to be used with said one or more executable code snippets; an output parameter active field for identifying output parameters that are required, by said node chosen, to be used with said one or more executable code snippets; an input/output parameter active field for identifying input/output parameters that are required, by said node chosen, to be used with said one or more executable code snippets; and a return value active field for identifying a return value that is required, by said node chosen, to be returned by said one or more executable code snippets; selecting at least one executable code snippet from said one or more executable code snippets displayed for correcting said semantic errors identified for said node chosen; and executing said code inputted in said integrated development environment, wherein said at least one executable code snippet has been previously collected by said integrated development environment, and wherein said at least one executable code snippet selected is automatically invoked to correct said semantic errors identified for said node chosen. | 1. A method for correcting semantic errors in code in an integrated development environment, said method comprising: inputting, using a code editor, code being developed in an integrated development environment; identifying, in a syntax tree constructed for said code inputted, one or more nodes containing semantic errors pertaining to use of a third-party library; choosing a node of said one or more nodes identified in said syntax tree constructed containing said semantic errors; displaying multiple suggestions for correcting said semantic errors identified for said node chosen, wherein each of said multiple suggestions include one or more executable code snippets associated with one or more collaboration records located for said node chosen, and wherein said one or more executable code snippets are submitted by peer developers to correct said semantic errors identified for said node chosen; displaying an executable code snippet configuration interface for configuring said one or more executable code snippets, wherein the executable code snippet configuration interface comprises: an input parameter active field for identifying input parameters that are required, by said node chosen, to be used with said one or more executable code snippets; an output parameter active field for identifying output parameters that are required, by said node chosen, to be used with said one or more executable code snippets; an input/output parameter active field for identifying input/output parameters that are required, by said node chosen, to be used with said one or more executable code snippets; and a return value active field for identifying a return value that is required, by said node chosen, to be returned by said one or more executable code snippets; selecting at least one executable code snippet from said one or more executable code snippets displayed for correcting said semantic errors identified for said node chosen; and executing said code inputted in said integrated development environment, wherein said at least one executable code snippet has been previously collected by said integrated development environment, and wherein said at least one executable code snippet selected is automatically invoked to correct said semantic errors identified for said node chosen. 4. The method according to claim 1 , wherein said identifying further comprises: creating, using said one or more collaboration records located, visual indicators within said code editor for said one or more nodes containing said semantic errors identified. | 0.766788 |
6,167,411 | 2 | 4 | 2. A method in accordance with claim 1, wherein said receiving step comprises receiving at least one handwritten new character in the handwriting capture widget. | 2. A method in accordance with claim 1, wherein said receiving step comprises receiving at least one handwritten new character in the handwriting capture widget. 4. A method in accordance with claim 2, wherein said receiving step further comprises: capturing each handwritten new character as it is being written in the handwriting capture widget; and visually representing each captured handwritten new character in the handwriting capture widget as it is being written. | 0.778653 |
9,240,184 | 12 | 14 | 12. A system comprising: one or more processors; memory; and machine-readable instructions stored in the memory, that upon execution by the one or more processors cause the system to carry out operations comprising: transforming an audio input signal into a first sequence of feature vectors and a second sequence of feature vectors, wherein both the first and second sequences of feature vectors correspond in common to a sequence of temporal frames of the audio input signal, and wherein each respective feature vector of the first sequence and a corresponding respective feature vector of the second sequence bear quantitative measures of acoustic properties of a corresponding, respective temporal frame of the sequence of temporal frames of the audio input signal, processing the first sequence of feature vectors with a neural network (NN) implemented by the system to generate a NN-based set of emission probabilities for a plurality of hidden Markov models (HMMs) implemented by the system, processing the second sequence of feature vectors with a Gaussian mixture model (GMM) implemented by the system to generate a GMM-based set of emission probabilities for the plurality of HMMs, by computing, for each temporal frame, weighted sums of the NN-based emission probabilities and the GMM-based emission probabilities, merging the NN-based set of emission probabilities with the GMM-based set of emission probabilities to generate a merged set of emission probabilities for the plurality of HMMs, and applying the merged set of emission probabilities to the plurality of HMMs to determine speech content corresponding to the sequence of temporal frames of the audio input signal, wherein the weighted sums are computed according to weights computationally-determined by at least one processor during to a training process that minimizes a computationally-determined difference between computationally-predicted speech in training temporal frames and predetermined speech in the training temporal frames. | 12. A system comprising: one or more processors; memory; and machine-readable instructions stored in the memory, that upon execution by the one or more processors cause the system to carry out operations comprising: transforming an audio input signal into a first sequence of feature vectors and a second sequence of feature vectors, wherein both the first and second sequences of feature vectors correspond in common to a sequence of temporal frames of the audio input signal, and wherein each respective feature vector of the first sequence and a corresponding respective feature vector of the second sequence bear quantitative measures of acoustic properties of a corresponding, respective temporal frame of the sequence of temporal frames of the audio input signal, processing the first sequence of feature vectors with a neural network (NN) implemented by the system to generate a NN-based set of emission probabilities for a plurality of hidden Markov models (HMMs) implemented by the system, processing the second sequence of feature vectors with a Gaussian mixture model (GMM) implemented by the system to generate a GMM-based set of emission probabilities for the plurality of HMMs, by computing, for each temporal frame, weighted sums of the NN-based emission probabilities and the GMM-based emission probabilities, merging the NN-based set of emission probabilities with the GMM-based set of emission probabilities to generate a merged set of emission probabilities for the plurality of HMMs, and applying the merged set of emission probabilities to the plurality of HMMs to determine speech content corresponding to the sequence of temporal frames of the audio input signal, wherein the weighted sums are computed according to weights computationally-determined by at least one processor during to a training process that minimizes a computationally-determined difference between computationally-predicted speech in training temporal frames and predetermined speech in the training temporal frames. 14. The system of claim 12 , wherein the plurality of HMMs collectively comprise a multiplicity of states, wherein generating the NN-based set of emission probabilities for the plurality of the HMMs comprises: for each respective feature vector of the first sequence, determining, for each respective state of the multiplicity of states, a respective NN-based conditional probability of emitting the respective feature vector of the first sequence given the respective state, wherein generating the GMM-based set of emission probabilities for the plurality of the GMMs comprises: for each respective feature vector of the second sequence, determining, for each respective state of the multiplicity of states, a respective GMM-based conditional probability of emitting the respective feature vector of the second sequence given the respective state, and wherein computing, for each temporal frame, the weighted sums of the NN-based emission probabilities and the GMM-based emission probabilities comprises: for each pair of a respective feature vector of the first sequence and a corresponding respective feature vector of the second sequence, determining, for each respective state of the multiplicity, a weighted sum of the respective NN-based conditional probability and the respective GMM-based conditional probability. | 0.5 |
7,856,472 | 70 | 185 | 70. A computer program product embodied on a non-transitory computer readable medium, comprising: computer code for displaying at least one window in connection with a website; computer code for displaying, utilizing the at least one window, a stock-related field; computer code for receiving a plurality of characters of text from a user as the user is typing the text utilizing the stock-related field; computer code for dynamically determining, after the user types each character in the received text, whether the characters typed so far match one or more text strings in one of a plurality of n-tuples including at least two text strings, each of the plurality of n-tuples including first text representing a stock ticker symbol and second text representing a company name corresponding to the stock ticker symbol; computer code for indicating to the user that a match has been found, utilizing the at least one window, if it is determined that the characters typed so far match the one or more text strings in the one of the plurality of n-tuples; computer code for displaying, utilizing the at least one window, a plurality of message summaries; wherein the plurality of message summaries comprise first information derived from a first message of a plurality of first messages and second information derived from a second message of a plurality of second messages associated with at least one online forum; computer code for displaying, utilizing the at least one window, a first set of representations; computer code for receiving first input from the user indicating a selection of one of the first set of representations; computer code for displaying a second set of representations representing a set of hyperlinks, utilizing the at least one window, in response to receiving the first input; computer code for receiving second input from the user indicating a selection of one of the second set of representations; and computer code for navigating to a destination specified by the selected one of the second set of representations, in response to receiving the second input. | 70. A computer program product embodied on a non-transitory computer readable medium, comprising: computer code for displaying at least one window in connection with a website; computer code for displaying, utilizing the at least one window, a stock-related field; computer code for receiving a plurality of characters of text from a user as the user is typing the text utilizing the stock-related field; computer code for dynamically determining, after the user types each character in the received text, whether the characters typed so far match one or more text strings in one of a plurality of n-tuples including at least two text strings, each of the plurality of n-tuples including first text representing a stock ticker symbol and second text representing a company name corresponding to the stock ticker symbol; computer code for indicating to the user that a match has been found, utilizing the at least one window, if it is determined that the characters typed so far match the one or more text strings in the one of the plurality of n-tuples; computer code for displaying, utilizing the at least one window, a plurality of message summaries; wherein the plurality of message summaries comprise first information derived from a first message of a plurality of first messages and second information derived from a second message of a plurality of second messages associated with at least one online forum; computer code for displaying, utilizing the at least one window, a first set of representations; computer code for receiving first input from the user indicating a selection of one of the first set of representations; computer code for displaying a second set of representations representing a set of hyperlinks, utilizing the at least one window, in response to receiving the first input; computer code for receiving second input from the user indicating a selection of one of the second set of representations; and computer code for navigating to a destination specified by the selected one of the second set of representations, in response to receiving the second input. 185. The computer program product of claim 70 , wherein the computer program product is configured such that the displaying the plurality of message summaries is carried out utilizing the website, and the first message and the second message are capable of being accessed utilizing the website. | 0.626904 |
10,133,551 | 12 | 13 | 12. A computer program product for compressing at least one floating point number, comprising a tangible machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps: obtaining said at least one floating point number represented using one or more bits to indicate a sign of said at least one floating point number and one or more additional bits to indicate an exponent at a given base and a significand of said at least one floating point number, wherein said significand has a length equal to a number of bits between a most significant bit of said significand and a least significant bit of said significand having a predefined binary value; applying, using at least one processing device, a plurality of distinct prediction algorithms to said at least one floating point number to generate a corresponding plurality of predictions; selecting, using the at least one processing device, a given one of said plurality of distinct prediction algorithms for said at least one floating point number by evaluating a compression metric applied to said plurality of predictions; and encoding, using the at least one processing device, said at least one floating point number by encoding said exponent and said length as a single code using a residual generated by said selected prediction algorithm. | 12. A computer program product for compressing at least one floating point number, comprising a tangible machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps: obtaining said at least one floating point number represented using one or more bits to indicate a sign of said at least one floating point number and one or more additional bits to indicate an exponent at a given base and a significand of said at least one floating point number, wherein said significand has a length equal to a number of bits between a most significant bit of said significand and a least significant bit of said significand having a predefined binary value; applying, using at least one processing device, a plurality of distinct prediction algorithms to said at least one floating point number to generate a corresponding plurality of predictions; selecting, using the at least one processing device, a given one of said plurality of distinct prediction algorithms for said at least one floating point number by evaluating a compression metric applied to said plurality of predictions; and encoding, using the at least one processing device, said at least one floating point number by encoding said exponent and said length as a single code using a residual generated by said selected prediction algorithm. 13. The computer program product of claim 12 , wherein said compression metric is related to selecting said given one of said plurality of predictions that generates a residual of smallest magnitude and a largest bit saving in a number of bits to be encoded. | 0.516854 |
8,909,591 | 1 | 2 | 1. A computer implemented method for identifying business listings, the method comprising: determining, using one or more processors, a first frequency value of a business listing characteristic within a first plurality of business listings received from a first source, the first plurality of business listings being associated with a particular business listing context; determining, using the one or more processors, a second frequency value of the business listing characteristic within a second plurality of business listings received from a second source, the second plurality of business listings being associated with the particular business listing context; determining, using the one or more processors, a frequency differential between the first frequency value and the second frequency value; in response to the frequency differential exceeding a threshold differential, identifying, using the one or more processors, the business listing characteristic as a differential characteristic; and identifying, using the one or more processors, a particular business listing of the plurality of business listings as a spam listing using the differential characteristic. | 1. A computer implemented method for identifying business listings, the method comprising: determining, using one or more processors, a first frequency value of a business listing characteristic within a first plurality of business listings received from a first source, the first plurality of business listings being associated with a particular business listing context; determining, using the one or more processors, a second frequency value of the business listing characteristic within a second plurality of business listings received from a second source, the second plurality of business listings being associated with the particular business listing context; determining, using the one or more processors, a frequency differential between the first frequency value and the second frequency value; in response to the frequency differential exceeding a threshold differential, identifying, using the one or more processors, the business listing characteristic as a differential characteristic; and identifying, using the one or more processors, a particular business listing of the plurality of business listings as a spam listing using the differential characteristic. 2. The method of claim 1 , wherein the business listing context is a type of business. | 0.884409 |
4,742,516 | 14 | 15 | 14. A method for transmitting voice information from at least one broadcasting station to a plurality of receivers, comprising the steps of: composing information packets each of which includes a text portion including a sequence of words and symbols and a classification code signifying a substance of the text portion; converting the information packets into digital signals; transmitting one time the information packets substantially as digital signals via a transmitting medium in succession; receiving at at least one of said receivers having at least one classification code stored therein, said transmitted information packets, said stored classification codes being changed, added as a new classification code or deleted by a user for signifying the substance of the text portion the user wishes to receive; selecting received information packets with a classification code corresponding to the stored classification code at said at least one receiver; accumulating in said at least one receiver selected information packets having a classification code matching the classification code previously stored in said receiver; and conveying to a user information corresponding to the texts of the accumulated information packets by at least one of visually displaying said information and enunciating said information over a speaker at each receiver according to an output order determined by said at least one receiver. | 14. A method for transmitting voice information from at least one broadcasting station to a plurality of receivers, comprising the steps of: composing information packets each of which includes a text portion including a sequence of words and symbols and a classification code signifying a substance of the text portion; converting the information packets into digital signals; transmitting one time the information packets substantially as digital signals via a transmitting medium in succession; receiving at at least one of said receivers having at least one classification code stored therein, said transmitted information packets, said stored classification codes being changed, added as a new classification code or deleted by a user for signifying the substance of the text portion the user wishes to receive; selecting received information packets with a classification code corresponding to the stored classification code at said at least one receiver; accumulating in said at least one receiver selected information packets having a classification code matching the classification code previously stored in said receiver; and conveying to a user information corresponding to the texts of the accumulated information packets by at least one of visually displaying said information and enunciating said information over a speaker at each receiver according to an output order determined by said at least one receiver. 15. A method according to claim 14 wherein the step of composing comprises the step of composing information packets wherein said text portion comprises at least one of the following types of characters: the square forms of Japanese syllabary, alphabets, numerals, Chinese character, pronunciation symbols, an accent symbol, a blank symbol a, period and comma. | 0.5 |
8,160,362 | 8 | 13 | 8. At least one computer storage media storing computer-executable instructions that, when executed by a computer, cause the computer to perform a method for recognizing handwritten input data, the method comprising combining, in response to recognizing the handwritten input data, a first set of scores provided by an offline recognizer with a second set of scores provided by an online recognizer in response to recognizing the handwritten input data, the combining based on a repeated base learning algorithm. | 8. At least one computer storage media storing computer-executable instructions that, when executed by a computer, cause the computer to perform a method for recognizing handwritten input data, the method comprising combining, in response to recognizing the handwritten input data, a first set of scores provided by an offline recognizer with a second set of scores provided by an online recognizer in response to recognizing the handwritten input data, the combining based on a repeated base learning algorithm. 13. The at least one computer storage media of claim 8 further comprising returning, in response to the combining, a single result that represents the recognized handwritten input data. | 0.604701 |
9,031,840 | 11 | 13 | 11. 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, by one or more processors, audio data that encodes (i) a spoken natural language query and (ii) music; determining, by the one or more processors, that one or more keywords in a transcription of the spoken natural language query are associated with a movie content type; and based on determining that the one or more keywords in the transcription of the spoken natural query are associated with the movie content type, identifying, by the one or more processors, a movie content item that is recognized using the music. | 11. 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, by one or more processors, audio data that encodes (i) a spoken natural language query and (ii) music; determining, by the one or more processors, that one or more keywords in a transcription of the spoken natural language query are associated with a movie content type; and based on determining that the one or more keywords in the transcription of the spoken natural query are associated with the movie content type, identifying, by the one or more processors, a movie content item that is recognized using the music. 13. The system of claim 11 , wherein the audio data that encodes the music is generated within a predetermined period of time before receiving the audio data that encodes the spoken natural language query. | 0.685583 |
9,224,172 | 1 | 7 | 1. A method comprising: detecting, by a computing device, the presence of a first user of an online social network; delivering, by a computing device, content to the first user, the content allowing enhancement by the first user; receiving, by the computing device, one or more enhancements to the content by the first user; detecting, by the computing device, the presence of a second user on the social network, the second user different than the first user, the detecting the presence of the second user comprising detecting that a first client node to which the content may be displayed is geographically proximate to a second client node associated with the second user; determining, by the computing device, a social context of the second user, the social context comprising data associated with the second user with respect to the social network, the data associated with the second user with respect to the social network comprising data related to an interaction with the particular content by friends or contacts of the second user; modifying, by the computing device, the content, the modifying based on the determined social context of the second user and the one or more enhancements to the content by the first user; notifying, by the computing device, the second user that the content has been modified by the first user, the notifying comprising displaying at least a portion of the modified content; and delivering, by the computing device, the modified content to the second user, the delivering comprising displaying the modified content to the first client node in response to the determination that the first client node is geographically proximate to the second client node. | 1. A method comprising: detecting, by a computing device, the presence of a first user of an online social network; delivering, by a computing device, content to the first user, the content allowing enhancement by the first user; receiving, by the computing device, one or more enhancements to the content by the first user; detecting, by the computing device, the presence of a second user on the social network, the second user different than the first user, the detecting the presence of the second user comprising detecting that a first client node to which the content may be displayed is geographically proximate to a second client node associated with the second user; determining, by the computing device, a social context of the second user, the social context comprising data associated with the second user with respect to the social network, the data associated with the second user with respect to the social network comprising data related to an interaction with the particular content by friends or contacts of the second user; modifying, by the computing device, the content, the modifying based on the determined social context of the second user and the one or more enhancements to the content by the first user; notifying, by the computing device, the second user that the content has been modified by the first user, the notifying comprising displaying at least a portion of the modified content; and delivering, by the computing device, the modified content to the second user, the delivering comprising displaying the modified content to the first client node in response to the determination that the first client node is geographically proximate to the second client node. 7. The method of claim 1 , wherein the delivered modified content is based on at least one of a temporal context, a geographical context, and a behavioral context of at least one of the first user and the second user. | 0.78125 |
9,274,820 | 18 | 19 | 18. The system of claim 15 , wherein the operations caused by the translator further comprise: processing a qualified mnemonic in the computer program comprising the mnemonic and a qualification character; interpreting the qualified mnemonic according to the user defined definition for the mnemonic in response to previously processing a local qualifier command associating the qualification character with the user defined definition, wherein a use of the qualification character with the mnemonic local qualifier command specifying the user defined definition overrides any mnemonic command specifying the mnemonic and the translator definition which precedes the local qualifier command specifying the user defined definition; and interpreting the qualified mnemonic according to the translator definition for the mnemonic in response to previously processing a local qualifier command associating the qualification character with the translator definition, wherein a use of the qualification character with the mnemonic local qualifier command specifying the user translator definition overrides any mnemonic command specifying the mnemonic and the user defined definition which precedes the local qualifier command specifying the translator definition. | 18. The system of claim 15 , wherein the operations caused by the translator further comprise: processing a qualified mnemonic in the computer program comprising the mnemonic and a qualification character; interpreting the qualified mnemonic according to the user defined definition for the mnemonic in response to previously processing a local qualifier command associating the qualification character with the user defined definition, wherein a use of the qualification character with the mnemonic local qualifier command specifying the user defined definition overrides any mnemonic command specifying the mnemonic and the translator definition which precedes the local qualifier command specifying the user defined definition; and interpreting the qualified mnemonic according to the translator definition for the mnemonic in response to previously processing a local qualifier command associating the qualification character with the translator definition, wherein a use of the qualification character with the mnemonic local qualifier command specifying the user translator definition overrides any mnemonic command specifying the mnemonic and the user defined definition which precedes the local qualifier command specifying the translator definition. 19. The system of claim 18 , wherein the local qualifier command and the local mnemonic command are defined within a macro in the computer program. | 0.5 |
9,576,009 | 4 | 5 | 4. A method comprising: a processor defining a communication goal data structure, wherein the defined communication goal data structure is associated with a communication goal of describing a status for a subject, the defined communication goal data structure comprising (1) first data that is indicative of a content block data structure associated with the communication goal data structure, the associated content block data structure comprising a parameterized model for a plurality of data components that need to be analyzed to generate a narrative and a parameterized model for a plurality of computational components for analyzing the data components to generate a narrative, and (2) second data that is indicative of a plurality of communication goal parameters whose values are variable, wherein the plurality of communication goal parameters are used by the parameterized models of the associated content block data structure; and the processor storing the defined communication goal data structure in a memory such that the defined communication goal data structure is accessible for use by a processor when automatically generating a narrative about data based on input about a communication goal to be satisfied by the automatically generated narrative; and wherein the second data comprises: a subject metric parameter; and a time frame parameter. | 4. A method comprising: a processor defining a communication goal data structure, wherein the defined communication goal data structure is associated with a communication goal of describing a status for a subject, the defined communication goal data structure comprising (1) first data that is indicative of a content block data structure associated with the communication goal data structure, the associated content block data structure comprising a parameterized model for a plurality of data components that need to be analyzed to generate a narrative and a parameterized model for a plurality of computational components for analyzing the data components to generate a narrative, and (2) second data that is indicative of a plurality of communication goal parameters whose values are variable, wherein the plurality of communication goal parameters are used by the parameterized models of the associated content block data structure; and the processor storing the defined communication goal data structure in a memory such that the defined communication goal data structure is accessible for use by a processor when automatically generating a narrative about data based on input about a communication goal to be satisfied by the automatically generated narrative; and wherein the second data comprises: a subject metric parameter; and a time frame parameter. 5. The method of claim 4 wherein the second data further comprises: a change threshold parameter. | 0.5 |
9,552,429 | 19 | 20 | 19. The method of claim 15 , wherein the set of usage elements are compared to feedback associated with individual features to determine whether each usage element is compatible with the individual features. | 19. The method of claim 15 , wherein the set of usage elements are compared to feedback associated with individual features to determine whether each usage element is compatible with the individual features. 20. The method of claim 19 , wherein the rating further analyzes user attributes in view of the known limitations and benefits. | 0.5 |
9,288,156 | 1 | 7 | 1. A method for maintaining a plurality of dialog sessions of a multi-modal dialog application in a server, the method comprising: storing session state information for at least one suspended dialog session of a plurality of dialog sessions with the multi-modal dialog application in the server, the session state information including a unique identifier; reducing an amount of the session state information stored; releasing resources associated with at least the amount reduced; and automatically resuming the at least one suspended dialog session with the reduced session state information stored based on the unique identifier and a detected interaction with the multi-modal dialog application. | 1. A method for maintaining a plurality of dialog sessions of a multi-modal dialog application in a server, the method comprising: storing session state information for at least one suspended dialog session of a plurality of dialog sessions with the multi-modal dialog application in the server, the session state information including a unique identifier; reducing an amount of the session state information stored; releasing resources associated with at least the amount reduced; and automatically resuming the at least one suspended dialog session with the reduced session state information stored based on the unique identifier and a detected interaction with the multi-modal dialog application. 7. The method of claim 1 wherein the session state information includes one or more data structures including at least one of the following: (i) information related to a user, (ii) disambiguation information for the at least one suspended dialog session, (iii) a dialog history for the at least one suspended dialog session, wherein the dialog history is a reduced dialog history based on a disambiguation context for the at least one suspended dialog session (iv) information related to a middleware application communicatively coupled to the multi-modal dialog application wherein the middleware application provides the information related to the user, or (v) an identifier of at least one log file used by the at least one suspended dialog session, wherein the automatically resuming further includes writing to the at least one log file located based on the identifier. | 0.5 |
9,900,498 | 1 | 6 | 1. A glass-type mobile terminal, comprising: a frame body configured to be worn as glasses by a user; a display mounted on the frame body; a camera mounted on the frame body; and a controller configured to: detect user's emotion information and user's linguistic expression information on an object, and control the camera to automatically capture an image corresponding to the object currently viewed with the glass-type mobile terminal based on the linguistic expression information and the emotion information of the user on the currently viewed object, wherein the controller is configured to detect a user's facial expression when the user is viewing the object and output the detected facial expression as the emotion information on the object and to recognize an utterance of the user when the user is viewing the object, and detect the recognized utterance as the user's linguistic expression information, wherein the controller is further configured to detect a user's gazing time at the currently viewed object, store the detected gazing time to a memory together with the image corresponding to the currently viewed object, set an order of priority on the at least one image based on the user's gazing time with respect to the currently viewed object, and differently display the at least one image in the order of priority, and wherein the controller is further configured to: display the emotion information of the user, the image and the linguistic expression information of the user on the display, display a plurality of images related to the emotion information on the display when a user gazes at the emotion information for a preset time, and differently change a display size of each of the plurality of images according to an order of interest of the user. | 1. A glass-type mobile terminal, comprising: a frame body configured to be worn as glasses by a user; a display mounted on the frame body; a camera mounted on the frame body; and a controller configured to: detect user's emotion information and user's linguistic expression information on an object, and control the camera to automatically capture an image corresponding to the object currently viewed with the glass-type mobile terminal based on the linguistic expression information and the emotion information of the user on the currently viewed object, wherein the controller is configured to detect a user's facial expression when the user is viewing the object and output the detected facial expression as the emotion information on the object and to recognize an utterance of the user when the user is viewing the object, and detect the recognized utterance as the user's linguistic expression information, wherein the controller is further configured to detect a user's gazing time at the currently viewed object, store the detected gazing time to a memory together with the image corresponding to the currently viewed object, set an order of priority on the at least one image based on the user's gazing time with respect to the currently viewed object, and differently display the at least one image in the order of priority, and wherein the controller is further configured to: display the emotion information of the user, the image and the linguistic expression information of the user on the display, display a plurality of images related to the emotion information on the display when a user gazes at the emotion information for a preset time, and differently change a display size of each of the plurality of images according to an order of interest of the user. 6. The glass-type mobile terminal of claim 1 , wherein the controller is further configured to: control the camera to automatically capture the image of the object, based on one of the user's facial expression, eye blink, voice, heart rate, and brain waves. | 0.733954 |
8,505,094 | 7 | 22 | 7. A method of detecting a malicious URL, said method comprising: retrieving HTML code representing a parent Web page; scanning said HTML code and identifying at least one embedded URL of said HTML code, wherein it is unknown as to whether said embedded URL is malicious or not; determining a parent page ranking for said parent Web page and a child page ranking for a child web page identified by said embedded URL; determining referring features of said URL using said parent and child page rankings; producing a numerical referring vector that indicates the presence of said referring features; processing said referring layout vector using a classifier algorithm; and outputting a score from said classifier algorithm indicating the likelihood that said embedded URL of said HTML code is a malicious URL, wherein said score is based upon said parent and child page rankings. | 7. A method of detecting a malicious URL, said method comprising: retrieving HTML code representing a parent Web page; scanning said HTML code and identifying at least one embedded URL of said HTML code, wherein it is unknown as to whether said embedded URL is malicious or not; determining a parent page ranking for said parent Web page and a child page ranking for a child web page identified by said embedded URL; determining referring features of said URL using said parent and child page rankings; producing a numerical referring vector that indicates the presence of said referring features; processing said referring layout vector using a classifier algorithm; and outputting a score from said classifier algorithm indicating the likelihood that said embedded URL of said HTML code is a malicious URL, wherein said score is based upon said parent and child page rankings. 22. A method as recited in claim 7 wherein said parent and child page rankings reflect the popularity respectively of said Web page and of said Web site identified by said embedded URL. | 0.782864 |
9,967,285 | 4 | 13 | 4. A computer-implemented method, the method comprising: receive a request for providing regulatory compliance evidence, which is signed and maintained, for a virtual computing service provider, using at least one hardware processor and a memory configured with executable instructions; identify key words associated with the request for the regulatory compliance evidence, using the at least one hardware processor and the memory; categorize the key words associated with the request, using the least one hardware processor and the memory; map categorized key words associated with the request to a control list, maintained for compliance regulations, that is mapped to the regulatory compliance evidence for associating the regulatory compliance evidence with the request, using the at least one hardware processor and the memory; develop a confidence level for the regulatory compliance evidence that indicates a statistical probability that the regulatory compliance evidence matches the key words associated with the request, and a statistical regression model is used to create a machine learning model to establish the statistical probability by comparing currently mapped key words with previously mapped key words in previous requests, using the at least one hardware processor and the memory; and provide a response with a summary of the regulatory compliance evidence and a set of digital signatures, using the at least one hardware processor and the memory. | 4. A computer-implemented method, the method comprising: receive a request for providing regulatory compliance evidence, which is signed and maintained, for a virtual computing service provider, using at least one hardware processor and a memory configured with executable instructions; identify key words associated with the request for the regulatory compliance evidence, using the at least one hardware processor and the memory; categorize the key words associated with the request, using the least one hardware processor and the memory; map categorized key words associated with the request to a control list, maintained for compliance regulations, that is mapped to the regulatory compliance evidence for associating the regulatory compliance evidence with the request, using the at least one hardware processor and the memory; develop a confidence level for the regulatory compliance evidence that indicates a statistical probability that the regulatory compliance evidence matches the key words associated with the request, and a statistical regression model is used to create a machine learning model to establish the statistical probability by comparing currently mapped key words with previously mapped key words in previous requests, using the at least one hardware processor and the memory; and provide a response with a summary of the regulatory compliance evidence and a set of digital signatures, using the at least one hardware processor and the memory. 13. The method of claim 4 , further comprising executable instructions that provide the response with source documentation as the regulatory compliance evidence. | 0.783602 |
8,219,817 | 29 | 30 | 29. A method for verifying matching content between a plurality of text documents implemented with a numerical computation device, comprising: electronically obtaining an image document derived from a first electronic text document; electronically obtaining a first authentication signature generated from the first electronic text document; transforming the image document to a second electronic text document; generating a second authentication signature from the second electronic text document; and determining if the first and second authentication signatures meet a match criterion. | 29. A method for verifying matching content between a plurality of text documents implemented with a numerical computation device, comprising: electronically obtaining an image document derived from a first electronic text document; electronically obtaining a first authentication signature generated from the first electronic text document; transforming the image document to a second electronic text document; generating a second authentication signature from the second electronic text document; and determining if the first and second authentication signatures meet a match criterion. 30. The method according to claim 29 , wherein obtaining includes receiving. | 0.5 |
9,940,317 | 1 | 22 | 1. A sentence parsing correction system comprising a computer processor, a display operatively linked therewith, and a user action element to effectuate select user actions in relation to display objects of said display, via user control and action signals, said computer processor receiving an input sentence signal encoding characters comprising a sentence and generating therefrom a meta-tag listing of text of the sentence and providing, according to a preselected parsing algorithm, an initial meta-tag listing signal encoding a parsed version of a noun, a verb, and phrases of the sentence, said computer processor selectively configurable as a display element in furtherance of receiving said initial meta-tag listing signal, and producing a parsed image display signal for displaying upon said display, said parsed image display signal displayed upon said display such that the text of the sentence is arranged in original text order, and has, on successive lines, the noun, the verb, and phrases of the sentence with indentations specifying parsing relationships among them, said computer processor further selectively configurable as a format element in furtherance of receiving said parsed image display signal, and creating a unique expandable container image for each phrase of the phrases of the sentence in an expandable container format, said expandable container format encoded in an expandable container format signal, said computer further selectively configurable as a correction element in furtherance of receiving said expandable container format signal and said user control and action signals, selecting a display object of said display objects wherein said display object comprises a unique expandable container image of a phrase of the phrases of the sentence, and altering a property of said display object and/or a relationship for said display object relative to other display objects and thereafter providing a display signal creating an image in furtherance of producing a final parsed image display signal creating on said display an altered parsing characterized by a topology for and between each unique expandable container image relative to other unique expandable container images wherein the sentence text has, on successive lines, the noun, the verb, and prepositional phrases of the sentence with indentations specifying the altered parsing. | 1. A sentence parsing correction system comprising a computer processor, a display operatively linked therewith, and a user action element to effectuate select user actions in relation to display objects of said display, via user control and action signals, said computer processor receiving an input sentence signal encoding characters comprising a sentence and generating therefrom a meta-tag listing of text of the sentence and providing, according to a preselected parsing algorithm, an initial meta-tag listing signal encoding a parsed version of a noun, a verb, and phrases of the sentence, said computer processor selectively configurable as a display element in furtherance of receiving said initial meta-tag listing signal, and producing a parsed image display signal for displaying upon said display, said parsed image display signal displayed upon said display such that the text of the sentence is arranged in original text order, and has, on successive lines, the noun, the verb, and phrases of the sentence with indentations specifying parsing relationships among them, said computer processor further selectively configurable as a format element in furtherance of receiving said parsed image display signal, and creating a unique expandable container image for each phrase of the phrases of the sentence in an expandable container format, said expandable container format encoded in an expandable container format signal, said computer further selectively configurable as a correction element in furtherance of receiving said expandable container format signal and said user control and action signals, selecting a display object of said display objects wherein said display object comprises a unique expandable container image of a phrase of the phrases of the sentence, and altering a property of said display object and/or a relationship for said display object relative to other display objects and thereafter providing a display signal creating an image in furtherance of producing a final parsed image display signal creating on said display an altered parsing characterized by a topology for and between each unique expandable container image relative to other unique expandable container images wherein the sentence text has, on successive lines, the noun, the verb, and prepositional phrases of the sentence with indentations specifying the altered parsing. 22. The system of claim 1 wherein said computer processor is further selectively configurable as a conversion element receiving the final parsed image display signal and converting said signal into sets of mark-up tags in a linear text listing for the original sentence, and encoding the sets of mark-up tags into a final meta-tag listing signal. | 0.5 |
7,480,645 | 5 | 8 | 5. A method according to claim 1 of estimating the relevance of a document with respect to a concept, including a preliminary step of detecting ambiguous concepts, i.e. concepts having a plurality of semantic clouds with different meanings in the same semantic neighborhood. | 5. A method according to claim 1 of estimating the relevance of a document with respect to a concept, including a preliminary step of detecting ambiguous concepts, i.e. concepts having a plurality of semantic clouds with different meanings in the same semantic neighborhood. 8. A method according to claim 5 of estimating the relevance of a document with respect to a concept, wherein, the concept belonging to a knowledge base obtained by merging a first knowledge base with a second knowledge base, the preliminary step of detecting ambiguous concepts is executed during merging. | 0.5 |
7,873,904 | 5 | 6 | 5. The method as recited in claim 4 , wherein the visual theme comprises an ocean visual theme or a seascape visual theme. | 5. The method as recited in claim 4 , wherein the visual theme comprises an ocean visual theme or a seascape visual theme. 6. The method as recited in claim 5 , wherein displaying the set of most popular queries according to an ocean visual theme includes: displaying a map of at least part of the world on the ocean floor; displaying each popular query as a variable-size bubble in ocean water; displaying a life cycle of each popular query as a movement of each respective bubble toward the surface of the ocean water; and displaying a popularity of each bubble as a dynamically changing relative size of each bubble. | 0.5 |
8,954,349 | 2 | 3 | 2. The method of accessing and controlling a server-administered function according to claim 1 , wherein the step of invoking the server-administered function establishes text messaging dialogue between said server and the device that communicated the self-addressed routing direction. | 2. The method of accessing and controlling a server-administered function according to claim 1 , wherein the step of invoking the server-administered function establishes text messaging dialogue between said server and the device that communicated the self-addressed routing direction. 3. The method of accessing and controlling a server-administered function according to claim 2 , wherein the text message dialogue is bi-directional between both said server and the device. | 0.5 |
9,836,192 | 1 | 7 | 1. A system to facilitate a graphical user interface in a computing environment, the system comprising a computing device configured to execute computer code in order to: interact with a graphical user interface (GUI) in a window, the GUI comprising at least one user interface (UI) object accessible using a mouse; receive a first command from a user to position a mouse cursor at a location in the window of the UI object in preparation for receiving a second command from the user to generate a marker representing the UI object, wherein the marker comprises a marker label; receive the second command from the user; display the marker label proximate the location of the UI object in the window; and receive a voice command from the user that identifies the marker label, and in response to the voice command, generate an input representing the user interfacing with the UI object. | 1. A system to facilitate a graphical user interface in a computing environment, the system comprising a computing device configured to execute computer code in order to: interact with a graphical user interface (GUI) in a window, the GUI comprising at least one user interface (UI) object accessible using a mouse; receive a first command from a user to position a mouse cursor at a location in the window of the UI object in preparation for receiving a second command from the user to generate a marker representing the UI object, wherein the marker comprises a marker label; receive the second command from the user; display the marker label proximate the location of the UI object in the window; and receive a voice command from the user that identifies the marker label, and in response to the voice command, generate an input representing the user interfacing with the UI object. 7. The system as recited in claim 1 , wherein the computing device is further configured to execute computer code in order to: generate an overlay comprising a plurality of markers, wherein each marker comprises a marker label; and display the marker labels of the overlay in the window in response to receiving a third command from the user. | 0.5 |
8,725,723 | 3 | 5 | 3. The method of claim 1 , wherein the analyzing of relevance between candidate search terms for which the peaks have occurred together with at least one of the search terms in the period as the result of the comparison and filtering out the candidate search term having no relevance comprises: analyzing the candidate search terms for which the peaks have occurred together in the period as the result of the comparison and determining whether relevance exists between the candidate search terms and the search terms; and filtering out a candidate search term having no relevance from the candidate search terms as the result of the determination. | 3. The method of claim 1 , wherein the analyzing of relevance between candidate search terms for which the peaks have occurred together with at least one of the search terms in the period as the result of the comparison and filtering out the candidate search term having no relevance comprises: analyzing the candidate search terms for which the peaks have occurred together in the period as the result of the comparison and determining whether relevance exists between the candidate search terms and the search terms; and filtering out a candidate search term having no relevance from the candidate search terms as the result of the determination. 5. The method of claim 3 , wherein the determining of whether relevance exists between the candidate search terms comprises measuring a number of search sessions in which the search terms are inputted and a number of search sessions in which a pair of search terms comprising at least one search term are inputted, and determining whether correlation exists between the candidate search terms and at least one of the search terms. | 0.600372 |
8,369,967 | 11 | 12 | 11. A method for controlling an alarm system, comprising: providing a packet data network interface port; communicating, by an automated controller, with security alarm sensors through a security alarm system interface port; receiving, by the automated controller, at least one input from the security alarm system interface port; processing, by the automated controller, the at least one input to determine an alarm condition; and communicating, by the automated controller, information defining a markup language interface comprising the alarm condition through the packet data network interface port. | 11. A method for controlling an alarm system, comprising: providing a packet data network interface port; communicating, by an automated controller, with security alarm sensors through a security alarm system interface port; receiving, by the automated controller, at least one input from the security alarm system interface port; processing, by the automated controller, the at least one input to determine an alarm condition; and communicating, by the automated controller, information defining a markup language interface comprising the alarm condition through the packet data network interface port. 12. The method according to claim 11 , wherein the packet data network interface port is configured to communicate with the Internet. | 0.730769 |
8,230,062 | 5 | 6 | 5. The method of claim 1 , wherein the step (a) includes crawling blog posts, which contain at least one link to the client website. | 5. The method of claim 1 , wherein the step (a) includes crawling blog posts, which contain at least one link to the client website. 6. The method of claim 5 , wherein the step (c) comprises collecting measurements of traffic referred from a subset of the crawled blog posts. | 0.5 |
6,161,130 | 8 | 10 | 8. The method in claim 4 further comprising the steps of: detecting whether each of a first group of predefined handcrafted features exists in the incoming message so as to yield first output data; analyzing text in the incoming message so as to break the text into a plurality of constituent tokens; ascertaining, using a word-oriented indexer and in response to said tokens, whether each of a second group of predefined word-oriented features exists in the incoming message so as to yield second output data, said first and second groups collectively defining an n-element feature space (where n is an integer greater than N); forming, in response to the first and second output data, an N-element feature vector which specifies whether each of said N features exists in the incoming message; and applying the feature vector as input to the probabilistic classifier so as to yield the output confidence level for the incoming message. | 8. The method in claim 4 further comprising the steps of: detecting whether each of a first group of predefined handcrafted features exists in the incoming message so as to yield first output data; analyzing text in the incoming message so as to break the text into a plurality of constituent tokens; ascertaining, using a word-oriented indexer and in response to said tokens, whether each of a second group of predefined word-oriented features exists in the incoming message so as to yield second output data, said first and second groups collectively defining an n-element feature space (where n is an integer greater than N); forming, in response to the first and second output data, an N-element feature vector which specifies whether each of said N features exists in the incoming message; and applying the feature vector as input to the probabilistic classifier so as to yield the output confidence level for the incoming message. 10. The method in claim 8 wherein the classes comprise a plurality of sub-classes and said one class is one of said sub-classes. | 0.761194 |
7,609,881 | 10 | 11 | 10. An image processing method according to the claim 9 , wherein said setting up step includes setting up an overlaying sequence to overlay unfilled closed areas in front of filled closed area. | 10. An image processing method according to the claim 9 , wherein said setting up step includes setting up an overlaying sequence to overlay unfilled closed areas in front of filled closed area. 11. An image processing method according to the claim 10 , wherein said step of recognizing attributes includes recognizing whether each extracted image areas is a line area that does not form any closed areas, and wherein said setting step includes setting overlaying sequence to overlay line areas in front of filled closed areas. | 0.5 |
7,739,658 | 17 | 21 | 17. Apparatus as claimed in claim 16 , wherein the authoring means comprises means for receiving data, and data conversion means for converting the data into a set of data objects each containing a respective version of the data suited to technical attributes of a respective device type. | 17. Apparatus as claimed in claim 16 , wherein the authoring means comprises means for receiving data, and data conversion means for converting the data into a set of data objects each containing a respective version of the data suited to technical attributes of a respective device type. 21. A method as claimed in claim 17 , wherein the set of data objects further comprises multiple versions corresponding to a set of available user preferences; and further comprising: determining a user preference from the request message whereby the selecting step selects the data object according to the user preference. | 0.5 |
7,917,838 | 9 | 11 | 9. A data file tangibly embodied on a computer-readable storage medium, comprising: a header portion containing an index portion; a first data type located near a terminus of the data file at a starting location referenced by the index portion, wherein the first data type is structured to be readable by a tool that ignores data in the data file after an end of file (EOF) marker in the data file, the first data type corresponding to compressed image data; and a second data type located between the header and the first data type, having the EOF marker at its terminus, wherein the second data type comprises a text file contextually associated with said first data type; and wherein the content of the data file are arranged to be compatible with Group-4 Tagged Image File Format (TIFF) specifications. | 9. A data file tangibly embodied on a computer-readable storage medium, comprising: a header portion containing an index portion; a first data type located near a terminus of the data file at a starting location referenced by the index portion, wherein the first data type is structured to be readable by a tool that ignores data in the data file after an end of file (EOF) marker in the data file, the first data type corresponding to compressed image data; and a second data type located between the header and the first data type, having the EOF marker at its terminus, wherein the second data type comprises a text file contextually associated with said first data type; and wherein the content of the data file are arranged to be compatible with Group-4 Tagged Image File Format (TIFF) specifications. 11. The data file of claim 9 , wherein a format of the second data is a proprietary format. | 0.821569 |
7,885,952 | 4 | 5 | 4. The system of claim 1 , further comprising a market value component configured to evaluate a market value of the at least one term, wherein the term score is based at least in part upon the market value. | 4. The system of claim 1 , further comprising a market value component configured to evaluate a market value of the at least one term, wherein the term score is based at least in part upon the market value. 5. The system of claim 4 , wherein the market value is based at least in part upon web advertisement monetization data. | 0.5 |
7,636,855 | 32 | 33 | 32. The method of claim 24 , further comprising enrolling a user by assigning a selection criterion from at least one of the following categories: (a) pass-phrases containing a given letter; (b) pass-phrases which rhyme with a given word; (c) pass-phrases selected from a list; (d) pass-phrases matching a semantic criterion; (e) pass-phrases containing double letters; (f) pass-phrases located in a specific position in a prompt; (g) pass-phrases having a relationship of at least one of alphabetical order or numerical value with respect to other pass-phrases in a prompt; (h) pass-phrases belonging to a specific class according to a pass-phrase ontology; (i) pass-phrases immediately following a pass-phrase matching one of the preceding criteria; or (j) combinations of the above criteria. | 32. The method of claim 24 , further comprising enrolling a user by assigning a selection criterion from at least one of the following categories: (a) pass-phrases containing a given letter; (b) pass-phrases which rhyme with a given word; (c) pass-phrases selected from a list; (d) pass-phrases matching a semantic criterion; (e) pass-phrases containing double letters; (f) pass-phrases located in a specific position in a prompt; (g) pass-phrases having a relationship of at least one of alphabetical order or numerical value with respect to other pass-phrases in a prompt; (h) pass-phrases belonging to a specific class according to a pass-phrase ontology; (i) pass-phrases immediately following a pass-phrase matching one of the preceding criteria; or (j) combinations of the above criteria. 33. The method of claim 32 , further comprising permitting the user to specify at least one of: (a) a letter that must be contained in a selected pass-phrase in accordance with a selection criterion assigned to the user; (b) a word with which a selected pass-phrase must rhyme in accordance with a selection criterion assigned to the user; (c) a specific position within a prompt in which a selected pass-phrase must be located in accordance with a selection criterion assigned to the user; (d) a relationship of at least one of alphabetical order or numerical value with respect to other pass-phrases in a prompt that a selected pass-phrase must exhibit in accordance with a selection criterion assigned to the user; or (e) a specific class to which a selected pass-phrase must belong in a pass-phrase ontology in accordance with a selection criterion assigned to the user. | 0.5 |
9,208,220 | 6 | 9 | 6. An apparatus comprising: one or more processors; memory; a parsing module stored in the memory and executable by the one or more processors that parses a text into one or more words; a filter module stored in the memory and executable by the one or more processors that filters the one or more words to provide one or more filtered words prior to classification of the text, wherein the filter module filters a word of the one or more words which frequencies of appearance vary across a plurality of categories at an amount less than a first variation threshold and greater than a second variation threshold, the second variation threshold being greater than zero; a query module stored in the memory and executable by the one or more processors that determines a word vector in a pre-constructed spherical space model for each filtered word of the one or more filtered words, a number of dimensions of the spherical space model being equal to a number of the plurality of categories; a calculation module stored in the memory and executable by the one or more processors that determines a distance between a sum of word vectors of the one or more filtered words and a respective category vector for each category, and accumulates normalized word frequency values of the one or more filtered words to obtain a normalized word vector sum; and a classification module stored in the memory and executable by the one or more processors that classifies the text into one or more categories based on the determined distance of each category, the one or more categories corresponding to a largest component of the normalized word vector sum. | 6. An apparatus comprising: one or more processors; memory; a parsing module stored in the memory and executable by the one or more processors that parses a text into one or more words; a filter module stored in the memory and executable by the one or more processors that filters the one or more words to provide one or more filtered words prior to classification of the text, wherein the filter module filters a word of the one or more words which frequencies of appearance vary across a plurality of categories at an amount less than a first variation threshold and greater than a second variation threshold, the second variation threshold being greater than zero; a query module stored in the memory and executable by the one or more processors that determines a word vector in a pre-constructed spherical space model for each filtered word of the one or more filtered words, a number of dimensions of the spherical space model being equal to a number of the plurality of categories; a calculation module stored in the memory and executable by the one or more processors that determines a distance between a sum of word vectors of the one or more filtered words and a respective category vector for each category, and accumulates normalized word frequency values of the one or more filtered words to obtain a normalized word vector sum; and a classification module stored in the memory and executable by the one or more processors that classifies the text into one or more categories based on the determined distance of each category, the one or more categories corresponding to a largest component of the normalized word vector sum. 9. The apparatus as recited in claim 6 , wherein the filter module is further configured to retain another word of the one or more words which frequencies of appearance vary across the plurality of categories at an amount greater than the first variation threshold. | 0.576677 |
9,600,231 | 1 | 3 | 1. A computer-implemented method for detecting a wakeword in a spoken utterance using a trained classifier, the method comprising: determining, for each of a first plurality of audio feature dimensions, a respective utility of each audio feature dimension for classifying audio data as including or not including a wakeword; determining a second plurality of audio feature dimensions using the respective utilities, wherein the second plurality of audio feature dimensions is a subset of the first plurality of audio feature dimensions; configuring a first support vector machine (SVM) classifier to classify whether input audio data includes a wakeword, the first SVM classifier comprising a first plurality of support vectors, each of the first plurality of support vectors including a value for each of the second plurality of audio feature dimensions; determining a positive pair of support vectors in the first plurality of support vectors, each support vector of the positive pair of support vectors being on a positive side of a binary classification of the first SVM classifier; combining the positive pair of support vectors into a first combined support vector; determining a negative pair of support vectors in the first plurality of support vectors, each support vector of the negative pair of support vectors being on a negative side of the binary classification of the first SVM classifier; combining the negative pair of support vectors into a second combined support vector; creating additional combined support vectors by alternating between combining a positive pair of support vectors and combining a negative pair of support vectors; determining a second plurality of support vectors including at least the first combined support vector, the second combined support vector, and the additional combined support vectors; configuring a second SVM classifier comprising the second plurality of support vectors; and determining, using the second SVM classifier, that first audio data includes a representation of a first wakeword. | 1. A computer-implemented method for detecting a wakeword in a spoken utterance using a trained classifier, the method comprising: determining, for each of a first plurality of audio feature dimensions, a respective utility of each audio feature dimension for classifying audio data as including or not including a wakeword; determining a second plurality of audio feature dimensions using the respective utilities, wherein the second plurality of audio feature dimensions is a subset of the first plurality of audio feature dimensions; configuring a first support vector machine (SVM) classifier to classify whether input audio data includes a wakeword, the first SVM classifier comprising a first plurality of support vectors, each of the first plurality of support vectors including a value for each of the second plurality of audio feature dimensions; determining a positive pair of support vectors in the first plurality of support vectors, each support vector of the positive pair of support vectors being on a positive side of a binary classification of the first SVM classifier; combining the positive pair of support vectors into a first combined support vector; determining a negative pair of support vectors in the first plurality of support vectors, each support vector of the negative pair of support vectors being on a negative side of the binary classification of the first SVM classifier; combining the negative pair of support vectors into a second combined support vector; creating additional combined support vectors by alternating between combining a positive pair of support vectors and combining a negative pair of support vectors; determining a second plurality of support vectors including at least the first combined support vector, the second combined support vector, and the additional combined support vectors; configuring a second SVM classifier comprising the second plurality of support vectors; and determining, using the second SVM classifier, that first audio data includes a representation of a first wakeword. 3. The computer-implemented method of claim 1 , wherein the determining the second plurality of support vectors comprises: combining the first combined support vector and the second combined support vector into a third combined support vector by: mapping the first combined support vector to high dimensional space to obtain a first mapped support vector; mapping the second combined support vector to high dimensional space to obtain a second mapped support vector; multiplying the first mapped support vector by a first weight to determine a first weighted mapped support vector; multiplying the second mapped support vector by a second weight to determine a second weighted mapped support vector; adding the first weighted mapped support vector to the second weighted mapped support vector to determine a merged support vector; and mapping the merged support vector to the feature space to determine the third combined support vector. | 0.5 |
10,115,389 | 1 | 4 | 1. A speech synthesis method, comprising: processing a text, on an electronic device comprising one or more processors and memory, to obtain a to-be-synthesized text, wherein processing the text comprises performing punctuation and sentence segmentation, part-of-speech tagging, numeric character processing, pinyin annotation, and rhythm and pause prediction processing for the text; if a network connection exists, sending the to-be-synthesized text to an online speech synthesis system for speech synthesis; and if a fault occurs in the online speech synthesis system in a process in which the online speech synthesis system performs speech synthesis or the network connection is disrupted in an actual use process, sending a text for which the online speech synthesis system has not completed speech synthesis to an offline speech synthesis system for speech synthesis. | 1. A speech synthesis method, comprising: processing a text, on an electronic device comprising one or more processors and memory, to obtain a to-be-synthesized text, wherein processing the text comprises performing punctuation and sentence segmentation, part-of-speech tagging, numeric character processing, pinyin annotation, and rhythm and pause prediction processing for the text; if a network connection exists, sending the to-be-synthesized text to an online speech synthesis system for speech synthesis; and if a fault occurs in the online speech synthesis system in a process in which the online speech synthesis system performs speech synthesis or the network connection is disrupted in an actual use process, sending a text for which the online speech synthesis system has not completed speech synthesis to an offline speech synthesis system for speech synthesis. 4. The method according to claim 1 , further comprising: after the speech synthesis is completed, concatenating speech data of the online speech synthesis system and speech data of the offline speech synthesis system, to obtain complete speech synthesis data. | 0.7986 |
8,032,305 | 14 | 17 | 14. A computer-readable recording medium on which a program is recorded, said program causing a computer system to perform a method for generating a cluster of child spliced sequences from a parent base sequence, said program causing said computer system to perform the steps of: recording a plurality of spliced sequences, wherein each spliced sequence is a query sequence; comparing a spliced edit distance between each query sequence and a parent base sequence read from a database with a predetermined maximum acceptable value; electing spliced sequences from the database for which the spliced edit distance between each query sequence and the parent base sequence is not more than said maximum acceptable value to generate a first cluster of child spliced sequences and recording said first cluster in storage means; and generating a second cluster of child spliced sequences comprising spliced pairs between each query sequence and the base sequence with a defined splice length for the base sequence and applying spliced alignment between each query sequence and the first cluster as aligned with the base sequence, wherein the degree of spliced alignment between each query sequence and the base sequence is determined by the number of spliced pairs of the second cluster at the defined splice length. | 14. A computer-readable recording medium on which a program is recorded, said program causing a computer system to perform a method for generating a cluster of child spliced sequences from a parent base sequence, said program causing said computer system to perform the steps of: recording a plurality of spliced sequences, wherein each spliced sequence is a query sequence; comparing a spliced edit distance between each query sequence and a parent base sequence read from a database with a predetermined maximum acceptable value; electing spliced sequences from the database for which the spliced edit distance between each query sequence and the parent base sequence is not more than said maximum acceptable value to generate a first cluster of child spliced sequences and recording said first cluster in storage means; and generating a second cluster of child spliced sequences comprising spliced pairs between each query sequence and the base sequence with a defined splice length for the base sequence and applying spliced alignment between each query sequence and the first cluster as aligned with the base sequence, wherein the degree of spliced alignment between each query sequence and the base sequence is determined by the number of spliced pairs of the second cluster at the defined splice length. 17. The computer-readable recording medium of claim 14 , wherein the storage means are selected from computer memory and a computer hard disk. | 0.871841 |
8,239,366 | 45 | 46 | 45. The at least one tangible computer readable medium of claim 34 , wherein generating the first of the at least two search queries further comprises performing speech recognition on the voice input using a language model different from the first language model. | 45. The at least one tangible computer readable medium of claim 34 , wherein generating the first of the at least two search queries further comprises performing speech recognition on the voice input using a language model different from the first language model. 46. The at least one tangible computer readable medium of claim 45 , wherein the language model different from the first language model is a general language model. | 0.5 |
8,078,645 | 1 | 10 | 1. A method implemented on a machine having at least one processor, storage, and communication platform, comprising: traversing through a data stream containing a plurality of name-value pairs organized in a multi-level nested data structure; and for each name-value pair encountered in the multi-level nested data structure, if the name-value pair is located at two levels outside an innermost level, then constructing a new table as a current table corresponding to the name-value pair, if the name-value pair is located at one level outside the innermost level, then constructing a new row within the current table as a current row corresponding to the name-value pair, and if the name-value pair is located at the innermost level, then adding the name-value pair to the current row of the current table as a field. | 1. A method implemented on a machine having at least one processor, storage, and communication platform, comprising: traversing through a data stream containing a plurality of name-value pairs organized in a multi-level nested data structure; and for each name-value pair encountered in the multi-level nested data structure, if the name-value pair is located at two levels outside an innermost level, then constructing a new table as a current table corresponding to the name-value pair, if the name-value pair is located at one level outside the innermost level, then constructing a new row within the current table as a current row corresponding to the name-value pair, and if the name-value pair is located at the innermost level, then adding the name-value pair to the current row of the current table as a field. 10. A method as recited in claim 1 , further comprising: discarding each name-value pair encountered that is located at more than two levels outside the innermost level. | 0.821353 |
4,862,504 | 1 | 3 | 1. A speech synthesis system comprising: a character analyzing means for analyzing a series of input characters to generate a series of syllabic symbols and a series of rhythmic symbols according to the series of input characters; a plurality of parameter file means for storing speech parameters determined by taking into consideration an influence of immediately preceding vowels of the syllabic symbols; a speech parameter generating means for generating a series of speech parameters by combining speech parameters obtained from said parameter file means in accordance with a determined vowel immediately preceding a syllabic symbol of said series of syllabic symbols; rhythmic parameter generating means for generating a series of rhythmic parameters according to the series of rhythmic symbols supplied from said character analyzing means; and a speech synthesizing means for synthesizing said series of speech parameters and said series of rhythmic parameters. | 1. A speech synthesis system comprising: a character analyzing means for analyzing a series of input characters to generate a series of syllabic symbols and a series of rhythmic symbols according to the series of input characters; a plurality of parameter file means for storing speech parameters determined by taking into consideration an influence of immediately preceding vowels of the syllabic symbols; a speech parameter generating means for generating a series of speech parameters by combining speech parameters obtained from said parameter file means in accordance with a determined vowel immediately preceding a syllabic symbol of said series of syllabic symbols; rhythmic parameter generating means for generating a series of rhythmic parameters according to the series of rhythmic symbols supplied from said character analyzing means; and a speech synthesizing means for synthesizing said series of speech parameters and said series of rhythmic parameters. 3. A system according to claim 1, further including means for linearly interpolating connecting portions of the speech parameters sequentially derived from said parameter files in correspondence with the series of input characters. | 0.633333 |
7,774,746 | 1 | 5 | 1. A method of generating code, comprising: receiving a specification of one or more translation patterns; using at least one of the one or more translation patterns to generate using a processor at least a portion of a first code associated with a first translator, wherein the first translator is configured to create a target object model, including by populating one or more elements of the target object model in a processing order at least in part associated with an order of elements in at least one of the one or more translation patterns; using at least one of the one or more translation patterns to generate at least a portion of a second code associated with a second translator; and connecting together the first translator and the second translator to form at least a portion of a converter. | 1. A method of generating code, comprising: receiving a specification of one or more translation patterns; using at least one of the one or more translation patterns to generate using a processor at least a portion of a first code associated with a first translator, wherein the first translator is configured to create a target object model, including by populating one or more elements of the target object model in a processing order at least in part associated with an order of elements in at least one of the one or more translation patterns; using at least one of the one or more translation patterns to generate at least a portion of a second code associated with a second translator; and connecting together the first translator and the second translator to form at least a portion of a converter. 5. A method as recited in claim 1 , wherein receiving the specification of the one or more translation patterns includes selecting one or more patterns from a library of previously created translation patterns. | 0.709945 |
7,917,528 | 8 | 15 | 8. A search system, comprising: a search engine to provide a search interface to a client, the search interface to facilitate entry of a search query, to display the search query in a search field in a user interface, and to receive a refinement request comprising context associated with the refinement request; a refinement store to store a plurality of refinements based upon the search query; a tokenization engine to tokenize the search query into two or more query tokens, wherein the refinement request is determined based on an indication of a user selection, the user selection being in the search field and being a selection of at least one and less than all of the query tokens, and the context is determined based on the selected query tokens and one or more unselected query tokens of the query; a refinement engine to retrieve one or more refinements from the refinement store and to process the retrieved refinements to prepare the retrieved refinements for filtering or reordering, the retrieved refinements for the selected query tokens, where the refinement engine is configured to perform operations comprising: recording votes on each of the one or more refinements, the votes corresponding to each word break in a region of overlap between the query tokens in the search query and refinement tokens in the refinement; and ordering the one or more refinements based on recorded votes; and the search engine to serve the retrieved refinements. | 8. A search system, comprising: a search engine to provide a search interface to a client, the search interface to facilitate entry of a search query, to display the search query in a search field in a user interface, and to receive a refinement request comprising context associated with the refinement request; a refinement store to store a plurality of refinements based upon the search query; a tokenization engine to tokenize the search query into two or more query tokens, wherein the refinement request is determined based on an indication of a user selection, the user selection being in the search field and being a selection of at least one and less than all of the query tokens, and the context is determined based on the selected query tokens and one or more unselected query tokens of the query; a refinement engine to retrieve one or more refinements from the refinement store and to process the retrieved refinements to prepare the retrieved refinements for filtering or reordering, the retrieved refinements for the selected query tokens, where the refinement engine is configured to perform operations comprising: recording votes on each of the one or more refinements, the votes corresponding to each word break in a region of overlap between the query tokens in the search query and refinement tokens in the refinement; and ordering the one or more refinements based on recorded votes; and the search engine to serve the retrieved refinements. 15. The system of claim 8 , wherein the refinement engine identifies the region of overlap between a set of query tokens comprising the search query and a set of refinement tokens comprising a selected retrieved refinement, records one vote for each word break in the overlapping region, defines mappings between the query tokens and the refinement tokens in the non-overlapping region, and sorts the refinements based upon the votes. | 0.5 |
9,536,200 | 1 | 6 | 1. A method of utilizing natural language processing (NLP) to analyze data logs, the method comprising: generating a data log in response to executing a software application, wherein the data log includes a plurality of entries that each comprise debugging data generated from execution of the software application; performing a sentiment analysis on the data log, wherein the sentiment analysis further comprises: identifying at least one negative connotation corresponding to at least one term located in at least a selected one of the plurality of entries; determining that the selected entry is within a predetermined spatial proximity of at least one known error message in the data log; and generating a proximity model score in response to the determination; generating a sentiment score for the selected entry based upon the proximity model score; and identifying for visual highlighting the selected entry based on the sentiment score. | 1. A method of utilizing natural language processing (NLP) to analyze data logs, the method comprising: generating a data log in response to executing a software application, wherein the data log includes a plurality of entries that each comprise debugging data generated from execution of the software application; performing a sentiment analysis on the data log, wherein the sentiment analysis further comprises: identifying at least one negative connotation corresponding to at least one term located in at least a selected one of the plurality of entries; determining that the selected entry is within a predetermined spatial proximity of at least one known error message in the data log; and generating a proximity model score in response to the determination; generating a sentiment score for the selected entry based upon the proximity model score; and identifying for visual highlighting the selected entry based on the sentiment score. 6. The method of claim 1 wherein the visually highlighting further comprises: displaying the sentiment score associated with the selected entry. | 0.811024 |
8,762,161 | 1 | 9 | 1. A computerized method for visualization of an interaction categorization, of an at least one interaction captured within an environment, the method comprising: capturing the at least one interaction by a computing platform executing one or more computer applications; receiving definition of at least two categories related to key-phrases and criteria for an interaction to be assigned to the at least two categories; based on the criteria, categorizing the at least one interaction to the at least two categories; determining by machine learning a category network of connections between the at least two categories; extracting an at least one key-phrase from at least one of the at least two categories; determining a key-phrase network; determining a layout for the key-phrase network; and visualizing the category network by the key-phrases. | 1. A computerized method for visualization of an interaction categorization, of an at least one interaction captured within an environment, the method comprising: capturing the at least one interaction by a computing platform executing one or more computer applications; receiving definition of at least two categories related to key-phrases and criteria for an interaction to be assigned to the at least two categories; based on the criteria, categorizing the at least one interaction to the at least two categories; determining by machine learning a category network of connections between the at least two categories; extracting an at least one key-phrase from at least one of the at least two categories; determining a key-phrase network; determining a layout for the key-phrase network; and visualizing the category network by the key-phrases. 9. The method of claim 1 wherein the key-phrase network is unified with the category network. | 0.716463 |
9,710,703 | 1 | 3 | 1. A method for detecting texts included in a specific image, comprising steps of: (a) an apparatus detecting or allowing another device to detect one or more text candidates in the specific image by referring to feature values of pixels in the specific image; (b) the apparatus classifying or allowing another device to classify one or more weak texts in the specific image as strong texts by referring to information on at least one text classified as a strong text in another image related to the specific image if more than a certain percentage of the detected text candidates are classified as the weak texts as a result of comparison between at least one threshold value and at least one feature value of at least one pixel selected in a region where the detected text candidates are included or a value converted from the feature value. | 1. A method for detecting texts included in a specific image, comprising steps of: (a) an apparatus detecting or allowing another device to detect one or more text candidates in the specific image by referring to feature values of pixels in the specific image; (b) the apparatus classifying or allowing another device to classify one or more weak texts in the specific image as strong texts by referring to information on at least one text classified as a strong text in another image related to the specific image if more than a certain percentage of the detected text candidates are classified as the weak texts as a result of comparison between at least one threshold value and at least one feature value of at least one pixel selected in a region where the detected text candidates are included or a value converted from the feature value. 3. The method of claim 1 , wherein, at the step of (b), (i) if the multiple text candidates detected in the specific image are adjacent to each other, and (ii) if more than the certain percentage of the multiple text candidates adjacent to each other are classified as the weak texts as the result of comparison between the at least one threshold value and the at least one feature value of the at least one pixel selected in the region where the detected multiple text candidates are included or the value converted from the feature value, the apparatus classifies or allows another device to classify the weak texts among the multiple text candidates as the strong texts by referring to the information on the text classified as the strong text in the another image related to the specific image. | 0.594924 |
9,134,215 | 16 | 17 | 16. A non-transitory computer-readable storage having instructions stored thereon for executing a method, the method comprising: labeling one or more portions of a content item with a system-generated label; analyzing the one or more labeled portions and the content item for sentiment and assigning a sentiment score to a first labeled portion of the one or more labeled portions and to the content item; causing a graphical user interface to display the content item; receiving, via the graphical user interface, a user-specified label for a user-selected portion of the content item and a user-specified sentiment for the user-specified label; responsive to receiving the user-specified label and user-specified sentiment, searching a data store comprising a plurality of content items to identify content items comprising the user-specified label; re-analyzing the identified content items to update their sentiment scores based on the user-specified sentiment score; and causing the graphical user interface to display the updated sentiment score of the content item. | 16. A non-transitory computer-readable storage having instructions stored thereon for executing a method, the method comprising: labeling one or more portions of a content item with a system-generated label; analyzing the one or more labeled portions and the content item for sentiment and assigning a sentiment score to a first labeled portion of the one or more labeled portions and to the content item; causing a graphical user interface to display the content item; receiving, via the graphical user interface, a user-specified label for a user-selected portion of the content item and a user-specified sentiment for the user-specified label; responsive to receiving the user-specified label and user-specified sentiment, searching a data store comprising a plurality of content items to identify content items comprising the user-specified label; re-analyzing the identified content items to update their sentiment scores based on the user-specified sentiment score; and causing the graphical user interface to display the updated sentiment score of the content item. 17. The non-transitory computer-readable storage of claim 16 , further comprising: identifying an author of a content item; creating an author profile of the author comprising an author sentiment score and one or more statistics on an activity of the author; and causing the graphical user interface to display the author profile. | 0.648936 |
7,805,710 | 1 | 13 | 1. A method of translating a subject code executable by a subject computing architecture into a target code executable by a second computing architecture, wherein the subject code includes at least a first program and a second program, comprising: providing a first translator instance which translates the subject code of the first program into the target code including translating a first portion of the subject code into a portion of the target code; caching said portion of the target code into a shared code cache facility; providing a second translator instance which is different from the first translator instance and which translates the subject code of the second program into the target code, wherein the second translator instance operates simultaneously with the first translator instance; retrieving the cached portion of the target code from the shared code cache facility upon a compatibility detection between said cached portion of the target code and a second portion of the subject code in the second program, including loading the portion of the target code in the shared code cache facility into a portion of memory which is shared amongst at least the first and second translator instances; and, copying at least one part of the shared code cache facility to a private portion of memory associated with the second translator instance upon modification of the at least one part of the shared code cache facility by the second translator instance. | 1. A method of translating a subject code executable by a subject computing architecture into a target code executable by a second computing architecture, wherein the subject code includes at least a first program and a second program, comprising: providing a first translator instance which translates the subject code of the first program into the target code including translating a first portion of the subject code into a portion of the target code; caching said portion of the target code into a shared code cache facility; providing a second translator instance which is different from the first translator instance and which translates the subject code of the second program into the target code, wherein the second translator instance operates simultaneously with the first translator instance; retrieving the cached portion of the target code from the shared code cache facility upon a compatibility detection between said cached portion of the target code and a second portion of the subject code in the second program, including loading the portion of the target code in the shared code cache facility into a portion of memory which is shared amongst at least the first and second translator instances; and, copying at least one part of the shared code cache facility to a private portion of memory associated with the second translator instance upon modification of the at least one part of the shared code cache facility by the second translator instance. 13. The method of claim 1 wherein the portion of target code cached consists of a single instruction. | 0.856125 |
7,499,910 | 1 | 15 | 1. A computer-readable program code embedded in a memory, wherein when a computer system executes the computer-readable program code embedded in the memory to perform a method for supporting query caching, the method comprising: determining a list of candidate queries from a plurality of queries that are stored in a cache in response to receiving a new query, wherein the list comprises a subset of the plurality of queries, wherein the subset is less than all of the plurality of queries, and wherein the determining comprises for each item in a select list of the new query, obtaining a set of cached queries that include the each item in the select list, for the each item in the select list of the new query without an exact match in the select list of at least one cached query, obtaining a set of cached queries using aggregate rewrite, and forming the list of candidate queries by intersecting the set of cached queries for the each item in the select list of the new query and the set of cached queries for the each item in the select list of the new query without an exact match in the select list of the at least one cached query; evaluating each of the candidate queries in the list to determine one candidate query that is a cache hit, the cache hit indicating a stored result of the one candidate query which answers the new query, wherein the evaluating is performed using a set of rules that allows for inexact matches between one or more aggregates included in the new query and one or more aggregates included in the candidate queries in the list; generating a result for the new query using the stored result for the one candidate query; and storing the result of the new query. | 1. A computer-readable program code embedded in a memory, wherein when a computer system executes the computer-readable program code embedded in the memory to perform a method for supporting query caching, the method comprising: determining a list of candidate queries from a plurality of queries that are stored in a cache in response to receiving a new query, wherein the list comprises a subset of the plurality of queries, wherein the subset is less than all of the plurality of queries, and wherein the determining comprises for each item in a select list of the new query, obtaining a set of cached queries that include the each item in the select list, for the each item in the select list of the new query without an exact match in the select list of at least one cached query, obtaining a set of cached queries using aggregate rewrite, and forming the list of candidate queries by intersecting the set of cached queries for the each item in the select list of the new query and the set of cached queries for the each item in the select list of the new query without an exact match in the select list of the at least one cached query; evaluating each of the candidate queries in the list to determine one candidate query that is a cache hit, the cache hit indicating a stored result of the one candidate query which answers the new query, wherein the evaluating is performed using a set of rules that allows for inexact matches between one or more aggregates included in the new query and one or more aggregates included in the candidate queries in the list; generating a result for the new query using the stored result for the one candidate query; and storing the result of the new query. 15. The computer-readable program code embedded in the memory of claim 1 , further comprises generating a query plan for the new query based on the one candidate query. | 0.800948 |
8,051,109 | 1 | 3 | 1. A document system comprising: a web server computer adapted to receive a document collection in a first format, the document collection including a plurality of documents in the first format, each document including an indication of a corresponding classification code; a database server computer coupled to the web server computer and including a document store adapted to store the documents; and wherein the web server computer is adapted to transform each document by substituting the classification code corresponding to that document with a keyword associated with the corresponding classification code, wherein the web server computer is adapted to convert each document, including its keyword, from the first format into a second format different from the first format, wherein the web server computer is adapted to transmit each converted document to the database server computer and the database server computer is adapted to store the converted document in the document store, and wherein the web server computer is adapted to provide converted documents corresponding to a request received from another system, different from the document system. | 1. A document system comprising: a web server computer adapted to receive a document collection in a first format, the document collection including a plurality of documents in the first format, each document including an indication of a corresponding classification code; a database server computer coupled to the web server computer and including a document store adapted to store the documents; and wherein the web server computer is adapted to transform each document by substituting the classification code corresponding to that document with a keyword associated with the corresponding classification code, wherein the web server computer is adapted to convert each document, including its keyword, from the first format into a second format different from the first format, wherein the web server computer is adapted to transmit each converted document to the database server computer and the database server computer is adapted to store the converted document in the document store, and wherein the web server computer is adapted to provide converted documents corresponding to a request received from another system, different from the document system. 3. The document system of claim 1 , wherein the second format is a display format. | 0.781915 |
9,195,952 | 6 | 7 | 6. A system comprising: a processor; and a memory coupled to the processor and storing instructions which, when executed by the processor, cause the processor to: receive a first set of statements relating to one or more business process controls, each statement in the first set of statements comprising at least one token; store the first set of statements in a repository; compare a first statement of the first set of statements with a second statement of the first set of statements; generate a result of comparing the first statement with the second statement; generate, based on the result, a matching score between the first statement and the second statement, the matching score being based on a total amount of unique tokens between the first statement and the second statement; remove the second statement of the first set of statements from the repository if the matching score is above a particular threshold; generate a chart including a plurality of bubbles, where a size of each bubble, of the plurality of bubbles, is based on a number of parameters relating to a particular business process control of the one or more business process controls, when generating the chart, the processor is to connect a first bubble, of the plurality of bubbles, to a second bubble, of the plurality of bubbles, when the matching score is above the particular threshold; provide, for display to a user device, the chart including the first bubble connected to the second bubble, the chart identifying redundancy relating to the first set of statements; receive, from the user device, a selection of a bubble of the plurality of bubbles of the chart provided for display; and provide, for display to the user device, information relating to the selected bubble, the information including parameters relating to a business process control of the one or more business process controls. | 6. A system comprising: a processor; and a memory coupled to the processor and storing instructions which, when executed by the processor, cause the processor to: receive a first set of statements relating to one or more business process controls, each statement in the first set of statements comprising at least one token; store the first set of statements in a repository; compare a first statement of the first set of statements with a second statement of the first set of statements; generate a result of comparing the first statement with the second statement; generate, based on the result, a matching score between the first statement and the second statement, the matching score being based on a total amount of unique tokens between the first statement and the second statement; remove the second statement of the first set of statements from the repository if the matching score is above a particular threshold; generate a chart including a plurality of bubbles, where a size of each bubble, of the plurality of bubbles, is based on a number of parameters relating to a particular business process control of the one or more business process controls, when generating the chart, the processor is to connect a first bubble, of the plurality of bubbles, to a second bubble, of the plurality of bubbles, when the matching score is above the particular threshold; provide, for display to a user device, the chart including the first bubble connected to the second bubble, the chart identifying redundancy relating to the first set of statements; receive, from the user device, a selection of a bubble of the plurality of bubbles of the chart provided for display; and provide, for display to the user device, information relating to the selected bubble, the information including parameters relating to a business process control of the one or more business process controls. 7. The system of claim 6 , where the memory further includes instructions which, when executed by the processor, cause the processor to: receive a second set of statements relating to the one or more business process controls; store the second set of statements in the repository; compare the first set of statements with the second set of statements; generate a comparison result of comparing the first set of statements with the second set of statements; generate another matching score between a statement in the first set of statements and a statement in the second set of statements based on the comparison result; and associate the statement in the first set of statements with the statement in the second set of statements in the repository if the other matching score is above a threshold. | 0.5 |
9,489,350 | 22 | 23 | 22. The method of claim 21 , wherein the graphic further comprises: a measure of a size of the first document, a measure of a size of the second document; and a measure of textual overlap between the first document and the second document. | 22. The method of claim 21 , wherein the graphic further comprises: a measure of a size of the first document, a measure of a size of the second document; and a measure of textual overlap between the first document and the second document. 23. The method of claim 22 , wherein at least one of the measure of textual overlap between the first document and the second document, the measure of relationship overlap between the first document and the second document, and the measure of relationship overlap between the first document and the selected portion of the second document is displayed as a colored bar having a length corresponding to a magnitude of the respective measure. | 0.5 |
7,587,320 | 1 | 2 | 1. A system that concatenates speech units to produce synthetic speech, the system comprising: a processor; a module configured to control the processor to train a set of Hidden Markov Models (HMMs) using seed data in a first iteration; a module configured to control the processor to align the set of HMMs to produce segmented unit labels; and a module configured to control the processor to adjust boundaries of the unit labels using spectral boundary correction, wherein the unit labels having adjusted boundaries are used to concatenate speech units to synthesize speech. | 1. A system that concatenates speech units to produce synthetic speech, the system comprising: a processor; a module configured to control the processor to train a set of Hidden Markov Models (HMMs) using seed data in a first iteration; a module configured to control the processor to align the set of HMMs to produce segmented unit labels; and a module configured to control the processor to adjust boundaries of the unit labels using spectral boundary correction, wherein the unit labels having adjusted boundaries are used to concatenate speech units to synthesize speech. 2. The system in claim 1 , wherein the module configured to control the processor to train the set of Hidden Markov Models further: initializes the set of HMMs using at least one of hand-labeled bootstrapped data, speaker-independent HMM bootstrapped data, and flat start data; re-estimates the set of HMMs; and performs an embedded re-estimation on the set of HMMs. | 0.731278 |
8,762,469 | 20 | 26 | 20. A method for operating an automated assistant, comprising: at a server computer system provided by a first entity, the server computer system comprising a processor and memory storing instructions for execution by the processor: receiving a voice command and contextual information from the portable electronic device; processing the voice command, using a speech recognition service provided by a second entity different from the first entity, to generate a text string from the voice command; processing the text string and the contextual information; and transmitting results associated with processing the text string and the contextual information to the portable electronic device. | 20. A method for operating an automated assistant, comprising: at a server computer system provided by a first entity, the server computer system comprising a processor and memory storing instructions for execution by the processor: receiving a voice command and contextual information from the portable electronic device; processing the voice command, using a speech recognition service provided by a second entity different from the first entity, to generate a text string from the voice command; processing the text string and the contextual information; and transmitting results associated with processing the text string and the contextual information to the portable electronic device. 26. The method of claim 20 , wherein processing the text string and the contextual information comprises: sending at least one of the text string and the contextual information to an online service operated separately from the server computer system; and receiving, from the online service, the results associated with processing the text string and the contextual information. | 0.554374 |
9,431,010 | 1 | 4 | 1. A speech-recognition device which acquires an internal recognition result from its recognition processing of input speech data and an external recognition result from recognition processing of said input speech data by one or more external recognition devices external to the speech-recognition device to determine a final recognition result, the speech-recognition device comprising: memory including: an acoustic model in which feature quantities of speeches are modeled; a language model in which notations and readings of recognition-object words of the speech-recognition device are stored; and a reading dictionary in which pairs of the notations and the readings of the recognition-object words and words other than the recognition-object words are stored; and circuitry configured to: transmit said input speech data to the one or more external recognition devices; analyze the input speech data to calculate a feature vector; perform, using the acoustic model, pattern collation between the calculated feature vector and each word stored in the language model to calculate their respective acoustic likelihoods; output, as the internal recognition result, a corresponding notation, a corresponding reading, and a corresponding acoustic likelihood of top one or more high-ranking words; acquire the external recognition result from recognition processing of the input speech data by the one or more external recognition devices, extract a reading corresponding to a notation included in said external recognition result using the reading dictionary, and output a result composed of said external recognition result and the extracted reading; perform, using the acoustic model, pattern collation between the calculated feature vector and the output result to calculate an acoustic likelihood for the external recognition result; and compare the corresponding acoustic likelihood of the internal recognition result with the acoustic likelihood of the external recognition result to determine the final recognition result. | 1. A speech-recognition device which acquires an internal recognition result from its recognition processing of input speech data and an external recognition result from recognition processing of said input speech data by one or more external recognition devices external to the speech-recognition device to determine a final recognition result, the speech-recognition device comprising: memory including: an acoustic model in which feature quantities of speeches are modeled; a language model in which notations and readings of recognition-object words of the speech-recognition device are stored; and a reading dictionary in which pairs of the notations and the readings of the recognition-object words and words other than the recognition-object words are stored; and circuitry configured to: transmit said input speech data to the one or more external recognition devices; analyze the input speech data to calculate a feature vector; perform, using the acoustic model, pattern collation between the calculated feature vector and each word stored in the language model to calculate their respective acoustic likelihoods; output, as the internal recognition result, a corresponding notation, a corresponding reading, and a corresponding acoustic likelihood of top one or more high-ranking words; acquire the external recognition result from recognition processing of the input speech data by the one or more external recognition devices, extract a reading corresponding to a notation included in said external recognition result using the reading dictionary, and output a result composed of said external recognition result and the extracted reading; perform, using the acoustic model, pattern collation between the calculated feature vector and the output result to calculate an acoustic likelihood for the external recognition result; and compare the corresponding acoustic likelihood of the internal recognition result with the acoustic likelihood of the external recognition result to determine the final recognition result. 4. The speech-recognition device of claim 1 , wherein the memory includes a result-determination language model in which pairs of words and language likelihoods are stored, and wherein the circuitry is configured to calculate, using the result-determination language model, a language likelihood of the internal recognition result and a language likelihood of the external recognition result, and compare the corresponding acoustic likelihood and the language likelihood of the internal recognition result with the acoustic likelihood and the language likelihood of the external recognition result to determine the final recognition result. | 0.580603 |
8,191,005 | 20 | 22 | 20. A computer-readable medium having computer-executable instructions for: performing a presentation, on a display device, of a customized visualization in an industrial automation environment, comprising further instructions for: determining data relating to an industrial automation environment in connection with presenting a first data visualization, including the data, on a first display device, wherein the first data visualization comprises a first display object subset; capturing context information pertaining to the first data visualization; determining a second display object subset facilitating presenting of the data as a function of the context information, wherein the first display object subset and second display object subset comprise at least one display object selected from a set of display objects and the first display object subset and the second display object subset are non-overlapping; selecting the second display object subset; and updating, dynamically, the first data visualization with a second data visualization displaying the data with the second display object subset. | 20. A computer-readable medium having computer-executable instructions for: performing a presentation, on a display device, of a customized visualization in an industrial automation environment, comprising further instructions for: determining data relating to an industrial automation environment in connection with presenting a first data visualization, including the data, on a first display device, wherein the first data visualization comprises a first display object subset; capturing context information pertaining to the first data visualization; determining a second display object subset facilitating presenting of the data as a function of the context information, wherein the first display object subset and second display object subset comprise at least one display object selected from a set of display objects and the first display object subset and the second display object subset are non-overlapping; selecting the second display object subset; and updating, dynamically, the first data visualization with a second data visualization displaying the data with the second display object subset. 22. The computer-readable medium of claim 20 , further comprising reformatting data in response to the data being in a format incompatible for display by the second display object subset. | 0.705975 |
9,224,385 | 11 | 13 | 11. A device for providing information to a user, the device comprising: a microphone; a display; a processor; and a memory including a computer program for audio recognition, wherein instructions of the computer program when executed by the processor perform operations for: detecting entry in an audio recognition mode, the detecting including receiving an audio stream via the microphone; analyzing one or more segments of the audio stream before a complete audio stream is received, wherein analyzing includes: sending the one or more segments to a first server for determining if the audio stream includes speech; and sending the one or more segments to a second server for determining if the audio stream is from a song; receiving a first confidence score from the first server and receiving a second confidence score from the second server; displaying a possible candidate on the display based on a partial identification of the audio stream using the first and second confidence scores while continuing analyzing additional segments as the audio stream is received until an end of the audio stream or until the first and second confidence scores determine that the audio stream has been identified as speech or music; and presenting results on the display based on the completed identification of the audio stream. | 11. A device for providing information to a user, the device comprising: a microphone; a display; a processor; and a memory including a computer program for audio recognition, wherein instructions of the computer program when executed by the processor perform operations for: detecting entry in an audio recognition mode, the detecting including receiving an audio stream via the microphone; analyzing one or more segments of the audio stream before a complete audio stream is received, wherein analyzing includes: sending the one or more segments to a first server for determining if the audio stream includes speech; and sending the one or more segments to a second server for determining if the audio stream is from a song; receiving a first confidence score from the first server and receiving a second confidence score from the second server; displaying a possible candidate on the display based on a partial identification of the audio stream using the first and second confidence scores while continuing analyzing additional segments as the audio stream is received until an end of the audio stream or until the first and second confidence scores determine that the audio stream has been identified as speech or music; and presenting results on the display based on the completed identification of the audio stream. 13. The device as recited in claim 11 , wherein sending the one or more segments to a first server and sending the one or more segments to a second server are performed in parallel and independently from each other. | 0.59434 |
9,594,741 | 1 | 10 | 1. A computer-implemented method practiced on a client device, comprising: receiving a new term from an application on the client device; segmenting the new term into a set of n-grams; applying a differential privacy algorithm to a selected n-gram in the set of n-grams, generating a differentially private n-gram sketch; selecting a row of the differentially private n-gram sketch; storing the new term and selected row of the differentially private n-gram sketch to a sample buffer of candidates for transmission to a new term learning server. | 1. A computer-implemented method practiced on a client device, comprising: receiving a new term from an application on the client device; segmenting the new term into a set of n-grams; applying a differential privacy algorithm to a selected n-gram in the set of n-grams, generating a differentially private n-gram sketch; selecting a row of the differentially private n-gram sketch; storing the new term and selected row of the differentially private n-gram sketch to a sample buffer of candidates for transmission to a new term learning server. 10. The method of claim 1 , further comprising: selecting a classification of terms, wherein at least one new term in the sample buffer has the selected classification; randomly selecting a new term having the classification from the sample buffer; and transmitting the new term and differentially private n-gram sketches to a new term learning server. | 0.591647 |
8,731,920 | 1 | 6 | 1. A method performed by a computer processor executing computer program instructions stored on a non-transitory computer-readable medium, the method for use with a system, the system including a first document, the first document containing at least some information in common with a spoken audio stream, the method comprising: (A) identifying text in the first document, wherein the text represents a concept; (B) identifying, based on the identified text and a repository of finite state grammars, a plurality of spoken forms of the concept, including at least one spoken form not contained in the first document, wherein all of the plurality of spoken forms have the same content as each other; (C) replacing the identified text with a finite state grammar specifying the plurality of spoken forms of the concept to produce a second document; (D) generating a document-specific language model based on the second document, comprising generating at least some of the document-specific language model based on the finite state grammar. | 1. A method performed by a computer processor executing computer program instructions stored on a non-transitory computer-readable medium, the method for use with a system, the system including a first document, the first document containing at least some information in common with a spoken audio stream, the method comprising: (A) identifying text in the first document, wherein the text represents a concept; (B) identifying, based on the identified text and a repository of finite state grammars, a plurality of spoken forms of the concept, including at least one spoken form not contained in the first document, wherein all of the plurality of spoken forms have the same content as each other; (C) replacing the identified text with a finite state grammar specifying the plurality of spoken forms of the concept to produce a second document; (D) generating a document-specific language model based on the second document, comprising generating at least some of the document-specific language model based on the finite state grammar. 6. The method of claim 1 , further comprising: (F) before (A), generating the first document based on the spoken audio stream. | 0.789298 |
7,853,586 | 7 | 11 | 7. A method performed by a device, comprising: providing, on a display associated with the device, a web browser application window that includes an input box and a button to initiate a highlighting operation; presenting a document within the web browser application window on the display; receiving one or more characters within the input box after presenting the document within the web browser application window; receiving selection of the button to initiate the highlighting operation after receiving the one or more characters within the input box; automatically locating and highlighting occurrences of the one or more characters within the document presented within the web browser application window in response to receiving the selection of the button; and presenting the document with the highlighted one or more characters on the display. | 7. A method performed by a device, comprising: providing, on a display associated with the device, a web browser application window that includes an input box and a button to initiate a highlighting operation; presenting a document within the web browser application window on the display; receiving one or more characters within the input box after presenting the document within the web browser application window; receiving selection of the button to initiate the highlighting operation after receiving the one or more characters within the input box; automatically locating and highlighting occurrences of the one or more characters within the document presented within the web browser application window in response to receiving the selection of the button; and presenting the document with the highlighted one or more characters on the display. 11. The method of claim 7 , where the document includes a list of search results. | 0.876147 |
9,972,305 | 1 | 6 | 1. An apparatus for normalizing input data of an acoustic model, the apparatus comprising: a window extractor configured to extract windows of frame data to be input to the acoustic model from frame data of a speech to be recognized; and a normalizer configured to normalize the frame data to be input to the acoustic model in units of the extracted windows, wherein the normalizer is configured to normalize frames belonging to a current window in consideration of frames belonging to preceding windows of the current window. | 1. An apparatus for normalizing input data of an acoustic model, the apparatus comprising: a window extractor configured to extract windows of frame data to be input to the acoustic model from frame data of a speech to be recognized; and a normalizer configured to normalize the frame data to be input to the acoustic model in units of the extracted windows, wherein the normalizer is configured to normalize frames belonging to a current window in consideration of frames belonging to preceding windows of the current window. 6. The apparatus of claim 1 , wherein the normalizer is further configured to normalize the frame data belonging to the extracted windows so that the frame data belonging to the extracted windows has an average of 0 and a standard deviation of 1. | 0.689394 |
8,832,108 | 8 | 11 | 8. A computer-implemented method for classifying documents that have different scales, the method comprising: counting, by a server computer, instances for each character size in a first document and instances for each character size in a second document; selecting, by the server computer, a first plurality of character sizes for the first document and a second plurality of character sizes for the second document, based on a corresponding count of instances associated with each corresponding character size; calculating, by the server computer, a plurality of scales, wherein each scale of the plurality of scales is based on a corresponding ratio of a corresponding one of the first plurality of character sizes relative to a corresponding one of the second plurality of character sizes; calculating, by the server computer, a plurality of scale products based on each corresponding count of instances for each character size range associated with the first plurality of character sizes multiplied by each corresponding count of instances for each corresponding character size range associated with the second plurality of character sizes, wherein the corresponding character size range is based on a corresponding one of the plurality of scales; calculating, by the server computer, a plurality of scale scores based on summing each of the plurality of scale products associated with each corresponding one of the plurality of scales; selecting, by the server computer, a scale of the plurality of scales based a highest one of the plurality of scale scores associated with a corresponding one the plurality of scales; determining, by the server computer, whether the second document is in a class associated with the first document based on a comparison of location information associated with the first document and location information associated with the second document, wherein the location information associated with second document is based on the scale; and classifying, by the server computer, the second document in the class associated with the first document in response to a determination that the second document is in the class associated with the first document. | 8. A computer-implemented method for classifying documents that have different scales, the method comprising: counting, by a server computer, instances for each character size in a first document and instances for each character size in a second document; selecting, by the server computer, a first plurality of character sizes for the first document and a second plurality of character sizes for the second document, based on a corresponding count of instances associated with each corresponding character size; calculating, by the server computer, a plurality of scales, wherein each scale of the plurality of scales is based on a corresponding ratio of a corresponding one of the first plurality of character sizes relative to a corresponding one of the second plurality of character sizes; calculating, by the server computer, a plurality of scale products based on each corresponding count of instances for each character size range associated with the first plurality of character sizes multiplied by each corresponding count of instances for each corresponding character size range associated with the second plurality of character sizes, wherein the corresponding character size range is based on a corresponding one of the plurality of scales; calculating, by the server computer, a plurality of scale scores based on summing each of the plurality of scale products associated with each corresponding one of the plurality of scales; selecting, by the server computer, a scale of the plurality of scales based a highest one of the plurality of scale scores associated with a corresponding one the plurality of scales; determining, by the server computer, whether the second document is in a class associated with the first document based on a comparison of location information associated with the first document and location information associated with the second document, wherein the location information associated with second document is based on the scale; and classifying, by the server computer, the second document in the class associated with the first document in response to a determination that the second document is in the class associated with the first document. 11. The computer-implemented method of claim 8 , wherein the comparison of the location information associated with the first document and the location information associated with the second document comprises: generating a plurality of word pairs, wherein each word pair comprises a word from the first document and a corresponding word from the second document; computing, for each word pair, first location information for the word that indicates a location of the word in the first document relative to other words in the first document; computing, for each word pair, second location information for the corresponding word that indicates a location of the corresponding word in the second document relative to other words in the second document; and comparing the first location information and the second location information. | 0.5 |
7,475,058 | 10 | 12 | 10. A computer-readable storage medium having stored thereon computer readable instructions for querying a data structure in a distributed computing environment when executed on a computing device, comprising: computer instructions for preparing a query specifying the constraints to be applied on at least two different data structures wherein each data structure comprises substantially the same information wherein the information is stored in a different data format type and where each data structure is queried according to a different format type wherein the data structure is stored as one of XML, database tables, and a programming language data structure; computer instructions for sending the query to at least two different objects wherein each object maintains one of the at least two different data structures in-memory and determines whether the in-memory data structure maintained by each object satisfies the query; and computer instructions for receiving the results from the query from the at least two different objects wherein the query results are returned in substantially identical formats. | 10. A computer-readable storage medium having stored thereon computer readable instructions for querying a data structure in a distributed computing environment when executed on a computing device, comprising: computer instructions for preparing a query specifying the constraints to be applied on at least two different data structures wherein each data structure comprises substantially the same information wherein the information is stored in a different data format type and where each data structure is queried according to a different format type wherein the data structure is stored as one of XML, database tables, and a programming language data structure; computer instructions for sending the query to at least two different objects wherein each object maintains one of the at least two different data structures in-memory and determines whether the in-memory data structure maintained by each object satisfies the query; and computer instructions for receiving the results from the query from the at least two different objects wherein the query results are returned in substantially identical formats. 12. The computer-readable storage medium as recited in claim 10 further comprising receiving a data value from at least one digital device indicative of the storage of the value in said digital device wherein one of the at least two different objects resides on the digital device. | 0.5 |
10,127,316 | 19 | 34 | 19. 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: receive, from a client system of a user of the social-networking system, instructions for generating a post in a news feed associated with the user on the social-networking system, wherein the post comprises unstructured text from the user; determine whether the unstructured text of the post comprises a request for a recommendation from other users of the social-networking system; parse the unstructured text to identify one or more first entities and one or more first entity types referenced in the unstructured text; generate a structured query based upon the one or more first entities and the one or more first entity types referenced in the unstructured text of the post; generate a plurality of search results corresponding to a plurality of second entities matching the structured query, wherein each of the second entities has an entity type matching at least one of the first entity types; and send, to the client system of the user responsive to receiving the instructions for generating the post, instructions for presenting one or more of the plurality of search results, wherein the search results are presented in association with the post by the user in the news feed associated with the user. | 19. 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: receive, from a client system of a user of the social-networking system, instructions for generating a post in a news feed associated with the user on the social-networking system, wherein the post comprises unstructured text from the user; determine whether the unstructured text of the post comprises a request for a recommendation from other users of the social-networking system; parse the unstructured text to identify one or more first entities and one or more first entity types referenced in the unstructured text; generate a structured query based upon the one or more first entities and the one or more first entity types referenced in the unstructured text of the post; generate a plurality of search results corresponding to a plurality of second entities matching the structured query, wherein each of the second entities has an entity type matching at least one of the first entity types; and send, to the client system of the user responsive to receiving the instructions for generating the post, instructions for presenting one or more of the plurality of search results, wherein the search results are presented in association with the post by the user in the news feed associated with the user. 34. The system of claim 19 , wherein the processors are further operable when executing instructions to: receive one or more comments from one or more users; and present the one or more comments in association with the news feed. | 0.783962 |
8,065,134 | 1 | 6 | 1. An information display system, comprising a large screen display device; an information display unit which displays information in the screen display device in a predetermined standard language; a user information input unit which inputs user information including information of a language desired by a user; and a language switch display unit which, when the user desired language input by the user information input unit is different from the standard language, switches all or part of the standard language of the screen display device to and displays the desired language of the user, and wherein the information display unit displays guide information, which comprises a public area common to all users and a plurality of individual areas dependent on respective users, in the standard language, when the input user desired language is different from the standard language, the language switch display unit displays the public area by time division by the standard language and the user desired language or displays the standard language and the user desired language in parallel by the area division and switches an individual area corresponding to the user desired language, and wherein the language switch display unit displays accompanying information relevant to the user in the language of the user so as to correspond to the individual area switched to and displayed in the user desired language. | 1. An information display system, comprising a large screen display device; an information display unit which displays information in the screen display device in a predetermined standard language; a user information input unit which inputs user information including information of a language desired by a user; and a language switch display unit which, when the user desired language input by the user information input unit is different from the standard language, switches all or part of the standard language of the screen display device to and displays the desired language of the user, and wherein the information display unit displays guide information, which comprises a public area common to all users and a plurality of individual areas dependent on respective users, in the standard language, when the input user desired language is different from the standard language, the language switch display unit displays the public area by time division by the standard language and the user desired language or displays the standard language and the user desired language in parallel by the area division and switches an individual area corresponding to the user desired language, and wherein the language switch display unit displays accompanying information relevant to the user in the language of the user so as to correspond to the individual area switched to and displayed in the user desired language. 6. The information display system according to claim 1 , wherein the information display unit displays a list of departure information of a transportation facility as the guide information, displays item names including, for example, departure time, flight number, route, destination, and company name as a public area of the list display, and displays a plurality of arrays of item values corresponding to the item names of the public area as individual areas of the list display. | 0.586059 |
7,882,116 | 12 | 16 | 12. A computer program product for localizing programming models, the computer program product comprising a computer readable storage medium having computer readable program code embodied therewith and controlling a processor to: given a programming model having one or more elements, each element having an element name and each element name being in a language spoken by an author of the programming model, for each element name, (a) pair the element name to corresponding character strings configured to generate the element name in different languages resulting in element name-character string pairs, and (b) store the resulting pairs in a set of property files of the model, a different property file for different languages; and during display of the given programming model in different geographic locales, for each of the different geographic locales: (i) obtain local processor system defined geographic locale, (ii) use the obtained local system defined geographic locale to (a) open the property file of the language corresponding to the obtained local system defined geographic locale, (b) look up the element name-character string pairs in the opened property file and (c) locate and obtain respective character strings of each element name in the respective language spoken in the obtained local system defined geographic locale and free of user specification of spoken language for that locale, and (iii) use the obtained character strings to display each element name in the respective language spoken in the obtained local system defined geographic locale in a manner free of manual translation across the different geographic locales and in a manner free of code changes, wherein said locating and obtaining respective character strings is automatically performed as a function of the obtained local system defined geographic locale and not as a function of a user specified spoken language. | 12. A computer program product for localizing programming models, the computer program product comprising a computer readable storage medium having computer readable program code embodied therewith and controlling a processor to: given a programming model having one or more elements, each element having an element name and each element name being in a language spoken by an author of the programming model, for each element name, (a) pair the element name to corresponding character strings configured to generate the element name in different languages resulting in element name-character string pairs, and (b) store the resulting pairs in a set of property files of the model, a different property file for different languages; and during display of the given programming model in different geographic locales, for each of the different geographic locales: (i) obtain local processor system defined geographic locale, (ii) use the obtained local system defined geographic locale to (a) open the property file of the language corresponding to the obtained local system defined geographic locale, (b) look up the element name-character string pairs in the opened property file and (c) locate and obtain respective character strings of each element name in the respective language spoken in the obtained local system defined geographic locale and free of user specification of spoken language for that locale, and (iii) use the obtained character strings to display each element name in the respective language spoken in the obtained local system defined geographic locale in a manner free of manual translation across the different geographic locales and in a manner free of code changes, wherein said locating and obtaining respective character strings is automatically performed as a function of the obtained local system defined geographic locale and not as a function of a user specified spoken language. 16. A computer program product as claimed in claim 12 wherein the different languages include any of French, Japanese, Italian, English, German and Russian. | 0.52439 |
4,339,806 | 6 | 7 | 6. The apparatus as set forth in claim 5, which further comprises a means responsive to the coincidence signals from the determining means for controlling the second addressing means so as to develop and transfer the one of the plurality of translations corresponding to each full-length word to the second indicating means. | 6. The apparatus as set forth in claim 5, which further comprises a means responsive to the coincidence signals from the determining means for controlling the second addressing means so as to develop and transfer the one of the plurality of translations corresponding to each full-length word to the second indicating means. 7. The apparatus as set forth claim 6, wherein the controlling means comprises: first means for detecting the termination of the transfer of each of the coincident full-length words from the first storing means; means responsive to the coincidence signals from the determining means for maintaining each of said coincidence signals for a certain time period; means responsive to the first detecting means and the maintaining means for providing translation control signals, and means responsive to the translation control signals from the providing means for enabling the transfer of the translation corresponding to the incident total word into the second indicating means. | 0.5 |
7,843,364 | 6 | 9 | 6. A handheld electronic device comprising: an input apparatus comprising a plurality of input members, at least some of the input members each having a plurality of linguistic elements assigned thereto; an output apparatus; and a processor apparatus that comprises a processor and a memory having stored therein a plurality of objects including a number of language objects, the memory further having stored therein a number of routines which, when executed on the processor, cause the handheld electronic device to perform operations comprising: providing as a first output a number of linguistic elements; detecting as an ambiguous editing input a number of input member actuations at a location that precedes at least a portion of the first output; employing a number of first language objects that each correspond with at least a portion of the editing input to generate a number of first linguistic results; employing a number of second language objects that each correspond with at least a portion of the editing input in combination with at least some of the linguistic elements of the at least a portion of the first output to generate a number of second linguistic results; and outputting at least one of: at least some of the first linguistic results, and at least some of the second linguistic results; wherein the operations further comprise determining that one of the first linguistic results is a complete word and that one of the second linguistic results is a complete word, outputting the one of the first linguistic results and the one of the second linguistic results in decreasing order of frequency value at a position of highest priority, and outputting the first linguistic results with at least some of the linguistic elements of the at least a portion of the first output. | 6. A handheld electronic device comprising: an input apparatus comprising a plurality of input members, at least some of the input members each having a plurality of linguistic elements assigned thereto; an output apparatus; and a processor apparatus that comprises a processor and a memory having stored therein a plurality of objects including a number of language objects, the memory further having stored therein a number of routines which, when executed on the processor, cause the handheld electronic device to perform operations comprising: providing as a first output a number of linguistic elements; detecting as an ambiguous editing input a number of input member actuations at a location that precedes at least a portion of the first output; employing a number of first language objects that each correspond with at least a portion of the editing input to generate a number of first linguistic results; employing a number of second language objects that each correspond with at least a portion of the editing input in combination with at least some of the linguistic elements of the at least a portion of the first output to generate a number of second linguistic results; and outputting at least one of: at least some of the first linguistic results, and at least some of the second linguistic results; wherein the operations further comprise determining that one of the first linguistic results is a complete word and that one of the second linguistic results is a complete word, outputting the one of the first linguistic results and the one of the second linguistic results in decreasing order of frequency value at a position of highest priority, and outputting the first linguistic results with at least some of the linguistic elements of the at least a portion of the first output. 9. The handheld electronic device of claim 6 , the operations further comprise outputting, as said at least some of the first linguistic results, at least some of the first linguistic results each having appended thereto at least some of the linguistic elements of the at least portion of the first output. | 0.696429 |
10,110,385 | 4 | 8 | 4. A system, comprising: at least one computing device that implements one or more services, wherein the one or more services: receive, from a signatory, a request to generate a signature for a document, wherein the signatory is associated with multiple sets of credentials, wherein a first set of credentials is associated with a first duress level, and wherein a second set of credentials is associated with a second duress level; obtain a document identifier for the document, the document identifier derived based at least in part from document contents; obtain a token identifier for the at least one computing device, wherein the token identifier is registered with an identity registrar and authorized by the identity registrar to generate signatures, and wherein the token identifier comprises a first private key corresponding to a first public key and a second private key corresponding to a second public key; generate a first signature based at least in part on the document identifier, the first set of credentials, and the identity verification identifier, wherein the first signature is based at least in part on encrypting the document identifier using the first private key; determine that the generated first signature is not a match to the received signature of the signatory; generate a second signature based at least in part on the document identifier, the second set of credentials, and the identity verification identifier, wherein the second signature is based at least in part on encrypting the document identifier using the second private key; determine that the generated second signature is a match to the received signature of the signatory; and perform an action in accordance with a duress level associated with the second set of credentials, wherein the action is one or more of hiding information associated with a first account, displaying information associated with a second account, notifying security personnel of a security incident indicated by the duress level, sending a message indicating an occurrence of the duress level, indicating the occurrence of the duress level in a data store, or repudiating transactions associated with the document. | 4. A system, comprising: at least one computing device that implements one or more services, wherein the one or more services: receive, from a signatory, a request to generate a signature for a document, wherein the signatory is associated with multiple sets of credentials, wherein a first set of credentials is associated with a first duress level, and wherein a second set of credentials is associated with a second duress level; obtain a document identifier for the document, the document identifier derived based at least in part from document contents; obtain a token identifier for the at least one computing device, wherein the token identifier is registered with an identity registrar and authorized by the identity registrar to generate signatures, and wherein the token identifier comprises a first private key corresponding to a first public key and a second private key corresponding to a second public key; generate a first signature based at least in part on the document identifier, the first set of credentials, and the identity verification identifier, wherein the first signature is based at least in part on encrypting the document identifier using the first private key; determine that the generated first signature is not a match to the received signature of the signatory; generate a second signature based at least in part on the document identifier, the second set of credentials, and the identity verification identifier, wherein the second signature is based at least in part on encrypting the document identifier using the second private key; determine that the generated second signature is a match to the received signature of the signatory; and perform an action in accordance with a duress level associated with the second set of credentials, wherein the action is one or more of hiding information associated with a first account, displaying information associated with a second account, notifying security personnel of a security incident indicated by the duress level, sending a message indicating an occurrence of the duress level, indicating the occurrence of the duress level in a data store, or repudiating transactions associated with the document. 8. The system of claim 4 , wherein: the multiple sets of credentials include: a location of a signing event corresponding to the signature; and a set of criteria for determining whether the location of the signing event is indicative of an occurrence of an unsanctioned signing event; and the one or more services that determine the multiple sets of credentials further determine whether the location of the signing event indicates the occurrence of the unsanctioned signing event. | 0.510183 |
9,513,879 | 14 | 20 | 14. A computer program product, comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, where the computer readable program code when executed on a computer causes the computer to: configure a principal model to facilitate automatic generation of at least one resource for use by a computer-executable application, where the principal model comprises a plurality of classes, references, attributes, and associations between any of the classes; identify at least one model item required for a task that is absent from the principal model; instantiate the absent model item in a decoration model that operates with the principal model to augment operational functionality of the principal model, where the decoration model comprises a class, a reference, and an attribute for any corresponding one of the plurality of classes, references, and attributes of the principal model, where a created reference is associated with a key that contains information about a source attribute and a target attribute of the key in the decoration model; and store both of the models within a memory. | 14. A computer program product, comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, where the computer readable program code when executed on a computer causes the computer to: configure a principal model to facilitate automatic generation of at least one resource for use by a computer-executable application, where the principal model comprises a plurality of classes, references, attributes, and associations between any of the classes; identify at least one model item required for a task that is absent from the principal model; instantiate the absent model item in a decoration model that operates with the principal model to augment operational functionality of the principal model, where the decoration model comprises a class, a reference, and an attribute for any corresponding one of the plurality of classes, references, and attributes of the principal model, where a created reference is associated with a key that contains information about a source attribute and a target attribute of the key in the decoration model; and store both of the models within a memory. 20. The computer program product of claim 14 , where the decoration model is mapped to the principal model, and the decoration model and the principal model are stored in a memory. | 0.842657 |
9,158,775 | 8 | 14 | 8. The method of claim 1 , further comprising receiving an update to the model based upon an interaction of the user with a website. | 8. The method of claim 1 , further comprising receiving an update to the model based upon an interaction of the user with a website. 14. The method of claim 8 , wherein the interaction with the website includes determining whether the user shared something in the stream of content. | 0.588398 |
9,875,239 | 19 | 22 | 19. A method for implementing an online document sharing community in a network environment including a plurality of participant computers operated by participants in the online document sharing community and comprising: maintaining, in a computer database, participant information for a plurality of participants in the online document sharing community, wherein the participant information for at least one of the participants is associated with document information in the computer database for at least one document owned by the participant, wherein the document information identifies a document in a storage system, an owner of the document, a public/private status flag indicating whether the document is public or private, a public description providing a description of the document that does not include all content of the document, a provide public description field indicating whether the public description is to be provided to requesting participants not in a group of participants allowed access to the document, and wherein the document information for at least one document indicated as private indicates the group of participants allowed to access the document; receiving a request for a page from a requesting participant computer, wherein the requesting participant computer comprises one of the participant computers operated by a requesting participant comprising one of the participants in the online document sharing community; determining a document to include in the page; determining whether the public/private status flag indicates whether the document is public or private; including in the page an access element to provide access to the content of the document in response to the determining that the public/private status flag indicates that the document is public; determining whether the requesting participant is a member of the group of participants allowed access to the document in response to determining that the public/private status flag indicates that the document is private; determining whether the provide public description field indicates that the public description is to be provided in response to the determining that the requesting participant is not a member of the group of participants allowed to access the document; including in the page access to the public description for the document in response to the determining that the public/private status flag indicates that the document is private, in response to the determining that the requesting participant is not being a member of the group of participants allowed to access the document, and in response to the determining that the provide public description field indicates that the public description is to be provided; including in the page the access element to provide access to the content of the document in response to the determining that the public/private status flag indicates that the document is private and in response to the determining that the requesting participant is a member of the group of participants allowed to access the document; and returning the page to the requesting participant computer. | 19. A method for implementing an online document sharing community in a network environment including a plurality of participant computers operated by participants in the online document sharing community and comprising: maintaining, in a computer database, participant information for a plurality of participants in the online document sharing community, wherein the participant information for at least one of the participants is associated with document information in the computer database for at least one document owned by the participant, wherein the document information identifies a document in a storage system, an owner of the document, a public/private status flag indicating whether the document is public or private, a public description providing a description of the document that does not include all content of the document, a provide public description field indicating whether the public description is to be provided to requesting participants not in a group of participants allowed access to the document, and wherein the document information for at least one document indicated as private indicates the group of participants allowed to access the document; receiving a request for a page from a requesting participant computer, wherein the requesting participant computer comprises one of the participant computers operated by a requesting participant comprising one of the participants in the online document sharing community; determining a document to include in the page; determining whether the public/private status flag indicates whether the document is public or private; including in the page an access element to provide access to the content of the document in response to the determining that the public/private status flag indicates that the document is public; determining whether the requesting participant is a member of the group of participants allowed access to the document in response to determining that the public/private status flag indicates that the document is private; determining whether the provide public description field indicates that the public description is to be provided in response to the determining that the requesting participant is not a member of the group of participants allowed to access the document; including in the page access to the public description for the document in response to the determining that the public/private status flag indicates that the document is private, in response to the determining that the requesting participant is not being a member of the group of participants allowed to access the document, and in response to the determining that the provide public description field indicates that the public description is to be provided; including in the page the access element to provide access to the content of the document in response to the determining that the public/private status flag indicates that the document is private and in response to the determining that the requesting participant is a member of the group of participants allowed to access the document; and returning the page to the requesting participant computer. 22. The method of claim 19 , wherein the public description is maintained separately from the document. | 0.911815 |
8,515,729 | 2 | 3 | 2. The method of claim 1 , further comprising determining when the application content associated with the site is modified and maintaining an identifier for the changed application content indicating whether the application content is translated and when the application content is un-translated. | 2. The method of claim 1 , further comprising determining when the application content associated with the site is modified and maintaining an identifier for the changed application content indicating whether the application content is translated and when the application content is un-translated. 3. The method of claim 2 , wherein maintaining the identifier for the changed application content includes maintaining a separate identifier for each of the strings that are associated with the user interface for the site. | 0.659509 |
8,442,976 | 9 | 10 | 9. A content item retrieval system comprising: a base location extractor module configured to determine multiple base locations, based on GPS information or user entry, each base location being a location from which to apply a corresponding criterion distance-determined granularity thresholding for setting a threshold for location similarity in selecting or rejecting target items for content item retrieval, wherein criterion distance-determined granularity thresholding is applied for each base location of the multiple base locations separately based on differences in distance between farther locations being less important than between equally distant closer locations, further wherein the farther in distance moved from a corresponding base location of the multiple base locations, the less important, in terms of determining similarity, are differences in distance between locations of different content items at the corresponding further distances from the corresponding base location; a location data extractor module configured to extract, as a first anchor item location, location data for a first identified anchor content item and to determine, as a criterion distance, a distance determined between a corresponding base location and the first anchor item location, the first identified content item for designating which candidate content items for which a content type is not known or specified by a user are to be retrieved; a threshold setter module configured to set a first threshold based on the criterion distance that the candidate content items must meet to be selected, wherein the first threshold comprises an assigned value on a scale of 1 to 10, where a value of 1 indicates a very small distance between a corresponding base location and candidate content item and a value of 10 indicates a great distance between the corresponding base location and candidate content item, and wherein the criterion distance is determined, using criterion distance-determined granularity thresholding, as a distance between the corresponding base location of the multiple base locations and the first anchor item location, further wherein the distance from the corresponding base location is ranked on the scale and as the distance from the corresponding base location increases, then longer distances are encompassed by fewer gradations of the scale, such that distance granularity on the scale is higher for locations geographically closer to the corresponding base location than for locations further away from the corresponding base location; said location data extractor module further being configured to extract, as a first candidate location, the location data for a first candidate content item, and to determine, as a first candidate distance, the distance between the corresponding base location of the multiple base locations and the first candidate location; a selector module configured to select the first candidate content item as similar for content item retrieval based on (i) the first candidate distance that corresponds to the distance between the corresponding base location of the multiple base locations and the first candidate location and (ii) the first threshold that is based upon the criterion distance, wherein the first candidate content item is selected as being similar to the first identified content item in response to the determined first candidate distance, when compared to the first threshold, being within or with the first threshold; a result output module configured to output a selection signal for indicating retrieval of the first candidate content item when the first candidate location of the candidate content item is selected as being similar to the first identified content item for content item retrieval; a controller configured to coordinate an overall functioning of respective modules; a user interface; and a storage device, wherein said controller is further configured to interact with the user interface and the storage device, the storage device for storage of content items subject to being retrieved, wherein the controller, the base location extractor, the location extractor, the threshold setter, the selector, and the result output modules, portions thereof, and the retrieval system as a whole, comprise a combination of hardware, software, and firmware configured to perform the corresponding functions of the respective controller and modules. | 9. A content item retrieval system comprising: a base location extractor module configured to determine multiple base locations, based on GPS information or user entry, each base location being a location from which to apply a corresponding criterion distance-determined granularity thresholding for setting a threshold for location similarity in selecting or rejecting target items for content item retrieval, wherein criterion distance-determined granularity thresholding is applied for each base location of the multiple base locations separately based on differences in distance between farther locations being less important than between equally distant closer locations, further wherein the farther in distance moved from a corresponding base location of the multiple base locations, the less important, in terms of determining similarity, are differences in distance between locations of different content items at the corresponding further distances from the corresponding base location; a location data extractor module configured to extract, as a first anchor item location, location data for a first identified anchor content item and to determine, as a criterion distance, a distance determined between a corresponding base location and the first anchor item location, the first identified content item for designating which candidate content items for which a content type is not known or specified by a user are to be retrieved; a threshold setter module configured to set a first threshold based on the criterion distance that the candidate content items must meet to be selected, wherein the first threshold comprises an assigned value on a scale of 1 to 10, where a value of 1 indicates a very small distance between a corresponding base location and candidate content item and a value of 10 indicates a great distance between the corresponding base location and candidate content item, and wherein the criterion distance is determined, using criterion distance-determined granularity thresholding, as a distance between the corresponding base location of the multiple base locations and the first anchor item location, further wherein the distance from the corresponding base location is ranked on the scale and as the distance from the corresponding base location increases, then longer distances are encompassed by fewer gradations of the scale, such that distance granularity on the scale is higher for locations geographically closer to the corresponding base location than for locations further away from the corresponding base location; said location data extractor module further being configured to extract, as a first candidate location, the location data for a first candidate content item, and to determine, as a first candidate distance, the distance between the corresponding base location of the multiple base locations and the first candidate location; a selector module configured to select the first candidate content item as similar for content item retrieval based on (i) the first candidate distance that corresponds to the distance between the corresponding base location of the multiple base locations and the first candidate location and (ii) the first threshold that is based upon the criterion distance, wherein the first candidate content item is selected as being similar to the first identified content item in response to the determined first candidate distance, when compared to the first threshold, being within or with the first threshold; a result output module configured to output a selection signal for indicating retrieval of the first candidate content item when the first candidate location of the candidate content item is selected as being similar to the first identified content item for content item retrieval; a controller configured to coordinate an overall functioning of respective modules; a user interface; and a storage device, wherein said controller is further configured to interact with the user interface and the storage device, the storage device for storage of content items subject to being retrieved, wherein the controller, the base location extractor, the location extractor, the threshold setter, the selector, and the result output modules, portions thereof, and the retrieval system as a whole, comprise a combination of hardware, software, and firmware configured to perform the corresponding functions of the respective controller and modules. 10. The system of claim 9 , further comprising said threshold setter module setting a second threshold based on the criterion distance, which second threshold together with the first threshold comprises a range, and said selector module for selecting the first candidate content item as being similar in response to the first candidate distance being within the range. | 0.687075 |
7,890,512 | 10 | 12 | 10. The process of claim 9 , wherein each pair-wise SDF f i (k) is given by the equation f β‘ ( x , y ) = β i = 1 n β’ W ii β‘ ( K β‘ ( x , x i ) - K β‘ ( y , x i ) ) 2 , wherein, x is a feature vector for a training image under consideration, y is a feature vector for the new image, W is a diagonal matrix with non-negative entries, and K( ) is a kernel given by the equation K(x,y)=Ο(x)Ο(y), wherein Ο( ) is a mapping function which maps a given feature vector to a very high dimensional vector. | 10. The process of claim 9 , wherein each pair-wise SDF f i (k) is given by the equation f β‘ ( x , y ) = β i = 1 n β’ W ii β‘ ( K β‘ ( x , x i ) - K β‘ ( y , x i ) ) 2 , wherein, x is a feature vector for a training image under consideration, y is a feature vector for the new image, W is a diagonal matrix with non-negative entries, and K( ) is a kernel given by the equation K(x,y)=Ο(x)Ο(y), wherein Ο( ) is a mapping function which maps a given feature vector to a very high dimensional vector. 12. The process of claim 10 , wherein the process action of estimating a cluster association probability p(k) for comprises actions of: generating a probability density function (PDF) which estimates the visual features in the training images in and utilizing the PDF to estimate the cluster association probability p(k). | 0.786285 |
9,565,521 | 11 | 17 | 11. An apparatus comprising: at least one processor; and a non-transitory processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including: recognizing an activity performed at a first place based on sensor data of an electronic device, wherein the first place comprises an unlabeled semantic place without an assigned semantic place label; determining a location for the first place by performing localization for the electronic device; determining an observed mapping between the activity and the location for the first place; determining a typical mapping between the activity and a second place, wherein the second place comprises a labeled semantic place with an assigned semantic place label; based on the observed mapping and the typical mapping, assigning the same sematic place label assigned to the labeled semantic place to the location for the first place; and updating a semantic place map to include the semantic place label assigned to the location for the first place. | 11. An apparatus comprising: at least one processor; and a non-transitory processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including: recognizing an activity performed at a first place based on sensor data of an electronic device, wherein the first place comprises an unlabeled semantic place without an assigned semantic place label; determining a location for the first place by performing localization for the electronic device; determining an observed mapping between the activity and the location for the first place; determining a typical mapping between the activity and a second place, wherein the second place comprises a labeled semantic place with an assigned semantic place label; based on the observed mapping and the typical mapping, assigning the same sematic place label assigned to the labeled semantic place to the location for the first place; and updating a semantic place map to include the semantic place label assigned to the location for the first place. 17. The apparatus of claim 11 , wherein the operations further comprise: eliminating location noise by differentiating neighboring places based on the activity. | 0.782609 |
9,978,022 | 11 | 12 | 11. A non-transitory computer readable storage medium having executable computer program instructions embodied therein, the computer program instructions comprising: maintaining social information for a social networking system user; receiving a search query from a user device associated with the user; receiving a location of the user device; determining a list of search results based on the search query, each search result comprising a search value, the search value indicating a quality of the match between the search query and the search result; associating each search result with a third-party content object; for each of the associated third party content objects, identifying a set of connected users of the user associated with the third-party content object, a connected user being associated with a third-party content object when the connected user takes an action with respect to the third-party content object; for each connected user in the set of connected users, determining a weighing factor for the connected user based on characteristics of the user's relationship to the connected user; and determining a connection value for the third-party content object based on the weighing factors of the connected users in the set of connected users; calculating a relevance score for each search result by matching the user location, social information, and search value to the third-party content object associated with the search result, wherein calculating the relevance score comprises: determining a location value for the third-party content object, the location value based on a proximity between a location assigned to the third-party content object and the user location; determining an interest value for the third-party content object, the interest value based on whether a category assigned to the third-party content object is included in one or more categories associated with affinity information for the user; determining a time value for the third-party content object, the time value based on whether a current time is within a delivery time range assigned to the third-party content object; and combining the search value, location value, interest value, connection value, and time value to determine the relevance score; adjusting the relevance score for each search result to determine an adjusted relevance score based on the connection value for the third-party content object associated with the search result; ranking the list of search results by the adjusted relevance scores of the third-party content objects associated with the search results; and providing the ranked list of search results to a notification controller. | 11. A non-transitory computer readable storage medium having executable computer program instructions embodied therein, the computer program instructions comprising: maintaining social information for a social networking system user; receiving a search query from a user device associated with the user; receiving a location of the user device; determining a list of search results based on the search query, each search result comprising a search value, the search value indicating a quality of the match between the search query and the search result; associating each search result with a third-party content object; for each of the associated third party content objects, identifying a set of connected users of the user associated with the third-party content object, a connected user being associated with a third-party content object when the connected user takes an action with respect to the third-party content object; for each connected user in the set of connected users, determining a weighing factor for the connected user based on characteristics of the user's relationship to the connected user; and determining a connection value for the third-party content object based on the weighing factors of the connected users in the set of connected users; calculating a relevance score for each search result by matching the user location, social information, and search value to the third-party content object associated with the search result, wherein calculating the relevance score comprises: determining a location value for the third-party content object, the location value based on a proximity between a location assigned to the third-party content object and the user location; determining an interest value for the third-party content object, the interest value based on whether a category assigned to the third-party content object is included in one or more categories associated with affinity information for the user; determining a time value for the third-party content object, the time value based on whether a current time is within a delivery time range assigned to the third-party content object; and combining the search value, location value, interest value, connection value, and time value to determine the relevance score; adjusting the relevance score for each search result to determine an adjusted relevance score based on the connection value for the third-party content object associated with the search result; ranking the list of search results by the adjusted relevance scores of the third-party content objects associated with the search results; and providing the ranked list of search results to a notification controller. 12. The non-transitory computer readable medium of claim 11 , wherein maintaining the social information further comprises: maintaining the affinity information for the user according to one or more categories; and maintaining a plurality of connections between the user and other users of the social networking system. | 0.695611 |
7,613,731 | 1 | 15 | 1. A computer system for presenting an electronic document to a viewer to facilitate comprehension and control display and speed of delivery, comprising: a plurality of knowledge databases in the computer system for use in assigning an emphasis value to each word in the electronic document; an annotation module in the computer system including a cognitive cluster parser configured to group selected word pairings as cognitive clusters to be treated as one word and an analysis engine configured to assign an emphasis value to each word and cognitive cluster, the cognitive cluster parser and analysis engine interacting to generate a first tagged file of assigned emphasis values for each word and cognitive cluster; a first analysis module in the computer system including a compiler engine configured to derive emphasis values for recognizability and comprehensibility and an author interface configured to facilitate tag editing, the first analysis module for processing the first tagged file to generate a second tagged file of derived emphasis values; a second analysis module in the computer system including a property editor configured to facilitate editing of properties of selected words and cognitive clusters in the electronic document, the second analysis module for processing the second tagged file to generate a deliverable file that dynamically controls the presentation of the electronic document to the viewer; a printer or an electronic display device; and a delivery display module operative with the property deliverable file and the printer or electronic display device to at least one of print or display the electronic document. | 1. A computer system for presenting an electronic document to a viewer to facilitate comprehension and control display and speed of delivery, comprising: a plurality of knowledge databases in the computer system for use in assigning an emphasis value to each word in the electronic document; an annotation module in the computer system including a cognitive cluster parser configured to group selected word pairings as cognitive clusters to be treated as one word and an analysis engine configured to assign an emphasis value to each word and cognitive cluster, the cognitive cluster parser and analysis engine interacting to generate a first tagged file of assigned emphasis values for each word and cognitive cluster; a first analysis module in the computer system including a compiler engine configured to derive emphasis values for recognizability and comprehensibility and an author interface configured to facilitate tag editing, the first analysis module for processing the first tagged file to generate a second tagged file of derived emphasis values; a second analysis module in the computer system including a property editor configured to facilitate editing of properties of selected words and cognitive clusters in the electronic document, the second analysis module for processing the second tagged file to generate a deliverable file that dynamically controls the presentation of the electronic document to the viewer; a printer or an electronic display device; and a delivery display module operative with the property deliverable file and the printer or electronic display device to at least one of print or display the electronic document. 15. The system for presenting an electronic document of claim 1 wherein the compiler engine is operative with a tempo function of the author interface to generate a tempo value for each word that is used to manipulate a rate of presentation for a section of the electronic document. | 0.599432 |
8,280,872 | 1 | 8 | 1. A method comprising: receiving a posting to a forum, the posting requesting a response; extracting a keyword from the posting; retrieving a responder identifier by executing a first query with the extracted keyword as a retrieval argument of the first query, the responder identifier corresponding to a person available to provide the response; obtaining a count of keywords by executing a second query with the responder identifier as a retrieval argument of the second query, the count of keywords quantifying a number of times the keyword is included in responses provided by the person available to provide the response; identifying the person as a responder to respond to the posting based on the count of keywords; and assigning the posting to the identified person to provide the response. | 1. A method comprising: receiving a posting to a forum, the posting requesting a response; extracting a keyword from the posting; retrieving a responder identifier by executing a first query with the extracted keyword as a retrieval argument of the first query, the responder identifier corresponding to a person available to provide the response; obtaining a count of keywords by executing a second query with the responder identifier as a retrieval argument of the second query, the count of keywords quantifying a number of times the keyword is included in responses provided by the person available to provide the response; identifying the person as a responder to respond to the posting based on the count of keywords; and assigning the posting to the identified person to provide the response. 8. The method of claim 1 , wherein the identifying of the person as the responder is based on the count of keywords failing to transgress a threshold percentage of a highest count among a plurality of counts of keywords. | 0.608541 |
8,285,744 | 7 | 9 | 7. The system of claim 1 , wherein the manufacturing process search engine is further configured to aggregate search results from the at least one data broker and at least one of the plurality of indexing agents to provide at least one search result object to a user. | 7. The system of claim 1 , wherein the manufacturing process search engine is further configured to aggregate search results from the at least one data broker and at least one of the plurality of indexing agents to provide at least one search result object to a user. 9. The system of claim 7 , wherein the search result object comprises at least one of a text object or a graphical object. | 0.5 |
8,566,277 | 3 | 5 | 3. The system of claim 2 , wherein the object model module creates a customized object model for the copybook using the REDEFINE criteria and stores the customized object model in the model library. | 3. The system of claim 2 , wherein the object model module creates a customized object model for the copybook using the REDEFINE criteria and stores the customized object model in the model library. 5. The system of claim 3 , wherein the REDEFINE module identifies a controlling numeric value for the REDEFINE clause, identifies an instance of the REDEFINE clause affecting a REDEFINE subset, recursively rereads the REDEFINE subset based at least in part on a new definition specified by the customized object model, and automatically forms each reread portion of the REDEFINE subset as the object instance. | 0.5 |
9,864,645 | 1 | 16 | 1. A system comprising: wrapping code coupled to an error handler of a routine, in which the routine produces an error and the wrapping code wraps the error with relevant information to provide a wrapped exception instance, including to use an exception type hierarchy to preserve information in the wrapped exception instance including an exception type, and wherein the wrapped exception instance comprises a single exception instance transformed from a series of errors; and an exception manager that receives the wrapped exception instance and determines one or more actions to take based upon the exception type of the wrapped exception instance. | 1. A system comprising: wrapping code coupled to an error handler of a routine, in which the routine produces an error and the wrapping code wraps the error with relevant information to provide a wrapped exception instance, including to use an exception type hierarchy to preserve information in the wrapped exception instance including an exception type, and wherein the wrapped exception instance comprises a single exception instance transformed from a series of errors; and an exception manager that receives the wrapped exception instance and determines one or more actions to take based upon the exception type of the wrapped exception instance. 16. The system of claim 1 further comprising a logging mechanism that logs data corresponding to the wrapped exception instance. | 0.84 |
9,886,496 | 9 | 10 | 9. A computing device comprising: a processor; a memory configured to be in communication with the processor, and effective to store instructions; and the processor effective to, in accordance with the instructions: identify contents of a database, wherein the contents of the database are categorized into intelligence fields, wherein the intelligence fields relate to particular types of information among the contents of the database, and wherein the database is stored in a first data structure stored in a memory; receive a selection of a particular intelligence field in the database; search a first data file in the database for matching data among the particular types of information, wherein the particular types of information are categorized within the selected particular intelligence field; identify a match between a first piece of data of the particular type of information and a second piece of data of the particular type of information, wherein the first piece of data is in the first data file and related to a first entity, the first piece of data includes a first link to a first source stored in the memory, the first source is stored in a second data structure different from the first data structure, and wherein the second piece of data is related to a second entity in a second data file, the second piece of data includes a second link to a second source stored in the memory, the second source is different from the first source, and the second source is stored in a third data structure different from the first data structure and the second data structure; determine that the first entity and the second entity are related based on the match; and update the first data file to produce an updated first data file, wherein the updated first data file indicates a relationship between the first entity and the second entity; wherein the memory is further effective to store the updated first data file; and wherein the processor is further effective to cause at least some of the contents of the first data file related to the first entity and the second entity to be displayed on a display. | 9. A computing device comprising: a processor; a memory configured to be in communication with the processor, and effective to store instructions; and the processor effective to, in accordance with the instructions: identify contents of a database, wherein the contents of the database are categorized into intelligence fields, wherein the intelligence fields relate to particular types of information among the contents of the database, and wherein the database is stored in a first data structure stored in a memory; receive a selection of a particular intelligence field in the database; search a first data file in the database for matching data among the particular types of information, wherein the particular types of information are categorized within the selected particular intelligence field; identify a match between a first piece of data of the particular type of information and a second piece of data of the particular type of information, wherein the first piece of data is in the first data file and related to a first entity, the first piece of data includes a first link to a first source stored in the memory, the first source is stored in a second data structure different from the first data structure, and wherein the second piece of data is related to a second entity in a second data file, the second piece of data includes a second link to a second source stored in the memory, the second source is different from the first source, and the second source is stored in a third data structure different from the first data structure and the second data structure; determine that the first entity and the second entity are related based on the match; and update the first data file to produce an updated first data file, wherein the updated first data file indicates a relationship between the first entity and the second entity; wherein the memory is further effective to store the updated first data file; and wherein the processor is further effective to cause at least some of the contents of the first data file related to the first entity and the second entity to be displayed on a display. 10. The computing device of claim 9 , wherein the first entity includes a person, a business, a product, a facility, or a service. | 0.889267 |
8,731,300 | 12 | 13 | 12. The method of claim 1 , wherein: the generating of the plurality of computer-generated images based on the query string includes, for each of a set of fonts, automatically generating images based on the query string; the training of the model based on the plurality of computer-generated images includes modeling the query string with a semi-continuous hidden Markov model, a subset of the parameters of the semi-continuous hidden Markov model being estimated based on features extracted from the images in the different fonts, and other parameters of the semi-continuous hidden Markov model being previously trained on sample handwritten word images without consideration of the query string; the scoring of the candidate handwritten word images includes scoring candidate handwritten word images of the collection against the trained semi-continuous hidden Markov model; and the identifying of the subset of the word images based on the scores includes labeling one or more of the candidate handwritten word images, or a document containing one or more of the candidate handwritten word images. | 12. The method of claim 1 , wherein: the generating of the plurality of computer-generated images based on the query string includes, for each of a set of fonts, automatically generating images based on the query string; the training of the model based on the plurality of computer-generated images includes modeling the query string with a semi-continuous hidden Markov model, a subset of the parameters of the semi-continuous hidden Markov model being estimated based on features extracted from the images in the different fonts, and other parameters of the semi-continuous hidden Markov model being previously trained on sample handwritten word images without consideration of the query string; the scoring of the candidate handwritten word images includes scoring candidate handwritten word images of the collection against the trained semi-continuous hidden Markov model; and the identifying of the subset of the word images based on the scores includes labeling one or more of the candidate handwritten word images, or a document containing one or more of the candidate handwritten word images. 13. The method of claim 12 , wherein the subset of parameters includes transition probabilities and mixture weights and the other parameters include means and covariance matrices. | 0.5 |
8,788,516 | 8 | 14 | 8. A computer-implemented method, comprising: determining, by one or more computing systems, a plurality of social interactions associated with a plurality of people, each social interaction comprising a particular person interacting with a particular social object of a plurality of social objects; generating, by the one or more computing systems, a social object matrix using the determined social interactions; generating, by the one or more computing systems, a social brain by performing Singular Value Decomposition (SVD) on the social object matrix, the social brain comprising a singular value representation of the social object matrix; determining, by the one or more computing systems, text from the social objects of the determined social interactions; generating, by the one or more computing systems, a term-document matrix (TDM) using the determined text; generating, by the one or more computing systems, a semantic brain by performing SVD on the TDM, the semantic brain comprising a singular value representation of the TDM; generating, by the one or more computing systems, an index using the determined text; and performing, by the one or more computing systems, a query using the social brain, the semantic brain, and the index. | 8. A computer-implemented method, comprising: determining, by one or more computing systems, a plurality of social interactions associated with a plurality of people, each social interaction comprising a particular person interacting with a particular social object of a plurality of social objects; generating, by the one or more computing systems, a social object matrix using the determined social interactions; generating, by the one or more computing systems, a social brain by performing Singular Value Decomposition (SVD) on the social object matrix, the social brain comprising a singular value representation of the social object matrix; determining, by the one or more computing systems, text from the social objects of the determined social interactions; generating, by the one or more computing systems, a term-document matrix (TDM) using the determined text; generating, by the one or more computing systems, a semantic brain by performing SVD on the TDM, the semantic brain comprising a singular value representation of the TDM; generating, by the one or more computing systems, an index using the determined text; and performing, by the one or more computing systems, a query using the social brain, the semantic brain, and the index. 14. The computer-implemented method of claim 8 , wherein the social object matrix comprises a social object-by-person matrix. | 0.915882 |
8,494,857 | 23 | 24 | 23. The method of claim 20 , further comprising monitoring changes in the fluency of the speech of the patient over time. | 23. The method of claim 20 , further comprising monitoring changes in the fluency of the speech of the patient over time. 24. The method of claim 23 , further comprising modifying a therapy regimen based on changes in the fluency of the speech of the patient. | 0.5 |
10,095,781 | 9 | 15 | 9. A non-transitory machine-readable storage medium storing instructions which, when executed, cause a processing device to: receive, by a processing device, a document comprising a first topic to be imported into a content management system; calculate a first signature of the first topic in view of content associated with the first topic; determine, by comparing the first signature with a respective signature of the plurality of topics stored in the content management system, whether the first topic of the document is a match to at least one of a plurality of topics stored in the content management system; and in response to a determination that the first topic of the document is the match to the at least one topic stored in the content management system: identify a plurality of hyperlinks embedded in the content associated with the first topic; determine whether at least one of the plurality of hyperlinks embedded in the content associated with the first topic matches any hyperlink associated with the at least one topic stored in the content management system to confirm whether the first topic of the document is a match to the at least one topic stored in the content management system; and in response to determining that the at least one of the plurality of hyperlinks embedded in the content associated with the first topic does not match any hyperlink associated with the at least one topic stored in the content management system: confirm that the first topic of the document is not a match; and add the first topic and the content associated with the first topic to the content management system. | 9. A non-transitory machine-readable storage medium storing instructions which, when executed, cause a processing device to: receive, by a processing device, a document comprising a first topic to be imported into a content management system; calculate a first signature of the first topic in view of content associated with the first topic; determine, by comparing the first signature with a respective signature of the plurality of topics stored in the content management system, whether the first topic of the document is a match to at least one of a plurality of topics stored in the content management system; and in response to a determination that the first topic of the document is the match to the at least one topic stored in the content management system: identify a plurality of hyperlinks embedded in the content associated with the first topic; determine whether at least one of the plurality of hyperlinks embedded in the content associated with the first topic matches any hyperlink associated with the at least one topic stored in the content management system to confirm whether the first topic of the document is a match to the at least one topic stored in the content management system; and in response to determining that the at least one of the plurality of hyperlinks embedded in the content associated with the first topic does not match any hyperlink associated with the at least one topic stored in the content management system: confirm that the first topic of the document is not a match; and add the first topic and the content associated with the first topic to the content management system. 15. The machine-readable storage medium of claim 9 , wherein to calculate the first signature, the processing device is further to: calculate a plurality of hash values in view of a hash function for a plurality of subsets of the content associated with the first topic stored in the content management system; determine a first subset of the plurality of subsets whose hash value is a minimum among the plurality of hash values; and assign the first subset of the content as the signature of the first topic. | 0.654681 |
7,493,658 | 1 | 12 | 1. A system implemented on a computer apparatus for scanning a computer file including source code of a computer program in a given computer language for malware, the system comprising: means for separating the source code into groups of constituent parts, each group comprising parts of a different type of structural part of the program; means for processing each group to count the number of occurrences in that group of characters of a character set to obtain a frequency distribution of characters in that group; means for comparing the character frequency distribution of each group with an expected range of frequency distributions; and means for flagging the file as suspect or not depending on the result of one or more comparisons by the comparing means. | 1. A system implemented on a computer apparatus for scanning a computer file including source code of a computer program in a given computer language for malware, the system comprising: means for separating the source code into groups of constituent parts, each group comprising parts of a different type of structural part of the program; means for processing each group to count the number of occurrences in that group of characters of a character set to obtain a frequency distribution of characters in that group; means for comparing the character frequency distribution of each group with an expected range of frequency distributions; and means for flagging the file as suspect or not depending on the result of one or more comparisons by the comparing means. 12. A system according to claim 1 , wherein the groups comprise at least one of a group of comments, a group of variable names, a group of subroutine names, and a group of strings. | 0.723926 |
8,751,284 | 1 | 2 | 1. A non-transitory computer-readable medium containing code executable by a computer processor to generate a workflow engine comprising: a Petri net domain model comprising a set of objects comprising a token object, a place object, an arc object, a transition object, and one or more trigger objects, wherein (a) each object represents a particular type of element of a Petri net model and (b) the one or more trigger objects represent triggering a transition object based at least in part on stimuli external to the workflow engine; and one or more runtime components configured for: reading source code representing a particular workflow, wherein the source code indicates elements of the particular workflow and connectors between elements of the particular workflow to sequence the elements of the particular workflow; loading the particular workflow into memory by mapping each element of the particular workflow to one or more objects of the set of objects and by mapping each connector of the particular workflow to one or more objects of the set of objects based on rules governing the Petri net model; and executing the particular workflow loaded into the memory, wherein the workflow engine further comprises one or more abstraction layer components comprising a transition layer from the Petri net domain model to an operating system, the abstraction layer configured to delegate one or more tasks associated with elements of the particular workflow of the Petri net domain to the operating system, via the transition layer, the delegated tasks to be performed by the operating system. | 1. A non-transitory computer-readable medium containing code executable by a computer processor to generate a workflow engine comprising: a Petri net domain model comprising a set of objects comprising a token object, a place object, an arc object, a transition object, and one or more trigger objects, wherein (a) each object represents a particular type of element of a Petri net model and (b) the one or more trigger objects represent triggering a transition object based at least in part on stimuli external to the workflow engine; and one or more runtime components configured for: reading source code representing a particular workflow, wherein the source code indicates elements of the particular workflow and connectors between elements of the particular workflow to sequence the elements of the particular workflow; loading the particular workflow into memory by mapping each element of the particular workflow to one or more objects of the set of objects and by mapping each connector of the particular workflow to one or more objects of the set of objects based on rules governing the Petri net model; and executing the particular workflow loaded into the memory, wherein the workflow engine further comprises one or more abstraction layer components comprising a transition layer from the Petri net domain model to an operating system, the abstraction layer configured to delegate one or more tasks associated with elements of the particular workflow of the Petri net domain to the operating system, via the transition layer, the delegated tasks to be performed by the operating system. 2. The computer-readable medium of claim 1 wherein the set of objects of the Petri net domain model further comprises one or more guard objects to represent conditional logic placed on one or more objects representing one or more connectors of the particular workflow. | 0.5 |
10,102,295 | 1 | 2 | 1. A method comprising, by one or more computing devices: receiving, from a first client system of a first user, a first query inputted by the first user at the first client system, the first query comprising one or more n-grams; identifying one or more ideograms, each ideogram being associated with one or more tags, each identified ideogram being associated with at least one tag matching at least one of the n-grams of the received first query; calculating, for each identified ideogram, a use-probability for the ideogram given the received first query, wherein the use-probability is based at least in part on a frequency of use associated with the ideogram; sending, to the first client system, instructions for presenting a first set of ideograms comprising one or more of the identified ideograms, the first set being determined based on the calculated use-probabilities associated with the ideograms; receiving a second query from a second client system of a second user, the second query inputted by the second user at the second client system, the second query comprising one or more n-grams; and identifying one or more ideograms, each identified ideogram associated with at least one tag matching at least one of the n-grams of the received second query. | 1. A method comprising, by one or more computing devices: receiving, from a first client system of a first user, a first query inputted by the first user at the first client system, the first query comprising one or more n-grams; identifying one or more ideograms, each ideogram being associated with one or more tags, each identified ideogram being associated with at least one tag matching at least one of the n-grams of the received first query; calculating, for each identified ideogram, a use-probability for the ideogram given the received first query, wherein the use-probability is based at least in part on a frequency of use associated with the ideogram; sending, to the first client system, instructions for presenting a first set of ideograms comprising one or more of the identified ideograms, the first set being determined based on the calculated use-probabilities associated with the ideograms; receiving a second query from a second client system of a second user, the second query inputted by the second user at the second client system, the second query comprising one or more n-grams; and identifying one or more ideograms, each identified ideogram associated with at least one tag matching at least one of the n-grams of the received second query. 2. The method of claim 1 , wherein at least one of the n-grams corresponds to an emoticon. | 0.914122 |
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