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24. A system comprising: a memory and a processor; a transaction analysis module stored in the memory and executable on the processor for: retrieving at least one activity record that represents at least one interaction between a visitor and a server-based system; identifying within the activity record at least one input provided by the visitor during the interaction; identifying within the activity record at least one item associated with the input; accessing at least one further activity record containing the input; comparing the activity record and the further activity record; identifying within the further activity record at least one further item associated with the input; establishing a similarity rating of the item and the further item based at least in part on the comparison of the activity record and the further activity record; a synonym recognition unit stored in the memory and executable on the processor, the synonym recognition unit being in communication with the transaction analysis module for: receiving indications of the item and the further item and for receiving indications of the similarity rating; obtaining a document associated with the item and obtaining at least a further document associated with the further item; comparing the document to the further document; determining, based at least in part on the similarity rating of the item and the further item, that the document associated with the item and the further document associated with the further item contain synonyms; designating a term that appears in the document associated with the item as a synonym for a different term that appears in the further document associated with the further item; and a search engine stored in the memory and executable on the processor, the search engine being in communication with the synonym recognition unit for: receiving a search query specifying at least the different term that appears in the further document; and returning, as search results for the search query, the further document associated with the further item and the document associated with the item containing the term that is the synonym for the different term. | 24. A system comprising: a memory and a processor; a transaction analysis module stored in the memory and executable on the processor for: retrieving at least one activity record that represents at least one interaction between a visitor and a server-based system; identifying within the activity record at least one input provided by the visitor during the interaction; identifying within the activity record at least one item associated with the input; accessing at least one further activity record containing the input; comparing the activity record and the further activity record; identifying within the further activity record at least one further item associated with the input; establishing a similarity rating of the item and the further item based at least in part on the comparison of the activity record and the further activity record; a synonym recognition unit stored in the memory and executable on the processor, the synonym recognition unit being in communication with the transaction analysis module for: receiving indications of the item and the further item and for receiving indications of the similarity rating; obtaining a document associated with the item and obtaining at least a further document associated with the further item; comparing the document to the further document; determining, based at least in part on the similarity rating of the item and the further item, that the document associated with the item and the further document associated with the further item contain synonyms; designating a term that appears in the document associated with the item as a synonym for a different term that appears in the further document associated with the further item; and a search engine stored in the memory and executable on the processor, the search engine being in communication with the synonym recognition unit for: receiving a search query specifying at least the different term that appears in the further document; and returning, as search results for the search query, the further document associated with the further item and the document associated with the item containing the term that is the synonym for the different term. 29. The system of claim 24 , wherein the transaction analysis module is for according higher weight to activity records indicating that the visitor completed a purchase of the item. | 0.542465 |
16. The method of claim 15 , further comprising, when a selection of a portion of the content, displayed in the WYSIWYG editor and corresponding to an inline element, is received, highlighting a representation of the corresponding inline element in the horizontal pane. | 16. The method of claim 15 , further comprising, when a selection of a portion of the content, displayed in the WYSIWYG editor and corresponding to an inline element, is received, highlighting a representation of the corresponding inline element in the horizontal pane. 17. The method of claim 16 , further comprising, in response to a user input, providing at least one menu for performing an operation on the corresponding inline element. | 0.962435 |
1. A method of guided search, comprising: receiving a new query; processing the new query to obtain a plurality of models, wherein the obtaining of the plurality of models comprises: identifying a central phrase comprising a plurality of words or a central word of the new query; and wherein: a model indicates a mapping relationship of a previously stored query and corresponding guidance information; the model includes information extracted from the new query, information transformed based on the new query, or both; and the model characterizes the new query; determining a corresponding plurality of similarities of the plurality of models relative to the new query, wherein the determining of the corresponding plurality of similarities comprises: computing a similarity of one of the models with the new query based on a property of a model word in the one of the models, a property of a skipped word in the one of the models, or a combination thereof; and in the event that the skipped word exists, calculating a penalty score based on the skipped word in the one of the models, comprising: determining a first penalty score of the skipped word based on a part of speech of the skipped word; determining a second penalty score based on a distance of the skipped word relative to the central phrase in the new query; determining a third penalty score based on a distance of the skipped word relative to the central word in the new query; and adjusting the similarity of the one of the models with the new query based on the first, second, and third penalty scores, comprising: weighing one of the first penalty score, second penalty score or third penalty score by a first weight to obtain a first weighted penalty score; weighing another one of the first penalty score, second penalty score or third penalty score by a second weight to obtain a second weighted penalty score, the first weight being different from the second weight; and adjusting the similarity of the one of the models with the new query based on the first and second weighted penalty scores; selecting at least one of the plurality of models based at least in part on the similarities; obtaining guidance information by using the selected model as an index to search a database comprising a plurality of mapping relationships of previously stored queries and corresponding guidance information; and sending the obtained guidance information to be displayed to a user. | 1. A method of guided search, comprising: receiving a new query; processing the new query to obtain a plurality of models, wherein the obtaining of the plurality of models comprises: identifying a central phrase comprising a plurality of words or a central word of the new query; and wherein: a model indicates a mapping relationship of a previously stored query and corresponding guidance information; the model includes information extracted from the new query, information transformed based on the new query, or both; and the model characterizes the new query; determining a corresponding plurality of similarities of the plurality of models relative to the new query, wherein the determining of the corresponding plurality of similarities comprises: computing a similarity of one of the models with the new query based on a property of a model word in the one of the models, a property of a skipped word in the one of the models, or a combination thereof; and in the event that the skipped word exists, calculating a penalty score based on the skipped word in the one of the models, comprising: determining a first penalty score of the skipped word based on a part of speech of the skipped word; determining a second penalty score based on a distance of the skipped word relative to the central phrase in the new query; determining a third penalty score based on a distance of the skipped word relative to the central word in the new query; and adjusting the similarity of the one of the models with the new query based on the first, second, and third penalty scores, comprising: weighing one of the first penalty score, second penalty score or third penalty score by a first weight to obtain a first weighted penalty score; weighing another one of the first penalty score, second penalty score or third penalty score by a second weight to obtain a second weighted penalty score, the first weight being different from the second weight; and adjusting the similarity of the one of the models with the new query based on the first and second weighted penalty scores; selecting at least one of the plurality of models based at least in part on the similarities; obtaining guidance information by using the selected model as an index to search a database comprising a plurality of mapping relationships of previously stored queries and corresponding guidance information; and sending the obtained guidance information to be displayed to a user. 9. The method of claim 1 , further comprising predicting guidance information in a machine learning mode. | 0.58129 |
1. An apparatus for performing an extended search, comprising: a processor, a receiving module configured to receive user-inputted keywords; an extending module configured to extend the user-inputted keywords according to geographical information to acquire extended keywords; a searching module configured to perform a search with the processor by using the extended keywords; and a returning module configured to return search results to the user. | 1. An apparatus for performing an extended search, comprising: a processor, a receiving module configured to receive user-inputted keywords; an extending module configured to extend the user-inputted keywords according to geographical information to acquire extended keywords; a searching module configured to perform a search with the processor by using the extended keywords; and a returning module configured to return search results to the user. 6. The apparatus of claim 1 , wherein the extending module comprises: a module configured to acquire an information type in the user-inputted keywords as a target functional attribute; a module configured to acquire geographical entities whose functional attributes are the target functional attribute; and a module configured to take names of the acquired geographical entities as an extension to the keywords. | 0.735558 |
9. A computer implemented method for analysing natural language contained in electronic text to determine a sentiment between two entities discussed in the natural language, comprising the following steps: receiving, via an input module, the electronic text containing the natural language at a processing circuitry; using an input/output subsystem of the processing circuitry to move the received natural language to a data storage; analysing the natural language in the data storage to determine a syntactic representation which shows the syntactic constituents of the analysed natural language together with determining a sentiment score of each constituent, wherein each constituent is a sentiment (sub)context; wherein the determining a sentiment score of each constituent comprises using a relation classifier to traverse a dependency path using a limited sliding window, each window position of which represents a (sub)context triple (z i−1 , z i , z i+1 ), and to determine a polarity distribution of each (sub)context; determining which constituents link the two entities; calculating an overall sentiment score for the sentiment between the two entities by processing the sentiment score of each constituent of the constituents determined to link the two entities, wherein calculating an overall sentiment score for the sentiment between the two entities comprises: calculating the overall sentiment scores for the sentiment between the two entities from the cumulative polarity distributions D 1 . . . D n across all (sub)contexts z 1 . . . z n , respectively, the cumulative scores for the three polarity counts (r.pos, r.ntr, r.neg) for sentiment r between the two entities (e 1 , e 2 ) in sentence s with n (sub)contexts being obtained through the following equation: r_scr ( r , e 1 , e 2 ) = ∑ i = 1 n fD i wherein z i is Constituent as sentiment (sub)context t i is Triple of previous current, and next (subcontext) along the path between e 1 and e 2 : (z i−1 , z i , z i+1 ) p is Polarity pε{POS, NTR, NEG} assigned to t, by sentiment grammar D is Polarity distribution of z i := { r · pos = α , r · ntr = 1 - α if p is POS r · neg = α , r · ntr = 1 - α if p is NEG r · ntr = α if p is NTR d is Dijkstra's shortest path distance between e 1 and e 2 heads α is ( Sub ) context score := 1 log 2 ( d ) . outputting, by a computer processor, the overall sentiment score for the sentiment between the two entities discussed by the natural language contained in the electronic text. | 9. A computer implemented method for analysing natural language contained in electronic text to determine a sentiment between two entities discussed in the natural language, comprising the following steps: receiving, via an input module, the electronic text containing the natural language at a processing circuitry; using an input/output subsystem of the processing circuitry to move the received natural language to a data storage; analysing the natural language in the data storage to determine a syntactic representation which shows the syntactic constituents of the analysed natural language together with determining a sentiment score of each constituent, wherein each constituent is a sentiment (sub)context; wherein the determining a sentiment score of each constituent comprises using a relation classifier to traverse a dependency path using a limited sliding window, each window position of which represents a (sub)context triple (z i−1 , z i , z i+1 ), and to determine a polarity distribution of each (sub)context; determining which constituents link the two entities; calculating an overall sentiment score for the sentiment between the two entities by processing the sentiment score of each constituent of the constituents determined to link the two entities, wherein calculating an overall sentiment score for the sentiment between the two entities comprises: calculating the overall sentiment scores for the sentiment between the two entities from the cumulative polarity distributions D 1 . . . D n across all (sub)contexts z 1 . . . z n , respectively, the cumulative scores for the three polarity counts (r.pos, r.ntr, r.neg) for sentiment r between the two entities (e 1 , e 2 ) in sentence s with n (sub)contexts being obtained through the following equation: r_scr ( r , e 1 , e 2 ) = ∑ i = 1 n fD i wherein z i is Constituent as sentiment (sub)context t i is Triple of previous current, and next (subcontext) along the path between e 1 and e 2 : (z i−1 , z i , z i+1 ) p is Polarity pε{POS, NTR, NEG} assigned to t, by sentiment grammar D is Polarity distribution of z i := { r · pos = α , r · ntr = 1 - α if p is POS r · neg = α , r · ntr = 1 - α if p is NEG r · ntr = α if p is NTR d is Dijkstra's shortest path distance between e 1 and e 2 heads α is ( Sub ) context score := 1 log 2 ( d ) . outputting, by a computer processor, the overall sentiment score for the sentiment between the two entities discussed by the natural language contained in the electronic text. 13. A method according to claim 9 , wherein processing the sentiment score of each constituent of the constituents determined to link the two entities includes using a windowed method to include a plurality of entities. | 0.5 |
4. The method of claim 3 , wherein the supplemental information for the verb variable text element comprises default information. | 4. The method of claim 3 , wherein the supplemental information for the verb variable text element comprises default information. 5. The method of claim 4 , wherein the default information comprises masculine gender, singular count, first person speech, and normal faction. | 0.933144 |
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. 5. The method as recited in claim 4 , wherein the audio stream is identified as speech, wherein the intermediate results include recognized words received in the audio stream. | 0.915926 |
1. An apparatus, comprising: a processing system receiving multiple language text corresponding to text of a plurality of languages including first and second text characters; a text-to-speech engine system receiving said text from said processing system, said text-to-speech engine system having a plurality of text-to-speech engines including a first language engine and a second language engine, each one text-to-speech engine among said plurality of text-to-speech engines corresponding to one language selected from among said plurality of languages, said text-to-speech engine system converting said text into audio wave data; an audio processor unit receiving said audio wave data and converting said audio wave data into analog audio signals; a speaker receiving said analog audio signals and converting said analog audio signals into sounds and outputting the sounds, wherein the sounds correspond to human speech; said processing system receiving said first text character and determining a first language corresponding to said first character, said first language being selected from among said plurality of languages; said first language engine receiving said first character outputted from said processing system and adding said first character to a buffer; said processing system receiving said second text character and determining a second language corresponding to said second character, said second language being selected from among said plurality of languages; said speaker outputting contents of said memory in form of the sounds corresponding to human speech when said first language of said first text character does not correspond to said second language of said second text character; and said second language engine receiving said second character outputted from said processing system and deleting contents of the buffer and adding said second character to the buffer, when said first language does not correspond to said second language. | 1. An apparatus, comprising: a processing system receiving multiple language text corresponding to text of a plurality of languages including first and second text characters; a text-to-speech engine system receiving said text from said processing system, said text-to-speech engine system having a plurality of text-to-speech engines including a first language engine and a second language engine, each one text-to-speech engine among said plurality of text-to-speech engines corresponding to one language selected from among said plurality of languages, said text-to-speech engine system converting said text into audio wave data; an audio processor unit receiving said audio wave data and converting said audio wave data into analog audio signals; a speaker receiving said analog audio signals and converting said analog audio signals into sounds and outputting the sounds, wherein the sounds correspond to human speech; said processing system receiving said first text character and determining a first language corresponding to said first character, said first language being selected from among said plurality of languages; said first language engine receiving said first character outputted from said processing system and adding said first character to a buffer; said processing system receiving said second text character and determining a second language corresponding to said second character, said second language being selected from among said plurality of languages; said speaker outputting contents of said memory in form of the sounds corresponding to human speech when said first language of said first text character does not correspond to said second language of said second text character; and said second language engine receiving said second character outputted from said processing system and deleting contents of the buffer and adding said second character to the buffer, when said first language does not correspond to said second language. 4. The apparatus of claim 1, wherein said multiple language text further comprises a plurality of characters. | 0.583333 |
1. A computer-implemented method of analyzing a clinical decision support (CDS) document and improving content analyzer system accuracy, the method comprising: improving content analyzer system accuracy in identifying CDS document deficiencies and consistencies with respect to reference content, the reference content comprising clinical guidelines, by repeatedly training a machine learning module, hosted by a content analyzer system, based on new incoming data, wherein the machine learning module is configured to automatically determine which content features are to be used to determine whether content is to be designated as relevant and matching to the reference content and which content is to be designated as non-relevant and to construct or modify an electronic model accordingly, wherein the features comprise one or more of text length, presence of a medication term, medical intervention language, use of a negation, or context, wherein improving content analyzer system accuracy by training the machine learning module comprises repeatedly: collecting positive and negative cases from CDS documents, training the electronic model using the collected positive and negative cases from CDS documents, taking as input a text segment extracted from a CDS document and returning a likelihood that the text segment matches a reference checklist item, and wherein the new incoming data indicates whether the likelihood that the text segment matches a reference checklist item is correct or incorrect; receiving at a computer system, including hardware and comprising an analytics engine, a clinical decision support document from a medical service provider system; accessing over a network from a remote system, by the computer system, reference content corresponding at least in part to the clinical decision support document, the reference content comprising clinical guidelines; using, by the computer system, the electronic model to identify and extract medical intervention content from the clinical decision support document; using feedback with respect to the identification of the medical intervention content to refine the electronic model; segmenting, by the computer system, at least a portion of the extracted medical intervention content into a first plurality of segments including: at least a first segment, comprising a first set of text, and a second segment comprising a second set of text, wherein a given segment in the first plurality of segments is evaluated to identify a core concept, wherein identifying the core concept further comprises determining whether a given segment includes a plurality of medications, and determining which of the plurality of medications are part of the core concept and which of the plurality of medications are not part of the core concept, and if the core concept of the given segment comprises at least one a medical intervention, determining whether a negation is associated with the at least one medical intervention; determining, by the trained machine learning engine, if the first segment corresponds to at least a first item included in the reference content, the first item comprising a third set of text comprising terminology not present in the first and second sets of text; at least partly in response to determining that the first segment, comprising the first set of text, corresponds to the first item included in the reference content, the first item comprising the third set of text, causing a version of the clinical decision support document to be generated to include a visual indication that the first segment corresponds to the first item included in the reference content; determining, by the trained machine learning engine, if a second item included in the reference content corresponds to at least one of the first plurality of segments; at least partly in response to determining that the second item included in the reference content does not correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to be generated to include a visual indication that the first plurality of segments fails to include at least one segment that corresponds to the second item included in the reference content; and at least partly in response to determining that the second item included in the reference content does correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to include a visual indication that the second item corresponds to at least one segment in the first plurality of segments. | 1. A computer-implemented method of analyzing a clinical decision support (CDS) document and improving content analyzer system accuracy, the method comprising: improving content analyzer system accuracy in identifying CDS document deficiencies and consistencies with respect to reference content, the reference content comprising clinical guidelines, by repeatedly training a machine learning module, hosted by a content analyzer system, based on new incoming data, wherein the machine learning module is configured to automatically determine which content features are to be used to determine whether content is to be designated as relevant and matching to the reference content and which content is to be designated as non-relevant and to construct or modify an electronic model accordingly, wherein the features comprise one or more of text length, presence of a medication term, medical intervention language, use of a negation, or context, wherein improving content analyzer system accuracy by training the machine learning module comprises repeatedly: collecting positive and negative cases from CDS documents, training the electronic model using the collected positive and negative cases from CDS documents, taking as input a text segment extracted from a CDS document and returning a likelihood that the text segment matches a reference checklist item, and wherein the new incoming data indicates whether the likelihood that the text segment matches a reference checklist item is correct or incorrect; receiving at a computer system, including hardware and comprising an analytics engine, a clinical decision support document from a medical service provider system; accessing over a network from a remote system, by the computer system, reference content corresponding at least in part to the clinical decision support document, the reference content comprising clinical guidelines; using, by the computer system, the electronic model to identify and extract medical intervention content from the clinical decision support document; using feedback with respect to the identification of the medical intervention content to refine the electronic model; segmenting, by the computer system, at least a portion of the extracted medical intervention content into a first plurality of segments including: at least a first segment, comprising a first set of text, and a second segment comprising a second set of text, wherein a given segment in the first plurality of segments is evaluated to identify a core concept, wherein identifying the core concept further comprises determining whether a given segment includes a plurality of medications, and determining which of the plurality of medications are part of the core concept and which of the plurality of medications are not part of the core concept, and if the core concept of the given segment comprises at least one a medical intervention, determining whether a negation is associated with the at least one medical intervention; determining, by the trained machine learning engine, if the first segment corresponds to at least a first item included in the reference content, the first item comprising a third set of text comprising terminology not present in the first and second sets of text; at least partly in response to determining that the first segment, comprising the first set of text, corresponds to the first item included in the reference content, the first item comprising the third set of text, causing a version of the clinical decision support document to be generated to include a visual indication that the first segment corresponds to the first item included in the reference content; determining, by the trained machine learning engine, if a second item included in the reference content corresponds to at least one of the first plurality of segments; at least partly in response to determining that the second item included in the reference content does not correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to be generated to include a visual indication that the first plurality of segments fails to include at least one segment that corresponds to the second item included in the reference content; and at least partly in response to determining that the second item included in the reference content does correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to include a visual indication that the second item corresponds to at least one segment in the first plurality of segments. 9. The method as defined in claim 1 , the method further comprising: determining whether a first character string in the clinical decision support document exceeds a first number of words or characters; and at least partly in response to determining that the first character string in the clinical decision support document exceeds the first number of words or characters, excluding the first character string from the first plurality of segments. | 0.542024 |
1. A system for context-based adaptive binary arithmetic encoding and decoding, the system comprising: a calculating device configured to calculate an index value for a first bin to be encoded or a second bin to be decoded; a memory device configured to store context models in clusters, wherein each cluster is loaded for processing as a single unit and at least one cluster includes multiple context models arranged in an order of syntax element processing, and wherein the index value for the first bin or the second bin is related to one of the context models; and a binary arithmetic unit configured to serve as an encoder for encoding the first bin based on the one context model or a decoder for decoding the second bin based on the one context model. | 1. A system for context-based adaptive binary arithmetic encoding and decoding, the system comprising: a calculating device configured to calculate an index value for a first bin to be encoded or a second bin to be decoded; a memory device configured to store context models in clusters, wherein each cluster is loaded for processing as a single unit and at least one cluster includes multiple context models arranged in an order of syntax element processing, and wherein the index value for the first bin or the second bin is related to one of the context models; and a binary arithmetic unit configured to serve as an encoder for encoding the first bin based on the one context model or a decoder for decoding the second bin based on the one context model. 10. The system of claim 1 , wherein the memory device includes a first register bank to store a context model cluster for a syntax element “last_significant_coeff_flag” (LSCF), and a second register bank to store context model clusters for other syntax elements. | 0.837757 |
1. A method for semi-supervised insight discovery, the method being implemented by one or more data processors and comprising: seeking, by at least one data processor, pair-wise relationships between large numbers of entities, in a variety of domain specific contexts, from appropriately filtered and customized transaction data; representing, by at least one data processor, the pair-wise relationships between the entities in a graph structure containing a set of nodes representing entities, and a set of edges representing strength of relationships between pairs of nodes; discovering, by at least one data processor, insights in the form of relationship patterns of interest that may be projected or scored on individual or groups of transactions or customers; and using, by at least one data processor, said insights to make data-driven-decisions for a variety of business goals; said graph structure comprising any of the following types of structures: a sub-graph comprising a subset of a graph, created by picking a subset of nodes and edges from an original graph, a sub-graph comprising any of: node based sub-graphs which are created by selecting a subset of the nodes and by keeping only those edges between selected nodes; and edge based sub-graphs which are created by pruning a set of edges from the graph and removing all nodes that are rendered disconnected from the graph; a neighborhood of a target product comprising a sub-graph that contains the target product and all the products that are connected to the target product with consistency strength above a predefined threshold to show the top most affiliated products for a given target product; a bundle structure comprising a sub-set of products wherein each product in the bundle has a high consistency connection with all the other products in the bundle, wherein each product in a bundle is assigned a product density with respect to the bundle which is high if the product has high consistency connection with other products in the bundle and low otherwise; and a bridge structure comprising a collection of two or more, otherwise disconnected, product groups that are bridged by one or more bridge product(s). | 1. A method for semi-supervised insight discovery, the method being implemented by one or more data processors and comprising: seeking, by at least one data processor, pair-wise relationships between large numbers of entities, in a variety of domain specific contexts, from appropriately filtered and customized transaction data; representing, by at least one data processor, the pair-wise relationships between the entities in a graph structure containing a set of nodes representing entities, and a set of edges representing strength of relationships between pairs of nodes; discovering, by at least one data processor, insights in the form of relationship patterns of interest that may be projected or scored on individual or groups of transactions or customers; and using, by at least one data processor, said insights to make data-driven-decisions for a variety of business goals; said graph structure comprising any of the following types of structures: a sub-graph comprising a subset of a graph, created by picking a subset of nodes and edges from an original graph, a sub-graph comprising any of: node based sub-graphs which are created by selecting a subset of the nodes and by keeping only those edges between selected nodes; and edge based sub-graphs which are created by pruning a set of edges from the graph and removing all nodes that are rendered disconnected from the graph; a neighborhood of a target product comprising a sub-graph that contains the target product and all the products that are connected to the target product with consistency strength above a predefined threshold to show the top most affiliated products for a given target product; a bundle structure comprising a sub-set of products wherein each product in the bundle has a high consistency connection with all the other products in the bundle, wherein each product in a bundle is assigned a product density with respect to the bundle which is high if the product has high consistency connection with other products in the bundle and low otherwise; and a bridge structure comprising a collection of two or more, otherwise disconnected, product groups that are bridged by one or more bridge product(s). 9. The method of claim 1 , further comprising: inferring, by at least one data processor, implicit product-product consistency relationships and customer-customer similarity relationships by viewing products in terms of customers and by viewing customers in terms of products. | 0.728785 |
1. A computer-implemented method comprising: a. automatically creating, by a template module of a computer, based on a current set of input, a first automated natural language sentence; b. removing, by the template module of the computer, at least one of the one or more input pieces from the current set of input to form a modified current set of input, the modified current set of input comprising at least one less input piece than the current set of input; c. creating, by the template module of the computer, a second natural language sentence based on the modified set of input; and d. providing, by the template module of the computer, the first automated natural language sentence and the second automated natural language sentence. | 1. A computer-implemented method comprising: a. automatically creating, by a template module of a computer, based on a current set of input, a first automated natural language sentence; b. removing, by the template module of the computer, at least one of the one or more input pieces from the current set of input to form a modified current set of input, the modified current set of input comprising at least one less input piece than the current set of input; c. creating, by the template module of the computer, a second natural language sentence based on the modified set of input; and d. providing, by the template module of the computer, the first automated natural language sentence and the second automated natural language sentence. 2. The method of claim 1 further comprising: a. repeating the removing step until the current set of input is null to create one or more additional natural language sentences; and b. providing the one or more additional natural language sentences. | 0.768582 |
1. A computer-based method for preparing a document for publication, the method comprising: receiving an electronic document previously prepared by an author using a markup formatting structure, the electronic document including a set of cited references and having a descriptive metadata associated with a set of components of the set of cited references embedded in accordance with the markup formatting structure; and executing on the electronic document a reference validation process adapted to recognize the markup formatting structure and the embedded metadata and further adapted to extract the embedded metadata and compare the extracted metadata against a set of at least one authority database to determine the validity of the set of cited references. | 1. A computer-based method for preparing a document for publication, the method comprising: receiving an electronic document previously prepared by an author using a markup formatting structure, the electronic document including a set of cited references and having a descriptive metadata associated with a set of components of the set of cited references embedded in accordance with the markup formatting structure; and executing on the electronic document a reference validation process adapted to recognize the markup formatting structure and the embedded metadata and further adapted to extract the embedded metadata and compare the extracted metadata against a set of at least one authority database to determine the validity of the set of cited references. 6. The method of claim 1 wherein the electronic document is received by a publisher and the publisher causes the reference validation process to be executed on the electronic document prior to publication of the electronic document. | 0.618341 |
10. At least one non-transitory computer-readable storage medium comprising a set of instructions that, in response to being executed on a computing device, cause the computing device to: determine one or more respective suitability metrics for each of one or more candidate partitioning policies for a set of pixel shader inputs for a graphics frame, each candidate partitioning policy comprising one or more rules for partitioning the set of pixel shader inputs for multi-phase pixel shading based on quality sensitivity values for the pixel shader inputs; select a partitioning policy for the set of pixel shader inputs from among the one or more candidate partitioning policies based on the determined suitability metrics; and construct a multi-phase pixel shader for the graphics frame by partitioning the set of pixel shader inputs into multiple classes according to the selected partitioning policy. | 10. At least one non-transitory computer-readable storage medium comprising a set of instructions that, in response to being executed on a computing device, cause the computing device to: determine one or more respective suitability metrics for each of one or more candidate partitioning policies for a set of pixel shader inputs for a graphics frame, each candidate partitioning policy comprising one or more rules for partitioning the set of pixel shader inputs for multi-phase pixel shading based on quality sensitivity values for the pixel shader inputs; select a partitioning policy for the set of pixel shader inputs from among the one or more candidate partitioning policies based on the determined suitability metrics; and construct a multi-phase pixel shader for the graphics frame by partitioning the set of pixel shader inputs into multiple classes according to the selected partitioning policy. 12. The at least one non-transitory computer-readable storage medium of claim 10 , the one or more respective suitability metrics for each candidate partitioning policy including a comparative image quality metric that describes a change in image quality associated with that candidate partitioning policy. | 0.542816 |
6. The computer based assessment tool of claim 1 , wherein the POF is broken down into multiple classes including at least lexicon, portfolios, project templates, activities and roles. | 6. The computer based assessment tool of claim 1 , wherein the POF is broken down into multiple classes including at least lexicon, portfolios, project templates, activities and roles. 11. The computer based assessment tool of claim 6 , wherein the portfolio class includes subclasses of sponsor, projects, roles and lexicons not clearly associated with a project or an activity, related portfolios and other metadata. | 0.93962 |
14. A speech recognizer according to claim 13 wherein said partitioning means comprises means responsive to said string of unknown spoken digits for generating a signal in each speech frame of said string representative of the classification of said speech frame as one of voiced speech, unvoiced speech, and silence, means responsive to said string of unknown spoken digits for generating a signal in each speech frame of said string representative of the speech energy in said frame; and means jointly responsive to said classification signals and said speech energy signals for identifying the unknown digit boundary frames. | 14. A speech recognizer according to claim 13 wherein said partitioning means comprises means responsive to said string of unknown spoken digits for generating a signal in each speech frame of said string representative of the classification of said speech frame as one of voiced speech, unvoiced speech, and silence, means responsive to said string of unknown spoken digits for generating a signal in each speech frame of said string representative of the speech energy in said frame; and means jointly responsive to said classification signals and said speech energy signals for identifying the unknown digit boundary frames. 15. A speech recognizer according to claim 14 wherein said boundary frame identifying means comprises means for scanning said classification signals, means for detecting each one of said unvoiced and silence frames immediately succeeding a voiced frame as a digit boundary frame, means for comparing the number of digit boundary frames with the number of unknown digits, means responsive to the number of boundary frames being less than said number of unknown digits for scanning said energy signals to detect minima energy frames, and means for identifying said energy minima frames as digit boundary frames until the number of digit boundary frames equals the number of unknown digits in the string. | 0.859987 |
1. A computer-implemented method of generating search queries based on digitized audio from conversations, the method executable by a computer including a processor and memory, comprising: providing a database stored in the memory containing a global hot-list comprising universal popular keywords or keyword phrases and containing a personalized entity list comprising keywords and keyword phrases used with a frequency above a determined threshold value in conversations involving a user; monitoring a conversation between at least two people, at least one of which is the user; identifying, by the processor, words or phrases in digitized audio of the monitored conversation through speech recognition; comparing, by the processor, the identified words or phrases to the keywords and keyword phrases in the database to find any matches; generating, by the processor, a search string, without the user requesting a search, based on words or phrases found to match the keyword or keyword phrases stored in the database; submitting, by the computer, the search string to a search engine as a search query; and serving, by the computer, a set of search results returned by the search engine based on the submitted search string to a display device of the user. | 1. A computer-implemented method of generating search queries based on digitized audio from conversations, the method executable by a computer including a processor and memory, comprising: providing a database stored in the memory containing a global hot-list comprising universal popular keywords or keyword phrases and containing a personalized entity list comprising keywords and keyword phrases used with a frequency above a determined threshold value in conversations involving a user; monitoring a conversation between at least two people, at least one of which is the user; identifying, by the processor, words or phrases in digitized audio of the monitored conversation through speech recognition; comparing, by the processor, the identified words or phrases to the keywords and keyword phrases in the database to find any matches; generating, by the processor, a search string, without the user requesting a search, based on words or phrases found to match the keyword or keyword phrases stored in the database; submitting, by the computer, the search string to a search engine as a search query; and serving, by the computer, a set of search results returned by the search engine based on the submitted search string to a display device of the user. 4. The method of claim 1 , wherein the conversations comprise conversations over telephones, the method further comprising: storing in a search diary of the telephone of the user all of the results generated during the conversation; and making available the search diary to the user through a display of the telephone. | 0.587534 |
1. A method of automatically generating an events dictionary in an Internet of Things (IoT) network, comprising: receiving a notification of a first event from a first IoT device in the IoT network; determining a state of the first IoT device before and after the first event; comparing the states of the first IoT device; determining a type of state change of the first event based on the comparing; determining whether the type of the state change of the first event is present in the events dictionary; creating a generic entry based on the type of the state change of the first event not being present in the events dictionary, wherein the type of the state change associated with the generic entry is common to IoT devices of a same type and/or class as the first IoT device; and storing, in the events dictionary, a mapping of an event description of the first event to the generic entry. | 1. A method of automatically generating an events dictionary in an Internet of Things (IoT) network, comprising: receiving a notification of a first event from a first IoT device in the IoT network; determining a state of the first IoT device before and after the first event; comparing the states of the first IoT device; determining a type of state change of the first event based on the comparing; determining whether the type of the state change of the first event is present in the events dictionary; creating a generic entry based on the type of the state change of the first event not being present in the events dictionary, wherein the type of the state change associated with the generic entry is common to IoT devices of a same type and/or class as the first IoT device; and storing, in the events dictionary, a mapping of an event description of the first event to the generic entry. 4. The method of claim 1 , wherein the generic entry comprises an enumeration and a text description of the type of the state change associated with the generic entry. | 0.788384 |
3. The method of claim 1 , wherein building the classifier comprises generating a plurality of clusters, each of the plurality of clusters comprising at least one partial set of words, each of the at least one partial set of words including a set of words from the beginning of the speech recognition result to the end of question part of the speech recognition result. | 3. The method of claim 1 , wherein building the classifier comprises generating a plurality of clusters, each of the plurality of clusters comprising at least one partial set of words, each of the at least one partial set of words including a set of words from the beginning of the speech recognition result to the end of question part of the speech recognition result. 5. The method of claim 3 , wherein the plurality of clusters are generated by grouping together end of question parts that include the same or similar words. | 0.952352 |
1. An electronic calculator comprising: keyboard input means including a plurality of keys for entering lines of one or more alphameric statements, including algebraic expressions, into the calculator, and a jump key for entering a jump statement that includes an algebraic expression into the calculator; memory means, coupled to said keyboard input means, for storing a program comprising a plurality of lines of one or more alphameric statements entered into the calculator, each one of said plurality of lines being associated with a separate line number; processing means, coupled to said keyboard input means and memory means, for processing the lines of one or more alphameric statements stored as a program in said memory means to perform selected functions and to calculate the numeric results of algebraic expressions, as specified by the lines of alphameric statements comprising that program, said processing means being responsive to a jump statement, including an algebraic expression, encountered during processing of a line of a program stored in said memory means, for evaluating that algebraic expression and for then transferring processing of that program to the line of one or more alphameric statements having an associated line number determined by the algebraic sum of the calculated numeric result of that algebraic expression and the line number associated with the line in which that jump statement was encountered; and output means, coupled to said processing means, for providing a visual indication of selected functions performed by the calculator. | 1. An electronic calculator comprising: keyboard input means including a plurality of keys for entering lines of one or more alphameric statements, including algebraic expressions, into the calculator, and a jump key for entering a jump statement that includes an algebraic expression into the calculator; memory means, coupled to said keyboard input means, for storing a program comprising a plurality of lines of one or more alphameric statements entered into the calculator, each one of said plurality of lines being associated with a separate line number; processing means, coupled to said keyboard input means and memory means, for processing the lines of one or more alphameric statements stored as a program in said memory means to perform selected functions and to calculate the numeric results of algebraic expressions, as specified by the lines of alphameric statements comprising that program, said processing means being responsive to a jump statement, including an algebraic expression, encountered during processing of a line of a program stored in said memory means, for evaluating that algebraic expression and for then transferring processing of that program to the line of one or more alphameric statements having an associated line number determined by the algebraic sum of the calculated numeric result of that algebraic expression and the line number associated with the line in which that jump statement was encountered; and output means, coupled to said processing means, for providing a visual indication of selected functions performed by the calculator. 2. An electronic calculator as in claim 1 wherein the calculated numeric result of the algebraic expression included within the jump statement is negative. | 0.747573 |
1. A system for conferring interactivity on a sheet having on a surface thereof a pre-printed image containing text, said system comprising a computer system configured for: (i) scanning said pre-printed image on said sheet to form a scanned image; (ii) printing a position-coding pattern on the sheet, said position-coding pattern identifying a plurality of absolute positions and an impression identity for the scanned image; (iii) receiving association data indicating an association between the impression identity, the plurality of absolute positions and the scanned image; (iv) performing Optical Character Recognition (OCR) on the scanned image to convert the text into computer text; (v) generating an input description for said scanned image, said input description describing words of computer text and zones of corresponding text in the scanned image; and (vi) storing a page description comprising said input description and said scanned image, said page description being indexed with said impression identity, wherein said page description is retrievable so as to confer interactivity on the image. | 1. A system for conferring interactivity on a sheet having on a surface thereof a pre-printed image containing text, said system comprising a computer system configured for: (i) scanning said pre-printed image on said sheet to form a scanned image; (ii) printing a position-coding pattern on the sheet, said position-coding pattern identifying a plurality of absolute positions and an impression identity for the scanned image; (iii) receiving association data indicating an association between the impression identity, the plurality of absolute positions and the scanned image; (iv) performing Optical Character Recognition (OCR) on the scanned image to convert the text into computer text; (v) generating an input description for said scanned image, said input description describing words of computer text and zones of corresponding text in the scanned image; and (vi) storing a page description comprising said input description and said scanned image, said page description being indexed with said impression identity, wherein said page description is retrievable so as to confer interactivity on the image. 2. The system of claim 1 , further comprising a scanner for scanning the pre-printed image. | 0.614345 |
11. A computing system comprising: a storage device to store search queries histories from all users of the computing system; a processing device coupled to the storage device, the processing device comprising a search engine and a prediction engine, the search engine to receive and track search queries of a user to generate a search query history, the prediction engine to compare a search query history of the user with search query histories of other users of the search engine, to determine a number of common search queries between the search query history of the user and the search query history of each other user in view of the comparison, to associate the search query history of the user with the search query history of at least one other user having the most number of common search queries, to determine where a test sequence of the search query history of the user is most similar to a reference sequence from at least one other user having the most number of common search queries, and to generate a predicted search query for the user that the user is predicted to use to perform a next search in relation to other possible searches from the search query history of at least one other user having the most number of common search queries in view of the test sequence and the reference sequence, wherein the predicted search query is the next sequential predicted search relative to the reference sequence, and wherein the search query histories of other users with greater similarities to the search query history of the user have a greater computation weight in generating the predicted search query than search query histories of other users with fewer similarities to the search query history of the user. | 11. A computing system comprising: a storage device to store search queries histories from all users of the computing system; a processing device coupled to the storage device, the processing device comprising a search engine and a prediction engine, the search engine to receive and track search queries of a user to generate a search query history, the prediction engine to compare a search query history of the user with search query histories of other users of the search engine, to determine a number of common search queries between the search query history of the user and the search query history of each other user in view of the comparison, to associate the search query history of the user with the search query history of at least one other user having the most number of common search queries, to determine where a test sequence of the search query history of the user is most similar to a reference sequence from at least one other user having the most number of common search queries, and to generate a predicted search query for the user that the user is predicted to use to perform a next search in relation to other possible searches from the search query history of at least one other user having the most number of common search queries in view of the test sequence and the reference sequence, wherein the predicted search query is the next sequential predicted search relative to the reference sequence, and wherein the search query histories of other users with greater similarities to the search query history of the user have a greater computation weight in generating the predicted search query than search query histories of other users with fewer similarities to the search query history of the user. 14. The computing system of claim 11 wherein the search queries of the user comprise orders placed by the user in view of results of the search queries. | 0.642322 |
1. A computer implemented method of providing visual feedback to a computer user during manipulation of selected text on a display device of a computer system, the computer system including a control device for interactively positioning a visible symbol and an insertion caret on the display device, the computer also having a signal generation device for signaling an active state and an inactive state, the method comprising the computer implemented steps of: a) in response to an active state of the signal generation device while the visible symbol is over the selected text at a source location on said display device: 1) creating and displaying a text object of the selected text, the text object including a visible portion of the selected text that is less than all of the selected text; 2) de-emphasizing the selected text at the source location; b) in a finite series of steps, moving the text object on the display device along a line between the source location and the visible symbol until the text object reaches the visible symbol; c) displaying the insertion caret near the visible symbol to indicate a point of insertion of the selected text; d) moving the visible symbol in response to the control device and moving the text object in response to movement of the visible symbol; e) in response to an inactive state of the signal generation device while the visible symbol is over a destination location: 1) on the display device zooming from a first bounding rectangle for the selected text at the source location to a second bounding rectangle for the selected block of text at the destination location such that the movement of the first bounding rectangle to the size and location of the second bounding rectangle at the destination location is animated; and 2) displaying on screen the selected text at the destination location. | 1. A computer implemented method of providing visual feedback to a computer user during manipulation of selected text on a display device of a computer system, the computer system including a control device for interactively positioning a visible symbol and an insertion caret on the display device, the computer also having a signal generation device for signaling an active state and an inactive state, the method comprising the computer implemented steps of: a) in response to an active state of the signal generation device while the visible symbol is over the selected text at a source location on said display device: 1) creating and displaying a text object of the selected text, the text object including a visible portion of the selected text that is less than all of the selected text; 2) de-emphasizing the selected text at the source location; b) in a finite series of steps, moving the text object on the display device along a line between the source location and the visible symbol until the text object reaches the visible symbol; c) displaying the insertion caret near the visible symbol to indicate a point of insertion of the selected text; d) moving the visible symbol in response to the control device and moving the text object in response to movement of the visible symbol; e) in response to an inactive state of the signal generation device while the visible symbol is over a destination location: 1) on the display device zooming from a first bounding rectangle for the selected text at the source location to a second bounding rectangle for the selected block of text at the destination location such that the movement of the first bounding rectangle to the size and location of the second bounding rectangle at the destination location is animated; and 2) displaying on screen the selected text at the destination location. 2. The method of claim 1 further comprising the computer implemented steps of: a) while the visible symbol is located over the selected text displaying the visible symbol on the display device as an arrow; and b) while the visible symbol is not located over the selected block text displaying the visible symbol on the display device as an I-beam. | 0.578748 |
8. A method for implementing authentication and verification by an authentication and verification system comprising one or more physical processors, the method comprising: obtaining a target personal identification sequence, wherein the target personal identification sequence is associated with an unidentified user; obtaining an input mapping between user-selectable input options and a set of prompts that represent words; obtaining a target sequence of prompts that corresponds to the target personal identification sequence; obtaining a redirection mapping and effectuating presentation of the redirection mapping to the unidentified user prior to effectuating presentation of a set of prompts; effectuating a presentation of the set of prompts to the unidentified user such that individual ones of the presented set of prompts are associated with individual ones of the user-selectable input options in accordance with the obtained input mapping; obtaining one or more audio files comprising sound generated by the unidentified user in response to the presentation; making a first determination whether the obtained one or more audio files represent a vocalization of the target sequence of prompts based on a reversal of a redirection in accordance with the redirection mapping; automatically authenticating the unidentified user responsive to a positive first determination; making a second determination whether the obtained one or more audio files match one or more audio characteristics of sounds generated by the unidentified user; automatically verifying the unidentified user responsive to a positive second determination; and effectuating a grant of access to the unidentified user responsive to authenticating and verifying the unidentified user. | 8. A method for implementing authentication and verification by an authentication and verification system comprising one or more physical processors, the method comprising: obtaining a target personal identification sequence, wherein the target personal identification sequence is associated with an unidentified user; obtaining an input mapping between user-selectable input options and a set of prompts that represent words; obtaining a target sequence of prompts that corresponds to the target personal identification sequence; obtaining a redirection mapping and effectuating presentation of the redirection mapping to the unidentified user prior to effectuating presentation of a set of prompts; effectuating a presentation of the set of prompts to the unidentified user such that individual ones of the presented set of prompts are associated with individual ones of the user-selectable input options in accordance with the obtained input mapping; obtaining one or more audio files comprising sound generated by the unidentified user in response to the presentation; making a first determination whether the obtained one or more audio files represent a vocalization of the target sequence of prompts based on a reversal of a redirection in accordance with the redirection mapping; automatically authenticating the unidentified user responsive to a positive first determination; making a second determination whether the obtained one or more audio files match one or more audio characteristics of sounds generated by the unidentified user; automatically verifying the unidentified user responsive to a positive second determination; and effectuating a grant of access to the unidentified user responsive to authenticating and verifying the unidentified user. 11. The method of claim 8 , wherein the input mapping is generated at least in part randomly, and wherein the input mapping is generated prior to the presentation of the set of prompts. | 0.84264 |
12. The system of claim 11 , wherein the action button comprises a license button enabling licensing of the music. | 12. The system of claim 11 , wherein the action button comprises a license button enabling licensing of the music. 13. The system of claim 12 , wherein the license button further enables adjustment of one or more terms of a commercial license of a music search result based on an intended use. | 0.972476 |
15. Apparatus for automatically modifying one or more real-time social signaling characteristics of an audio input signal to produce a modified audio output signal comprising, in combination, a digital signal analyzer for determining the boundaries between speech segments and non-speech segments of said audio input signal, a digital signal processor for modifying one or more controllable parameters of said speech segments to produce modified speech segments having one or more modified real-time social signaling characteristics, and output means for combining said modified speech segments with said non-speech segments to produce said modified audio output signal, wherein said audio input signal is from a microphone. | 15. Apparatus for automatically modifying one or more real-time social signaling characteristics of an audio input signal to produce a modified audio output signal comprising, in combination, a digital signal analyzer for determining the boundaries between speech segments and non-speech segments of said audio input signal, a digital signal processor for modifying one or more controllable parameters of said speech segments to produce modified speech segments having one or more modified real-time social signaling characteristics, and output means for combining said modified speech segments with said non-speech segments to produce said modified audio output signal, wherein said audio input signal is from a microphone. 20. The apparatus of claim 15 , wherein the audio input signal is processed in real time to produce the modified audio output signal. | 0.67384 |
1. A method comprising: obtaining a priority of each of at least one task objective based on a structure of a hierarchy of task objectives; associating the at least one task objective from the hierarchy of task objectives with a first input received from a user, wherein each task objective is assigned a corresponding priority; determining an order of implementation of the at least one task objective based on the corresponding priority assigned to each of the task objectives associated with the first input; associating the at least one task objective from the hierarchy of task objectives with a second input received from a user; revising the order of implementation of the at least one task objective based on the corresponding priority assigned to each of the task objectives associated with the first input and the second input, to yield a revised order of implementation; and implementing the task objectives based on the revised order of implementation. | 1. A method comprising: obtaining a priority of each of at least one task objective based on a structure of a hierarchy of task objectives; associating the at least one task objective from the hierarchy of task objectives with a first input received from a user, wherein each task objective is assigned a corresponding priority; determining an order of implementation of the at least one task objective based on the corresponding priority assigned to each of the task objectives associated with the first input; associating the at least one task objective from the hierarchy of task objectives with a second input received from a user; revising the order of implementation of the at least one task objective based on the corresponding priority assigned to each of the task objectives associated with the first input and the second input, to yield a revised order of implementation; and implementing the task objectives based on the revised order of implementation. 10. The system of claim 1 , the instructions further controlling the processor to apply a threshold to task objectives associated with the first input, wherein task objectives below the threshold are not implemented. | 0.5704 |
15. The method of claim 14 wherein the rules include a pan rule, a zoom rule, a fast pan rule and a fixed rule. | 15. The method of claim 14 wherein the rules include a pan rule, a zoom rule, a fast pan rule and a fixed rule. 16. The method of claim 15 wherein the pan rule includes extracting a plurality of frames to cover the space of environment while reducing the spatial overlap among the frames from a pan segment. | 0.912775 |
13. A non-transitory computer-readable medium storing instructions executable by a processor for performing a method, comprising: storing a compressed file containing an XML document of one of a plurality of document types in a memory connected to the processor; using the processor, opening the compressed file; at the processor, loading a relationships file of the XML document and parsing contents of the relationships file of the XML document to detect an identifier of the one of the plurality of document types in the relationships file; and selecting a distiller corresponding to the one of the plurality of document types from among a plurality of distillers, and executing, using the processor, the distiller that is selected to parse data in the XML document to generate a Document Object Model (DOM) for storage in the memory. | 13. A non-transitory computer-readable medium storing instructions executable by a processor for performing a method, comprising: storing a compressed file containing an XML document of one of a plurality of document types in a memory connected to the processor; using the processor, opening the compressed file; at the processor, loading a relationships file of the XML document and parsing contents of the relationships file of the XML document to detect an identifier of the one of the plurality of document types in the relationships file; and selecting a distiller corresponding to the one of the plurality of document types from among a plurality of distillers, and executing, using the processor, the distiller that is selected to parse data in the XML document to generate a Document Object Model (DOM) for storage in the memory. 18. The non-transitory computer-readable medium of claim 13 , the method further comprising executing a decorator to convert the DOM into a format for transmission to a mobile electronic device. | 0.519639 |
1. A speech recognition circuit comprising: input means for receiving processed speech parameters; lexical memory means containing lexical data for word recognition, said lexical data comprising a plurality of lexical tree data structures, each lexical tree data structure comprising a model of words having common prefix components, an initial component of each lexical tree data structure being unique; a plurality of lexical tree processors connected in parallel to said input means for processing the speech parameters in parallel to perform parallel lexical tree processing for word recognition by accessing said lexical data in said lexical memory means; results memory means connected to said lexical tree processors for storing processing results from said lexical tree processors and lexical tree identifiers to identify lexical trees to be processed by said lexical tree processors; and control processor means for controlling said lexical tree processors to process lexical trees identified in said results memory means by performing parallel processing on a plurality of said lexical tree data structures. | 1. A speech recognition circuit comprising: input means for receiving processed speech parameters; lexical memory means containing lexical data for word recognition, said lexical data comprising a plurality of lexical tree data structures, each lexical tree data structure comprising a model of words having common prefix components, an initial component of each lexical tree data structure being unique; a plurality of lexical tree processors connected in parallel to said input means for processing the speech parameters in parallel to perform parallel lexical tree processing for word recognition by accessing said lexical data in said lexical memory means; results memory means connected to said lexical tree processors for storing processing results from said lexical tree processors and lexical tree identifiers to identify lexical trees to be processed by said lexical tree processors; and control processor means for controlling said lexical tree processors to process lexical trees identified in said results memory means by performing parallel processing on a plurality of said lexical tree data structures. 18. A speech recognition circuit according to claim 1 , wherein said input means is arranged to receive said speech parameters as feature vectors. | 0.653823 |
1. A system including at least one non-transitory, computer-readable medium, comprising: a plurality of modules; and a plurality of instructions stored on the at least one non-transitory, computer-readable medium that, when executed by a processor, cause the processor to provide a preprocessor utility for: determining whether each of the plurality of modules includes a consumer tag having a consumer tag unique identifier and a consumer tag method name and, if so, associating an event name for the consumer tag method name with the consumer tag unique identifier in at least one array that includes a hash table; determining whether each of the plurality of modules includes a producer tag having a producer tag unique identifier and a producer tag method name and, if so, replacing the producer tag method name with an event name in the at least one array; creating a unique event method for each unique identifier in the at least one array; and replacing a portion of each of the plurality of modules that includes the producer tag with a respective one of the unique event methods. | 1. A system including at least one non-transitory, computer-readable medium, comprising: a plurality of modules; and a plurality of instructions stored on the at least one non-transitory, computer-readable medium that, when executed by a processor, cause the processor to provide a preprocessor utility for: determining whether each of the plurality of modules includes a consumer tag having a consumer tag unique identifier and a consumer tag method name and, if so, associating an event name for the consumer tag method name with the consumer tag unique identifier in at least one array that includes a hash table; determining whether each of the plurality of modules includes a producer tag having a producer tag unique identifier and a producer tag method name and, if so, replacing the producer tag method name with an event name in the at least one array; creating a unique event method for each unique identifier in the at least one array; and replacing a portion of each of the plurality of modules that includes the producer tag with a respective one of the unique event methods. 4. The system of claim 1 , wherein the preprocessor utility is further for: determining whether the event name exists for the consumer tag unique identifier in the at least one array and, if not, generating the event name for association with the consumer tag unique identifier. | 0.5 |
4. The method of claim 1 , wherein the converting of the text box into the table comprises displaying two addition visual elements such that an addition visual element is displayed to the right of the table and another addition visual element is displayed below the table. | 4. The method of claim 1 , wherein the converting of the text box into the table comprises displaying two addition visual elements such that an addition visual element is displayed to the right of the table and another addition visual element is displayed below the table. 5. The method of claim 4 , wherein when the addition visual element displayed to the right of the table is selected, a new column is added to the table, and wherein, when the addition visual element displayed below the table is selected, a new row is added to the table. | 0.899927 |
12. The computer-readable memory of claim 11 , wherein the continuous query language query statement includes at least one predicate. | 12. The computer-readable memory of claim 11 , wherein the continuous query language query statement includes at least one predicate. 14. The computer-implemented method of claim 12 , wherein the predicate includes one or more other logical operators. | 0.970158 |
15. An apparatus comprising: a server, including a processor, to: receive a request from a user device for a document; retrieve the document, wherein the document includes embedded instructions; identify at least a text segment of the document based on the embedded instructions; determine phrases of interest in the at least a text segment based on a reference and logged information specific to the user, wherein the reference includes a plurality of phrases and associations between the plurality of phrases; indicate the phrases of interest in a response to the user device; transmit the response to the user device, wherein the response enables the user device to display the document, visually distinguish the phrases of interest indicated in the response from the remaining text within the document that conveys to a user a capability of user input corresponding to the phrases of interest, the response also includes search results for the phrases of interest from a plurality of data sources, wherein the searches were performed prior to the response to the user device, and in response to user input corresponding to a phrase of interest within the document, display at least a portion of the search results corresponding to the phrase of interest concurrently with the document, wherein the phrase of interest and the at least portion of the search results are viewable simultaneously. | 15. An apparatus comprising: a server, including a processor, to: receive a request from a user device for a document; retrieve the document, wherein the document includes embedded instructions; identify at least a text segment of the document based on the embedded instructions; determine phrases of interest in the at least a text segment based on a reference and logged information specific to the user, wherein the reference includes a plurality of phrases and associations between the plurality of phrases; indicate the phrases of interest in a response to the user device; transmit the response to the user device, wherein the response enables the user device to display the document, visually distinguish the phrases of interest indicated in the response from the remaining text within the document that conveys to a user a capability of user input corresponding to the phrases of interest, the response also includes search results for the phrases of interest from a plurality of data sources, wherein the searches were performed prior to the response to the user device, and in response to user input corresponding to a phrase of interest within the document, display at least a portion of the search results corresponding to the phrase of interest concurrently with the document, wherein the phrase of interest and the at least portion of the search results are viewable simultaneously. 16. The apparatus of claim 15 , wherein the server is further configured to: retrieve respective search results associated with each phrase of interest from a cache; and include the respective search results in the response. | 0.624424 |
13. A computer-implemented NLP-based content recommendation system, comprising: a memory; and a content recommender module, having instructions that are configured to, when executed, receive a text segment for processing; identify, one or more named entities to which a received text segment refers based, at least in part, upon a natural language processing (NLP) parsing and linguistic analysis of the text segment; and derive and present related content based at least in part upon a natural language processing parsing and linguistic analysis of entity based information and of context related information, wherein the related content includes a representation of connections to at least one identified named entity, and wherein the related content is determined as relevant using context information associated with the derived named entities. | 13. A computer-implemented NLP-based content recommendation system, comprising: a memory; and a content recommender module, having instructions that are configured to, when executed, receive a text segment for processing; identify, one or more named entities to which a received text segment refers based, at least in part, upon a natural language processing (NLP) parsing and linguistic analysis of the text segment; and derive and present related content based at least in part upon a natural language processing parsing and linguistic analysis of entity based information and of context related information, wherein the related content includes a representation of connections to at least one identified named entity, and wherein the related content is determined as relevant using context information associated with the derived named entities. 14. The system of claim 13 , wherein the module is further configured, when executed, to display one or more indicators for navigating to the related content. | 0.656344 |
19. The method of claim 18 , the presenting of the list comprising: presenting, in a window of a graphical user interface, two or more icons, each representing one of the two or more definitions of business object types; and presenting, in the window, an area representing the new definition of a business object type. | 19. The method of claim 18 , the presenting of the list comprising: presenting, in a window of a graphical user interface, two or more icons, each representing one of the two or more definitions of business object types; and presenting, in the window, an area representing the new definition of a business object type. 20. The method of claim 19 , the receiving of the input comprising: receiving input effected by dragging at least one of the two or more icons into the area representing the new definition of a business object type. | 0.825516 |
4. The device as recited in claim 2 , wherein the encoding unit is configured for encoding the data words by adding a predefined number of bits to each data word as a function of the at least one encoding parameter. | 4. The device as recited in claim 2 , wherein the encoding unit is configured for encoding the data words by adding a predefined number of bits to each data word as a function of the at least one encoding parameter. 5. The device as recited in claim 4 , wherein the encoding unit is configured for selecting the predefined number of added bits in such a way that a predefined portion of all encoded data words has at least one of a predefined Hamming distance and a predefined Hamming weight among each other. | 0.908662 |
19. An application server for computer-based instruction, the application server comprising: a central processing unit; a memory module; a non-transitory computer readable medium storing computer program instructions for the computer-based instruction; and a communications network interface configured to: receive input from user equipment operated by a student; and send output to the user equipment; wherein the application server is configured to: receive a plurality of selections, wherein the plurality of selections is sent by the student, and wherein the plurality of selections select an animated instructor, an animated avatar representing the student, at least one animated classmate, and a first lesson; in response to the plurality of selections, execute at least a portion of the computer program instructions to: present on the user equipment a first portion of the first lesson by the animated instructor, wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the first portion of the first lesson; and present, on the user equipment, a first interactive question based on the first portion of the first lesson; receive from the student a first answer to the first interactive question; in response to the first answer, execute at least a portion of the computer program instructions to determine whether the first answer is correct or incorrect; upon determining that the first answer is correct, execute at least a portion of the computer program instructions to present, on the user equipment, a second portion of the first lesson by the animated instructor, wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the second portion of the first lesson; upon determining that the first answer is incorrect: execute at least a portion of the computer program instructions to: present, on the user equipment, at least a portion of a second lesson by the animated instructor, wherein the second lesson is based on the incorrect first answer, and wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the at least a portion of the second lesson; and present, on the user equipment, a second interactive question based on the at least a portion of the second lesson previously presented by the animated instructor; receive from the student a second answer to the second interactive question; execute at least a portion of the computer program instructions to determine whether the second answer is correct or incorrect; upon determining that the second answer is correct, execute at least a portion of the computer program instructions to return to the first lesson; upon determining that the second answer is incorrect: execute at least a portion of the computer program instructions to: present, on the user equipment, at least a portion of a third lesson by the animated instructor, wherein the third lesson is based on the incorrect second answer, and wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the at least a portion of the third lesson; and present, on the user equipment, a third interactive question based on the at least a portion of the third lesson previously presented by the animated instructor; receive from the student a third answer to the third interactive question; in response to the third answer, execute at least a portion of the computer program instructions to determine whether the third answer is correct or incorrect; upon determining that the third answer is correct, execute at least a portion of the computer program instructions to return to the second lesson; and upon determining that the third answer is incorrect, execute at least a portion of the computer program instructions to present, on the user equipment, at least a portion of a fourth lesson by the animated instructor, wherein the fourth lesson is based on the incorrect third answer, and wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the at least a portion of the fourth lesson. | 19. An application server for computer-based instruction, the application server comprising: a central processing unit; a memory module; a non-transitory computer readable medium storing computer program instructions for the computer-based instruction; and a communications network interface configured to: receive input from user equipment operated by a student; and send output to the user equipment; wherein the application server is configured to: receive a plurality of selections, wherein the plurality of selections is sent by the student, and wherein the plurality of selections select an animated instructor, an animated avatar representing the student, at least one animated classmate, and a first lesson; in response to the plurality of selections, execute at least a portion of the computer program instructions to: present on the user equipment a first portion of the first lesson by the animated instructor, wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the first portion of the first lesson; and present, on the user equipment, a first interactive question based on the first portion of the first lesson; receive from the student a first answer to the first interactive question; in response to the first answer, execute at least a portion of the computer program instructions to determine whether the first answer is correct or incorrect; upon determining that the first answer is correct, execute at least a portion of the computer program instructions to present, on the user equipment, a second portion of the first lesson by the animated instructor, wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the second portion of the first lesson; upon determining that the first answer is incorrect: execute at least a portion of the computer program instructions to: present, on the user equipment, at least a portion of a second lesson by the animated instructor, wherein the second lesson is based on the incorrect first answer, and wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the at least a portion of the second lesson; and present, on the user equipment, a second interactive question based on the at least a portion of the second lesson previously presented by the animated instructor; receive from the student a second answer to the second interactive question; execute at least a portion of the computer program instructions to determine whether the second answer is correct or incorrect; upon determining that the second answer is correct, execute at least a portion of the computer program instructions to return to the first lesson; upon determining that the second answer is incorrect: execute at least a portion of the computer program instructions to: present, on the user equipment, at least a portion of a third lesson by the animated instructor, wherein the third lesson is based on the incorrect second answer, and wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the at least a portion of the third lesson; and present, on the user equipment, a third interactive question based on the at least a portion of the third lesson previously presented by the animated instructor; receive from the student a third answer to the third interactive question; in response to the third answer, execute at least a portion of the computer program instructions to determine whether the third answer is correct or incorrect; upon determining that the third answer is correct, execute at least a portion of the computer program instructions to return to the second lesson; and upon determining that the third answer is incorrect, execute at least a portion of the computer program instructions to present, on the user equipment, at least a portion of a fourth lesson by the animated instructor, wherein the fourth lesson is based on the incorrect third answer, and wherein the animated instructor, the animated avatar, and the at least one animated classmate are displayed on the user equipment during the entirety of the at least a portion of the fourth lesson. 21. The application server of claim 19 , wherein the application server is further configured to execute at least a portion of the computer instructions to: present, on the user equipment, an internal question by at least one of: the animated avatar; or the at least one animated classmate; and receive, on the user equipment, an answer to the internal question from at least one of: the animated instructor; the animated avatar; or the at least one animated classmate. | 0.578339 |
283. The system of claim 279 , wherein to set the term of experience to the time difference, the processor is further configured to: compute a repeated entry time difference for each of the searchable phrases that is a repeated entry and is associated with an other experience range; and add to the time difference each repeated entry time difference, wherein the other experience range includes an other start time and an other end time, and wherein the other start time and the start time are different, or the other end time and the end time are different. | 283. The system of claim 279 , wherein to set the term of experience to the time difference, the processor is further configured to: compute a repeated entry time difference for each of the searchable phrases that is a repeated entry and is associated with an other experience range; and add to the time difference each repeated entry time difference, wherein the other experience range includes an other start time and an other end time, and wherein the other start time and the start time are different, or the other end time and the end time are different. 284. The system of claim 283 , wherein to store the parsed resume, the processor is further configured to: store each of the searchable phrases having an identical term of experience as an element defined by a markup language, the element comprising a start tag, content data, and an end tag, wherein the start tag and the end tag include the identical term of experience, and wherein the content data includes each of the searchable phrases having the identical term of experience. | 0.8248 |
1. A method performed by a computing device for creating a model structure, the method comprising: creating multiple submodels comprising sentence weights that are assigned to a sentence comprising multiple words having assigned word weights, the multiple submodels including an individual submodel that comprises an individual sentence weight; arranging the multiple submodels in layers based on a selected permutation of the multiple words of the sentence; assigning, as an input to the individual submodel, an individual word weight that is assigned to an individual word of the sentence; and defining the model structure to include the multiple submodels arranged in the layers, wherein the individual submodel is configured to perform an operation on the individual sentence weight assigned to the sentence and the individual word weight assigned to the individual word of the sentence. | 1. A method performed by a computing device for creating a model structure, the method comprising: creating multiple submodels comprising sentence weights that are assigned to a sentence comprising multiple words having assigned word weights, the multiple submodels including an individual submodel that comprises an individual sentence weight; arranging the multiple submodels in layers based on a selected permutation of the multiple words of the sentence; assigning, as an input to the individual submodel, an individual word weight that is assigned to an individual word of the sentence; and defining the model structure to include the multiple submodels arranged in the layers, wherein the individual submodel is configured to perform an operation on the individual sentence weight assigned to the sentence and the individual word weight assigned to the individual word of the sentence. 4. The method according to claim 1 , further comprising: update the sentence weights and the word weights using a gradient of an objective function with respect to outputs of the multiple submodels. | 0.506263 |
1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learned user preferences, the method comprising: providing a set of content items, each content item having at least one associated descriptive term to describe the content item; receiving input entered by a user for identifying desired content items; in response to the input entered by the user, presenting a subset of content items; receiving selection actions of selected content items of the subset from the user; learning preferred descriptive terms of the user by analyzing descriptive terms associated with the selected content items and by analyzing the date, day, and time of the selection actions and the descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items, wherein similarity of the similar content items is determined by comparing the descriptive terms associated with the selected content items with a previously selected content item, and wherein the periodicity indicates an amount of time between the selection actions of the similar content items relative to a reference point; associating the periodicity with the preferred descriptive terms associated with the similar content items; associating the preferred descriptive terms with the user; determining a measurement collection having measurements associated with the preferred descriptive terms, wherein the measurements represent relative preferences of the user for the preferred descriptive terms, wherein the measurement collection includes groups of the preferred descriptive terms, wherein the relative preferences of the user for the preferred descriptive terms in a particular group are treated as equal and the groups differentiate the relative preferences of the user for the preferred descriptive terms between the groups, and wherein a preferred descriptive term is included in the particular group at least in part based on at least one of (i) smoothing relatively smaller probability weights associated with less commonly expressed preferences of the relative preferences and (ii) aging preferences of the relative preferences of relative preferences captured in a relatively more distant past, and based further on bounding a range of values of the particular group using a relevance scale factor; and in response to receiving subsequent input entered by the user, selecting and ordering a collection of content items by promoting rankings of content items of the collection of content items associated with the preferred descriptive terms of the user according to differentiation provided by the measurement collection and further based on promoting rankings of those content items of the collection of content items associated with the preferred descriptive terms further associated with periodicities similar to the date, day, and time of the subsequent input; wherein at least one of the input and the subsequent input are entered by the user on an input constrained device. | 1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learned user preferences, the method comprising: providing a set of content items, each content item having at least one associated descriptive term to describe the content item; receiving input entered by a user for identifying desired content items; in response to the input entered by the user, presenting a subset of content items; receiving selection actions of selected content items of the subset from the user; learning preferred descriptive terms of the user by analyzing descriptive terms associated with the selected content items and by analyzing the date, day, and time of the selection actions and the descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items, wherein similarity of the similar content items is determined by comparing the descriptive terms associated with the selected content items with a previously selected content item, and wherein the periodicity indicates an amount of time between the selection actions of the similar content items relative to a reference point; associating the periodicity with the preferred descriptive terms associated with the similar content items; associating the preferred descriptive terms with the user; determining a measurement collection having measurements associated with the preferred descriptive terms, wherein the measurements represent relative preferences of the user for the preferred descriptive terms, wherein the measurement collection includes groups of the preferred descriptive terms, wherein the relative preferences of the user for the preferred descriptive terms in a particular group are treated as equal and the groups differentiate the relative preferences of the user for the preferred descriptive terms between the groups, and wherein a preferred descriptive term is included in the particular group at least in part based on at least one of (i) smoothing relatively smaller probability weights associated with less commonly expressed preferences of the relative preferences and (ii) aging preferences of the relative preferences of relative preferences captured in a relatively more distant past, and based further on bounding a range of values of the particular group using a relevance scale factor; and in response to receiving subsequent input entered by the user, selecting and ordering a collection of content items by promoting rankings of content items of the collection of content items associated with the preferred descriptive terms of the user according to differentiation provided by the measurement collection and further based on promoting rankings of those content items of the collection of content items associated with the preferred descriptive terms further associated with periodicities similar to the date, day, and time of the subsequent input; wherein at least one of the input and the subsequent input are entered by the user on an input constrained device. 5. The method of claim 1 , wherein the measurements of the measurement collection associated with the preferred descriptive terms are based on numbers of selections of the selected content items associated with said preferred descriptive terms. | 0.567331 |
22. A system for converting a textual passage to a synthesized image sequence, comprising: processing electronics configured to determine a reading speed of a user, to determine a first textual passage of a text source currently being read by the user, to predict a second textual passage of the text source that will be read by the user based on the reading speed of the user and an amount of text between the first textual passage and the second textual passage, and to generate a synthesized image sequence associated with the second textual passage. | 22. A system for converting a textual passage to a synthesized image sequence, comprising: processing electronics configured to determine a reading speed of a user, to determine a first textual passage of a text source currently being read by the user, to predict a second textual passage of the text source that will be read by the user based on the reading speed of the user and an amount of text between the first textual passage and the second textual passage, and to generate a synthesized image sequence associated with the second textual passage. 23. The system of claim 22 , wherein the amount of text between the first textual passage and the second textual passage comprises a number of pages. | 0.55787 |
13. A system comprising: one or more processors configured to perform operations comprising: receiving a collection of text; identifying a first collection of instance-class pairs for the collection of text, wherein the first collection of instance-class pairs are identified by applying one or more template patterns to a collection of documents; clustering a collection of semantically similar phrases using the collection of text; determining, for each class in the first collection of instance-class pairs: whether a threshold number of instances within a cluster in the semantically similar phrase clusters are labeled by the class, and whether a threshold number of clusters in the semantically similar phrase clusters include at least one instance that is labeled by the class; in response to determining that a threshold number of instances within a cluster are labeled by a class and a threshold number of clusters in the semantically similar phrase clusters include at least one instance that is labeled by the class, selecting each instance in the first collection of instance-class pairs that are labeled by the class to be included in a second collection of instance-class pairs; and storing the second collection of instance-class pairs for use in information retrieval. | 13. A system comprising: one or more processors configured to perform operations comprising: receiving a collection of text; identifying a first collection of instance-class pairs for the collection of text, wherein the first collection of instance-class pairs are identified by applying one or more template patterns to a collection of documents; clustering a collection of semantically similar phrases using the collection of text; determining, for each class in the first collection of instance-class pairs: whether a threshold number of instances within a cluster in the semantically similar phrase clusters are labeled by the class, and whether a threshold number of clusters in the semantically similar phrase clusters include at least one instance that is labeled by the class; in response to determining that a threshold number of instances within a cluster are labeled by a class and a threshold number of clusters in the semantically similar phrase clusters include at least one instance that is labeled by the class, selecting each instance in the first collection of instance-class pairs that are labeled by the class to be included in a second collection of instance-class pairs; and storing the second collection of instance-class pairs for use in information retrieval. 14. The system of claim 13 , where the threshold number of instances is a minimum number of instances paired with the class found within the cluster. | 0.869969 |
14. The computing system of claim 12 further comprising: an inference engine that generates inferred equivalence pairs based on one or more inference rules in the semantic model and where the clique building logic iteratively inputs, into operating memory, batches of one or more inferred equivalence pairs, and updates the clique map with one or more cliques that consolidate groups of equivalent resources, as determined from the inferred equivalence pairs, to a canonical representative resource; and where the triple consolidation logic accesses the updated clique map to consolidate triples in the semantic model and inferred triples by replacing triple resources that are in a clique with their corresponding canonical representative resources. | 14. The computing system of claim 12 further comprising: an inference engine that generates inferred equivalence pairs based on one or more inference rules in the semantic model and where the clique building logic iteratively inputs, into operating memory, batches of one or more inferred equivalence pairs, and updates the clique map with one or more cliques that consolidate groups of equivalent resources, as determined from the inferred equivalence pairs, to a canonical representative resource; and where the triple consolidation logic accesses the updated clique map to consolidate triples in the semantic model and inferred triples by replacing triple resources that are in a clique with their corresponding canonical representative resources. 15. The computing system of claim 14 where the inference engine stores an output of rules that infer an equivalence pair in a temporary table that is input to the clique building logic. | 0.781609 |
3. The computer-implemented method of claim 1 , wherein the authorship identifier includes an email address for a purported author of or contributor to the first document, and wherein the email address is an email address in the first domain. | 3. The computer-implemented method of claim 1 , wherein the authorship identifier includes an email address for a purported author of or contributor to the first document, and wherein the email address is an email address in the first domain. 4. The computer-implemented method of claim 3 , wherein prior to conditionally confirming the authorship of the first document, the method includes requesting that the entity confirm that the email address associated with the profile for the entity is the email address that the entity includes in documents authored by the entity. | 0.854315 |
1. A method, using a processor, for providing a search query, the method comprising: receiving at least one image from a user terminal; comparing the at least one image with a plurality of stored images; determining whether there is a match between the received image and at least one of the plurality of stored images; determining a search keyword when the match is detected between the received image and at least one of the plurality of stored images based on the match determination; receiving location information of the user terminal when no match is detected between the received image and one of the plurality of stored images after the match determination; acquiring textual documents uploaded by users of other terminals located within a reference distance from the user terminal based on the received location information; detecting keywords commonly appearing at least a predetermined number of times in each of at least two of the acquired documents; determining, by the processor, a search keyword based on the commonly appearing keywords, the determined search keyword being associated with at least one topic related to the location information; providing, to the user terminal, at least one search query associated with the determined search keyword based on the match between the received image and the at least one of the plurality of stored images when the match is detected, and based on the commonly appearing keywords when no match is detected based on the match determination, wherein the at least one search query associated with the determined search keyword based on the commonly appearing keywords is used to search for additional information about the at least one topic in a search engine; displaying, on a display of the user terminal, the at least one search query, and enabling a user of the user terminal to select one of the at least one search query displayed on the display for a search conducted in the search engine, without the user entering a search keyword in text in the user terminal; receiving a search query selected by the user from the provided at least one search query; and providing lower search queries corresponding to the selected search query; wherein the at least one search query associated with the determined search keyword based on the detection of the match or on the commonly appearing keywords comprises a real-time rush keyword corresponding to the determined search keyword based on the detection of the match or on the commonly appearing keywords. | 1. A method, using a processor, for providing a search query, the method comprising: receiving at least one image from a user terminal; comparing the at least one image with a plurality of stored images; determining whether there is a match between the received image and at least one of the plurality of stored images; determining a search keyword when the match is detected between the received image and at least one of the plurality of stored images based on the match determination; receiving location information of the user terminal when no match is detected between the received image and one of the plurality of stored images after the match determination; acquiring textual documents uploaded by users of other terminals located within a reference distance from the user terminal based on the received location information; detecting keywords commonly appearing at least a predetermined number of times in each of at least two of the acquired documents; determining, by the processor, a search keyword based on the commonly appearing keywords, the determined search keyword being associated with at least one topic related to the location information; providing, to the user terminal, at least one search query associated with the determined search keyword based on the match between the received image and the at least one of the plurality of stored images when the match is detected, and based on the commonly appearing keywords when no match is detected based on the match determination, wherein the at least one search query associated with the determined search keyword based on the commonly appearing keywords is used to search for additional information about the at least one topic in a search engine; displaying, on a display of the user terminal, the at least one search query, and enabling a user of the user terminal to select one of the at least one search query displayed on the display for a search conducted in the search engine, without the user entering a search keyword in text in the user terminal; receiving a search query selected by the user from the provided at least one search query; and providing lower search queries corresponding to the selected search query; wherein the at least one search query associated with the determined search keyword based on the detection of the match or on the commonly appearing keywords comprises a real-time rush keyword corresponding to the determined search keyword based on the detection of the match or on the commonly appearing keywords. 3. The method of claim 1 , wherein determining the at least one search keyword based on the commonly appearing keywords comprises: counting a number of the acquired documents having the commonly appearing keywords for each of the commonly appearing keywords; and determining, as the search keyword, a commonly appearing keyword appearing in the highest counted number of acquired documents. | 0.51336 |
17. A method comprising: receiving system diagnostic trouble codes (DTCs), component DTCs, and root causes; determining a first level model comprising first level causal relationships and first level causal weights between system DTCs and component DTCs; determining a second level model comprising second level causal relationships and second level causal weights between component DTCs and root causes; and generating an acyclic graphical model based on the first level model and the second level model. | 17. A method comprising: receiving system diagnostic trouble codes (DTCs), component DTCs, and root causes; determining a first level model comprising first level causal relationships and first level causal weights between system DTCs and component DTCs; determining a second level model comprising second level causal relationships and second level causal weights between component DTCs and root causes; and generating an acyclic graphical model based on the first level model and the second level model. 18. The method of claim 17 , comprising: receiving one or more individual vehicle system DTCs and one or more individual vehicle component DTCs; calculating a causal probability using the acyclic graphical model in conjunction with the individual vehicle system DTCs and one or more individual vehicle component DTCs; determining one or more highly likely root causes based on the causal probability; and displaying the one or more highly likely root causes to a user. | 0.691041 |
15. A non-transitory computer-readable storage device 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 search query; generating a potential substitute term that is related to a query term of the search query; identifying an original set of documents that are responsive to the search query; weighting each potential substitute term that appears in a document in the original set based on a prevalence of the potential substitute term in the original set of documents; producing a pruned set of terms whose weight satisfies a condition; determining that the potential substitute term is a member of the pruned set of terms; and in response to determining that the potential substitute term is a member of the pruned set of terms, modifying the search query to include the potential substitute term. | 15. A non-transitory computer-readable storage device 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 search query; generating a potential substitute term that is related to a query term of the search query; identifying an original set of documents that are responsive to the search query; weighting each potential substitute term that appears in a document in the original set based on a prevalence of the potential substitute term in the original set of documents; producing a pruned set of terms whose weight satisfies a condition; determining that the potential substitute term is a member of the pruned set of terms; and in response to determining that the potential substitute term is a member of the pruned set of terms, modifying the search query to include the potential substitute term. 20. The non-transitory computer-readable storage device of claim 15 , wherein weighting each potential substitute term comprises using inverse document frequency to measure the prevalence of the substitute term. | 0.586301 |
5. The system of claim 1 wherein the computational module further comprises a regional click rate (RCR) computation module that generates a third metric value based on selections among a plurality of results generated by initiating the first search query. | 5. The system of claim 1 wherein the computational module further comprises a regional click rate (RCR) computation module that generates a third metric value based on selections among a plurality of results generated by initiating the first search query. 8. The system of claim 5 wherein the computational module further comprises a meta-query classifier (metaclass) computation module that generates a fourth metric value based on a combination of the first value, the second metric value, and the third metric value and determines that the first search query is a regional specific query when the fourth metric value exceeds a metaclass threshold. | 0.83617 |
1. A method of responding to a search query to a user with a computing system comprising: a) processing a query from a user including a first search term with the computing system; b) automatically determining a geographic region associated with said user with the computing system; c) automatically determining if said first search term relates to a news story describing events or topics associated with said geographic region; wherein said determining is performed by analyzing content of published online news stories from local sources within or proximate to said geographic region with the computing system; d) automatically selecting first news content from said local sources relating to said first search term with the computing system when said first search term relates to a news story published within a first time interval; e) presenting search results to said user with the computing system for said query including said first news content in response to said query directed to said first search term; and f) presenting advertising within said interface based on an expected mental state of said user predicted based on a state of an event identified in said first topic news content. | 1. A method of responding to a search query to a user with a computing system comprising: a) processing a query from a user including a first search term with the computing system; b) automatically determining a geographic region associated with said user with the computing system; c) automatically determining if said first search term relates to a news story describing events or topics associated with said geographic region; wherein said determining is performed by analyzing content of published online news stories from local sources within or proximate to said geographic region with the computing system; d) automatically selecting first news content from said local sources relating to said first search term with the computing system when said first search term relates to a news story published within a first time interval; e) presenting search results to said user with the computing system for said query including said first news content in response to said query directed to said first search term; and f) presenting advertising within said interface based on an expected mental state of said user predicted based on a state of an event identified in said first topic news content. 17. The method of claim 1 further including a step: pricing advertisements for an advertising auction presented with said search results based on a prediction of an expected time for said user to complete reviewing said search results. | 0.626866 |
1. A computer-implemented method comprising: using a processor to mine user related content information, wherein the information is mined from an information repository; filtering the mined user related content information from the information repository, wherein the filtering comprises identifying a subset of the mined user related content information comprising information related to a predetermined category; identifying, using a cosine similarity measure, a plurality of words having a similarity to a seed set of words by analyzing the subset, using a plurality of analyzers, wherein each analyzer is configured to capture a plurality of representational variations, from the information repository, related to the seed set of words; classifying, based on the analyzing, the plurality of representational variations, wherein the classifying comprises ranking the filtered user related content information; combining the classified plurality of representational variations of the user related content information from each of the plurality of analyzers, wherein the combining comprises identifying a relevancy of a representational variation to a user intent based upon the ranking of the filtered user related content information; and training a classifier for characterizing real-time intention content from information repositories using the combined plurality of representational variations. | 1. A computer-implemented method comprising: using a processor to mine user related content information, wherein the information is mined from an information repository; filtering the mined user related content information from the information repository, wherein the filtering comprises identifying a subset of the mined user related content information comprising information related to a predetermined category; identifying, using a cosine similarity measure, a plurality of words having a similarity to a seed set of words by analyzing the subset, using a plurality of analyzers, wherein each analyzer is configured to capture a plurality of representational variations, from the information repository, related to the seed set of words; classifying, based on the analyzing, the plurality of representational variations, wherein the classifying comprises ranking the filtered user related content information; combining the classified plurality of representational variations of the user related content information from each of the plurality of analyzers, wherein the combining comprises identifying a relevancy of a representational variation to a user intent based upon the ranking of the filtered user related content information; and training a classifier for characterizing real-time intention content from information repositories using the combined plurality of representational variations. 7. The method as claimed in claim 1 , wherein said analyzing comprises analyzing in parallel the mined user related content information from the information repository by each of the plurality of analyzers. | 0.60755 |
13. At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for use in connection with generating text, the method comprising: obtaining a plurality of items of content and associated metadata, the associated metadata comprising information indicative of how persuasive at least one of the plurality of items of content is likely to be to a person; obtaining a schema specifying a first set of one or more rhetorical relations; identifying a second set of one or more rhetorical relations among items of content in the plurality of items of content based, at least in part, on the associated metadata, wherein the second set of rhetorical relations is not in the schema; generating a document plan comprising a plurality of rhetorical relations among the items of content in the plurality of items of content, the plurality of rhetorical relations including the first set of rhetorical relations and the second set of rhetorical relations; generating an electronic document comprising natural language text based, at least in part, on the document plan; and providing the electronic document to the person. | 13. At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for use in connection with generating text, the method comprising: obtaining a plurality of items of content and associated metadata, the associated metadata comprising information indicative of how persuasive at least one of the plurality of items of content is likely to be to a person; obtaining a schema specifying a first set of one or more rhetorical relations; identifying a second set of one or more rhetorical relations among items of content in the plurality of items of content based, at least in part, on the associated metadata, wherein the second set of rhetorical relations is not in the schema; generating a document plan comprising a plurality of rhetorical relations among the items of content in the plurality of items of content, the plurality of rhetorical relations including the first set of rhetorical relations and the second set of rhetorical relations; generating an electronic document comprising natural language text based, at least in part, on the document plan; and providing the electronic document to the person. 16. The at least one non-transitory computer-readable storage medium of claim 13 , wherein generating the document plan comprises generating a hierarchy of rhetorical relations comprising the plurality of rhetorical relations. | 0.636412 |
8. A method, comprising: retrieving a first set of electronic documents; recording user interactions with contextual shortcuts in a second set of electronic documents, wherein said contextual shortcuts comprise associated keyword anchors and user-selectable links relating to said keyword anchors, said keyword anchors being associated with a context vector representing at least a portion of content of a given electronic document of said second set of electronic documents; updating an index of said second set of electronic documents based, at least in part, on said user interactions from said plurality of users, said updating comprising adding at least one previously non-indexed electronic document to said index at least partially in response to receiving one or more user interactions with said one or more contextual shortcuts contained within said at least one previously non-indexed electronic document, wherein said index is searchable by said plurality of users; determining a score for said given electronic document based, at least in part, on user selections of said user-selectable links of said contextual shortcuts within said given electronic document; and updating said index based, at least in part, on said first set of electronic documents and said score; and updating a ranking of individual ones of the first set of electronic documents is determined based, at least in part, on the recorded said user interactions from said plurality of users. | 8. A method, comprising: retrieving a first set of electronic documents; recording user interactions with contextual shortcuts in a second set of electronic documents, wherein said contextual shortcuts comprise associated keyword anchors and user-selectable links relating to said keyword anchors, said keyword anchors being associated with a context vector representing at least a portion of content of a given electronic document of said second set of electronic documents; updating an index of said second set of electronic documents based, at least in part, on said user interactions from said plurality of users, said updating comprising adding at least one previously non-indexed electronic document to said index at least partially in response to receiving one or more user interactions with said one or more contextual shortcuts contained within said at least one previously non-indexed electronic document, wherein said index is searchable by said plurality of users; determining a score for said given electronic document based, at least in part, on user selections of said user-selectable links of said contextual shortcuts within said given electronic document; and updating said index based, at least in part, on said first set of electronic documents and said score; and updating a ranking of individual ones of the first set of electronic documents is determined based, at least in part, on the recorded said user interactions from said plurality of users. 9. The method of claim 8 , wherein said first and second set of electronic documents share at least one common electronic document. | 0.787336 |
11. A system for assigning text to a record from an image of the record, comprising: one or more processors; and a memory, the memory storing instructions that are executable by the one or more processors and configure the system to: obtain a scanned image of a record; determine at an optical character recognition system that at least some words in the scanned image are unidentified; evaluate the record image in order to locate each of multiple word images corresponding to the unidentified words; for each located word image, identify multiple word features of that word image; assign each of the multiple word images that have similar word features to one of a plurality of word clusters; select a representative word image in each of the word clusters as a centroid; receive, from an analyst, the centroid in each of the word clusters, and corresponding data representing text for the centroid; and assign the representing text for the centroid to all other word images in the same word cluster as the centroid. | 11. A system for assigning text to a record from an image of the record, comprising: one or more processors; and a memory, the memory storing instructions that are executable by the one or more processors and configure the system to: obtain a scanned image of a record; determine at an optical character recognition system that at least some words in the scanned image are unidentified; evaluate the record image in order to locate each of multiple word images corresponding to the unidentified words; for each located word image, identify multiple word features of that word image; assign each of the multiple word images that have similar word features to one of a plurality of word clusters; select a representative word image in each of the word clusters as a centroid; receive, from an analyst, the centroid in each of the word clusters, and corresponding data representing text for the centroid; and assign the representing text for the centroid to all other word images in the same word cluster as the centroid. 12. The system of claim 11 , wherein the stored instructions further configure the system to: receive, from the analyst, at least one sampling of word images in at least one word cluster; determine, based on judgment of the analyst, whether the sampled word images are the same word as the centroid for the word cluster and whether the sampled words have been correctly included in the word cluster; determine that a threshold number of the sampled word images have not been correctly included in the word cluster; and in response to determining that a threshold number of words have not been correctly included, mark the cluster as suspicious. | 0.5 |
6. A storage medium including machine readable indicia, said storage medium being selectively coupled to a reading device, said reading device being selectively coupled to processing circuitry within a computer system, said reading device being selectively operable to read said machine readable indicia and provide program signals representative thereof, said program signals being effective to cause said computer system to selectively provide a listing of hyperlinks assembled from a designated network page, said program signals being selectively operable to effect an accomplishment of the steps of: presenting a selection screen to the user whereby the user may make a selection to provide a listing of hyperlinks included on at least one designated network page; enabling a user to select a level value representative of a level of linked pages to be perused in providing said listing of hyperlinks; and displaying said listing of hyperlinks to the user. | 6. A storage medium including machine readable indicia, said storage medium being selectively coupled to a reading device, said reading device being selectively coupled to processing circuitry within a computer system, said reading device being selectively operable to read said machine readable indicia and provide program signals representative thereof, said program signals being effective to cause said computer system to selectively provide a listing of hyperlinks assembled from a designated network page, said program signals being selectively operable to effect an accomplishment of the steps of: presenting a selection screen to the user whereby the user may make a selection to provide a listing of hyperlinks included on at least one designated network page; enabling a user to select a level value representative of a level of linked pages to be perused in providing said listing of hyperlinks; and displaying said listing of hyperlinks to the user. 7. The storage medium as set forth in claim 6 wherein said listing of hyperlinks comprises hyperlinks present on said designated network page listed separately from, and exclusive of, the context displayed on said designated network page. | 0.71345 |
1. A method, comprising: obtaining an electronic document conforming to one of a plurality of print formats; parsing the electronic document according to the one of the plurality of print formats to generate an intermediate data structure conforming to an intermediate format such that the electronic document is converted to the intermediate format, wherein the intermediate format is different from the plurality of print formats; applying one or more rules to obtain data for a plurality of regions of the electronic document from the intermediate data structure; and storing or providing the data for the plurality of regions of the electronic document that has been obtained from the intermediate data structure, thereby enabling a report to be generated using at least a portion of the data for the plurality of regions that has been stored or provided. | 1. A method, comprising: obtaining an electronic document conforming to one of a plurality of print formats; parsing the electronic document according to the one of the plurality of print formats to generate an intermediate data structure conforming to an intermediate format such that the electronic document is converted to the intermediate format, wherein the intermediate format is different from the plurality of print formats; applying one or more rules to obtain data for a plurality of regions of the electronic document from the intermediate data structure; and storing or providing the data for the plurality of regions of the electronic document that has been obtained from the intermediate data structure, thereby enabling a report to be generated using at least a portion of the data for the plurality of regions that has been stored or provided. 5. The method as recited in claim 1 , wherein at least one of the one or more rules includes a search pattern to be identified in the electronic document, and wherein the intermediate data structure includes the search pattern. | 0.741253 |
7. A computer-implemented method of analyzing a corpus of text to provide a recommendation to a user, the method comprising: as implemented by a computer system comprising one or more computing devices, the computer system configured with specific executable instructions, receiving an indication to provide a recommendation to a user device associated with a first user, wherein one or more ratings are associated with the first user, and wherein each rating is assigned to a literary work by the first user; retrieving a positive model associated with the first user, wherein the positive model is generated based on a first set of literary works that are assigned a rating above a first threshold value by the first user; retrieving at least one fingerprint stored in a literary works fingerprint database, wherein each fingerprint is associated with one literary work in a plurality of literary works; comparing the positive model with the retrieved fingerprints; identifying one or more second literary works in the plurality of literary works that are associated with a fingerprint that is within a second threshold value of the positive model; and transmitting an identity of the one or more second literary works to the user device. | 7. A computer-implemented method of analyzing a corpus of text to provide a recommendation to a user, the method comprising: as implemented by a computer system comprising one or more computing devices, the computer system configured with specific executable instructions, receiving an indication to provide a recommendation to a user device associated with a first user, wherein one or more ratings are associated with the first user, and wherein each rating is assigned to a literary work by the first user; retrieving a positive model associated with the first user, wherein the positive model is generated based on a first set of literary works that are assigned a rating above a first threshold value by the first user; retrieving at least one fingerprint stored in a literary works fingerprint database, wherein each fingerprint is associated with one literary work in a plurality of literary works; comparing the positive model with the retrieved fingerprints; identifying one or more second literary works in the plurality of literary works that are associated with a fingerprint that is within a second threshold value of the positive model; and transmitting an identity of the one or more second literary works to the user device. 12. The computer-implemented method of claim 7 , wherein comparing the positive model with the retrieved fingerprints comprises: selecting metrics of the positive model and metrics of the retrieved fingerprints using machine-learning techniques; and comparing the selected metrics of the positive model with the selected metrics of the retrieved fingerprints. | 0.686057 |
1. A method of using a graphical display for communication based on character recognition between a sender and a receiver, wherein said graphical display is configured from a set of characters associated with a predetermined language and said graphical display enables expeditious discovery of said sender-selected character by said receiver, said graphical display comprising: a plurality of distinct quadrants, wherein said plurality of distinct quadrants each having an array of coordinate locations, wherein each said coordinate location possesses a location value based on the effort required to reach each said coordinate location when said receiver utilizes a top-left based scanning routine, and each said coordinate location is populated with a unique character selected from said set of characters, wherein each said unique character possesses a character value substantially derived from a set of character guidelines, wherein said guidelines provides a character ranking system primarily directed to enable early character detection and selection, said character ranking system includes assigning a higher rank to said unique characters having a greater use frequency within said predetermined language, and said coordinate locations with a higher ranked said location value are substantially populated with a corresponding said unique characters having higher ranked said character values, wherein said higher ranked said coordinate locations are substantially populated with the higher ranked said unique characters, said method comprising the steps of: (a) having a communication that can be conveyed using a written language that a sender or receiver wishes to communicate, wherein the communication is comprised of words constructed from a plurality of characters; said communication commences with the identification of a first letter corresponding to a first word; (b) positioning the graphical display such that the display is visible to both the sender and the receiver; (c) initiating a sequential scan of quadrants located on the graphical display, using a top-left based scanning routine performed by the receiver; (d) observing the sender for a selection motion, thereby communicating to the receiver the particular quadrant containing the first letter of the first word of the communication of step(a); (e) initiating sequential scan of characters contained within the selected quadrant in step(d), using a top-left based scanning routine performed by the receiver; (f) observing the sender for a selection motion, thereby communicating to the receiver the first character of the first word of step(a); and noting the selected first character; (g) repeating steps (c) through (f) wherein each subsequent cycle provides additional characters, thereby enabling the completion of words that shape the foundation of the communication of step(a). | 1. A method of using a graphical display for communication based on character recognition between a sender and a receiver, wherein said graphical display is configured from a set of characters associated with a predetermined language and said graphical display enables expeditious discovery of said sender-selected character by said receiver, said graphical display comprising: a plurality of distinct quadrants, wherein said plurality of distinct quadrants each having an array of coordinate locations, wherein each said coordinate location possesses a location value based on the effort required to reach each said coordinate location when said receiver utilizes a top-left based scanning routine, and each said coordinate location is populated with a unique character selected from said set of characters, wherein each said unique character possesses a character value substantially derived from a set of character guidelines, wherein said guidelines provides a character ranking system primarily directed to enable early character detection and selection, said character ranking system includes assigning a higher rank to said unique characters having a greater use frequency within said predetermined language, and said coordinate locations with a higher ranked said location value are substantially populated with a corresponding said unique characters having higher ranked said character values, wherein said higher ranked said coordinate locations are substantially populated with the higher ranked said unique characters, said method comprising the steps of: (a) having a communication that can be conveyed using a written language that a sender or receiver wishes to communicate, wherein the communication is comprised of words constructed from a plurality of characters; said communication commences with the identification of a first letter corresponding to a first word; (b) positioning the graphical display such that the display is visible to both the sender and the receiver; (c) initiating a sequential scan of quadrants located on the graphical display, using a top-left based scanning routine performed by the receiver; (d) observing the sender for a selection motion, thereby communicating to the receiver the particular quadrant containing the first letter of the first word of the communication of step(a); (e) initiating sequential scan of characters contained within the selected quadrant in step(d), using a top-left based scanning routine performed by the receiver; (f) observing the sender for a selection motion, thereby communicating to the receiver the first character of the first word of step(a); and noting the selected first character; (g) repeating steps (c) through (f) wherein each subsequent cycle provides additional characters, thereby enabling the completion of words that shape the foundation of the communication of step(a). 14. The method of claim 1 , wherein each distinct quadrant further includes a quadrant background and a quadrant perimeter; and at least one said distinct quadrant further includes a border disposed about at least a portion of its said perimeter, wherein said border possesses a contrasting appearance for substantially differentiating each said distinct quadrant from each other, whereby a clear, visible distinction among each said quadrant helps remove ambiguity as to which quadrant is being referred to during use. | 0.5 |
9. A method of claim 1 , wherein the estimating word alignment models step includes the substeps of: estimating a lexical model from the word alignments; generating lexical constraints from the lexical model; constraining the learning of word alignments from an unlabelled parallel corpus; processing the constrained word alignments to generate one or more processed word alignments; generating one or more word alignment models as a function of the processed word alignments, the alignment features, and the estimated word alignments estimated from said alignment patterns; and generating one or more final word alignments. | 9. A method of claim 1 , wherein the estimating word alignment models step includes the substeps of: estimating a lexical model from the word alignments; generating lexical constraints from the lexical model; constraining the learning of word alignments from an unlabelled parallel corpus; processing the constrained word alignments to generate one or more processed word alignments; generating one or more word alignment models as a function of the processed word alignments, the alignment features, and the estimated word alignments estimated from said alignment patterns; and generating one or more final word alignments. 10. The method of claim 9 further including the following step: using said word alignment models to generate word alignments from an unlabelled parallel corpus. | 0.769986 |
1. A method of assessing fraud in a document using a computer system, comprising: providing a document to the computer system, wherein the document comprises at least one information field; and for at least one of the information fields of the document, comparing handwriting in the information field to at least one handwriting profile representation from at least two information fields of at least one other document, wherein at least one handwriting profile representation has been stored in a computer system; assessing fraud in the document using at least one comparison, wherein evidence of fraud comprises a failure of at least a portion of the handwriting in at least one of the information fields of the document to approximately match at least one handwriting profile representation. | 1. A method of assessing fraud in a document using a computer system, comprising: providing a document to the computer system, wherein the document comprises at least one information field; and for at least one of the information fields of the document, comparing handwriting in the information field to at least one handwriting profile representation from at least two information fields of at least one other document, wherein at least one handwriting profile representation has been stored in a computer system; assessing fraud in the document using at least one comparison, wherein evidence of fraud comprises a failure of at least a portion of the handwriting in at least one of the information fields of the document to approximately match at least one handwriting profile representation. 28. The method of claim 1 , wherein at least one handwriting profile representation comprises at least one handwriting variant of an example of at least one type of handwritten information, and wherein at least one type of handwritten information comprises a character type. | 0.80441 |
1. A keyword ranking determining system including a computer and a data storage device coupled to the computer, the system comprising using the computer to perform: grouping data of a weblog according to a theme in a predefined set of themes, the theme being one of user groups included in the weblog, and the user groups being groups of users who selected a document using a keyword, wherein the document is a website, wherein the keyword is one entered by a user leading to selection of the document and included in the data of the weblog; storing the data of the weblog, the data of the weblog including information on the document, the keyword used in selecting the document, the user that entered the keyword used in selecting the document, and a selection number indicating a number of times the document is selected using the keyword; calculating a document concentration of the document corresponding to the theme using a relationship between a probability that the document corresponds to the theme and a probability that the document corresponds to all themes in the predefined set of themes, and to apply a weight corresponding to the document concentration to the data of the weblog to adjust the selection number with respect to the document; generating at least one data set by grouping the data of the weblog applied with the weight according to a search intention, wherein determining the search intention involves grouping keywords used to select a same document as the same search intention, and/or by grouping keywords among which similarity is greater than or equal to a value as the same search intention, and determining rankings of the at least one data set according to the theme, thereby ranking the keywords in the at least one data set according to an associated user group, and further calculating a share of a data set included in the theme using the selection number adjusted according to the document and to determine rankings of the at least one data set with respect to the theme according to the calculated share; and determining a main keyword representing each of the at least one data set from each of the at least one data set as generated and ranked; wherein the system further provides a set of ranked keywords for each of said user groups. | 1. A keyword ranking determining system including a computer and a data storage device coupled to the computer, the system comprising using the computer to perform: grouping data of a weblog according to a theme in a predefined set of themes, the theme being one of user groups included in the weblog, and the user groups being groups of users who selected a document using a keyword, wherein the document is a website, wherein the keyword is one entered by a user leading to selection of the document and included in the data of the weblog; storing the data of the weblog, the data of the weblog including information on the document, the keyword used in selecting the document, the user that entered the keyword used in selecting the document, and a selection number indicating a number of times the document is selected using the keyword; calculating a document concentration of the document corresponding to the theme using a relationship between a probability that the document corresponds to the theme and a probability that the document corresponds to all themes in the predefined set of themes, and to apply a weight corresponding to the document concentration to the data of the weblog to adjust the selection number with respect to the document; generating at least one data set by grouping the data of the weblog applied with the weight according to a search intention, wherein determining the search intention involves grouping keywords used to select a same document as the same search intention, and/or by grouping keywords among which similarity is greater than or equal to a value as the same search intention, and determining rankings of the at least one data set according to the theme, thereby ranking the keywords in the at least one data set according to an associated user group, and further calculating a share of a data set included in the theme using the selection number adjusted according to the document and to determine rankings of the at least one data set with respect to the theme according to the calculated share; and determining a main keyword representing each of the at least one data set from each of the at least one data set as generated and ranked; wherein the system further provides a set of ranked keywords for each of said user groups. 4. The keyword ranking determining system of claim 1 , further comprising determining the main keyword among a plurality of keywords, using at least one weight selected from a weight based on a morpheme-based redundant number calculated by analyzing morphemes of the keywords included in the at least one data set, a weight according to a selection number of the document caused by the corresponding keyword, and a weight according to a length of the keyword. | 0.520597 |
1. A computer-implemented method comprising: obtaining information regarding selections of search results provided in response to a plurality of search queries, the obtained information for one or more of the selected search results comprising one or more presentation bias features of a presentation of the search result and one or more relevancy features of the search result, wherein at least one of the presentation bias features is a rank of the search result in the search results; training a model using the obtained information, wherein the model is trained to predict a click through rate based on input comprising the one or more presentation bias features and the one or more relevancy features; and providing the model for use with a search engine, wherein the search engine is configured to provide presentation bias and relevancy features of given search results as input to the model and to use predictive outputs of the model to reduce presentation bias in a presentation of the given search results by determining a quality score for each of the given search results and factoring out independent effects of presentation bias from the quality scores using the predictive outputs of the model, wherein the predictive outputs used to reduce the presentation bias in the presentation of the given search results include a predicted click through rate predicted based on the presentation bias and relevancy features of the given search results and the model. | 1. A computer-implemented method comprising: obtaining information regarding selections of search results provided in response to a plurality of search queries, the obtained information for one or more of the selected search results comprising one or more presentation bias features of a presentation of the search result and one or more relevancy features of the search result, wherein at least one of the presentation bias features is a rank of the search result in the search results; training a model using the obtained information, wherein the model is trained to predict a click through rate based on input comprising the one or more presentation bias features and the one or more relevancy features; and providing the model for use with a search engine, wherein the search engine is configured to provide presentation bias and relevancy features of given search results as input to the model and to use predictive outputs of the model to reduce presentation bias in a presentation of the given search results by determining a quality score for each of the given search results and factoring out independent effects of presentation bias from the quality scores using the predictive outputs of the model, wherein the predictive outputs used to reduce the presentation bias in the presentation of the given search results include a predicted click through rate predicted based on the presentation bias and relevancy features of the given search results and the model. 4. The method of claim 1 wherein the one or more relevancy features include an information retrieval score of the search result, an information retrieval score of another search result returned along with the search result, a language of the query, or a count of words in the query. | 0.577457 |
43. A method of culturing a fungus of Chrysosporium lucknowense Garg 27K having accession number VKM F-3500D in a medium containing inorganic salts, carbon sources, and organic nitrogen sources, at a pH between about 5 and 8. | 43. A method of culturing a fungus of Chrysosporium lucknowense Garg 27K having accession number VKM F-3500D in a medium containing inorganic salts, carbon sources, and organic nitrogen sources, at a pH between about 5 and 8. 45. A method of culturing a fungus of the genus Chrysosporium according to claim 43 , wherein the pH is between about 6.9 and 7.1. | 0.820359 |
1. A method that uses a processor to determine a document rank, comprising: calculating, using the processor, a document rank score of a second document based on a first term relationship score of a first document and a first contribution score, the first contribution score being determined based on a common keyword between the first document and the second document; changing the first term relationship score to a second term relationship score; and updating the document rank score of the second document based on the second term relationship score, wherein the first document is linked by a link to the second document, wherein the first term relationship score is determined based on content of the first document and the link, and wherein updating the document rank score comprises determining whether each of a plurality of contribution scores is greater than a predetermined threshold value, whereby if one of the contribution scores is less than or equal to the predetermined threshold value, that contribution score is set to a zero value. | 1. A method that uses a processor to determine a document rank, comprising: calculating, using the processor, a document rank score of a second document based on a first term relationship score of a first document and a first contribution score, the first contribution score being determined based on a common keyword between the first document and the second document; changing the first term relationship score to a second term relationship score; and updating the document rank score of the second document based on the second term relationship score, wherein the first document is linked by a link to the second document, wherein the first term relationship score is determined based on content of the first document and the link, and wherein updating the document rank score comprises determining whether each of a plurality of contribution scores is greater than a predetermined threshold value, whereby if one of the contribution scores is less than or equal to the predetermined threshold value, that contribution score is set to a zero value. 5. The method of claim 1 , wherein: the second term relationship score is a term relationship score with respect to the common keyword of the content of the first document. | 0.82327 |
17. A method for accessing an enterprise data system via a voice communications device, comprising: enabling a communications connection to a voice access system; authenticating a login through the communications connection using a user identifier, wherein the authenticating comprises: querying a database with the user identifier, and in response to the querying, verifying the user identifier and receiving from the database an enterprise data system log-in data comprising a password for the enterprise data system; automatically logging into the enterprise data system using the enterprise data system log-in data; enabling access to a domain of the enterprise system after the logging into the enterprise data system, each of a plurality of domains corresponding to a respective object or type of data; determining a navigation context; receiving a navigation command; updating the navigation context in response to the navigation command; providing a system prompt based on the navigation context; enabling a request that a query be performed using a spoken language query corresponding to the navigation context; converting the spoken language query into a data query and executing the data query to retrieve data that corresponds to the data query in the accessed domain; providing feedback data in a verbal format via the communications connection, wherein the feedback data corresponds to data retrieved from the accessed domain and is based, at least in part, on the navigation context, and the providing the feedback data comprises: generating audio data by performing a text-to-speech conversion on retrieved data; and generating a verbalized system response by interspersing the audio data with waveform data of prompts. | 17. A method for accessing an enterprise data system via a voice communications device, comprising: enabling a communications connection to a voice access system; authenticating a login through the communications connection using a user identifier, wherein the authenticating comprises: querying a database with the user identifier, and in response to the querying, verifying the user identifier and receiving from the database an enterprise data system log-in data comprising a password for the enterprise data system; automatically logging into the enterprise data system using the enterprise data system log-in data; enabling access to a domain of the enterprise system after the logging into the enterprise data system, each of a plurality of domains corresponding to a respective object or type of data; determining a navigation context; receiving a navigation command; updating the navigation context in response to the navigation command; providing a system prompt based on the navigation context; enabling a request that a query be performed using a spoken language query corresponding to the navigation context; converting the spoken language query into a data query and executing the data query to retrieve data that corresponds to the data query in the accessed domain; providing feedback data in a verbal format via the communications connection, wherein the feedback data corresponds to data retrieved from the accessed domain and is based, at least in part, on the navigation context, and the providing the feedback data comprises: generating audio data by performing a text-to-speech conversion on retrieved data; and generating a verbalized system response by interspersing the audio data with waveform data of prompts. 24. The method of claim 17 , further comprising: enabling navigation in the accessed domain using spoken navigation commands. | 0.66513 |
14. A computer implemented method for password pre-verification comprising: (a) executing a translation module on a processor that causes the processor to translate user input, in the form of a character string that can represent a password, to obtain a symbolic representation of the user input; (b) executing an output module on the processor that causes the processor to receive the symbolic representation from the translation module and, based on the user input, provide output to the user in the form of visual, audio or haptic cues, wherein the visual, audio or haptic cues alert a user as to whether the input character string is correctly or incorrectly entered based upon a variance or similarity of the cue as compared to a previous cue provided to the user during a previous attempt to enter the input character, without providing an objective indicator, that allows an unauthorized user to discover the password, as to whether or not the password has been entered correctly if the user is unfamiliar with the variance, and wherein the output does not change until after a predetermined number of characters has been input and thereafter changes in a distinguishable way with the entry of each successive character that is input. | 14. A computer implemented method for password pre-verification comprising: (a) executing a translation module on a processor that causes the processor to translate user input, in the form of a character string that can represent a password, to obtain a symbolic representation of the user input; (b) executing an output module on the processor that causes the processor to receive the symbolic representation from the translation module and, based on the user input, provide output to the user in the form of visual, audio or haptic cues, wherein the visual, audio or haptic cues alert a user as to whether the input character string is correctly or incorrectly entered based upon a variance or similarity of the cue as compared to a previous cue provided to the user during a previous attempt to enter the input character, without providing an objective indicator, that allows an unauthorized user to discover the password, as to whether or not the password has been entered correctly if the user is unfamiliar with the variance, and wherein the output does not change until after a predetermined number of characters has been input and thereafter changes in a distinguishable way with the entry of each successive character that is input. 16. The method of claim 14 , further comprising executing an input module to receive user input from a physical or on-screen keyboard. | 0.560842 |
12. Apparatus according to claim 8 wherein the scanned image comprises a scanned-in sequence of elements and the typed-in image comprises a matching typed-in sequence of elements. | 12. Apparatus according to claim 8 wherein the scanned image comprises a scanned-in sequence of elements and the typed-in image comprises a matching typed-in sequence of elements. 13. Apparatus according to claim 12 wherein the tracker is operative to generate information regarding the user's current location in an individual one of said sequences, and wherein the synchronizer is operative: to receive said information, to identify a matching location, within she other of said sequences, which matches said current location, and to display the matching location. | 0.7142 |
1. A method of processing a query, comprising: providing a pre-defined logical schema to a user of a database system, wherein the pre-defined logical schema is mapped to at least two relational database entities of different databases storing data therein; receiving a logical query for data stored in the databases from the user, wherein the logical query is written in an object-oriented query language utilizing the pre-defined logical schema, and comprises two or more predicates and an operator specifying an action to take with one or more of the predicates; in response to receiving the logical query, interpreting the logical query using the pre-defined logical schema to determine which of the relational database entities is a subject of the logical query, and which of the relational database entities is associated with each predicate; requesting the database of each determined relational database entity of the logical query to: translate each of the associated predicates of that database in the logical query into a query language specific to that database, wherein at least two different databases translate an associated predicate; and apply an authorization rule with each of the associated predicates of that database in the logical query, wherein the authorization rule identifies unauthorized data: receiving a translated predicate query for each determined predicate of the logical query from its associated database of the relational database entity, wherein each translated predicate query is written in a relational query language specific to the associated database and is a translation of one of the object-oriented predicates in the logical query; combining each translated predicate query received from the databases of the determined relational database entities into a master query using the operator; executing the master query against the databases of the relational database entities that are subjects of the logical query; in response to executing the master query, receiving a query result set from the databases of the relational database entities that are subjects of the logical query; and providing the query result set to the user, wherein the query result set lacks the unauthorized data. | 1. A method of processing a query, comprising: providing a pre-defined logical schema to a user of a database system, wherein the pre-defined logical schema is mapped to at least two relational database entities of different databases storing data therein; receiving a logical query for data stored in the databases from the user, wherein the logical query is written in an object-oriented query language utilizing the pre-defined logical schema, and comprises two or more predicates and an operator specifying an action to take with one or more of the predicates; in response to receiving the logical query, interpreting the logical query using the pre-defined logical schema to determine which of the relational database entities is a subject of the logical query, and which of the relational database entities is associated with each predicate; requesting the database of each determined relational database entity of the logical query to: translate each of the associated predicates of that database in the logical query into a query language specific to that database, wherein at least two different databases translate an associated predicate; and apply an authorization rule with each of the associated predicates of that database in the logical query, wherein the authorization rule identifies unauthorized data: receiving a translated predicate query for each determined predicate of the logical query from its associated database of the relational database entity, wherein each translated predicate query is written in a relational query language specific to the associated database and is a translation of one of the object-oriented predicates in the logical query; combining each translated predicate query received from the databases of the determined relational database entities into a master query using the operator; executing the master query against the databases of the relational database entities that are subjects of the logical query; in response to executing the master query, receiving a query result set from the databases of the relational database entities that are subjects of the logical query; and providing the query result set to the user, wherein the query result set lacks the unauthorized data. 6. The method according to claim 1 , further comprising: saving the query result set in the database system. | 0.935579 |
1. An electronic apparatus comprising: a display device; an input unit; a storage which includes: dictionary information that causes an entry word in a first language to correspond to explanatory information in a second language which is a language different from the first language, reading-kanji correspondence information that causes a kanji character in the second language to correspond to a reading in the second language, and kanji correspondence information that causes a kanji character in the first language to correspond to a kanji character in the second language; a processor which accepts the input of a reading in the second language via the input unit, reads a kanji character in the second language corresponding to the input reading in the second language from the reading-kanji correspondence information stored in the storage, reads a kanji character in the first language corresponding to the read kanji character in the second language from the kanji correspondence information stored in the storage and performs display control of the read kanji character on the display device, and reads explanatory information that uses a character string including the kanji character in the first language subjected to display control as an entry word from dictionary information stored in the storage and performs display control of the explanatory information on the display device; wherein the storage further includes multiple kanji correspondence information that causes a plurality of kanji kanji characters in the first language to correspond to a plurality of kanji characters in the second language, and the processor reads a kanji character in the second language corresponding to the reading in the second language input via the input unit from the reading-kanji correspondence information stored in the storage and determines whether the kanji character in the second language is in the plurality of kanji characters included in the multiple kanji correspondence information stored in the storage, reads the plurality of kanji characters in the first language corresponding to the plurality of kanji characters in the second language from the multiple kanji correspondence information and performs display control of the read kanji characters on the display device, if it has been determined that the kanji character in the second language is in the plurality of kanji characters included in the multiple kanji correspondence information, and reads a kanji character in the first language corresponding to the kanji character in the second language from the kanji correspondence information stored in the storage and performs display control of the read kanji character on the display device, if it has been determined that the kanji character in the second language is not in the plurality of kanji characters included in the multiple kanji correspondence information. | 1. An electronic apparatus comprising: a display device; an input unit; a storage which includes: dictionary information that causes an entry word in a first language to correspond to explanatory information in a second language which is a language different from the first language, reading-kanji correspondence information that causes a kanji character in the second language to correspond to a reading in the second language, and kanji correspondence information that causes a kanji character in the first language to correspond to a kanji character in the second language; a processor which accepts the input of a reading in the second language via the input unit, reads a kanji character in the second language corresponding to the input reading in the second language from the reading-kanji correspondence information stored in the storage, reads a kanji character in the first language corresponding to the read kanji character in the second language from the kanji correspondence information stored in the storage and performs display control of the read kanji character on the display device, and reads explanatory information that uses a character string including the kanji character in the first language subjected to display control as an entry word from dictionary information stored in the storage and performs display control of the explanatory information on the display device; wherein the storage further includes multiple kanji correspondence information that causes a plurality of kanji kanji characters in the first language to correspond to a plurality of kanji characters in the second language, and the processor reads a kanji character in the second language corresponding to the reading in the second language input via the input unit from the reading-kanji correspondence information stored in the storage and determines whether the kanji character in the second language is in the plurality of kanji characters included in the multiple kanji correspondence information stored in the storage, reads the plurality of kanji characters in the first language corresponding to the plurality of kanji characters in the second language from the multiple kanji correspondence information and performs display control of the read kanji characters on the display device, if it has been determined that the kanji character in the second language is in the plurality of kanji characters included in the multiple kanji correspondence information, and reads a kanji character in the first language corresponding to the kanji character in the second language from the kanji correspondence information stored in the storage and performs display control of the read kanji character on the display device, if it has been determined that the kanji character in the second language is not in the plurality of kanji characters included in the multiple kanji correspondence information. 2. The electronic apparatus according to claim 1 , wherein each of the first language and the second language is any one of Japanese, Korean, and Chinese. | 0.916756 |
3. A computer-readable medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform operations comprising: receiving a request for an advertisement to embed in a Web page identifying one or more search results, the search results responsive to a user-submitted search query that includes a name of an individual; identifying one or more name-based profiles associated with the name, the name-based profiles including characteristics of persons derived from the name; selecting one or more advertisements based on the characteristics of persons included in the one or more name-based profiles. | 3. A computer-readable medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform operations comprising: receiving a request for an advertisement to embed in a Web page identifying one or more search results, the search results responsive to a user-submitted search query that includes a name of an individual; identifying one or more name-based profiles associated with the name, the name-based profiles including characteristics of persons derived from the name; selecting one or more advertisements based on the characteristics of persons included in the one or more name-based profiles. 14. The computer-readable medium of claim 3 , wherein the operations further comprise: identifying, from one or more third-party Web sites, publicly-available information associated with specific individuals having the name; generating a person profile for the specific individual based, at least in part, on the publicly-available information; and identifying the one or more advertisements using the person profile and the one or more name-based profiles. | 0.55972 |
6. The communication apparatus of claim 1 , wherein the voice-to-text processor comprises a dictation apparatus to convert the spoken words and informative sounds into the text message. | 6. The communication apparatus of claim 1 , wherein the voice-to-text processor comprises a dictation apparatus to convert the spoken words and informative sounds into the text message. 7. The communication apparatus of claim 6 , wherein the voice-to-text processor further comprises a language translator to translate the text message from a first language to a second language. | 0.91901 |
1. A method comprising: receiving a request from a user of a client computing device for a video file, wherein the video file comprises: audibly spoken words in a source language; subtitles in the source language that are synchronized with the audibly spoken words, the subtitles segmented into a plurality of source language segments, each source language segment comprising a respective plurality of source language words; and subtitles in a target language that are segmented into a plurality of target language segments, each target language segment comprising a respective plurality of target language words, each source language segment in the source language segments mapped to a respective target language segment in the target language segments, and each source language word in each source language segment mapped to a respective target language word in the respective target language segment; and causing the video file to be streamed to the client computing device responsive to receipt of the request, wherein the video file, when viewed at the client computing device, comprises the subtitles in the source language and the subtitles in the target language that are displayed in synchronization with the audibly spoken words, wherein the video file, when viewed at the client computing device, includes words in the subtitles in the source language and words in the subtitles in the target language that are highlighted as corresponding audibly spoken words are output. | 1. A method comprising: receiving a request from a user of a client computing device for a video file, wherein the video file comprises: audibly spoken words in a source language; subtitles in the source language that are synchronized with the audibly spoken words, the subtitles segmented into a plurality of source language segments, each source language segment comprising a respective plurality of source language words; and subtitles in a target language that are segmented into a plurality of target language segments, each target language segment comprising a respective plurality of target language words, each source language segment in the source language segments mapped to a respective target language segment in the target language segments, and each source language word in each source language segment mapped to a respective target language word in the respective target language segment; and causing the video file to be streamed to the client computing device responsive to receipt of the request, wherein the video file, when viewed at the client computing device, comprises the subtitles in the source language and the subtitles in the target language that are displayed in synchronization with the audibly spoken words, wherein the video file, when viewed at the client computing device, includes words in the subtitles in the source language and words in the subtitles in the target language that are highlighted as corresponding audibly spoken words are output. 11. The method of claim 1 , wherein the video file is caused to be streamed to a web browser executing on the client computing device. | 0.684235 |
1. A method comprising: creating a database by a first method comprising: determining a first vocabulary for numbers to be represented, said first vocabulary being from a first language and comprising vocabulary words; assigning a vocabulary index for each of said first vocabulary words; creating a two dimensional table comprising levels and said first vocabulary words assigned to said levels, each of said levels being one of a sequence of said first vocabulary words representing said numbers; grouping said two dimensional table by a radix to create groups of said levels, each of said groups of said levels being arranged by sublevels representing incremental numbers within each of said groups; and representing said two dimensional table using a set of patterns and said vocabulary indexes, each of said sublevels being defined with one of said patterns and at least one vocabulary index representing one of said vocabulary words applied by said one of said patterns; performing a second method on a computer processor, said second method comprising: receiving a text string representing a number in said first language; separating said text string into words; determining at least one distribution of said words within said database; analyzing each of said at least one distribution of said words to find an intersection; and determining said number based on said intersection. | 1. A method comprising: creating a database by a first method comprising: determining a first vocabulary for numbers to be represented, said first vocabulary being from a first language and comprising vocabulary words; assigning a vocabulary index for each of said first vocabulary words; creating a two dimensional table comprising levels and said first vocabulary words assigned to said levels, each of said levels being one of a sequence of said first vocabulary words representing said numbers; grouping said two dimensional table by a radix to create groups of said levels, each of said groups of said levels being arranged by sublevels representing incremental numbers within each of said groups; and representing said two dimensional table using a set of patterns and said vocabulary indexes, each of said sublevels being defined with one of said patterns and at least one vocabulary index representing one of said vocabulary words applied by said one of said patterns; performing a second method on a computer processor, said second method comprising: receiving a text string representing a number in said first language; separating said text string into words; determining at least one distribution of said words within said database; analyzing each of said at least one distribution of said words to find an intersection; and determining said number based on said intersection. 7. The method of claim 1 , said text string being created by translating an audio stream into said text string. | 0.530956 |
9. A computer program products comprising a physical computer usable medium having computer readable code embodied therein for causing a physical computer to effect: obtaining a dataset comprising a plurality of records, the dataset being characterized by a vocabulary and each of the plurality of records being characterized by at least one term within the vocabulary; preprocessing the obtained dataset, wherein the preprocessing step comprises the steps of mapping each record onto a system ontology and cleaning the obtained dataset: identifying a plurality of nearest neighbors for each term within the vocabulary; imputing a degree of membership for each nearest neighbor of the plurality of nearest neighbors identified for each term within the vocabulary, thereby providing an imputed degree of membership; augmenting the obtained dataset with the imputed degree of membership, thereby increasing a similarity between each record by reducing a sparsity of the obtained dataset while fixing a dimension of the vocabulary, and thereby providing an augmented dataset; and generating a taxonomy of the augmented dataset, thereby satisfying a user's similarity-biased intuition while taking into account a plurality of factors, the plurality of factors comprising a similarity between each record and a dissimilarity between each record. | 9. A computer program products comprising a physical computer usable medium having computer readable code embodied therein for causing a physical computer to effect: obtaining a dataset comprising a plurality of records, the dataset being characterized by a vocabulary and each of the plurality of records being characterized by at least one term within the vocabulary; preprocessing the obtained dataset, wherein the preprocessing step comprises the steps of mapping each record onto a system ontology and cleaning the obtained dataset: identifying a plurality of nearest neighbors for each term within the vocabulary; imputing a degree of membership for each nearest neighbor of the plurality of nearest neighbors identified for each term within the vocabulary, thereby providing an imputed degree of membership; augmenting the obtained dataset with the imputed degree of membership, thereby increasing a similarity between each record by reducing a sparsity of the obtained dataset while fixing a dimension of the vocabulary, and thereby providing an augmented dataset; and generating a taxonomy of the augmented dataset, thereby satisfying a user's similarity-biased intuition while taking into account a plurality of factors, the plurality of factors comprising a similarity between each record and a dissimilarity between each record. 11. The computer program product of claim 9 , further comprising a computer usable medium having computer readable code embodied therein for causing a computer to effect: representing each record as a multi-component vector, wherein a value assigned to each vector component represents the presence of a term within the record, wherein augmenting the dataset comprises: adding a value representing the degree of membership for each nearest neighbor to the value assigned to each vector component. | 0.749634 |
14. The method of claim 13 wherein the scanner comprises a hand-held scanning optical scanning device. | 14. The method of claim 13 wherein the scanner comprises a hand-held scanning optical scanning device. 16. The method of claim 14 wherein encrypting the captured information includes encrypting the captured information using a public/private key pair. | 0.861201 |
1. A method of operating a user equipment (UE), comprising: determining to load a web page via a mobile browsing application on the UE; obtaining, from a web server associated with the web page, web page resource information that is required to load the web page on the UE; loading the web page on the UE using the web page resource information; determining, by the UE based on the web page resource information obtained from the web server, one or more hints for assisting one or more mobile browsing applications on at least one other UE to perform an initial load of the web page without interacting with the web server to acquire the web page resource information; and reporting the one or more hints to a hints server for selective distribution of the one or more hints to the at least one other UE. | 1. A method of operating a user equipment (UE), comprising: determining to load a web page via a mobile browsing application on the UE; obtaining, from a web server associated with the web page, web page resource information that is required to load the web page on the UE; loading the web page on the UE using the web page resource information; determining, by the UE based on the web page resource information obtained from the web server, one or more hints for assisting one or more mobile browsing applications on at least one other UE to perform an initial load of the web page without interacting with the web server to acquire the web page resource information; and reporting the one or more hints to a hints server for selective distribution of the one or more hints to the at least one other UE. 4. The method of claim 1 , further comprising: determining, in response to the determination to load the web page, that no cached web page resource information acquired via a previous loading of the web page is available on the UE, wherein the obtaining is performed in response to the unavailability determination. | 0.569277 |
10. The printing server according to claim 9 , wherein said transmit further comprises transmit a page which does not include said figure to a monochromatic printer, wherein the page which does not include said figure is part of the digital document. | 10. The printing server according to claim 9 , wherein said transmit further comprises transmit a page which does not include said figure to a monochromatic printer, wherein the page which does not include said figure is part of the digital document. 11. The printing server according to claim 10 , wherein said monochromatic printer is a grayscale printer. | 0.860123 |
1. A method, implemented in at least one processor, for constructing a database to deduce a disease, the method comprising: the at least one processor: analyzing U-Health information, which includes a disease, a symptom, and a treatment, and constructing a plurality of U-Health ontologies required for service provision; setting a meta-model defining cause-and-effect relationships between the constructed U-Health ontologies; and selecting a subset of the constructed U-Health ontologies comprising at least two specific U-Health ontologies from among the plurality of U-Health ontologies, setting the selected U-Health ontologies as nodes, generating a Bayesian network by applying the meta-model to the set nodes to thereby construct a customized database for inferring a disease of a user; whereby upon receiving personal information of the user, a disease of a user is inferred using a probabilistic analysis of said Bayesian network. | 1. A method, implemented in at least one processor, for constructing a database to deduce a disease, the method comprising: the at least one processor: analyzing U-Health information, which includes a disease, a symptom, and a treatment, and constructing a plurality of U-Health ontologies required for service provision; setting a meta-model defining cause-and-effect relationships between the constructed U-Health ontologies; and selecting a subset of the constructed U-Health ontologies comprising at least two specific U-Health ontologies from among the plurality of U-Health ontologies, setting the selected U-Health ontologies as nodes, generating a Bayesian network by applying the meta-model to the set nodes to thereby construct a customized database for inferring a disease of a user; whereby upon receiving personal information of the user, a disease of a user is inferred using a probabilistic analysis of said Bayesian network. 2. The method as claimed in claim 1 , wherein said analyzing comprises: setting common ontologies which comprises various kinds of ontologies required for disease deduction; and setting an intermediate ontology, which comprises at least one specific ontology relating to a specific disease, from among the common ontologies. | 0.649665 |
11. A computer program product for an email receiver to rate an email sender, the computer program product comprising: a non-transitory computer-usable storage medium for storing instructions that when executed on a computer system causes the computer to perform a method comprising: receiving an email from an email server, a person sending said email being an email sender, and a person receiving said email being an email receiver; retrieving, in advance of said receiver opening said email, information associated with said receiver which indicates whether said sender is a person whose emails are to be rated by said receiver; and in response to said sender being a person whose emails are to be rated: retrieving a local overall rating from storage; determining whether a cumulative email rating score for said sender exists and, in response to said cumulative email rating score existing, retrieving said cumulative email rating score; displaying said cumulative email rating score on any of: said email and a pop-up window associated with said opened email; displaying a plurality of selectable rating categories, wherein said selectable rating categories comprise any of: usefulness, educational, motivational, helpfulness, productivity, and spelling/grammar; and in response to said receiver electing to rate said email from said sender: said receiver rating said email by selecting one of said displayed rating categories; and in response to said email having been rated: adding said rating to a said cumulative email rating score; and updating a historical database with said cumulative email rating score. | 11. A computer program product for an email receiver to rate an email sender, the computer program product comprising: a non-transitory computer-usable storage medium for storing instructions that when executed on a computer system causes the computer to perform a method comprising: receiving an email from an email server, a person sending said email being an email sender, and a person receiving said email being an email receiver; retrieving, in advance of said receiver opening said email, information associated with said receiver which indicates whether said sender is a person whose emails are to be rated by said receiver; and in response to said sender being a person whose emails are to be rated: retrieving a local overall rating from storage; determining whether a cumulative email rating score for said sender exists and, in response to said cumulative email rating score existing, retrieving said cumulative email rating score; displaying said cumulative email rating score on any of: said email and a pop-up window associated with said opened email; displaying a plurality of selectable rating categories, wherein said selectable rating categories comprise any of: usefulness, educational, motivational, helpfulness, productivity, and spelling/grammar; and in response to said receiver electing to rate said email from said sender: said receiver rating said email by selecting one of said displayed rating categories; and in response to said email having been rated: adding said rating to a said cumulative email rating score; and updating a historical database with said cumulative email rating score. 14. The computer program product of claim 11 , further comprising generating at least one rule to sort emails received from said sender based on said cumulative email rating, score. | 0.532438 |
1. A handheld motion control system comprising: a motion detector within a handheld motion device, the motion detector having a three-axis acceleration sensor; a device locator within the handheld motion device, the device locator configured to: receive motion information obtained by the motion detector within the handheld motion device, wherein the motion information comprises a set of points traversed by the handheld motion device between a starting point base reference position and a stopping point; identify and select a device to be controlled from among a plurality of devices based on the motion information and device selection information, the device selection information correlating different motion information with different devices of the plurality of devices; and wherein the starting point base reference position is associated with an orientation of the handheld motion device relative to the device identified and selected to be controlled; a wireless communication interface configured to communicate device commands for the selected device, the device commands communicated from the handheld motion device to a processing apparatus of the selected device configured to obtain the information from the wireless communication interface to process the device commands; and wherein the motion detector is operable to allow a user to reset the starting point base reference position of the handheld motion device. | 1. A handheld motion control system comprising: a motion detector within a handheld motion device, the motion detector having a three-axis acceleration sensor; a device locator within the handheld motion device, the device locator configured to: receive motion information obtained by the motion detector within the handheld motion device, wherein the motion information comprises a set of points traversed by the handheld motion device between a starting point base reference position and a stopping point; identify and select a device to be controlled from among a plurality of devices based on the motion information and device selection information, the device selection information correlating different motion information with different devices of the plurality of devices; and wherein the starting point base reference position is associated with an orientation of the handheld motion device relative to the device identified and selected to be controlled; a wireless communication interface configured to communicate device commands for the selected device, the device commands communicated from the handheld motion device to a processing apparatus of the selected device configured to obtain the information from the wireless communication interface to process the device commands; and wherein the motion detector is operable to allow a user to reset the starting point base reference position of the handheld motion device. 9. The motion control system according to claim 1 , wherein the handheld device is operable to provide feedback information to notify a user that a particular motion of the handheld device is recognized as a particular gesture. | 0.507165 |
15. A computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform at least the following steps: receiving queries over information sources; for each of the queries, generating a polynomial by defining each query against the information sources as input parameters in polynomial form; providing irreducible polynomials over a finite field of a degree of orthogonality by factoring polynomials generated from the queries; adjusting the degree of orthogonality of the irreducible polynomials to provide adjusted irreducible polynomials, each of which is a signature and corresponds to a signature fragment, such that the signature fragments have overlapping portions; and causing at least in part storage of the signatures or the signature fragments in a signature domain over an information space. | 15. A computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform at least the following steps: receiving queries over information sources; for each of the queries, generating a polynomial by defining each query against the information sources as input parameters in polynomial form; providing irreducible polynomials over a finite field of a degree of orthogonality by factoring polynomials generated from the queries; adjusting the degree of orthogonality of the irreducible polynomials to provide adjusted irreducible polynomials, each of which is a signature and corresponds to a signature fragment, such that the signature fragments have overlapping portions; and causing at least in part storage of the signatures or the signature fragments in a signature domain over an information space. 20. A computer-readable storage medium of claim 15 , wherein the signature fragment constitutes a finest lossless component in an information representation format or structure that has one or more overlapping portions with one or more other signature fragments. | 0.513105 |
11. A computing system configured to act as an intermediary to facilitate performance by task performers of tasks submitted by task requesters, comprising: one or more processors; and a task fulfillment facilitator system configured to, when executed by at least one of the one or more processors, facilitate performance of multiple tasks by, for each of the tasks: receiving information from an executing program of a task requester about the task available to be performed, the information received via a defined programmatic application program interface and indicating one or more criteria for satisfactory performance of the task; providing information about the task to one or more human task performers who are qualified to satisfactorily perform the task based on the indicated criteria; and after receiving results based on performance of the available task by one or more of the human task performers to whom the information about the task was provided, supplying the received results via the defined programmatic interface to a program of the task requester. | 11. A computing system configured to act as an intermediary to facilitate performance by task performers of tasks submitted by task requesters, comprising: one or more processors; and a task fulfillment facilitator system configured to, when executed by at least one of the one or more processors, facilitate performance of multiple tasks by, for each of the tasks: receiving information from an executing program of a task requester about the task available to be performed, the information received via a defined programmatic application program interface and indicating one or more criteria for satisfactory performance of the task; providing information about the task to one or more human task performers who are qualified to satisfactorily perform the task based on the indicated criteria; and after receiving results based on performance of the available task by one or more of the human task performers to whom the information about the task was provided, supplying the received results via the defined programmatic interface to a program of the task requester. 17. The computing system of claim 11 further comprising the application programming interface, the application programming interface being provided by the task fulfillment facilitator system and being configured to allow executing programs of task requesters to submit tasks available to be performed and to allow the executing programs to receive information about results of performance of submitted tasks. | 0.653061 |
2. The method of claim 1 , wherein the computer processor is contained in a wearable device. | 2. The method of claim 1 , wherein the computer processor is contained in a wearable device. 3. The method of claim 2 , wherein the wearable device is worn by the second person having an autism spectrum disorder. | 0.954023 |
1. A computer-implemented method comprising: receiving a plurality of a particular user's interactions with a plurality of items in a social networking system, each item associated with a category; ranking the items based on the particular user's interactions with the items, the ranking based at least in part on an interaction type for each interaction with an item; for the particular user, ranking the categories based on the item rankings of the items within each category; for the particular user, generating by the social networking system a bookmark link for each item, each bookmark link providing a link to the corresponding item in the social networking system; and sending the bookmark links grouped by category to the particular user for display, the bookmark links ordered within each category based on the item rankings of the corresponding items within the category, and the categories ordered based on the ranking of the categories. | 1. A computer-implemented method comprising: receiving a plurality of a particular user's interactions with a plurality of items in a social networking system, each item associated with a category; ranking the items based on the particular user's interactions with the items, the ranking based at least in part on an interaction type for each interaction with an item; for the particular user, ranking the categories based on the item rankings of the items within each category; for the particular user, generating by the social networking system a bookmark link for each item, each bookmark link providing a link to the corresponding item in the social networking system; and sending the bookmark links grouped by category to the particular user for display, the bookmark links ordered within each category based on the item rankings of the corresponding items within the category, and the categories ordered based on the ranking of the categories. 8. The method of claim 1 , wherein a bookmark link is generated for the particular user in response to the viewing user requesting a page. | 0.576271 |
12. The method of a claim 7 , further comprising: identifying a third type of concept referred to by the secondary user intent for the first substring; identifying a third substring from the first substring corresponding to the third type of concept; and determining a tertiary user intent for the third substring, wherein performing the task flow is further based on the tertiary user intent for the third substring. | 12. The method of a claim 7 , further comprising: identifying a third type of concept referred to by the secondary user intent for the first substring; identifying a third substring from the first substring corresponding to the third type of concept; and determining a tertiary user intent for the third substring, wherein performing the task flow is further based on the tertiary user intent for the third substring. 13. The method of claim 12 , wherein the third type of concept comprises a place, a time, an event, or a person. | 0.930097 |
16. A non-transitory computer readable storage medium storing one or more programs for execution by a computer system, the one or more programs comprising instructions to: receive a search query; in response to receiving the search query: obtain, from a message repository, conversations relevant to the search query; create a list of conversations representing at least a subset of the obtained conversations, wherein each conversation in the list of conversations is represented as a single item, and at least one of the conversations in the list of conversations comprises two or more electronic messages from distinct senders; identify, for each conversation in the list of conversations, a portion of conversation content relevant to the search query; and produce, for concurrent display at a client, a search result including at least the list of conversations, and the identified portion of conversation content for each conversation in the list of conversations. | 16. A non-transitory computer readable storage medium storing one or more programs for execution by a computer system, the one or more programs comprising instructions to: receive a search query; in response to receiving the search query: obtain, from a message repository, conversations relevant to the search query; create a list of conversations representing at least a subset of the obtained conversations, wherein each conversation in the list of conversations is represented as a single item, and at least one of the conversations in the list of conversations comprises two or more electronic messages from distinct senders; identify, for each conversation in the list of conversations, a portion of conversation content relevant to the search query; and produce, for concurrent display at a client, a search result including at least the list of conversations, and the identified portion of conversation content for each conversation in the list of conversations. 22. The storage medium of claim 16 , wherein the one or more programs further comprise instructions to obtain, for one or more conversations in the list of conversations, one or more labels associated with the respective conversation; and wherein producing the search result for concurrent display at the client includes producing the obtained labels for concurrent display with the list of conversations and the identified portion of conversation content for each conversation in the list of conversations. | 0.556154 |
1. A method comprising: receiving item category data by a computing device, wherein the item category data comprises a plurality of nodes and each node is associated with an item category of a plurality of item categories; receiving a plurality of queries by the computing device; for each query of the plurality of queries, providing an indicator of one or more items that are responsive to the query of a plurality of items by the computing device, wherein the plurality of items comprise at least one of products or services; for each query of the plurality of queries, receiving a selection of an item indicated by the provided indicator of one or more items by the computing device; receiving item data that associates an item category of the plurality of item categories with each item of the plurality of items by the computing device; based on the selected item for each query of the plurality of queries and the item category associated with each item, generating training data by the computing device, wherein the training data comprises a mapping of queries to item categories; for each query and item category in the mapping, determining a count of the number of times that the item category is associated with the query in the mapping by the computing device; combining the training data and item category data by, for each determined count for each item category, associating the determined count with the node of the plurality of nodes associated with the item category by the computing device; receiving another query by the computing device; receiving a classifier by the computing device, wherein the classifier, when applied to a node of the plurality of nodes using the received query by the computing device, results in a generated probability that the received query is intended for the item category associated with the node; applying the classifier to the plurality of nodes using the received query by the computing device until a generated probability for a node is below a threshold probability resulting in a list of item categories and a generated probability for each item category; ranking the item categories in the list of item categories based on the generated probabilities by the computing device; and providing the item categories in a ranked order by the computing device through a network. | 1. A method comprising: receiving item category data by a computing device, wherein the item category data comprises a plurality of nodes and each node is associated with an item category of a plurality of item categories; receiving a plurality of queries by the computing device; for each query of the plurality of queries, providing an indicator of one or more items that are responsive to the query of a plurality of items by the computing device, wherein the plurality of items comprise at least one of products or services; for each query of the plurality of queries, receiving a selection of an item indicated by the provided indicator of one or more items by the computing device; receiving item data that associates an item category of the plurality of item categories with each item of the plurality of items by the computing device; based on the selected item for each query of the plurality of queries and the item category associated with each item, generating training data by the computing device, wherein the training data comprises a mapping of queries to item categories; for each query and item category in the mapping, determining a count of the number of times that the item category is associated with the query in the mapping by the computing device; combining the training data and item category data by, for each determined count for each item category, associating the determined count with the node of the plurality of nodes associated with the item category by the computing device; receiving another query by the computing device; receiving a classifier by the computing device, wherein the classifier, when applied to a node of the plurality of nodes using the received query by the computing device, results in a generated probability that the received query is intended for the item category associated with the node; applying the classifier to the plurality of nodes using the received query by the computing device until a generated probability for a node is below a threshold probability resulting in a list of item categories and a generated probability for each item category; ranking the item categories in the list of item categories based on the generated probabilities by the computing device; and providing the item categories in a ranked order by the computing device through a network. 3. The method of claim 1 , further comprising: determining a subset of the ranked item categories; determining one or more items responsive to the received query that are associated with the item categories of the subset of ranked item categories; and providing indicators of the determined one or more items. | 0.512558 |
19. A computer system as defined in claim 18 which further includes: means for generating a refresh signal in response to a periodic control signal to cause a refresh cycle of said speech memory. | 19. A computer system as defined in claim 18 which further includes: means for generating a refresh signal in response to a periodic control signal to cause a refresh cycle of said speech memory. 20. A computer system as defined in claim 19 which further includes: a refresh counter, having its output connected to the address inputs of said speech memory and incremented by said refresh signal, for selectively causing a refresh cycle for sequential locations of said speech memory. | 0.895151 |
17. A method of generating a personal ranking of information specific to a user, the method comprising: interfacing with online data, stored user data and user device data; analysing information derived or inferred, at least in part, from the online data, stored user data and user device data, wherein the analysis incorporates criteria derived or inferred from both the stored user data and the user device data; generating an analysis output selected from a group consisting of personalised attention profile outputs, personalized psychometric profile outputs or a combination thereof; storing information relating to data from at least one data source selected from a group consisting of online data, stored user data and analysis output data; and generating a personalised attention ranking of the analysed information, wherein the personalised attention ranking is accessible to the user through the user device. | 17. A method of generating a personal ranking of information specific to a user, the method comprising: interfacing with online data, stored user data and user device data; analysing information derived or inferred, at least in part, from the online data, stored user data and user device data, wherein the analysis incorporates criteria derived or inferred from both the stored user data and the user device data; generating an analysis output selected from a group consisting of personalised attention profile outputs, personalized psychometric profile outputs or a combination thereof; storing information relating to data from at least one data source selected from a group consisting of online data, stored user data and analysis output data; and generating a personalised attention ranking of the analysed information, wherein the personalised attention ranking is accessible to the user through the user device. 18. The method according to claim 17 , wherein the step of analysing the information comprises steps of: retrieving at least part of one or more structured, un-structured, or semi-structured information of the online data and the stored user data; conducting natural language processing of the information to identify and extract a concept; and mapping an extracted concept into at least part of an ontology. | 0.611217 |
4. The method of claim 1 , further comprising determining the at least one task associated with the task suggestion. | 4. The method of claim 1 , further comprising determining the at least one task associated with the task suggestion. 6. The method of claim 4 , wherein determining the at least one task comprises determining the at least one task based on user data associated with the user. | 0.95496 |
15. A perception and planning system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations of operating an autonomous vehicle, the operations including perceiving driving behaviors of one or more vehicles surrounding a first autonomous vehicle, transmitting one or more driving style elements representing the driving behaviors of the surrounding vehicles from the first autonomous vehicle to a remote server over a network, receiving from the remote server a driving style that was determined based on the driving style elements at the remote server, the driving style including information describing how the first autonomous vehicle should drive in view of the surrounding vehicles at a point in time, generating planning and control data based on the driving style, and controlling and driving the first autonomous vehicle based on the planning and control data. | 15. A perception and planning system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations of operating an autonomous vehicle, the operations including perceiving driving behaviors of one or more vehicles surrounding a first autonomous vehicle, transmitting one or more driving style elements representing the driving behaviors of the surrounding vehicles from the first autonomous vehicle to a remote server over a network, receiving from the remote server a driving style that was determined based on the driving style elements at the remote server, the driving style including information describing how the first autonomous vehicle should drive in view of the surrounding vehicles at a point in time, generating planning and control data based on the driving style, and controlling and driving the first autonomous vehicle based on the planning and control data. 18. The system of claim 15 , wherein the driving style elements associated with the surrounding vehicles comprise at least one of a driving speed of each surrounding vehicle, distances between the surrounding vehicles, a deceleration rate, and a distance of deceleration. | 0.610935 |
1. A computer-implemented method for automatically generating a specialized dictionary, comprising: extracting a plurality of potential phrases from a document corpus including a plurality of documents; assigning each of the plurality of potential phrases to a cluster from a plurality of clusters; identifying, for each of the plurality of clusters, a set of documents from the plurality of documents that include at least one potential phrase assigned to the cluster; determining, for each of the plurality of potential phrases, a score based on a number of documents that include the potential phrase from the set of documents identified for the cluster to which the potential phrase is assigned; selecting, from the plurality of potential phrases, potential phrases as dictionary phrases, each dictionary phrase selected based on the score determined for the dictionary phrase; extracting, for each dictionary phrase, a definition from the document corpus; and storing each dictionary phrase and the definition extracted for the dictionary phrase. | 1. A computer-implemented method for automatically generating a specialized dictionary, comprising: extracting a plurality of potential phrases from a document corpus including a plurality of documents; assigning each of the plurality of potential phrases to a cluster from a plurality of clusters; identifying, for each of the plurality of clusters, a set of documents from the plurality of documents that include at least one potential phrase assigned to the cluster; determining, for each of the plurality of potential phrases, a score based on a number of documents that include the potential phrase from the set of documents identified for the cluster to which the potential phrase is assigned; selecting, from the plurality of potential phrases, potential phrases as dictionary phrases, each dictionary phrase selected based on the score determined for the dictionary phrase; extracting, for each dictionary phrase, a definition from the document corpus; and storing each dictionary phrase and the definition extracted for the dictionary phrase. 10. The computer-implemented method of claim 1 , wherein selecting potential phrases as dictionary phrases comprises identifying positions of occurrences of the dictionary phrases within the plurality of documents. | 0.684555 |
26. A method for displaying content in an integrated display of a touchscreen display of at least a current page of a first document and at least one other page from said first document or from a second document, comprising: processing by a processor configured for controlling the displaying of content displayed by at least the current page of the first document and said at least one other page from said first document or from a second document on the touchscreen display; and displaying content in an integrated display of the current page of the first document and said at least one other page from said first document or from a second document according to proportional distance criteria, and wherein the current page and said at least one other page from said first document or from the second document comprise text, image, graphics, or a combination thereof; wherein the current page or said at least one other page from said first document or from the second document is displayed on the touchscreen display in response to a pan command; and wherein the pan command causes display according to inertia and velocity sensed by the device. | 26. A method for displaying content in an integrated display of a touchscreen display of at least a current page of a first document and at least one other page from said first document or from a second document, comprising: processing by a processor configured for controlling the displaying of content displayed by at least the current page of the first document and said at least one other page from said first document or from a second document on the touchscreen display; and displaying content in an integrated display of the current page of the first document and said at least one other page from said first document or from a second document according to proportional distance criteria, and wherein the current page and said at least one other page from said first document or from the second document comprise text, image, graphics, or a combination thereof; wherein the current page or said at least one other page from said first document or from the second document is displayed on the touchscreen display in response to a pan command; and wherein the pan command causes display according to inertia and velocity sensed by the device. 30. The method according to claim 26 , wherein the proportional distance criteria comprises an order of having been displayed previously on the touchscreen display. | 0.548585 |
16. A method comprising: receiving a model, the model including information relating to a computation, the information relating to the computation being included in a file that includes information for executing the model, the file including one or more parameter files; identifying information about the model, identifying the information about the model including interpreting the model to identify the information about the model, the identified information including: information identifying a number of input ports for the model, information identifying a number of output ports for the model, information indicating a complexity of the model, and at least one of: information indicating whether the model uses continuous time integration or discrete time integration, information indicating whether the model is self-contained, information identifying a number of charts in the model, information identifying a number of discrete states in the model, annotations added to the model, or review information about an author of the model; displaying the identified information to a user when the user requests the identified information; associating the identified information with the model to make the model available for model searching; and performing a search for the model using the identified information, the receiving, the identifying, the displaying, the associating, and the performing being performed by a computing device. | 16. A method comprising: receiving a model, the model including information relating to a computation, the information relating to the computation being included in a file that includes information for executing the model, the file including one or more parameter files; identifying information about the model, identifying the information about the model including interpreting the model to identify the information about the model, the identified information including: information identifying a number of input ports for the model, information identifying a number of output ports for the model, information indicating a complexity of the model, and at least one of: information indicating whether the model uses continuous time integration or discrete time integration, information indicating whether the model is self-contained, information identifying a number of charts in the model, information identifying a number of discrete states in the model, annotations added to the model, or review information about an author of the model; displaying the identified information to a user when the user requests the identified information; associating the identified information with the model to make the model available for model searching; and performing a search for the model using the identified information, the receiving, the identifying, the displaying, the associating, and the performing being performed by a computing device. 17. The method of claim 16 , where the identified information further includes at least one of: information identifying one or more characteristics of the input ports, information identifying one or more characteristics of the output ports, information indicating whether the model is self-contained, information identifying a number of warnings that an editing style is violated, information identifying a number of subsystems within the model, information identifying a number of charts in the model, information identifying a number of discrete states in the model, information indicating whether the model adheres to one or more modeling standards, or information indicating whether an editing style is violated. | 0.5 |
16. An apparatus adapted to parse a hierarchically organized data document, comprising: a preparser configured to determine a logical tree structure of the data document, for use by a plurality of parsing processors, each of the plurality of parsing processors being configured to receive at least one of a plurality of divided sections of the data document, in dependence on the logical tree structure, wherein each section comprises at least a beginning of a logical section of the logical tree structure, with sufficient context to resolve any ambiguities wherein a set of tasks is generated by iteratively removing a remaining branch of the hierarchical skeleton which represents a largest task, and elevating the root of the subtree represented by the largest task to a next higher level, wherein a sufficient number of tasks is generated to permit efficient balancing; and a distribution control configured to distribute the plurality of sections to respective ones of the plurality of parsing processors for parsing of the sections of the data document. | 16. An apparatus adapted to parse a hierarchically organized data document, comprising: a preparser configured to determine a logical tree structure of the data document, for use by a plurality of parsing processors, each of the plurality of parsing processors being configured to receive at least one of a plurality of divided sections of the data document, in dependence on the logical tree structure, wherein each section comprises at least a beginning of a logical section of the logical tree structure, with sufficient context to resolve any ambiguities wherein a set of tasks is generated by iteratively removing a remaining branch of the hierarchical skeleton which represents a largest task, and elevating the root of the subtree represented by the largest task to a next higher level, wherein a sufficient number of tasks is generated to permit efficient balancing; and a distribution control configured to distribute the plurality of sections to respective ones of the plurality of parsing processors for parsing of the sections of the data document. 20. The apparatus according to claim 16 , wherein said preparser further comprises a data-dependent data distributor. | 0.596835 |
7. A scheduler system for a search engine crawler, comprising: a computer; a history log containing document identifiers corresponding to documents on a network previously indexed by the search engine crawler, wherein each document identifier has a corresponding document; and a scheduler, executed by the computer, and configured to process each document identifier in a set of the document identifiers in the history log by determining a query-independent score indicative of a page rank of the corresponding document relative to other documents on the network, determining a content change frequency of the document corresponding to the document identifier by comparing information stored for successive downloads of the corresponding document, determining an age of the corresponding document, wherein the age is associated with the time of the last download of the corresponding document by the crawler, determining a first score for the document identifier that is a function of the query-independent score and the determined content change frequency and the determined age of the corresponding document, comparing the first score against a threshold value, and conditionally scheduling the corresponding document for indexing based on the results of the comparison, wherein the history log and scheduler are stored on computer-readable media. | 7. A scheduler system for a search engine crawler, comprising: a computer; a history log containing document identifiers corresponding to documents on a network previously indexed by the search engine crawler, wherein each document identifier has a corresponding document; and a scheduler, executed by the computer, and configured to process each document identifier in a set of the document identifiers in the history log by determining a query-independent score indicative of a page rank of the corresponding document relative to other documents on the network, determining a content change frequency of the document corresponding to the document identifier by comparing information stored for successive downloads of the corresponding document, determining an age of the corresponding document, wherein the age is associated with the time of the last download of the corresponding document by the crawler, determining a first score for the document identifier that is a function of the query-independent score and the determined content change frequency and the determined age of the corresponding document, comparing the first score against a threshold value, and conditionally scheduling the corresponding document for indexing based on the results of the comparison, wherein the history log and scheduler are stored on computer-readable media. 8. The scheduler system of claim 7 , wherein the document is scheduled for a particular index segment indicated by a segment identifier in the history log. | 0.545946 |
5. The method of claim 3 , further comprising building a respective normalized dominance threshold associated with each of the cognitive motivation orientations. | 5. The method of claim 3 , further comprising building a respective normalized dominance threshold associated with each of the cognitive motivation orientations. 6. The method of claim 5 , wherein building a respective normalized dominance threshold associated with for each of the cognitive motivation orientations comprises: receiving a tuning corpus of tuning documents; each tuning document comprising a plurality of meaningfully arranged words; each tuning document being annotated with a respective document-level annotation, wherein the respective document-level annotation identifies a dominant cognitive motivation orientation set assigned to the particular tuning document annotated by that particular document-level annotation; obtaining, for each tuning document, respective document raw confidence weight scores associated with every cognitive motivation orientation expressed in that particular tuning document by, for each tuning document: for each indicator n-gram: identifying each instance of a matching n-gram appearing in that particular tuning document, wherein the matching n-gram matches the particular indicator n-gram; and for each instance of a matching n-gram, incrementing a document raw confidence weight score associated with the cognitive motivation orientation that is associated with that particular indicator n-gram matched by the matching n-gram, wherein the incrementing is according to the confidence weight assigned to that particular n-gram; for each tuning document, normalizing the document raw confidence weight scores for each cognitive motivation orientation expressed in that particular tuning document by: dividing the raw confidence weight score associated with each cognitive motivation orientation expressed in that particular tuning document by a number of tokens contained in that particular tuning document to assign normalized document confidence weight scores to each cognitive motivation orientation expressed in that particular tuning document; and selecting, for each cognitive motivation orientation, a normalized dominance threshold, wherein the normalized dominance threshold minimizes a number of incorrectly classified tuning documents, wherein: incorrect classification of a given tuning document with respect to a particular cognitive motivation orientation is a condition selected from the group consisting of: (a) the normalized document confidence weight score associated with that cognitive motivation orientation with respect to that particular tuning document exceeds the normalized dominance threshold but that cognitive motivation orientation is absent from the document-level annotation annotating that particular tuning document; and (b) the normalized document confidence weight score associated with that cognitive motivation orientation with respect to that particular tuning document is less than or equal to the normalized dominance threshold but that cognitive motivation orientation is present in the document-level annotation annotating that particular tuning document. | 0.757839 |
13. A non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed, cause a data processing system to perform the steps of: receiving a closure rule and at least one input object, the closure rule having at least one closure rule clause to collect data required for a consolidation as a logically consistent set of data objects, the at least one input object is in a primary class of objects to be included in the consolidation; identifying a first closure rule clause from the at least one closure rule clause to be evaluated for the at least one input object; constructing a recursive database query corresponding to the first closure rule clause; querying a database using the recursive database query, wherein the instructions that cause the data processing system to query the database further include instructions that cause the data processing system to, in response to encountering a first object of the primary class of objects, process and traverse, according to the closure rule, a second object referenced by the first object even when the second object is in a secondary class of objects; and receiving and storing results from the recursive database query. | 13. A non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed, cause a data processing system to perform the steps of: receiving a closure rule and at least one input object, the closure rule having at least one closure rule clause to collect data required for a consolidation as a logically consistent set of data objects, the at least one input object is in a primary class of objects to be included in the consolidation; identifying a first closure rule clause from the at least one closure rule clause to be evaluated for the at least one input object; constructing a recursive database query corresponding to the first closure rule clause; querying a database using the recursive database query, wherein the instructions that cause the data processing system to query the database further include instructions that cause the data processing system to, in response to encountering a first object of the primary class of objects, process and traverse, according to the closure rule, a second object referenced by the first object even when the second object is in a secondary class of objects; and receiving and storing results from the recursive database query. 17. The computer-readable storage medium of claim 13 , wherein the recursive database query performs a bulk query corresponding to the at least one input object and a plurality of other objects that are relationally dependent on the at least one input object. | 0.71 |
7. A method for converting a document in an electronic format into a representation containing structure and layout metadata, the method comprising: sending textual data, including a set of available structure and layout information, in a first electronic format to a layout extraction engine, the layout extraction engine configured to convert the textual data from the first electronic format to an independent interface format different from the first electronic format by extracting the set of available structure and layout information from the textual data in the first electronic format, the independent interface format including coordinates to one or more structural elements of the textual data the independent interface format enabling common analysis procedures to be carried out on textual data received in a variety of electronic formats, the layout extraction engine configured to perform a structure and layout analysis of the textual data to generate a set of additional structure and layout information by: having a set of specialized sub engines for extracting metadata, applying a subset of the set of specialized sub engines to the textual data of the independent interface format, each of the specialized sub engines extracting only metadata for generating the set of additional structure and layout information that is not already identified in the set of available structure and layout information from the first electronic format, thereby avoiding redundant extraction of metadata, and integrating the additional structure and layout information metadata extracted from each of the specialized sub engines into the available structure and layout information of the independent interface format to convert the independent interface format into an enriched interface format; sending textual data in a second electronic format to the layout extraction engine, wherein the layout extraction engine is configured to convert the textual data from the second electronic format to the independent interface format different from both the first electronic format and the second electronic format; and receiving the textual data, the additional structure and layout information, and the extracted set of available structure and layout information in the enriched interface format, the enriched interface format providing for search and navigation of the textual data. | 7. A method for converting a document in an electronic format into a representation containing structure and layout metadata, the method comprising: sending textual data, including a set of available structure and layout information, in a first electronic format to a layout extraction engine, the layout extraction engine configured to convert the textual data from the first electronic format to an independent interface format different from the first electronic format by extracting the set of available structure and layout information from the textual data in the first electronic format, the independent interface format including coordinates to one or more structural elements of the textual data the independent interface format enabling common analysis procedures to be carried out on textual data received in a variety of electronic formats, the layout extraction engine configured to perform a structure and layout analysis of the textual data to generate a set of additional structure and layout information by: having a set of specialized sub engines for extracting metadata, applying a subset of the set of specialized sub engines to the textual data of the independent interface format, each of the specialized sub engines extracting only metadata for generating the set of additional structure and layout information that is not already identified in the set of available structure and layout information from the first electronic format, thereby avoiding redundant extraction of metadata, and integrating the additional structure and layout information metadata extracted from each of the specialized sub engines into the available structure and layout information of the independent interface format to convert the independent interface format into an enriched interface format; sending textual data in a second electronic format to the layout extraction engine, wherein the layout extraction engine is configured to convert the textual data from the second electronic format to the independent interface format different from both the first electronic format and the second electronic format; and receiving the textual data, the additional structure and layout information, and the extracted set of available structure and layout information in the enriched interface format, the enriched interface format providing for search and navigation of the textual data. 8. The method of claim 7 , wherein the enriched interface format includes one or more sections identifying one or more portions in the textual data having a role in the organization of the document. | 0.643005 |
15. A computer program product, comprising a computer-readable, tangible storage device having a computer-readable program code stored therein, said computer-readable program code containing instructions configured to be executed by a processor of a computer system to implement a method of automatically verifying a change to a target database, said method comprising: receiving an input file that indicates a change to a target database, wherein said input file includes a test query for said target database and a predefined output expected from executing said test query on said target database; automatically executing said test query on said target database subsequent to said receiving said input file; retrieving an actual output resulting from said automatically executing said test query; comparing said actual output with said predefined output; automatically identifying a mismatch between said actual output and said predefined output based on a result of said comparing said actual output with said predefined output; and storing an indication of a failure in a computer data storage unit, wherein said failure indicates said mismatch, and wherein said failure further indicates that said change to said target database is invalid and said change to said target database initiates a defect in an application coupled to said target database. | 15. A computer program product, comprising a computer-readable, tangible storage device having a computer-readable program code stored therein, said computer-readable program code containing instructions configured to be executed by a processor of a computer system to implement a method of automatically verifying a change to a target database, said method comprising: receiving an input file that indicates a change to a target database, wherein said input file includes a test query for said target database and a predefined output expected from executing said test query on said target database; automatically executing said test query on said target database subsequent to said receiving said input file; retrieving an actual output resulting from said automatically executing said test query; comparing said actual output with said predefined output; automatically identifying a mismatch between said actual output and said predefined output based on a result of said comparing said actual output with said predefined output; and storing an indication of a failure in a computer data storage unit, wherein said failure indicates said mismatch, and wherein said failure further indicates that said change to said target database is invalid and said change to said target database initiates a defect in an application coupled to said target database. 16. The program product of claim 15 , wherein said receiving said input file comprises receiving said input file that further indicates a second change to said target database and that further includes a second test query for said target database and a second predefined output expected from executing said second test query on said target database, and wherein said method further comprises: automatically executing said second test query subsequent to said receiving said input file; retrieving a second actual output resulting from said automatically executing said second test query; comparing said second actual output with said second predefined output; and automatically identifying a match between said second actual output and said second predefined output based on a result of said comparing said second actual output with said second predefined output. | 0.733812 |
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