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8,086,590 | 7 | 10 | 7. A computer-implemented method for providing product suggestion information, the computer-implemented method using a computing device having memory, processor, and database subsystems and comprising: crawling a plurality of sources via a computer system processor; extracting product information comprising product identifiers, name, size, color, and price for each individual product included in the plurality of sources; storing the product identifiers, name, size, color, and price for each individual product in a product database; categorizing each individual product based on a type, a trade name, and the price; generating a product suggestions message in response to a specific product query from a user, the product suggestions message comprising one or more marked product suggestions linked to a respective one or more product details pages; and continuously returning the one or more product details pages for each marked product suggestion stored in the product database that is relevant to partial query strings formulated by the user as the user inputs additional query strings until the user completes the query strings. | 7. A computer-implemented method for providing product suggestion information, the computer-implemented method using a computing device having memory, processor, and database subsystems and comprising: crawling a plurality of sources via a computer system processor; extracting product information comprising product identifiers, name, size, color, and price for each individual product included in the plurality of sources; storing the product identifiers, name, size, color, and price for each individual product in a product database; categorizing each individual product based on a type, a trade name, and the price; generating a product suggestions message in response to a specific product query from a user, the product suggestions message comprising one or more marked product suggestions linked to a respective one or more product details pages; and continuously returning the one or more product details pages for each marked product suggestion stored in the product database that is relevant to partial query strings formulated by the user as the user inputs additional query strings until the user completes the query strings. 10. The computer-implemented method of claim 7 , wherein the product information is returned via a graphical user interface element that displays the product identifiers, name, size, color, or price for each relevant product that corresponds to the query strings received from the user. | 0.731707 |
7,587,069 | 5 | 8 | 5. A facial expression learning method for learning data to be used by a facial expression recognition apparatus, the facial expression recognition apparatus being adapted for recognizing an expression of a provided face image by using an expression learning data set including plural face images representing specific expressions as recognition targets and plural face images representing expressions different from the specific expressions, the facial expression learning method, performed by a processor, comprising an expression learning step of learning data to be used by the facial expression recognition apparatus, the facial expression recognition apparatus identifying the face images representing the specific expressions from provided face images on the basis of a face feature extracted from the expression learning data set by using a Gabor filter. | 5. A facial expression learning method for learning data to be used by a facial expression recognition apparatus, the facial expression recognition apparatus being adapted for recognizing an expression of a provided face image by using an expression learning data set including plural face images representing specific expressions as recognition targets and plural face images representing expressions different from the specific expressions, the facial expression learning method, performed by a processor, comprising an expression learning step of learning data to be used by the facial expression recognition apparatus, the facial expression recognition apparatus identifying the face images representing the specific expressions from provided face images on the basis of a face feature extracted from the expression learning data set by using a Gabor filter. 8. The facial expression learning method, performed by a processor, as claimed in claim 5 wherein the expression learning step includes: a weak hypothesis generation step of repeating processing to generate a weak hypothesis for estimating whether a provided face image is of the specific expression or not on the basis of the result of filtering by one Gabor filter selected from plural Gabor filters; a reliability calculation step of calculating reliability of the weak hypothesis generated at the weak hypothesis generation step on the basis of estimation performance of the weak hypothesis with respect to the expression learning data set; a data weighting update step of updating data weighting set for the expression learning data set on the basis of the reliability; and a support vector learning step of learning a support vector for identifying a face image representing the specific expression on the basis of the face feature extracted form the expression learning data set by a predetermined Gabor filter, and wherein the weak hypothesis generation step includes generating the weak hypothesis while selecting one Gabor filter having the highest estimation performance with respect to the expression learning data set every time the data weighting is updated, and the support vector learning step includes extracting the face feature by using the Gabor filter selected by the weak hypothesis generated by the weak hypothesis generation unit, and thus learning the support vector. | 0.500335 |
9,711,134 | 9 | 14 | 9. A method comprising: receiving, by a device comprising a processor, first text data and second text data; converting, by the device, at least a part of the first text data into first synthesized voice data based, at least in part, on first voice data; converting, by the device, at least a part of the second text data into second synthesized voice data based, at least in part, on transformed second voice data that is transformed from second voice data by a voice transformation function, wherein the voice transformation function relates to a power spectrum difference between the first voice data and the transformed second voice data; sending, by the device, the first synthesized voice data to a first speaker to render the first synthesized voice data and the second synthesized voice data to a second speaker to render the second synthesized voice data, wherein the first synthesized voice data and the second synthesized voice data are to be rendered substantially simultaneously, and wherein the voice transformation function facilitates distinguishing the first voice data from the second voice data as distinct data sources; and in response to receiving, by the device, via an input device, an indication that corresponds to a selection of the first synthesized voice data or a selection of the second synthesized voice data, causing, the device to generate sound, via at least one of the first speaker or the second speaker, that represents additional data corresponding to the first synthesized voice data or the second synthesized voice data based on the indication. | 9. A method comprising: receiving, by a device comprising a processor, first text data and second text data; converting, by the device, at least a part of the first text data into first synthesized voice data based, at least in part, on first voice data; converting, by the device, at least a part of the second text data into second synthesized voice data based, at least in part, on transformed second voice data that is transformed from second voice data by a voice transformation function, wherein the voice transformation function relates to a power spectrum difference between the first voice data and the transformed second voice data; sending, by the device, the first synthesized voice data to a first speaker to render the first synthesized voice data and the second synthesized voice data to a second speaker to render the second synthesized voice data, wherein the first synthesized voice data and the second synthesized voice data are to be rendered substantially simultaneously, and wherein the voice transformation function facilitates distinguishing the first voice data from the second voice data as distinct data sources; and in response to receiving, by the device, via an input device, an indication that corresponds to a selection of the first synthesized voice data or a selection of the second synthesized voice data, causing, the device to generate sound, via at least one of the first speaker or the second speaker, that represents additional data corresponding to the first synthesized voice data or the second synthesized voice data based on the indication. 14. The method of claim 9 , wherein the first speaker and the second speaker are on a headset, and wherein the sensor comprises a gyro sensor in the headset to detect a headset tilt gesture substantially in the direction of the first speaker or the second speaker. | 0.65625 |
9,454,966 | 9 | 10 | 9. A 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 recognition output associated with speech from a user; receiving an error model characterizing how automatic speech recognition transcription errors are made; generating guesses of a true transcription based on an error model-based algorithm, wherein the error model-based algorithm uses the error model and the speech recognition output; and generating, based on the guesses of the true transcription, a personalized user model associated with a user voiceprint of the user, wherein the generating of the personalized user model comprises iteratively guessing the true transcription until a threshold is met. | 9. A 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 recognition output associated with speech from a user; receiving an error model characterizing how automatic speech recognition transcription errors are made; generating guesses of a true transcription based on an error model-based algorithm, wherein the error model-based algorithm uses the error model and the speech recognition output; and generating, based on the guesses of the true transcription, a personalized user model associated with a user voiceprint of the user, wherein the generating of the personalized user model comprises iteratively guessing the true transcription until a threshold is met. 10. The system of claim 9 , wherein generating of the guesses further comprises repeating, until a threshold is met, steps comprising: guessing the true transcription from a current guess of the personalized user model, to yield a current guess of the true transcription; and guessing the personalized user model based on the current guess of the true transcription. | 0.509383 |
9,589,012 | 1 | 17 | 1. A computer-implemented method, comprising: receiving from a user a selection of an object among one or more objects included in a data model, the selection made through an object-selection interface; retrieving from computer memory a previously stored object definition that corresponds to the selected object, the previously stored object definition includes: an object query that, when executed, retrieves a set of time stamped events from a data store on a computing device, each event including a portion of raw machine data reflecting activity in an information technology environment; and an object schema identifying a set of one or more fields, each field defined by an extraction rule or regular expression that locates the field in the raw machine data and can be used to extract a field value from the field location from the raw machine data in each event in a subset of the set of time stamped events, each extraction rule or regular expression operating on the raw machine data in an event without modifying the event's raw machine data; and executing, against events in the data store that meet filtering criteria of the object query, a search query that references only field values that are extracted using the object schema and that produces a result based at least in part on the data reflecting the activity of the information technology environment. | 1. A computer-implemented method, comprising: receiving from a user a selection of an object among one or more objects included in a data model, the selection made through an object-selection interface; retrieving from computer memory a previously stored object definition that corresponds to the selected object, the previously stored object definition includes: an object query that, when executed, retrieves a set of time stamped events from a data store on a computing device, each event including a portion of raw machine data reflecting activity in an information technology environment; and an object schema identifying a set of one or more fields, each field defined by an extraction rule or regular expression that locates the field in the raw machine data and can be used to extract a field value from the field location from the raw machine data in each event in a subset of the set of time stamped events, each extraction rule or regular expression operating on the raw machine data in an event without modifying the event's raw machine data; and executing, against events in the data store that meet filtering criteria of the object query, a search query that references only field values that are extracted using the object schema and that produces a result based at least in part on the data reflecting the activity of the information technology environment. 17. The method of claim 1 , wherein the search query includes event-filtering criteria different than filtering criteria of the object query, and wherein the method further comprises: populating a pivot graphical user interface with data-manipulation controls that correspond to the set of one or more fields in the object schema for the selected object, and wherein the pivot graphical user interface enables a user to develop the search query and specify the event-filtering criteria that is different than the filtering criteria of the object query. | 0.725373 |
9,111,011 | 3 | 4 | 3. The method of claim 1 , further comprising: determining a first overall count representing a total number of times the first query was issued by users; and providing the first category-specific query suggestion in accordance with the first overall count. | 3. The method of claim 1 , further comprising: determining a first overall count representing a total number of times the first query was issued by users; and providing the first category-specific query suggestion in accordance with the first overall count. 4. The method of claim 3 , wherein providing the first category-specific query suggestion in accordance with the first overall count comprises: ranking the first category-specific query suggestion within the two query suggestions in accordance with the first overall count. | 0.932056 |
7,908,279 | 22 | 24 | 22. The electronic market place of claim 21 , wherein selecting a field of the first document comprises selecting a field from the group of fields comprising a product number field, a model number field, and a catalogue number field. | 22. The electronic market place of claim 21 , wherein selecting a field of the first document comprises selecting a field from the group of fields comprising a product number field, a model number field, and a catalogue number field. 24. The electronic market place of claim 22 , wherein the threshold comprises a percentage of all of the tokens in the first document. | 0.96665 |
8,041,741 | 1 | 8 | 1. A computer-implemented method for collecting information about a group of documents, comprising: under control of one or more computer systems configured with executable instructions, seeding the group of documents with one or more seed documents; identifying, in at least a portion of the group of documents, at least one reference to at least one secondary document; adding to the group of documents at least one secondary document obtainable using the identified at least one reference; identifying, for at least a portion of the documents in the group of documents, two or more attribute values associated with at least one aspect of the document; adding at least a portion of the identified attribute values to a set of information associated with the group of documents, the set of information enabling documents in the group of documents to be located based at least in part upon any of the attribute values in the set of information; applying a test to at least one secondary document; and adding the attribute values identified for the at least one secondary document to the set of information associated with the group of documents only if the applied test is satisfied. | 1. A computer-implemented method for collecting information about a group of documents, comprising: under control of one or more computer systems configured with executable instructions, seeding the group of documents with one or more seed documents; identifying, in at least a portion of the group of documents, at least one reference to at least one secondary document; adding to the group of documents at least one secondary document obtainable using the identified at least one reference; identifying, for at least a portion of the documents in the group of documents, two or more attribute values associated with at least one aspect of the document; adding at least a portion of the identified attribute values to a set of information associated with the group of documents, the set of information enabling documents in the group of documents to be located based at least in part upon any of the attribute values in the set of information; applying a test to at least one secondary document; and adding the attribute values identified for the at least one secondary document to the set of information associated with the group of documents only if the applied test is satisfied. 8. The computer-implemented method of claim 1 , wherein the attribute values are obtained for at least a portion of the group of documents by traversing references between documents of the group and parsing the contents of traversed-to documents. | 0.637168 |
10,127,927 | 3 | 7 | 3. The method of claim 2 , further comprising: receiving one or more test speech samples; generating a set of test data by extracting one or more acoustic features from every frame of the one or more test speech samples; and transforming the set of test data into transformed data using the PLDA model to capture emotion and/or speaking style in the transformed data; and using the transformed data for clustering and/or classification to discover speech with emotion or speaking styles similar to that captured in the transformed data. | 3. The method of claim 2 , further comprising: receiving one or more test speech samples; generating a set of test data by extracting one or more acoustic features from every frame of the one or more test speech samples; and transforming the set of test data into transformed data using the PLDA model to capture emotion and/or speaking style in the transformed data; and using the transformed data for clustering and/or classification to discover speech with emotion or speaking styles similar to that captured in the transformed data. 7. The method of claim 3 , wherein using the transformed data for clustering and/or classification to discover speech with similar emotion or speaking styles to that captured in the transformed data includes: training one or more emotional speech models from scratch using the transformed data; and performing speech recognition using the one or more trained emotional models. | 0.777778 |
7,882,148 | 2 | 4 | 2. The computer system according to claim 1 , wherein the activation attribute has a value. | 2. The computer system according to claim 1 , wherein the activation attribute has a value. 4. The computer system according to claim 2 , wherein the value of the activation attribute of the at least one context entity varies in a time dependent manner. | 0.957765 |
9,270,736 | 1 | 4 | 1. A method to provide semantic cache cloud services to connected devices, comprising: receiving, by a caching reverse proxy server, web traffic from semantic device applications; selectively filtering the received web traffic using dynamically created and updated lists of semantic web linked data namespaces based on global public and private data registries; automatically registering semantic objects representing semantic devices and semantic device applications into a public cached namespace, a private shadow namespace that is a shadow copy of the public cached namespace, and an activated private Q-context namespace that is a secure namespace, opaque to users, wherein: the private shadow namespace includes each of the semantic objects automatically instrumented by a time, a location, and a context, and the activated private Q-context namespace includes a Q-context that defines Q-context assertions, queries, and rules relevant to each of the instrumented semantic objects; recording the time, the location, and the context for each of the semantic objects; and coordinating and synchronizing the semantic objects within a semantic object plexus representing cooperating semantic devices and semantic device applications by applying the Q-context assertions, the queries, and the rules defined via Q-context processing for an interlock. | 1. A method to provide semantic cache cloud services to connected devices, comprising: receiving, by a caching reverse proxy server, web traffic from semantic device applications; selectively filtering the received web traffic using dynamically created and updated lists of semantic web linked data namespaces based on global public and private data registries; automatically registering semantic objects representing semantic devices and semantic device applications into a public cached namespace, a private shadow namespace that is a shadow copy of the public cached namespace, and an activated private Q-context namespace that is a secure namespace, opaque to users, wherein: the private shadow namespace includes each of the semantic objects automatically instrumented by a time, a location, and a context, and the activated private Q-context namespace includes a Q-context that defines Q-context assertions, queries, and rules relevant to each of the instrumented semantic objects; recording the time, the location, and the context for each of the semantic objects; and coordinating and synchronizing the semantic objects within a semantic object plexus representing cooperating semantic devices and semantic device applications by applying the Q-context assertions, the queries, and the rules defined via Q-context processing for an interlock. 4. The method according to claim 1 , wherein automatically registering the semantic objects into the public cached namespace, the private shadow namespace, and the activated private Q-context namespace comprises: registering semantic resources, caches, and proxy detected semantic web namespace traffic into the public cached namespace, the private shadow namespace, and the activated private Q-context namespace. | 0.844853 |
9,529,908 | 2 | 3 | 2. The one or more computer storage media of claim 1 , wherein querying the search index comprises: determining that a single atom is identified from the search query; identifying a posting list corresponding with the single atom; retrieving postings from only the first tier of the posting list; and providing search results from the first tier of the posting list. | 2. The one or more computer storage media of claim 1 , wherein querying the search index comprises: determining that a single atom is identified from the search query; identifying a posting list corresponding with the single atom; retrieving postings from only the first tier of the posting list; and providing search results from the first tier of the posting list. 3. The one or more computer storage media of claim 2 , wherein providing search results from the first tier of the posting list comprises providing search results corresponding with a predetermined number of postings with the highest ranks from the first tier of the posting list. | 0.916963 |
7,860,899 | 18 | 19 | 18. A computer readable storage medium comprising instructions which, when executed by one or more processors, cause the one of more processors to perform: receiving a logical description that references an abstract datatype in a database system, wherein the database system supports multiple database representations for the abstract datatype; receiving one or more sample queries that reference the abstract datatype; evaluating the one or more sample queries, wherein evaluating one or more sample queries comprises: determining a plurality of database representations that the database system supports for the abstract datatype; re-writing the one or more sample queries based on the plurality of database representations; performing a cost analysis of the re-written sample queries; based on the logical description and said evaluating said one or more sample queries, determining a database representation for the abstract datatype; wherein said database representation includes one or more base structures that are used to store data for the abstract datatype. | 18. A computer readable storage medium comprising instructions which, when executed by one or more processors, cause the one of more processors to perform: receiving a logical description that references an abstract datatype in a database system, wherein the database system supports multiple database representations for the abstract datatype; receiving one or more sample queries that reference the abstract datatype; evaluating the one or more sample queries, wherein evaluating one or more sample queries comprises: determining a plurality of database representations that the database system supports for the abstract datatype; re-writing the one or more sample queries based on the plurality of database representations; performing a cost analysis of the re-written sample queries; based on the logical description and said evaluating said one or more sample queries, determining a database representation for the abstract datatype; wherein said database representation includes one or more base structures that are used to store data for the abstract datatype. 19. The computer readable storage medium of claim 18 , further including instructions for performing: based on determining a database representation for the abstract datatype, generating a code to execute to create the database representation for the abstract datatype. | 0.501852 |
8,412,525 | 11 | 16 | 11. A computer-readable storage device storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising: converting a plurality of feature vectors into a plurality of log probability sets using a classifier ensemble that includes a plurality of classifiers, the plurality of feature vectors representing a speech utterance; transforming the plurality of log probability sets into a plurality of output symbol sequences using a decoder ensemble that includes a plurality of decoders; combining the plurality of output symbol sequences into a fusion output symbol sequence using a block fusion algorithm, the block fusion algorithm to use convergent iterative a-priori probability calculations to produce the fusion output sequence; and retrieving one or more stored speech utterances from a speech database based on at least one of the plurality of output symbol sequences or the fusion output symbol sequence. | 11. A computer-readable storage device storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising: converting a plurality of feature vectors into a plurality of log probability sets using a classifier ensemble that includes a plurality of classifiers, the plurality of feature vectors representing a speech utterance; transforming the plurality of log probability sets into a plurality of output symbol sequences using a decoder ensemble that includes a plurality of decoders; combining the plurality of output symbol sequences into a fusion output symbol sequence using a block fusion algorithm, the block fusion algorithm to use convergent iterative a-priori probability calculations to produce the fusion output sequence; and retrieving one or more stored speech utterances from a speech database based on at least one of the plurality of output symbol sequences or the fusion output symbol sequence. 16. The computer-readable storage device of claim 11 , wherein the retrieving includes using an n-gram based scoring algorithm that associates the plurality of output symbol sequences in an union operation during retrieval of one or more speech utterances from a speech database. | 0.867395 |
7,603,267 | 24 | 25 | 24. The method of claim 19 and further comprising: training a rules based grammar configured to map portions of the natural language input to the slots. | 24. The method of claim 19 and further comprising: training a rules based grammar configured to map portions of the natural language input to the slots. 25. The method of claim 24 , and further comprising using the rules based grammar and the statistical model to map terms from the natural language input to the slots and the preterminals derived from the schema, wherein using comprises: using only one of the statistical model and the rules based grammar to map terms from the natural language input to the slots of the schema; and using only one of the statistical model and the rules based grammar to map terms from the natural language input to the preterminals of the schema, wherein the rules based grammar is utilized to map terms from the natural language input to the slots and the statistical model is utilized to map terms from the natural language input to the preterminals. | 0.864641 |
8,510,323 | 1 | 7 | 1. A method, comprising: receiving a plurality of identity records, wherein the identity records provide attributes of entities, wherein the identity records may provide different or same values for the attributes; grouping the received identity records into a group of identity records; generating a composite query on values for selected attributes of the identity records in the group; applying the composite query to an entity database to obtain composite results of entity records in the entity database matching the attribute values of the composite query; for the identity records in the group, performing an individual query on attributes of one of the identity records against the composite results of the entity records to determine a candidate list of the entity records from the entity database for the identity record; for the identity records in the group, applying resolution rules to determine the entity records in the determined candidate list that are related to one of the identity records in the group according to the resolution rules; and storing entity relationship information on the determined entity records that are related to the identity records. | 1. A method, comprising: receiving a plurality of identity records, wherein the identity records provide attributes of entities, wherein the identity records may provide different or same values for the attributes; grouping the received identity records into a group of identity records; generating a composite query on values for selected attributes of the identity records in the group; applying the composite query to an entity database to obtain composite results of entity records in the entity database matching the attribute values of the composite query; for the identity records in the group, performing an individual query on attributes of one of the identity records against the composite results of the entity records to determine a candidate list of the entity records from the entity database for the identity record; for the identity records in the group, applying resolution rules to determine the entity records in the determined candidate list that are related to one of the identity records in the group according to the resolution rules; and storing entity relationship information on the determined entity records that are related to the identity records. 7. The method of claim 1 , further comprising: determining the received identity records that satisfy a grouping attribute, wherein the received identity records that satisfy the grouping attributes are included in the group to process; and for each of the identity records not satisfying the grouping attribute: query the entity database to obtain results of the entity records in the entity database that satisfy the attributes of the identity record; apply resolution rules to determine the entity records satisfying the query that are related to the identity record according to the resolution rules; and store entity relationship information on the determined entity records that are related to the identity record. | 0.500693 |
9,161,007 | 1 | 6 | 1. A method performed on at least one computing device that includes at least one processor and memory, the method comprising: receiving, by the at least one computing device, a selection of a theme script; and generating, by the at least one computing device a story, where the generated story comprises at least a portion of user assets organized according to the theme script, and where the generating comprises: applying a plurality of rules from the theme script to the user assets, selecting at least the portion of the user assets according to the theme script and meta data of the user assets, automatically generating an introduction section according to the theme script at a beginning of the generated story, where the introduction section comprises a title generated according to the meta data of the user assets in response to the meta data being sufficient for generating the title, and where the introduction section comprises a generic title in response to the meta data being insufficient for generating the title, and automatically generating a conclusion section according to the theme script at an end of the generated story. | 1. A method performed on at least one computing device that includes at least one processor and memory, the method comprising: receiving, by the at least one computing device, a selection of a theme script; and generating, by the at least one computing device a story, where the generated story comprises at least a portion of user assets organized according to the theme script, and where the generating comprises: applying a plurality of rules from the theme script to the user assets, selecting at least the portion of the user assets according to the theme script and meta data of the user assets, automatically generating an introduction section according to the theme script at a beginning of the generated story, where the introduction section comprises a title generated according to the meta data of the user assets in response to the meta data being sufficient for generating the title, and where the introduction section comprises a generic title in response to the meta data being insufficient for generating the title, and automatically generating a conclusion section according to the theme script at an end of the generated story. 6. The method of claim 1 where the theme script is configured for comprising an effects rule as part of the plurality of rules, the transition rule configured for defining visual effects and sound effects to be imposed on the portion of the user assets. | 0.724401 |
8,430,301 | 17 | 21 | 17. A computer program product comprising a computer usable medium having a computer readable program code embedded therein for controlling a data processing apparatus, the computer readable program code configured to cause the data processing apparatus to execute a process for authenticating a printed document, the printed document including a hierarchical barcode stamp and a document image printed on a front side of a recording medium and first barcode stamps printed on a front or back side of the same recording medium, the process comprising: (a) receiving a front side image and a back side image from the printed document; (b) extracting the document image and the hierarchical barcode stamp from the front side image; (c) processing the document image extracted in step (b) to obtain first processed data; (d) extracting a first code from the hierarchical barcode stamp extracted in step (b); (e) reading and decoding the first barcode stamps in the front side image and/or the back side image to obtain second processed data encoded therein; (f) calculating a second code from the second processed data; (g) comparing the first code and the second code to determine whether the first barcode stamps have been altered; and (h) comparing first processed data and second processed data to determine whether the printed document has been altered. | 17. A computer program product comprising a computer usable medium having a computer readable program code embedded therein for controlling a data processing apparatus, the computer readable program code configured to cause the data processing apparatus to execute a process for authenticating a printed document, the printed document including a hierarchical barcode stamp and a document image printed on a front side of a recording medium and first barcode stamps printed on a front or back side of the same recording medium, the process comprising: (a) receiving a front side image and a back side image from the printed document; (b) extracting the document image and the hierarchical barcode stamp from the front side image; (c) processing the document image extracted in step (b) to obtain first processed data; (d) extracting a first code from the hierarchical barcode stamp extracted in step (b); (e) reading and decoding the first barcode stamps in the front side image and/or the back side image to obtain second processed data encoded therein; (f) calculating a second code from the second processed data; (g) comparing the first code and the second code to determine whether the first barcode stamps have been altered; and (h) comparing first processed data and second processed data to determine whether the printed document has been altered. 21. The computer program product of claim 17 , further comprising: (i) extracting first metadata from the hierarchical barcode stamp extracted in step (b); (j) obtaining second metadata encoded in the first barcode stamps; and (k) comparing the first metadata and the second metadata to determine whether the printed document has been altered. | 0.515537 |
8,774,515 | 13 | 15 | 13. A method for labeling images comprising: providing a structured prediction model in memory which represents predictive correlations between labels in a set of labels, the structured prediction model comprising a tree structure in which each of the labels in the set of labels is in exactly one node of the tree structure and edges between the nodes define pairs of nodes for which predicted correlations between values of pairs of labels is stored, the prediction of the value for the at least one label value being based on the predicted correlations; receiving an image to be labeled; eliciting a plurality of the label values for the image from a user; generating feature-based predictions for values of labels in the set of labels based on features extracted from the image; and with a processor, predicting a value for at least one label from the set of labels for the image based on the feature-based label predictions and predictive correlations of the structured prediction model, and based on an assigned label value for at least one other label, the assigned label value comprising the elicited label values, the prediction of the value for the at least one label for the input image including modifying at least one of the predicted correlations between values of the node which includes the label whose value has been elicited and at least those nodes linked by edges to that node based on the elicited value. | 13. A method for labeling images comprising: providing a structured prediction model in memory which represents predictive correlations between labels in a set of labels, the structured prediction model comprising a tree structure in which each of the labels in the set of labels is in exactly one node of the tree structure and edges between the nodes define pairs of nodes for which predicted correlations between values of pairs of labels is stored, the prediction of the value for the at least one label value being based on the predicted correlations; receiving an image to be labeled; eliciting a plurality of the label values for the image from a user; generating feature-based predictions for values of labels in the set of labels based on features extracted from the image; and with a processor, predicting a value for at least one label from the set of labels for the image based on the feature-based label predictions and predictive correlations of the structured prediction model, and based on an assigned label value for at least one other label, the assigned label value comprising the elicited label values, the prediction of the value for the at least one label for the input image including modifying at least one of the predicted correlations between values of the node which includes the label whose value has been elicited and at least those nodes linked by edges to that node based on the elicited value. 15. The method of claim 13 , wherein in the tree structure, nodes of the tree have at most a predefined number k of labels, and wherein a plurality of the nodes have more than one label. | 0.909357 |
8,412,531 | 17 | 18 | 17. The computer readable storage medium of claim 16 , further comprising identifying a contextual data associated with the contextual area on the touch screen interface. | 17. The computer readable storage medium of claim 16 , further comprising identifying a contextual data associated with the contextual area on the touch screen interface. 18. The computer readable storage medium of claim 17 , further comprising receiving the spoken utterance via the activated listening mechanism of the speech recognition module related to the identified contextual data. | 0.919079 |
6,012,072 | 1 | 6 | 1. An apparatus suitable for simultaneously displaying multiple documents, said apparatus comprising: a display device; memory means comprising a plurality of documents, each said document comprising one or more associated attributes stored internally to said document; processor means coupled with said memory means and with said display device, said processor means for displaying a plurality of document display outlines in a workspace, each said document display outline corresponding to one said document; and document rendering means coupled with said memory means, for rendering each said document within a corresponding said document display outline, said document rendering means responsive to said document attributes for restricting a view of said selected document in said workspace by selectively defining a contiguous portion of said corresponding document for display within said corresponding document display outline. | 1. An apparatus suitable for simultaneously displaying multiple documents, said apparatus comprising: a display device; memory means comprising a plurality of documents, each said document comprising one or more associated attributes stored internally to said document; processor means coupled with said memory means and with said display device, said processor means for displaying a plurality of document display outlines in a workspace, each said document display outline corresponding to one said document; and document rendering means coupled with said memory means, for rendering each said document within a corresponding said document display outline, said document rendering means responsive to said document attributes for restricting a view of said selected document in said workspace by selectively defining a contiguous portion of said corresponding document for display within said corresponding document display outline. 6. The apparatus of claim 1 wherein said processor means displays said document display outlines in a piled configuration comprising a staggered overlapping arrangement. | 0.866297 |
9,905,228 | 7 | 9 | 7. A computer-readable storage device storing instructions which, when executed by a processor, cause the processor to perform operations comprising: receiving text as part of a message, a placeholder within the text and audio, wherein the device comprises an embedded speech recognition system that accesses private user data on a computing device; receiving one of a garbage model, phonemic language model, a language model according to a standard list, and a language model built on the private user data to yield a received component; determining a privacy level of the private user data; recognizing the audio using the received component, the embedded speech recognition system and by accessing the private user data according to the privacy level to yield a recognition result; and replacing the placeholder with the recognition result in the text to yield an updated message. | 7. A computer-readable storage device storing instructions which, when executed by a processor, cause the processor to perform operations comprising: receiving text as part of a message, a placeholder within the text and audio, wherein the device comprises an embedded speech recognition system that accesses private user data on a computing device; receiving one of a garbage model, phonemic language model, a language model according to a standard list, and a language model built on the private user data to yield a received component; determining a privacy level of the private user data; recognizing the audio using the received component, the embedded speech recognition system and by accessing the private user data according to the privacy level to yield a recognition result; and replacing the placeholder with the recognition result in the text to yield an updated message. 9. The computer-readable storage device of claim 7 , wherein the computer-readable storage device stores additional instructions which, when executed by the processor, cause the processor to perform operations further comprising: identifying a location of the computing device. | 0.600865 |
7,496,563 | 1 | 3 | 1. A computer-implemented method of providing a personal search agent enabling a user to search for electronic documents that the user has previously accessed regardless of the location of the electronic documents, the method comprising: monitoring one or more of a plurality of electronic documents previously accessed by the user for one or more predetermined events; automatically converting each of the monitored electronic documents into a common format for creating an index of said monitored electronic documents, wherein an index entry that comprises content data found within each of said monitored electronic documents is created for each of said monitored electronic documents by searching one or more browser caches and intercepting one or more commands to access one or more applications regardless of the location of said monitored electronic documents; and providing a graphical user interface enabling said user to enter one or more keywords to create a search query to be used to search said index, and wherein in response to receiving and executing said search query, presenting said search results including at least some content data from said index as text and using one or more graphical displays. | 1. A computer-implemented method of providing a personal search agent enabling a user to search for electronic documents that the user has previously accessed regardless of the location of the electronic documents, the method comprising: monitoring one or more of a plurality of electronic documents previously accessed by the user for one or more predetermined events; automatically converting each of the monitored electronic documents into a common format for creating an index of said monitored electronic documents, wherein an index entry that comprises content data found within each of said monitored electronic documents is created for each of said monitored electronic documents by searching one or more browser caches and intercepting one or more commands to access one or more applications regardless of the location of said monitored electronic documents; and providing a graphical user interface enabling said user to enter one or more keywords to create a search query to be used to search said index, and wherein in response to receiving and executing said search query, presenting said search results including at least some content data from said index as text and using one or more graphical displays. 3. The method of claim 1 , wherein said one or more graphical displays includes a calendar providing indicia of said search results. | 0.807018 |
8,041,565 | 13 | 14 | 13. A method of converting speech into text comprising: receiving audio information representing speech of a person; delivering said audio information to a plurality of speech-to-text conversion engines; converting said audio information by said conversion engines into a plurality of text files, each conversion engine producing one of said text files; fragmenting each of said text files into a plurality of overlapping text fragments, each of said text files being fragmented according to a set of previously determined time boundaries, said set of boundaries being the same for each of said text files, said overlapping creating duplicative information; comparing said plurality of text fragments a set at a time, each set corresponding to a single portion of said audio information; outputting a hybrid text fragment for each of said sets, each of said hybrid text fragments corresponding to a distinct portion of said audio information; and concatenating said hybrid text fragments into a text file that represents said audio information by deleting said duplicative information. | 13. A method of converting speech into text comprising: receiving audio information representing speech of a person; delivering said audio information to a plurality of speech-to-text conversion engines; converting said audio information by said conversion engines into a plurality of text files, each conversion engine producing one of said text files; fragmenting each of said text files into a plurality of overlapping text fragments, each of said text files being fragmented according to a set of previously determined time boundaries, said set of boundaries being the same for each of said text files, said overlapping creating duplicative information; comparing said plurality of text fragments a set at a time, each set corresponding to a single portion of said audio information; outputting a hybrid text fragment for each of said sets, each of said hybrid text fragments corresponding to a distinct portion of said audio information; and concatenating said hybrid text fragments into a text file that represents said audio information by deleting said duplicative information. 14. A method as recited in claim 13 further comprising: creating said hybrid text fragment by selecting words from said group of text fragments that are agreed upon by a majority of said conversion engines. | 0.863757 |
8,365,086 | 1 | 2 | 1. A computer-implemented system for building instrument or front panels, comprising: a computational device providing access to a graphical panel building application, where: the graphical panel building application generates a graphical user interface (GUI) for rendering and placing components on an instrument or front panel, where: at least one component on the instrument or front panel: directly corresponds with, and interacts only with, a section of code in a textual language program, displays input or output values for the textual language program during execution of the textual language program, and does not interact with or correspond with a graphical diagram, the textual language program includes code for the execution of one or more components on the instrument or front panel, and the instrument or front panel runs in a continuous mode of operation during the execution of the textual language program; and a display device in communication with the computational device, the display device displaying the GUI, the GUI: displaying the instrument or front panel, separately displaying the textual language program, and displaying in a window with the textual language program at least one control allowing a user to switch between threads of execution of the textual language program, the threads of execution comprising two or more of: an initialization procedure, a foreground task, a background task, a timed execution task, an event-driven execution task, and a termination procedure. | 1. A computer-implemented system for building instrument or front panels, comprising: a computational device providing access to a graphical panel building application, where: the graphical panel building application generates a graphical user interface (GUI) for rendering and placing components on an instrument or front panel, where: at least one component on the instrument or front panel: directly corresponds with, and interacts only with, a section of code in a textual language program, displays input or output values for the textual language program during execution of the textual language program, and does not interact with or correspond with a graphical diagram, the textual language program includes code for the execution of one or more components on the instrument or front panel, and the instrument or front panel runs in a continuous mode of operation during the execution of the textual language program; and a display device in communication with the computational device, the display device displaying the GUI, the GUI: displaying the instrument or front panel, separately displaying the textual language program, and displaying in a window with the textual language program at least one control allowing a user to switch between threads of execution of the textual language program, the threads of execution comprising two or more of: an initialization procedure, a foreground task, a background task, a timed execution task, an event-driven execution task, and a termination procedure. 2. The system of claim 1 , wherein there is a one-to-one correspondence between each of the components on the instrument or front panel to corresponding sections of code in the textual language program. | 0.874222 |
9,973,519 | 1 | 6 | 1. A computer system for protecting one or more server computers by identifying a browser on a client computer comprising: one or more hardware processors; a memory coupled to the one or more hardware processors and storing one or more instructions, which when executed by the one or more hardware processors cause the one or more hardware processors to: supplement a set of web code with a set of instrumentation code, which when executed on the client computer collects a set of information that describes a document object model created by the client computer after the client computer executes the set of web code; send the set of web code and the set of instrumentation code to the client computer; receive the set of information from the client computer; identify the browser on the client computer based on the set of information that describes the document object model. | 1. A computer system for protecting one or more server computers by identifying a browser on a client computer comprising: one or more hardware processors; a memory coupled to the one or more hardware processors and storing one or more instructions, which when executed by the one or more hardware processors cause the one or more hardware processors to: supplement a set of web code with a set of instrumentation code, which when executed on the client computer collects a set of information that describes a document object model created by the client computer after the client computer executes the set of web code; send the set of web code and the set of instrumentation code to the client computer; receive the set of information from the client computer; identify the browser on the client computer based on the set of information that describes the document object model. 6. The computer system of claim 1 , wherein the one or more instructions, when executed, cause the one or more hardware processors to determine that the browser is a malicious browser, and in response, terminate one or more requests for one or more sets of data from the client computer without sending the one or more sets of data to the client computer. | 0.683036 |
8,429,526 | 9 | 10 | 9. A non-transitory machine-readable storage medium storing instructions, which when executed by one or more processors, causes: receiving a hash level indicating a particular depth in a first document and a second document, wherein: the first document comprises the first plurality of nodes, wherein one or more nodes of the first plurality of nodes are at a depth that is greater than the particular depth; and the second document comprises the second plurality of nodes, wherein one or more nodes of the second plurality of nodes are at a depth that is greater than the particular depth; based on the hash level, identifying a first subset, of the first plurality of nodes, that do not include any nodes, in the first plurality of nodes, that are greater than the particular depth, wherein the first subset includes at least two nodes that are at the same depth; based on the hash level, identifying a second subset, of the second plurality of nodes, that do not include any nodes, in the second plurality of nodes, that are greater than the particular depth, wherein the second subset includes at least two nodes that are at the same depth; for each node included in the first and the second subsets, computing a hash value based on said each node and one or more descendants of said each node; performing a plurality of comparisons between hash values of different nodes that are only in the first and second subsets; and based on the plurality of comparisons, generating an edit script. | 9. A non-transitory machine-readable storage medium storing instructions, which when executed by one or more processors, causes: receiving a hash level indicating a particular depth in a first document and a second document, wherein: the first document comprises the first plurality of nodes, wherein one or more nodes of the first plurality of nodes are at a depth that is greater than the particular depth; and the second document comprises the second plurality of nodes, wherein one or more nodes of the second plurality of nodes are at a depth that is greater than the particular depth; based on the hash level, identifying a first subset, of the first plurality of nodes, that do not include any nodes, in the first plurality of nodes, that are greater than the particular depth, wherein the first subset includes at least two nodes that are at the same depth; based on the hash level, identifying a second subset, of the second plurality of nodes, that do not include any nodes, in the second plurality of nodes, that are greater than the particular depth, wherein the second subset includes at least two nodes that are at the same depth; for each node included in the first and the second subsets, computing a hash value based on said each node and one or more descendants of said each node; performing a plurality of comparisons between hash values of different nodes that are only in the first and second subsets; and based on the plurality of comparisons, generating an edit script. 10. The non-transitory machine-readable storage medium of claim 9 , wherein the first document and the second document are in XML or HTML. | 0.939527 |
9,263,027 | 1 | 9 | 1. A broadcast signal receiver comprising: a text data receiver configured to receive broadcast text data and to transmit the broadcast text data to a user interface, wherein the broadcast text data includes at least one word; a text-to-speech (TTS) converter configured to convert received text data into an audio speech sound, wherein the TTS converter is configured to: detect whether the at least one word is also included in a stored list of words, and when the at least one word is also included in the stored list of words, convert the at least one word according to a conversion defined by the stored list, and when the at least one word is not included in the stored list of words, convert the at least one word according to a set of predetermined conversion rules; a conversion memory configured to store the list of words as initial data; an update receiver configured to receive, from a conversion repository, and via a network connection, update data, wherein the update data includes updated words, associated conversions, and updated conversion rules, and configured to store, in the conversion memory, the update data; and a commander circuitry configured to control an operation of the broadcast signal receiver, wherein the commander circuitry is configured to receive a user control input, wherein the user control input indicates an incorrect conversion carried out by the TTS converter; and wherein the broadcast signal receiver is configured to, in response to the user control input, send a message to a data provider, and thereby request update data, wherein the message indicates a conversion problem and indicates text which was converted, by the TTS converter, into speech. | 1. A broadcast signal receiver comprising: a text data receiver configured to receive broadcast text data and to transmit the broadcast text data to a user interface, wherein the broadcast text data includes at least one word; a text-to-speech (TTS) converter configured to convert received text data into an audio speech sound, wherein the TTS converter is configured to: detect whether the at least one word is also included in a stored list of words, and when the at least one word is also included in the stored list of words, convert the at least one word according to a conversion defined by the stored list, and when the at least one word is not included in the stored list of words, convert the at least one word according to a set of predetermined conversion rules; a conversion memory configured to store the list of words as initial data; an update receiver configured to receive, from a conversion repository, and via a network connection, update data, wherein the update data includes updated words, associated conversions, and updated conversion rules, and configured to store, in the conversion memory, the update data; and a commander circuitry configured to control an operation of the broadcast signal receiver, wherein the commander circuitry is configured to receive a user control input, wherein the user control input indicates an incorrect conversion carried out by the TTS converter; and wherein the broadcast signal receiver is configured to, in response to the user control input, send a message to a data provider, and thereby request update data, wherein the message indicates a conversion problem and indicates text which was converted, by the TTS converter, into speech. 9. The receiver according to claim 1 , wherein the update data includes update acronyms and update abbreviations. | 0.874165 |
9,026,676 | 5 | 6 | 5. A processing system for prepending nonce labels to DNS queries, the system comprising: at least one processor; a nonce label analyzer module associated with the at least one processor, the nonce label analyzer module being configured: to evaluate whether a log contains a past entry of a domain name resolution query to a name server, to determine whether the log contains a recent entry of the domain name resolution query to the name server for a full domain name that resulted in a negative indicating that the full domain name did not exist, to determine whether querying the name server with a nonce-less query for the full domain name currently results in a reply indicating that the full domain name exists, to determine whether querying the name server with a nonce label prepended query for the full domain name currently results in the positive reply indicating that the full domain name exists, and to flag the full domain name as inappropriate for nonce label prepending, when it is determined that querying the name server with the nonce label prepended query indicates that the full domain name does not exist. | 5. A processing system for prepending nonce labels to DNS queries, the system comprising: at least one processor; a nonce label analyzer module associated with the at least one processor, the nonce label analyzer module being configured: to evaluate whether a log contains a past entry of a domain name resolution query to a name server, to determine whether the log contains a recent entry of the domain name resolution query to the name server for a full domain name that resulted in a negative indicating that the full domain name did not exist, to determine whether querying the name server with a nonce-less query for the full domain name currently results in a reply indicating that the full domain name exists, to determine whether querying the name server with a nonce label prepended query for the full domain name currently results in the positive reply indicating that the full domain name exists, and to flag the full domain name as inappropriate for nonce label prepending, when it is determined that querying the name server with the nonce label prepended query indicates that the full domain name does not exist. 6. The processing system of claim 5 , further comprising a DNS resolver. | 0.825243 |
7,574,433 | 22 | 30 | 22. A computerized method for the retrieval of classified documents comprising: initiating a connection between a client software application in a client computer and a server computer; and causing at least one request by said client software application in said client computer, wherein said request initiates a method comprising: retrieving a document from a document collection, said document collection comprising at least one document(s), said document(s) having been classified according to a predefined classification scheme, said predefined classification scheme comprising classification codes, said classification codes comprising title(s) and definition(s), said document(s) further comprising at least one retrieval code, wherein said retrieval code corresponds with at least one of said classification code title(s) or classification code definition(s); retrieving from a database at least one keyword derived from at least one of said classification code title(s) or classification code definition(s); inserting said keyword into said document(s) to create a tagged document; and transmitting said tagged document to said search engine. | 22. A computerized method for the retrieval of classified documents comprising: initiating a connection between a client software application in a client computer and a server computer; and causing at least one request by said client software application in said client computer, wherein said request initiates a method comprising: retrieving a document from a document collection, said document collection comprising at least one document(s), said document(s) having been classified according to a predefined classification scheme, said predefined classification scheme comprising classification codes, said classification codes comprising title(s) and definition(s), said document(s) further comprising at least one retrieval code, wherein said retrieval code corresponds with at least one of said classification code title(s) or classification code definition(s); retrieving from a database at least one keyword derived from at least one of said classification code title(s) or classification code definition(s); inserting said keyword into said document(s) to create a tagged document; and transmitting said tagged document to said search engine. 30. The computerized method for the indexing and retrieval of classified documents of claim 22 , wherein the client software application is a web content repackager. | 0.644397 |
4,849,732 | 1 | 12 | 1. An input device operated by a single hand and used to transmit information between the operator of said device and an electronic apparatus in communication with said device, comprising: a housing; a first key array having a plurality of input means mounted to said housing and operable by the thumb of the hand; a second key array having a plurality of input means mounted to said housing, said second array disposed on an array axis which is aligned along a longitudinal axis of and operable by the index finger of the hand when the thumb of the hand is operably positioned proximal to said first array; a third key array having a plurality of input means mounted to said housing, said third array disposed on an array axis which is aligned along a longitudinal axis of and operable by the middle finger of the hand when the thumb and the first finger of the hand are operably positioned proximal to said first and second arrays, respectively; a fourth key array having a plurality of input means mounted to said housing, said fourth array disposed on an array axis which is aligned along a longitudinal axis of and operable by the fourth finger of the hand when the thumb and the first finger are operably positioned proximal to said first and second arrays, respectively; a fifth key array having a plurality of input means mounted to said housing, said fifth array disposed on an array axis which is aligned along a longitudinal axis of and operable by the fifth finger of the hand when the thumb and the first finger are operably positioned proximal to said first and second arrays; and label means disposed in close association with a corresponding key, laterally offset from said corresponding key and outwardly disposed from said key array axis, said label means being viewable while the fingers are in contact with said corresponding key, wherein said first, second, third, fourth and fifth key arrays are disposed to conform to the positions, motion and range of the respective fingers of the hand allowing activation of the keys without movement of the hand relative to the housing and without movement of said fingers to other of said key arrays, and each said key array includes an axis being non-parallel to the axis of other key arrays. | 1. An input device operated by a single hand and used to transmit information between the operator of said device and an electronic apparatus in communication with said device, comprising: a housing; a first key array having a plurality of input means mounted to said housing and operable by the thumb of the hand; a second key array having a plurality of input means mounted to said housing, said second array disposed on an array axis which is aligned along a longitudinal axis of and operable by the index finger of the hand when the thumb of the hand is operably positioned proximal to said first array; a third key array having a plurality of input means mounted to said housing, said third array disposed on an array axis which is aligned along a longitudinal axis of and operable by the middle finger of the hand when the thumb and the first finger of the hand are operably positioned proximal to said first and second arrays, respectively; a fourth key array having a plurality of input means mounted to said housing, said fourth array disposed on an array axis which is aligned along a longitudinal axis of and operable by the fourth finger of the hand when the thumb and the first finger are operably positioned proximal to said first and second arrays, respectively; a fifth key array having a plurality of input means mounted to said housing, said fifth array disposed on an array axis which is aligned along a longitudinal axis of and operable by the fifth finger of the hand when the thumb and the first finger are operably positioned proximal to said first and second arrays; and label means disposed in close association with a corresponding key, laterally offset from said corresponding key and outwardly disposed from said key array axis, said label means being viewable while the fingers are in contact with said corresponding key, wherein said first, second, third, fourth and fifth key arrays are disposed to conform to the positions, motion and range of the respective fingers of the hand allowing activation of the keys without movement of the hand relative to the housing and without movement of said fingers to other of said key arrays, and each said key array includes an axis being non-parallel to the axis of other key arrays. 12. The input device according to claim 1, wherein at least one of said arrays of a plurality of input means comprise character keys. | 0.865112 |
9,324,324 | 1 | 6 | 1. A computer system, comprising: one or more hardware processors; and one or more non-transitory computer-readable media having stored thereon computer-executable instructions that are structured such that, when the computer-executable instructions are executed by the one or more hardware processors, the computer system generates text captions from speech data, including at least the following: receiving, from a first communications device, the speech data based on a remote party's voice: generating, at the one or more hardware processors, first text captions from the speech data using a speech recognition algorithm; determining, at the one or more hardware processors, whether the generated first text captions meet a first predetermined quality threshold; and when the first text captions meet the first predetermined quality threshold, sending the first text captions to a second communications device for display at a display device; or when the first text captions do not meet the first predetermined quality threshold, performing at least the following: generating, at the one or more hardware processors, second text captions from the speech data based on user input to the speech recognition algorithm from a human user; and sending the second text captions to the second communications device for display at the display device when the second text captions meet a second predetermined quality threshold. | 1. A computer system, comprising: one or more hardware processors; and one or more non-transitory computer-readable media having stored thereon computer-executable instructions that are structured such that, when the computer-executable instructions are executed by the one or more hardware processors, the computer system generates text captions from speech data, including at least the following: receiving, from a first communications device, the speech data based on a remote party's voice: generating, at the one or more hardware processors, first text captions from the speech data using a speech recognition algorithm; determining, at the one or more hardware processors, whether the generated first text captions meet a first predetermined quality threshold; and when the first text captions meet the first predetermined quality threshold, sending the first text captions to a second communications device for display at a display device; or when the first text captions do not meet the first predetermined quality threshold, performing at least the following: generating, at the one or more hardware processors, second text captions from the speech data based on user input to the speech recognition algorithm from a human user; and sending the second text captions to the second communications device for display at the display device when the second text captions meet a second predetermined quality threshold. 6. The computer system as recited in claim 1 , wherein generating the second text captions from the speech data based on the user input to the speech recognition algorithm from the human user comprises generating text captions that are annotated with one or more visual cues. | 0.739583 |
8,346,526 | 1 | 6 | 1. A computer-implemented method to generate cumulative metric data for a test in a test environment, the method comprising: specifying, from within the test environment, a plurality of test elements within an iterations part of the test, the plurality of test elements including: a first test element being a simulation model having a block property, the simulation model executed from within a second environment that is separate from the test environment, and a second test element that is not a simulation model, the second test element being executable; generating, from within the test environment, at least one test condition for the test, the at least one test condition specifying a number of values for the block property of the simulation model; specifying at least one metric setting for the simulation model; defining a test variable within the test environment; mapping, from within the test environment, the at least one metric setting of the simulation model to the test variable; running, by a computer, the test from within the test environment such that the simulation model is executed within the second environment for a plurality of iterations based on the number of block property values specified in the at least one test condition, and the second test element is executed for the plurality of iterations; generating, during each iteration of the simulation model, metric data based on the at least one metric setting for the simulation model, the metric data generated within the second environment; assigning the metric data generated in the second environment to the test variable of the test environment as a result of the mapping of the at least one metric setting of the simulation model to the test variable so that the metric data from the iterations of the simulation model is accumulated; and providing access to the accumulated metric data from within the test environment. | 1. A computer-implemented method to generate cumulative metric data for a test in a test environment, the method comprising: specifying, from within the test environment, a plurality of test elements within an iterations part of the test, the plurality of test elements including: a first test element being a simulation model having a block property, the simulation model executed from within a second environment that is separate from the test environment, and a second test element that is not a simulation model, the second test element being executable; generating, from within the test environment, at least one test condition for the test, the at least one test condition specifying a number of values for the block property of the simulation model; specifying at least one metric setting for the simulation model; defining a test variable within the test environment; mapping, from within the test environment, the at least one metric setting of the simulation model to the test variable; running, by a computer, the test from within the test environment such that the simulation model is executed within the second environment for a plurality of iterations based on the number of block property values specified in the at least one test condition, and the second test element is executed for the plurality of iterations; generating, during each iteration of the simulation model, metric data based on the at least one metric setting for the simulation model, the metric data generated within the second environment; assigning the metric data generated in the second environment to the test variable of the test environment as a result of the mapping of the at least one metric setting of the simulation model to the test variable so that the metric data from the iterations of the simulation model is accumulated; and providing access to the accumulated metric data from within the test environment. 6. The method as in claim 1 , wherein the at least one metric setting is provided by the simulation model. | 0.886022 |
8,312,005 | 12 | 14 | 12. The computer-readable storage medium of claim 10 , wherein relating attributes to semantic equivalents includes generating first tables associating different terms with corresponding attributes, wherein the different terms associated with a given attribute include the semantic equivalents thereof, and generating at least one second table including master names corresponding to one of the terms associated with the attributes. | 12. The computer-readable storage medium of claim 10 , wherein relating attributes to semantic equivalents includes generating first tables associating different terms with corresponding attributes, wherein the different terms associated with a given attribute include the semantic equivalents thereof, and generating at least one second table including master names corresponding to one of the terms associated with the attributes. 14. The computer-readable storage medium of claim 12 , wherein the determination of the data to retrieve includes eliminating semantic duplicates based on associating a semantic project operation with the terms of at least one of the first table and the second table. | 0.881439 |
9,753,767 | 12 | 16 | 12. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, the computer-program product including instructions operable to cause a processor to perform operations comprising: retrieve node data, from a node device, wherein the node data is descriptive of at least one characteristic of an execution environment of the node device; generate a current data set model, wherein the current data set model is descriptive of at least one characteristic of a current data set; compare the current data set model to at least one previously generated data set model of at least one previously analyzed data set to detect a match in the at least one characteristic that meets a similarity threshold, wherein the at least one previously generated data set model is descriptive of the at least one characteristic of the at least one previously analyzed data set; in response to detection of a match that meets the similarity threshold between the at least one characteristic described in the current data set model and the at least one characteristic described in the at least one previously generated data set model: retrieve an indication from a correlation database of at least one previously performed action previously performed on the at least one previously analyzed data set, wherein the at least one previously performed action comprises at least one of normalization, a transform or a task performed on the at least one previously analyzed data set; select a computer language based on the node data; generate node instructions, in the selected computer language and based on at least the current data set model, to cause the node device to perform the at least one previously performed action on at least a portion of the current data set; and transmit the node instructions to the node device; in response to detection of a lack of a match that meets the similarity threshold between the at least one characteristic described in the current data set model and the at least one characteristic described in the at least one previously generated data set model present a request for input indicative of a current action to perform on the current data set, wherein the current action comprises at least one of normalization, a transform or a task to be performed on the current data set; monitor previously performed actions performed on each previously analyzed data set of the at least one previously analyzed data set; for each previously analyzed data set of the at least one previously analyzed data set, store previously generated data set model that corresponds to the previously analyzed data set correlated to an indication of a previously performed action performed on the previously analyzed data set in the correlation database; derive a correlation between the at least one characteristic and the at least one previously performed action from the correlation database; and determine the at least one previously performed action to cause the node device to perform on the current data set based on the correlation. | 12. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, the computer-program product including instructions operable to cause a processor to perform operations comprising: retrieve node data, from a node device, wherein the node data is descriptive of at least one characteristic of an execution environment of the node device; generate a current data set model, wherein the current data set model is descriptive of at least one characteristic of a current data set; compare the current data set model to at least one previously generated data set model of at least one previously analyzed data set to detect a match in the at least one characteristic that meets a similarity threshold, wherein the at least one previously generated data set model is descriptive of the at least one characteristic of the at least one previously analyzed data set; in response to detection of a match that meets the similarity threshold between the at least one characteristic described in the current data set model and the at least one characteristic described in the at least one previously generated data set model: retrieve an indication from a correlation database of at least one previously performed action previously performed on the at least one previously analyzed data set, wherein the at least one previously performed action comprises at least one of normalization, a transform or a task performed on the at least one previously analyzed data set; select a computer language based on the node data; generate node instructions, in the selected computer language and based on at least the current data set model, to cause the node device to perform the at least one previously performed action on at least a portion of the current data set; and transmit the node instructions to the node device; in response to detection of a lack of a match that meets the similarity threshold between the at least one characteristic described in the current data set model and the at least one characteristic described in the at least one previously generated data set model present a request for input indicative of a current action to perform on the current data set, wherein the current action comprises at least one of normalization, a transform or a task to be performed on the current data set; monitor previously performed actions performed on each previously analyzed data set of the at least one previously analyzed data set; for each previously analyzed data set of the at least one previously analyzed data set, store previously generated data set model that corresponds to the previously analyzed data set correlated to an indication of a previously performed action performed on the previously analyzed data set in the correlation database; derive a correlation between the at least one characteristic and the at least one previously performed action from the correlation database; and determine the at least one previously performed action to cause the node device to perform on the current data set based on the correlation. 16. The computer-program product of claim 12 , wherein the node data comprises an indication of at least one of a processor type of the node device, a quantity of storage space within the node device, a type of operating system executed by the node device, a type of language interpreter executed by the node device, or a configuration setting of the node device. | 0.863122 |
8,065,527 | 5 | 6 | 5. A method as in claim 1 , further comprising adding a plurality of signing individuals to the signer list to enable the signature tag to be associated with one of the plurality of signing individuals, and verifying that the plurality of signing individuals added to the list includes no duplicate names using a signer list module. | 5. A method as in claim 1 , further comprising adding a plurality of signing individuals to the signer list to enable the signature tag to be associated with one of the plurality of signing individuals, and verifying that the plurality of signing individuals added to the list includes no duplicate names using a signer list module. 6. A method as in claim 5 , further comprising placing a unique signature tag associated with the selected signing individual from the plurality of signing individuals at every location throughout the placeholder electronic document where the signing individual will sign. | 0.924065 |
6,070,160 | 9 | 12 | 9. A memory device programmable by a programmer, and operable by a user to be operably connectable to a processor for storing and accessing information corresponding to articles sought by a user, the memory device comprising: a first memory block programmed to store a first database comprising at least one first record comprising at least one first field containing first field data; a second memory block programmed to store a second database, unrelated to the first database, and comprising at least one second record comprising at least one second field containing second field data; a third memory block programmed to store at least one index comprising at least one index record linking the at least one first record to the at least one second record according to a fuzzy logic relationship identified by the processor and corresponding to a subjective observation programmed into the processor by the user; and a fourth memory block containing a search engine for execution by the processor for conducting a search, the engine being programmed to selectively control interaction and sequencing of sub-engines during the search, the sub-engines comprising: a standard search sub-engine for pefoming a deterministic search, a key-word search sub-enginie for performing a textual search, and a query search sub-engine for performing a fuzzy logic seach, the sub-engines substantially simultaneously loaded to run in the processor to search the set of databases. | 9. A memory device programmable by a programmer, and operable by a user to be operably connectable to a processor for storing and accessing information corresponding to articles sought by a user, the memory device comprising: a first memory block programmed to store a first database comprising at least one first record comprising at least one first field containing first field data; a second memory block programmed to store a second database, unrelated to the first database, and comprising at least one second record comprising at least one second field containing second field data; a third memory block programmed to store at least one index comprising at least one index record linking the at least one first record to the at least one second record according to a fuzzy logic relationship identified by the processor and corresponding to a subjective observation programmed into the processor by the user; and a fourth memory block containing a search engine for execution by the processor for conducting a search, the engine being programmed to selectively control interaction and sequencing of sub-engines during the search, the sub-engines comprising: a standard search sub-engine for pefoming a deterministic search, a key-word search sub-enginie for performing a textual search, and a query search sub-engine for performing a fuzzy logic seach, the sub-engines substantially simultaneously loaded to run in the processor to search the set of databases. 12. The imemory device of claim 9, wherein the first memory block is programmed to store a plurality of databases, the second memory block is programmed to store a plurality of second databases, and the third memory block is programmmed to store a plurality of indices. | 0.763204 |
8,516,012 | 8 | 14 | 8. A non-transitory computer readable storage device comprising a resource management software module that is operative, when executed by a processor, to perform a method, the method comprising: defining a plurality of translating references for an object; generating a common information model (CIM), the CIM comprising one or more functional object attributes of the object; generating a first instantiation of a user information model (UIM), the first instantiation of the UIM comprising one or more user-associated attributes of the object; interfacing with the CIM using the first instantiation of the UIM; translating one or more user-associated attributes of the first instantiation of the UIM to the one or more functional object attributes of the CIM using the plurality of translating references; generating a second instantiation of a user information model (UIM); interfacing with the CIM using the second instantiation of the UIM; translating one or more user-associated attributes of the second instantiation of the UIM to the one or more functional object attributes of the CIM using the plurality of translating references; and providing at least a portion of the CIM. | 8. A non-transitory computer readable storage device comprising a resource management software module that is operative, when executed by a processor, to perform a method, the method comprising: defining a plurality of translating references for an object; generating a common information model (CIM), the CIM comprising one or more functional object attributes of the object; generating a first instantiation of a user information model (UIM), the first instantiation of the UIM comprising one or more user-associated attributes of the object; interfacing with the CIM using the first instantiation of the UIM; translating one or more user-associated attributes of the first instantiation of the UIM to the one or more functional object attributes of the CIM using the plurality of translating references; generating a second instantiation of a user information model (UIM); interfacing with the CIM using the second instantiation of the UIM; translating one or more user-associated attributes of the second instantiation of the UIM to the one or more functional object attributes of the CIM using the plurality of translating references; and providing at least a portion of the CIM. 14. The computer readable storage device of claim 8 , wherein the object is a radio. | 0.810811 |
9,336,256 | 17 | 21 | 17. At least one non-transitory computer-readable medium storing computer-readable instructions that, when executed by one or more computing devices, cause at least one of the one or more computing devices to: receive a request directed to a tokenized database, wherein the tokenized database contains one or more tokenized data values and wherein the request does not include any tokenized data values; apply one or more rules to the request; rewrite the request based on at least one of the one or more rules, wherein the rewritten request is configured to cause one or more non-tokenized data values specified in the request to be tokenized by a software agent resident on the tokenized database when data is added to the tokenized database as a result of the request and wherein the rewritten request is configured to cause the tokenized database to return non-tokenized data values when data is received from the tokenized database as a result of the request; and transmit the rewritten request to the tokenized database. | 17. At least one non-transitory computer-readable medium storing computer-readable instructions that, when executed by one or more computing devices, cause at least one of the one or more computing devices to: receive a request directed to a tokenized database, wherein the tokenized database contains one or more tokenized data values and wherein the request does not include any tokenized data values; apply one or more rules to the request; rewrite the request based on at least one of the one or more rules, wherein the rewritten request is configured to cause one or more non-tokenized data values specified in the request to be tokenized by a software agent resident on the tokenized database when data is added to the tokenized database as a result of the request and wherein the rewritten request is configured to cause the tokenized database to return non-tokenized data values when data is received from the tokenized database as a result of the request; and transmit the rewritten request to the tokenized database. 21. The at least one non-transitory computer-readable medium of claim 17 , wherein applying one or more rules and rewriting the request comprises: selecting a filter terms rule when the request includes one or more filter data values, the one or more filter data values limiting the request to a subset of records related to the one or more filter data values; and rewriting the request to insert a tokenize command into the request, the tokenize command signaling to a software agent resident on the tokenized database to tokenize the one or more filter data values prior to the execution of the request on the tokenized database. | 0.709752 |
8,688,451 | 1 | 3 | 1. A speech recognition method comprising the steps of: (a) receiving input speech from a user via a microphone associated with an automatic speech recognition system; (b) processing the input speech using a first grammar to obtain parameter values of a first N-best list of vocabulary using at least one processor associated with the automatic speech recognition system; (c) comparing at least one parameter value of a top result of the first N-best list to at least one predetermined threshold value; and (d) subsequently processing the input speech using a second grammar to obtain parameter values of a second N-best list of vocabulary, if the compared at least one parameter value is below the at least one predetermined threshold value; (e) determining the input speech to be in-vocabulary, if any of the results of the first N-best list is also present within the second N-best list, but out-of vocabulary if any of the results of the first N-best list is not within the second N-best list; and (f) providing audible feedback to the user if the input speech is determined to be out-of-vocabulary. | 1. A speech recognition method comprising the steps of: (a) receiving input speech from a user via a microphone associated with an automatic speech recognition system; (b) processing the input speech using a first grammar to obtain parameter values of a first N-best list of vocabulary using at least one processor associated with the automatic speech recognition system; (c) comparing at least one parameter value of a top result of the first N-best list to at least one predetermined threshold value; and (d) subsequently processing the input speech using a second grammar to obtain parameter values of a second N-best list of vocabulary, if the compared at least one parameter value is below the at least one predetermined threshold value; (e) determining the input speech to be in-vocabulary, if any of the results of the first N-best list is also present within the second N-best list, but out-of vocabulary if any of the results of the first N-best list is not within the second N-best list; and (f) providing audible feedback to the user if the input speech is determined to be out-of-vocabulary. 3. The method of claim 1 , wherein step (d) is disabled if frequency of out-of-vocabulary utterances falls below a predetermined threshold. | 0.699134 |
8,423,498 | 1 | 5 | 1. A computer system for analyzing a scenario task to determine contextual characteristics thereof, said system comprising: a processor including: an analytical processing component configured to parse a scenario task into parsed task requirements, the scenario task being at least partially associated with a cultural group of human subjects; an associative processing component configured to receive the parsed task requirements and to: associate a task requirement identifier with each parsed task requirement; associate each parsed task requirement with a task requirement resolution factor; and associate the task requirement identifiers to define a task requirement model; a correlative processing component configured to correlate the task requirement model with a database of scenario data elements, each scenario data element having one of a scenario data element identifier and a scenario resolution factor associated therewith, by correlating one of the task requirement identifiers and the task requirement resolution factors with the respective one of the scenario data element identifiers and the scenario resolution factors; and a compilation processing component configured to compile the correlated scenario data element identifiers and scenario resolution factors to form contextual characteristics having a hierarchical structure, the hierarchical structure having a plurality of categories, each category being defined by at least one attribute, wherein each attribute includes at least one parameter, with each parameter having a parameter value, the categories, attributes, parameters, and parameter values being associated with the correlated scenario data element identifiers and scenario resolution factors, the contextual characteristics cooperating to populate and provide context to the task requirement model. | 1. A computer system for analyzing a scenario task to determine contextual characteristics thereof, said system comprising: a processor including: an analytical processing component configured to parse a scenario task into parsed task requirements, the scenario task being at least partially associated with a cultural group of human subjects; an associative processing component configured to receive the parsed task requirements and to: associate a task requirement identifier with each parsed task requirement; associate each parsed task requirement with a task requirement resolution factor; and associate the task requirement identifiers to define a task requirement model; a correlative processing component configured to correlate the task requirement model with a database of scenario data elements, each scenario data element having one of a scenario data element identifier and a scenario resolution factor associated therewith, by correlating one of the task requirement identifiers and the task requirement resolution factors with the respective one of the scenario data element identifiers and the scenario resolution factors; and a compilation processing component configured to compile the correlated scenario data element identifiers and scenario resolution factors to form contextual characteristics having a hierarchical structure, the hierarchical structure having a plurality of categories, each category being defined by at least one attribute, wherein each attribute includes at least one parameter, with each parameter having a parameter value, the categories, attributes, parameters, and parameter values being associated with the correlated scenario data element identifiers and scenario resolution factors, the contextual characteristics cooperating to populate and provide context to the task requirement model. 5. A system according to claim 1 , wherein the contextual characteristics incompletely populate the task requirement model and define a gap, and the processor of the system further comprises an extrapolation processing component configured to extrapolate between relevant contextual characteristics to determine an approximated contextual characteristic for filling the gap. | 0.774699 |
8,156,160 | 14 | 18 | 14. A computer program product stored on a computer storage device, the product for generating a poet personality to configure a processor to: analyze a plurality of poems, each of the poems containing a plurality of words; generate a plurality of analysis models, each of said analysis models representing one of said plurality of poems; receive for the words in the poems rhyme numbers, with words that rhyme with each other having the same rhyme number; generate a data structure that specifies n-grams found in the text, with each analysis model having a set of weights, bigram, trigram and quadgram exponents; and store in computer storage the plurality of analysis models in a personality data structure including a set of parameters that control poetry generation using the personality data structure. | 14. A computer program product stored on a computer storage device, the product for generating a poet personality to configure a processor to: analyze a plurality of poems, each of the poems containing a plurality of words; generate a plurality of analysis models, each of said analysis models representing one of said plurality of poems; receive for the words in the poems rhyme numbers, with words that rhyme with each other having the same rhyme number; generate a data structure that specifies n-grams found in the text, with each analysis model having a set of weights, bigram, trigram and quadgram exponents; and store in computer storage the plurality of analysis models in a personality data structure including a set of parameters that control poetry generation using the personality data structure. 18. The program of claim 14 wherein the program further configures the processing unit to: define values of weight for each of a bigram, trigram and quadgram structures for an author within the personality data structure; and define values of exponents for each of a bigram, trigram and quadgram structures for an author within the poet personality. | 0.501429 |
9,786,277 | 1 | 6 | 1. A computer-implemented method for eliciting command variants from crowd users to train a natural language processing system, the method being implemented in a computer system having one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the computer system to perform the method, the method comprising: identifying a computer recognized command to be processed by a computer; generating at least a first task, wherein the first task includes command creation instructions that prompt a user to input a variant of the computer recognized command; transmitting the first task to a plurality of users via a network; receiving, from at least a first user among the plurality of users, a first user-defined variant of the computer recognized command; receiving, from at least a second user among the plurality of users, a second user-defined variant of the computer recognized command; storing the first user-defined variant and the second user-defined variant in association with the computer recognized command; generating at least a second task, wherein the second task includes command review instructions that prompt a given user to verify the first user-defined variant; transmitting the second task to a second plurality of users via the network; receiving, from at least a second user among the second plurality of users, a verification of the first user-defined variant; and training the natural language processing system to recognize variants of the computer recognized command based on the verified first user-defined variant and the second user-defined variant. | 1. A computer-implemented method for eliciting command variants from crowd users to train a natural language processing system, the method being implemented in a computer system having one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the computer system to perform the method, the method comprising: identifying a computer recognized command to be processed by a computer; generating at least a first task, wherein the first task includes command creation instructions that prompt a user to input a variant of the computer recognized command; transmitting the first task to a plurality of users via a network; receiving, from at least a first user among the plurality of users, a first user-defined variant of the computer recognized command; receiving, from at least a second user among the plurality of users, a second user-defined variant of the computer recognized command; storing the first user-defined variant and the second user-defined variant in association with the computer recognized command; generating at least a second task, wherein the second task includes command review instructions that prompt a given user to verify the first user-defined variant; transmitting the second task to a second plurality of users via the network; receiving, from at least a second user among the second plurality of users, a verification of the first user-defined variant; and training the natural language processing system to recognize variants of the computer recognized command based on the verified first user-defined variant and the second user-defined variant. 6. The method of claim 1 , wherein the command review instructions instruct the second plurality of users to compare the first user-defined variant with model text language variants for the computer recognized command. | 0.798521 |
9,400,769 | 1 | 2 | 1. A method for generating a document, comprising: receiving a first configuration of the document that includes a set of content items; generating, using a processor, alignment data representing a measure for an alignment of the content items in the first configuration, the alignment data including a plurality of alignment lines; and determining, using the alignment data, whether the content items are aligned with one another with an acceptable alignment, wherein the content items are determined to be aligned with one another with an acceptable alignment when a distance between any two adjacent alignment lines of the plurality of alignment lines is less than a first defined value or greater than a second defined value; wherein generating the alignment data comprises: using the processor to determine a set of collinear points for the first configuration defined by the edges of the content items; and using the determined set of points to generate the plurality of alignment lines for the first configuration; wherein the first defined value is 2 mm and the second defined value is 5 mm. | 1. A method for generating a document, comprising: receiving a first configuration of the document that includes a set of content items; generating, using a processor, alignment data representing a measure for an alignment of the content items in the first configuration, the alignment data including a plurality of alignment lines; and determining, using the alignment data, whether the content items are aligned with one another with an acceptable alignment, wherein the content items are determined to be aligned with one another with an acceptable alignment when a distance between any two adjacent alignment lines of the plurality of alignment lines is less than a first defined value or greater than a second defined value; wherein generating the alignment data comprises: using the processor to determine a set of collinear points for the first configuration defined by the edges of the content items; and using the determined set of points to generate the plurality of alignment lines for the first configuration; wherein the first defined value is 2 mm and the second defined value is 5 mm. 2. The method as claimed in claim 1 , further comprising: on the basis of the determination that the content items are not aligned with one another with an acceptable alignment, generating, using a layout engine, a second configuration for the document, wherein the second configuration is different from the first configuration. | 0.568241 |
8,545,299 | 16 | 17 | 16. A system comprising: a processor; and a memory containing instructions that, when executed, cause the processor to: present to a first player of a computer-generated crossword puzzle, a first clue to derive a first solution of the computer-generated crossword puzzle; receive, from the first player, an alternate clue request identifying a desired level of difficulty for an alternate clue, different than the first clue, to derive the first solution; and upon a determination that an alternate clue, different than the first clue, of the desired level of difficulty is available, present, to the first player, the alternate clue to derive the first solution. | 16. A system comprising: a processor; and a memory containing instructions that, when executed, cause the processor to: present to a first player of a computer-generated crossword puzzle, a first clue to derive a first solution of the computer-generated crossword puzzle; receive, from the first player, an alternate clue request identifying a desired level of difficulty for an alternate clue, different than the first clue, to derive the first solution; and upon a determination that an alternate clue, different than the first clue, of the desired level of difficulty is available, present, to the first player, the alternate clue to derive the first solution. 17. The system of claim 16 , wherein the instructions further cause the processor to: receive, from the first player, a desired level of puzzle difficulty for the computer-generated crossword puzzle; and generate the computer-generated crossword puzzle based, at least partially, on the desired level of puzzle difficulty received from the first user. | 0.664436 |
8,155,678 | 1 | 2 | 1. An email communications system comprising: a plurality of wireless communications devices each supporting at least one markup language from among a plurality of different markup languages; and an electronic mail (email) server comprising an account provisioning module for determining the at least one markup language supported by a given wireless communications device, providing at least one respective markup language provisioning template to said given wireless communications device based upon the supported at least one markup language, and generating a provisioned email account for said given wireless communications device based upon at least one provisioning parameter returned via the at least one markup language provisioning template, said account provisioning module storing a plurality of device profiles for said plurality of wireless communications devices, determining the at least one markup language supported by said given wireless communications device based upon the device profiles and information included in at least one communication with said given wireless communications device, and updating the plurality of device profiles based upon new mobile wireless communications devices attempting to access said account provisioning module, and a mail-user agent module for forwarding emails to said given wireless communications device based upon the provisioned email account therefor. | 1. An email communications system comprising: a plurality of wireless communications devices each supporting at least one markup language from among a plurality of different markup languages; and an electronic mail (email) server comprising an account provisioning module for determining the at least one markup language supported by a given wireless communications device, providing at least one respective markup language provisioning template to said given wireless communications device based upon the supported at least one markup language, and generating a provisioned email account for said given wireless communications device based upon at least one provisioning parameter returned via the at least one markup language provisioning template, said account provisioning module storing a plurality of device profiles for said plurality of wireless communications devices, determining the at least one markup language supported by said given wireless communications device based upon the device profiles and information included in at least one communication with said given wireless communications device, and updating the plurality of device profiles based upon new mobile wireless communications devices attempting to access said account provisioning module, and a mail-user agent module for forwarding emails to said given wireless communications device based upon the provisioned email account therefor. 2. The email communications system of claim 1 wherein each wireless communications device has at least one unique identification (ID) associated therewith; and wherein the at least one provisioning parameter returned via the at least one markup language provisioning template comprises the at least one unique ID of said given wireless communications device. | 0.526455 |
8,938,451 | 8 | 10 | 8. An apparatus for linking documents, comprising: means for obtaining a set of documents, wherein documents in the set of documents are not interlinked with other documents via one or more hyperlinks; means for storing a plurality of clusters from the set of documents, each cluster comprising one or more documents; means for building a cluster page for each cluster to represent the documents in the cluster; and means for building links based on analysis of the contents of the clusters and the documents; wherein, when a similarity of a topic of a first document of the set of documents to a second document of the set of documents is greater than a threshold, a link is built from the first document to the second document, the similarity being a cosine function of an angle between a topic vector of the first document and the document vector of the second document; wherein the link building means comprises one or more of the following units: a cluster page linking unit that builds the link between the cluster pages; a cluster-document linking unit that builds the link from the cluster page to the document; a document-cluster linking unit that builds the link from the document to the cluster page; and a document linking unit that builds the link between the documents; wherein, when the number of documents commonly owned by a first cluster and a second cluster is greater than or equal to a threshold, the link building means builds the links between the cluster page of the first cluster and the cluster page of the second cluster; and wherein, when the proportion of the number of commonly owned documents in the first cluster is greater than that in the second cluster, the link building means generates a link from the cluster page of the second cluster to the cluster page of the first cluster, otherwise the link building means generates a link from the cluster page of the first cluster to the cluster page of the second cluster. | 8. An apparatus for linking documents, comprising: means for obtaining a set of documents, wherein documents in the set of documents are not interlinked with other documents via one or more hyperlinks; means for storing a plurality of clusters from the set of documents, each cluster comprising one or more documents; means for building a cluster page for each cluster to represent the documents in the cluster; and means for building links based on analysis of the contents of the clusters and the documents; wherein, when a similarity of a topic of a first document of the set of documents to a second document of the set of documents is greater than a threshold, a link is built from the first document to the second document, the similarity being a cosine function of an angle between a topic vector of the first document and the document vector of the second document; wherein the link building means comprises one or more of the following units: a cluster page linking unit that builds the link between the cluster pages; a cluster-document linking unit that builds the link from the cluster page to the document; a document-cluster linking unit that builds the link from the document to the cluster page; and a document linking unit that builds the link between the documents; wherein, when the number of documents commonly owned by a first cluster and a second cluster is greater than or equal to a threshold, the link building means builds the links between the cluster page of the first cluster and the cluster page of the second cluster; and wherein, when the proportion of the number of commonly owned documents in the first cluster is greater than that in the second cluster, the link building means generates a link from the cluster page of the second cluster to the cluster page of the first cluster, otherwise the link building means generates a link from the cluster page of the first cluster to the cluster page of the second cluster. 10. The apparatus according to claim 8 , wherein the cluster forming means is further configured to form the clusters by using one or more of the following processes: taking a folder in a document system as a cluster, the documents under a folder belonging to the corresponding cluster; taking a category in taxonomy as a cluster, the documents in the category belonging to the corresponding cluster; selecting a clustering algorithm, and splitting a document set into m clusters, where m can be varied many times; and fixing the number of the clusters, and applying n different clustering algorithms to the document set to foam the cluster. | 0.608191 |
7,526,459 | 1 | 2 | 1. An adaptive social network system, comprising: a computer-implemented structural aspect comprising a plurality of objects and at least one relationship between a first object and a second object; a representation of one or more users of the system as one or more objects of the plurality of objects; a usage aspect, comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users; a function to determine an affinity between a first user represented as an object and a second user represented as an object of the plurality of objects based, at least in part, on inferred interests derived from the e plurality of usage behaviors associated with the one or more users; and a function to generate a user accessible social network representation, wherein the social network representation comprises a plurality of users represented as objects and the affinity between the representation of a first user as an object and the representation of a second user as an object. | 1. An adaptive social network system, comprising: a computer-implemented structural aspect comprising a plurality of objects and at least one relationship between a first object and a second object; a representation of one or more users of the system as one or more objects of the plurality of objects; a usage aspect, comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users; a function to determine an affinity between a first user represented as an object and a second user represented as an object of the plurality of objects based, at least in part, on inferred interests derived from the e plurality of usage behaviors associated with the one or more users; and a function to generate a user accessible social network representation, wherein the social network representation comprises a plurality of users represented as objects and the affinity between the representation of a first user as an object and the representation of a second user as an object. 2. The adaptive social network system of claim 1 , the usage aspect further comprising one or more usage behaviors, wherein each usage behavior is associated with either a user, one or more user communities, or a user and one or more user communities simultaneously, wherein the user comprises a single-member subset of the one or more users and a community of the one or more user communities comprises a multiple-member subset of the one or more users. | 0.705577 |
8,311,737 | 11 | 13 | 11. A method for labeling objects on a digital map, the method comprising: determining, by a computer, a reference count for each of the objects with respect to a corpus of documents, the reference count indicating a frequency with which the object appears in the corpus; associating, by the computer, a priority with each of the objects according to the reference count for the object; and rendering, by the computer, a digital map including at least some of the objects, each object including a label, the label having an appearance reflecting the priority associated with the labeled object. | 11. A method for labeling objects on a digital map, the method comprising: determining, by a computer, a reference count for each of the objects with respect to a corpus of documents, the reference count indicating a frequency with which the object appears in the corpus; associating, by the computer, a priority with each of the objects according to the reference count for the object; and rendering, by the computer, a digital map including at least some of the objects, each object including a label, the label having an appearance reflecting the priority associated with the labeled object. 13. The method of claim 11 wherein each object is a city. | 0.912037 |
7,529,729 | 1 | 5 | 1. A method of analyzing table access in a database system, comprising: defining an incorrect rule set and a related correct rule set from a database model associated with the database system; retrieving index definitions for the database system; comparing the index definitions with the incorrect rule set to identify improper indexes, wherein the improper indexes are identified independently of any SQL processing; generating a list of index definitions that match a rule in the incorrect rule set; retrieving application programs for the database system; comparing application programs with the index definitions that match a rule in the incorrect rule set; generating a list of application programs which match a rule in the incorrect rule set; merging the list of index definitions with the list of application programs; reporting the merged list of index definitions and application programs; identifying and storing to memory application programs that depend on the improper indexes; and using the related correct rule set to propose changes to the improper indexes and the application programs that depend on the improper indexes. | 1. A method of analyzing table access in a database system, comprising: defining an incorrect rule set and a related correct rule set from a database model associated with the database system; retrieving index definitions for the database system; comparing the index definitions with the incorrect rule set to identify improper indexes, wherein the improper indexes are identified independently of any SQL processing; generating a list of index definitions that match a rule in the incorrect rule set; retrieving application programs for the database system; comparing application programs with the index definitions that match a rule in the incorrect rule set; generating a list of application programs which match a rule in the incorrect rule set; merging the list of index definitions with the list of application programs; reporting the merged list of index definitions and application programs; identifying and storing to memory application programs that depend on the improper indexes; and using the related correct rule set to propose changes to the improper indexes and the application programs that depend on the improper indexes. 5. The method of claim 1 , wherein the index definitions are retrieved from a set of database catalog tables. | 0.904553 |
9,442,916 | 1 | 3 | 1. A method for providing annotations for revising a message, comprising: receiving, using a processor of a first computing device, a message to be sent from a sender at the first computing device to a recipient at a second computing device, wherein the first computing device and the second computing device are coupled together via a communication network; receiving selection of a dialect for the sender and a dialect for the recipient; receiving a level of misunderstanding that is acceptable to the recipient; selecting a meaning map associated with the sender based on the dialect for the sender to determine a first context of the message that indicates a first way in which the message is understood; selecting a meaning map associated with the recipient based on the dialect for the recipient to determine a second context of the message that indicates a second way in which the message is understood; parsing the message into sub-constructs; comparing the sub-constructs in the meaning map associated with the sender and the meaning map associated with the recipient to identify words and phrases where there are differences between perceptions of the sender and the recipient; and in response to the comparisons showing that the differences are greater than a threshold that is based on the level of misunderstanding that is acceptable, identifying an alternative language for the sub-constructs in the message; and providing annotations for the message to the sender at the first computing device based on the alternative language before the message is sent from the sender at the first computing device to the recipient at the second computing device, wherein the annotations indicate the second context of the message; and sending the annotated message from the first computing device to the second computing device, wherein the message is received by the recipient at the second computing device, wherein the meaning map associated with the sender is obtained at the second computing device, and wherein annotations are provided to the recipient at the second computing device on how the sender meant the message to be interpreted based on the meaning map associated with the sender. | 1. A method for providing annotations for revising a message, comprising: receiving, using a processor of a first computing device, a message to be sent from a sender at the first computing device to a recipient at a second computing device, wherein the first computing device and the second computing device are coupled together via a communication network; receiving selection of a dialect for the sender and a dialect for the recipient; receiving a level of misunderstanding that is acceptable to the recipient; selecting a meaning map associated with the sender based on the dialect for the sender to determine a first context of the message that indicates a first way in which the message is understood; selecting a meaning map associated with the recipient based on the dialect for the recipient to determine a second context of the message that indicates a second way in which the message is understood; parsing the message into sub-constructs; comparing the sub-constructs in the meaning map associated with the sender and the meaning map associated with the recipient to identify words and phrases where there are differences between perceptions of the sender and the recipient; and in response to the comparisons showing that the differences are greater than a threshold that is based on the level of misunderstanding that is acceptable, identifying an alternative language for the sub-constructs in the message; and providing annotations for the message to the sender at the first computing device based on the alternative language before the message is sent from the sender at the first computing device to the recipient at the second computing device, wherein the annotations indicate the second context of the message; and sending the annotated message from the first computing device to the second computing device, wherein the message is received by the recipient at the second computing device, wherein the meaning map associated with the sender is obtained at the second computing device, and wherein annotations are provided to the recipient at the second computing device on how the sender meant the message to be interpreted based on the meaning map associated with the sender. 3. The method of claim 1 , further comprising: associating a different level of misunderstanding that is acceptable with each of multiple recipients who are to receive the message. | 0.831776 |
8,467,533 | 3 | 5 | 3. A method for a computer to generate and export a data structure containing encrypted data and accompanying metadata with a specified offset, comprising: a. having a one-time-pad communications key; b. using said one-time-Dad communications key to encrypt a data set starting at an offset location within said key thereby creating an encrypted data set; c. generating a unitary data structure containing said encrypted data set and metadata comprising an offset number specifying said offset location and a key identification number identifying said one-time-pad communications key; and d. exporting said data structure for transmission to a remote recipient. | 3. A method for a computer to generate and export a data structure containing encrypted data and accompanying metadata with a specified offset, comprising: a. having a one-time-pad communications key; b. using said one-time-Dad communications key to encrypt a data set starting at an offset location within said key thereby creating an encrypted data set; c. generating a unitary data structure containing said encrypted data set and metadata comprising an offset number specifying said offset location and a key identification number identifying said one-time-pad communications key; and d. exporting said data structure for transmission to a remote recipient. 5. The method of claim 3 further comprising transmitting the data structure containing said encrypted data set and metadata on a computer network. | 0.719231 |
8,983,962 | 7 | 8 | 7. A question and answer data editing method of editing the content of a dialogue to generate question and answer data, comprising: detecting a first question part or a first answer part from a history data of the content of the dialogue as being similar to a first question and answer data in existing question and answer data; extracting a) a second answer part from the history data, when a second question part in the history data is similar to the first question and answer data and the second answer part in the history data is not similar to the first question and answer data, and b) a third question part from the history data, when a third answer part in the history data is similar to the first question and answer data and the third question part is not similar to the first question and answer data; and registering said third question part or said second answer part from said content of the dialogue as a variation of said first question and answer data. | 7. A question and answer data editing method of editing the content of a dialogue to generate question and answer data, comprising: detecting a first question part or a first answer part from a history data of the content of the dialogue as being similar to a first question and answer data in existing question and answer data; extracting a) a second answer part from the history data, when a second question part in the history data is similar to the first question and answer data and the second answer part in the history data is not similar to the first question and answer data, and b) a third question part from the history data, when a third answer part in the history data is similar to the first question and answer data and the third question part is not similar to the first question and answer data; and registering said third question part or said second answer part from said content of the dialogue as a variation of said first question and answer data. 8. The question and answer data editing method according to claim 7 , further comprising a step of presenting said extracted question and answer data in said content of the dialogue or a part of said extracted question and answer data so as to be edited in correlation with said existing question and answer data extracted. | 0.864286 |
8,452,746 | 9 | 16 | 9. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a search query input and identification of a context file from a third-party content provider, wherein the third-party content provider is configured to receive the search query input from a client device and is configured to determine the identification of the context file based on the search query input; processing the context file to identify one or more commands for processing the search query input and one or more commands for processing search results wherein one or more of the commands for processing the search query input each specifies a respective precondition for evaluation of another of the commands for processing the search query input; processing the search query input according to the one or more commands for processing the search query input to produce one or more context processed search queries; obtaining a plurality of search results responsive to the context processed search queries; processing the search results according to the one or more commands for processing search results to produce a plurality of context processed search results; analyzing, for each of one or more of the context processed search results of the plurality of context processed search results, whether a corresponding entry in an annotation file refers to spam; and removing from the plurality of context processed search results one or more of the context processed search results that each have a corresponding entry in the annotation file that refers to spam to create a plurality of modified search results; and providing the modified search results to the client device. | 9. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a search query input and identification of a context file from a third-party content provider, wherein the third-party content provider is configured to receive the search query input from a client device and is configured to determine the identification of the context file based on the search query input; processing the context file to identify one or more commands for processing the search query input and one or more commands for processing search results wherein one or more of the commands for processing the search query input each specifies a respective precondition for evaluation of another of the commands for processing the search query input; processing the search query input according to the one or more commands for processing the search query input to produce one or more context processed search queries; obtaining a plurality of search results responsive to the context processed search queries; processing the search results according to the one or more commands for processing search results to produce a plurality of context processed search results; analyzing, for each of one or more of the context processed search results of the plurality of context processed search results, whether a corresponding entry in an annotation file refers to spam; and removing from the plurality of context processed search results one or more of the context processed search results that each have a corresponding entry in the annotation file that refers to spam to create a plurality of modified search results; and providing the modified search results to the client device. 16. The computer storage medium of claim 9 , wherein each of the entries in the annotation file comprises a respective link to a respective web page, the method further comprising determining whether the respective web page contains spam. | 0.744635 |
5,488,719 | 9 | 10 | 9. The article of claim 1 in which the category set information includes, for each category in the first subset of categories, a respective category data unit in the first string's ending subsequence; each category's category data unit having a value indicating the category. | 9. The article of claim 1 in which the category set information includes, for each category in the first subset of categories, a respective category data unit in the first string's ending subsequence; each category's category data unit having a value indicating the category. 10. The article of claim 9 in which the first subset of categories includes at least two categories, the category set information including at least two category data units in the first string's ending subsequence. | 0.945987 |
9,430,563 | 9 | 11 | 9. The apparatus of claim 1 , wherein the probabilistic topic model comprises a Gaussian mixture model (GMM) including K-dimensional Gaussian components, the GMM including M Gaussian components corresponding to the M semantic topics. | 9. The apparatus of claim 1 , wherein the probabilistic topic model comprises a Gaussian mixture model (GMM) including K-dimensional Gaussian components, the GMM including M Gaussian components corresponding to the M semantic topics. 11. The apparatus of claim 9 , wherein in the performed document processing method, the applying includes: applying the learned GMM to generate one or more Fisher Vectors representing the input document. | 0.948214 |
9,224,385 | 4 | 6 | 4. The method as recited in claim 1 , further including: presenting intermediate results, on the display, while the audio stream is being received based on one or more first confidence scores and one or more second confidence scores, the intermediate results including one or more possible results. | 4. The method as recited in claim 1 , further including: presenting intermediate results, on the display, while the audio stream is being received based on one or more first confidence scores and one or more second confidence scores, the intermediate results including one or more possible results. 6. The method as recited in claim 4 , wherein the audio stream is identified as music, wherein the intermediate results include candidate result songs for the audio stream. | 0.942165 |
4,386,416 | 15 | 16 | 15. The system of claim 14 wherein the processor means includes means for detecting an answer back from the second station indicating whether the second station is equipped with decompression capability and for sending the data stream in standard form if the second station does not have decompression capability. | 15. The system of claim 14 wherein the processor means includes means for detecting an answer back from the second station indicating whether the second station is equipped with decompression capability and for sending the data stream in standard form if the second station does not have decompression capability. 16. The system of claim 15 wherein the processor means includes: means for detecting whether a received data stream is in standard form or compressed form, and for sending a standard data stream received from the second station to the first station in standard form. | 0.950558 |
6,154,222 | 13 | 19 | 13. The method of claim 1, wherein said object includes characteristics of a human being and said method further includes the step of defining a major animation parameter representing a human expression, said step includes the sub-steps of: defining a plurality of animation parameters representing said human expression; and storing said plurality of animation parameters as said major parameter. | 13. The method of claim 1, wherein said object includes characteristics of a human being and said method further includes the step of defining a major animation parameter representing a human expression, said step includes the sub-steps of: defining a plurality of animation parameters representing said human expression; and storing said plurality of animation parameters as said major parameter. 19. The method of claim 13, wherein said major animation parameter represents a tongue roll. | 0.944645 |
9,613,138 | 1 | 7 | 1. A computer implemented method of scoring one or more partitions of a composition of ontological subjects, said method comprises execution of a set of instructions, by one or more processors, configured to perform: identifying a plurality of ontological subjects, wherein said plurality of ontological subjects assigned with a first predefined order; partitioning, using one or more data processing or computing devices, the composition into one or more pluralities of partitions, wherein at least one plurality of said one more pluralities of partitions is assigned with a second predefined order; constructing at least one set of data structures corresponding to at least one ordered array of data, said at least one ordered array of data represents participation of some of said ontological subjects of the first predefined order into some of said partitions assigned with the second predefined order by having a non-zero value in the corresponding entries of the ordered array of data; and calculating frequency of occurrences of the ontological subjects of the first predefined order in the partitions of the composition assigned with a predefined order; and scoring, using one or more data processing or computing devices, an importance of one or more of the partitions of the second predefined order using the data of said at least one ordered array of data and the frequency of occurrences of the ontological subjects of the first predefined order; and storing the data, representing said scores of the importance of one or more of the partitions of the second predefined order or the ordered array of data, in one or more non-transitory computer readable storage medium for further use by an application server. | 1. A computer implemented method of scoring one or more partitions of a composition of ontological subjects, said method comprises execution of a set of instructions, by one or more processors, configured to perform: identifying a plurality of ontological subjects, wherein said plurality of ontological subjects assigned with a first predefined order; partitioning, using one or more data processing or computing devices, the composition into one or more pluralities of partitions, wherein at least one plurality of said one more pluralities of partitions is assigned with a second predefined order; constructing at least one set of data structures corresponding to at least one ordered array of data, said at least one ordered array of data represents participation of some of said ontological subjects of the first predefined order into some of said partitions assigned with the second predefined order by having a non-zero value in the corresponding entries of the ordered array of data; and calculating frequency of occurrences of the ontological subjects of the first predefined order in the partitions of the composition assigned with a predefined order; and scoring, using one or more data processing or computing devices, an importance of one or more of the partitions of the second predefined order using the data of said at least one ordered array of data and the frequency of occurrences of the ontological subjects of the first predefined order; and storing the data, representing said scores of the importance of one or more of the partitions of the second predefined order or the ordered array of data, in one or more non-transitory computer readable storage medium for further use by an application server. 7. The method of claim 1 , wherein said composition is composed of one or more of: a genetic code corresponding to one or more deoxyribonucleic acid molecule; genetic code having symbols representing at least one of chemical bases of adenine, thymine, guanine, cytosine, and uracil; a visual content or one or more digital images composed of a plurality of pixels; a textual content composed of textual characters; an audio content composed of digital signals; electrical signals; one or more contents obtained from Internet; a digital signal string having ones and zeros. | 0.536467 |
8,972,372 | 12 | 17 | 12. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: receiving a first specification that identifies program code behavior associated with a plurality of documents, wherein the specification comprises an input-output pair including a first data entity and a second data entity; for each module of program code of a plurality of modules of program code, supplying to a constraint solver one or more input-output constraints based on the input-output pair of the first specification and one or more code constraints based on the module of program code; receiving from the constraint solver, for each module of program code, a result indicating whether the code constraints based on the module of program code are satisfiable with the input-output constraints; and generating search results referencing one or more modules of program code having a positive result from the constraint solver and providing the search results to a user. | 12. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: receiving a first specification that identifies program code behavior associated with a plurality of documents, wherein the specification comprises an input-output pair including a first data entity and a second data entity; for each module of program code of a plurality of modules of program code, supplying to a constraint solver one or more input-output constraints based on the input-output pair of the first specification and one or more code constraints based on the module of program code; receiving from the constraint solver, for each module of program code, a result indicating whether the code constraints based on the module of program code are satisfiable with the input-output constraints; and generating search results referencing one or more modules of program code having a positive result from the constraint solver and providing the search results to a user. 17. The computer-readable medium of claim 12 , wherein the first data entity is an extensible markup language (XML) file type and the second data entity is a Structure Query Language (SQL) file type. | 0.775901 |
8,396,714 | 2 | 5 | 2. The method of claim 1 , wherein the text string is generated based on metadata associated with or identifying a media asset. | 2. The method of claim 1 , wherein the text string is generated based on metadata associated with or identifying a media asset. 5. The method of claim 2 , wherein the text string includes one or more fields of information extracted the metadata and omits at least one field of information available in the metadata. | 0.898148 |
8,527,276 | 11 | 17 | 11. 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: training a neural network implemented by the system to map one or more training-time sequences of phonetic-context descriptors received by the neural network into training-time predicted feature vectors that correspond to acoustic properties of predefined speech waveforms, wherein the one or more training-time sequences of phonetic-context descriptors correspond to phonetic transcriptions of training-time text strings, and the training-time text strings correspond to written transcriptions of speech carried in the predefined speech waveforms, receiving, by a text analysis module implemented by the system, a run-time text string, processing the received run-time text string with the text analysis module to generate a run-time sequence of phonetic-context descriptors that corresponds to a phonetic transcription of the run-time text string, wherein each phonetic-context descriptor of the run-time sequence includes a respective label identifying a phonetic speech unit of a plurality of phonetic speech units, data indicating phonetic context of the identified phonetic speech unit, and data indicating time duration of the identified phonetic speech unit, processing the run-time sequence of the phonetic-context descriptors with the trained neural network in a corresponding sequence of neural network time steps to generate one or more run-time predicted feature vectors, and processing the one or more run-time predicted feature vectors with a signal generation module to produce and output a run-time speech waveform corresponding to a spoken rendering of the received run-time text string, wherein processing the received run-time text string with the text analysis module to generate the run-time sequence of phonetic-context descriptors comprises: generating a run-time transcription sequence of phonetic speech units that corresponds to the phonetic transcription of the run-time text string; and determining a respective number of consecutive phonetic-context descriptors to generate for each of the phonetic speech units of the run-time transcription sequence. | 11. 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: training a neural network implemented by the system to map one or more training-time sequences of phonetic-context descriptors received by the neural network into training-time predicted feature vectors that correspond to acoustic properties of predefined speech waveforms, wherein the one or more training-time sequences of phonetic-context descriptors correspond to phonetic transcriptions of training-time text strings, and the training-time text strings correspond to written transcriptions of speech carried in the predefined speech waveforms, receiving, by a text analysis module implemented by the system, a run-time text string, processing the received run-time text string with the text analysis module to generate a run-time sequence of phonetic-context descriptors that corresponds to a phonetic transcription of the run-time text string, wherein each phonetic-context descriptor of the run-time sequence includes a respective label identifying a phonetic speech unit of a plurality of phonetic speech units, data indicating phonetic context of the identified phonetic speech unit, and data indicating time duration of the identified phonetic speech unit, processing the run-time sequence of the phonetic-context descriptors with the trained neural network in a corresponding sequence of neural network time steps to generate one or more run-time predicted feature vectors, and processing the one or more run-time predicted feature vectors with a signal generation module to produce and output a run-time speech waveform corresponding to a spoken rendering of the received run-time text string, wherein processing the received run-time text string with the text analysis module to generate the run-time sequence of phonetic-context descriptors comprises: generating a run-time transcription sequence of phonetic speech units that corresponds to the phonetic transcription of the run-time text string; and determining a respective number of consecutive phonetic-context descriptors to generate for each of the phonetic speech units of the run-time transcription sequence. 17. The system of claim 11 , wherein each of the one or more run-time predicted feature vectors corresponds to a respective temporal frame of acoustic data in the run-time speech waveform, and wherein the data indicating time duration of the identified phonetic speech unit comprises a number that specifies how many consecutive temporal frames of acoustic data over which an acoustic rendering of the identified phonetic speech unit in the run-time speech waveform should last. | 0.837746 |
7,636,945 | 15 | 16 | 15. An apparatus for detecting script language viruses, comprising: a script language processor, wherein the script language processor prepares language description data corresponding to at least one script language; a detection data processor, wherein the detection data processor prepares detection data for viral code corresponding to a script language virus and wherein the detection data processor generates viral code detection data by analyzing a plurality of samples of polymorphic script language viral code; and a detection engine, wherein the detection engine converts a data stream to a stream of tokens using lexical analysis, wherein the tokens correspond to respective language constructs, wherein the detection engine lexically analyzes the stream of tokens using the language description data and the detection data to identify the script language virus. | 15. An apparatus for detecting script language viruses, comprising: a script language processor, wherein the script language processor prepares language description data corresponding to at least one script language; a detection data processor, wherein the detection data processor prepares detection data for viral code corresponding to a script language virus and wherein the detection data processor generates viral code detection data by analyzing a plurality of samples of polymorphic script language viral code; and a detection engine, wherein the detection engine converts a data stream to a stream of tokens using lexical analysis, wherein the tokens correspond to respective language constructs, wherein the detection engine lexically analyzes the stream of tokens using the language description data and the detection data to identify the script language virus. 16. The apparatus of claim 15 , wherein the language description data correspond to Dynamic Finite Automata data. | 0.799645 |
9,990,428 | 1 | 2 | 1. A search system comprising: a device controller configured to provision a device to execute a copy of a selected mobile application selected from among a plurality of mobile applications; a crawler configured to extract content and metadata from a plurality of states of the copy of the selected mobile application using the device; a search input state classifier configured to identify search input states within the plurality of states of the selected mobile application, wherein identifying search input states is performed based on a first set of heuristics, wherein the first set of heuristics includes recognition of user-visible search indicia from the extracted content and metadata of the selected mobile application and recognition of items of the extracted metadata of the selected mobile application that correlate with search functionality; a parameter identifier configured to identify, for each of the search input states of the selected mobile application, necessary input parameters for a search function corresponding to the search input state; a search function data store configured to store a record for each identified search function in the selected mobile application, wherein each record includes: a navigation path within the selected mobile application to reach a search input state of the corresponding search function, an indication of input parameters required to be supplied to the corresponding search function, and a mapping of the input parameters to user interface widgets of the search input state; and a query processing system configured to, in response to a query, (i) identify a first search function record from the search function data store based on the query, (ii) within a first mobile application corresponding to the first search function record, navigate to the search input state of the first search function record according to the navigation path of the first search function record; (iii) based on parameters specified by the query, selectively perform the search function of the first search function record in the first mobile application, (iv) scrape content from a resulting search results state of the first mobile application, and (v) present the scraped content to a user. | 1. A search system comprising: a device controller configured to provision a device to execute a copy of a selected mobile application selected from among a plurality of mobile applications; a crawler configured to extract content and metadata from a plurality of states of the copy of the selected mobile application using the device; a search input state classifier configured to identify search input states within the plurality of states of the selected mobile application, wherein identifying search input states is performed based on a first set of heuristics, wherein the first set of heuristics includes recognition of user-visible search indicia from the extracted content and metadata of the selected mobile application and recognition of items of the extracted metadata of the selected mobile application that correlate with search functionality; a parameter identifier configured to identify, for each of the search input states of the selected mobile application, necessary input parameters for a search function corresponding to the search input state; a search function data store configured to store a record for each identified search function in the selected mobile application, wherein each record includes: a navigation path within the selected mobile application to reach a search input state of the corresponding search function, an indication of input parameters required to be supplied to the corresponding search function, and a mapping of the input parameters to user interface widgets of the search input state; and a query processing system configured to, in response to a query, (i) identify a first search function record from the search function data store based on the query, (ii) within a first mobile application corresponding to the first search function record, navigate to the search input state of the first search function record according to the navigation path of the first search function record; (iii) based on parameters specified by the query, selectively perform the search function of the first search function record in the first mobile application, (iv) scrape content from a resulting search results state of the first mobile application, and (v) present the scraped content to a user. 2. The search system of claim 1 further comprising a search results state classifier configured to identify search results states within the plurality of states based on a second set of heuristics, wherein the second set of heuristics includes identification of a list of repeated widget groups. | 0.881811 |
8,347,391 | 11 | 12 | 11. A networking device for detecting a network activity of interest, comprising: a network port for connecting to a network infrastructure, wherein the network port is adapted to obtain a plurality of network packets, wherein the obtained plurality of network packets comprises network packets categorized as Transmission Control Protocol (TCP) packets and Internet Protocol (IP) packets, wherein the obtained plurality of network packets include the network activity of interest; a processor connected to the network port, wherein the processor is adapted to: create a plurality of combined packets, from at least a subset of the plurality of TCP packets and IP packets, wherein: a first combined packet of the plurality of combined packets comprises a portion of at least one of the TCP packets and a portion of at least one of the IP packets, and a second combined packet of the plurality of combined packets comprises a portion of at least one of the TCP packets and a portion of at least one of the IP packets, wherein the second combined packet is different from the first combined packet; create a first sequence by converting bitwise content of at least a portion of the first combined packet into a first plurality of integers, wherein the first sequence includes the first plurality of integers; create a second sequence by converting bitwise content of at least a portion of the second combined packet into a second plurality of integers, wherein the second sequence includes the second plurality of integers; determine a similarity metric between the first sequence and the second sequence based on a distance function; create a third sequence based on the similarity metric, wherein the third sequence comprises a third plurality of integers common to the first sequence and the second sequence, in the first order; and create a fourth sequence, wherein the fourth sequence is a meta-expression that: comprises a subset of the third plurality of integers of the third list, in the first order, and corresponds to the presence of the network activity of interest in the network traffic; and a memory connected to the processor, wherein the memory is adapted to store the meta-expression, wherein the stored meta-expression is used to detect the presence of the network activity of interest. | 11. A networking device for detecting a network activity of interest, comprising: a network port for connecting to a network infrastructure, wherein the network port is adapted to obtain a plurality of network packets, wherein the obtained plurality of network packets comprises network packets categorized as Transmission Control Protocol (TCP) packets and Internet Protocol (IP) packets, wherein the obtained plurality of network packets include the network activity of interest; a processor connected to the network port, wherein the processor is adapted to: create a plurality of combined packets, from at least a subset of the plurality of TCP packets and IP packets, wherein: a first combined packet of the plurality of combined packets comprises a portion of at least one of the TCP packets and a portion of at least one of the IP packets, and a second combined packet of the plurality of combined packets comprises a portion of at least one of the TCP packets and a portion of at least one of the IP packets, wherein the second combined packet is different from the first combined packet; create a first sequence by converting bitwise content of at least a portion of the first combined packet into a first plurality of integers, wherein the first sequence includes the first plurality of integers; create a second sequence by converting bitwise content of at least a portion of the second combined packet into a second plurality of integers, wherein the second sequence includes the second plurality of integers; determine a similarity metric between the first sequence and the second sequence based on a distance function; create a third sequence based on the similarity metric, wherein the third sequence comprises a third plurality of integers common to the first sequence and the second sequence, in the first order; and create a fourth sequence, wherein the fourth sequence is a meta-expression that: comprises a subset of the third plurality of integers of the third list, in the first order, and corresponds to the presence of the network activity of interest in the network traffic; and a memory connected to the processor, wherein the memory is adapted to store the meta-expression, wherein the stored meta-expression is used to detect the presence of the network activity of interest. 12. The networking device of claim 11 , wherein: the network activity of interest is a threat based on a computer virus. | 0.909502 |
9,471,874 | 4 | 5 | 4. The method of claim 1 further comprising: identifying a plurality of follow-up postings corresponding to the identified question; analyzing each of the follow-up postings to identify a conversational move corresponding to each of the follow up postings, wherein at least one of the conversational postings is selected from the group consisting of an answer, a clarification, a rejection, or a different conversational move; and generating a contribution tree based on the follow-up postings and their identified conversational moves. | 4. The method of claim 1 further comprising: identifying a plurality of follow-up postings corresponding to the identified question; analyzing each of the follow-up postings to identify a conversational move corresponding to each of the follow up postings, wherein at least one of the conversational postings is selected from the group consisting of an answer, a clarification, a rejection, or a different conversational move; and generating a contribution tree based on the follow-up postings and their identified conversational moves. 5. The method of claim 4 further comprising: pruning one or more of the follow-up postings from the contribution tree based on a contribution analysis, wherein the pruned follow-up postings have a contribution analysis result selected from the group consisting of an answer leading to a new question, an overly deep follow-up posting, and another pruning criteria. | 0.929946 |
8,903,174 | 54 | 60 | 54. The method of claim 47 wherein the optimal recognition position of a display element of the at least some display elements is located at a position, referred to as an optimal proportionate position, along a reading direction of the display element that is a distance in pixels from a beginning of the display element, the distance in pixels being a proportion of the display element's pixel width. | 54. The method of claim 47 wherein the optimal recognition position of a display element of the at least some display elements is located at a position, referred to as an optimal proportionate position, along a reading direction of the display element that is a distance in pixels from a beginning of the display element, the distance in pixels being a proportion of the display element's pixel width. 60. The method of claim 54 wherein the optimal proportionate position provides an offset amount for a pixel at the beginning of the display element, referred to as a beginning pixel, the method further comprising displaying the display element such that the beginning pixel of the display element is at a position that is the offset amount away from the fixed display location. | 0.883137 |
7,493,338 | 6 | 7 | 6. A computer-implemented method for providing full-text search integration in XQuery for a binary object XML repository, comprising: interpreting XQuery code and accepting a search query comprising one or more search expressions having one or more search terms; identifying all matching variants in a lexicon for each search term within the search expressions; collecting posting lists for each variant of each search term comprising sequence offset values to candidate elements containing that search term; dispatching an XQuery function calling mechanism comprising at least one argument for the search query based on the search expressions; searching and interpreting a full-text search query syntax for the XQuery function calling mechanism as a built-in search function specified as part of an XQuery language, comprising: applying search query Boolean logic to the sequence offset values; and filtering the posting lists for relations specified in the search query; determining the candidate elements that satisfy at least one of the search terms; returning all of the determined candidate elements that match at least one of the search expressions based on the search query Boolean logic and the relations, wherein each search expression corresponds to a piece of XML syntax; and returning a score that reflects strength of each match. | 6. A computer-implemented method for providing full-text search integration in XQuery for a binary object XML repository, comprising: interpreting XQuery code and accepting a search query comprising one or more search expressions having one or more search terms; identifying all matching variants in a lexicon for each search term within the search expressions; collecting posting lists for each variant of each search term comprising sequence offset values to candidate elements containing that search term; dispatching an XQuery function calling mechanism comprising at least one argument for the search query based on the search expressions; searching and interpreting a full-text search query syntax for the XQuery function calling mechanism as a built-in search function specified as part of an XQuery language, comprising: applying search query Boolean logic to the sequence offset values; and filtering the posting lists for relations specified in the search query; determining the candidate elements that satisfy at least one of the search terms; returning all of the determined candidate elements that match at least one of the search expressions based on the search query Boolean logic and the relations, wherein each search expression corresponds to a piece of XML syntax; and returning a score that reflects strength of each match. 7. The method according to claim 6 , further comprising: identifying at least one of the matching variants using one or more of case-insensitive, spelling correction, and stemming routines. | 0.547847 |
7,685,252 | 1 | 33 | 1. A method of generating an application accessible by a user through one or more computer-based devices, the method comprising the steps of: representing interactions that the user is permitted to have with the one or more computer-based devices used to access the application by interaction-based programming components, wherein the interaction-based programming components are independent of content/application logic and presentation requirements associated with the application, and further wherein the interaction-based programming components may be transcoded on a component by component basis to generate one or more modality-specific renderings of the application renderable in accordance with one or more modality-specific browsers associated with the one or more computer-based devices, such that the interaction-based programming components are independent of any modality and any modality-specific browser; and authoring the application using at least a portion of the interaction-based programming components; wherein representation by the interaction-based programming components permits synchronization of the one or more modality-specific renderings of the application on the one or more computer-based devices. | 1. A method of generating an application accessible by a user through one or more computer-based devices, the method comprising the steps of: representing interactions that the user is permitted to have with the one or more computer-based devices used to access the application by interaction-based programming components, wherein the interaction-based programming components are independent of content/application logic and presentation requirements associated with the application, and further wherein the interaction-based programming components may be transcoded on a component by component basis to generate one or more modality-specific renderings of the application renderable in accordance with one or more modality-specific browsers associated with the one or more computer-based devices, such that the interaction-based programming components are independent of any modality and any modality-specific browser; and authoring the application using at least a portion of the interaction-based programming components; wherein representation by the interaction-based programming components permits synchronization of the one or more modality-specific renderings of the application on the one or more computer-based devices. 33. The method of claim 1 , wherein representation by the interaction-based programming components supports a natural language understanding environment. | 0.794906 |
9,704,481 | 2 | 6 | 2. The computer-implemented method of claim 1 , further comprising persisting each generated parsing rule and data describing the action with which the generated parsing rule is associated to command models data that is accessible by a language processing system that parses command input sentences by the generated parsing rules and determines actions to perform based on a successful parses. | 2. The computer-implemented method of claim 1 , further comprising persisting each generated parsing rule and data describing the action with which the generated parsing rule is associated to command models data that is accessible by a language processing system that parses command input sentences by the generated parsing rules and determines actions to perform based on a successful parses. 6. The computer-implemented method of claim 2 , wherein the parsing rules are grammar based rules. | 0.971528 |
7,599,580 | 14 | 15 | 14. The method of claim 1 wherein the distinguished text capture operation is performed using a text capture device, and wherein the received supplemental information includes information about an ambient environment in which the distinguished text capture operation was performed, and wherein the determining comprises determine a configuration of a distinguished capability of the device. | 14. The method of claim 1 wherein the distinguished text capture operation is performed using a text capture device, and wherein the received supplemental information includes information about an ambient environment in which the distinguished text capture operation was performed, and wherein the determining comprises determine a configuration of a distinguished capability of the device. 15. The method of claim 14 wherein the distinguished text capture operation involves capturing an image of the captured text from the distinguished rendered document, and wherein the received supplemental information includes an indication of a frequency of light received in the image capture. | 0.856725 |
10,164,995 | 1 | 7 | 1. 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 data representing aggregate network traffic data as a bipartite graph between nodes representing first entities and nodes representing second entities, wherein each edge of the bipartite graph connects a node representing a respective first entity with a node representing a respective second entity, each edge of the bipartite graph has an edge weight representing a measure of the aggregate network traffic between the entities represented by nodes of the graph connected by the edge, the aggregate network traffic data represents network traffic between the first entities and the second entities, and each of the entities is an entity communicating with one or more other entities on a data communication network; receiving an initial collection of ground truth label values for some of the first entities, some of the second entities, or both, wherein each ground truth label value for an entity indicates that the entity is known to be safe or unsafe, and wherein each ground truth label value is either −r or +r, wherein r is a positive real number; computing a respective initial score for each of the first entities and each of the second entities, each initial score being a non-zero value for a respective entity that has a known ground truth label value indicating that the entity is known to be safe or unsafe or a zero value for entities that do not have a known ground truth label value, each entity with a ground truth value of −r being assigned an initial score of −r/B, and each entity with a ground truth value of +r being assigned an initial score of +r/A, wherein B is a count of how many values of −r were present in the initial collection, and wherein A is a count of how many values of +r were present in the initial collection; iteratively computing a respective final score for each of the first entities and the second entities from the initial scores and the edge weights, the final score indicating malware infection risk of a corresponding entity; identifying, based on the final scores, one or more first entities, one or more second entities, or both, that are likely infected with malware; and reporting the identified one or more first entities or one or more second entities to a user. | 1. 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 data representing aggregate network traffic data as a bipartite graph between nodes representing first entities and nodes representing second entities, wherein each edge of the bipartite graph connects a node representing a respective first entity with a node representing a respective second entity, each edge of the bipartite graph has an edge weight representing a measure of the aggregate network traffic between the entities represented by nodes of the graph connected by the edge, the aggregate network traffic data represents network traffic between the first entities and the second entities, and each of the entities is an entity communicating with one or more other entities on a data communication network; receiving an initial collection of ground truth label values for some of the first entities, some of the second entities, or both, wherein each ground truth label value for an entity indicates that the entity is known to be safe or unsafe, and wherein each ground truth label value is either −r or +r, wherein r is a positive real number; computing a respective initial score for each of the first entities and each of the second entities, each initial score being a non-zero value for a respective entity that has a known ground truth label value indicating that the entity is known to be safe or unsafe or a zero value for entities that do not have a known ground truth label value, each entity with a ground truth value of −r being assigned an initial score of −r/B, and each entity with a ground truth value of +r being assigned an initial score of +r/A, wherein B is a count of how many values of −r were present in the initial collection, and wherein A is a count of how many values of +r were present in the initial collection; iteratively computing a respective final score for each of the first entities and the second entities from the initial scores and the edge weights, the final score indicating malware infection risk of a corresponding entity; identifying, based on the final scores, one or more first entities, one or more second entities, or both, that are likely infected with malware; and reporting the identified one or more first entities or one or more second entities to a user. 7. The system of claim 1 , wherein reporting the identified one or more first entities or one or more second entities comprises: displaying multiple final scores in a sorted order, including displaying each of the multiple final score with an identifier of the first entity or the second entity of the final score. | 0.846829 |
7,821,426 | 12 | 13 | 12. A computer-readable medium storing data and instructions to cause a processor to perform a method comprising: storing a first plurality of literal symbols as a compressed plurality of literal symbols in a first block of data; and storing a second plurality of literal symbols as an encoded plurality of literal symbols in a second block of data, wherein each of the second plurality of literal symbols occurs subsequently in a symbol stream to a literal symbol with the same value in the first plurality of literal symbols. | 12. A computer-readable medium storing data and instructions to cause a processor to perform a method comprising: storing a first plurality of literal symbols as a compressed plurality of literal symbols in a first block of data; and storing a second plurality of literal symbols as an encoded plurality of literal symbols in a second block of data, wherein each of the second plurality of literal symbols occurs subsequently in a symbol stream to a literal symbol with the same value in the first plurality of literal symbols. 13. The computer-readable medium storing data and instructions of claim 12 wherein the storing the first plurality comprises: storing the first block of data as an uninterrupted series of compressed codes. | 0.725936 |
9,317,589 | 11 | 17 | 11. The computer readable storage medium of claim 10 , wherein selecting the first word usage sense from the plurality of word usage senses associated with the first search term, comprises: for each of the plurality of word usage senses: determining a plurality of lexical component scores by applying a plurality of lexical analysis techniques using a respective word usage sense and a context window of words surrounding the first search term, applying a weighting factor to each lexical component score, and adding the lexical component scores to determine a total score for a respective word usage sense; and selecting the word usage sense with a highest total score as the first word usage sense. | 11. The computer readable storage medium of claim 10 , wherein selecting the first word usage sense from the plurality of word usage senses associated with the first search term, comprises: for each of the plurality of word usage senses: determining a plurality of lexical component scores by applying a plurality of lexical analysis techniques using a respective word usage sense and a context window of words surrounding the first search term, applying a weighting factor to each lexical component score, and adding the lexical component scores to determine a total score for a respective word usage sense; and selecting the word usage sense with a highest total score as the first word usage sense. 17. The computer readable storage medium of claim 11 , wherein one of the lexical analysis techniques comprises calculating a value based upon the number of words in the context window of words that are a meronym or holonym of the first word usage sense. | 0.872105 |
8,661,046 | 10 | 13 | 10. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for inferring activity-related context information from a message, the method comprising: scanning for a keyword in the message, wherein the keyword is associated with an activity category in a content database; determining that the keyword indicates the associated activity category; inferring message-related context information from the keyword in the message; and recommending an activity in the activity category based on the inferred message-related context information, wherein recommending the activity involves:— identifying one or more content items associated with the keyword, wherein each content item corresponds to a recommendable activity; generating a combined model based on the message-related context information, wherein the combined model includes an activity model for one or more activities associated with the message-related context information, and includes a user preference model for the user; scoring the one or more content items using at least the activity model and the user preference model of the combined model; and returning a content item associated with a top score. | 10. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for inferring activity-related context information from a message, the method comprising: scanning for a keyword in the message, wherein the keyword is associated with an activity category in a content database; determining that the keyword indicates the associated activity category; inferring message-related context information from the keyword in the message; and recommending an activity in the activity category based on the inferred message-related context information, wherein recommending the activity involves:— identifying one or more content items associated with the keyword, wherein each content item corresponds to a recommendable activity; generating a combined model based on the message-related context information, wherein the combined model includes an activity model for one or more activities associated with the message-related context information, and includes a user preference model for the user; scoring the one or more content items using at least the activity model and the user preference model of the combined model; and returning a content item associated with a top score. 13. The computer-readable storage medium of claim 10 , wherein the method further comprises: identifying location and time indicators in the message; and using the identified location and time indicators to facilitate recommending the activity to the user. | 0.622419 |
10,083,229 | 1 | 16 | 1. A computer-implemented method for pairing a new document to a document from a plurality of documents in a document repository, comprising: for each of the new document and the plurality of documents in the document repository, generating a vector uniquely associated with a document of the new document and the plurality of documents, wherein: the vector comprises a number of elements equal to a number of terms of interest; and for each term of interest, an associated element value of the vector is assigned as zero if the term of interest does not occur in the document and one if the term does occur in the document; for each document from the plurality of documents, determining a similarity between the vector for the new document and the vector for the document from the plurality of documents comprising calculating a cosine measurement of similarity between the vector for the new document and the vector for the document from the plurality of documents; if it is determined that the similarity between the vector for the new document and the vector for a document from the plurality of documents is greater than or equal to a threshold value then: selecting the document from the plurality of documents; generating a merged document by merging the new document with the document from the plurality of documents in response to the document from the plurality of documents being selected, wherein the merging comprises combining at least a portion of the new document with at least a portion of the selected document into the merged document; removing the selected document from the document repository and adding the merged document to the document repository; and generating a new vector for the merged document; and if it is determined that the similarity is less than the threshold value then adding the new document to the document repository without merging the new document. | 1. A computer-implemented method for pairing a new document to a document from a plurality of documents in a document repository, comprising: for each of the new document and the plurality of documents in the document repository, generating a vector uniquely associated with a document of the new document and the plurality of documents, wherein: the vector comprises a number of elements equal to a number of terms of interest; and for each term of interest, an associated element value of the vector is assigned as zero if the term of interest does not occur in the document and one if the term does occur in the document; for each document from the plurality of documents, determining a similarity between the vector for the new document and the vector for the document from the plurality of documents comprising calculating a cosine measurement of similarity between the vector for the new document and the vector for the document from the plurality of documents; if it is determined that the similarity between the vector for the new document and the vector for a document from the plurality of documents is greater than or equal to a threshold value then: selecting the document from the plurality of documents; generating a merged document by merging the new document with the document from the plurality of documents in response to the document from the plurality of documents being selected, wherein the merging comprises combining at least a portion of the new document with at least a portion of the selected document into the merged document; removing the selected document from the document repository and adding the merged document to the document repository; and generating a new vector for the merged document; and if it is determined that the similarity is less than the threshold value then adding the new document to the document repository without merging the new document. 16. The computer-implemented method of claim 1 , wherein the associated element value for each term of interest is weighted based on a position of the term of interest within a structural hierarchy of the document. | 0.90028 |
10,152,655 | 1 | 5 | 1. A method for automatically identifying a query object within a visual medium, wherein the method includes one or more processing devices performing operations comprising: receiving, as input to a neural network, a query object and a visual medium including the query object; generating, from a first subset of layers in the neural network, representations of the query object and the visual medium defining features of the query object and the visual medium; generating a plurality of heat maps by applying a second subset of layers in the neural network to the representations, wherein the plurality of heat maps include (i) a first heat map generated from a first combination of a first query object representation and a first visual medium representation and (ii) a second heat map generated from a second combination of a second query object representation and a second visual medium representation; pooling the first heat map and the second heat map to create a set of concatenated layers; generating an output heat map from the set of concatenated layers, wherein the output heat map identifies a location of pixels corresponding to the query object within the visual medium; and generating an updated visual medium using the output heat map to highlight the query object within the visual medium. | 1. A method for automatically identifying a query object within a visual medium, wherein the method includes one or more processing devices performing operations comprising: receiving, as input to a neural network, a query object and a visual medium including the query object; generating, from a first subset of layers in the neural network, representations of the query object and the visual medium defining features of the query object and the visual medium; generating a plurality of heat maps by applying a second subset of layers in the neural network to the representations, wherein the plurality of heat maps include (i) a first heat map generated from a first combination of a first query object representation and a first visual medium representation and (ii) a second heat map generated from a second combination of a second query object representation and a second visual medium representation; pooling the first heat map and the second heat map to create a set of concatenated layers; generating an output heat map from the set of concatenated layers, wherein the output heat map identifies a location of pixels corresponding to the query object within the visual medium; and generating an updated visual medium using the output heat map to highlight the query object within the visual medium. 5. The method of claim 1 , further comprising training, using a batch of images, neurons in the neural network to identify pixels corresponding to the query object within the visual medium. | 0.817568 |
8,526,739 | 49 | 58 | 49. A method, comprising: receiving an image of a part or all of a document selected from a group consisting of: an invoice, a bill, a receipt, a sales order, an insurance claim, a medical insurance document, and a benefits document; performing optical character recognition (OCR) on the image; extracting at least a partial address of a sender of the document; comparing the at least partial address of the sender to a plurality of addresses in a first database; and identifying one or more of: textual information specific to the sender; and data formatting specific to the sender. | 49. A method, comprising: receiving an image of a part or all of a document selected from a group consisting of: an invoice, a bill, a receipt, a sales order, an insurance claim, a medical insurance document, and a benefits document; performing optical character recognition (OCR) on the image; extracting at least a partial address of a sender of the document; comparing the at least partial address of the sender to a plurality of addresses in a first database; and identifying one or more of: textual information specific to the sender; and data formatting specific to the sender. 58. The method as recited in claim 49 , further comprising comparing one or more portions of the document to one or more portions of a complementary document. | 0.874204 |
8,190,985 | 6 | 7 | 6. A method as set forth in claim 3 , wherein said step of using comprises performing a comparison of said object, attribute or attribute value to a corresponding set of objects, attributes, or attribute values defined by said first schema. | 6. A method as set forth in claim 3 , wherein said step of using comprises performing a comparison of said object, attribute or attribute value to a corresponding set of objects, attributes, or attribute values defined by said first schema. 7. A method as set forth in claim 6 , wherein said step of using comprises using said comparison to convert said set of data from said first form to said second form. | 0.941301 |
8,468,122 | 27 | 28 | 27. A system for responding to a query initiated at a user device, comprising: one or more data stores having a knowledge base stored therein that includes data representing first knowledge about a plurality of objects using a plurality of relationships among the objects, wherein selected ones of the objects are associated with class objects identifying corresponding classes for the selected objects using class member objects that define class member relationships between the selected objects and the corresponding classes; and one or more computing devices configured to generate a response to the query using second knowledge not statically stored or represented in the at least one knowledge base prior to receipt of the query, the second knowledge being generated by inference from the first knowledge in response to the query, the inference including retrieving one or more first facts included in the first knowledge, the first facts corresponding to first ones of the objects and first ones of the relationships, and generating one or more second facts from the first facts that express at least one new relationship for at least one of the one or more first objects. | 27. A system for responding to a query initiated at a user device, comprising: one or more data stores having a knowledge base stored therein that includes data representing first knowledge about a plurality of objects using a plurality of relationships among the objects, wherein selected ones of the objects are associated with class objects identifying corresponding classes for the selected objects using class member objects that define class member relationships between the selected objects and the corresponding classes; and one or more computing devices configured to generate a response to the query using second knowledge not statically stored or represented in the at least one knowledge base prior to receipt of the query, the second knowledge being generated by inference from the first knowledge in response to the query, the inference including retrieving one or more first facts included in the first knowledge, the first facts corresponding to first ones of the objects and first ones of the relationships, and generating one or more second facts from the first facts that express at least one new relationship for at least one of the one or more first objects. 28. The system of claim 27 wherein the one or more computing devices are configured to infer the second knowledge with reference to the relationships. | 0.899194 |
9,058,322 | 9 | 11 | 9. A method of providing a two-way automatic interpretation and translation service, comprising: providing an interpretation service for receiving an input conversation in a first language or a second language in a form of speech and outputting results of interpretation of the input conversation in a form of speech in the second language or the first language, and a translation service for receiving an input conversation in the first language or the second language in a form of text and outputting results of translation of the input conversation in a form of text in the second language or the first language, using a first interpretation and translation unit or a second interpretation and translation unit; and sharing and managing, by a context information management unit receiving conversational context and translation history information processed when translation is performed from the first and second interpretation and translation units, the conversational context and the translation history information. | 9. A method of providing a two-way automatic interpretation and translation service, comprising: providing an interpretation service for receiving an input conversation in a first language or a second language in a form of speech and outputting results of interpretation of the input conversation in a form of speech in the second language or the first language, and a translation service for receiving an input conversation in the first language or the second language in a form of text and outputting results of translation of the input conversation in a form of text in the second language or the first language, using a first interpretation and translation unit or a second interpretation and translation unit; and sharing and managing, by a context information management unit receiving conversational context and translation history information processed when translation is performed from the first and second interpretation and translation units, the conversational context and the translation history information. 11. The method of claim 9 , wherein the providing the interpretation and translation service using the second interpretation and translation unit is configured to receive an input conversation of a second language user in the form of speech, receive an context of conversation and an information of translation history information from a context information management unit and converts the conversation into text in the first language, separate sentences and words from the input text, and tag each separated word with part-of-speech tags, and sets syntactic relationships between the words, and generates a second language syntactic tree indicative of results of the setting, and converts the second language syntactic tree into a first language syntactic tree, and translates second language vocabularies corresponding to a terminal node into first language vocabularies and generates first language sentences from the first language vocabularies. | 0.764268 |
9,189,967 | 1 | 13 | 1. A method comprising: providing access to an online discussion, wherein the online discussion includes one or more threads; accepting, for the one or more threads of the online discussion: a posting by an author participating in the discussion; and one or more responses to the posting by other individuals participating in the discussion; automatically producing for the author a recommendation for amending the posting to increase the likelihood of response to the posting by other individuals participating in the discussion via: triggering an agent based on fulfillment of at least one predetermined criterion; the at least one predetermined criterion being based on: time elapsed since a last login or view time by the author; a number of replies to the posting; and at least one of: a number of views of the posting, a number of times that explicit feedback has been provided by at least one other individual, and a degree of similarity with respect to an existing posting; and thereafter employing the agent to make a determination for performing at least one of: providing explicit feedback from at least one other individual, and classifying the posting to suggest further action to the author. | 1. A method comprising: providing access to an online discussion, wherein the online discussion includes one or more threads; accepting, for the one or more threads of the online discussion: a posting by an author participating in the discussion; and one or more responses to the posting by other individuals participating in the discussion; automatically producing for the author a recommendation for amending the posting to increase the likelihood of response to the posting by other individuals participating in the discussion via: triggering an agent based on fulfillment of at least one predetermined criterion; the at least one predetermined criterion being based on: time elapsed since a last login or view time by the author; a number of replies to the posting; and at least one of: a number of views of the posting, a number of times that explicit feedback has been provided by at least one other individual, and a degree of similarity with respect to an existing posting; and thereafter employing the agent to make a determination for performing at least one of: providing explicit feedback from at least one other individual, and classifying the posting to suggest further action to the author. 13. The method according to claim 1 , wherein the at least one predetermined criterion is based on at least one of: whether time elapsed since a last login or view time by the author is greater than a predetermined threshold; whether a number of replies to the posting is less than a predetermined threshold; whether a number of views of the posting is greater than a predetermined threshold; whether a number of times that explicit feedback has been provided by at least one other individual is greater than a predetermined threshold; and whether a degree of similarity with respect to an existing posting is greater than predetermined threshold. | 0.50761 |
10,083,016 | 14 | 25 | 14. One or more memories collectively having contents adapted to cause a computing system to perform a method, the method comprising: receiving a formula that, when evaluated with respect to a particular row of each of a plurality of database rows, produces a respective value for a distinguished database field of the particular row, the formula expressed as a method in a distinguished procedural programming language; transforming the formula method into a first syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row; traversing the first syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row, wherein the traversal identifies one or more prohibited language features of the distinguished procedural programming language; generating an error in relation to the identified one or more prohibited language features of the distinguished procedural programming language; receiving a modification to the formula method that excludes the prohibited language features of the distinguished procedural programming language; transforming the modified formula method into a second syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row; modifying the second syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row, to provide variable context, field projection, runtimetype dynamic property accessors, and/or field path mapping; and compiling the modified second syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row, into a second version of the formula method in the distinguished procedural programming language. | 14. One or more memories collectively having contents adapted to cause a computing system to perform a method, the method comprising: receiving a formula that, when evaluated with respect to a particular row of each of a plurality of database rows, produces a respective value for a distinguished database field of the particular row, the formula expressed as a method in a distinguished procedural programming language; transforming the formula method into a first syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row; traversing the first syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row, wherein the traversal identifies one or more prohibited language features of the distinguished procedural programming language; generating an error in relation to the identified one or more prohibited language features of the distinguished procedural programming language; receiving a modification to the formula method that excludes the prohibited language features of the distinguished procedural programming language; transforming the modified formula method into a second syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row; modifying the second syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row, to provide variable context, field projection, runtimetype dynamic property accessors, and/or field path mapping; and compiling the modified second syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row, into a second version of the formula method in the distinguished procedural programming language. 25. The one or more memories of claim 14 , wherein the modifying the second syntax tree that defines, for the particular row of each of the plurality of database rows, the respective value for the distinguished database field of the particular row, is performed by providing a composite projection type having properties comprising distinct field value accessors, unique aggregate calls factoring, a target field of each formula, and an identifier field of the record being operated on. | 0.605519 |
9,213,996 | 10 | 11 | 10. A system for trend detection, the system comprising one or more processors and one or more memory devices operably coupled to the one or more processors, the one or more memory devices storing executable and operational data effective to cause the one or more processors to: receive a seed expert set comprising expert contributors to social media content with respect to a category; identify a second tier set comprising highly influential users with respect to the social media content with respect to the category from the seed expert set; maintain a record for a least a portion of the social media content generated by at least one of the seed expert set or the second tier set for a window of time; identify new concepts in the social media content with respect to the category generated by at least one of the seed expert set or the second tier set; monitor trends for the new concepts in activity of a general population of social media users; and output at least a portion of the monitored trends for the new concepts; wherein: the one or more memory devices storing the executable and operational data effective to cause the one or more processors to identify the new concepts further comprises comparing the social media content generated by the at least one of the seed expert set or the second tier set to the record for at least the portion of the social media content generated by at least one of the seed expert set or the second tier set for the window of time; and the window of time comprises a set period of time minus a current time. | 10. A system for trend detection, the system comprising one or more processors and one or more memory devices operably coupled to the one or more processors, the one or more memory devices storing executable and operational data effective to cause the one or more processors to: receive a seed expert set comprising expert contributors to social media content with respect to a category; identify a second tier set comprising highly influential users with respect to the social media content with respect to the category from the seed expert set; maintain a record for a least a portion of the social media content generated by at least one of the seed expert set or the second tier set for a window of time; identify new concepts in the social media content with respect to the category generated by at least one of the seed expert set or the second tier set; monitor trends for the new concepts in activity of a general population of social media users; and output at least a portion of the monitored trends for the new concepts; wherein: the one or more memory devices storing the executable and operational data effective to cause the one or more processors to identify the new concepts further comprises comparing the social media content generated by the at least one of the seed expert set or the second tier set to the record for at least the portion of the social media content generated by at least one of the seed expert set or the second tier set for the window of time; and the window of time comprises a set period of time minus a current time. 11. The system of claim 10 , wherein the executable and operational data are further effective to cause the one or more processors to monitor for the new concepts in the activity of the general population of social media users by at least one of: detecting local trends in accordance with a location of users in the general population of social media users; or detecting demographic trends in accordance with demographic attributes of users in the general population of social media users. | 0.50102 |
8,285,273 | 6 | 7 | 6. The method of claim 5 , further comprising: receiving advertisements; and displaying the advertisements with the search results on the display of the wireless mobile device. | 6. The method of claim 5 , further comprising: receiving advertisements; and displaying the advertisements with the search results on the display of the wireless mobile device. 7. The method of claim 6 , further comprising formatting the advertisements and the search results on the wireless mobile device. | 0.930645 |
7,720,787 | 11 | 19 | 11. A system for analyzing a set of exhaustive and exclusive hypotheses in estimative intelligence, said system comprising: at least one processor for executing instructions, a memory coupled to the processor, a data storage system for reading media having sequences of instructions stored thereon coupled to the data storage system, input/output for delivering data to and from the memory or to and from the data storage system and a user interface allowing for interaction with said instruction sequences by users, which sequences also causes said at least one processor to execute the steps of: assessing and assigning base rates for each hypothesis; determining a set of items of evidence that are relevant to, have a causal influence on or would disconfirm more than one hypothesis; assessing and assigning base rates for each item of evidence; deciding, for each item of evidence, whether the item should be treated as being a causal influence or diagnostic indicator with respect to the set of the hypotheses; if the item of evidence is to be treated as a causal influence, making a judgment as to the likelihood of each hypothesis, both if the evidence were true, and also if the evidence were false; if the item of evidence is to be treated as a diagnostic indicator, making a judgment as to the evidence being true if the hypothesis were true; assessing the belief for each item of evidence being true; deciding a set of interim beliefs in each hypothesis for each individual item of evidence by: employing a conditional inference operator for evidence that is to be treated as a causal influence; and employing a reverse conditional inference operator for evidence that is to be treated as a diagnostic indicator; deciding the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs; and outputting a set of beliefs representing the certainty and likelihood of each hypothesis. | 11. A system for analyzing a set of exhaustive and exclusive hypotheses in estimative intelligence, said system comprising: at least one processor for executing instructions, a memory coupled to the processor, a data storage system for reading media having sequences of instructions stored thereon coupled to the data storage system, input/output for delivering data to and from the memory or to and from the data storage system and a user interface allowing for interaction with said instruction sequences by users, which sequences also causes said at least one processor to execute the steps of: assessing and assigning base rates for each hypothesis; determining a set of items of evidence that are relevant to, have a causal influence on or would disconfirm more than one hypothesis; assessing and assigning base rates for each item of evidence; deciding, for each item of evidence, whether the item should be treated as being a causal influence or diagnostic indicator with respect to the set of the hypotheses; if the item of evidence is to be treated as a causal influence, making a judgment as to the likelihood of each hypothesis, both if the evidence were true, and also if the evidence were false; if the item of evidence is to be treated as a diagnostic indicator, making a judgment as to the evidence being true if the hypothesis were true; assessing the belief for each item of evidence being true; deciding a set of interim beliefs in each hypothesis for each individual item of evidence by: employing a conditional inference operator for evidence that is to be treated as a causal influence; and employing a reverse conditional inference operator for evidence that is to be treated as a diagnostic indicator; deciding the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs; and outputting a set of beliefs representing the certainty and likelihood of each hypothesis. 19. The estimative intelligence analysis system of claim 11 wherein deciding the belief in each interim hypothesis for each item of evidence is considered apart from the general set of evidence under consideration in relation to the hypothesis. | 0.688776 |
8,566,353 | 1 | 6 | 1. A computer-implemented method for annotating a digital video stored in a video repository, the method comprising: identifying a visual object displayed within the video using visual object recognition; determining whether a user, who did not contribute the digital video to the video repository, is authorized to annotate the digital video; responsive to the user being authorized to annotate the digital video, providing to the user a first web-based user interface portion for annotating the digital video, the first web-based user interface portion comprising visual representations of a plurality of different annotation types and a visual suggestion to annotate the visual object; responsive to receiving a user selection of one of the annotation types and approval of the suggestion of annotating the visual object, providing to the user a second web-based user interface portion comprising at least one input area for specifying a URL for a new annotation for the visual object, the URL separately encoding both an identifier of a target video and a time stamp of a moment within the target video; receiving a request from the user via the second user interface portion to add an annotation of the selected annotation type to the visual object, the request comprising a designation of the URL for the annotation; tracking a spatial position of the visual object across frames of the video to identify a plurality of spatial positions of the visual object as the visual object moves within the video; and adding the annotation to the digital video such that the annotation is displayed during playback of the digital video and moves responsive to the tracked plurality of spatial positions of the visual object, and such that selection of the annotation causes playback of the target video at the moment in the target video specified by the time stamp, the target video being different than the digital video. | 1. A computer-implemented method for annotating a digital video stored in a video repository, the method comprising: identifying a visual object displayed within the video using visual object recognition; determining whether a user, who did not contribute the digital video to the video repository, is authorized to annotate the digital video; responsive to the user being authorized to annotate the digital video, providing to the user a first web-based user interface portion for annotating the digital video, the first web-based user interface portion comprising visual representations of a plurality of different annotation types and a visual suggestion to annotate the visual object; responsive to receiving a user selection of one of the annotation types and approval of the suggestion of annotating the visual object, providing to the user a second web-based user interface portion comprising at least one input area for specifying a URL for a new annotation for the visual object, the URL separately encoding both an identifier of a target video and a time stamp of a moment within the target video; receiving a request from the user via the second user interface portion to add an annotation of the selected annotation type to the visual object, the request comprising a designation of the URL for the annotation; tracking a spatial position of the visual object across frames of the video to identify a plurality of spatial positions of the visual object as the visual object moves within the video; and adding the annotation to the digital video such that the annotation is displayed during playback of the digital video and moves responsive to the tracked plurality of spatial positions of the visual object, and such that selection of the annotation causes playback of the target video at the moment in the target video specified by the time stamp, the target video being different than the digital video. 6. The computer-implemented method of claim 1 , further comprising determining whether the user is authorized to annotate a particular temporal or spatial portion of the digital video. | 0.771144 |
7,668,823 | 26 | 27 | 26. The method of claim 15 , further comprising: aggregating the topic corpus; and generating a web page based upon aggregating the topic corpus. | 26. The method of claim 15 , further comprising: aggregating the topic corpus; and generating a web page based upon aggregating the topic corpus. 27. The method of claim 26 , wherein the web page allows a user to access the topic corpus from a single web page. | 0.960608 |
8,751,511 | 1 | 8 | 1. A computer-implemented method for generating a ranked list of resources in response to a query, comprising: pairing the query with a plurality of microblog resource identifiers, wherein each microblog resource identifier comprises a resource identifier obtained from monitoring a received data stream of microblog posts, thereby generating a plurality of query/microblog resource identifier pairs; generating a feature set for each query/microblog resource identifier pair, wherein generating the feature set includes generating at least one textual feature by analyzing text of one or more microblog posts that refer to the microblog resource identifier in conjunction with text of the query or generating at least one social networking feature by analyzing one or more characteristics associated with one or more microblog users that issued or received the microblog resource identifier via the microblog; processing the feature sets associated with each query/microblog resource identifier pair in a first machine learned ranker to produce a ranking for each microblog resource identifier; and generating one single combined ranked list of resources by combining the rankings for each microblog resource identifier produced by the first machine learned ranker with rankings generated for a plurality of network resource identifiers, wherein each network resource identifier comprises a resource identifier obtained from resources other than the received data stream of microblog posts. | 1. A computer-implemented method for generating a ranked list of resources in response to a query, comprising: pairing the query with a plurality of microblog resource identifiers, wherein each microblog resource identifier comprises a resource identifier obtained from monitoring a received data stream of microblog posts, thereby generating a plurality of query/microblog resource identifier pairs; generating a feature set for each query/microblog resource identifier pair, wherein generating the feature set includes generating at least one textual feature by analyzing text of one or more microblog posts that refer to the microblog resource identifier in conjunction with text of the query or generating at least one social networking feature by analyzing one or more characteristics associated with one or more microblog users that issued or received the microblog resource identifier via the microblog; processing the feature sets associated with each query/microblog resource identifier pair in a first machine learned ranker to produce a ranking for each microblog resource identifier; and generating one single combined ranked list of resources by combining the rankings for each microblog resource identifier produced by the first machine learned ranker with rankings generated for a plurality of network resource identifiers, wherein each network resource identifier comprises a resource identifier obtained from resources other than the received data stream of microblog posts. 8. The method of claim 1 , wherein combining the rankings for each microblog resource identifier produced by the first machine learned ranker with rankings generated for a plurality of network resource identifiers comprises: pairing the query with the plurality of network resource identifiers, thereby generating a plurality of query/network resource identifier pairs; generating a feature set for each query/network resource identifier pair; and processing the feature sets associated with each query/network resource identifier pair in a second machine learned ranker to produce the ranking for each network resource identifier. | 0.701796 |
7,739,221 | 9 | 14 | 9. A computer-implemented method of performing a multi-dimensional search, comprising: employing a processor to execute computer executable instructions stored on a computer readable storage medium to perform the following acts: receiving an input, the input comprises visual data and at least one of text or audio data, wherein the visual data is from an image file; extracting a plurality of features from the input comprising: utilizing a pattern recognition component to extract a plurality of features from the image file, wherein the features comprise physical attributes that are visually identifiable in the image file; establishing a plurality of search terms based at least in part upon a subset of the extracted features; retrieving a plurality of results based at least in part upon a subset of the search terms; collecting and indexing search-related information across a plurality of dimensions, and building an index based upon features associated with the input; automatically and dynamically extracting features from searchable items and making the features available for search based upon a particular query or set of queries; learning, based on context information, historical data, or feedback, what results are desired in view of a determined and inferred query, as well as how the results should be rendered, and generating a statistical model with the results; and filtering a subset of the plurality of results in accordance with at least one of a user context, a user preference, a relevancy factor with respect to the input, or a device context. | 9. A computer-implemented method of performing a multi-dimensional search, comprising: employing a processor to execute computer executable instructions stored on a computer readable storage medium to perform the following acts: receiving an input, the input comprises visual data and at least one of text or audio data, wherein the visual data is from an image file; extracting a plurality of features from the input comprising: utilizing a pattern recognition component to extract a plurality of features from the image file, wherein the features comprise physical attributes that are visually identifiable in the image file; establishing a plurality of search terms based at least in part upon a subset of the extracted features; retrieving a plurality of results based at least in part upon a subset of the search terms; collecting and indexing search-related information across a plurality of dimensions, and building an index based upon features associated with the input; automatically and dynamically extracting features from searchable items and making the features available for search based upon a particular query or set of queries; learning, based on context information, historical data, or feedback, what results are desired in view of a determined and inferred query, as well as how the results should be rendered, and generating a statistical model with the results; and filtering a subset of the plurality of results in accordance with at least one of a user context, a user preference, a relevancy factor with respect to the input, or a device context. 14. The method of claim 9 , further comprising ranking the subset of the plurality of results based at least in part upon one of a user preference or a relevancy factor. | 0.786616 |
8,078,643 | 9 | 12 | 9. A computer implemented process for modifying a schema implemented on a computer programmed to execute computer code comprising instructions to: obtain an original schema corresponding to an original database, said original schema comprising a field and an original field type selection; perform rule-based structural and semantic checking comprising checking fields and relationships of said original schema for data integrity due to based on at least one of nested structure denormalization, lookup tables that can hold an unlimited number of records, inspection of taxonomy defined on a non-main table, and augmenting a particular table of said original schema; determine at least one suggested field type based on said checking, wherein said at least one suggest field type is selected from a first field type of qualifier type, wherein said first field type of said qualifier type is a lookup into the records of a qualified table of a database comprising sparse data placed in said qualified table and eliminated from a primary table of said database, a second field type of multi-lingual type, wherein said second field type of multi-lingual type is associated with a targeted audience, wherein only data that is different with respect to a second audience is entered for said targeted audience in said database, and unentered values are inherited form data entered for said second audience in said database, and a third field type of calculation type, wherein said third field type of calculation type is configured to store calculated values of said third field type in memory at runtime and not in said database; accept a field type selection from said first field type, said second field type, and said third field type, wherein said field type selection is different from said original field type selection; generate a modified schema comprising said field and said field type selection based on said original schema and said at least one suggested field type, wherein said modified schema conforms to requirements of a master data management schema; and load said modified schema into a desired database, wherein said desired database comprises data from said original database, wherein said data is optimized based on said field type selection. | 9. A computer implemented process for modifying a schema implemented on a computer programmed to execute computer code comprising instructions to: obtain an original schema corresponding to an original database, said original schema comprising a field and an original field type selection; perform rule-based structural and semantic checking comprising checking fields and relationships of said original schema for data integrity due to based on at least one of nested structure denormalization, lookup tables that can hold an unlimited number of records, inspection of taxonomy defined on a non-main table, and augmenting a particular table of said original schema; determine at least one suggested field type based on said checking, wherein said at least one suggest field type is selected from a first field type of qualifier type, wherein said first field type of said qualifier type is a lookup into the records of a qualified table of a database comprising sparse data placed in said qualified table and eliminated from a primary table of said database, a second field type of multi-lingual type, wherein said second field type of multi-lingual type is associated with a targeted audience, wherein only data that is different with respect to a second audience is entered for said targeted audience in said database, and unentered values are inherited form data entered for said second audience in said database, and a third field type of calculation type, wherein said third field type of calculation type is configured to store calculated values of said third field type in memory at runtime and not in said database; accept a field type selection from said first field type, said second field type, and said third field type, wherein said field type selection is different from said original field type selection; generate a modified schema comprising said field and said field type selection based on said original schema and said at least one suggested field type, wherein said modified schema conforms to requirements of a master data management schema; and load said modified schema into a desired database, wherein said desired database comprises data from said original database, wherein said data is optimized based on said field type selection. 12. The computer implemented process of claim 9 wherein said computer readable instruction code is further configured to: accept a manually entered field, wherein said modified schema comprises said manually entered field. | 0.834821 |
10,121,467 | 1 | 4 | 1. A computer-implemented method comprising: receiving audio data; determining first text data corresponding to the audio data, the first text data including a first word sequence and a next word following the first word sequence; determining, using a language model having data for a plurality of states, where each state of the plurality of states corresponds to a respective word sequence, a first state, of the plurality of states, corresponding to the first word sequence; determining, using the language model, that no second state of the language model following the first state corresponds to the next word; determining, using the language model, a first score corresponding to a similarity between use of the first word sequence in a text corpus and use of a second word sequence in the text corpus; determining, based on determining that no second state following the first state corresponds to the next word and based on the first score, second text data by replacing the first word sequence in the first text data with the second word sequence; and determining output text data corresponding to the audio data, the output text data including the second text data. | 1. A computer-implemented method comprising: receiving audio data; determining first text data corresponding to the audio data, the first text data including a first word sequence and a next word following the first word sequence; determining, using a language model having data for a plurality of states, where each state of the plurality of states corresponds to a respective word sequence, a first state, of the plurality of states, corresponding to the first word sequence; determining, using the language model, that no second state of the language model following the first state corresponds to the next word; determining, using the language model, a first score corresponding to a similarity between use of the first word sequence in a text corpus and use of a second word sequence in the text corpus; determining, based on determining that no second state following the first state corresponds to the next word and based on the first score, second text data by replacing the first word sequence in the first text data with the second word sequence; and determining output text data corresponding to the audio data, the output text data including the second text data. 4. The computer-implemented method of claim 1 , wherein the first score is based on a difference between a first vector representing how the first word sequence is used in the text corpus and a second vector representing how the second word sequence is used in the text corpus. | 0.765651 |
8,832,088 | 44 | 46 | 44. The system of claim 41 , wherein determining Q comprises determining Q from a maximum Max(QFval) of one or more of the following query freshness values: a percentile value of a number of occurrences of the query being received within a first recent time period; percentile value of a number of occurrences of the query on blog web pages within a second recent time period; a percentile value of a number of occurrences of the query on news web pages within a third recent time period; a percentile value of a number of occurrences of the query on social network web pages within a fourth recent time period; a percentile value of a number of occurrences of the query requesting news search results within a fifth recent time period; a percentile value of a ratio of a number of occurrences of the query requesting news search results within a sixth recent time period to a number of occurrences of the query requesting web search results within the sixth recent time period; a percentile value of a number of user selections of news search results provided in response to the query within a seventh recent time period; and a percentile value of a ratio of a number of user selections of news search results provided in response to the query within an eighth recent time period to a number of user selections of web search results provided in response to the query within the eighth recent time period. | 44. The system of claim 41 , wherein determining Q comprises determining Q from a maximum Max(QFval) of one or more of the following query freshness values: a percentile value of a number of occurrences of the query being received within a first recent time period; percentile value of a number of occurrences of the query on blog web pages within a second recent time period; a percentile value of a number of occurrences of the query on news web pages within a third recent time period; a percentile value of a number of occurrences of the query on social network web pages within a fourth recent time period; a percentile value of a number of occurrences of the query requesting news search results within a fifth recent time period; a percentile value of a ratio of a number of occurrences of the query requesting news search results within a sixth recent time period to a number of occurrences of the query requesting web search results within the sixth recent time period; a percentile value of a number of user selections of news search results provided in response to the query within a seventh recent time period; and a percentile value of a ratio of a number of user selections of news search results provided in response to the query within an eighth recent time period to a number of user selections of web search results provided in response to the query within the eighth recent time period. 46. The system of claim 44 , wherein Q satisfies:
Q =Max( QFval )× QtoA+ 1. | 0.936153 |
7,617,199 | 4 | 6 | 4. The method of claim 1 , wherein the search query is based on a first aspect of a user context, the first aspect of the user context including data indicative of text being accessed by a user, the query being different than the user context. | 4. The method of claim 1 , wherein the search query is based on a first aspect of a user context, the first aspect of the user context including data indicative of text being accessed by a user, the query being different than the user context. 6. The method of claim 4 , including comparing data indicative of a plurality of search results to data indicative of a second aspect of the user context to determine a plurality of relevance scores associated with the plurality of search results, the second aspect of the user context including data indicative of at least one task in which the user is engaged out of a plurality of possible user tasks. | 0.878606 |
9,643,722 | 4 | 5 | 4. The system of claim 3 wherein the aerial drone device is configured to locate a person based on where the breach occurred and by detecting the person, using a camera of the aerial drone device and object recognition. | 4. The system of claim 3 wherein the aerial drone device is configured to locate a person based on where the breach occurred and by detecting the person, using a camera of the aerial drone device and object recognition. 5. The system of claim 4 wherein the aerial drone device is configured to track the person and transmit tracking location information to the security system or another device, wherein the aerial drone device tracks the person by acquiring image data of the person and maintaining the image data of the person within a view of the camera of the aerial drone device. | 0.870463 |
9,495,376 | 16 | 17 | 16. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to: receive profile information associated with a source system; extract, from the profile information: file identification information and file structure information associated with content, of the source system, browser information associated with browsing directory hierarchies of the source system, content information associated with retrieving the content of the source system, and attribute information associated with retrieving metadata corresponding to the content of the source system, the metadata including a property pertaining to the content, the property including one or more of author information and keyword information; present the content and the metadata for display based on one or more of the file identification information and file structure information, the browser information, the content information, and the attribute information; receive a selection of content and metadata, from the presented content and metadata, to be copied to the target system; create, based on the selection, a batch file that includes file identification information and file structure information for the selected content and metadata; and execute the batch file to cause the selected content and metadata to be copied to the target system. | 16. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to: receive profile information associated with a source system; extract, from the profile information: file identification information and file structure information associated with content, of the source system, browser information associated with browsing directory hierarchies of the source system, content information associated with retrieving the content of the source system, and attribute information associated with retrieving metadata corresponding to the content of the source system, the metadata including a property pertaining to the content, the property including one or more of author information and keyword information; present the content and the metadata for display based on one or more of the file identification information and file structure information, the browser information, the content information, and the attribute information; receive a selection of content and metadata, from the presented content and metadata, to be copied to the target system; create, based on the selection, a batch file that includes file identification information and file structure information for the selected content and metadata; and execute the batch file to cause the selected content and metadata to be copied to the target system. 17. The non-transitory computer-readable medium of claim 16 , where the property includes the author information, the keyword information, title information, and subject information. | 0.730769 |
7,849,042 | 1 | 3 | 1. A computer-implemented information searching method, comprising the steps of: extracting one or more non-content characteristics of a document from a document set; analyzing the extracted non-content characteristics; generating an optimizing tool based on analyzing results; and optimizing a preliminary search result with the generated optimizing tool, wherein the step of optimizing includes the steps of: selecting, from the preliminary search result, a resulting document sequence having relatively high reliability; calculating a distance from each of the documents in the preliminary search results to each document of the resulting document sequence; adjusting the reliability values of the documents in the resulting document sequence; and arranging the documents in descending order of the adjusted reliability values to obtain optimized results, and wherein the distance from each of the documents in the preliminary search results to the document of the resulting document sequence equals a sum of weights of sides passed through by a directional path between two documents, the distance is infinite when there is no path between the document in the preliminary search results and the document in the resulting document sequence, the distance from one document to itself is zero, the distance L between the document in the preliminary search results and the document in the resulting document sequence is expressed by the following equation when there are plural paths between the two documents, L=1/((1/L 1 )+(1/L 2 )+ . . . +(1/LX)) where, L represents the distance between two documents, L 1 represents the distance of a path 1 , L 2 represents the distance of a path 2 , and LX represents the distance of a path X. | 1. A computer-implemented information searching method, comprising the steps of: extracting one or more non-content characteristics of a document from a document set; analyzing the extracted non-content characteristics; generating an optimizing tool based on analyzing results; and optimizing a preliminary search result with the generated optimizing tool, wherein the step of optimizing includes the steps of: selecting, from the preliminary search result, a resulting document sequence having relatively high reliability; calculating a distance from each of the documents in the preliminary search results to each document of the resulting document sequence; adjusting the reliability values of the documents in the resulting document sequence; and arranging the documents in descending order of the adjusted reliability values to obtain optimized results, and wherein the distance from each of the documents in the preliminary search results to the document of the resulting document sequence equals a sum of weights of sides passed through by a directional path between two documents, the distance is infinite when there is no path between the document in the preliminary search results and the document in the resulting document sequence, the distance from one document to itself is zero, the distance L between the document in the preliminary search results and the document in the resulting document sequence is expressed by the following equation when there are plural paths between the two documents, L=1/((1/L 1 )+(1/L 2 )+ . . . +(1/LX)) where, L represents the distance between two documents, L 1 represents the distance of a path 1 , L 2 represents the distance of a path 2 , and LX represents the distance of a path X. 3. The information searching method as claimed in claim 1 , wherein the preliminary search result includes a result obtained by searching a predetermined data group with a search engine, or a result obtained by another optimizing tool, the result obtained by searching the predetermined data group or by the other optimizing tool is a document sequence, and each document in the document sequence has a reliability value indicating a probability of the document being a target document. | 0.783229 |
8,285,698 | 19 | 20 | 19. A method for deploying a system for securing search queries, comprising; providing computer infrastructure being operable to: receive a search query; analyze the search query to determine a subject matter of the search query; generate a set of securing search queries that have the subject matter of the search query; submit the search query and the set of securing search queries to a search engine; and filter results received from the search engine to remove any hits that resulted from the set of securing search queries. | 19. A method for deploying a system for securing search queries, comprising; providing computer infrastructure being operable to: receive a search query; analyze the search query to determine a subject matter of the search query; generate a set of securing search queries that have the subject matter of the search query; submit the search query and the set of securing search queries to a search engine; and filter results received from the search engine to remove any hits that resulted from the set of securing search queries. 20. The method of claim 19 , the computer infrastructure being further operable to: randomly choose keywords related to the subject matter; and create at least one combination of the keywords. | 0.76699 |
8,069,033 | 35 | 38 | 35. An apparatus comprising: a processor; a storage device coupled to the processor and configurable for storing instructions, which, when executed by the processor, cause the processor to perform operations comprising: identifying in a document a first character, wherein the first character is a recognized non-alphabetic character that is ambiguous because it can be represented by more than one character code in an output file that is interpretable by a word processing application; identifying a string of characters including the first character, wherein the string begins with the character that follows the first blank space preceding the first character and ends with the character that precedes the first blank space that follows the first character; creating a set of candidate solutions from the string of characters, wherein each solution in the set comprises one or more sub-strings created by representing the first character with a unique one of the more than one character codes that can represent the first character; searching a dictionary stored on a computer storage device for the one or more sub-strings in each of the solutions in the set of candidate solutions; using the dictionary search results to determine which one of the more than one character codes should be used to represent the first character in the output file; and writing the character code to the output file. | 35. An apparatus comprising: a processor; a storage device coupled to the processor and configurable for storing instructions, which, when executed by the processor, cause the processor to perform operations comprising: identifying in a document a first character, wherein the first character is a recognized non-alphabetic character that is ambiguous because it can be represented by more than one character code in an output file that is interpretable by a word processing application; identifying a string of characters including the first character, wherein the string begins with the character that follows the first blank space preceding the first character and ends with the character that precedes the first blank space that follows the first character; creating a set of candidate solutions from the string of characters, wherein each solution in the set comprises one or more sub-strings created by representing the first character with a unique one of the more than one character codes that can represent the first character; searching a dictionary stored on a computer storage device for the one or more sub-strings in each of the solutions in the set of candidate solutions; using the dictionary search results to determine which one of the more than one character codes should be used to represent the first character in the output file; and writing the character code to the output file. 38. The apparatus of claim 35 , wherein the dictionary search finds all of the one or more substrings in more than one solution in the set of candidate solutions, the storage device further configurable for storing instructions which, when executed by the processor, cause the processor to perform operations comprising: prompting a user to manually enter the character code that represents the first character. | 0.509547 |
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