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9,087,507 | 17 | 23 |
17. One or more non-transitory computer-readable media storing instructions which, when executed by one or more computing devices, cause performance of a computer-implemented method for aurally scrolling an information source comprising the steps of: analyzing an information source; wherein the information source comprises a plurality of markup tags; wherein analyzing the information source comprises using the plurality of markup tags to identify a plurality of segments of the information source from which to derive corresponding marker texts; generating and storing, separate from the information source, a set of a plurality of marker texts based at least on the analyzing of the information source including generating each marker text in the set of marker texts based at least on an analysis of a corresponding segment, of the plurality of identified segments, of the information source; wherein the analysis of a particular segment, of the plurality of identified segments, corresponding to a particular marker text of the set of marker texts comprises applying a summarization technique to the particular segment to derive the particular marker text; wherein the analysis of the particular segment comprises determining a significance of the particular segment based at least in part on a relative amount of text content of the particular segment; generating and storing data that comprises, for each marker text in the set of marker texts, an association between the marker text and a location within the information source, the location corresponding to the segment of the information source that corresponds to the marker text; arranging the plurality of marker texts in a sequence, the particular marker text having an order in the sequence; wherein the order of the particular marker text in the sequence is dependent on the determined significance of the particular segment that was determined based at least in part on the relative amount of text content of the particular segment; initiating an aural presentation of the sequence, the aural presentation comprising computerized text-to-speech synthesis of at least a portion of the sequence; during the aural presentation of the sequence, receiving input while the particular marker text of the set of marker texts is being aurally presented; and in response to the input: ceasing the aural presentation of the particular marker text; inspecting the data to identify the location associated with the particular marker text; and initiating an aural presentation of the information source at the location associated with the particular marker text, the aural presentation comprising computerized text-to-speech synthesis of at least a portion of the information source.
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17. One or more non-transitory computer-readable media storing instructions which, when executed by one or more computing devices, cause performance of a computer-implemented method for aurally scrolling an information source comprising the steps of: analyzing an information source; wherein the information source comprises a plurality of markup tags; wherein analyzing the information source comprises using the plurality of markup tags to identify a plurality of segments of the information source from which to derive corresponding marker texts; generating and storing, separate from the information source, a set of a plurality of marker texts based at least on the analyzing of the information source including generating each marker text in the set of marker texts based at least on an analysis of a corresponding segment, of the plurality of identified segments, of the information source; wherein the analysis of a particular segment, of the plurality of identified segments, corresponding to a particular marker text of the set of marker texts comprises applying a summarization technique to the particular segment to derive the particular marker text; wherein the analysis of the particular segment comprises determining a significance of the particular segment based at least in part on a relative amount of text content of the particular segment; generating and storing data that comprises, for each marker text in the set of marker texts, an association between the marker text and a location within the information source, the location corresponding to the segment of the information source that corresponds to the marker text; arranging the plurality of marker texts in a sequence, the particular marker text having an order in the sequence; wherein the order of the particular marker text in the sequence is dependent on the determined significance of the particular segment that was determined based at least in part on the relative amount of text content of the particular segment; initiating an aural presentation of the sequence, the aural presentation comprising computerized text-to-speech synthesis of at least a portion of the sequence; during the aural presentation of the sequence, receiving input while the particular marker text of the set of marker texts is being aurally presented; and in response to the input: ceasing the aural presentation of the particular marker text; inspecting the data to identify the location associated with the particular marker text; and initiating an aural presentation of the information source at the location associated with the particular marker text, the aural presentation comprising computerized text-to-speech synthesis of at least a portion of the information source. 23. The one or more non-transitory computer-readable media as recited in claim 17 , wherein the set of marker texts comprises a first set of marker texts and a second set of marker texts, the method further comprising: storing metadata that indicates that the first set of marker texts have a first logical significance and that the at least second set of marker texts have at least a second logical significance.
| 0.778433 |
8,555,243 | 11 | 14 |
11. A computer-implemented method, comprising: utilizing a computer to perform: providing a graphical program development environment comprising a graphical specification and constraint language that allows specification of a model of computation and explicit declaration of constraints; creating a graphical program in a graphical specification and constraint language that allows specification of a model of computation and explicit declaration of constraints in response to user input, wherein the graphical program comprises: a specified model of computation; a plurality of interconnected functional blocks that visually indicate functionality of the graphical program; and graphically indicated specifications or constraints for at least one functional block of the functional blocks in the graphical program; wherein the specifications or constraints comprise: input count (IC), comprising a number of tokens consumed at an input terminal of the at least one functional block by one firing of the at least one functional block; output count (OC), comprising a number of tokens produced at an output terminal of the at least one functional block by one firing of the at least one functional block; execution time (ET), comprising a number of cycles needed by the functional block to complete firing; initiation interval (II), comprising a minimum number of cycles between firings of the functional block; input pattern (IP), comprising a sequence of Boolean values of length at most II, wherein the sequence of Boolean values aligns with the beginning of firing of the functional block, wherein each true value in the sequence denotes consumption of a token by the functional block; and output pattern (OP), comprising a sequence of Boolean values of length at most II, wherein the sequence of Boolean values aligns with the end of firing of the functional block, wherein each true value in the sequence denotes production of a token by the functional block; and analyzing the graphical program, including analyzing the specifications or constraints based on the specified model of computation; and automatically generating a timing accurate simulation of the graphical program.
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11. A computer-implemented method, comprising: utilizing a computer to perform: providing a graphical program development environment comprising a graphical specification and constraint language that allows specification of a model of computation and explicit declaration of constraints; creating a graphical program in a graphical specification and constraint language that allows specification of a model of computation and explicit declaration of constraints in response to user input, wherein the graphical program comprises: a specified model of computation; a plurality of interconnected functional blocks that visually indicate functionality of the graphical program; and graphically indicated specifications or constraints for at least one functional block of the functional blocks in the graphical program; wherein the specifications or constraints comprise: input count (IC), comprising a number of tokens consumed at an input terminal of the at least one functional block by one firing of the at least one functional block; output count (OC), comprising a number of tokens produced at an output terminal of the at least one functional block by one firing of the at least one functional block; execution time (ET), comprising a number of cycles needed by the functional block to complete firing; initiation interval (II), comprising a minimum number of cycles between firings of the functional block; input pattern (IP), comprising a sequence of Boolean values of length at most II, wherein the sequence of Boolean values aligns with the beginning of firing of the functional block, wherein each true value in the sequence denotes consumption of a token by the functional block; and output pattern (OP), comprising a sequence of Boolean values of length at most II, wherein the sequence of Boolean values aligns with the end of firing of the functional block, wherein each true value in the sequence denotes production of a token by the functional block; and analyzing the graphical program, including analyzing the specifications or constraints based on the specified model of computation; and automatically generating a timing accurate simulation of the graphical program. 14. The computer-implemented method of claim 11 , further comprising: utilizing the computer to perform: providing token flow probes at the level of the model of computation and specifications or constraints.
| 0.595331 |
9,996,670 | 1 | 7 |
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.
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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. 7. The method as defined in claim 1 , wherein the clinical decision support document comprises field identifiers and corresponding data.
| 0.847875 |
9,014,481 | 1 | 2 |
1. A method of font recognition comprising: calculating a plurality of feature values for a sample text, wherein the feature values correspond to a plurality of font features, and the plurality of font features includes curvature features for the sample text; determining a Euclidian distance between the plurality of the feature values for the sample text and respective model feature values for each of a plurality of predefined fonts, wherein the Euclidian distance for the i th predefined font is given by D i = ∑ j = 1 n ( T ij - V j ) 2 , where n is the number of feature values, T ij is an element corresponding to the i th row and j th column of a matrix of the model values, and V j is a j th element a vector containing the plurality of the feature values for the sample text; and signaling that the font of the sample text is the font from the plurality of predefined fonts corresponding to the smallest Euclidian distance.
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1. A method of font recognition comprising: calculating a plurality of feature values for a sample text, wherein the feature values correspond to a plurality of font features, and the plurality of font features includes curvature features for the sample text; determining a Euclidian distance between the plurality of the feature values for the sample text and respective model feature values for each of a plurality of predefined fonts, wherein the Euclidian distance for the i th predefined font is given by D i = ∑ j = 1 n ( T ij - V j ) 2 , where n is the number of feature values, T ij is an element corresponding to the i th row and j th column of a matrix of the model values, and V j is a j th element a vector containing the plurality of the feature values for the sample text; and signaling that the font of the sample text is the font from the plurality of predefined fonts corresponding to the smallest Euclidian distance. 2. The method according to claim 1 , wherein the plurality of font features includes the center of gravity features of the sample text.
| 0.5 |
9,164,778 | 11 | 12 |
11. The system for modal progress dialog of claim 10 , where the action request is received at the system interface from an application.
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11. The system for modal progress dialog of claim 10 , where the action request is received at the system interface from an application. 12. The system for modal progress dialog of claim 11 , where execution of the application is blocked until a result of the action request is received by the application.
| 0.5 |
9,449,279 | 2 | 4 |
2. The tangible machine-readable medium of claim 1 , wherein the behavior model includes time-stamped vectors.
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2. The tangible machine-readable medium of claim 1 , wherein the behavior model includes time-stamped vectors. 4. The tangible machine-readable medium of claim 2 , wherein the instructions, when executed, cause the machine to correlate two or more of the time-stamped vectors to identify a change in a usage pattern of one of the identified applications.
| 0.677719 |
9,898,456 | 5 | 6 |
5. The method of claim 4 wherein the first pair of characters is a pair of letters of an alphabet and wherein the second pair of characters is another pair of letters of the alphabet.
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5. The method of claim 4 wherein the first pair of characters is a pair of letters of an alphabet and wherein the second pair of characters is another pair of letters of the alphabet. 6. The method of claim 5 wherein the alphabet is of a language and wherein the displaying of one of the second pair of characters comprises displaying the one of the second pair of characters that is most likely to follow the displayed one of the first pair of characters in the language.
| 0.5 |
8,930,337 | 20 | 29 |
20. A system for presenting visual feedback in an interface, the system including: an input data storage system storing an input dataset; an output data storage system storing an output dataset; and a data processing system configured to provide an interface for receiving user input and presenting results of data processing, including receiving one or more mapped relationships between a given output and one or more inputs represented by input variables, at least one of the mapped relationships including a transformational expression executable on a data processing system, the transformational expression defining an output of a mapped relationship based on at least one input variable mapped to an element of an input dataset; receiving identification of elements of an output dataset mapped to outputs of respective mapped relationships; generating output data from the data processing system according to the transformational expression based on input data from the input dataset associated with the element of the input dataset mapped to the input variable, including applying the transformational expressions to input values in respective fields of input records of the input dataset and storing output values in respective fields of output records of the output dataset, including executing a dataflow graph including nodes representing data processing components, links representing data flows between the data processing components, a node representing the input dataset providing a data flow of the input records, and a node representing the output dataset receiving a data flow of the output records; determining validation information in response to the generated output data based on validation criteria defining one or more characteristics of valid values associated with one or more of the identified elements of the output dataset; and presenting in the interface visual feedback based on the determined validation information.
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20. A system for presenting visual feedback in an interface, the system including: an input data storage system storing an input dataset; an output data storage system storing an output dataset; and a data processing system configured to provide an interface for receiving user input and presenting results of data processing, including receiving one or more mapped relationships between a given output and one or more inputs represented by input variables, at least one of the mapped relationships including a transformational expression executable on a data processing system, the transformational expression defining an output of a mapped relationship based on at least one input variable mapped to an element of an input dataset; receiving identification of elements of an output dataset mapped to outputs of respective mapped relationships; generating output data from the data processing system according to the transformational expression based on input data from the input dataset associated with the element of the input dataset mapped to the input variable, including applying the transformational expressions to input values in respective fields of input records of the input dataset and storing output values in respective fields of output records of the output dataset, including executing a dataflow graph including nodes representing data processing components, links representing data flows between the data processing components, a node representing the input dataset providing a data flow of the input records, and a node representing the output dataset receiving a data flow of the output records; determining validation information in response to the generated output data based on validation criteria defining one or more characteristics of valid values associated with one or more of the identified elements of the output dataset; and presenting in the interface visual feedback based on the determined validation information. 29. The system of claim 20 , further including generating second output data from the data processing system according to a second transformational expression based on the input data from the input dataset.
| 0.781316 |
7,756,915 | 17 | 18 |
17. The digital music library builder of claim 1 wherein the extracted song is selectively stored based upon pre-defined characteristics.
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17. The digital music library builder of claim 1 wherein the extracted song is selectively stored based upon pre-defined characteristics. 18. The digital music library builder of claim 17 wherein the pre-defined characteristics relate to the identified meta-data.
| 0.5 |
9,342,626 | 13 | 16 |
13. The method of claim 1 , wherein the one or more past queries include a plurality of past queries.
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13. The method of claim 1 , wherein the one or more past queries include a plurality of past queries. 16. The method of claim 13 , further comprising determining, for each of the past entity collections, a cumulative ranking of the past entity collection based on a ranking of the past entity collection for each of the past queries.
| 0.717604 |
9,779,141 | 4 | 5 |
4. The system of claim 3 wherein an abstract syntax tree is generated for each comparative expression in the one or more identified documents and a corresponding term in the modified query.
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4. The system of claim 3 wherein an abstract syntax tree is generated for each comparative expression in the one or more identified documents and a corresponding term in the modified query. 5. The system of claim 4 wherein a constraint is used to evaluate each abstract syntax tree to determine whether the abstract syntax tree is a true statement or a false statement, and wherein those identified documents associated with an abstract syntax tree that is a false statement are differentiated from identified documents not associated with an abstract tree that is a false statement.
| 0.5 |
9,576,074 | 15 | 20 |
15. A computing system comprising: a processor; and computer storage memory having computer-executable instructions stored thereon which, when executed by the processor, configure the computing system to: receive a user input at an input mechanism (“IME”) program running on a computing device, the IME program enabling an associated input mechanism that provides input to multiple applications on the computing device, the user input comprising letters that do not presently constitute a full word; communicate the user input from the IME program to an active application running on the computing device; communicating the user input from the IME program to a remote contextual-service provider; receiving at the IME program a contextual-service instruction comprising information needed to provide one or more contextual services from the remote contextual-service provider; generate, by the IME program, a contextual interface that offers to provide one or more contextual services related to the user input and context in the active application; receive a user selection of a contextual service offered in the contextual interface; and initiate the contextual service.
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15. A computing system comprising: a processor; and computer storage memory having computer-executable instructions stored thereon which, when executed by the processor, configure the computing system to: receive a user input at an input mechanism (“IME”) program running on a computing device, the IME program enabling an associated input mechanism that provides input to multiple applications on the computing device, the user input comprising letters that do not presently constitute a full word; communicate the user input from the IME program to an active application running on the computing device; communicating the user input from the IME program to a remote contextual-service provider; receiving at the IME program a contextual-service instruction comprising information needed to provide one or more contextual services from the remote contextual-service provider; generate, by the IME program, a contextual interface that offers to provide one or more contextual services related to the user input and context in the active application; receive a user selection of a contextual service offered in the contextual interface; and initiate the contextual service. 20. The computing system of claim 15 , wherein the computing system is further configured to communicate the user selection to a contextual service provider that uses the user selection to determine circumstances when the contextual service is relevant to a user.
| 0.5 |
4,783,810 | 3 | 10 |
3. A device as claimed in claim 1, wherein the data-processor unit further comprises position determining means for determining the position of at least one letter of a word consisting made up of characters presented and for introducing a second modification in the speech pattern for said letter while maintaining the its identity, said second modification comprising a modification of a pitch component and/or a voice-characterizing component of the speech pattern.
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3. A device as claimed in claim 1, wherein the data-processor unit further comprises position determining means for determining the position of at least one letter of a word consisting made up of characters presented and for introducing a second modification in the speech pattern for said letter while maintaining the its identity, said second modification comprising a modification of a pitch component and/or a voice-characterizing component of the speech pattern. 10. A device as claimed in claim 3, wherein said second modification of a pitch component of the speech pattern comprises a lower pitch with respect to a mean pitch component of the speech pattern.
| 0.591286 |
8,451,475 | 6 | 7 |
6. A method as recited in claim 1 , wherein the facsimile text does not contain confirmation destination information.
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6. A method as recited in claim 1 , wherein the facsimile text does not contain confirmation destination information. 7. A method as recited in claim 6 , wherein the confirmation destination information not contained in the facsimile text includes names of intended facsimile recipients and destination data.
| 0.5 |
10,127,287 | 1 | 3 |
1. A method comprising: retrieving a plurality of related content items that are related to a user in an online service; identifying a plurality of topics of interest to the user using the plurality of related content items; ranking the topics by relevance to one of a plurality of content items in a stream of content and based on a relationship between an author of the topics and the user; associating an identified topic to a content item in the stream of content where the identified topic is ranked as being relevant to the content item; generating a marker for the identified topic; generating a user interface including one or more tiles and the generated marker rendered with a tile of the one or more tiles, the one or more tiles being an element on the user interface, and each of the one or more tiles corresponding to the content item of the plurality of content items in the stream of content; detecting a cursor movement towards the marker; in response to the cursor movement towards the marker, updating the user interface by expanding the marker in size to reveal an overlay of the identified topic on the tile; receiving a selection of the identified topic by the user; in response to the selection of the identified topic, replacing the tile with at least one of the plurality of related content items on the identified topic such that the user views related information when viewing the stream of content without having to transition to a different element of the user interface; and providing the user interface for display.
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1. A method comprising: retrieving a plurality of related content items that are related to a user in an online service; identifying a plurality of topics of interest to the user using the plurality of related content items; ranking the topics by relevance to one of a plurality of content items in a stream of content and based on a relationship between an author of the topics and the user; associating an identified topic to a content item in the stream of content where the identified topic is ranked as being relevant to the content item; generating a marker for the identified topic; generating a user interface including one or more tiles and the generated marker rendered with a tile of the one or more tiles, the one or more tiles being an element on the user interface, and each of the one or more tiles corresponding to the content item of the plurality of content items in the stream of content; detecting a cursor movement towards the marker; in response to the cursor movement towards the marker, updating the user interface by expanding the marker in size to reveal an overlay of the identified topic on the tile; receiving a selection of the identified topic by the user; in response to the selection of the identified topic, replacing the tile with at least one of the plurality of related content items on the identified topic such that the user views related information when viewing the stream of content without having to transition to a different element of the user interface; and providing the user interface for display. 3. The method of claim 1 wherein the plurality of related content items are also related to a social graph of the user.
| 0.762 |
5,584,698 | 11 | 15 |
11. A method of increasing the reading efficiency of a dyslexic, comprising the step of: placing a transparent sheet of material over at least a portion of an area of text to be read, said transparent sheet of material having a plurality of colors, each color comprising a plurality of color gradients forming horizontal lines, each color gradient covering at least a portion of one line of text, whereby each color gradient creates a less distracting environment and allows the dyslexic to more readily focus and concentrate on the lines of text to be read.
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11. A method of increasing the reading efficiency of a dyslexic, comprising the step of: placing a transparent sheet of material over at least a portion of an area of text to be read, said transparent sheet of material having a plurality of colors, each color comprising a plurality of color gradients forming horizontal lines, each color gradient covering at least a portion of one line of text, whereby each color gradient creates a less distracting environment and allows the dyslexic to more readily focus and concentrate on the lines of text to be read. 15. The teaching aid according to claim 11, wherein said plurality of color gradients for each color progresses from a darker color gradient to a lighter color gradient when said transparent sheet of material is placed over the area of text to be read.
| 0.5 |
8,211,155 | 7 | 8 |
7. An implantable spine stabilization device comprising: an elongated bone anchor having a distal end and a proximal end and a longitudinal axis; a deflectable post having a distal end and a proximal end and coaxially aligned with the longitudinal axis of the anchor; a joint which secures the distal end of the post to the proximal end of the bone anchor such that the deflectable post may pivot relative to the bone anchor; a tubular extension of the bone anchor which extends over a portion of the deflectable post; and a compliant member disposed between the distal portion of the deflectable post and the tubular extension of the bone anchor whereby the compliant member biases the deflectable post into alignment with the bone anchor with the compliant member being shaped to control deflection of the deflectable post from alignment with the longitudinal axis of the bone anchor.
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7. An implantable spine stabilization device comprising: an elongated bone anchor having a distal end and a proximal end and a longitudinal axis; a deflectable post having a distal end and a proximal end and coaxially aligned with the longitudinal axis of the anchor; a joint which secures the distal end of the post to the proximal end of the bone anchor such that the deflectable post may pivot relative to the bone anchor; a tubular extension of the bone anchor which extends over a portion of the deflectable post; and a compliant member disposed between the distal portion of the deflectable post and the tubular extension of the bone anchor whereby the compliant member biases the deflectable post into alignment with the bone anchor with the compliant member being shaped to control deflection of the deflectable post from alignment with the longitudinal axis of the bone anchor. 8. The dynamic pedicle screw of claim 7 , wherein: said tubular extension is associated with a limit surface positioned to contact the deflectable post after a predetermined amount of deflection of the deflectable post away from alignment with the longitudinal axis of the bone anchor; and wherein the compliant member comprises a surface relief to prevent the compliant member from being pinched between the deflectable post and the limit surface.
| 0.653787 |
8,504,909 | 1 | 3 |
1. A system comprising: one or more computer readable storage media; computer-readable instructions on the one or more computer readable storage media which, when executed, provide a load time optimizer configured to: in a first pass, catalog certain objects that occur in a markup language description associated with a document in a resource dictionary by setting or incrementing a reference count for the certain objects in the resource dictionary, and insert a resource key associated with each of the objects in an associated object model; and in a second pass, determine non-reoccurring objects based on the reference count for the certain objects, remove the non-reoccurring objects from the resource dictionary, and insert an associated object in the object model in place of an associated resource key for the non-reoccurring objects.
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1. A system comprising: one or more computer readable storage media; computer-readable instructions on the one or more computer readable storage media which, when executed, provide a load time optimizer configured to: in a first pass, catalog certain objects that occur in a markup language description associated with a document in a resource dictionary by setting or incrementing a reference count for the certain objects in the resource dictionary, and insert a resource key associated with each of the objects in an associated object model; and in a second pass, determine non-reoccurring objects based on the reference count for the certain objects, remove the non-reoccurring objects from the resource dictionary, and insert an associated object in the object model in place of an associated resource key for the non-reoccurring objects. 3. The system of claim 1 , wherein said load time optimizer is configured to build an in-memory representation of the object model.
| 0.58805 |
8,365,071 | 1 | 5 |
1. A method of enabling input on a handheld electronic device that comprises an output apparatus, an input apparatus comprising a plurality of input keys, and a processor apparatus comprising a memory having stored therein a plurality of objects that comprise a plurality of language objects, a plurality of characters, and a plurality of words, at least some of the language objects each being associated with a plurality of the characters, each word comprising a number of the characters, at least some of the input keys each having a number of linguistic elements assigned thereto, each language object comprising a number of the linguistic elements, the method comprising: detecting an ambiguous text input comprising a number of selections of a number of input keys; generating a string of language objects that corresponds with at least an initial portion of the ambiguous input; outputting one of the language objects of the string of language objects and at least one variant language object as an alternative to the one of the language objects of the string of language objects in a first region of the output apparatus, the one of the language objects of the string of language objects and the at least one variant language object being selectable; outputting a character interpretation that comprises a number of words comprising characters that correspond with at least a portion of the string of language objects in a second region of the output apparatus, the character interpretation being selectable; applying a selection focus to the one of the language objects of the string of language objects, the at least one variant language object, or the character interpretation; and in response to detecting a selection of the one of the language objects of the string of language objects or the at least one variant language object, outputting a second one of the language objects of the string of language objects and at least one second variant language object in the first region, wherein the second one of the language objects of the string of language objects comprises a different portion of the string of language objects than the one of the language objects of the string of language objects.
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1. A method of enabling input on a handheld electronic device that comprises an output apparatus, an input apparatus comprising a plurality of input keys, and a processor apparatus comprising a memory having stored therein a plurality of objects that comprise a plurality of language objects, a plurality of characters, and a plurality of words, at least some of the language objects each being associated with a plurality of the characters, each word comprising a number of the characters, at least some of the input keys each having a number of linguistic elements assigned thereto, each language object comprising a number of the linguistic elements, the method comprising: detecting an ambiguous text input comprising a number of selections of a number of input keys; generating a string of language objects that corresponds with at least an initial portion of the ambiguous input; outputting one of the language objects of the string of language objects and at least one variant language object as an alternative to the one of the language objects of the string of language objects in a first region of the output apparatus, the one of the language objects of the string of language objects and the at least one variant language object being selectable; outputting a character interpretation that comprises a number of words comprising characters that correspond with at least a portion of the string of language objects in a second region of the output apparatus, the character interpretation being selectable; applying a selection focus to the one of the language objects of the string of language objects, the at least one variant language object, or the character interpretation; and in response to detecting a selection of the one of the language objects of the string of language objects or the at least one variant language object, outputting a second one of the language objects of the string of language objects and at least one second variant language object in the first region, wherein the second one of the language objects of the string of language objects comprises a different portion of the string of language objects than the one of the language objects of the string of language objects. 5. The method of claim 1 , further comprising outputting the one of the language objects of the string of language objects, the at least one variant language object, and the character interpretation as objects that are selectable by one or more predetermined inputs.
| 0.693548 |
9,395,876 | 1 | 4 |
1. A computer-implemented method, comprising: receiving, by a computing system, a first search query that was typed by a first user input at a computing device into a search input box of a mapping application program at the computing device; parsing, by the computing system, the first search query in order to determine that one or more words in the first search query name a particular geographical location; conducting, by the computing system, a search for first search results that: (i) are responsive to the first search query, and (ii) identify respective first businesses that are geographically located around the particular geographical location that is named by the one or more words in the first search query; sending, by the computing system and for receipt by the computing device, information that identifies the first search results, so as to cause the computing device to present a display of a first geographical area of a map with first graphical interface elements that identify the first search results overlaying the map at locations that correspond to locations on the map of the first businesses; receiving, by the computing system, a second search query that was typed by a second user input at the computing device into the search input box of the mapping application program into which the first search query was typed, wherein the second search query does not include one or more words that name any geographical location; parsing, by the computing system, the second search query in order to identify whether the second search query includes one or more words that name any geographical location; receiving, by the computing system, an indication of a geographical location that is indicated by a presently-displayed geographical area of the map that is being presented by the computing device; conducting, by the computing system, a search for second search results that: (i) are responsive to the second search query, and (ii) identify respective second businesses that are geographically located around the geographical location that is indicated by the presently-displayed geographical area of the map that is being presented by the computing device; and sending, by the computing system and for receipt by the computing device, information that identifies the second search results, so as to cause the computing device to present a display of a second geographical area of the map with second graphical interface elements that identify the second search results overlaying the map at locations that correspond to locations on the map of the second businesses.
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1. A computer-implemented method, comprising: receiving, by a computing system, a first search query that was typed by a first user input at a computing device into a search input box of a mapping application program at the computing device; parsing, by the computing system, the first search query in order to determine that one or more words in the first search query name a particular geographical location; conducting, by the computing system, a search for first search results that: (i) are responsive to the first search query, and (ii) identify respective first businesses that are geographically located around the particular geographical location that is named by the one or more words in the first search query; sending, by the computing system and for receipt by the computing device, information that identifies the first search results, so as to cause the computing device to present a display of a first geographical area of a map with first graphical interface elements that identify the first search results overlaying the map at locations that correspond to locations on the map of the first businesses; receiving, by the computing system, a second search query that was typed by a second user input at the computing device into the search input box of the mapping application program into which the first search query was typed, wherein the second search query does not include one or more words that name any geographical location; parsing, by the computing system, the second search query in order to identify whether the second search query includes one or more words that name any geographical location; receiving, by the computing system, an indication of a geographical location that is indicated by a presently-displayed geographical area of the map that is being presented by the computing device; conducting, by the computing system, a search for second search results that: (i) are responsive to the second search query, and (ii) identify respective second businesses that are geographically located around the geographical location that is indicated by the presently-displayed geographical area of the map that is being presented by the computing device; and sending, by the computing system and for receipt by the computing device, information that identifies the second search results, so as to cause the computing device to present a display of a second geographical area of the map with second graphical interface elements that identify the second search results overlaying the map at locations that correspond to locations on the map of the second businesses. 4. The computer-implemented method of claim 1 , wherein the one or more words that name the particular geographical location is one or more words that name a zip code or a city.
| 0.821212 |
9,747,365 | 3 | 6 |
3. The method of claim 1 , wherein generating the retrieval query based on the parsed queries comprises: generating an intent query based on the parsed queries, wherein: the intent query is a tree data structure that includes a plurality of connected intent query nodes, and the plurality of connected intent query nodes includes one or more intent query leaf nodes that each store a respective one of the parsed tokens; and generating the retrieval query based on the intent query.
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3. The method of claim 1 , wherein generating the retrieval query based on the parsed queries comprises: generating an intent query based on the parsed queries, wherein: the intent query is a tree data structure that includes a plurality of connected intent query nodes, and the plurality of connected intent query nodes includes one or more intent query leaf nodes that each store a respective one of the parsed tokens; and generating the retrieval query based on the intent query. 6. The method of claim 3 further comprising: generating a scoring query based on the intent query and the search query, wherein the scoring query is nested data structure, and determining the result scores of each record in the consideration set based on the scoring query.
| 0.643603 |
8,930,386 | 2 | 3 |
2. The computer program product of claim 1 wherein the terminology content comprises concepts that are associated with at least one terminology code system.
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2. The computer program product of claim 1 wherein the terminology content comprises concepts that are associated with at least one terminology code system. 3. The computer program product of claim 2 wherein the seed concept has a coded data type.
| 0.7 |
9,461,834 | 22 | 25 |
22. An electronic document provision system or online meetings, the system comprising: a meeting server having a repository for a first online meeting having a first URL address, the meeting server receiving an electronically formatted document sent to a first email address assigned to the first online meeting, at a time selected from a group consisting of prior to, during, and after the first online meeting, and storing the document in the online meeting repository; and, a network-connected first computer device logging into the meeting server via the first email address and, without the requirement of participating in the first online meeting, accessing the document from the online meeting repository at a time selected from a group consisting of prior to, during, and after the first online meeting.
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22. An electronic document provision system or online meetings, the system comprising: a meeting server having a repository for a first online meeting having a first URL address, the meeting server receiving an electronically formatted document sent to a first email address assigned to the first online meeting, at a time selected from a group consisting of prior to, during, and after the first online meeting, and storing the document in the online meeting repository; and, a network-connected first computer device logging into the meeting server via the first email address and, without the requirement of participating in the first online meeting, accessing the document from the online meeting repository at a time selected from a group consisting of prior to, during, and after the first online meeting. 25. The system of claim 22 further comprising: a second computer device, not participating in the first online meeting receiving a document request from the first computer device, and sending the document to the first communication address.
| 0.757085 |
9,092,528 | 19 | 22 |
19. The computer program product of claim 17 , wherein determining, using a document-to-query-to-document model, that the first resource is relevant to a first suggested query different from the initial search query comprises: obtaining a first plurality of previously submitted search queries, each of the first plurality of previously submitted search queries being associated with the first resource identified by the first search result; and selecting a first query from the first plurality of previously submitted search queries as the first suggested query, the first query having at least one term that does not occur in the initial search query.
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19. The computer program product of claim 17 , wherein determining, using a document-to-query-to-document model, that the first resource is relevant to a first suggested query different from the initial search query comprises: obtaining a first plurality of previously submitted search queries, each of the first plurality of previously submitted search queries being associated with the first resource identified by the first search result; and selecting a first query from the first plurality of previously submitted search queries as the first suggested query, the first query having at least one term that does not occur in the initial search query. 22. The computer program product of claim 19 , wherein selecting a first query from the first plurality of previously submitted search queries as the first suggested query comprises: determining a respective measure of diversity for each of the first plurality of previously submitted search queries, the respective measure of diversity being based on resources associated with each of the first plurality of previously submitted search queries and resources identified by the plurality of search results responsive to the initial search query; and selecting a first query having a measure of diversity that satisfies a threshold.
| 0.585526 |
9,262,722 | 10 | 11 |
10. A computer-implemented method comprising: (a) maintaining at least one registration data file within a computer-readable storage medium for receiving registration of at least one or more social networking websites for a social networker for measuring the social networker's collective influence on the registered at least one or more social networking websites; (b) automatically collecting empirical data using a computer processor, regarding the social networker's use and activity level for the registered at least one or more social networking websites; (c) measuring frequency of an activity posting by the social networker to the at least one or more registered social networking websites within a predetermined period; (d) determining number of responses to the activity posting; (e) measuring the length, quantitative and qualitative discussion emanating from the social networker's activity posting; measuring one or more third party's use of the social networker's activity posting; (f) assigning individual weighted scores for each quantitative and qualitative empirical data; (g) generating an impact score by tabulating an aggregate of the individual weighted scores derived from the plurality of weighted scores; and (h) posting the impact score on a social networking website viewable by the social networker and a community of friends.
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10. A computer-implemented method comprising: (a) maintaining at least one registration data file within a computer-readable storage medium for receiving registration of at least one or more social networking websites for a social networker for measuring the social networker's collective influence on the registered at least one or more social networking websites; (b) automatically collecting empirical data using a computer processor, regarding the social networker's use and activity level for the registered at least one or more social networking websites; (c) measuring frequency of an activity posting by the social networker to the at least one or more registered social networking websites within a predetermined period; (d) determining number of responses to the activity posting; (e) measuring the length, quantitative and qualitative discussion emanating from the social networker's activity posting; measuring one or more third party's use of the social networker's activity posting; (f) assigning individual weighted scores for each quantitative and qualitative empirical data; (g) generating an impact score by tabulating an aggregate of the individual weighted scores derived from the plurality of weighted scores; and (h) posting the impact score on a social networking website viewable by the social networker and a community of friends. 11. The computer-implemented method according to claim 10 , further comprising automatically assigning a weighted network score for the registered social networking websites.
| 0.644898 |
8,356,087 | 17 | 18 |
17. The method of claim 13 , wherein the translating comprises applying a device-specific translation table to the generic gateway configuration document.
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17. The method of claim 13 , wherein the translating comprises applying a device-specific translation table to the generic gateway configuration document. 18. The method of claim 17 , further comprising the step of receiving the device-specific translation table.
| 0.614286 |
8,312,032 | 31 | 32 |
31. A server system for processing query information, comprising: one or more processors; and memory to store data and one or more programs to be executed by the one or more processors, the one or more programs including instructions for: prior to a user of a client device signaling completion of a search query: receiving from a search requestor a partial search query, the search requestor located remotely from the server system; predicting from the partial search query a set of predicted complete queries relevant to the partial search query, where the predicted complete queries comprise previously submitted complete queries submitted by a community of users, wherein the partial search query and the set of predicted complete queries are in a first language; subsequent to the predicting, obtaining translations of at least a subset of the set of predicted complete queries, wherein the translations are in a second language different from the first language, and the subset comprises multiple predicted complete queries, wherein the first and second languages are predicted based, at least in part, on the partial search query; and conveying both the set of predicted complete queries and the corresponding translations to the search requestor for concurrent display.
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31. A server system for processing query information, comprising: one or more processors; and memory to store data and one or more programs to be executed by the one or more processors, the one or more programs including instructions for: prior to a user of a client device signaling completion of a search query: receiving from a search requestor a partial search query, the search requestor located remotely from the server system; predicting from the partial search query a set of predicted complete queries relevant to the partial search query, where the predicted complete queries comprise previously submitted complete queries submitted by a community of users, wherein the partial search query and the set of predicted complete queries are in a first language; subsequent to the predicting, obtaining translations of at least a subset of the set of predicted complete queries, wherein the translations are in a second language different from the first language, and the subset comprises multiple predicted complete queries, wherein the first and second languages are predicted based, at least in part, on the partial search query; and conveying both the set of predicted complete queries and the corresponding translations to the search requestor for concurrent display. 32. The server system of claim 31 , including: data, stored in the memory, the data representing complete queries previously submitted by a community of users, and translations of the complete queries; wherein the instructions for predicting and for obtaining comprise instructions for extracting from the stored data, data representing the predicted complete queries and the translations of the set of predicted complete queries.
| 0.5 |
7,971,157 | 1 | 6 |
1. A method for predicting a gesture made by a user to a first application, comprising: receiving image data captured by a camera and sound data captured by a microphone, wherein the image data is representative of a gesture performed by the user and the sound data is representative of a sound made by the user; applying a filter to the image data to interpret the gesture, wherein the sound data at least one of: augments, distinguishes or clarifies the gesture and wherein the filter comprises a first parameter about the gesture and a second parameter about the gesture, the first parameter corresponding to an earlier part of the gesture than the second parameter; determining, from the applied filter, an output corresponding to the gesture being performed, wherein determining the output includes determining the output corresponds to a high confidence level when the first parameter corresponds to a high confidence level and the second parameter does not correspond to a high confidence level; and sending the first application the output.
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1. A method for predicting a gesture made by a user to a first application, comprising: receiving image data captured by a camera and sound data captured by a microphone, wherein the image data is representative of a gesture performed by the user and the sound data is representative of a sound made by the user; applying a filter to the image data to interpret the gesture, wherein the sound data at least one of: augments, distinguishes or clarifies the gesture and wherein the filter comprises a first parameter about the gesture and a second parameter about the gesture, the first parameter corresponding to an earlier part of the gesture than the second parameter; determining, from the applied filter, an output corresponding to the gesture being performed, wherein determining the output includes determining the output corresponds to a high confidence level when the first parameter corresponds to a high confidence level and the second parameter does not correspond to a high confidence level; and sending the first application the output. 6. The method of claim 1 , further comprising: receiving second sound and second image data; applying the filter to the second image data to interpret the gesture, wherein the second sound data at least one of: augments, distinguishes or clarifies the gesture; determining from the applied filter that the gesture has been performed; determining a second output based on the applied filter; and sending the first application the second output.
| 0.563116 |
9,378,740 | 8 | 13 |
8. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, performing automatic speech recognition using audio data received from a client device to obtain a transcript of the audio data, the transcript including first text for the audio data; selecting an intent definition from a plurality of intent definitions, the intent definition selected based on a first textual expression included in the intent definition and on the first text; and generating second text using the first text and the intent definition, wherein the second text corresponds to a second textual expression included in the intent definition, and wherein a portion of the second text matches the first text.
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8. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, performing automatic speech recognition using audio data received from a client device to obtain a transcript of the audio data, the transcript including first text for the audio data; selecting an intent definition from a plurality of intent definitions, the intent definition selected based on a first textual expression included in the intent definition and on the first text; and generating second text using the first text and the intent definition, wherein the second text corresponds to a second textual expression included in the intent definition, and wherein a portion of the second text matches the first text. 13. The computer-implemented method of claim 8 , wherein performing automatic speech recognition comprises generating alternate text corresponding to the received audio data, and wherein the intent definition is selected using the alternate text corresponding to the received audio data.
| 0.701042 |
9,613,267 | 1 | 10 |
1. A computer implemented method of extracting structural label and value pairwise data associated with a digital version of a document, the method comprising: a) performing a layout analysis of the digital version of the document to generate one or more layout structures associated with the document, each layout structure including a plurality of structural elements vertically or horizontally aligned where each structural element is defined as a typographical box including one or more lines of textual elements associated with the digital version of the document; b) processing the one or more lines of textual elements to identify and tag textual elements associated with label and value pairwise data; c) processing the one or more layout structures and associated tagged textual elements to generate one or more respective label:value sequences of tagged textual elements representative of a respective layout structure; and d) extracting label and value pairwise data from the one or more label:value sequences of tagged elements, wherein step c) generates the one or more respective label:value sequences of tagged textual element using a sequence-based method to hierarchically segment a sequence of elements associated with the generated one or more layout structures associated with the digital version of the document; and wherein the sequence-based method includes: generating a set of n-prams from the sequence of elements, an n-gram including an ordered sequence of n features provided by a sequence of n named elements; electing sequential n-grams from the set of n-grams, the sequential n-grams defined as similar contiguous n-grams; selecting a most frequent sequential n-pram from the elected sequential n-grams; and generating a new sequence of the elements by matching the selected most frequent sequential n-gram against the sequence of elements associated with the document, replacing matched elements of the sequence of elements with a respective node, and associating the matched elements of the sequence of elements as children of the respective node.
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1. A computer implemented method of extracting structural label and value pairwise data associated with a digital version of a document, the method comprising: a) performing a layout analysis of the digital version of the document to generate one or more layout structures associated with the document, each layout structure including a plurality of structural elements vertically or horizontally aligned where each structural element is defined as a typographical box including one or more lines of textual elements associated with the digital version of the document; b) processing the one or more lines of textual elements to identify and tag textual elements associated with label and value pairwise data; c) processing the one or more layout structures and associated tagged textual elements to generate one or more respective label:value sequences of tagged textual elements representative of a respective layout structure; and d) extracting label and value pairwise data from the one or more label:value sequences of tagged elements, wherein step c) generates the one or more respective label:value sequences of tagged textual element using a sequence-based method to hierarchically segment a sequence of elements associated with the generated one or more layout structures associated with the digital version of the document; and wherein the sequence-based method includes: generating a set of n-prams from the sequence of elements, an n-gram including an ordered sequence of n features provided by a sequence of n named elements; electing sequential n-grams from the set of n-grams, the sequential n-grams defined as similar contiguous n-grams; selecting a most frequent sequential n-pram from the elected sequential n-grams; and generating a new sequence of the elements by matching the selected most frequent sequential n-gram against the sequence of elements associated with the document, replacing matched elements of the sequence of elements with a respective node, and associating the matched elements of the sequence of elements as children of the respective node. 10. The computer implemented method of extracting label and value pairwise data according to claim 1 , wherein the label and value pairwise data is associated with an invoice.
| 0.767287 |
8,285,736 | 13 | 14 |
13. A system, comprising: a processor; and a memory containing a program, which when executed on the processor, performs an operation, comprising: presenting, in a query interface, an abstract query comprising a plurality of conditional expressions arranged within multiple nested logical levels separated by one or more logical operators, wherein the plurality of conditional expressions is defined in an abstraction model that defines logical fields, wherein each of the defined logical fields is associated with a respective access method specifying a manner of mapping the logical field to a corresponding one or more physical fields of the underlying physical data, and wherein the respective access method is selected from two or more different types of access methods; receiving a selection, from the plurality of conditional expressions, of one or more conditional expressions to be disabled in the abstract query; presenting the abstract query in the query interface, wherein each of the selected one or more conditional expressions is presented with an indication communicating that the respective conditional expression is disabled, such that each of the selected one or more conditional expressions is maintained in a respective original location within the multiple nested logical levels of the abstract query, wherein the indication comprises at least one of: (i) a font color, (ii) a struck-out font, (iii) a background color, and (iv) a symbol; executing the abstract query excluding the selected one or more conditional expressions, comprising: generating a temporary abstract query based on the abstract query and the selected one or more conditional expressions, wherein the temporary abstract query excludes the selected one or more conditional expressions; transforming the generated temporary abstract query into an executable query; and executing the executable query against the underlying physical data to produce a set of query results; and returning a set of results of executing the abstract query to the query interface.
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13. A system, comprising: a processor; and a memory containing a program, which when executed on the processor, performs an operation, comprising: presenting, in a query interface, an abstract query comprising a plurality of conditional expressions arranged within multiple nested logical levels separated by one or more logical operators, wherein the plurality of conditional expressions is defined in an abstraction model that defines logical fields, wherein each of the defined logical fields is associated with a respective access method specifying a manner of mapping the logical field to a corresponding one or more physical fields of the underlying physical data, and wherein the respective access method is selected from two or more different types of access methods; receiving a selection, from the plurality of conditional expressions, of one or more conditional expressions to be disabled in the abstract query; presenting the abstract query in the query interface, wherein each of the selected one or more conditional expressions is presented with an indication communicating that the respective conditional expression is disabled, such that each of the selected one or more conditional expressions is maintained in a respective original location within the multiple nested logical levels of the abstract query, wherein the indication comprises at least one of: (i) a font color, (ii) a struck-out font, (iii) a background color, and (iv) a symbol; executing the abstract query excluding the selected one or more conditional expressions, comprising: generating a temporary abstract query based on the abstract query and the selected one or more conditional expressions, wherein the temporary abstract query excludes the selected one or more conditional expressions; transforming the generated temporary abstract query into an executable query; and executing the executable query against the underlying physical data to produce a set of query results; and returning a set of results of executing the abstract query to the query interface. 14. The system of claim 13 , wherein transforming the abstract query is performed on the basis of the abstraction model.
| 0.71564 |
9,208,134 | 1 | 2 |
1. A method implemented in a computer infrastructure, comprising: determining an attribute of a current character in input text, the attribute of the current character indicating one or more classes of characters the current character is assigned thereto; determining one or more attributes of one or more next characters in the input text, the one or more attributes of the one or more next characters indicating the one or more classes the one or more next characters are assigned thereto; and constructing a token of the input text that comprises the current character and the one or more next characters, the attribute of the current character and the one or more attributes of the one or more next characters intersecting with each other, wherein the attribute of the current character and the one or more attributes of the one or more next characters comprises an attribute data structure which comprises a one-byte array, and wherein the one-byte array comprises a plurality of binary bits and each bit of the binary bits indicates a different class from remaining bits of the binary bits.
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1. A method implemented in a computer infrastructure, comprising: determining an attribute of a current character in input text, the attribute of the current character indicating one or more classes of characters the current character is assigned thereto; determining one or more attributes of one or more next characters in the input text, the one or more attributes of the one or more next characters indicating the one or more classes the one or more next characters are assigned thereto; and constructing a token of the input text that comprises the current character and the one or more next characters, the attribute of the current character and the one or more attributes of the one or more next characters intersecting with each other, wherein the attribute of the current character and the one or more attributes of the one or more next characters comprises an attribute data structure which comprises a one-byte array, and wherein the one-byte array comprises a plurality of binary bits and each bit of the binary bits indicates a different class from remaining bits of the binary bits. 2. The method of claim 1 , further comprising setting the attribute data structure of the current character and the one or more next characters, to assign the current character and the one or more next characters to the one or more classes.
| 0.643917 |
8,649,572 | 21 | 23 |
21. A non-transitory computer readable medium that stores instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: analyzing an image to detect a portion of the image that corresponds to a person, and to recognize information from the image for the person; determining a set of metadata for the image, wherein the set of metadata includes the recognized information, and data that identifies the portion of the image apart from a remainder of the image; associating a set of permissions with the set of metadata; providing the set of metadata with the image in accordance with the set of permissions; wherein providing the set of metadata includes: enabling the portion of the image depicting the person to be selectable separate from the remainder of the image, and providing the recognized information with the image of the person in response to a user selecting the portion of the image that corresponds to the person.
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21. A non-transitory computer readable medium that stores instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: analyzing an image to detect a portion of the image that corresponds to a person, and to recognize information from the image for the person; determining a set of metadata for the image, wherein the set of metadata includes the recognized information, and data that identifies the portion of the image apart from a remainder of the image; associating a set of permissions with the set of metadata; providing the set of metadata with the image in accordance with the set of permissions; wherein providing the set of metadata includes: enabling the portion of the image depicting the person to be selectable separate from the remainder of the image, and providing the recognized information with the image of the person in response to a user selecting the portion of the image that corresponds to the person. 23. The computer-readable medium of claim 21 , wherein the instructions for analyzing the image include instructions for determining an identifier of the person depicted in the image based on the recognized information.
| 0.638614 |
10,163,061 | 1 | 2 |
1. A computer-implemented method of quality-directed adaptive analytic retraining, comprising: receiving training example data with which to retrain a machine learning model that has been previously trained; storing the training example data in a memory; evaluating the machine learning model at least by running the machine learning model with the training example data; determining a normalized quality measure based on the evaluating; determining whether to retrain the machine learning model at least based on the normalized quality measure; and responsive to determining that the machine learning model is to be retrained, retraining the machine learning model, wherein the machine learning model is not retrained if it is determined that the machine learning model is not to be retrained, wherein determining whether to retrain the machine learning model at least based on the normalized quality measure, comprises: determining whether the quality measure is below a quality threshold; and determining whether a number of available data items comprising at least the training example data meet a specified number of inertia window data items; wherein responsive to determining that the quality measure is below the quality threshold and the number of available data items comprising at least the training example data meets the specified number of inertia window data items, the machine learning model is retrained.
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1. A computer-implemented method of quality-directed adaptive analytic retraining, comprising: receiving training example data with which to retrain a machine learning model that has been previously trained; storing the training example data in a memory; evaluating the machine learning model at least by running the machine learning model with the training example data; determining a normalized quality measure based on the evaluating; determining whether to retrain the machine learning model at least based on the normalized quality measure; and responsive to determining that the machine learning model is to be retrained, retraining the machine learning model, wherein the machine learning model is not retrained if it is determined that the machine learning model is not to be retrained, wherein determining whether to retrain the machine learning model at least based on the normalized quality measure, comprises: determining whether the quality measure is below a quality threshold; and determining whether a number of available data items comprising at least the training example data meet a specified number of inertia window data items; wherein responsive to determining that the quality measure is below the quality threshold and the number of available data items comprising at least the training example data meets the specified number of inertia window data items, the machine learning model is retrained. 2. The method of claim 1 , wherein the evaluating comprises performing a prediction by running the machine learning model with the training example data, and updating one or more of a loss function for supervised learning or a cost function for unsupervised learning.
| 0.562295 |
9,405,835 | 15 | 20 |
15. A non-transitory machine-readable medium comprising stored instructions, wherein the instructions, when executed, cause a machine to: receive a plurality of factors, a test set of items, and an ordering solution representing a preferred ordering of the test set of items; generate a potential ranking function based on the plurality of factors; apply, using a processor, the potential ranking function to each item in the test set of items to generate an ordering of items associated with the potential ranking function; compare the ordering of items with the ordering solution; identify, based on the result of the comparing, the potential ranking function as a solution ranking function; and assign a ranking score to items in a set of active items, the ranking scores assigned to the items determined based on the solution ranking function.
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15. A non-transitory machine-readable medium comprising stored instructions, wherein the instructions, when executed, cause a machine to: receive a plurality of factors, a test set of items, and an ordering solution representing a preferred ordering of the test set of items; generate a potential ranking function based on the plurality of factors; apply, using a processor, the potential ranking function to each item in the test set of items to generate an ordering of items associated with the potential ranking function; compare the ordering of items with the ordering solution; identify, based on the result of the comparing, the potential ranking function as a solution ranking function; and assign a ranking score to items in a set of active items, the ranking scores assigned to the items determined based on the solution ranking function. 20. The non-transitory machine-readable medium of claim 15 , further comprising instructions, which when executed, cause a machine to: modify the potential ranking function using another ranking function.
| 0.813187 |
9,430,563 | 1 | 3 |
1. An apparatus comprising: an electronic data processing device configured to: perform a modeling method including: constructing a set of word embedding transforms by operations including generating a term-document matrix whose elements represent occurrence frequencies for text words in documents of a set of documents and include inverse document frequency (IDF) scaling; applying the set of word embedding transforms to transform text words of a set of documents into K-dimensional word vectors in order to generate sets or sequences of word vectors representing the documents of the set of documents where K is an integer greater than or equal to two; and learning a probabilistic topic model comprising a mixture model including M mixture components representing M topics using the sets or sequences of word vectors representing the documents of the set of documents wherein the learned probabilistic topic model operates to assign probabilities for the topics of the probabilistic topic model to an input set or sequence of K-dimensional embedded word vectors; and perform a document processing method including: applying the set of word embedding transforms to transform text words of an input document into K-dimensional word vectors in order to generate a set or sequence of word vectors representing the input document; and applying the learned mixture model to the set or sequence of word vectors representing the input document in order to generate one of (1) a vector or histogram of topic probabilities representing the input document or (2) one or more Fisher vectors representing the input document.
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1. An apparatus comprising: an electronic data processing device configured to: perform a modeling method including: constructing a set of word embedding transforms by operations including generating a term-document matrix whose elements represent occurrence frequencies for text words in documents of a set of documents and include inverse document frequency (IDF) scaling; applying the set of word embedding transforms to transform text words of a set of documents into K-dimensional word vectors in order to generate sets or sequences of word vectors representing the documents of the set of documents where K is an integer greater than or equal to two; and learning a probabilistic topic model comprising a mixture model including M mixture components representing M topics using the sets or sequences of word vectors representing the documents of the set of documents wherein the learned probabilistic topic model operates to assign probabilities for the topics of the probabilistic topic model to an input set or sequence of K-dimensional embedded word vectors; and perform a document processing method including: applying the set of word embedding transforms to transform text words of an input document into K-dimensional word vectors in order to generate a set or sequence of word vectors representing the input document; and applying the learned mixture model to the set or sequence of word vectors representing the input document in order to generate one of (1) a vector or histogram of topic probabilities representing the input document or (2) one or more Fisher vectors representing the input document. 3. The apparatus of claim 1 , wherein the document processing method further includes: identifying one or more documents other than the input document as being similar to the input document based on the vector, Fisher vector, or histogram of topic probabilities representing the input document.
| 0.611111 |
8,033,744 | 1 | 5 |
1. A compact one-handed keyboard for a hand held computer device including an array of keys comprising characters of an alphabet of a language, wherein the array includes: (a) first keys arranged together in a contiguous alphabetical sequence of at least five characters, said first keys, when pressed, providing computer input sending signals corresponding to respective frequently used vowel characters; (b) second keys arranged adjacent to the first keys, said keys, when pressed, providing computer input sending signals corresponding to respective frequently used consonant characters; and (c) third keys arranged in positions remote from the first keys, said third keys, when pressed, providing computer input sending signals corresponding to respective infrequently used consonant characters, wherein the second keys are arranged in alphabetical order adjacent to the first keys, wherein a first set of the second keys substantially forming the first third of the letters of the alphabet are arranged in a first row; a second set of the second keys substantially forming the middle third of the letters of the alphabet are arranged in a left corner; and a third set of the second keys substantially forming the last third of the letters of the alphabet are arranged in a right corner; and the first keys are arranged in a another row located between left and right corners, wherein one or more of the second keys are arranged adjacent to the first keys that they are frequently paired together with in words of the language, and wherein the array of keys includes seven or less columns and four or more rows.
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1. A compact one-handed keyboard for a hand held computer device including an array of keys comprising characters of an alphabet of a language, wherein the array includes: (a) first keys arranged together in a contiguous alphabetical sequence of at least five characters, said first keys, when pressed, providing computer input sending signals corresponding to respective frequently used vowel characters; (b) second keys arranged adjacent to the first keys, said keys, when pressed, providing computer input sending signals corresponding to respective frequently used consonant characters; and (c) third keys arranged in positions remote from the first keys, said third keys, when pressed, providing computer input sending signals corresponding to respective infrequently used consonant characters, wherein the second keys are arranged in alphabetical order adjacent to the first keys, wherein a first set of the second keys substantially forming the first third of the letters of the alphabet are arranged in a first row; a second set of the second keys substantially forming the middle third of the letters of the alphabet are arranged in a left corner; and a third set of the second keys substantially forming the last third of the letters of the alphabet are arranged in a right corner; and the first keys are arranged in a another row located between left and right corners, wherein one or more of the second keys are arranged adjacent to the first keys that they are frequently paired together with in words of the language, and wherein the array of keys includes seven or less columns and four or more rows. 5. The one-handed keyboard claimed in claim 1 , wherein one or more of the third keys are arranged adjacent to the second keys that they are frequently paired together with in words of the language.
| 0.674342 |
9,697,824 | 10 | 11 |
10. A system for facilitating a user to control a driving apparatus through a voice command, the system comprising one or more processors configured by machine-readable instructions to perform: receiving a user voice input; determining the user voice input is associated with a language dialect; translating the voice input to a standard voice pattern based on the language dialect associated with the user voice input; based on the standard voice pattern, determining a control command corresponding to the user voice input for maneuvering the driving apparatus; and effectuating execution of the control command to control the driving apparatus.
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10. A system for facilitating a user to control a driving apparatus through a voice command, the system comprising one or more processors configured by machine-readable instructions to perform: receiving a user voice input; determining the user voice input is associated with a language dialect; translating the voice input to a standard voice pattern based on the language dialect associated with the user voice input; based on the standard voice pattern, determining a control command corresponding to the user voice input for maneuvering the driving apparatus; and effectuating execution of the control command to control the driving apparatus. 11. The system of claim 10 , wherein the user voice input includes information indicating a translational and/or a rotational maneuver of the driving apparatus.
| 0.879336 |
7,606,425 | 4 | 5 |
4. The method of claim 1 , wherein the feature vector is a multi-dimensional vector.
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4. The method of claim 1 , wherein the feature vector is a multi-dimensional vector. 5. The method of claim 4 , wherein said multi-dimensional vector is a three-dimensional vector.
| 0.5 |
10,049,032 | 9 | 10 |
9. A data management computing device comprising: a processor; and a memory coupled to the processor which is configured to be capable of executing programmed instructions comprising and stored in the memory to: extract a set of textual attributes and a set of textual attribute properties from a requirement specification; frame a constraint representation syntax from the extracted set of attribute properties; model a structured diagram from the framed constraint representation syntax and a set of use cases; construct a set of path predicates from the structured diagram; determine one or more attribute classes from the set of path predicates based on an attribute constraint and an attribute dependency; generate the negative test input data for the one or more attribute classes; and execute the generated negative test input data to test a model of an application.
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9. A data management computing device comprising: a processor; and a memory coupled to the processor which is configured to be capable of executing programmed instructions comprising and stored in the memory to: extract a set of textual attributes and a set of textual attribute properties from a requirement specification; frame a constraint representation syntax from the extracted set of attribute properties; model a structured diagram from the framed constraint representation syntax and a set of use cases; construct a set of path predicates from the structured diagram; determine one or more attribute classes from the set of path predicates based on an attribute constraint and an attribute dependency; generate the negative test input data for the one or more attribute classes; and execute the generated negative test input data to test a model of an application. 10. The device of claim 9 , wherein the attribute class comprises a string attribute class, a numeric attribute class with boundary constraints, or a numeric attribute class with relational constraints.
| 0.717877 |
8,510,302 | 1 | 5 |
1. A method, performed in a computer, for synthesizing one or more relationships between a plurality of concept definitions automatically derived from a faceted domain of information, wherein the information of the domain is classifiable according to a plurality of facets each having a plurality of facet attributes, wherein each of the concept definitions comprises at least one of the plurality of facet attributes, the method comprising: receiving user input specifying an active concept definition; identifying at least one facet attribute in the active concept definition; determining whether any explicit relationships exist between the active concept definition and a first concept definition of the plurality of concept definitions derived from the domain of information, wherein an explicit relationship is determined to exist if a facet attribute of the active concept definition and a facet attribute of the first concept definition are of a same lineage in at least one facet attribute hierarchy of the plurality of facet attributes; determining whether any implicit relationships exist between the active concept definition and the first concept definition, wherein an implicit relationship is determined to exist if the active concept definition and the first concept definition share at least one common facet attribute; in response to determining that at least one explicit relationship, at least one implicit relationship, or at least one explicit relationship and at least one implicit relationship exist between the active concept definition and the first concept definition, synthesizing, using the computer, a relationship between the active concept definition and the first concept definition; and generating a dimensional concept hierarchy based on dimensional concept relationships synthesized between the active concept definition and the plurality of concept definitions derived from the domain of information.
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1. A method, performed in a computer, for synthesizing one or more relationships between a plurality of concept definitions automatically derived from a faceted domain of information, wherein the information of the domain is classifiable according to a plurality of facets each having a plurality of facet attributes, wherein each of the concept definitions comprises at least one of the plurality of facet attributes, the method comprising: receiving user input specifying an active concept definition; identifying at least one facet attribute in the active concept definition; determining whether any explicit relationships exist between the active concept definition and a first concept definition of the plurality of concept definitions derived from the domain of information, wherein an explicit relationship is determined to exist if a facet attribute of the active concept definition and a facet attribute of the first concept definition are of a same lineage in at least one facet attribute hierarchy of the plurality of facet attributes; determining whether any implicit relationships exist between the active concept definition and the first concept definition, wherein an implicit relationship is determined to exist if the active concept definition and the first concept definition share at least one common facet attribute; in response to determining that at least one explicit relationship, at least one implicit relationship, or at least one explicit relationship and at least one implicit relationship exist between the active concept definition and the first concept definition, synthesizing, using the computer, a relationship between the active concept definition and the first concept definition; and generating a dimensional concept hierarchy based on dimensional concept relationships synthesized between the active concept definition and the plurality of concept definitions derived from the domain of information. 5. The method of claim 1 , further comprising: defining a limit on a number of relationships to synthesize between the active concept definition and the plurality of concept definitions derived from the domain of information.
| 0.663174 |
7,966,327 | 2 | 4 |
2. A method of searching a plurality of stored objects according to claim 1 , wherein said step of finding objects comprises the steps of: defining a similarity distance between said objects using a weighted distance function based upon said collection of sketches; and finding objects closest to a query object based upon said weighted distance function.
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2. A method of searching a plurality of stored objects according to claim 1 , wherein said step of finding objects comprises the steps of: defining a similarity distance between said objects using a weighted distance function based upon said collection of sketches; and finding objects closest to a query object based upon said weighted distance function. 4. A method of searching a plurality of stored objects according to claim 2 , wherein said weighted distance function comprises an Earth Mover's Distance.
| 0.798956 |
9,031,845 | 30 | 31 |
30. The method of claim 18 , further comprising: identifying, by the one or more physical processors, a second command or query based on the natural language utterance; determining, by the one or more physical processors, based on the natural language utterance, whether the second command or query is to be executed on-board or off-board the vehicle; executing, by the one or more physical processors, the second command or query at the vehicle in response to a determination that the second command or query is to be executed on-board the vehicle; and invoking, by the one or more physical processors, a device that communicates wirelessly over a wide area network to process the second command or query such that the second command or query is executed off-board the vehicle in response to a determination that the second command or query is to be executed off-board the vehicle.
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30. The method of claim 18 , further comprising: identifying, by the one or more physical processors, a second command or query based on the natural language utterance; determining, by the one or more physical processors, based on the natural language utterance, whether the second command or query is to be executed on-board or off-board the vehicle; executing, by the one or more physical processors, the second command or query at the vehicle in response to a determination that the second command or query is to be executed on-board the vehicle; and invoking, by the one or more physical processors, a device that communicates wirelessly over a wide area network to process the second command or query such that the second command or query is executed off-board the vehicle in response to a determination that the second command or query is to be executed off-board the vehicle. 31. The method of claim 30 , wherein the command or query is executed at the vehicle and the second command or query is executed off-board the vehicle, or wherein the command or query is executed off-board the vehicle and the second command or query is executed on-board the vehicle.
| 0.5 |
7,985,243 | 1 | 12 |
1. An implant adapted to be implanted in a spine of a patient comprising: a shield with a shield cavity; a deflection rod that is mounted in said shield cavity; said deflection rod able to be deflected in said shield cavity and the motion of said deflection rod is limited by the shield cavity; a connector located at an end of said deflection rod; a rod connected to said connector; and a mount extending from said shield.
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1. An implant adapted to be implanted in a spine of a patient comprising: a shield with a shield cavity; a deflection rod that is mounted in said shield cavity; said deflection rod able to be deflected in said shield cavity and the motion of said deflection rod is limited by the shield cavity; a connector located at an end of said deflection rod; a rod connected to said connector; and a mount extending from said shield. 12. The implant of claim 1 wherein said shield has a first end and a second end with said deflection rod extending out of said second end and with said mount mounted adjacent one of said first end and said second end.
| 0.652244 |
8,874,605 | 12 | 13 |
12. A computer-readable non-transitory storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising: receiving a request from a user for one or more recommendations; determining a location distribution from historical location information associated with the user, wherein the location distribution indicates one or more probability distributions for one or more locations; determining a time distribution from historical context information associated with the user, wherein the time distribution indicates one or more probability distributions for one or more contexts, wherein a respective context includes contextual variables corresponding to at least one of: a location; a time; a weather condition; and a venue type; determining a joint distribution of two or more of the contextual variables that are based on the location distribution and the time distribution, wherein the joint distribution comprises statistical attributes that characterize dependent relationships between the two or more of the contextual variables, the statistical attributes including one or more of: a mean value; a median value; a mode; a maximum value; and a minimum value of the contextual variables; determining a hypothetical context based on the statistical attributes of the joint distribution and a current context, wherein the hypothetical context indicates one or more user preferences that are outside of the user's current context and explicit historical contexts; producing one or more recommendations for the user based on the hypothetical context; constructing a mapping function that maps a user context to a hypothetical context, wherein constructing the mapping function involves using one or more of: historical contexts associated with the request; prior hypothetical contexts; and the user's interactions associated with the user's past requests for a recommendation; and providing the one or more recommendations to the user.
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12. A computer-readable non-transitory storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising: receiving a request from a user for one or more recommendations; determining a location distribution from historical location information associated with the user, wherein the location distribution indicates one or more probability distributions for one or more locations; determining a time distribution from historical context information associated with the user, wherein the time distribution indicates one or more probability distributions for one or more contexts, wherein a respective context includes contextual variables corresponding to at least one of: a location; a time; a weather condition; and a venue type; determining a joint distribution of two or more of the contextual variables that are based on the location distribution and the time distribution, wherein the joint distribution comprises statistical attributes that characterize dependent relationships between the two or more of the contextual variables, the statistical attributes including one or more of: a mean value; a median value; a mode; a maximum value; and a minimum value of the contextual variables; determining a hypothetical context based on the statistical attributes of the joint distribution and a current context, wherein the hypothetical context indicates one or more user preferences that are outside of the user's current context and explicit historical contexts; producing one or more recommendations for the user based on the hypothetical context; constructing a mapping function that maps a user context to a hypothetical context, wherein constructing the mapping function involves using one or more of: historical contexts associated with the request; prior hypothetical contexts; and the user's interactions associated with the user's past requests for a recommendation; and providing the one or more recommendations to the user. 13. The computer-readable non-transitory storage medium of claim 12 , wherein determining the hypothetical context comprises: applying the mapping function to a current context; and producing the hypothetical context as an output of the mapping function.
| 0.863294 |
9,871,715 | 7 | 8 |
7. The method according to claim 1 , wherein the web server hosts a Web-based application, and wherein the target communication protocol pertains to the Web-based application.
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7. The method according to claim 1 , wherein the web server hosts a Web-based application, and wherein the target communication protocol pertains to the Web-based application. 8. The method according to claim 7 , wherein the Web-based application comprises one of a Web-based e-mail application, an instant-messaging application and a social network application.
| 0.5 |
7,860,705 | 14 | 19 |
14. A context adaptable speech-to-speech translation system comprising: a memory at least one processor implementing: a plurality of classifiers, wherein each of the plurality of classifiers receives a corresponding input signal and generates a corresponding set of paralinguistic attribute values; a fusion module that receives a plurality of sets of paralinguistic attribute values from the plurality of classifiers and generates a final set of paralinguistic attribute values; and speech-to-speech translation modules comprising a speech recognition module, a translation module, and a text-to-speech module, wherein performance of at least one of the speech recognition module, the translation module and the text-to-speech module are modified in accordance with the final set of paralinguistic attribute values for the plurality of input signals; wherein the set of paralinguistic attribute values that each classifier generates is represented by a vector signal output by the classifier, the vector signal comprising two or more values corresponding to two or more paralinguistic attributes of interest such that the step of generating the final set of paralinguistic attribute values performed by the fusion module comprises combining each of the vector signals from each of the classifiers by combining values of common paralinguistic attributes of interest across the vector signals to yield a separate decision value for each of the two or more paralinguistic attributes of interest, the final set of paralinguistic attribute values comprising a plurality of decision values corresponding to respective ones of the two or more paralinguistic attributes of interest.
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14. A context adaptable speech-to-speech translation system comprising: a memory at least one processor implementing: a plurality of classifiers, wherein each of the plurality of classifiers receives a corresponding input signal and generates a corresponding set of paralinguistic attribute values; a fusion module that receives a plurality of sets of paralinguistic attribute values from the plurality of classifiers and generates a final set of paralinguistic attribute values; and speech-to-speech translation modules comprising a speech recognition module, a translation module, and a text-to-speech module, wherein performance of at least one of the speech recognition module, the translation module and the text-to-speech module are modified in accordance with the final set of paralinguistic attribute values for the plurality of input signals; wherein the set of paralinguistic attribute values that each classifier generates is represented by a vector signal output by the classifier, the vector signal comprising two or more values corresponding to two or more paralinguistic attributes of interest such that the step of generating the final set of paralinguistic attribute values performed by the fusion module comprises combining each of the vector signals from each of the classifiers by combining values of common paralinguistic attributes of interest across the vector signals to yield a separate decision value for each of the two or more paralinguistic attributes of interest, the final set of paralinguistic attribute values comprising a plurality of decision values corresponding to respective ones of the two or more paralinguistic attributes of interest. 19. The context adaptable speech-to-speech translation system of claim 14 , wherein the plurality of decision values comprise values for at least two of gender, accent, age, intonation, emotion, social background and educational level of a speaker.
| 0.769517 |
8,910,120 | 1 | 4 |
1. A computer-implemented process for creating a database of characterized software bug descriptions, comprising: using a computer to perform the following process actions: (a) inputting a software bug description which provides information about a previously resolved or yet to be resolved software bug occurring in a software program, and which comprises more than the software code associated with a software bug; (b) employing a group of software-specific feature extractors designed for said software program, wherein each extractor recognizes and extracts a different feature from the software bug description, and wherein said software-specific feature extractors recognize and extract features which comprise more than the software code associated with the software bug; (c) generating a typed document comprising features extracted from the software bug description; (d) transforming the typed document into a bag of words, wherein order of the extracted features of the typed document in the bag of words is irrelevant and a bag of words having extracted features exhibiting a first order is equivalent to a bag of words having the same extracted features in a different order; (e) storing the bag of words in a database; (f) repeating actions (a)-(e) for a plurality of additional software bug descriptions associated with previously resolved or yet to be resolved software bugs, or both, that occurred in said software program; and (g) generating a full-text searchable index of the bags of words representing the first-input and additional software bug descriptions in the database.
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1. A computer-implemented process for creating a database of characterized software bug descriptions, comprising: using a computer to perform the following process actions: (a) inputting a software bug description which provides information about a previously resolved or yet to be resolved software bug occurring in a software program, and which comprises more than the software code associated with a software bug; (b) employing a group of software-specific feature extractors designed for said software program, wherein each extractor recognizes and extracts a different feature from the software bug description, and wherein said software-specific feature extractors recognize and extract features which comprise more than the software code associated with the software bug; (c) generating a typed document comprising features extracted from the software bug description; (d) transforming the typed document into a bag of words, wherein order of the extracted features of the typed document in the bag of words is irrelevant and a bag of words having extracted features exhibiting a first order is equivalent to a bag of words having the same extracted features in a different order; (e) storing the bag of words in a database; (f) repeating actions (a)-(e) for a plurality of additional software bug descriptions associated with previously resolved or yet to be resolved software bugs, or both, that occurred in said software program; and (g) generating a full-text searchable index of the bags of words representing the first-input and additional software bug descriptions in the database. 4. The process of claim 1 , wherein the process action of generating a full-text searchable index of the bags of words representing the first-input and additional software bug descriptions in the database, comprises the action of employing a Term Frequency and Inverse Document Frequency (TF-IDF) search engine to generate the index.
| 0.5 |
8,504,355 | 1 | 9 |
1. A method of resolving both semantic and syntactic ambiguity, comprising: generating, by a computer, combinations of semantic interpretation choices for each of at least two alternative syntactic parses of a natural language expression or part thereof; and selecting, by the computer, one or more best combinations from those generated for all of the alternative syntactic parses.
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1. A method of resolving both semantic and syntactic ambiguity, comprising: generating, by a computer, combinations of semantic interpretation choices for each of at least two alternative syntactic parses of a natural language expression or part thereof; and selecting, by the computer, one or more best combinations from those generated for all of the alternative syntactic parses. 9. The method of claim 1 , wherein each combination comprises exactly one choice for each ambiguous aspect in the corresponding alternative syntactic parse.
| 0.633803 |
8,078,961 | 1 | 8 |
1. An SGML validation system comprising: an XML validation engine; a translator configured to convert an SGML document to a translated XML document using a p-isomorphic translation; an XML schema against which the XML validation engine validates the translated XML document, the XML schema configured such that errors detected by the validation of the translated XML document correspond to SGML errors in the SGML document; and a report generator configured to generate an error report identifying SGML errors corresponding with errors detected by the validation and linking the identified SGML errors with corresponding locations in the SGML document; wherein the XML validation engine, the translator, and the report generator are embodied as a digital processing device.
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1. An SGML validation system comprising: an XML validation engine; a translator configured to convert an SGML document to a translated XML document using a p-isomorphic translation; an XML schema against which the XML validation engine validates the translated XML document, the XML schema configured such that errors detected by the validation of the translated XML document correspond to SGML errors in the SGML document; and a report generator configured to generate an error report identifying SGML errors corresponding with errors detected by the validation and linking the identified SGML errors with corresponding locations in the SGML document; wherein the XML validation engine, the translator, and the report generator are embodied as a digital processing device. 8. The SGML validation system as set forth in claim 1 , further comprising: an business rules analyzer configured to generate at least a portion of the XML schema from a set of business rules.
| 0.786667 |
8,432,368 | 18 | 24 |
18. A computing device, comprising: a case; a processor positioned within the case; a memory coupled to the processor, the memory storing a reference signal template; and a force sensitive sensor positioned on the case and coupled to the processor, wherein the processor is configured with processor-executable instructions to perform operations comprising: receiving an electrical signal from the force sensitive sensor; comparing the received electrical signal to each of a plurality of reference signal templates; calculating cross-correlation values of the received electrical signal and each of the plurality of reference signal templates; determining a best match reference signal template for the received electrical signal based on the cross-correlation values; identifying a functionality associated with the best match reference signal template; and implementing the identified functionality on the computing device.
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18. A computing device, comprising: a case; a processor positioned within the case; a memory coupled to the processor, the memory storing a reference signal template; and a force sensitive sensor positioned on the case and coupled to the processor, wherein the processor is configured with processor-executable instructions to perform operations comprising: receiving an electrical signal from the force sensitive sensor; comparing the received electrical signal to each of a plurality of reference signal templates; calculating cross-correlation values of the received electrical signal and each of the plurality of reference signal templates; determining a best match reference signal template for the received electrical signal based on the cross-correlation values; identifying a functionality associated with the best match reference signal template; and implementing the identified functionality on the computing device. 24. The computing device of claim 18 , wherein the processor is configured with processor-executable instructions such that comparing the received electrical signal with a reference signal template, and determining whether the received electrical signal matches the reference signal template comprises: converting at least a portion of the received electrical signal into a frequency domain signal portion; calculating cross-correlation values of the frequency domain portion and each of a plurality of reference templates; determining a best correlation value; and determining whether the correlation value is above a threshold value.
| 0.580581 |
8,266,148 | 1 | 3 |
1. A machine-implemented method for a pipelined process of capture, classification and dimensioning of data from a plurality of data sources that include unstructured data having no explicit dimensions associated with the unstructured data to generate a domain-relevant classified data index that is useable by a plurality of different intelligence metrics to perform different kinds of business intelligence analytics, the method comprising: using a data processing machine to collect ingested data as one or more documents from each of the plurality of data sources that include unstructured data and automatically generate and store an ingested data index representing the ingested data that includes at least a hyperlink and extracted meta data for each document; using a data processing machine to automatically classify each of the one or more documents into one or more relevance classifications that are stored with the ingested data index for that document to form a domain-relevant classified data index representing the ingested data, wherein the relevance classifications are based on a plurality of dynamically generated topics that are generated in response to machine analysis that includes machine-defined classifiers and in response to machine-prompted user input that distinguishes between user-defined named-entities and user-defined keywords and includes hierarchy information for establishing a hierarchical relationship among the one or more relevance classifications; and using a data processing machine to automatically process the plurality of data sources with a plurality of different intelligence metric modules independent of and after the one or more documents have been initially ingested and classified by utilizing the domain-relevant classified data index to generate analytics results that are presented for a user, including processing at least one of the documents in the ingested data with each intelligence metric module based upon a plurality of dimensions abstracted from the relevance classifications and the extracted metadata that includes at least one implicit dimension derived from one or more of the user-defined named-entities, wherein the intelligence metric modules do not modify the ingested data index, and the dynamically generated topics upon which the relevance classifications are based are not determined prior to using the data processing machine to collect ingested data based upon analytic requirements of the intelligence metric modules such that the relevance classifications are separated in the pipelined process from analytic requirements of one or more of the any given intelligence metric modules.
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1. A machine-implemented method for a pipelined process of capture, classification and dimensioning of data from a plurality of data sources that include unstructured data having no explicit dimensions associated with the unstructured data to generate a domain-relevant classified data index that is useable by a plurality of different intelligence metrics to perform different kinds of business intelligence analytics, the method comprising: using a data processing machine to collect ingested data as one or more documents from each of the plurality of data sources that include unstructured data and automatically generate and store an ingested data index representing the ingested data that includes at least a hyperlink and extracted meta data for each document; using a data processing machine to automatically classify each of the one or more documents into one or more relevance classifications that are stored with the ingested data index for that document to form a domain-relevant classified data index representing the ingested data, wherein the relevance classifications are based on a plurality of dynamically generated topics that are generated in response to machine analysis that includes machine-defined classifiers and in response to machine-prompted user input that distinguishes between user-defined named-entities and user-defined keywords and includes hierarchy information for establishing a hierarchical relationship among the one or more relevance classifications; and using a data processing machine to automatically process the plurality of data sources with a plurality of different intelligence metric modules independent of and after the one or more documents have been initially ingested and classified by utilizing the domain-relevant classified data index to generate analytics results that are presented for a user, including processing at least one of the documents in the ingested data with each intelligence metric module based upon a plurality of dimensions abstracted from the relevance classifications and the extracted metadata that includes at least one implicit dimension derived from one or more of the user-defined named-entities, wherein the intelligence metric modules do not modify the ingested data index, and the dynamically generated topics upon which the relevance classifications are based are not determined prior to using the data processing machine to collect ingested data based upon analytic requirements of the intelligence metric modules such that the relevance classifications are separated in the pipelined process from analytic requirements of one or more of the any given intelligence metric modules. 3. The machine-implemented method of claim 1 wherein the plurality of data sources include text, images, video and audio and wherein using a data processing machine to collect ingested data includes: using data source connectors to access the plurality of data sources, wherein the data source connectors include one or more of internal file system connectors, web site connectors, blog connectors, subscription connectors, email connectors, short-message-service connectors.
| 0.71247 |
8,145,632 | 21 | 23 |
21. A computer system, comprising: memory; one or more processors; one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including: instructions for identifying multiple resource identifiers in accordance with a first set of predefined criteria for selecting a respective document that satisfies user-specified search keywords, each resource identifier corresponding to a document at a respective data source; instructions for retrieving the corresponding document from the respective document source for at least one of the resource identifiers; instructions for identifying within the retrieved document a chunk by applying a second set of user-specified criteria to the retrieved document; and instructions for displaying the identified chunk and a link to the identified chunk within the document to the user, wherein the first set of predefined criteria requires that all the search keywords be found within an identified respective document, and the second set of predefined criteria requires that all the search keywords be found within an identified chunk.
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21. A computer system, comprising: memory; one or more processors; one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including: instructions for identifying multiple resource identifiers in accordance with a first set of predefined criteria for selecting a respective document that satisfies user-specified search keywords, each resource identifier corresponding to a document at a respective data source; instructions for retrieving the corresponding document from the respective document source for at least one of the resource identifiers; instructions for identifying within the retrieved document a chunk by applying a second set of user-specified criteria to the retrieved document; and instructions for displaying the identified chunk and a link to the identified chunk within the document to the user, wherein the first set of predefined criteria requires that all the search keywords be found within an identified respective document, and the second set of predefined criteria requires that all the search keywords be found within an identified chunk. 23. The computer system of claim 21 , wherein the identified chunk includes an identical instance of the search keywords appearing as a phrase.
| 0.87412 |
8,402,032 | 31 | 32 |
31. The method of claim 30 , wherein the score for a name-context pair is derived from the context consistency measure for the name-context pair and an average context consistency measure for the context and all entity names in the distinct name-context pairs.
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31. The method of claim 30 , wherein the score for a name-context pair is derived from the context consistency measure for the name-context pair and an average context consistency measure for the context and all entity names in the distinct name-context pairs. 32. The method of claim 31 , wherein the score for a name-context pair is further derived from a number of times the entity name and the context term of the name-context pair appear in queries submitted to a search engine during a period of time.
| 0.5 |
8,762,225 | 11 | 12 |
11. A system comprising: one or more processors to: receive a search term; determine a location from which the search query is received; perform a search to locate a book related to the search term; determine a first score for the book based on a factor related to the search term; determine a second score for the book based on the location from which the search query is received, and a best seller list, the second score being higher when the best seller list includes the book and is for a location that matches the location from which the search query is received than when the best seller list is for a location that is different from the location from which the search query is received; assign a score to the book based on a combination of the first score and the second score; and provide the book as a search result based on the assigned score.
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11. A system comprising: one or more processors to: receive a search term; determine a location from which the search query is received; perform a search to locate a book related to the search term; determine a first score for the book based on a factor related to the search term; determine a second score for the book based on the location from which the search query is received, and a best seller list, the second score being higher when the best seller list includes the book and is for a location that matches the location from which the search query is received than when the best seller list is for a location that is different from the location from which the search query is received; assign a score to the book based on a combination of the first score and the second score; and provide the book as a search result based on the assigned score. 12. The system of claim 11 , where the factor related to the search term includes at least one of: a number of occurrences of the search term within the book, a location of the search term within the book, or a characteristic of the search term within the book.
| 0.5 |
7,624,105 | 10 | 11 |
10. The search engine of claim 1 , wherein the microprogram comprises: a bitcheck command including a bitmap embodying the specified set of characters; and a count command including at least one boundary value embodying the specified range.
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10. The search engine of claim 1 , wherein the microprogram comprises: a bitcheck command including a bitmap embodying the specified set of characters; and a count command including at least one boundary value embodying the specified range. 11. The search engine of claim 10 , wherein the microcontroller delegates the bitcheck command to the first co-processor and delegates the count command to the second co-processor.
| 0.734513 |
9,465,694 | 10 | 15 |
10. An apparatus for recovering a partition based on file system metadata, comprising: an unallocated area determination unit for determining an unallocated area in a disk or an evidence image; a Master File Table (MFT) entry collection unit for collecting MFT entries from the unallocated area; an MFT entry analysis unit for generating MFT partition candidate information by analyzing the MFT entries; and an MFT partition creation unit for creating information enabling a layout of a partition to be reconfigured based on the MFT partition candidate information, and creating a tree structure using the created information and the MFT entries, wherein the MFT partition creation unit is configured to analyze MFT entries of a corresponding sector by reading the MFT partition candidate information, calculate a location of a boot record, and calculate a total size of a volume using an MFT entry corresponding to a metadata file, and is configured to, if the calculated volume size satisfies a preset minimum size, parse the collected MFT entries and restore parsed results to a tree structure.
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10. An apparatus for recovering a partition based on file system metadata, comprising: an unallocated area determination unit for determining an unallocated area in a disk or an evidence image; a Master File Table (MFT) entry collection unit for collecting MFT entries from the unallocated area; an MFT entry analysis unit for generating MFT partition candidate information by analyzing the MFT entries; and an MFT partition creation unit for creating information enabling a layout of a partition to be reconfigured based on the MFT partition candidate information, and creating a tree structure using the created information and the MFT entries, wherein the MFT partition creation unit is configured to analyze MFT entries of a corresponding sector by reading the MFT partition candidate information, calculate a location of a boot record, and calculate a total size of a volume using an MFT entry corresponding to a metadata file, and is configured to, if the calculated volume size satisfies a preset minimum size, parse the collected MFT entries and restore parsed results to a tree structure. 15. The apparatus of claim 10 , wherein the MFT partition creation unit obtains information of the metadata file using a real attribute size item of a non-resident attribute header of $DATA attributes of the MFT entry corresponding to the metadata file, and calculates a total number of sectors of the volume based on the information of the metadata file.
| 0.704659 |
7,683,916 | 1 | 3 |
1. A system, comprising: a processor and memory; an editable-object selector capable of selecting at least one editable object; a graphics editor capable of producing a user-defined graphics edit, wherein the user-defined graphics editor is capable of editing at least part of the at least one selected editable object by adjusting at least one parameter associated with the at least one selected editable object; a graphical-template selector capable of selecting a graphical template comprising a foreground image with at least one cutout region; and a graphics-edit importer capable of importing at least a part of the user-defined graphics edit into the cutout region.
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1. A system, comprising: a processor and memory; an editable-object selector capable of selecting at least one editable object; a graphics editor capable of producing a user-defined graphics edit, wherein the user-defined graphics editor is capable of editing at least part of the at least one selected editable object by adjusting at least one parameter associated with the at least one selected editable object; a graphical-template selector capable of selecting a graphical template comprising a foreground image with at least one cutout region; and a graphics-edit importer capable of importing at least a part of the user-defined graphics edit into the cutout region. 3. The system of claim 1 , wherein the graphics-edit importer automatically places at least a part of the user-defined graphics edit into the cutout region.
| 0.648649 |
9,160,801 | 4 | 8 |
4. The system of claim 1 , further comprising: logic to synchronize the local social media context into the global social media context according to a configurable set of rules.
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4. The system of claim 1 , further comprising: logic to synchronize the local social media context into the global social media context according to a configurable set of rules. 8. The system of claim 4 , further comprising: logic to synchronize the local social media context into the global social media context prioritizing content for one or more of close friends, family, or co-workers.
| 0.585603 |
10,055,461 | 17 | 20 |
17. A computer program product, encoded on one or more non-transitory computer storage media, comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving, by a distributed search system, a collection of training data comprising a plurality of training instances that each identify a respective first document selected by a particular user when the first document was identified in search results provided by the search system to the particular user in response to particular search query issued by the particular user; partitioning the collection of training data over a plurality of computing devices of the distributed search system; generating, by the distributed search system, a ranking model that produces a likelihood that a particular user will select a particular document when identified by one or more search results provided in response to a particular search query submitted by the particular user, including processing, by each computing device of the plurality of computing devices, training instances assigned to the computing device, including: selecting, by the computing device, a candidate condition, wherein the candidate condition specifies values for one or more user features, one or more query features, and one or more document features, sending, by the computing device, to each other computing device of the plurality of computing devices, a request to compute local statistics for the candidate condition, receiving, by the computing device from each other computing device of one or more other computing devices, respective computed statistics for the candidate condition computed by the other computing device using values of local training instances assigned to the other computing device, computing, by the computing device, a weight for the candidate condition according to the computed statistics received from the one or more other computing devices for the candidate condition; determining, by the computing device, that a new rule comprising the candidate condition and the computed weight should be added to the ranking model, and in response, adding the new rule to the ranking model and providing, by the computing device, to each other computing device of the plurality of computing devices, an indication that the new rule comprising the candidate condition and the computed weight should be added to the ranking model; receiving a search query submitted by a first user; obtaining a plurality of search results that satisfy the search query, wherein each search result identifies a respective document of a plurality of documents; determining one or more features of the first user and one or more features of the search query submitted by the first user; using the one or more features of the first user and the one or more features of the search query as input to the ranking model to compute, for each document identified by the search results, a respective likelihood that the first user will select the document when provided in response to the search query; and ranking the plurality of search results based on a respective computed likelihood for each document, the computed likelihood for each document being a likelihood that the first user will select the document when provided in response to the search query.
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17. A computer program product, encoded on one or more non-transitory computer storage media, comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving, by a distributed search system, a collection of training data comprising a plurality of training instances that each identify a respective first document selected by a particular user when the first document was identified in search results provided by the search system to the particular user in response to particular search query issued by the particular user; partitioning the collection of training data over a plurality of computing devices of the distributed search system; generating, by the distributed search system, a ranking model that produces a likelihood that a particular user will select a particular document when identified by one or more search results provided in response to a particular search query submitted by the particular user, including processing, by each computing device of the plurality of computing devices, training instances assigned to the computing device, including: selecting, by the computing device, a candidate condition, wherein the candidate condition specifies values for one or more user features, one or more query features, and one or more document features, sending, by the computing device, to each other computing device of the plurality of computing devices, a request to compute local statistics for the candidate condition, receiving, by the computing device from each other computing device of one or more other computing devices, respective computed statistics for the candidate condition computed by the other computing device using values of local training instances assigned to the other computing device, computing, by the computing device, a weight for the candidate condition according to the computed statistics received from the one or more other computing devices for the candidate condition; determining, by the computing device, that a new rule comprising the candidate condition and the computed weight should be added to the ranking model, and in response, adding the new rule to the ranking model and providing, by the computing device, to each other computing device of the plurality of computing devices, an indication that the new rule comprising the candidate condition and the computed weight should be added to the ranking model; receiving a search query submitted by a first user; obtaining a plurality of search results that satisfy the search query, wherein each search result identifies a respective document of a plurality of documents; determining one or more features of the first user and one or more features of the search query submitted by the first user; using the one or more features of the first user and the one or more features of the search query as input to the ranking model to compute, for each document identified by the search results, a respective likelihood that the first user will select the document when provided in response to the search query; and ranking the plurality of search results based on a respective computed likelihood for each document, the computed likelihood for each document being a likelihood that the first user will select the document when provided in response to the search query. 20. The computer program product of claim 17 , wherein the operations further comprise: generating, by each computing device of the plurality of computing device using local training instances assigned to the computing device, a feature-to-instance index that maps each value of a feature to one or more training instances having the value for the feature; receiving, by a first computing device of the plurality of computing devices, a request to compute local statistics for the candidate condition; obtaining, by the first computing device, training instances matching the candidate condition by using one or more features of the candidate condition as input to the feature-to-instance index; computing local statistics for the candidate condition using matching training instances obtained using the feature-to-instance index; and providing, by the first computing device, the computed local statistics in response to the request to compute local statistics for the candidate condition.
| 0.5 |
10,013,890 | 13 | 21 |
13. A computer program product comprising a non-transitory computer readable medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device implemented a natural language processing (NLP) system, causes the computing device to be configured to perform natural language processing on natural language statements in electronic communications and correlate terms in the natural language statements with terms associated with objectives of performances, and to operate to: receive, by the NLP system, performance data for a performance to be provided by a human performer, wherein the performance data comprises at least one objective to be achieved by the performance, specified in an objective data structure of the performance data, wherein the objective data structure comprises one or more indicators of goals or objectives that are being attempted to be achieved by the performer with respect to the performance; monitor, by the NLP system, one or more channels of electronic communication of one or more communication service computing systems, to identify the natural language statements exchanged over the one or more channels of electronic communication directed to the performance while the performance is being presented; extract, by the NLP system, feedback information from the natural language statements based on the natural language processing of the natural language statements; generate, by the NLP system, aggregate feedback information based on the identified feedback information; evaluate, by the NLP system, an alignment of the aggregate feedback information with the at least one objective in the performance data at least by correlating terms extracted from the natural language statements by the NLP system with terms associated with the at least one objective in the performance data; and output, by the NLP system, a modified presentation output to an audience and the performer based on results of evaluating the alignment of the aggregate feedback information with the at least one objective in the performance data, wherein the presentation is automatically modified so that the performance is more likely to achieve the at least one objective based on the aggregate feedback information and continuously modifying the presentation output based on a machined learned aggregate audience model, wherein extracting, by the NLP system, feedback information from the natural language statements based on natural language processing of the natural language statements comprises identifying terms or phrases in the natural language statements indicative of feedback on a performance in either a positive or a negative context, and wherein evaluating an alignment of the aggregate feedback information with the at least one objective in the performance data further comprises generating, by the NLP system, an aggregate audience model based on machine learning of weights associated with personality traits specified in user models of audience members, wherein the aggregate audience model specifies at least one aggregate audience personality trait generated by aggregating personality traits determined from the user models of the audience members weighted according to the machine learned weights, wherein generating the at least one aggregate audience personality trait comprises retaining only one personality trait having a higher number of audience members of that trait if two or more mutually exclusive personality traits are found in an audience.
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13. A computer program product comprising a non-transitory computer readable medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device implemented a natural language processing (NLP) system, causes the computing device to be configured to perform natural language processing on natural language statements in electronic communications and correlate terms in the natural language statements with terms associated with objectives of performances, and to operate to: receive, by the NLP system, performance data for a performance to be provided by a human performer, wherein the performance data comprises at least one objective to be achieved by the performance, specified in an objective data structure of the performance data, wherein the objective data structure comprises one or more indicators of goals or objectives that are being attempted to be achieved by the performer with respect to the performance; monitor, by the NLP system, one or more channels of electronic communication of one or more communication service computing systems, to identify the natural language statements exchanged over the one or more channels of electronic communication directed to the performance while the performance is being presented; extract, by the NLP system, feedback information from the natural language statements based on the natural language processing of the natural language statements; generate, by the NLP system, aggregate feedback information based on the identified feedback information; evaluate, by the NLP system, an alignment of the aggregate feedback information with the at least one objective in the performance data at least by correlating terms extracted from the natural language statements by the NLP system with terms associated with the at least one objective in the performance data; and output, by the NLP system, a modified presentation output to an audience and the performer based on results of evaluating the alignment of the aggregate feedback information with the at least one objective in the performance data, wherein the presentation is automatically modified so that the performance is more likely to achieve the at least one objective based on the aggregate feedback information and continuously modifying the presentation output based on a machined learned aggregate audience model, wherein extracting, by the NLP system, feedback information from the natural language statements based on natural language processing of the natural language statements comprises identifying terms or phrases in the natural language statements indicative of feedback on a performance in either a positive or a negative context, and wherein evaluating an alignment of the aggregate feedback information with the at least one objective in the performance data further comprises generating, by the NLP system, an aggregate audience model based on machine learning of weights associated with personality traits specified in user models of audience members, wherein the aggregate audience model specifies at least one aggregate audience personality trait generated by aggregating personality traits determined from the user models of the audience members weighted according to the machine learned weights, wherein generating the at least one aggregate audience personality trait comprises retaining only one personality trait having a higher number of audience members of that trait if two or more mutually exclusive personality traits are found in an audience. 21. The computer program product of claim 13 , wherein the computer readable program further causes the computing device to generate aggregate feedback information based on the identified feedback information at least by updating the aggregate feedback information based on the identified terms or phrases, wherein updating the aggregate feedback information comprises at least one of generating a new entry in the aggregate feedback information corresponding to a type of feedback associated with the identified terms or phrases, or updating a count of a number of sources of feedback providing the type of feedback.
| 0.524653 |
7,831,494 | 16 | 21 |
16. A system for providing remote web-based financial portfolio coaching comprising: at least one memory to store data and instructions; and at least one processor configured to access the at least one memory and execute instructions to: receive a selection, from a user, of a service agreement for the user, wherein the selected service agreement is chosen from a plurality of different service agreements providing various service levels related to portfolio modeling and coaching, and wherein the various service levels define distinct combinations of support, financial models, portfolio modeling, and coaching services to the user; identify a current financial portfolio for the user; generate, based upon a financial model selected from a set of financial models defined by the selected service agreement, a user profile based on personal financial parameters of the user, wherein the user profile includes at least a risk tolerance level; providing, via an internet connection, automated financial coaching in a natural language format; and providing, to the user, recommended changes to the current financial portfolio based on the user profile and the distinct combination of services defined by the selected service agreement, including providing customized financial coaching tailored to life intentions of the user and providing suggestions of financial products and recommended securities for the user to purchase.
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16. A system for providing remote web-based financial portfolio coaching comprising: at least one memory to store data and instructions; and at least one processor configured to access the at least one memory and execute instructions to: receive a selection, from a user, of a service agreement for the user, wherein the selected service agreement is chosen from a plurality of different service agreements providing various service levels related to portfolio modeling and coaching, and wherein the various service levels define distinct combinations of support, financial models, portfolio modeling, and coaching services to the user; identify a current financial portfolio for the user; generate, based upon a financial model selected from a set of financial models defined by the selected service agreement, a user profile based on personal financial parameters of the user, wherein the user profile includes at least a risk tolerance level; providing, via an internet connection, automated financial coaching in a natural language format; and providing, to the user, recommended changes to the current financial portfolio based on the user profile and the distinct combination of services defined by the selected service agreement, including providing customized financial coaching tailored to life intentions of the user and providing suggestions of financial products and recommended securities for the user to purchase. 21. The system of claim 16 , wherein the at least one processor is further configured to: filter a list of securities based on the user profile to generate recommended securities; and present the recommended securities to the user for swapping.
| 0.617555 |
8,909,623 | 1 | 4 |
1. A method comprising: collecting, using a computing device over a network, a plurality of search queries from at least one search query data source; determining, using the computing device, a lifetime value for each of the plurality of search queries to provide a plurality of lifetime values, each of the plurality of lifetime values reflecting a respective estimate of respective total online revenue a respective search query is expected to generate over its lifetime, wherein determining the respective lifetime value for each of the plurality of search queries includes matching each respective search query to at least one search term having a known lifetime value, wherein matching each respective search query to the at least one search term includes computing a matching score for the respective search term using the respective search term and the respective search query, and wherein the matching score is computed according to the equation:
MatchingScore=( W m ÷W ltv ) 2 ×( W m ÷W t ), where W ltv is a weighted sum of words in the respective search term, W t is a weighted count of words in the respective search query, W m is a weighted count of unique matching words between the respective search term and the respective search query; selecting, using the computing device, a plurality of potential titles from the plurality of search queries using at least one selection criteria associated with the plurality of lifetime values; and providing the plurality of potential titles to at least one content author.
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1. A method comprising: collecting, using a computing device over a network, a plurality of search queries from at least one search query data source; determining, using the computing device, a lifetime value for each of the plurality of search queries to provide a plurality of lifetime values, each of the plurality of lifetime values reflecting a respective estimate of respective total online revenue a respective search query is expected to generate over its lifetime, wherein determining the respective lifetime value for each of the plurality of search queries includes matching each respective search query to at least one search term having a known lifetime value, wherein matching each respective search query to the at least one search term includes computing a matching score for the respective search term using the respective search term and the respective search query, and wherein the matching score is computed according to the equation:
MatchingScore=( W m ÷W ltv ) 2 ×( W m ÷W t ), where W ltv is a weighted sum of words in the respective search term, W t is a weighted count of words in the respective search query, W m is a weighted count of unique matching words between the respective search term and the respective search query; selecting, using the computing device, a plurality of potential titles from the plurality of search queries using at least one selection criteria associated with the plurality of lifetime values; and providing the plurality of potential titles to at least one content author. 4. The method of claim 1 wherein if one or more of the plurality of search queries do not match any of the plurality of search terms, the lifetime value of the respective search query is set to a default value.
| 0.58498 |
7,882,047 | 7 | 8 |
7. An information processing method for constructing an information analysis processing configuration to be applied to information analysis processing in an observation domain including an uncertainty, said information processing method comprising the steps of: generating, with a data processing unit, an intra Bayesian Network for each observation element included in an observation space of a Partially Observable Markov Decision Process; combining, with the data processing unit, a plurality of Bayesian Networks, each generated for one observation element included in said observation space, all of the plurality of as Bayesian Networks having the same event observation domain, in order to construct a combined Bayesian Network; generating, with the data processing unit, a Dynamic Bayesian Network by analyzing relationships between elements in a plurality of combined Bayesian Networks, each of the combined Bayesian Networks corresponding to a different event observation domain, in order to construct a Dynamic Bayesian Network including information on relations between elements in said different event observation domains; and generating, based on the Dynamic Bayesian Network and with the data processing unit, a Factored Partially Observable Markov Decision Process including information on relations between elements pertaining to information spaces of said Partially Observable Markov Decision Process, the information spaces including at least the observation space.
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7. An information processing method for constructing an information analysis processing configuration to be applied to information analysis processing in an observation domain including an uncertainty, said information processing method comprising the steps of: generating, with a data processing unit, an intra Bayesian Network for each observation element included in an observation space of a Partially Observable Markov Decision Process; combining, with the data processing unit, a plurality of Bayesian Networks, each generated for one observation element included in said observation space, all of the plurality of as Bayesian Networks having the same event observation domain, in order to construct a combined Bayesian Network; generating, with the data processing unit, a Dynamic Bayesian Network by analyzing relationships between elements in a plurality of combined Bayesian Networks, each of the combined Bayesian Networks corresponding to a different event observation domain, in order to construct a Dynamic Bayesian Network including information on relations between elements in said different event observation domains; and generating, based on the Dynamic Bayesian Network and with the data processing unit, a Factored Partially Observable Markov Decision Process including information on relations between elements pertaining to information spaces of said Partially Observable Markov Decision Process, the information spaces including at least the observation space. 8. The information processing method according claim 7 , wherein: said information spaces include a state space, an action space, a reward space and said observation space; and the intra Bayesian Network is generated by determining relationships between the observation element and an element included in at least one of said information spaces other than said observation space based on learning sample data.
| 0.586032 |
8,996,622 | 1 | 3 |
1. A method, comprising: generating by a network device one or more graphs using data obtained from a query log, the one or more graphs including an anticlick graph, wherein the anticlick graph represents information pertaining to documents in previously provided search results that, according to the data obtained from the query log, have not been clicked by a user that submitted a corresponding search query and does not represent information pertaining to documents in the previously provided search results that, according to the data obtained from the query log, have been clicked by the user that submitted the corresponding search query, wherein the anticlick graph includes one or more nodes representing or corresponding to documents that, according to the data obtained from the query log, have not been clicked by the user that submitted the corresponding search query; ascertaining by the network device values of one or more syntactic features of the one or more graphs; determining by the network device values of one or more semantic features of the one or more graphs by propagating categories from a web directory among nodes in each of the one or more graphs; and detecting by the network device spam hosts based upon the values of the syntactic features and the semantic features; wherein the anti-click graph includes a host-based graph or a document-based graph, wherein the nodes of the host-based graph includes one or more host nodes representing hosts corresponding to the documents that, according to the data obtained from the query log, have not been clicked by the user that submitted the corresponding search query, and wherein the nodes of the document-based graph includes one or more document nodes representing the documents that, according to the data obtained from the query log, have not been clicked by the user that submitted the corresponding search query.
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1. A method, comprising: generating by a network device one or more graphs using data obtained from a query log, the one or more graphs including an anticlick graph, wherein the anticlick graph represents information pertaining to documents in previously provided search results that, according to the data obtained from the query log, have not been clicked by a user that submitted a corresponding search query and does not represent information pertaining to documents in the previously provided search results that, according to the data obtained from the query log, have been clicked by the user that submitted the corresponding search query, wherein the anticlick graph includes one or more nodes representing or corresponding to documents that, according to the data obtained from the query log, have not been clicked by the user that submitted the corresponding search query; ascertaining by the network device values of one or more syntactic features of the one or more graphs; determining by the network device values of one or more semantic features of the one or more graphs by propagating categories from a web directory among nodes in each of the one or more graphs; and detecting by the network device spam hosts based upon the values of the syntactic features and the semantic features; wherein the anti-click graph includes a host-based graph or a document-based graph, wherein the nodes of the host-based graph includes one or more host nodes representing hosts corresponding to the documents that, according to the data obtained from the query log, have not been clicked by the user that submitted the corresponding search query, and wherein the nodes of the document-based graph includes one or more document nodes representing the documents that, according to the data obtained from the query log, have not been clicked by the user that submitted the corresponding search query. 3. The method as recited in claim 1 , wherein the one or more semantic features comprise one or more measures of dispersion of each of the document nodes in the document-based graph.
| 0.85624 |
9,378,196 | 1 | 7 |
1. A method, comprising: identifying, by one or more processors, a task of a user; determining, by one or more of the processors, a category of the task; identifying, by one or more of the processors and based on the category, a plurality of annotation fields for the task; determining, by one or more of the processors, annotation field specific information related to the task, the annotation field specific information being related to one or more of the annotation fields for the task; and populating, by one or more of the processors, one or more of the annotation fields based on the annotation field specific information; determining, by one or more of the processors, a second category for the task; identifying, by one or more of the processors and based on the second category, a second plurality of annotation fields for the task, wherein at least one of the second plurality of annotation fields is unique from the plurality of annotation fields; determining, by one or more of the processors, additional annotation field specific information related to the task, the additional annotation field specific information being related to one or more of the second plurality of annotation fields for the task; and populating, by one or more of the processors, one or more of the second plurality of annotation fields based on the additional annotation field specific information.
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1. A method, comprising: identifying, by one or more processors, a task of a user; determining, by one or more of the processors, a category of the task; identifying, by one or more of the processors and based on the category, a plurality of annotation fields for the task; determining, by one or more of the processors, annotation field specific information related to the task, the annotation field specific information being related to one or more of the annotation fields for the task; and populating, by one or more of the processors, one or more of the annotation fields based on the annotation field specific information; determining, by one or more of the processors, a second category for the task; identifying, by one or more of the processors and based on the second category, a second plurality of annotation fields for the task, wherein at least one of the second plurality of annotation fields is unique from the plurality of annotation fields; determining, by one or more of the processors, additional annotation field specific information related to the task, the additional annotation field specific information being related to one or more of the second plurality of annotation fields for the task; and populating, by one or more of the processors, one or more of the second plurality of annotation fields based on the additional annotation field specific information. 7. The method of claim 1 , further comprising: receiving, by a given application, the task and one or more of the populated annotation fields; determining, by the given application, a suggested completion step for the task based on the populated annotation fields; and providing, by the given application, the suggested completion step for the task to the user.
| 0.547619 |
9,141,668 | 1 | 6 |
1. A computer-implemented method to determine individuals having desired skills, the method comprising: receiving, by an application and from a requesting entity, a request to identify individuals having a desired skill specified in the request, wherein the application includes a semantic layer, a knowledge management system (KMS) layer, and a collaboration layer, wherein the request comprises a query composed according to a query language not supported by the KMS layer; reformulating the query by the semantic layer and into a different query language supported by the KMS layer; identifying, by accessing a data store using the reformulated query: (i) a first individual having the specified skill and (ii) a first characterization of a skill level of the first individual in the specified skill, wherein the first characterization is stored in the data store and is determined based on input from a plurality of individuals, wherein the data store contains a body of knowledge represented by the KMS layer; and upon determining, by the collaboration layer and by operation of one or more computer processors, that a count of the plurality of individuals is less than a desired count of individuals characterizing the skill level of the first individual, confirming the skill level of the first individual by requesting at least a second individual to provide a second characterization of the skill level of the first individual in the specified skill, wherein the second individual is not included in the plurality of individuals.
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1. A computer-implemented method to determine individuals having desired skills, the method comprising: receiving, by an application and from a requesting entity, a request to identify individuals having a desired skill specified in the request, wherein the application includes a semantic layer, a knowledge management system (KMS) layer, and a collaboration layer, wherein the request comprises a query composed according to a query language not supported by the KMS layer; reformulating the query by the semantic layer and into a different query language supported by the KMS layer; identifying, by accessing a data store using the reformulated query: (i) a first individual having the specified skill and (ii) a first characterization of a skill level of the first individual in the specified skill, wherein the first characterization is stored in the data store and is determined based on input from a plurality of individuals, wherein the data store contains a body of knowledge represented by the KMS layer; and upon determining, by the collaboration layer and by operation of one or more computer processors, that a count of the plurality of individuals is less than a desired count of individuals characterizing the skill level of the first individual, confirming the skill level of the first individual by requesting at least a second individual to provide a second characterization of the skill level of the first individual in the specified skill, wherein the second individual is not included in the plurality of individuals. 6. The computer-implemented method according to claim 1 , further comprising: upon programmatically determining that the second characterization is different from the first characterization, determining a refined characterization of the skill level of the first individual in the specified skill, based on the second characterization, wherein the refined characterization is different from the first characterization, wherein the data store is updated to reflect the refined characterization of the skill level of the first individual in the specified skill, wherein the refined characterization is output to the requesting entity responsive to the request.
| 0.5 |
8,972,458 | 12 | 16 |
12. A computer-implemented method for managing comment data generated when interacting with a page module, the computer-implemented method comprising: detecting at least one comment data being expressed when interacting with the page module, the comment data being text; analyzing content of the comment data to identify a context for the comment data; if the comment data is identified to be associated with a context based on the analyzing, tagging the comment data with a context association, and if the comment data is not associated with a context then maintaining the comment data associated only with the page module; and carrying over the comment data as text to one or more other page modules that were identified to have the context association with the comment data; wherein the populating enables display presentation of the comment data aggregated at the page module and carried over to one or more page modules having the context association; wherein the page module and the related page modules each have a subject context that is contextually related to a word.
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12. A computer-implemented method for managing comment data generated when interacting with a page module, the computer-implemented method comprising: detecting at least one comment data being expressed when interacting with the page module, the comment data being text; analyzing content of the comment data to identify a context for the comment data; if the comment data is identified to be associated with a context based on the analyzing, tagging the comment data with a context association, and if the comment data is not associated with a context then maintaining the comment data associated only with the page module; and carrying over the comment data as text to one or more other page modules that were identified to have the context association with the comment data; wherein the populating enables display presentation of the comment data aggregated at the page module and carried over to one or more page modules having the context association; wherein the page module and the related page modules each have a subject context that is contextually related to a word. 16. The computer-implemented method for managing comment data as recited in claim 12 , wherein the populating includes adding the comment data to comment table in a relational database and associating the comment data to identifiers of one or more other page modules that were identified to have a context association with the comment data.
| 0.5 |
8,886,676 | 24 | 25 |
24. The method of claim 22 , wherein determining that the first list in the first column continues with the second list in the second column comprises: generating, using a label generation function, a next list item label for the first list; and determining that the next list item label matches a label of a first list item of the second list in the second column.
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24. The method of claim 22 , wherein determining that the first list in the first column continues with the second list in the second column comprises: generating, using a label generation function, a next list item label for the first list; and determining that the next list item label matches a label of a first list item of the second list in the second column. 25. The method of claim 24 further comprising: detecting more than one matching list between the first column and the second column; and comparing indentations of the matching lists to determine a best matching list between the first column and the second column.
| 0.5 |
5,434,955 | 7 | 9 |
7. A fuzzy inference apparatus comprising: an input device for entering input data relating to phenomena; a storage device for storing knowledge of expert users which expresses relationships between phenomena and conclusions as a plurality of membership functions, wherein said knowledge can include relationships of multiple phenomena to a single conclusion which are conjoined in an OR relationship; a grade calculating device for determining a grade measure for said input data with respect to each of said plurality of membership functions stored in said storage device and for determining a grade for each group of said phenomena which are conjoined in an OR relationship; a fuzzy information amount calculating device for calculating a fuzzy information amount for each phenomenon using said grade measures; and a possibility calculating device for calculating the possibility of a conclusion using said grade measures, said grade for each of said groups, and said fuzzy information amounts.
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7. A fuzzy inference apparatus comprising: an input device for entering input data relating to phenomena; a storage device for storing knowledge of expert users which expresses relationships between phenomena and conclusions as a plurality of membership functions, wherein said knowledge can include relationships of multiple phenomena to a single conclusion which are conjoined in an OR relationship; a grade calculating device for determining a grade measure for said input data with respect to each of said plurality of membership functions stored in said storage device and for determining a grade for each group of said phenomena which are conjoined in an OR relationship; a fuzzy information amount calculating device for calculating a fuzzy information amount for each phenomenon using said grade measures; and a possibility calculating device for calculating the possibility of a conclusion using said grade measures, said grade for each of said groups, and said fuzzy information amounts. 9. The fuzzy inference apparatus of claim 7, wherein said storage device stores said knowledge in a form which expresses that various multiple phenomena about which a single conclusion is to be drawn are in an OR relationship.
| 0.5 |
7,761,787 | 1 | 2 |
1. A document generation system for producing a document from information derived from an information repository, comprising: a source of code representing a document template including, data fields containing placeholder items to be replaced by desired data items, and also including a repetition identifier indicating one of said data fields is to be replicated to provide a group of data fields to be replaced by a plurality of said desired data items; a source of document generation control information supporting insertion of said desired data items derived from said information repository in said data fields; and a document processor for applying said control information in replacing template document data field placeholder items with desired data items, to produce a generated document.
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1. A document generation system for producing a document from information derived from an information repository, comprising: a source of code representing a document template including, data fields containing placeholder items to be replaced by desired data items, and also including a repetition identifier indicating one of said data fields is to be replicated to provide a group of data fields to be replaced by a plurality of said desired data items; a source of document generation control information supporting insertion of said desired data items derived from said information repository in said data fields; and a document processor for applying said control information in replacing template document data field placeholder items with desired data items, to produce a generated document. 2. The system according to claim 1 , wherein said control information contains at least one of, (a) an identification of data fields in said template document available to be replaced by desired data items, (b) an identification of a location in said information repository of a desired data item associated with an individual data field, and (c) an identification of a location in said information repository of a first data item for insertion in said individual data field of said group of data fields and data items sequentially linked to said first data item are inserted in remaining data fields of said group of data fields.
| 0.5 |
9,696,969 | 7 | 10 |
7. A system, comprising: a memory, communicatively coupled to a processor, the memory storing the computer-executable components comprising: an editor component configured to: infer a first industrial programming language of a plurality of industrial programming languages to utilize for programming an industrial controller and infer a second industrial programming language of the plurality of industrial programming languages to utilize in combination with the first industrial programming language for programming the industrial controller to create a custom programming language that is optimal for programming the industrial controller based upon at least one criteria comprising a code function to be implemented in the industrial controller, wherein the at least one criteria further comprises user tendencies associated with respective industrial programming languages of the industrial programming languages; and combine at least a portion of the first industrial programming language with at least another portion of the second industrial programming language to create the custom programming language for programming the industrial controller, wherein the first industrial programming language, the industrial second programming language and the custom programming language are disparate.
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7. A system, comprising: a memory, communicatively coupled to a processor, the memory storing the computer-executable components comprising: an editor component configured to: infer a first industrial programming language of a plurality of industrial programming languages to utilize for programming an industrial controller and infer a second industrial programming language of the plurality of industrial programming languages to utilize in combination with the first industrial programming language for programming the industrial controller to create a custom programming language that is optimal for programming the industrial controller based upon at least one criteria comprising a code function to be implemented in the industrial controller, wherein the at least one criteria further comprises user tendencies associated with respective industrial programming languages of the industrial programming languages; and combine at least a portion of the first industrial programming language with at least another portion of the second industrial programming language to create the custom programming language for programming the industrial controller, wherein the first industrial programming language, the industrial second programming language and the custom programming language are disparate. 10. The system of claim 7 , wherein at least one of the first industrial programming language or the second industrial programming language relates to an IEC61499 standard.
| 0.764384 |
7,590,224 | 1 | 16 |
1. An automated task classification system that operates on a task objective of a user, through a natural language dialog with the user in which system prompts are not ordered in a menu, the system comprising: a recognizer that spots at least one of a plurality of meaningful phrases in substantially simultaneous user natural language verbal and non-verbal input, wherein the natural language verbal and non-verbal input each convey different information and are associated with a coordinated message that achieves an appropriate response, each of the plurality of meaningful phrases having an association with at least one of a predetermined set of task objectives, and the predetermined set of task objectives based, at least partly, on a salience measure of one of the plurality of meaningful phrases to a specified one of the predetermined task objectives, wherein the salience measure is represented as a conditional probability of the task objective being requested given an appearance of one of the plurality of meaningful phrases in the input communication, the conditional probability being a highest value in a distribution of conditional probabilities over the set of predetermined task objectives; and a task classifier that makes a classification decision based, at least partly, on the spotted at least one of the plurality of meaningful phrases.
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1. An automated task classification system that operates on a task objective of a user, through a natural language dialog with the user in which system prompts are not ordered in a menu, the system comprising: a recognizer that spots at least one of a plurality of meaningful phrases in substantially simultaneous user natural language verbal and non-verbal input, wherein the natural language verbal and non-verbal input each convey different information and are associated with a coordinated message that achieves an appropriate response, each of the plurality of meaningful phrases having an association with at least one of a predetermined set of task objectives, and the predetermined set of task objectives based, at least partly, on a salience measure of one of the plurality of meaningful phrases to a specified one of the predetermined task objectives, wherein the salience measure is represented as a conditional probability of the task objective being requested given an appearance of one of the plurality of meaningful phrases in the input communication, the conditional probability being a highest value in a distribution of conditional probabilities over the set of predetermined task objectives; and a task classifier that makes a classification decision based, at least partly, on the spotted at least one of the plurality of meaningful phrases. 16. The automated task classification system of claim 1 , wherein the task classifier makes the classification decision using a confidence function.
| 0.876461 |
8,776,024 | 8 | 17 |
8. A non-transitory computer-readable medium having stored therein instructions executable by a computing device to cause the computing device to perform functions comprising: compiling a source code of a software application provided in a first language into an intermediate code provided in a second language different from the first language, wherein compiling the source code into the intermediate code comprises: inserting, within the intermediate code, complementary instructions that indicate (1) instructions for controlling an intermediate debugging program configured to debug the intermediate code and (2) instructions for translating debugging information between the intermediate debugging program and a source debugging program configured to debug the source code; and formatting the intermediate code such that a line of the source code points to at least one line of the intermediate code; compiling the intermediate code into an executable form; and debugging the source code, wherein debugging the source code comprises translating at least one piece of debugging information between the intermediate debugging program and the source debugging program according to the complementary instructions.
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8. A non-transitory computer-readable medium having stored therein instructions executable by a computing device to cause the computing device to perform functions comprising: compiling a source code of a software application provided in a first language into an intermediate code provided in a second language different from the first language, wherein compiling the source code into the intermediate code comprises: inserting, within the intermediate code, complementary instructions that indicate (1) instructions for controlling an intermediate debugging program configured to debug the intermediate code and (2) instructions for translating debugging information between the intermediate debugging program and a source debugging program configured to debug the source code; and formatting the intermediate code such that a line of the source code points to at least one line of the intermediate code; compiling the intermediate code into an executable form; and debugging the source code, wherein debugging the source code comprises translating at least one piece of debugging information between the intermediate debugging program and the source debugging program according to the complementary instructions. 17. The non-transitory computer-readable medium of claim 8 , further comprising instructions executable by the computing device to cause the computing device to perform functions comprising: before debugging the source code, receiving the at least one piece of debugging information from the source debugging program.
| 0.711818 |
8,676,833 | 16 | 20 |
16. A system for requesting social services from group of users, the system comprises of: a central server unit; at least one service provider and/or searcher computer system providing a service and/or performing a search in response to a service request and/or query from a requestor or searching user; at least one search system receiving request that the group service providers and/or group searching is required, presenting a list of groups of service providers and/or groups of searchers having human service providers and/or searcher members with domain specific expertise and rating, receiving selection of a desired service and/or search groups, prompting for and receiving an authentication information from the requestor or searching user, validating the authentication information, determining whether the requestor or searching user may submit the service request and/or query to a human service provider and/or searcher member of the related or selected service providers group and/or searcher group or a service provider or searcher who is not the human service provider or searcher member based on the match making, connections and subscriptions, and accepting the request or query from the requestor or searching user; a service providers and searchers group database including a service providers and search service of searchers service profiles, service name, ID, service types, service details, service categories, taxonomy, ontology, keywords, ranking, hit statistics, comments, service data including request and response resources, list and profile details of group members; a user system including at least one requestor or searching user interface using which the required service providers or searchers group is selected, the list of service providers or searchers groups being presented in an order based at least in part on the ranking of the service providers or searchers groups; a service providers or searchers of groups database including a service provider or searcher profile(s) including name, location, language, resume, qualification, background, experience, identity data, group identity data, authentication information, user data, ranking hit statistics; and an authorized requestors or searching users or registered subscribers database including the service providers or searchers group identity, an authorized user identity and authentication information, and where at least one of the human service provider or searcher members is selected to provide service or perform the search in response to the request or query from the requester or searching user based on a combination of a categories or keywords of the service or search, two way match making preferences, connections and subscriptions.
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16. A system for requesting social services from group of users, the system comprises of: a central server unit; at least one service provider and/or searcher computer system providing a service and/or performing a search in response to a service request and/or query from a requestor or searching user; at least one search system receiving request that the group service providers and/or group searching is required, presenting a list of groups of service providers and/or groups of searchers having human service providers and/or searcher members with domain specific expertise and rating, receiving selection of a desired service and/or search groups, prompting for and receiving an authentication information from the requestor or searching user, validating the authentication information, determining whether the requestor or searching user may submit the service request and/or query to a human service provider and/or searcher member of the related or selected service providers group and/or searcher group or a service provider or searcher who is not the human service provider or searcher member based on the match making, connections and subscriptions, and accepting the request or query from the requestor or searching user; a service providers and searchers group database including a service providers and search service of searchers service profiles, service name, ID, service types, service details, service categories, taxonomy, ontology, keywords, ranking, hit statistics, comments, service data including request and response resources, list and profile details of group members; a user system including at least one requestor or searching user interface using which the required service providers or searchers group is selected, the list of service providers or searchers groups being presented in an order based at least in part on the ranking of the service providers or searchers groups; a service providers or searchers of groups database including a service provider or searcher profile(s) including name, location, language, resume, qualification, background, experience, identity data, group identity data, authentication information, user data, ranking hit statistics; and an authorized requestors or searching users or registered subscribers database including the service providers or searchers group identity, an authorized user identity and authentication information, and where at least one of the human service provider or searcher members is selected to provide service or perform the search in response to the request or query from the requester or searching user based on a combination of a categories or keywords of the service or search, two way match making preferences, connections and subscriptions. 20. The system as claimed in claim 16 , wherein enabling a requestor or searching user to provide a service and/or perform a search in response to a service request and/or query based on one or more communication, collaboration, sharing, searching, messaging, responses via one or more applications including email, instant messaging, video, voice, services including communication services, devices including mobile, networks including interne, intranet and technologies including voice enabled, voice to text or text to voice, SMS, MMS, translation system.
| 0.809035 |
4,597,055 | 15 | 25 |
15. An improved electronic translator wherein a first sentence in a first language is translated into a second sentence in a second language, the translator comprising input means for entering the first sentence into the translator, a first memory means for storing a plurality of words and sentences in the first language, the plurality of words and sentences being stored at respectively specific addresses, a second memory means for storing a like plurality of words and sentences in the second language, each of the plurality of words and sentences in the second memory means being stored at a specific address that is equivalent to the address of a corresponding word or sentence in the first memory means, first access means responsive to the input means for retrieving a sentence from those sentences stored in the first memory means, first detection means responsive to the first access means and the input means for detecting the sentence from those sentences stored in the first memory means that is most equivalent to the first sentence entered by the input means and for identifying any differences that exist between the first sentence and the most equivalent sentence, second access means responsive to the first detection means for retrieving a word from those words stored in the first memory means, second detection means responsive to the first detection means and the second access means for detecting whether a word from those words stored in the first memory means is equivalent to any difference identified by the first detection means between the first sentence and the most equivalent sentence and for identifying any differences that continue to exist between the first sentence and the most equivalent sentence, first insertion means responsive to the second detection means for inserting each word detected by the second detection means into the most equivalent sentence, third access means responsive to the first and second detection means and the first insertion means for retrieving a sentence and words from addresses in the second memory means equivalent to those of the most equivalent sentence detected by the first detection means and the words detected by the second detection means to achieve a second, translated sentence in the second language that is most equivalent to the first sentence, second insertion means responsive to the second detection means and third access means to insert any continuing differences identified by the second detection means into the second sentence untranslated, and output means responsive to the third access means and second, insertion means for outputting the second translated sentence in the second language.
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15. An improved electronic translator wherein a first sentence in a first language is translated into a second sentence in a second language, the translator comprising input means for entering the first sentence into the translator, a first memory means for storing a plurality of words and sentences in the first language, the plurality of words and sentences being stored at respectively specific addresses, a second memory means for storing a like plurality of words and sentences in the second language, each of the plurality of words and sentences in the second memory means being stored at a specific address that is equivalent to the address of a corresponding word or sentence in the first memory means, first access means responsive to the input means for retrieving a sentence from those sentences stored in the first memory means, first detection means responsive to the first access means and the input means for detecting the sentence from those sentences stored in the first memory means that is most equivalent to the first sentence entered by the input means and for identifying any differences that exist between the first sentence and the most equivalent sentence, second access means responsive to the first detection means for retrieving a word from those words stored in the first memory means, second detection means responsive to the first detection means and the second access means for detecting whether a word from those words stored in the first memory means is equivalent to any difference identified by the first detection means between the first sentence and the most equivalent sentence and for identifying any differences that continue to exist between the first sentence and the most equivalent sentence, first insertion means responsive to the second detection means for inserting each word detected by the second detection means into the most equivalent sentence, third access means responsive to the first and second detection means and the first insertion means for retrieving a sentence and words from addresses in the second memory means equivalent to those of the most equivalent sentence detected by the first detection means and the words detected by the second detection means to achieve a second, translated sentence in the second language that is most equivalent to the first sentence, second insertion means responsive to the second detection means and third access means to insert any continuing differences identified by the second detection means into the second sentence untranslated, and output means responsive to the third access means and second, insertion means for outputting the second translated sentence in the second language. 25. An improved electronic translator according to claim 15, wherein the output means comprises a display.
| 0.857143 |
8,566,787 | 1 | 10 |
1. A computer system for improving modularity of a software source code, comprising: i. a user interface for receiving source code; ii. a source code model extractor for parsing and forming a model of the source code; iii. a source code model database for storing the source code model, refactoring operators, and a record of refactoring changes, wherein: each refactoring operator comprises a defined source code function, and each refactoring change comprises an alteration to the source code such that external behavior of the source code subsequent to alteration conforms to external behavior of the source code prior to alteration; and the source code model database is stored in a non-transitory memory; iv. a modularization diagnosis reader for evaluating modularity of the source code and generating a modularity problem diagnosis data, wherein the modularity problem diagnosis data comprises information corresponding to identified modularity problems; v. a modularity improvement analyzer for reading the source code model and modularity problem diagnosis data and generating a set of prescriptions, the set of prescriptions comprising a list of operations for improving modularity of the source code; vi. an optimal improvement suggestion selector for evaluating and electing one or more prescriptions from the set of prescriptions, based on a predetermined criteria; and vii. a refactoring engine for receiving selected prescriptions and applying them on the source code; wherein modularity is a logical partitioning of the software source code into software modules, each module comprising a logical unit.
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1. A computer system for improving modularity of a software source code, comprising: i. a user interface for receiving source code; ii. a source code model extractor for parsing and forming a model of the source code; iii. a source code model database for storing the source code model, refactoring operators, and a record of refactoring changes, wherein: each refactoring operator comprises a defined source code function, and each refactoring change comprises an alteration to the source code such that external behavior of the source code subsequent to alteration conforms to external behavior of the source code prior to alteration; and the source code model database is stored in a non-transitory memory; iv. a modularization diagnosis reader for evaluating modularity of the source code and generating a modularity problem diagnosis data, wherein the modularity problem diagnosis data comprises information corresponding to identified modularity problems; v. a modularity improvement analyzer for reading the source code model and modularity problem diagnosis data and generating a set of prescriptions, the set of prescriptions comprising a list of operations for improving modularity of the source code; vi. an optimal improvement suggestion selector for evaluating and electing one or more prescriptions from the set of prescriptions, based on a predetermined criteria; and vii. a refactoring engine for receiving selected prescriptions and applying them on the source code; wherein modularity is a logical partitioning of the software source code into software modules, each module comprising a logical unit. 10. The system as claimed in claim 1 , wherein the optimal improvement suggestion selector module further comprises: i. an improvement suggestion selector module for receiving a prescription list from improvement analyzer module; ii. a refactoring history repository for archiving prescription recommended for previous source code along with defects and gain suggested for various modules; and iii. a conflict resolution heuristics for assessing prescriptions and its effect on modularity.
| 0.73395 |
5,560,060 | 27 | 30 |
27. A method according to claim 25, wherein an operating cycle comprises at least one pre-wash fill cycle, a main wash fill cycle, a rinse fill cycle, and a final rinse fill cycle.
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27. A method according to claim 25, wherein an operating cycle comprises at least one pre-wash fill cycle, a main wash fill cycle, a rinse fill cycle, and a final rinse fill cycle. 30. A method according to claim 27, further comprising varying the duration of at least one of the fill cycles as a function of liquid temperature.
| 0.5 |
6,130,670 | 1 | 12 |
1. A method for providing simple generalized conservative visibility, comprising the steps of: providing one or more occluder nodes, wherein said one or more occluder nodes are planes having a given width and height in object space that block the rendering of objects behind them in world space; providing an activationDistance field which specifies how far a camera must be from an occluder node for said occluder node to be active; using said one or more occluder nodes to compute a conservative set of visible objects from a current view point; and rendering said conservative set of visible objects.
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1. A method for providing simple generalized conservative visibility, comprising the steps of: providing one or more occluder nodes, wherein said one or more occluder nodes are planes having a given width and height in object space that block the rendering of objects behind them in world space; providing an activationDistance field which specifies how far a camera must be from an occluder node for said occluder node to be active; using said one or more occluder nodes to compute a conservative set of visible objects from a current view point; and rendering said conservative set of visible objects. 12. The method of claim 1, further comprising the step of: altering the number of occluder nodes in a scene to optimize said method.
| 0.869307 |
9,251,279 | 19 | 25 |
19. A method as claimed in claim 1 , wherein the displayed set of search results is an ordered set.
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19. A method as claimed in claim 1 , wherein the displayed set of search results is an ordered set. 25. A method as claimed in claim 19 , wherein the displayed set of search results is limited to one or more of the groups created and the groups joined by members connected to the searcher in a social network.
| 0.727865 |
9,389,890 | 14 | 19 |
14. A computer system comprising: a logical processor; a memory in operable communication with the logical processor; a computer program source code residing in at least one source code file in the memory, the computer program source code written in one or more programming languages; a compiler; and a directives document residing in the memory outside a compilation portion of the computer program source code and having runtime behavior characteristic directives which are written in a human-readable software-parsable format and are not written in the programming languages; and wherein upon execution by the logical processor(s) the compiler compiles at least the compilation portion of the computer program source code from the source code file(s) into at least one native code file as directed by the directives document, including performing at least two of the following: (a) making a type T of the computer program source code be a required type, an optional type, or a prohibited type in the environment, (b) making a type member M of the computer program source code be a required type member, an optional type member, or a prohibited type member in the environment, or (c) enabling or disabling a degree D for a type T or a type member M in the environment.
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14. A computer system comprising: a logical processor; a memory in operable communication with the logical processor; a computer program source code residing in at least one source code file in the memory, the computer program source code written in one or more programming languages; a compiler; and a directives document residing in the memory outside a compilation portion of the computer program source code and having runtime behavior characteristic directives which are written in a human-readable software-parsable format and are not written in the programming languages; and wherein upon execution by the logical processor(s) the compiler compiles at least the compilation portion of the computer program source code from the source code file(s) into at least one native code file as directed by the directives document, including performing at least two of the following: (a) making a type T of the computer program source code be a required type, an optional type, or a prohibited type in the environment, (b) making a type member M of the computer program source code be a required type member, an optional type member, or a prohibited type member in the environment, or (c) enabling or disabling a degree D for a type T or a type member M in the environment. 19. The system of claim 14 , wherein the directives document comprises runtime behavior characteristic directives which recite at least two of the following degree values: Required-All, Required-PublicAndInternal, Required-Public, All, PublicAndInternal, Public, Included, Auto, or Excluded.
| 0.639851 |
7,516,161 | 5 | 10 |
5. The process control system set forth in claim 4 wherein: the server determines whether the specified condition is determined by executing the second particular query.
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5. The process control system set forth in claim 4 wherein: the server determines whether the specified condition is determined by executing the second particular query. 10. The process control system set forth in claim 5 wherein: the set of process representations indicates hierarchies thereof; and a query representation specifies one or more members of a hierarchy to which a process representation belongs upon which the administrative activity indicated by the administrative activity representation to which the trigger representation is related is being performed.
| 0.5 |
7,598,942 | 78 | 85 |
78. The method of claim 3 , comprising specifying the gesture at a plurality of levels.
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78. The method of claim 3 , comprising specifying the gesture at a plurality of levels. 85. The method of claim 78 , wherein the plurality of levels include a third level comprising a combination of poses and positions, the combination of poses comprising a third pose of at least one appendage of the body and a fourth pose of at least one appendage of a second body.
| 0.5 |
9,037,606 | 1 | 3 |
1. A computer-implemented method comprising: clustering hierarchical database records into a first set of clusters having corresponding first cluster identifications (IDs), each hierarchical database record comprising one or more field values, the clustering based at least in part on determining similarity among corresponding field values of the hierarchical database records; determining parent-child hierarchical relationships among the hierarchical database records; associating related hierarchical database records by: determining highest compelling linkages among the hierarchical database records, the determining comprising: identifying mutually preferred pairs of records from the hierarchical database records, each mutually preferred pair of records consisting of a first record and a second record, the first record consisting of a preferred record associated with the second record and the second record consisting of a preferred record associated with the first record, wherein the mutually preferred pairs of records each has a match score that meets pre-specified match criteria; assigning, for each record from the hierarchical database records, at least one associated preferred record, wherein a match value assigned to a given record together with its associated preferred record is at least as great as a match value assigned to the record together with any other record in the database records; and forming and storing a plurality of entity representations in the database, each entity representation of the plurality of entity representations comprising at least one linked pair of mutually preferred records; applying a hierarchal directional linking process, the hierarchal directional linking process comprising selecting and applying at least an upward process based on the determined parent-child hierarchical relationship wherein the upward process comprises: determining, from the parent-child hierarchical relationships, similarity among a plurality of child records having initial separate parent records; in response to determining a threshold similarity among the plurality of child records, inferring that the initial separate parent records correspond to the same entity; and linking, responsive to the inferring, the initial separate parent records as inferred common parent records; re-clustering at least a portion of the database records into a second set of clusters having corresponding second cluster IDs, the re-clustering based at least in part on the associating related hierarchical database records and on the determining similarity among corresponding field values of the database records; and outputting database record information, based at least in part on the re-clustering.
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1. A computer-implemented method comprising: clustering hierarchical database records into a first set of clusters having corresponding first cluster identifications (IDs), each hierarchical database record comprising one or more field values, the clustering based at least in part on determining similarity among corresponding field values of the hierarchical database records; determining parent-child hierarchical relationships among the hierarchical database records; associating related hierarchical database records by: determining highest compelling linkages among the hierarchical database records, the determining comprising: identifying mutually preferred pairs of records from the hierarchical database records, each mutually preferred pair of records consisting of a first record and a second record, the first record consisting of a preferred record associated with the second record and the second record consisting of a preferred record associated with the first record, wherein the mutually preferred pairs of records each has a match score that meets pre-specified match criteria; assigning, for each record from the hierarchical database records, at least one associated preferred record, wherein a match value assigned to a given record together with its associated preferred record is at least as great as a match value assigned to the record together with any other record in the database records; and forming and storing a plurality of entity representations in the database, each entity representation of the plurality of entity representations comprising at least one linked pair of mutually preferred records; applying a hierarchal directional linking process, the hierarchal directional linking process comprising selecting and applying at least an upward process based on the determined parent-child hierarchical relationship wherein the upward process comprises: determining, from the parent-child hierarchical relationships, similarity among a plurality of child records having initial separate parent records; in response to determining a threshold similarity among the plurality of child records, inferring that the initial separate parent records correspond to the same entity; and linking, responsive to the inferring, the initial separate parent records as inferred common parent records; re-clustering at least a portion of the database records into a second set of clusters having corresponding second cluster IDs, the re-clustering based at least in part on the associating related hierarchical database records and on the determining similarity among corresponding field values of the database records; and outputting database record information, based at least in part on the re-clustering. 3. The method of claim 1 , wherein determining the similarity among the corresponding field values of the database records comprises: assigning a hyperspace attribute to each database record, wherein the hyperspace attribute corresponding to two database records is correlated with a similarity of the corresponding field values of the two database records; determining membership of each database record in a plurality of hyperspace clusters based at least in part on the hyperspace attributes; assigning, to each record, a cluster ID and a match value reflecting a likelihood that the record is a member of a particular hyperspace cluster; and linking related records based at least in part on the cluster ID and the match value.
| 0.5 |
9,127,950 | 19 | 25 |
19. A system for determining a location for recommendation to a user, comprising: a non-transitory computer readable storage medium comprising executable computer modules configured to: receive a signal representing an utterance from the user, the utterance specifying a location attribute and a landmark; identify a set of candidate locations based on the specified location attribute; identify a set of landmarks based on the specified landmark; generate an associated kernel model for each landmark in the set of landmarks, each kernel model comprising a three-dimensional model centered on a map at the location of a landmark associated with the kernel model; rank the candidate locations based on kernel model amplitudes at each candidate location, wherein at least one candidate location is associated with multiple kernel models, and wherein the at least one candidate location is ranked based on a sum of the amplitudes of the multiple kernel models at the at least one candidate location; and select a location to provide to the user based on the ranked candidate locations; and a processor configured to execute the computer modules.
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19. A system for determining a location for recommendation to a user, comprising: a non-transitory computer readable storage medium comprising executable computer modules configured to: receive a signal representing an utterance from the user, the utterance specifying a location attribute and a landmark; identify a set of candidate locations based on the specified location attribute; identify a set of landmarks based on the specified landmark; generate an associated kernel model for each landmark in the set of landmarks, each kernel model comprising a three-dimensional model centered on a map at the location of a landmark associated with the kernel model; rank the candidate locations based on kernel model amplitudes at each candidate location, wherein at least one candidate location is associated with multiple kernel models, and wherein the at least one candidate location is ranked based on a sum of the amplitudes of the multiple kernel models at the at least one candidate location; and select a location to provide to the user based on the ranked candidate locations; and a processor configured to execute the computer modules. 25. The system of claim 19 , wherein the amplitude of at least one kernel model is based on at least one of: a confidence score of the landmark associated with the kernel model, one or more attributes of the landmark associated with the kernel model, a landmark type of the associated landmark, and reviews associated with the associated landmark.
| 0.582933 |
8,607,311 | 8 | 10 |
8. A method of facilitating access to a resource, the method comprising: using a processor to perform acts comprising: abducing a first plurality of assertions from information that comprises an access request and a policy under which a guard controls access to the resource, a system that performs said abducing not having in possession said first plurality of assertions, said abducing being performed by acts comprising: receiving a first answer set and a second answer set, said first answer set comprising said first plurality of assertions, said second answer set comprising a second plurality of assertions, said first plurality of assertions and said second plurality of assertions each satisfying a condition that either said first plurality of assertions or said second plurality of assertions will, when presented to a guard of the resource, cause said guard to find that a query evaluates to true under a policy implemented by said guard; and determining that said first answer set is not subsumed by said second answer set; receiving, from a first principal, a template that specifies said first plurality of assertions and that further specifies a first token that satisfies a first one of said first plurality of assertions; determining from the template that a second one of said first plurality of assertions can be satisfied by a second token containing an assertion made by a second principal; retrieving or generating said second token; sending the guard of the resource said access request which includes a set of tokens which satisfy the template; and gaining access to the resource based on the request.
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8. A method of facilitating access to a resource, the method comprising: using a processor to perform acts comprising: abducing a first plurality of assertions from information that comprises an access request and a policy under which a guard controls access to the resource, a system that performs said abducing not having in possession said first plurality of assertions, said abducing being performed by acts comprising: receiving a first answer set and a second answer set, said first answer set comprising said first plurality of assertions, said second answer set comprising a second plurality of assertions, said first plurality of assertions and said second plurality of assertions each satisfying a condition that either said first plurality of assertions or said second plurality of assertions will, when presented to a guard of the resource, cause said guard to find that a query evaluates to true under a policy implemented by said guard; and determining that said first answer set is not subsumed by said second answer set; receiving, from a first principal, a template that specifies said first plurality of assertions and that further specifies a first token that satisfies a first one of said first plurality of assertions; determining from the template that a second one of said first plurality of assertions can be satisfied by a second token containing an assertion made by a second principal; retrieving or generating said second token; sending the guard of the resource said access request which includes a set of tokens which satisfy the template; and gaining access to the resource based on the request. 10. The method of claim 8 , wherein said acts further comprise: using a key of said second principal to sign said second token.
| 0.781034 |
9,977,510 | 1 | 4 |
1. A method comprising: receiving of gesture event data by a client application of a gesture-driven introduction system running on a user device of a first human actor, wherein the gesture event data comprises a gesture and at least one ancillary condition regarding performance of the gesture by a second human actor, wherein the gesture is representative of a discrete non-empty set of deliberate motions whose execution utilizes at least one visible and movable body part of the second human actor, wherein the first human actor and the second human actor are registered members of the gesture-driven introduction system; assessing the received gesture event data with respect to at least one introduction definition created by the first human actor, wherein an introduction definition defines triggering parameters for exchanging predetermined introduction data, wherein the triggering parameters comprise at least one gesture and at least one ancillary condition; and when the gesture event data is assessed as satisfying the triggering parameters expressed in an introduction definition, automatically transmitting the predetermined introduction data of the respective introduction definition to a user device of the second human actor, wherein said transmission occurs without direct physical or verbal interaction between the first and second human actors.
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1. A method comprising: receiving of gesture event data by a client application of a gesture-driven introduction system running on a user device of a first human actor, wherein the gesture event data comprises a gesture and at least one ancillary condition regarding performance of the gesture by a second human actor, wherein the gesture is representative of a discrete non-empty set of deliberate motions whose execution utilizes at least one visible and movable body part of the second human actor, wherein the first human actor and the second human actor are registered members of the gesture-driven introduction system; assessing the received gesture event data with respect to at least one introduction definition created by the first human actor, wherein an introduction definition defines triggering parameters for exchanging predetermined introduction data, wherein the triggering parameters comprise at least one gesture and at least one ancillary condition; and when the gesture event data is assessed as satisfying the triggering parameters expressed in an introduction definition, automatically transmitting the predetermined introduction data of the respective introduction definition to a user device of the second human actor, wherein said transmission occurs without direct physical or verbal interaction between the first and second human actors. 4. The method of claim 1 , wherein the user device is one of a smartphone, a tablet computer, a notebook computer, a fitness tracking device, a smart watch, a portable gaming system, a pair of smart glasses, and a personal computing device having a transceiver for communicating with the gesture-driven introduction system and having a sensor for sensing an occurrence of one or more user gestures.
| 0.709913 |
8,280,733 | 12 | 15 |
12. A computer-implemented method for adapting a speech recognition system, comprising: detecting a user-initiated change to dictated text; automatically assigning a categorization to the user-initiated change to the dictated text, the categorization being automatically assigned based at least in part upon a measurement of a number of words changed as a result of the user-initiated change, wherein automatically assigning the categorization comprises automatically identifying, based at least in part upon the measurement of the number of words, the user-initiated change as being either a correction or an edit operation; determining a number of times and a most recent time that the user-initiated change has occurred; and selectively adapting the speech recognition engine by temporarily adding a word pair associated with the user-initiated change to a lexicon of the speech recognition engine based at least in part upon the categorization, the number of times the user-initiated change has occurred, and the most recent time that the user-initiated change has occurred, and wherein selectively adapting the speech recognition engine further comprises increasing a probability of a pronunciation associated with the word pair.
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12. A computer-implemented method for adapting a speech recognition system, comprising: detecting a user-initiated change to dictated text; automatically assigning a categorization to the user-initiated change to the dictated text, the categorization being automatically assigned based at least in part upon a measurement of a number of words changed as a result of the user-initiated change, wherein automatically assigning the categorization comprises automatically identifying, based at least in part upon the measurement of the number of words, the user-initiated change as being either a correction or an edit operation; determining a number of times and a most recent time that the user-initiated change has occurred; and selectively adapting the speech recognition engine by temporarily adding a word pair associated with the user-initiated change to a lexicon of the speech recognition engine based at least in part upon the categorization, the number of times the user-initiated change has occurred, and the most recent time that the user-initiated change has occurred, and wherein selectively adapting the speech recognition engine further comprises increasing a probability of a pronunciation associated with the word pair. 15. The method of claim 12 , wherein selectively adapting comprises not adapting if the categorization indicates that the user-initiated correction is not due to a recognition error made by the speech recognition engine.
| 0.73236 |
7,548,909 | 11 | 13 |
11. A method that facilitates displaying search results, comprising: employing a processor to execute computer readable instructions stored on a computer storage readable medium to perform the following acts: displaying, in a same page of a web search engine, a query window to receive queries, a relevance pane including one or more information categories that correspond to a query and to a selected tab identifying a user role, and a channel pane to display selectable information channels corresponding to the query and to the selected tab identifying the user role, wherein a relevance indicator graphically conveys, in terms of relative length of the relevance indicator, relevance of each of the one or more information categories; receiving a search query relating to medical/health information from a user; receiving a selection of a tab identifying a user role; executing the search query; analyzing results of the search query; selectively filtering, organizing, and presenting the search query results as a function of relevancy to the user and the selection of the tab identifying a user role; and based on user interaction with a relevance indicator in the relevance pane, displaying a menu window in the same page, wherein the menu window provides information concerning a concept and a query corresponding to the relevance indicator, identifies additional concepts that are relevant to the concept and the query, and enables a user to request additional information regarding the concept and the query.
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11. A method that facilitates displaying search results, comprising: employing a processor to execute computer readable instructions stored on a computer storage readable medium to perform the following acts: displaying, in a same page of a web search engine, a query window to receive queries, a relevance pane including one or more information categories that correspond to a query and to a selected tab identifying a user role, and a channel pane to display selectable information channels corresponding to the query and to the selected tab identifying the user role, wherein a relevance indicator graphically conveys, in terms of relative length of the relevance indicator, relevance of each of the one or more information categories; receiving a search query relating to medical/health information from a user; receiving a selection of a tab identifying a user role; executing the search query; analyzing results of the search query; selectively filtering, organizing, and presenting the search query results as a function of relevancy to the user and the selection of the tab identifying a user role; and based on user interaction with a relevance indicator in the relevance pane, displaying a menu window in the same page, wherein the menu window provides information concerning a concept and a query corresponding to the relevance indicator, identifies additional concepts that are relevant to the concept and the query, and enables a user to request additional information regarding the concept and the query. 13. The method of claim 11 , further comprising displaying an advertisement in the same page of the web search engine, based on the query and the selected role identifier.
| 0.5 |
9,195,952 | 1 | 20 |
1. A computer-implemented method of designing business process controls utilizing a contextual mapping engine, the computer-implemented method comprising: receiving, in a computer system, a plurality of statements relating to one or more business process controls, each statement, in the plurality of statements, comprising at least one token; storing the plurality of statements in a repository of the computer system; comparing, using the contextual mapping engine in the computer system, a first statement of the plurality of statements with a second statement of the plurality of statements; generating, using the contextual mapping engine in the computer system, a matching score between the first statement and the second statement, the matching score being based on a total amount of unique tokens between the first statement and the second statement; removing the second statement of the plurality of statements from the repository if the matching score is above a particular threshold; generating a chart including a plurality of bubbles, generating the chart including: connecting a first bubble, of the plurality of bubbles, to a second bubble, of the plurality of bubbles, when the matching score is above the particular threshold, where a size of each bubble, of the plurality of bubbles, is based on a number of parameters relating to a particular business process control of the one or more business process controls; providing the chart, including the first bubble connected to the second bubble, for display to a user device; receiving, from the user device, a selection of a bubble of the plurality of bubbles; and providing, for display, information relating to the selected bubble, the information including parameters relating to a business process control of the one or more business process controls.
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1. A computer-implemented method of designing business process controls utilizing a contextual mapping engine, the computer-implemented method comprising: receiving, in a computer system, a plurality of statements relating to one or more business process controls, each statement, in the plurality of statements, comprising at least one token; storing the plurality of statements in a repository of the computer system; comparing, using the contextual mapping engine in the computer system, a first statement of the plurality of statements with a second statement of the plurality of statements; generating, using the contextual mapping engine in the computer system, a matching score between the first statement and the second statement, the matching score being based on a total amount of unique tokens between the first statement and the second statement; removing the second statement of the plurality of statements from the repository if the matching score is above a particular threshold; generating a chart including a plurality of bubbles, generating the chart including: connecting a first bubble, of the plurality of bubbles, to a second bubble, of the plurality of bubbles, when the matching score is above the particular threshold, where a size of each bubble, of the plurality of bubbles, is based on a number of parameters relating to a particular business process control of the one or more business process controls; providing the chart, including the first bubble connected to the second bubble, for display to a user device; receiving, from the user device, a selection of a bubble of the plurality of bubbles; and providing, for display, information relating to the selected bubble, the information including parameters relating to a business process control of the one or more business process controls. 20. The method of claim 1 , further comprising: replacing the first statement of the plurality of statements with a redrafted statement if the matching score is above the particular threshold.
| 0.852308 |
9,430,585 | 5 | 6 |
5. The method of claim 1 , wherein providing the media object and the query to the plurality of second users further comprises: including the media object and the query pair in a list of additional media object and query pairs, the media object and query pair having a position in the list; and selecting the media object and query pair from the list for presentation to the plurality of second users, the selection based on the position of the media object and query pair in the list.
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5. The method of claim 1 , wherein providing the media object and the query to the plurality of second users further comprises: including the media object and the query pair in a list of additional media object and query pairs, the media object and query pair having a position in the list; and selecting the media object and query pair from the list for presentation to the plurality of second users, the selection based on the position of the media object and query pair in the list. 6. The method of claim 5 , further comprising: modifying a position of the media object and query pair in the list based on indications of interest in the media object and query pair from one or more second users of the plurality of second users; and selecting the media object and query pair from the list for presentation to one or more third users, the selection based on the modified position of the media object and query pair in the list.
| 0.5 |
8,965,754 | 8 | 13 |
8. A computer system, comprising: a processor; and a storage device connected to the processor, wherein the storage device has stored thereon a program, and wherein the processor is configured to execute instructions of the program to perform operations, wherein the operations comprise: dynamically generating an environment dictionary for a document that is being viewed based on information from one or more social networks associated with the user; and in response to receiving a portion of a word for the document, receiving a list of words for use in completing the portion of the word, wherein each word in the list of words has an associated weight; for at least one word in the list of words, obtaining an environment weight from the dynamically created environment dictionary; updating the associated weight of the at least one word using the obtained environment weight; and ordering the words in the list of words based on the updated, associated weight of each of the words.
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8. A computer system, comprising: a processor; and a storage device connected to the processor, wherein the storage device has stored thereon a program, and wherein the processor is configured to execute instructions of the program to perform operations, wherein the operations comprise: dynamically generating an environment dictionary for a document that is being viewed based on information from one or more social networks associated with the user; and in response to receiving a portion of a word for the document, receiving a list of words for use in completing the portion of the word, wherein each word in the list of words has an associated weight; for at least one word in the list of words, obtaining an environment weight from the dynamically created environment dictionary; updating the associated weight of the at least one word using the obtained environment weight; and ordering the words in the list of words based on the updated, associated weight of each of the words. 13. The computer system of claim 8 , wherein the operations further comprise: receiving the list of words from an auto-completion process that generates the list of words as suggestions for completion of the portion of the word input by the user.
| 0.523256 |
8,032,535 | 12 | 13 |
12. A non-transitory computer-readable medium comprising instructions which, when performed by a computer, cause: receiving a query from a user terminal including a query word; identifying a first search result and a second search result relevant to the query, wherein the first search result has a first result word and the second search result has a second result word; combining the query and the search results to obtain a set of symbol pairs, wherein a first symbol pair contains the query word and the first result word and a second symbol pair contains the query word and the second result word; accessing a data storage device for weights for the symbol pairs which were stored during a previous search; determining an estimate of relevance for each search result with the weights; ranking the search results according to their estimates of relevance; displaying the search results according to their ranks; detecting browsing activities on the search results; updating the weights when a search result is clicked on and storing the updated weights in the data storage device; updating the estimates of relevance with the updated weights; and reordering the search results according to the updated estimates of relevance.
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12. A non-transitory computer-readable medium comprising instructions which, when performed by a computer, cause: receiving a query from a user terminal including a query word; identifying a first search result and a second search result relevant to the query, wherein the first search result has a first result word and the second search result has a second result word; combining the query and the search results to obtain a set of symbol pairs, wherein a first symbol pair contains the query word and the first result word and a second symbol pair contains the query word and the second result word; accessing a data storage device for weights for the symbol pairs which were stored during a previous search; determining an estimate of relevance for each search result with the weights; ranking the search results according to their estimates of relevance; displaying the search results according to their ranks; detecting browsing activities on the search results; updating the weights when a search result is clicked on and storing the updated weights in the data storage device; updating the estimates of relevance with the updated weights; and reordering the search results according to the updated estimates of relevance. 13. The non-transitory computer-readable medium of claim 12 , further comprising instructions which, when performed by the computer, cause: displaying the search results reordered according to the updated estimates of relevance.
| 0.746102 |
9,471,565 | 1 | 3 |
1. A method comprising: performing a generic web crawl to identify a first webpage in a first language having a link thereon which points to a second webpage in a second language, wherein the first webpage and the second webpage comprise a bilingual website comprising the first webpage and the second webpage in respective languages; based on an analysis of parameters on the first webpage comprising at least two of: the link pointing to the second webpage, a title, a link neighborhood, a link context and data indicating a separate version of the first webpage, classifying the first webpage as a root page and as an entry point for the bilingual website via the link to the second webpage; identifying, using a visitation policy which constrains web-crawling to a graph neighborhood of bilingual websites, a pattern of links within between the first webpage and the second webpage, to yield a bipartite graph; ranking a relevance of candidate links which point to parallel text in the first webpage and the second webpage, to yield classifications of links based on the bipartite graph and the relevance; performing, based on the relevance, a bidirectional web crawl of the candidate links, to identify the first webpage and the second webpage as a bilingual website, the bidirectional web crawl utilizing the classifications of links to avoid links having a low respective relevance; analyzing the first webpage and the second webpage to identify information pairs in the first language and the second language; extracting the information pairs from the first webpage and the second webpage for use in a language translation model, the information pairs comprising at least one of a sentence pair and a paragraph pair; and updating a statistical model with domain representative data using the information pairs.
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1. A method comprising: performing a generic web crawl to identify a first webpage in a first language having a link thereon which points to a second webpage in a second language, wherein the first webpage and the second webpage comprise a bilingual website comprising the first webpage and the second webpage in respective languages; based on an analysis of parameters on the first webpage comprising at least two of: the link pointing to the second webpage, a title, a link neighborhood, a link context and data indicating a separate version of the first webpage, classifying the first webpage as a root page and as an entry point for the bilingual website via the link to the second webpage; identifying, using a visitation policy which constrains web-crawling to a graph neighborhood of bilingual websites, a pattern of links within between the first webpage and the second webpage, to yield a bipartite graph; ranking a relevance of candidate links which point to parallel text in the first webpage and the second webpage, to yield classifications of links based on the bipartite graph and the relevance; performing, based on the relevance, a bidirectional web crawl of the candidate links, to identify the first webpage and the second webpage as a bilingual website, the bidirectional web crawl utilizing the classifications of links to avoid links having a low respective relevance; analyzing the first webpage and the second webpage to identify information pairs in the first language and the second language; extracting the information pairs from the first webpage and the second webpage for use in a language translation model, the information pairs comprising at least one of a sentence pair and a paragraph pair; and updating a statistical model with domain representative data using the information pairs. 3. The method of claim 1 , further comprising bootstrapping the language translation model using the information pairs.
| 0.776316 |
8,838,562 | 17 | 20 |
17. An apparatus for providing query parameters to a search engine, comprising: a computer memory; and a selection processing unit that is stored in the computer memory to: (i) receive one or more selection indicators that include at least two locations of a graphical user interface, wherein the at least two locations define an area of the graphical user interface that has been selected by a user of the apparatus, (ii) identify one or more selected elements that are contained within the defined area of the graphical user interface, wherein the one or more selected elements are unable to receive information from the user, and (iii) fetch search data associated with the one or more selected elements in the graphical user interface that are contained within the defined area of the graphical user interface; and a query creation unit that is stored in the memory to: (i) convert the search data to textual search data at a time subsequent to fetching the search data if the search data is not text, (ii) determine that the one or more selected elements comprise a partial word, (iii) identify one or more unselected elements of the graphical user interface that the user did not select, wherein the one or more unselected elements are identified from among a plurality of unselected elements that are located outside of the defined area of the graphical user interface based on a determination that the one or more unselected elements, when appended to the one or more selected elements, will complete the partial word, (iv) append unselected textual search data associated with the one or more unselected elements to the textual search data of the one or more selected elements, (v) determine a context associated with the one or more selected elements based on the identified one or more unselected elements, (vi) generate one or more query terms based on the determined context, wherein the one or more query terms are different than the unselected textual search data, (vii) create a query based on the textual search data appended with the unselected textual search data and the one or more query terms, and (viii) transmit the created query to the search engine.
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17. An apparatus for providing query parameters to a search engine, comprising: a computer memory; and a selection processing unit that is stored in the computer memory to: (i) receive one or more selection indicators that include at least two locations of a graphical user interface, wherein the at least two locations define an area of the graphical user interface that has been selected by a user of the apparatus, (ii) identify one or more selected elements that are contained within the defined area of the graphical user interface, wherein the one or more selected elements are unable to receive information from the user, and (iii) fetch search data associated with the one or more selected elements in the graphical user interface that are contained within the defined area of the graphical user interface; and a query creation unit that is stored in the memory to: (i) convert the search data to textual search data at a time subsequent to fetching the search data if the search data is not text, (ii) determine that the one or more selected elements comprise a partial word, (iii) identify one or more unselected elements of the graphical user interface that the user did not select, wherein the one or more unselected elements are identified from among a plurality of unselected elements that are located outside of the defined area of the graphical user interface based on a determination that the one or more unselected elements, when appended to the one or more selected elements, will complete the partial word, (iv) append unselected textual search data associated with the one or more unselected elements to the textual search data of the one or more selected elements, (v) determine a context associated with the one or more selected elements based on the identified one or more unselected elements, (vi) generate one or more query terms based on the determined context, wherein the one or more query terms are different than the unselected textual search data, (vii) create a query based on the textual search data appended with the unselected textual search data and the one or more query terms, and (viii) transmit the created query to the search engine. 20. The apparatus of claim 17 , wherein the one or more selection indicators include a mouse down-click and a mouse up-click.
| 0.720982 |
9,092,440 | 15 | 16 |
15. An electronic system comprising: one or more processing devices; and one or more machine-readable hardware storage devices storing instructions that are executable by the one or more processing devices to perform operations comprising: obtaining a definition of a data structure, the definition specifying one or more fields of the data structure; obtaining a definition of one or more operations to be performed on the data structure, the definition of the one or more operations being in accordance with a first syntax; receiving, from a client device, a request to translate (i) instructions to perform the one or more operations in the first syntax on the one or more fields of the data structure, into (ii) instructions to perform the one or more operations in a second syntax on the one or more fields of the data structure; in response to the request to translate received from the client device, generating a translation of the obtained definition of the one or more operations from the first syntax to the second syntax specified in the request; generating, in accordance with the second syntax based on the obtained definition of the data structure, access instructions for accessing the one or more fields of the data structure as defined in the obtained definition in the second syntax specified in the request; and at least partly based on the translation of the definition of the one or more operations from the first syntax to the second syntax, generating the requested translation for performing in the second syntax specified in the request the one or more operations on the one or more fields of the data structure, with the requested instructions comprising the generated access instructions that are in the second syntax and are for accessing the one or more fields of the data structure.
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15. An electronic system comprising: one or more processing devices; and one or more machine-readable hardware storage devices storing instructions that are executable by the one or more processing devices to perform operations comprising: obtaining a definition of a data structure, the definition specifying one or more fields of the data structure; obtaining a definition of one or more operations to be performed on the data structure, the definition of the one or more operations being in accordance with a first syntax; receiving, from a client device, a request to translate (i) instructions to perform the one or more operations in the first syntax on the one or more fields of the data structure, into (ii) instructions to perform the one or more operations in a second syntax on the one or more fields of the data structure; in response to the request to translate received from the client device, generating a translation of the obtained definition of the one or more operations from the first syntax to the second syntax specified in the request; generating, in accordance with the second syntax based on the obtained definition of the data structure, access instructions for accessing the one or more fields of the data structure as defined in the obtained definition in the second syntax specified in the request; and at least partly based on the translation of the definition of the one or more operations from the first syntax to the second syntax, generating the requested translation for performing in the second syntax specified in the request the one or more operations on the one or more fields of the data structure, with the requested instructions comprising the generated access instructions that are in the second syntax and are for accessing the one or more fields of the data structure. 16. The electronic system of claim 15 , wherein the operations further comprise: generating, based on the definition of the data structure and the definition of one or more operations, a mapping specifying a relationship among the one or more fields and input to the one or more operations; wherein generating the requested instructions for performing the one or more operations on the one or more fields of the data structure comprises: generating, at least partly based on the translation, the access instructions for accessing the one or more fields of the data structure, and the mapping, the requested instructions in accordance with the second syntax for performing the one or more operations on the one or more fields of the data structure.
| 0.5 |
9,710,466 | 9 | 12 |
9. A computer-implemented method of annotating a shared document, the method being executed on a computer and comprising: responsive to a request from a user to retrieve a shared document, retrieving, in a computer processor, the shared document from a document data storage as document data; inputting, in the computer in response to the user, at least one annotation to be applied to said shared document in association with a section-dependent, display-independent location; inputting, in the computer processor in response to the user, a section of the document data to be associated with the at least one annotation; providing , in the computer processor, the shared document as a marked-up document for display at a user interface, the at least one annotation being visually reproduced in association with the section of the shared document, the shared document having plural sections each having a user-selectable length, the plural sections including the section of the share document within which the annotation data is to be embedded, wherein the at least one annotation includes, specific to the section of said shared document to which the at least one annotation is applied, a pre-defined conflict indication which is user-selected from at least two of the following: yes, possible and/or no; revising, in the computer processor in response to the user, the at least one annotation from the marked-up document displayed at the user interface, and a scope of the section of said shared document to which the at least one annotation is applied; and responsive to a request from the user to store the shared document, extracting, from the marked-up document, the annotation data and the scope of the section of said shared document to which the at least one annotation is applied, updating, in the annotation data storage, from the annotation data that was extracted, the at least one annotation and the scope of the section of said shared document to which the at least one annotation is applied, and storing, in the annotation data storage for later retrieval, the at least one annotation and the scope of the annotation data that are revised.
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9. A computer-implemented method of annotating a shared document, the method being executed on a computer and comprising: responsive to a request from a user to retrieve a shared document, retrieving, in a computer processor, the shared document from a document data storage as document data; inputting, in the computer in response to the user, at least one annotation to be applied to said shared document in association with a section-dependent, display-independent location; inputting, in the computer processor in response to the user, a section of the document data to be associated with the at least one annotation; providing , in the computer processor, the shared document as a marked-up document for display at a user interface, the at least one annotation being visually reproduced in association with the section of the shared document, the shared document having plural sections each having a user-selectable length, the plural sections including the section of the share document within which the annotation data is to be embedded, wherein the at least one annotation includes, specific to the section of said shared document to which the at least one annotation is applied, a pre-defined conflict indication which is user-selected from at least two of the following: yes, possible and/or no; revising, in the computer processor in response to the user, the at least one annotation from the marked-up document displayed at the user interface, and a scope of the section of said shared document to which the at least one annotation is applied; and responsive to a request from the user to store the shared document, extracting, from the marked-up document, the annotation data and the scope of the section of said shared document to which the at least one annotation is applied, updating, in the annotation data storage, from the annotation data that was extracted, the at least one annotation and the scope of the section of said shared document to which the at least one annotation is applied, and storing, in the annotation data storage for later retrieval, the at least one annotation and the scope of the annotation data that are revised. 12. The method of claim 9 , wherein the at least one annotation further includes at least one of: a user-provided text, a user-defined attribute, a reference to a URL, a reference to another section of the shared document, and a reference to another data storage.
| 0.5 |
7,665,015 | 13 | 14 |
13. The method of claim 12 , wherein: if the token comprises character data, said token header identifies where the character data are stored.
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13. The method of claim 12 , wherein: if the token comprises character data, said token header identifies where the character data are stored. 14. The method of claim 13 , further comprising: forwarding said token header and said character data in separate data streams.
| 0.5 |
10,043,069 | 1 | 3 |
1. A computer-implemented method, comprising: receiving social network data associated with a first user profile corresponding to a mobile computing device, the social network data including information related to one or more different user profiles linked, within a social network, to the first user profile; receiving image data from the mobile computing device; performing image processing on the image data to identify text in the image data; recognizing actionable text in the identified text; determining a plurality of potential user-executable functions, wherein the determining is based at least in part on the actionable text and wherein the plurality of potential user-executable functions includes at least one of creating a new contact entry based on the actionable text, adding information in the actionable text to an existing contact, showing a map based on the actionable text, dialing a telephone number included in the actionable text, or accessing a website included in the actionable text; determining, based at least in part on the social network data, a primary user-executable function of the plurality of potential user-executable functions; causing the mobile computing device to display an icon, the icon including at least two user-selectable portions including: a first user-selectable portion that, when selected, causes execution of the primary user-executable function, and a second user-selectable portion that, when selected, causes execution of an action to display additional user-executable functions of the plurality of potential user-executable functions; receiving an indication of a user selection of the first user-selectable portion corresponding to the primary user-executable function; and executing the primary user-executable function.
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1. A computer-implemented method, comprising: receiving social network data associated with a first user profile corresponding to a mobile computing device, the social network data including information related to one or more different user profiles linked, within a social network, to the first user profile; receiving image data from the mobile computing device; performing image processing on the image data to identify text in the image data; recognizing actionable text in the identified text; determining a plurality of potential user-executable functions, wherein the determining is based at least in part on the actionable text and wherein the plurality of potential user-executable functions includes at least one of creating a new contact entry based on the actionable text, adding information in the actionable text to an existing contact, showing a map based on the actionable text, dialing a telephone number included in the actionable text, or accessing a website included in the actionable text; determining, based at least in part on the social network data, a primary user-executable function of the plurality of potential user-executable functions; causing the mobile computing device to display an icon, the icon including at least two user-selectable portions including: a first user-selectable portion that, when selected, causes execution of the primary user-executable function, and a second user-selectable portion that, when selected, causes execution of an action to display additional user-executable functions of the plurality of potential user-executable functions; receiving an indication of a user selection of the first user-selectable portion corresponding to the primary user-executable function; and executing the primary user-executable function. 3. The computer-implemented method of claim 1 , wherein the actionable text comprises at least one of a physical address, a telephone number, a website address, or an email address.
| 0.777641 |
8,140,556 | 1 | 2 |
1. A method of generating a query for querying an ontology, the method comprising: receiving a first query in a first language, wherein the first language is a natural language; checking the first query to determine if the first query complies with a predefined grammar and to determine if the first query comprises one or more terms from a vocabulary used in the ontology; in response to determining that at least one aspect of the first query does not comply with the predetermined grammar and does not comprise one or more terms from the vocabulary used in the ontology, providing guiding formulation of the first query by providing one or more constraints, wherein the one or more constraints are based upon the predefined grammar and the vocabulary comprising terms used in the ontology, and the predefined grammar is based upon a set of one or more rules; based on the one or more constraints, constraining the first query to comply with the predetermined grammar and the vocabulary used in the ontology; and generating, based upon the constrained first query, a second query in a second language, wherein the second query complies with the predetermined grammar and the vocabulary used in the ontology, and wherein the second language is different from the first language and the ontology is capable of being queried using the second query in the second language.
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1. A method of generating a query for querying an ontology, the method comprising: receiving a first query in a first language, wherein the first language is a natural language; checking the first query to determine if the first query complies with a predefined grammar and to determine if the first query comprises one or more terms from a vocabulary used in the ontology; in response to determining that at least one aspect of the first query does not comply with the predetermined grammar and does not comprise one or more terms from the vocabulary used in the ontology, providing guiding formulation of the first query by providing one or more constraints, wherein the one or more constraints are based upon the predefined grammar and the vocabulary comprising terms used in the ontology, and the predefined grammar is based upon a set of one or more rules; based on the one or more constraints, constraining the first query to comply with the predetermined grammar and the vocabulary used in the ontology; and generating, based upon the constrained first query, a second query in a second language, wherein the second query complies with the predetermined grammar and the vocabulary used in the ontology, and wherein the second language is different from the first language and the ontology is capable of being queried using the second query in the second language. 2. The method of claim 1 wherein the first query is a natural language query.
| 0.915385 |
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