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7,944,448 | 32 | 50 | 32. A method for causing a processor to execute computer-readable instructions stored on a non-transitory computer-readable medium, the computer readable instructions comprising: generating emotional responses for a software agent, comprising: receiving and interpreting an input event based on stored social characteristics and outputting a social event based on the interpretation of the input event; receiving the social event, an output from an emotional state register and an output from a predefined personality trait register, and updating at least one of a current state of the emotional state register and a social response message stored an event buffer; outputting an emotion response message based on at least one of the social response message stored in the event buffer, one or more outputs of the predefined personality trait register, or one or more outputs of the emotional state register; and receiving the emotion response message output from the emotion generator and converting the emotion response message into a behavior message based on stored social characteristics. | 32. A method for causing a processor to execute computer-readable instructions stored on a non-transitory computer-readable medium, the computer readable instructions comprising: generating emotional responses for a software agent, comprising: receiving and interpreting an input event based on stored social characteristics and outputting a social event based on the interpretation of the input event; receiving the social event, an output from an emotional state register and an output from a predefined personality trait register, and updating at least one of a current state of the emotional state register and a social response message stored an event buffer; outputting an emotion response message based on at least one of the social response message stored in the event buffer, one or more outputs of the predefined personality trait register, or one or more outputs of the emotional state register; and receiving the emotion response message output from the emotion generator and converting the emotion response message into a behavior message based on stored social characteristics. 50. The method according to claim 32 , wherein the method further comprises generating the emotion response message after the social event has been processed, and outputting the behavior message based on social characteristics applied to the emotional response message. | 0.736791 |
7,779,355 | 39 | 43 | 39. A computer program product stored on a computer-readable medium for creating a composite electronic representation of a document having text using information recorded during a presentation, the computer program product comprising: code for scanning a paper document to generate an electronic representation of the document for the presentation, the electronic representation including text; code for applying an optical character recognition (OCR) algorithm to the text from the electronic representation to generate OCR determined text; code for accessing recorded information including audio and visual information recorded during the presentation, and using a matching algorithm to compare the OCR determined text to the audio of the recorded information to determine a portion of the recorded information that matches the OCR determined text, the matching algorithm configured to map the OCR determined text to a portion of any of a plurality of recorded information with audio that matches the OCR determined text and generate matching information in response thereto; code for generating composite information based on the portion of the recorded information that corresponds to the OCR determined text and the electronic representation of the document; code for generating a user selectable object providing a user with access to the portion of the audio of the recorded information corresponding to the OCR determined text, and inserting the user selectable object and metadata including the matching information into the electronic representation of the document when the computer system locates a portion of the recorded information corresponding to the OCR determined text, the computer system thus creating a composite electronic representation of the document including the user selectable object and the metadata, the user selectable object being placed in a position associated with the text and allowing the user to access the composite information in an application displaying the composite electronic representation or a separate application by selecting the user selectable object, the user-selectable object being able to access the portion of the recorded information using an embedded video link in the user selectable object; and code for storing the composite electronic representation as a PDF, HyperText Transfer Language (HTML), Flash or Word formatted document for access by the user or another user accessing the composite electronic document. | 39. A computer program product stored on a computer-readable medium for creating a composite electronic representation of a document having text using information recorded during a presentation, the computer program product comprising: code for scanning a paper document to generate an electronic representation of the document for the presentation, the electronic representation including text; code for applying an optical character recognition (OCR) algorithm to the text from the electronic representation to generate OCR determined text; code for accessing recorded information including audio and visual information recorded during the presentation, and using a matching algorithm to compare the OCR determined text to the audio of the recorded information to determine a portion of the recorded information that matches the OCR determined text, the matching algorithm configured to map the OCR determined text to a portion of any of a plurality of recorded information with audio that matches the OCR determined text and generate matching information in response thereto; code for generating composite information based on the portion of the recorded information that corresponds to the OCR determined text and the electronic representation of the document; code for generating a user selectable object providing a user with access to the portion of the audio of the recorded information corresponding to the OCR determined text, and inserting the user selectable object and metadata including the matching information into the electronic representation of the document when the computer system locates a portion of the recorded information corresponding to the OCR determined text, the computer system thus creating a composite electronic representation of the document including the user selectable object and the metadata, the user selectable object being placed in a position associated with the text and allowing the user to access the composite information in an application displaying the composite electronic representation or a separate application by selecting the user selectable object, the user-selectable object being able to access the portion of the recorded information using an embedded video link in the user selectable object; and code for storing the composite electronic representation as a PDF, HyperText Transfer Language (HTML), Flash or Word formatted document for access by the user or another user accessing the composite electronic document. 43. The computer program product of claim 39 , further comprising code for performing at least one of emailing, printing, storing, displaying, playing, and copying the created representation. | 0.5 |
4,384,329 | 5 | 6 | 5. A method according to claim 1 or claim 3 including the step of run-length encoding the displacement of said binary bits in said two-dimensional array. | 5. A method according to claim 1 or claim 3 including the step of run-length encoding the displacement of said binary bits in said two-dimensional array. 6. A method according to claim 5 including the step of appending a code to the run-length encoded displacement of said binary bits indicative of the direction of displacement of each succeeding bit from the preceding bit. | 0.5 |
7,653,244 | 12 | 17 | 12. A method for linking external data files to an application program executing on at least one processor, wherein during execution the application program provides displays of a user interface substantially entirely as a raster pattern of pixels and which is not capable of directly rendering data in the external data file, the method comprising: causing display data displayed in a graphic representation by the application program during execution thereof to be retrieved, the display data comprising a selected data area corresponding to at least one area of the graphic representation; processing, at the at least one processor, the display data through an artificial neural network trained to identify recognized data from selected data within the selected data area of the graphic representation thereof, the artificial neural network comprising a raster neural network, a feature neural network, and a vector neural network, and wherein analyzing the character data comprises: analyzing rectangular coordinates and a binary color of the character data at the raster neural network to generate a first character data value; analyzing feature data of the character data at feature neural network to generate a second character data value; analyzing vectors corresponding to an outline of the character data at the vector neural network to generate a third character data value; and voting on the first, second, and third character data values to identify the recognized data; storing recognized data from the graphic representation thereof in a non-graphic format; and associating the recognized selected data with an external data file external to the application program at the at least one processor, the application program being incapable of directly rendering data from the external data file. | 12. A method for linking external data files to an application program executing on at least one processor, wherein during execution the application program provides displays of a user interface substantially entirely as a raster pattern of pixels and which is not capable of directly rendering data in the external data file, the method comprising: causing display data displayed in a graphic representation by the application program during execution thereof to be retrieved, the display data comprising a selected data area corresponding to at least one area of the graphic representation; processing, at the at least one processor, the display data through an artificial neural network trained to identify recognized data from selected data within the selected data area of the graphic representation thereof, the artificial neural network comprising a raster neural network, a feature neural network, and a vector neural network, and wherein analyzing the character data comprises: analyzing rectangular coordinates and a binary color of the character data at the raster neural network to generate a first character data value; analyzing feature data of the character data at feature neural network to generate a second character data value; analyzing vectors corresponding to an outline of the character data at the vector neural network to generate a third character data value; and voting on the first, second, and third character data values to identify the recognized data; storing recognized data from the graphic representation thereof in a non-graphic format; and associating the recognized selected data with an external data file external to the application program at the at least one processor, the application program being incapable of directly rendering data from the external data file. 17. The method of claim 12 wherein: the graphic representation of the selected data comprises a raster representation of the selected data; a set of graphic features are extracted from the raster representation; and the artificial neural network system identifies the recognized data from the set of graphic features. | 0.769287 |
8,364,685 | 7 | 8 | 7. A non-transitory computer readable media comprising program code that when executed by a programmable processor causes the processor to execute a method for annotating and ranking a user review personalized to prior user experience, the computer readable media comprising: program code for generating a collection of content items for which a user has previously expressed interest, the collection of content items including objective attributes of interest indicated by the user for each of the content items, subjective attributes of interest indicated by the user for each of the content items by assigning a user-defined subjective value and a reference to the collection of content items stored in a user profile; program code for identifying a new content item that is not contained in the collection of content items, the new content item comprising one or more objective attributes and one or more subjective attributes, wherein each of the one or more subjective attributes includes a subjective value; and program code for providing a reference framework to interpret the new content item in view of one or more common objective attributes of interest by the user for a given one of the one or more content items in the collection of content items, wherein the number of common objective attributes satisfy a threshold, and in view of the one or more subjective attributes for a given one of the one or more content items in the collection of content items, wherein a difference between the user-defined subjective value and the subjective value of each of the one or more subjective attributes for a given one of the one or more content items in the collection of content items is below a threshold. | 7. A non-transitory computer readable media comprising program code that when executed by a programmable processor causes the processor to execute a method for annotating and ranking a user review personalized to prior user experience, the computer readable media comprising: program code for generating a collection of content items for which a user has previously expressed interest, the collection of content items including objective attributes of interest indicated by the user for each of the content items, subjective attributes of interest indicated by the user for each of the content items by assigning a user-defined subjective value and a reference to the collection of content items stored in a user profile; program code for identifying a new content item that is not contained in the collection of content items, the new content item comprising one or more objective attributes and one or more subjective attributes, wherein each of the one or more subjective attributes includes a subjective value; and program code for providing a reference framework to interpret the new content item in view of one or more common objective attributes of interest by the user for a given one of the one or more content items in the collection of content items, wherein the number of common objective attributes satisfy a threshold, and in view of the one or more subjective attributes for a given one of the one or more content items in the collection of content items, wherein a difference between the user-defined subjective value and the subjective value of each of the one or more subjective attributes for a given one of the one or more content items in the collection of content items is below a threshold. 8. The computer readable media of claim 7 , comprising program code for creating the user profile by explicitly collecting user data. | 0.809456 |
8,452,983 | 1 | 4 | 1. A computer implemented method for protecting numerical control codes, the method comprising: decrypting, by one or more computers, an encrypted text file that defines how an event for a tool path data set is processed, wherein the encrypted text file includes an encrypted seed number that has a fixed number of first random characters padded before and after a translated seed number, and includes second random characters inserted after each character in a translated instruction string; processing, by the one or more computers, said decrypted text file to obtain a set of instructions; formatting, by the one or more computers, said set of instructions according to a definition file, wherein the definition file contains static information related to a machine tool and a corresponding controller for the machine tool; and outputting, by the one or more computers, said set of formatted instructions, whereby postprocessed machine controls are written that can be executed by the controller. | 1. A computer implemented method for protecting numerical control codes, the method comprising: decrypting, by one or more computers, an encrypted text file that defines how an event for a tool path data set is processed, wherein the encrypted text file includes an encrypted seed number that has a fixed number of first random characters padded before and after a translated seed number, and includes second random characters inserted after each character in a translated instruction string; processing, by the one or more computers, said decrypted text file to obtain a set of instructions; formatting, by the one or more computers, said set of instructions according to a definition file, wherein the definition file contains static information related to a machine tool and a corresponding controller for the machine tool; and outputting, by the one or more computers, said set of formatted instructions, whereby postprocessed machine controls are written that can be executed by the controller. 4. The method of claim 1 , wherein said encrypted text file contains license information. | 0.942581 |
8,832,126 | 1 | 6 | 1. A method of suggesting custodians subject to a litigation hold, comprising: receiving a set of keywords or queries; identifying, by one or more processing devices, a set of documents relevant to the set of keywords or queries; identifying, by one or more processing devices, one or more custodians associated with one or more documents in the set of documents; and determining a set of one or more candidates for the litigation hold from the identified one or more custodians based upon a comparison of the identified one or more custodians to a known set of one or more custodians relevant to the litigation hold, wherein each of the identified one or more custodians that is external to the known set of one or more custodians is added to the set of one or more candidates, providing, by the one or more processing devices, the determined set of one or more candidates for the litigation hold to a user. | 1. A method of suggesting custodians subject to a litigation hold, comprising: receiving a set of keywords or queries; identifying, by one or more processing devices, a set of documents relevant to the set of keywords or queries; identifying, by one or more processing devices, one or more custodians associated with one or more documents in the set of documents; and determining a set of one or more candidates for the litigation hold from the identified one or more custodians based upon a comparison of the identified one or more custodians to a known set of one or more custodians relevant to the litigation hold, wherein each of the identified one or more custodians that is external to the known set of one or more custodians is added to the set of one or more candidates, providing, by the one or more processing devices, the determined set of one or more candidates for the litigation hold to a user. 6. The method of claim 1 , wherein the providing comprises: performing a search for one or more additional documents relevant to the litigation hold based upon the determined set of one or more candidates for the litigation hold; and providing the one or more additional documents as being relevant to the litigation hold, wherein the one or more additional documents are external to the set of documents relevant to the set of keywords or queries. | 0.5 |
7,853,869 | 12 | 13 | 12. A non-transient computer-readable storage medium having computer-executable instructions, for generating a logically structured document from an unstructured document, that when executed on a computer via a microprocessor perform a method comprising: obtaining text runs from an unstructured markup language representation of a document, comprising a paper specification format, where the text runs comprise one or more representations of markup language glyphs; constructing semantic block containers by determining which text runs correspond to same semantic blocks and placing those text runs corresponding to the same semantic block into the same semantic block container; ordering the text runs within respective semantic blocks to match a logical order present in the document; and ordering the semantic blocks to match a logical order present in the document to generate a structured markup language representation of the document, comprising selectable text. | 12. A non-transient computer-readable storage medium having computer-executable instructions, for generating a logically structured document from an unstructured document, that when executed on a computer via a microprocessor perform a method comprising: obtaining text runs from an unstructured markup language representation of a document, comprising a paper specification format, where the text runs comprise one or more representations of markup language glyphs; constructing semantic block containers by determining which text runs correspond to same semantic blocks and placing those text runs corresponding to the same semantic block into the same semantic block container; ordering the text runs within respective semantic blocks to match a logical order present in the document; and ordering the semantic blocks to match a logical order present in the document to generate a structured markup language representation of the document, comprising selectable text. 13. The non-transient computer readable medium of claim 12 , constructing the semantic block containers comprising: determining whether a current text block corresponds to an existing semantic block; and creating a new semantic block to include the current text run, if the current text block does not correspond to the existing semantic block. | 0.894994 |
9,594,831 | 15 | 16 | 15. The hardware computer readable storage medium of claim 14 , the computer readable instructions further comprising: logic configured to create at least one virtual node, the at least one virtual node associated with an entity e i , the at least one virtual node conveying counterpart reference information about the entity e i ; and logic configured to link the at least one virtual node to the graph. | 15. The hardware computer readable storage medium of claim 14 , the computer readable instructions further comprising: logic configured to create at least one virtual node, the at least one virtual node associated with an entity e i , the at least one virtual node conveying counterpart reference information about the entity e i ; and logic configured to link the at least one virtual node to the graph. 16. The hardware computer readable storage medium of claim 15 , the computer readable instructions further comprising logic configured to associate a mention-node-to-virtual-node similarity score to each edge between the at least one virtual node and a node corresponding to a candidate mention (e i′ , d j′ ), associated with entity e i′ and document d j′ , the mention-node-to-virtual-node similarity score being an associated unmodified context similarity score μ ij,i′j′ , providing that e i =e i′ , the mention-node-to-virtual-node similarity score being an associated context similarity score μ ij,i′j′ , modified by an adjustment parameter β, providing that e i ≠e i′ , and where the adjustment parameter β describes an extent to which the counterpart reference information applies to the particular subject matter domain. | 0.5 |
9,172,610 | 8 | 13 | 8. A Diameter Routing Agent (DRA) for enabling a user to define message processing rules, the DRA comprising: a user interface and a processor with a coupled memory, wherein the processor is configured to: receive, via the user interface, a request to define a behavior rule for the DRA; retrieve metadata associated with a context object; determine that a component specified by the metadata is an enumerated type component; present, via the user interface, a first displayed component for the enumerated type component, wherein the first displayed component is associated with a numeric value; present, via the user interface, a second displayed component for the enumerated type component, wherein the second displayed component is associated with an alphanumeric value, wherein the first displayed component and the second displayed component represent a same enumerated type component; and receive, via the user interface, a rule definition, wherein receiving the rule definition comprises receiving a selection of at least one of the first displayed component and the second displayed component. | 8. A Diameter Routing Agent (DRA) for enabling a user to define message processing rules, the DRA comprising: a user interface and a processor with a coupled memory, wherein the processor is configured to: receive, via the user interface, a request to define a behavior rule for the DRA; retrieve metadata associated with a context object; determine that a component specified by the metadata is an enumerated type component; present, via the user interface, a first displayed component for the enumerated type component, wherein the first displayed component is associated with a numeric value; present, via the user interface, a second displayed component for the enumerated type component, wherein the second displayed component is associated with an alphanumeric value, wherein the first displayed component and the second displayed component represent a same enumerated type component; and receive, via the user interface, a rule definition, wherein receiving the rule definition comprises receiving a selection of at least one of the first displayed component and the second displayed component. 13. The DRA of claim 8 wherein the user interface is further configured to: receive a request to publish a rule set; and generate a rule tree based on the rule definition. | 0.887054 |
8,631,350 | 2 | 3 | 2. The method of claim 1 wherein the graphical context menu further comprises a menu item for displaying a full menu. | 2. The method of claim 1 wherein the graphical context menu further comprises a menu item for displaying a full menu. 3. The method of claim 2 further comprising rendering, in response to actuating the menu item for displaying the full menu, a graphical list of menu items which includes menu items not displayed in the graphical context menu. | 0.551793 |
9,852,219 | 19 | 20 | 19. The computer program product of claim 17 , wherein the file is in accordance with International Organization for Standardization (ISO) base media file format. | 19. The computer program product of claim 17 , wherein the file is in accordance with International Organization for Standardization (ISO) base media file format. 20. The computer program product of claim 19 , wherein the metadata type is indicated by a grouping type and grouping instance data, the grouping type specifying semantics of the grouping instance data and the metadata content. | 0.5 |
6,049,339 | 38 | 39 | 38. A graphical processing system comprising: a receiver adapted to receive a page description language representation of graphical objects, the graphical objects having transparency characteristics and color characteristics; and a converter operatively coupled to the receiver, the converter adapted to convert a portion of the page description language representation into a planar map representation, the planar map representation having regions wherein each region is associated with one or more of the graphical objects, and assign a color to a planar map region as a function of the transparency characteristics and color characteristics of the graphical objects associated with the planar map region. | 38. A graphical processing system comprising: a receiver adapted to receive a page description language representation of graphical objects, the graphical objects having transparency characteristics and color characteristics; and a converter operatively coupled to the receiver, the converter adapted to convert a portion of the page description language representation into a planar map representation, the planar map representation having regions wherein each region is associated with one or more of the graphical objects, and assign a color to a planar map region as a function of the transparency characteristics and color characteristics of the graphical objects associated with the planar map region. 39. The system of claim 38 further comprising a sorter operatively coupled to the converter, wherein the sorter is adapted to sort the planar map regions into a print order. | 0.514045 |
8,368,738 | 7 | 9 | 7. A computer-implemented communications system, comprising: an invite component for receiving an invitation for an invitee to participate in a conferencing session, the invitation received via one or more communications modes that include text messaging, interactive voice call, email, SMS, or MMS; a join component for detecting whether the conferencing session exists and if so, automatically joining the invitee into the conferencing session in response to the invitation, the invitee joined using a conferencing method that corresponds to the conferencing session; and a microprocessor that executes computer-executable instructions associated with at least one of the invite component or the join component. | 7. A computer-implemented communications system, comprising: an invite component for receiving an invitation for an invitee to participate in a conferencing session, the invitation received via one or more communications modes that include text messaging, interactive voice call, email, SMS, or MMS; a join component for detecting whether the conferencing session exists and if so, automatically joining the invitee into the conferencing session in response to the invitation, the invitee joined using a conferencing method that corresponds to the conferencing session; and a microprocessor that executes computer-executable instructions associated with at least one of the invite component or the join component. 9. The system of claim 7 , wherein the join component mixes audio and video of the conferencing session. | 0.706215 |
9,098,493 | 4 | 5 | 4. The computer implemented method of claim 3 further including comparing the first and second signs relative to each other to determine the accuracy of the match between the first sign and the first gesture. | 4. The computer implemented method of claim 3 further including comparing the first and second signs relative to each other to determine the accuracy of the match between the first sign and the first gesture. 5. The computer implemented method of claim 4 wherein the step of detecting further includes detecting a third sign after the second sign and further including comparing the first sign and the third sign to the second sign to determine the accuracy of the second sign to the second gesture. | 0.5 |
9,274,910 | 11 | 19 | 11. A computer system including a processor and a computer readable storage medium storing computer instructions configured to cause the processor to perform a computer implemented method of identifying and extracting data included in test responses from system verification testing of a system under test, the method comprising the steps of: receiving a first test response including a first block of unstructured text in one or more formats, the first block of unstructured text including a plurality of lines of words separated by white spaces; processing the first block of unstructured text to discover the one or more formats of the first block of unstructured text without a priori knowledge of the format of the first block of unstructured text and without a priori knowledge of a template for the format; generating a response map from the discovered formats for use in parsing unstructured text from a test response; and applying the response map to a second test response, including a second block of unstructured text in the discovered formats, to identify and extract textual data from the second block of unstructured text. | 11. A computer system including a processor and a computer readable storage medium storing computer instructions configured to cause the processor to perform a computer implemented method of identifying and extracting data included in test responses from system verification testing of a system under test, the method comprising the steps of: receiving a first test response including a first block of unstructured text in one or more formats, the first block of unstructured text including a plurality of lines of words separated by white spaces; processing the first block of unstructured text to discover the one or more formats of the first block of unstructured text without a priori knowledge of the format of the first block of unstructured text and without a priori knowledge of a template for the format; generating a response map from the discovered formats for use in parsing unstructured text from a test response; and applying the response map to a second test response, including a second block of unstructured text in the discovered formats, to identify and extract textual data from the second block of unstructured text. 19. The computer system of claim 11 , wherein the first test response and the second test response are the same. | 0.829268 |
8,825,614 | 6 | 17 | 6. A method of performing XBRL taxonomy migration comprising: receiving an XBRL document having XBRL tags of a first version of an XBRL taxonomy; migrating, by a processor, the received XBRL document to a second version of the XBRL taxonomy by gathering metadata that corresponds to the first version of the XBRL taxonomy and replacing XBRL concepts of the first version of the XBRL taxonomy in the received XBRL document with XBRL concepts of the second version of the XBRL taxonomy; detecting dependencies in calculations in the received XBRL document using the XBRL concepts in the received XBRL document; when dependencies are detected, determining whether a balance type of the first version XBRL taxonomy concept matches a balance type of a related second version XBRL taxonomy concept; when the balance type of the first version XBRL taxonomy concept matches the balance type of the related second version XBRL taxonomy concept, replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept of the matched balance type; and when the balance type of the first version XBRL taxonomy concept does not match the balance type of the related second version XBRL taxonomy concept, adjusting a weight of an arc using the related second version XBRL taxonomy concept in a calculation assertion when replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept, wherein after completion of the method of performing XBRL taxonomy migration, the migrated XBRL document no longer uses the first version of the XBRL taxonomy. | 6. A method of performing XBRL taxonomy migration comprising: receiving an XBRL document having XBRL tags of a first version of an XBRL taxonomy; migrating, by a processor, the received XBRL document to a second version of the XBRL taxonomy by gathering metadata that corresponds to the first version of the XBRL taxonomy and replacing XBRL concepts of the first version of the XBRL taxonomy in the received XBRL document with XBRL concepts of the second version of the XBRL taxonomy; detecting dependencies in calculations in the received XBRL document using the XBRL concepts in the received XBRL document; when dependencies are detected, determining whether a balance type of the first version XBRL taxonomy concept matches a balance type of a related second version XBRL taxonomy concept; when the balance type of the first version XBRL taxonomy concept matches the balance type of the related second version XBRL taxonomy concept, replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept of the matched balance type; and when the balance type of the first version XBRL taxonomy concept does not match the balance type of the related second version XBRL taxonomy concept, adjusting a weight of an arc using the related second version XBRL taxonomy concept in a calculation assertion when replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept, wherein after completion of the method of performing XBRL taxonomy migration, the migrated XBRL document no longer uses the first version of the XBRL taxonomy. 17. The method of claim 6 , wherein the migrating comprises a many-to-one mapping in which a plurality of deprecated XBRL concepts of the first version of the XBRL taxonomy are mapped to a single XBRL concept of the second version of the XBRL taxonomy, the single XBRL concept of the second version aggregating the plurality of deprecated XBRL concepts of the first version which have greater specificity than the single XBRL concept of the second version. | 0.5 |
9,411,855 | 1 | 7 | 1. A method comprising: receiving a feed item, the feed item being displayable in a feed of a social networking system implemented using a database system, the feed being displayable on a display device; processing textual content of the feed item to detect a designated keyword in the textual content, the designated keyword associated with a data record creation rule; responsive to detecting the designated keyword in the textual content of the feed item, automatically: causing a data record to be created as a data object in a database of the database system, the created data record being accessible via a cloud-based computing services environment; identifying information of the feed item or of one or more feed items associated with the feed item related to the created data record; determining that the created data record is related to a first customer relationship management (CRM) record of a CRM system; and causing one or more data fields of the created data record to be populated with the identified information of the feed item and information of the first CRM record. | 1. A method comprising: receiving a feed item, the feed item being displayable in a feed of a social networking system implemented using a database system, the feed being displayable on a display device; processing textual content of the feed item to detect a designated keyword in the textual content, the designated keyword associated with a data record creation rule; responsive to detecting the designated keyword in the textual content of the feed item, automatically: causing a data record to be created as a data object in a database of the database system, the created data record being accessible via a cloud-based computing services environment; identifying information of the feed item or of one or more feed items associated with the feed item related to the created data record; determining that the created data record is related to a first customer relationship management (CRM) record of a CRM system; and causing one or more data fields of the created data record to be populated with the identified information of the feed item and information of the first CRM record. 7. The method recited in claim 1 , wherein causing the created data record to be created comprises: determining a data type for the created data record, the created data record being created in accordance with the determined data type. | 0.655425 |
5,412,566 | 1 | 12 | 1. A variable replacement apparatus which replaces variable names in a text with corresponding variable values, each variable name formed by at least one character and each character in the variable name having a corresponding format, the apparatus comprising: variable name extracting means for extracting a variable name from the text; variable value acquisition means for obtaining a variable value corresponding to the variable name extracted by said variable name extracting means; variable name analyzing means for analyzing the corresponding format of each character forming the variable name; variable value converting means for converting the variable value obtained by said variable value acquisition means so that the variable value has a format which is determined in accordance with the corresponding format of each character forming the variable name; and variable replacing means for replacing the variable name in the text by the converted variable value. | 1. A variable replacement apparatus which replaces variable names in a text with corresponding variable values, each variable name formed by at least one character and each character in the variable name having a corresponding format, the apparatus comprising: variable name extracting means for extracting a variable name from the text; variable value acquisition means for obtaining a variable value corresponding to the variable name extracted by said variable name extracting means; variable name analyzing means for analyzing the corresponding format of each character forming the variable name; variable value converting means for converting the variable value obtained by said variable value acquisition means so that the variable value has a format which is determined in accordance with the corresponding format of each character forming the variable name; and variable replacing means for replacing the variable name in the text by the converted variable value. 12. The variable replacement apparatus as claimed in claim 1, wherein the same format corresponds to each character in the variable name and said variable value converting means converts the entire variable value in accordance with the corresponding same format of the characters in the variable name. | 0.700199 |
8,458,602 | 2 | 5 | 2. The method of claim 1 , wherein said subset of said set of local characters for display is selected to be less than or equal to a predetermined maximum number of characters for display in a particular display area. | 2. The method of claim 1 , wherein said subset of said set of local characters for display is selected to be less than or equal to a predetermined maximum number of characters for display in a particular display area. 5. The method of claim 2 , further comprising the step of further determining said subset of said set of local characters for display to said particular user as being present in said portion of said common area by also using another selected criteria to determine said subset only if the subset determined by using said selected criteria is more than said maximum to further reduce the number of characters in said subset to be at or below said maximum. | 0.5 |
8,799,186 | 10 | 11 | 10. A method for computationally performing an online choice model, the method comprising: receiving a plurality of attributes from a user wherein each attribute has an associated plurality of attribute levels; generating a survey experimental design and an associated set of treatments, comprising the steps of: determining the signature of the attribute space from the received plurality of attributes and associated attribute levels; selecting one or more experimental designs from a library of experimental designs; for each selected experimental design; performing one or more transformations until the signature of the transformed experimental design matches the signature of the attribute space to obtain one or more matching transformed experimental designs; wherein each transformation preserves the information properties of the untransformed experimental design; selecting a survey experimental design from the one or more matching transformed experimental designs; obtaining a set of treatments from the selected survey experimental design; assembling an online survey, comprising the steps of: creating a plurality of survey templates pages; creating a plurality of treatment representations based on the set of treatments associated with the survey experimental design; assembling the plurality of survey templates pages and plurality of treatment representations to form an online survey; conducting an online survey, the online survey comprising allocating each treatment to one or more respondents; providing a plurality of combinations of treatments to the one or more respondents; receiving the responses of the one or more respondents; generating a model based upon the received responses to obtain a plurality of model parameter estimates and errors from which a utility estimate can be obtained for each attribute level; and providing a model explorer user interface for allowing the user to enter one or more attribute levels and obtain a model prediction of the expected utility; wherein the one or more transformations comprise one or more of the following group of transformations: factorial splitting of a factor F into two sub factors A, B where A×B=F and A or B match at least one unmatched factor in the signature; factorial expansion of a factor F into a new factor A×F where A×F matches at least one unmatched factor in the signature; factor truncation of a factor F into a new factor F−A where F−A matches at least one unmatched factor in the signature; full factorization by generation of a new factor F where F matches at least one unmatched factor in the signature; and deleting a factor F when all the other factors in the signature are matched. | 10. A method for computationally performing an online choice model, the method comprising: receiving a plurality of attributes from a user wherein each attribute has an associated plurality of attribute levels; generating a survey experimental design and an associated set of treatments, comprising the steps of: determining the signature of the attribute space from the received plurality of attributes and associated attribute levels; selecting one or more experimental designs from a library of experimental designs; for each selected experimental design; performing one or more transformations until the signature of the transformed experimental design matches the signature of the attribute space to obtain one or more matching transformed experimental designs; wherein each transformation preserves the information properties of the untransformed experimental design; selecting a survey experimental design from the one or more matching transformed experimental designs; obtaining a set of treatments from the selected survey experimental design; assembling an online survey, comprising the steps of: creating a plurality of survey templates pages; creating a plurality of treatment representations based on the set of treatments associated with the survey experimental design; assembling the plurality of survey templates pages and plurality of treatment representations to form an online survey; conducting an online survey, the online survey comprising allocating each treatment to one or more respondents; providing a plurality of combinations of treatments to the one or more respondents; receiving the responses of the one or more respondents; generating a model based upon the received responses to obtain a plurality of model parameter estimates and errors from which a utility estimate can be obtained for each attribute level; and providing a model explorer user interface for allowing the user to enter one or more attribute levels and obtain a model prediction of the expected utility; wherein the one or more transformations comprise one or more of the following group of transformations: factorial splitting of a factor F into two sub factors A, B where A×B=F and A or B match at least one unmatched factor in the signature; factorial expansion of a factor F into a new factor A×F where A×F matches at least one unmatched factor in the signature; factor truncation of a factor F into a new factor F−A where F−A matches at least one unmatched factor in the signature; full factorization by generation of a new factor F where F matches at least one unmatched factor in the signature; and deleting a factor F when all the other factors in the signature are matched. 11. The method as claimed in claim 10 , wherein each transformation preserves the information properties of the untransformed experimental design. | 0.933636 |
7,546,334 | 49 | 56 | 49. An information processing system for filtering and securing from input data, one or more security sensitive words, characters or data objects with an adaptive filter in a computer system, said adaptive filter used in conjunction with a compilation of additional data, the system comprising: means for identifying said security sensitive words, characters or data objects in said compilation of additional data; means for retrieving at least one of contextual, semiotic and taxonomic words, characters or data objects from said compilation of additional data related to said security sensitive words, characters or data objects; a filter compiled from said security sensitive words, characters or data objects and the retrieved data related to said security sensitive words, characters or data objects; and an extractor, cooperating with said filter, for extracting, from said input data, said security sensitive words, characters or data objects and said retrieved data to obtain extracted data and remainder data therefrom; and means for storing either the extracted data separately from said remainder data or storing partial versions of said extracted data with said remainder data based upon multiple security levels unique to each partial version. | 49. An information processing system for filtering and securing from input data, one or more security sensitive words, characters or data objects with an adaptive filter in a computer system, said adaptive filter used in conjunction with a compilation of additional data, the system comprising: means for identifying said security sensitive words, characters or data objects in said compilation of additional data; means for retrieving at least one of contextual, semiotic and taxonomic words, characters or data objects from said compilation of additional data related to said security sensitive words, characters or data objects; a filter compiled from said security sensitive words, characters or data objects and the retrieved data related to said security sensitive words, characters or data objects; and an extractor, cooperating with said filter, for extracting, from said input data, said security sensitive words, characters or data objects and said retrieved data to obtain extracted data and remainder data therefrom; and means for storing either the extracted data separately from said remainder data or storing partial versions of said extracted data with said remainder data based upon multiple security levels unique to each partial version. 56. An information processing system as claimed in claim 49 wherein said means for retrieving includes means for retrieving semiotic words, characters or data objects from said compilation of additional data related to said security sensitive words, characters or data objects and is based upon synonyms, antonyms, and pseudonyms of said security sensitive words, characters or data objects; syntactics of said security sensitive words, characters or data objects as reflected in said compilation of additional data; and pragmatics of said security sensitive words, characters or data objects as reflected in said compilation of additional data. | 0.5 |
9,436,912 | 7 | 12 | 7. A non-transitory computer readable medium having a method encoded thereon represented by computer-readable programming code, the method comprising the steps of: providing a parent schema; generating a plurality of distinct and domain-specific instantiations of the parent schema; replacing one or more existing Boolean features in one or more cases of a case-based reasoning system with the domain-specific instantiations of the parent schema; evolving non-zero weights for the cases including the domain-specific instantiations of the parent schema; user-validating at least two of the domain-specific instantiations of the parent schema; and creating a symmetric schema by combining the user-validated domain-specific instantiations of the parent schema. | 7. A non-transitory computer readable medium having a method encoded thereon represented by computer-readable programming code, the method comprising the steps of: providing a parent schema; generating a plurality of distinct and domain-specific instantiations of the parent schema; replacing one or more existing Boolean features in one or more cases of a case-based reasoning system with the domain-specific instantiations of the parent schema; evolving non-zero weights for the cases including the domain-specific instantiations of the parent schema; user-validating at least two of the domain-specific instantiations of the parent schema; and creating a symmetric schema by combining the user-validated domain-specific instantiations of the parent schema. 12. The non-transitory computer readable medium of claim 7 further comprising the step of generating an instantiation of the symmetric schema. | 0.702929 |
7,954,050 | 1 | 2 | 1. A computer server system comprising: a memory server system configured to include an original document object model that comprises a data structure for representing documents on the computer server system; and a processing server system configured to communicate with the memory server system and with a client computing system comprising a duplicate copy of the original document object model and the processing server system is configured to implement the steps of: receive information from the client computing system, the information representing a change in a state of a software component of the original document object model; update portions of the original document object model based on the change; translate only the updated portions of the original document object model into a mark-up language string; and transmit the string to the client computing system for incorporation into the duplicate copy of the original document object model. | 1. A computer server system comprising: a memory server system configured to include an original document object model that comprises a data structure for representing documents on the computer server system; and a processing server system configured to communicate with the memory server system and with a client computing system comprising a duplicate copy of the original document object model and the processing server system is configured to implement the steps of: receive information from the client computing system, the information representing a change in a state of a software component of the original document object model; update portions of the original document object model based on the change; translate only the updated portions of the original document object model into a mark-up language string; and transmit the string to the client computing system for incorporation into the duplicate copy of the original document object model. 2. The computer system of claim 1 wherein the original document object model comprises HTML code. | 0.748705 |
7,720,827 | 9 | 14 | 9. A network meta-data library that is part of a telecommunications network hardware component operable to: receive at least one or more changes associated with one or more telecommunication models; link one or more portions of one or more stored telecommunication models to the received changes; generate one or more maps of the linked portions; generate one or more transformation models using one or more of the generated maps; and forward the one or more transformation models to a mediation unit to enable the generation of one or more normalized models. | 9. A network meta-data library that is part of a telecommunications network hardware component operable to: receive at least one or more changes associated with one or more telecommunication models; link one or more portions of one or more stored telecommunication models to the received changes; generate one or more maps of the linked portions; generate one or more transformation models using one or more of the generated maps; and forward the one or more transformation models to a mediation unit to enable the generation of one or more normalized models. 14. The meta-data library as in claim 9 wherein the one or more, generated normalized models are recognizable to one or more operations support systems in response to the receipt of the received changes. | 0.64386 |
7,836,428 | 61 | 63 | 61. The method of claim 59 , wherein obtaining a new declarative element type comprises: defining a new logical behavior; and associating one or more declarative properties with said new logical behavior. | 61. The method of claim 59 , wherein obtaining a new declarative element type comprises: defining a new logical behavior; and associating one or more declarative properties with said new logical behavior. 63. The method of claim 61 , wherein associating one or more declarative properties comprises: defining one or more control points for said new logical behavior; and assigning one or more attributes to said one or more control points. | 0.5 |
9,390,161 | 6 | 7 | 6. The method of claim 5 , characterized in that it further comprises utilizing the knowledge-based dictionary to build a hash table for each word in the natural text. | 6. The method of claim 5 , characterized in that it further comprises utilizing the knowledge-based dictionary to build a hash table for each word in the natural text. 7. The method of claim 6 , characterized in that it further comprises evaluating each word in accordance with a lookup table as to whether the word has one or more part of speech tags. | 0.5 |
8,327,255 | 38 | 39 | 38. The system of claim 30 , wherein the processor is further configured to provide, via the executable viewer file, the one or more electronic transcript files and the one or more electronic exhibit files substantially simultaneously via a user interface having a transcript pane and an exhibit pane to respectively provide the one or more electronic transcript files and the or more electronic exhibit files. | 38. The system of claim 30 , wherein the processor is further configured to provide, via the executable viewer file, the one or more electronic transcript files and the one or more electronic exhibit files substantially simultaneously via a user interface having a transcript pane and an exhibit pane to respectively provide the one or more electronic transcript files and the or more electronic exhibit files. 39. The system of claim 38 , wherein the transcript pane and the exhibit pane associated with the user interface include independent controls to respectively add, view, and remove the one or more electronic transcript files and the one or more electronic exhibit files in the bundle. | 0.5 |
8,165,985 | 13 | 24 | 13. A method for performing discovery of digital information in a subject area, comprising: designating through a user interface of a computer a corpus comprising electronically-stored digital information, which are maintained in a storage device; selecting one or more topics and training material for the selected topics comprising on topic information and off topic information; building candidate topic models on the computer comprising: selecting seed words for each of the selected topics; and generating patterns from the seed words for each topic as candidate topic models for that topic; evaluating the candidate topic models for each selected topic against the training material comprising: matching the patterns in each candidate topic model to the training material; rating each candidate topic model for the selected topic comprising: assigning a higher score to each candidate topic model that matches the on topic information for the selected topic; assigning a lower score to each candidate topic model that does not match the on topic information for the selected topic; assigning a higher score to each candidate topic model that does not match the off topic information for the selected topic; and assigning a lower score to each candidate topic model that matches the off topic information for the selected topic; and building an evergreen index comprising topic models for each of the selected topics by pairing each topic to the candidate topic model that has the best overall score. | 13. A method for performing discovery of digital information in a subject area, comprising: designating through a user interface of a computer a corpus comprising electronically-stored digital information, which are maintained in a storage device; selecting one or more topics and training material for the selected topics comprising on topic information and off topic information; building candidate topic models on the computer comprising: selecting seed words for each of the selected topics; and generating patterns from the seed words for each topic as candidate topic models for that topic; evaluating the candidate topic models for each selected topic against the training material comprising: matching the patterns in each candidate topic model to the training material; rating each candidate topic model for the selected topic comprising: assigning a higher score to each candidate topic model that matches the on topic information for the selected topic; assigning a lower score to each candidate topic model that does not match the on topic information for the selected topic; assigning a higher score to each candidate topic model that does not match the off topic information for the selected topic; and assigning a lower score to each candidate topic model that matches the off topic information for the selected topic; and building an evergreen index comprising topic models for each of the selected topics by pairing each topic to the candidate topic model that has the best overall score. 24. A method according to claim 13 , further comprising: biasing the candidate topic models for the selected topic that have term overlap with topic labels associated with the selected topic by assigning a higher score to the overlapping candidate topic models. | 0.648248 |
8,156,060 | 13 | 15 | 13. The interactive virtual agent (avatar) system of claim 12 , wherein the character interface is a visual character interface configured to respond to spoken input with spoken output. | 13. The interactive virtual agent (avatar) system of claim 12 , wherein the character interface is a visual character interface configured to respond to spoken input with spoken output. 15. The interactive virtual agent (avatar) system of claim 13 , wherein the visual interface is a cartoon character interface. | 0.5 |
8,892,438 | 1 | 9 | 1. A method comprising: selecting a plurality of language models; for each period of a plurality of time periods: identifying a first utterance and a second utterance received during each time period, wherein the first utterance was recognized using a first language model of the plurality of language models and the second utterance was recognized using a second language model of the plurality of language models; identifying distinctions between the first utterance and the second utterance for each of the plurality of time periods; determining when a significant word usage change has occurred within the first language model and the second language model by comparing the distinctions to previously recorded distinctions; and when the significant word usage change is detected: identifying a word corresponding to the significant word usage change; generating, from the utterances, a first cluster of utterances comprising the word; generating, from the utterances, a second cluster of utterances not comprising the word; and updating the plurality of language models using the first cluster of utterances and the second cluster of utterances. | 1. A method comprising: selecting a plurality of language models; for each period of a plurality of time periods: identifying a first utterance and a second utterance received during each time period, wherein the first utterance was recognized using a first language model of the plurality of language models and the second utterance was recognized using a second language model of the plurality of language models; identifying distinctions between the first utterance and the second utterance for each of the plurality of time periods; determining when a significant word usage change has occurred within the first language model and the second language model by comparing the distinctions to previously recorded distinctions; and when the significant word usage change is detected: identifying a word corresponding to the significant word usage change; generating, from the utterances, a first cluster of utterances comprising the word; generating, from the utterances, a second cluster of utterances not comprising the word; and updating the plurality of language models using the first cluster of utterances and the second cluster of utterances. 9. The method of claim 1 , further comprising: pooling the plurality of utterances from the plurality of time periods, to yield pooled utterances; assigning each utterance of the pooled utterances to one of a plurality of subpopulations; generating a language model for each of the plurality of subpopulations; reassigning each utterance to one of the plurality of subpopulations according to a reassignment criterion; determining whether any of the plurality of subpopulations fulfill a splitting criterion; and splitting ones of the plurality of subpopulations that fulfill the splitting criterion, wherein: examining a subset of the plurality of utterances from each of the time periods, determining one of a significant word change within the plurality of spoken utterances, generating, from the subset of the plurality of utterances, a first cluster of utterances including a word corresponding to the significant word change, and generating, from the subset of the plurality of utterances, a second cluster of utterances not including the word corresponding to the significant word change are performed after pooling, assigning, generating a language model, reassigning, determining whether any of the subpopulations fulfill the splitting criterion, and splitting. | 0.5 |
9,020,962 | 19 | 22 | 19. The system of claim 13 , wherein the executable and operational data are further effective to cause the one or more processors to evaluate similarity of the candidate articles to the principal article by evaluating similarity of out-links of the principal article and the candidate articles. | 19. The system of claim 13 , wherein the executable and operational data are further effective to cause the one or more processors to evaluate similarity of the candidate articles to the principal article by evaluating similarity of out-links of the principal article and the candidate articles. 22. The system of claim 19 , wherein the executable and operational data are further effective to cause the one or more processors to evaluate similarity of out-links of the principal article and the candidate articles by evaluating a Jaccard distance between the out-links of the principal article and the candidate articles. | 0.538244 |
9,282,180 | 1 | 2 | 1. A process, residing in the memory of a Smartphone or cellular phone, that is used to invoke a compound wireless mobile communication service also known as a Smartphone app or its diminutive app, by using a Boolean expression of events, which when a combination of events that either occur or events that do not occur result in a “True” evaluation of the Boolean expression, the aforementioned compound wireless mobile communication service will commence its execution, where the Boolean expression is contained within a service termed Invoke facility service; such that the builder of an Invoke facility service associated the said Invoke facility service with the aforementioned compound wireless mobile communication service; wherein the contained Boolean expression is built with events interconnected by the logical connectives of “And”, “Or”, “Not”, “If X, Then Y”, “And Then”; such that the negation operation “Not” establishes a contradictory event, signifying that an event and its contradictory event are both considered to be events, where if the event did not occur (is “False”), then its contradictory event did occur (is “True”) and conversely; and such that “And Then” is a conjunctive connective, meaning it has the same logical operation as “And”; whereas, “And Then” includes a sequential implication for testing the occurrence of events; as illustrated in the partial Boolean expression “PrecedingExpression And Then FollowingExpression”, for which “PrecedingExpression” and “FollowingExpression” are both logic expressions each containing one or more events, wherein the events within “PrecedingExpression” are tested for occurrences prior to testing occurrences of the events within “FollowingExpression”; where both logic expressions contain events or their contradictory events that are observable when tested by the wireless mobile terminal; such that events observed by or linked to the Smartphone or cellular telephone, are of the following types or their contradictions to enhance vehicular safety by denying the invocation (“False” Boolean expression evaluation) of an app that may distract a vehicle's driver: a) a wireless mobile terminal moving beyond a specified threshold speed, as can be determined via a GPS measurement differential of known time duration or a communication link to the vehicle's speedometer; b) a wireless mobile terminal detected to be in proximity of the vehicle driver's seat, as can be determined using a Bluetooth or other radio wave position detection via a high directivity antenna; c) a voice actuated wireless mobile terminal Compound Wireless Service app that detects the voice of the vehicle's driver, as can be determined by means of a high directivity microphone for a wireless mobile terminal or voice recognition of a driver's recording issuing the command to actuate the Compound Wireless Service app; d) a distracting app execution disapproval by an outside source communication denial, as can be performed by a service provider or law enforcement that remotely sets a parameter state to restrict invocation of the distracting app; e) a wireless mobile terminal is in a restricted area, as can be determined when on a spatially defined roadway by means of a GPS measurement or by means of a service provider's location service; f) a wireless mobile terminal is in a particular vehicle, as can be determined when the particular vehicle possesses a Bluetooth or other radio wave generator that signals an invocation restriction of one or more distracting apps; g) the driver's seat of a vehicle is occupied and the vehicle's engine is running, as can be determined via a pressure transducer that senses weight on the seat and communicates with the wireless mobile terminal to restrict one or more distracting apps; h) temporal events of a vehicle travelling during time periods of heavy traffic or during time periods requiring attentive driving; wherein safety enhancing Boolean expressions include: a) not being able to send a text message while in a moving vehicle, that may be expressed as {SendTextMessage And Then (Not VehiclelsMoving)}; b) not invoking a voice command by a vehicle's driver, unless the vehicle is not moving, that may be expressed as {AppVoiceCommand And Then ((Not Driver Voice) Or (Not VehicleMoving))} c) not invoking a voice command in a vehicle unless the vehicle's engine is not running or the mobile communication terminal is in the vehicle's cradle and either the vehicle is not moving or the voice command was not spoken by the driver, that may be expressed as {AppVoiceCommand And Then ((Not EngineRunning) Or (InCradle And ((Not VehicleMoving) Or (Not DriverVoice))))} d) not invoking a voice command in a vehicle unless the vehicle's engine is not running or the vehicle is not moving or the voice command was not spoken by the driver, that may be expressed as {AppVoiceCommand And Then ((Not EngineRunning) Or (Not VehicleMoving) Or (Not DriverVoice))} e) not invoking a manufacturer's default app in a vehicle unless the vehicle's engine is not running or the vehicle is not moving or the driver is not in possession of the smartphone, that may be expressed as {ManufacturerDefault And Then ((Not EngineRunning) Or (Not VehicleMoving) Or (Not DriverHasSmartphone))} f) not invoking a voice command in a vehicle unless the vehicle's engine is not running, that may be expressed as {AppVoiceCommand And Then (Not EngineRunning)} g) not invoking a manufacturer's default app in a vehicle unless the vehicle's engine is not running, that may be expressed as {ManufacturerDefault And Then (Not EngineRunning)} h) not invoking a voice command in a vehicle unless the vehicle is not moving, that may be expressed as {AppVoiceCommand And Then (Not VehicleMoving)} i) not invoking a manufacturer's default app in a vehicle unless the vehicle is not moving, that may be expressed as {ManufacturerDefault And Then (Not VehicleMoving)}; whereby an Invoke facility service consists of the parameters: 1) {OptionalEventExpression} which is a Boolean expression containing assertions of recognized events that are to control the invocation of an associated Smartphone app or wireless mobile terminal app; 2) (OptionalEventDurationList) which is a list of triples; where each triple designates an event name, its value of “Sustained” or “Momentary”, and the testing repetition interval for a “Sustained” event or a null entry for a “Momentary” event; 3) (OptionalObservationRepetitionInterval) which is a parameter pair that specifies the time interval between successive evaluations of the {OptionalEventExpression} truth value and the time interval to reset all event test values to an “Untested” flag; 4) (OptionalPasswordPair) which is a parameter pair intended to provide password protection of a Smartphone app or wireless mobile terminal app that contains a password name and a password value; 5) (OptionalDisableQuadruple) which is a quadruple of entries consisting of the name of maximum permitted number of failed password attempts, the value of maximum permitted number of failed password attempts, the name of amount of time the Smartphone app or wireless mobile terminal app will be disabled if the number of failed attempts reaches the maximum, and the value of the amount of time the Smartphone app or wireless mobile terminal app will be disabled if the number of failed attempts reaches the maximum; 6) OptionalEnablingSwitch which is a parameter to either enable or disable the Smartphone app or wireless mobile terminal app invocation; 7) (OptionalInitialConditionsAssignmentList) which is a list of pairs that consist of builder selected Smartphone app or wireless mobile terminal app parameter names and their initial values when the Smartphone app or wireless mobile terminal app begins its execution; 8) (OptionalLinkedCWSList) which is a list of pairs that indicate the names of potentially linked Smartphone apps or wireless mobile terminal apps and their addresses for the purpose of having an invoked Smartphone app or wireless mobile terminal app receive data from a linked Smartphone app or wireless mobile terminal app; 9) (OptionalLinkedCWSPasswords) which is a list of pairs that associate potentially linked Smartphone apps or wireless mobile terminal apps with passwords for those potentially linked Smartphone apps or wireless mobile terminal apps having password protection; 10) (OptionalLinkedCWSData) which is a list of pairs; where the first member of a pair is a linked Smartphone app or wireless mobile terminal app name and the second member is a list of pairs that associate named constants and variables with memory locations into which a linked Smartphone app or wireless mobile terminal app will enter values; 11) (OptionalNotifications) which is a n-tuple to provide notification concerning the operation of the Invoke facility service after the Boolean expression was evaluated as “True”; 12) (OptionalEventFailureNotification) which is a n-tuple to provide a notification when an event test path results in a “False” evaluation of a Smartphone app's or wireless mobile terminal app's Invoke facility service Boolean expression; 13) (OptionalEventPathData) which is a parameter consisting of set of triples and a set of pairs that represent the potential event test paths, in the form of an inverted, binary, directed tree, to evaluate the Smartphone app's or wireless mobile terminal app's Boolean expression; 14) {OptionalEventFailureNotificationSchema} which is a set of pairs, of the form (event test path label, notification) or (event test path label, notification pointer), that provides the failure notification parameter value to be identified in the (OptionalEventFailureNotification) output for its associated Smartphone app or wireless mobile terminal app. | 1. A process, residing in the memory of a Smartphone or cellular phone, that is used to invoke a compound wireless mobile communication service also known as a Smartphone app or its diminutive app, by using a Boolean expression of events, which when a combination of events that either occur or events that do not occur result in a “True” evaluation of the Boolean expression, the aforementioned compound wireless mobile communication service will commence its execution, where the Boolean expression is contained within a service termed Invoke facility service; such that the builder of an Invoke facility service associated the said Invoke facility service with the aforementioned compound wireless mobile communication service; wherein the contained Boolean expression is built with events interconnected by the logical connectives of “And”, “Or”, “Not”, “If X, Then Y”, “And Then”; such that the negation operation “Not” establishes a contradictory event, signifying that an event and its contradictory event are both considered to be events, where if the event did not occur (is “False”), then its contradictory event did occur (is “True”) and conversely; and such that “And Then” is a conjunctive connective, meaning it has the same logical operation as “And”; whereas, “And Then” includes a sequential implication for testing the occurrence of events; as illustrated in the partial Boolean expression “PrecedingExpression And Then FollowingExpression”, for which “PrecedingExpression” and “FollowingExpression” are both logic expressions each containing one or more events, wherein the events within “PrecedingExpression” are tested for occurrences prior to testing occurrences of the events within “FollowingExpression”; where both logic expressions contain events or their contradictory events that are observable when tested by the wireless mobile terminal; such that events observed by or linked to the Smartphone or cellular telephone, are of the following types or their contradictions to enhance vehicular safety by denying the invocation (“False” Boolean expression evaluation) of an app that may distract a vehicle's driver: a) a wireless mobile terminal moving beyond a specified threshold speed, as can be determined via a GPS measurement differential of known time duration or a communication link to the vehicle's speedometer; b) a wireless mobile terminal detected to be in proximity of the vehicle driver's seat, as can be determined using a Bluetooth or other radio wave position detection via a high directivity antenna; c) a voice actuated wireless mobile terminal Compound Wireless Service app that detects the voice of the vehicle's driver, as can be determined by means of a high directivity microphone for a wireless mobile terminal or voice recognition of a driver's recording issuing the command to actuate the Compound Wireless Service app; d) a distracting app execution disapproval by an outside source communication denial, as can be performed by a service provider or law enforcement that remotely sets a parameter state to restrict invocation of the distracting app; e) a wireless mobile terminal is in a restricted area, as can be determined when on a spatially defined roadway by means of a GPS measurement or by means of a service provider's location service; f) a wireless mobile terminal is in a particular vehicle, as can be determined when the particular vehicle possesses a Bluetooth or other radio wave generator that signals an invocation restriction of one or more distracting apps; g) the driver's seat of a vehicle is occupied and the vehicle's engine is running, as can be determined via a pressure transducer that senses weight on the seat and communicates with the wireless mobile terminal to restrict one or more distracting apps; h) temporal events of a vehicle travelling during time periods of heavy traffic or during time periods requiring attentive driving; wherein safety enhancing Boolean expressions include: a) not being able to send a text message while in a moving vehicle, that may be expressed as {SendTextMessage And Then (Not VehiclelsMoving)}; b) not invoking a voice command by a vehicle's driver, unless the vehicle is not moving, that may be expressed as {AppVoiceCommand And Then ((Not Driver Voice) Or (Not VehicleMoving))} c) not invoking a voice command in a vehicle unless the vehicle's engine is not running or the mobile communication terminal is in the vehicle's cradle and either the vehicle is not moving or the voice command was not spoken by the driver, that may be expressed as {AppVoiceCommand And Then ((Not EngineRunning) Or (InCradle And ((Not VehicleMoving) Or (Not DriverVoice))))} d) not invoking a voice command in a vehicle unless the vehicle's engine is not running or the vehicle is not moving or the voice command was not spoken by the driver, that may be expressed as {AppVoiceCommand And Then ((Not EngineRunning) Or (Not VehicleMoving) Or (Not DriverVoice))} e) not invoking a manufacturer's default app in a vehicle unless the vehicle's engine is not running or the vehicle is not moving or the driver is not in possession of the smartphone, that may be expressed as {ManufacturerDefault And Then ((Not EngineRunning) Or (Not VehicleMoving) Or (Not DriverHasSmartphone))} f) not invoking a voice command in a vehicle unless the vehicle's engine is not running, that may be expressed as {AppVoiceCommand And Then (Not EngineRunning)} g) not invoking a manufacturer's default app in a vehicle unless the vehicle's engine is not running, that may be expressed as {ManufacturerDefault And Then (Not EngineRunning)} h) not invoking a voice command in a vehicle unless the vehicle is not moving, that may be expressed as {AppVoiceCommand And Then (Not VehicleMoving)} i) not invoking a manufacturer's default app in a vehicle unless the vehicle is not moving, that may be expressed as {ManufacturerDefault And Then (Not VehicleMoving)}; whereby an Invoke facility service consists of the parameters: 1) {OptionalEventExpression} which is a Boolean expression containing assertions of recognized events that are to control the invocation of an associated Smartphone app or wireless mobile terminal app; 2) (OptionalEventDurationList) which is a list of triples; where each triple designates an event name, its value of “Sustained” or “Momentary”, and the testing repetition interval for a “Sustained” event or a null entry for a “Momentary” event; 3) (OptionalObservationRepetitionInterval) which is a parameter pair that specifies the time interval between successive evaluations of the {OptionalEventExpression} truth value and the time interval to reset all event test values to an “Untested” flag; 4) (OptionalPasswordPair) which is a parameter pair intended to provide password protection of a Smartphone app or wireless mobile terminal app that contains a password name and a password value; 5) (OptionalDisableQuadruple) which is a quadruple of entries consisting of the name of maximum permitted number of failed password attempts, the value of maximum permitted number of failed password attempts, the name of amount of time the Smartphone app or wireless mobile terminal app will be disabled if the number of failed attempts reaches the maximum, and the value of the amount of time the Smartphone app or wireless mobile terminal app will be disabled if the number of failed attempts reaches the maximum; 6) OptionalEnablingSwitch which is a parameter to either enable or disable the Smartphone app or wireless mobile terminal app invocation; 7) (OptionalInitialConditionsAssignmentList) which is a list of pairs that consist of builder selected Smartphone app or wireless mobile terminal app parameter names and their initial values when the Smartphone app or wireless mobile terminal app begins its execution; 8) (OptionalLinkedCWSList) which is a list of pairs that indicate the names of potentially linked Smartphone apps or wireless mobile terminal apps and their addresses for the purpose of having an invoked Smartphone app or wireless mobile terminal app receive data from a linked Smartphone app or wireless mobile terminal app; 9) (OptionalLinkedCWSPasswords) which is a list of pairs that associate potentially linked Smartphone apps or wireless mobile terminal apps with passwords for those potentially linked Smartphone apps or wireless mobile terminal apps having password protection; 10) (OptionalLinkedCWSData) which is a list of pairs; where the first member of a pair is a linked Smartphone app or wireless mobile terminal app name and the second member is a list of pairs that associate named constants and variables with memory locations into which a linked Smartphone app or wireless mobile terminal app will enter values; 11) (OptionalNotifications) which is a n-tuple to provide notification concerning the operation of the Invoke facility service after the Boolean expression was evaluated as “True”; 12) (OptionalEventFailureNotification) which is a n-tuple to provide a notification when an event test path results in a “False” evaluation of a Smartphone app's or wireless mobile terminal app's Invoke facility service Boolean expression; 13) (OptionalEventPathData) which is a parameter consisting of set of triples and a set of pairs that represent the potential event test paths, in the form of an inverted, binary, directed tree, to evaluate the Smartphone app's or wireless mobile terminal app's Boolean expression; 14) {OptionalEventFailureNotificationSchema} which is a set of pairs, of the form (event test path label, notification) or (event test path label, notification pointer), that provides the failure notification parameter value to be identified in the (OptionalEventFailureNotification) output for its associated Smartphone app or wireless mobile terminal app. 2. The process of claim 1 , wherein further improvements include the use of an interactive graphical compiler that conducts dialog with the builder of an Invoke facility service to assist the construction of a Boolean expression of events and assistance for the other parameter assignments of an Invoke facility service by graphically presenting queries and providing guidance for successive Invoke facility service parameter entries. | 0.5 |
8,677,312 | 3 | 4 | 3. The method of claim 1 , wherein the architecture description comprises information organized in a hierarchical format. | 3. The method of claim 1 , wherein the architecture description comprises information organized in a hierarchical format. 4. The method of claim 3 , wherein automatically generating the compiler description comprises flattening the information organized in the hierarchical format to produce a non-hierarchical formatted compiler description. | 0.5 |
9,558,747 | 1 | 3 | 1. A high intelligibility voice announcement system, comprising: an announcement station, including: a speech to text engine configured to convert a spoken announcement from a user to converted text data; an error recognition engine configured to identify any errors in the converted text data; a display on the announcement station configured to display the converted text data, wherein: the identified errors in the converted text data are identified for the user; and corrected text data is displayed, via the display on the announcement station, in a queue system including other announcements until corrected text data of the spoken announcement is ready to be sent; an input device allowing the user to correct any identified errors in the converted text data, resulting in the corrected text data; a number of zones each including one or more speakers; and a transmitter configured to send the corrected text data to the one or more speakers of the number of zones; and a text to speech engine configured to convert the corrected text data to an audible message in preparation for broadcast via the one or more speakers, wherein the broadcast of the audible message is delayed via a configurable delay that is different for the one or more speakers in different ones of the number of zones. | 1. A high intelligibility voice announcement system, comprising: an announcement station, including: a speech to text engine configured to convert a spoken announcement from a user to converted text data; an error recognition engine configured to identify any errors in the converted text data; a display on the announcement station configured to display the converted text data, wherein: the identified errors in the converted text data are identified for the user; and corrected text data is displayed, via the display on the announcement station, in a queue system including other announcements until corrected text data of the spoken announcement is ready to be sent; an input device allowing the user to correct any identified errors in the converted text data, resulting in the corrected text data; a number of zones each including one or more speakers; and a transmitter configured to send the corrected text data to the one or more speakers of the number of zones; and a text to speech engine configured to convert the corrected text data to an audible message in preparation for broadcast via the one or more speakers, wherein the broadcast of the audible message is delayed via a configurable delay that is different for the one or more speakers in different ones of the number of zones. 3. The high intelligibility voice announcement system of claim 1 , wherein the identified errors in the converted text data are corrected using one or more of user speech inputs and a keyboard. | 0.610887 |
8,918,431 | 1 | 4 | 1. A computing system including at least one processor, the system comprises: a user interface to allow a user to view and input data related to sales associated with the user; an observation sub-system having a processor configured to centralize data and to identify each of a plurality of sales concepts; a conceptualization sub-system configured to generate a plurality of nodes within an ontological mapping, wherein each of the plurality of nodes corresponds to a certain one of the plurality of identified sales concepts identified by the observation sub-system, the conceptualization sub-system configured to generate new nodes within the ontological mapping responsive to a determination that a new concept exists in the system; a relationship identification sub-system configured to create relationships between at least some of the plurality of identified sales concepts, and attribute affinity weights to the relationships, wherein the affinity weight quantifies a strength of the relationship between the sales concepts using concept occurrence and co-concept occurrence, and each concept occupies a node on the ontological mapping, the relationship identification sub-system to select relationships between the nodes with the greatest affinity index and then update the ontology to with the affinity index for the selected relationships; a change refinement sub-system configured to modify at least one of the plurality of nodes, affinity weights and relationships based upon information associated with the user; and a non-transitory knowledge store configured to the information associated with the user pertaining to a sub-plurality of the plurality of identified sales concepts. | 1. A computing system including at least one processor, the system comprises: a user interface to allow a user to view and input data related to sales associated with the user; an observation sub-system having a processor configured to centralize data and to identify each of a plurality of sales concepts; a conceptualization sub-system configured to generate a plurality of nodes within an ontological mapping, wherein each of the plurality of nodes corresponds to a certain one of the plurality of identified sales concepts identified by the observation sub-system, the conceptualization sub-system configured to generate new nodes within the ontological mapping responsive to a determination that a new concept exists in the system; a relationship identification sub-system configured to create relationships between at least some of the plurality of identified sales concepts, and attribute affinity weights to the relationships, wherein the affinity weight quantifies a strength of the relationship between the sales concepts using concept occurrence and co-concept occurrence, and each concept occupies a node on the ontological mapping, the relationship identification sub-system to select relationships between the nodes with the greatest affinity index and then update the ontology to with the affinity index for the selected relationships; a change refinement sub-system configured to modify at least one of the plurality of nodes, affinity weights and relationships based upon information associated with the user; and a non-transitory knowledge store configured to the information associated with the user pertaining to a sub-plurality of the plurality of identified sales concepts. 4. The system of claim 1 , wherein the change refinement sub-system comprises: a removal module configured to remove a certain one of the plurality of nodes responsive to a determination that a certain affinity index pertaining an identified sales concept corresponding to the certain node has dropped below a predetermined threshold. | 0.5 |
7,581,206 | 11 | 12 | 11. A system for using templates having associated metadata files, said system comprising: a computer; and at least one subsystem coupled to the computer for: parsing each of a plurality of metadata files, wherein each metadata file is associated with a template, wherein at least one of the metadata files determines an associated template's placement under a certain subcategory; storing a single file for each template in a UI (“User Interface”) accessible location comprising an associated template and associated code data based upon whether the template is a user-created template, a pre-defined template, an item template or a project template; building an index of all the templates; displaying the index in a customized UI, wherein the UI displays an identifier for each template in a format depending upon whether the respective template is a user-created template, a pre-defined template, an item template or a project template; receiving a selection of a template from the index; invoking a template engine upon the selecting of the template from the index, the template engine causing reading of a metadata file associated with the selected template, the metadata file including three sections, a TemplateData section that defines an appearance of the selected template, a TemplateContent section that defines content and creation parameters of the selected template and a ReplaceParameters section, wherein the metadata file provides information regarding the template contents and template creation process and wherein the metadata file determines whether data to be created will be created in a separate folder for the data, wherein the metadata file is created by using inference rules to index at least one file by determining elements of the at least one file to be generalized; creating a project according to specifications of the metadata file; performing parameter substitution to enable replacement of key parameters on template instantiation. | 11. A system for using templates having associated metadata files, said system comprising: a computer; and at least one subsystem coupled to the computer for: parsing each of a plurality of metadata files, wherein each metadata file is associated with a template, wherein at least one of the metadata files determines an associated template's placement under a certain subcategory; storing a single file for each template in a UI (“User Interface”) accessible location comprising an associated template and associated code data based upon whether the template is a user-created template, a pre-defined template, an item template or a project template; building an index of all the templates; displaying the index in a customized UI, wherein the UI displays an identifier for each template in a format depending upon whether the respective template is a user-created template, a pre-defined template, an item template or a project template; receiving a selection of a template from the index; invoking a template engine upon the selecting of the template from the index, the template engine causing reading of a metadata file associated with the selected template, the metadata file including three sections, a TemplateData section that defines an appearance of the selected template, a TemplateContent section that defines content and creation parameters of the selected template and a ReplaceParameters section, wherein the metadata file provides information regarding the template contents and template creation process and wherein the metadata file determines whether data to be created will be created in a separate folder for the data, wherein the metadata file is created by using inference rules to index at least one file by determining elements of the at least one file to be generalized; creating a project according to specifications of the metadata file; performing parameter substitution to enable replacement of key parameters on template instantiation. 12. A system according to claim 11 wherein each template corresponds to a project in an integrated development environment (IDE). | 0.649457 |
9,787,722 | 8 | 10 | 8. The method of claim 1 , further comprising: performing a regular expressions search of the security rule parameters of each security rule in the opened configuration file for matches to regular expressions defined for the configuration file; and for each security rule parameter found to match one of the regular expressions, generating for display a visual indication associated with the security rule parameter found to match the one of the regular expressions. | 8. The method of claim 1 , further comprising: performing a regular expressions search of the security rule parameters of each security rule in the opened configuration file for matches to regular expressions defined for the configuration file; and for each security rule parameter found to match one of the regular expressions, generating for display a visual indication associated with the security rule parameter found to match the one of the regular expressions. 10. The method of claim 8 , wherein: wherein the performing the regular expression search further includes searching the opened configuration file for delineated remarks interspersed among the security rules and that have no effect on the network security device; and for each found remark, generating for display a visual remark indication to visually differentiate the found remark from the security rules. | 0.587045 |
9,866,645 | 3 | 4 | 3. The processor-implemented method of claim 2 , further comprising: assigning, by the notification server, a registration ID in response to the received enrollment request, wherein the registration ID is associated with the user; storing, by the notification server, the registration ID; and transmitting, by the notification server, the registration ID to the user device. | 3. The processor-implemented method of claim 2 , further comprising: assigning, by the notification server, a registration ID in response to the received enrollment request, wherein the registration ID is associated with the user; storing, by the notification server, the registration ID; and transmitting, by the notification server, the registration ID to the user device. 4. The processor-implemented method of claim 3 , wherein the trigger message includes the registration ID, and wherein the step of determining user information is based at least on the registration ID included in the trigger message. | 0.5 |
9,008,416 | 12 | 13 | 12. A computer-implemented method as in claim 11 , wherein the steps of providing one or more goals, receiving from the first plurality of untrained providers images created in response to the step of providing one or more goals, sending to a second plurality of untrained providers requests to rate, and receiving ratings of the second plurality of untrained providers are performed by a computer-based system over a network coupling the computer-based system to provider machines of the providers of the first and second pluralities of untrained providers. | 12. A computer-implemented method as in claim 11 , wherein the steps of providing one or more goals, receiving from the first plurality of untrained providers images created in response to the step of providing one or more goals, sending to a second plurality of untrained providers requests to rate, and receiving ratings of the second plurality of untrained providers are performed by a computer-based system over a network coupling the computer-based system to provider machines of the providers of the first and second pluralities of untrained providers. 13. A computer-implemented method as in claim 12 , wherein the step of providing one or more goals comprises sending instructions over the network from the computer-based system to the provider machines of the providers of the first plurality of untrained providers. | 0.654545 |
8,005,816 | 11 | 14 | 11. A computer program product embedded in a computer readable storage medium for automatically generating suggested links in a search system, comprising: program code for initiating a first crawl across an enterprise corpus owned by an enterprise; program code for discovering during the first crawl a link pointing to a data source, the data source being mis-characterized during the first crawl as outside a boundary of the enterprise corpus owned by the enterprise; program code for automatically storing the link as a first suggested link with other suggested links in a memory; program code for initiating a second crawl across the enterprise corpus after the automatically storing, the second crawl having a different seed uniform resource locator (URL) or different boundary rules than the first crawl; program code for encountering during the second crawl the data source within the same boundary of the enterprise corpus; program code for removing the first suggested link from the other suggested links based on the encountering the data source, previously characterized as outside the boundary of the enterprise corpus, within the same boundary of the enterprise corpus during the second crawl; and program code for determining relevancy scoring for the other suggested links. | 11. A computer program product embedded in a computer readable storage medium for automatically generating suggested links in a search system, comprising: program code for initiating a first crawl across an enterprise corpus owned by an enterprise; program code for discovering during the first crawl a link pointing to a data source, the data source being mis-characterized during the first crawl as outside a boundary of the enterprise corpus owned by the enterprise; program code for automatically storing the link as a first suggested link with other suggested links in a memory; program code for initiating a second crawl across the enterprise corpus after the automatically storing, the second crawl having a different seed uniform resource locator (URL) or different boundary rules than the first crawl; program code for encountering during the second crawl the data source within the same boundary of the enterprise corpus; program code for removing the first suggested link from the other suggested links based on the encountering the data source, previously characterized as outside the boundary of the enterprise corpus, within the same boundary of the enterprise corpus during the second crawl; and program code for determining relevancy scoring for the other suggested links. 14. A computer program product according to claim 11 , further comprising: program code for configuring a crawler to conduct one of the first and second crawls with boundary rules. | 0.634146 |
8,200,704 | 1 | 5 | 1. A computer-implemented method, comprising: identifying, in a computer, a plurality of structured documents having a same structured data format; parsing each structured document in the plurality of structured documents and extracting a plurality of data sets from each structured document; obtaining distinct metadata from a plurality of sources for each structured document in the plurality of structured documents, wherein the distinct metadata for each structured document comprises a rank of the structured document, a popularity of the structured document, or a number of downloads of the structured document, and wherein one or more sources in the plurality of sources comprises a different source than the structured document itself; merging the distinct metadata from the plurality of sources, including removing duplicate metadata; associating the distinct metadata for each respective structured document with each data set extracted from the respective structured document; adding a plurality of record items to a searchable database, wherein each record item corresponds to one of the extracted data sets, and wherein each record item is associated with the distinct metadata associated with the corresponding data set; receiving a search query; using the distinct metadata associated with each of one or more of the record items to calculate a query-independent score for at least one record item in the searchable database and using the query-independent score to identify the at least one record item as a response to the search query; and returning the identified at least one record item in response to the search query. | 1. A computer-implemented method, comprising: identifying, in a computer, a plurality of structured documents having a same structured data format; parsing each structured document in the plurality of structured documents and extracting a plurality of data sets from each structured document; obtaining distinct metadata from a plurality of sources for each structured document in the plurality of structured documents, wherein the distinct metadata for each structured document comprises a rank of the structured document, a popularity of the structured document, or a number of downloads of the structured document, and wherein one or more sources in the plurality of sources comprises a different source than the structured document itself; merging the distinct metadata from the plurality of sources, including removing duplicate metadata; associating the distinct metadata for each respective structured document with each data set extracted from the respective structured document; adding a plurality of record items to a searchable database, wherein each record item corresponds to one of the extracted data sets, and wherein each record item is associated with the distinct metadata associated with the corresponding data set; receiving a search query; using the distinct metadata associated with each of one or more of the record items to calculate a query-independent score for at least one record item in the searchable database and using the query-independent score to identify the at least one record item as a response to the search query; and returning the identified at least one record item in response to the search query. 5. The method of claim 1 , wherein the distinct metadata for each structured document comprises a rank of the structured document. | 0.844869 |
8,352,229 | 13 | 14 | 13. The computer-implemented method of claim 12 , further comprising, during a simulation wherein the simulation engine is executing, registering a callback function of the third party application with an event that invokes the callback function. | 13. The computer-implemented method of claim 12 , further comprising, during a simulation wherein the simulation engine is executing, registering a callback function of the third party application with an event that invokes the callback function. 14. The computer-implemented method of claim 13 , wherein during the simulation, responsive to the event being generated, the simulation engine invokes the callback function of the third party application through which state information from the block of memory is passed using a pointer. | 0.5 |
9,092,406 | 3 | 5 | 3. The method of claim 1 , wherein the intermediate file facilitates providing additional content associated with the image file. | 3. The method of claim 1 , wherein the intermediate file facilitates providing additional content associated with the image file. 5. The method of claim 3 , wherein the additional content is provided in an environment associated with a web browser. | 0.725581 |
8,438,054 | 2 | 3 | 2. The method as recited in claim 1 , wherein input to one or more of the plurality of queries is received from a plurality of users over a computer network. | 2. The method as recited in claim 1 , wherein input to one or more of the plurality of queries is received from a plurality of users over a computer network. 3. The method as recited in claim 2 , the method further comprising allowing one or more of the plurality of users to generate and associate comments to at least a portion of the new argument. | 0.5 |
9,514,108 | 1 | 3 | 1. A method for reference note generation, comprising: receiving a first user input that identifies a designated insertion point in a destination document; subsequent to receiving the first user input, receiving a second user input that causes an information element to be copied to a transfer buffer from a source application; collecting source reference information associated with the information element, wherein the source reference information includes a source identifier indicative of an origin of the information element; generating a reference note based on the source reference information and a reference note format specification; inserting the information element into the destination document, wherein the information element is inserted automatically at the designated insertion point without receiving a further user input from a user interface that is associated with the destination document; and inserting the reference note into the destination document, wherein the reference note is associated with the information element and the reference note is inserted without receiving a user input from a user interface that is associated with the destination document. | 1. A method for reference note generation, comprising: receiving a first user input that identifies a designated insertion point in a destination document; subsequent to receiving the first user input, receiving a second user input that causes an information element to be copied to a transfer buffer from a source application; collecting source reference information associated with the information element, wherein the source reference information includes a source identifier indicative of an origin of the information element; generating a reference note based on the source reference information and a reference note format specification; inserting the information element into the destination document, wherein the information element is inserted automatically at the designated insertion point without receiving a further user input from a user interface that is associated with the destination document; and inserting the reference note into the destination document, wherein the reference note is associated with the information element and the reference note is inserted without receiving a user input from a user interface that is associated with the destination document. 3. The method of claim 1 , wherein the source identifier includes a Uniform Resource Locator of a web browser application from which the information element has been copied. | 0.664729 |
8,838,587 | 1 | 4 | 1. A computer-implemented method comprising: receiving, at a computer system, a request to determine whether to assign a classification to a first query; selecting, by the computer system, a plurality of search entities that are associated with the first query based on respective user behavior data for the first query associated with each of the search entities; determining a first measure of how many of the plurality of search entities have been assigned the classification; determining that the first measure of how many of the plurality of search entities have been assigned the classification satisfies a classification threshold; in response to determining that the first measure of how many of the plurality of search entities have been assigned the classification satisfies the classification threshold, determining a second measure of how many of a top group of search entities from the plurality of search entities have been assigned the classification, each search entity in the top group of search entities being in a subset of the plurality of search entities having highest respective measures of relevance to the query based on the user behavior data; determining, by the computer system that the first measure of how many of the plurality of search entities have been assigned the classification is consistent with the second measure of how many of the top group of search entities have been assigned the classification; and in response to determining that the first measure of how many of the plurality of search entities have been assigned the classification is consistent with the second measure of how many of the top group of search entities have been assigned the classification, assigning the classification to the first query. | 1. A computer-implemented method comprising: receiving, at a computer system, a request to determine whether to assign a classification to a first query; selecting, by the computer system, a plurality of search entities that are associated with the first query based on respective user behavior data for the first query associated with each of the search entities; determining a first measure of how many of the plurality of search entities have been assigned the classification; determining that the first measure of how many of the plurality of search entities have been assigned the classification satisfies a classification threshold; in response to determining that the first measure of how many of the plurality of search entities have been assigned the classification satisfies the classification threshold, determining a second measure of how many of a top group of search entities from the plurality of search entities have been assigned the classification, each search entity in the top group of search entities being in a subset of the plurality of search entities having highest respective measures of relevance to the query based on the user behavior data; determining, by the computer system that the first measure of how many of the plurality of search entities have been assigned the classification is consistent with the second measure of how many of the top group of search entities have been assigned the classification; and in response to determining that the first measure of how many of the plurality of search entities have been assigned the classification is consistent with the second measure of how many of the top group of search entities have been assigned the classification, assigning the classification to the first query. 4. The method of claim 1 , wherein the plurality of identified search entities comprises a plurality of web domains, and wherein the respective user behavior data associated with each web domain of the plurality of web domains represents user behavior relative to one or more documents in the web domain when the one or more documents were provided as search results for the first query. | 0.745059 |
8,671,387 | 16 | 18 | 16. A system, comprising: means for receiving an application, the application comprising a stack and a database, the stack comprising one or more pages for a user interface to the application; and means for executing the application comprising: means for locating a compiled script for the application based on a global identifier assigned to the compiled script, the compiled script including a scripting-language instruction for the application, wherein the global identifier comprises an application identifier for specifically identifying the application in a plurality of applications and an object identifier for specifically identifying an object of a computational model, and wherein the computational model is either the stack or the database; means for injecting the compiled script into the application; means for executing the injected script in the application, wherein the injected script is configured to perform at least one transaction of the computational model, wherein the means for locating the compiled script comprises: means for determining that the script is to be compiled; means for requesting that the script be compiled comprises: means for receiving the script, the script comprising at least one scripting-language instruction for the application, wherein the at least one instruction comprises a script extension for specifying use of the computational model, wherein the script extension comprises a key character and an identifier of the computational model; means for determining the script extension within the script based on the key character; means for determining the identifier for the computational model specified by the script extension; and means for generating the compiled script corresponding to the script, wherein the compiled script comprises scripting-language code that replaces the script extension, and wherein the scripting-language code is configured to access the computational model identified by the identifier of the computational model; and means for receiving the compiled script, wherein the compiled script corresponds to the script. | 16. A system, comprising: means for receiving an application, the application comprising a stack and a database, the stack comprising one or more pages for a user interface to the application; and means for executing the application comprising: means for locating a compiled script for the application based on a global identifier assigned to the compiled script, the compiled script including a scripting-language instruction for the application, wherein the global identifier comprises an application identifier for specifically identifying the application in a plurality of applications and an object identifier for specifically identifying an object of a computational model, and wherein the computational model is either the stack or the database; means for injecting the compiled script into the application; means for executing the injected script in the application, wherein the injected script is configured to perform at least one transaction of the computational model, wherein the means for locating the compiled script comprises: means for determining that the script is to be compiled; means for requesting that the script be compiled comprises: means for receiving the script, the script comprising at least one scripting-language instruction for the application, wherein the at least one instruction comprises a script extension for specifying use of the computational model, wherein the script extension comprises a key character and an identifier of the computational model; means for determining the script extension within the script based on the key character; means for determining the identifier for the computational model specified by the script extension; and means for generating the compiled script corresponding to the script, wherein the compiled script comprises scripting-language code that replaces the script extension, and wherein the scripting-language code is configured to access the computational model identified by the identifier of the computational model; and means for receiving the compiled script, wherein the compiled script corresponds to the script. 18. The system of claim 16 , wherein the at least one scripting-language instruction of the script further comprises a method extension, wherein the method extension corresponds to an invocation of a function of the computational model, and wherein the compiled script further comprises additional scripting-language code to replace the method extension with the invocation of the function of the computational model. | 0.5 |
7,483,947 | 1 | 6 | 1. A system that facilitates message filtering, comprising: A computer readable storage medium comprising: a pre-rendering component that receives a message and renders the message into a first format, the received message including one or more compressed images, for each of the one or more compressed images a first hash is generated, wherein the one or more compressed images are rendered in an uncompressed mode, for each of the one or more compressed images rendered in the uncompressed mode a second hash is generated, the first and second hashes are compared to determine if the message contains junk indicia; a converting component that converts the message in the first format into a character-only message and a filtering component that processes the character-only message for predetermined content and routes the received message based upon the predetermined content. | 1. A system that facilitates message filtering, comprising: A computer readable storage medium comprising: a pre-rendering component that receives a message and renders the message into a first format, the received message including one or more compressed images, for each of the one or more compressed images a first hash is generated, wherein the one or more compressed images are rendered in an uncompressed mode, for each of the one or more compressed images rendered in the uncompressed mode a second hash is generated, the first and second hashes are compared to determine if the message contains junk indicia; a converting component that converts the message in the first format into a character-only message and a filtering component that processes the character-only message for predetermined content and routes the received message based upon the predetermined content. 6. The system of claim 1 , the message analyzed for at least one of embedded tags and comments. | 0.82852 |
9,652,725 | 1 | 2 | 1. A unified activity manager for use in a collaborative environment comprising: at least one computer system in which the unified activity manager executes; an activity list view provided by the unified activity manager, the activity list view comprising a hierarchical listing of activities; an activity view provided by the unified activity manager, the activity view comprising a rendering of properties associated with a selected activity in said activity list view; a persons and roles view displayed concurrently in a single screen with the activity view and provided by the unified activity manager, the persons and roles view comprising at least a listing of collaborators available for association with said selected activity in said activity list view; and, a placeholder management module coupled to the unified activity manager, the module comprising program code enabled to display a placeholder for a collaborator designated to perform a task in an activity in the activity view in lieu of reference to a specific collaborator. | 1. A unified activity manager for use in a collaborative environment comprising: at least one computer system in which the unified activity manager executes; an activity list view provided by the unified activity manager, the activity list view comprising a hierarchical listing of activities; an activity view provided by the unified activity manager, the activity view comprising a rendering of properties associated with a selected activity in said activity list view; a persons and roles view displayed concurrently in a single screen with the activity view and provided by the unified activity manager, the persons and roles view comprising at least a listing of collaborators available for association with said selected activity in said activity list view; and, a placeholder management module coupled to the unified activity manager, the module comprising program code enabled to display a placeholder for a collaborator designated to perform a task in an activity in the activity view in lieu of reference to a specific collaborator. 2. The unified activity manager of claim 1 , wherein said activity view further comprises a listing of collaborators associated with said selected activity in said activity list view, a listing of roles associated with said selected activity in said activity list view, a listing of resources associated with said selected activity in said activity list view, and a listing of events associated with said selected activity in said activity list view. | 0.5 |
8,494,836 | 7 | 8 | 7. An apparatus comprising: a processing unit; a host controller coupled with the processing unit; and a machine-readable storage medium coupled with the host controller, the machine-readable storage medium having program instructions stored thereon, the program instructions to, retrieve a plurality of web pages of a website in response to an input of an address to the website; select a subset of the plurality of web pages based, at least in part, on positions of each of the plurality of web pages within a hierarchy of the website; machine translate the subset of the plurality of web pages from a first language to a second language to generate translations of the subset of web pages; receive a verification result from verification of the translations of the subset of web pages, wherein the verification result includes corrected translations of phrases in the subset of web pages; update a translation dictionary for machine translations, wherein said updating the translation dictionary corrects mistranslated phrases in the translations of the subset of web pages with the corrected translations of the phrases; and machine translate the plurality of web pages from the first language to the second language using the updated translation dictionary. | 7. An apparatus comprising: a processing unit; a host controller coupled with the processing unit; and a machine-readable storage medium coupled with the host controller, the machine-readable storage medium having program instructions stored thereon, the program instructions to, retrieve a plurality of web pages of a website in response to an input of an address to the website; select a subset of the plurality of web pages based, at least in part, on positions of each of the plurality of web pages within a hierarchy of the website; machine translate the subset of the plurality of web pages from a first language to a second language to generate translations of the subset of web pages; receive a verification result from verification of the translations of the subset of web pages, wherein the verification result includes corrected translations of phrases in the subset of web pages; update a translation dictionary for machine translations, wherein said updating the translation dictionary corrects mistranslated phrases in the translations of the subset of web pages with the corrected translations of the phrases; and machine translate the plurality of web pages from the first language to the second language using the updated translation dictionary. 8. The apparatus of claim 7 , wherein the program instructions to select the subset of the plurality of web pages based, at least in part, on positions of each of the plurality of web pages within the hierarchy of the website comprises program instructions to determine, for each of the plurality of web pages, a number of hyperlinks to traverse from a top web page of the web site to the web page, wherein the positions of each of the plurality of web pages within the hierarchy of the website corresponds to the number of hyperlinks. | 0.5 |
7,640,232 | 1 | 3 | 1. A process implemented across a network having a search engine, the search engine having access to a content source, the process comprising the steps of: receiving at an application module a search query from a user at a user terminal, the search query comprising at least one search parameter specified by the user; receiving at the application module an identification of at least one ratings service specified by the user, wherein the at least one ratings service is external to the application module, to the search engine, and to the content source, and wherein the ratings service is accessible to the application module across the network; responsive to the receipt of the user-specified ratings services, sending a data query from the application module to at least one of the user-specified ratings services; receiving rating information at the application module across the network from at least one of the user-specified ratings services in response to the sent data query, wherein the rating information is independently maintained by the at least one ratings service; providing a refined search through the application module, wherein the refinement comprises any of at the application module, using the received rating information from at least one of the user-specified ratings services in conjunction with the search parameters received from the user to perform the search at the search engine through retrieval of content from the content source, wherein search results received from the search engine at the application module comply with both the search parameters received from the user and the received rating information from at least one of the user-specified ratings services, performing the search at the search engine through retrieval of content from the content source with the search parameters received from the user and subsequently filtering search results received from the search engine at the application module with the received rating information from at least one of the user-specified ratings services, and performing the search at the search engine through retrieval of content from the content source with the search parameters received from the user and subsequently providing any of organizing and sorting of the search results received from the search engine at the application module with the received rating information from at least one of the user-specified ratings services; and returning the results of the refined search from the application module to any of the user at the user terminal and a recipient at a recipient terminal, the recipient other than the user. | 1. A process implemented across a network having a search engine, the search engine having access to a content source, the process comprising the steps of: receiving at an application module a search query from a user at a user terminal, the search query comprising at least one search parameter specified by the user; receiving at the application module an identification of at least one ratings service specified by the user, wherein the at least one ratings service is external to the application module, to the search engine, and to the content source, and wherein the ratings service is accessible to the application module across the network; responsive to the receipt of the user-specified ratings services, sending a data query from the application module to at least one of the user-specified ratings services; receiving rating information at the application module across the network from at least one of the user-specified ratings services in response to the sent data query, wherein the rating information is independently maintained by the at least one ratings service; providing a refined search through the application module, wherein the refinement comprises any of at the application module, using the received rating information from at least one of the user-specified ratings services in conjunction with the search parameters received from the user to perform the search at the search engine through retrieval of content from the content source, wherein search results received from the search engine at the application module comply with both the search parameters received from the user and the received rating information from at least one of the user-specified ratings services, performing the search at the search engine through retrieval of content from the content source with the search parameters received from the user and subsequently filtering search results received from the search engine at the application module with the received rating information from at least one of the user-specified ratings services, and performing the search at the search engine through retrieval of content from the content source with the search parameters received from the user and subsequently providing any of organizing and sorting of the search results received from the search engine at the application module with the received rating information from at least one of the user-specified ratings services; and returning the results of the refined search from the application module to any of the user at the user terminal and a recipient at a recipient terminal, the recipient other than the user. 3. The process of claim 1 , wherein at least one of the user-specified ratings services comprises any of a person and an entity associated with the user. | 0.809227 |
9,798,800 | 1 | 2 | 1. A computer-implemented method for automatically generating answers to questions comprising the steps of: analyzing a corpus of documents to identify a document containing a list, wherein said list contains item-delimiting markup; parsing said list to identify type information and entities in said list indicated by said markup, wherein to identify said type information and entities in said list comprises: extracting a type from a title indicating said list; determining a presence of item-delimeter mark-up associated with said list, each mark-up delimeter including or not including one or more associated hyperlinks, and for each determined item-delimiter mark-up item: if a hyperlink is included: obtaining an instance of a hyperlink in closest proximity to the mark-up item-delimeter, and extracting an entity from a target of said hyperlink instance; and if a hyperlink is not included: using an annotator to identify phrases included in text associated with the item-delimeter mark-up, and extracting a most salient phrase as said entity; creating entity-type pairs, wherein said entity-type pairs comprise said extracted entities and the identified type from said list; receiving a lexical answer type associated with an input query; receiving a candidate answer to said query; determining whether said candidate answer is associated with an entity in said created entity-type pairs; for any associated entity-type pairs, comparing said extracted type in said associated entity-type pair with said lexical answer type; generating a type-matching score, wherein said type-matching score is indicative of a quality of said obtained candidate answer based on matching types; and using said type-matching score to evaluate said candidate answer as an answer to said query; wherein a hardware processor automatically performs one or more of said steps. | 1. A computer-implemented method for automatically generating answers to questions comprising the steps of: analyzing a corpus of documents to identify a document containing a list, wherein said list contains item-delimiting markup; parsing said list to identify type information and entities in said list indicated by said markup, wherein to identify said type information and entities in said list comprises: extracting a type from a title indicating said list; determining a presence of item-delimeter mark-up associated with said list, each mark-up delimeter including or not including one or more associated hyperlinks, and for each determined item-delimiter mark-up item: if a hyperlink is included: obtaining an instance of a hyperlink in closest proximity to the mark-up item-delimeter, and extracting an entity from a target of said hyperlink instance; and if a hyperlink is not included: using an annotator to identify phrases included in text associated with the item-delimeter mark-up, and extracting a most salient phrase as said entity; creating entity-type pairs, wherein said entity-type pairs comprise said extracted entities and the identified type from said list; receiving a lexical answer type associated with an input query; receiving a candidate answer to said query; determining whether said candidate answer is associated with an entity in said created entity-type pairs; for any associated entity-type pairs, comparing said extracted type in said associated entity-type pair with said lexical answer type; generating a type-matching score, wherein said type-matching score is indicative of a quality of said obtained candidate answer based on matching types; and using said type-matching score to evaluate said candidate answer as an answer to said query; wherein a hardware processor automatically performs one or more of said steps. 2. The computer-implemented method of claim 1 , further comprising: storing, in a further memory storage device, said created entity-type pairs, each entity-pair structure representing said one or more entities and associated type, wherein said determining whether said candidate answer is associated with an entity in said entity-type pairs comprises: accessing said stored entity-type pairs to identify a type from an entity-type pair. | 0.544792 |
8,171,453 | 4 | 10 | 4. The method as recited in claim 1 , further comprising: an act of the computer system receiving a third user interface gesture explicitly specifying a third region of the computer program source code this is to be associated with one or more of: a semantic behavior that specifies whether a type inference is applicable or not applicable to the corresponding first or second semantic region; a semantic behavior that specifies whether or not a variable needs to be declared prior to utilization of the variable within the corresponding first or second semantic region; a semantic behavior that specifies whether a particular type conversion is allowed or disallowed within the corresponding first or second semantic region; or a semantic behavior that specifies type safety for the corresponding first or second semantic region, the method further comprising: resolving proper semantics for the third region of the computer program source code using rules that define a hierarchy of semantic behaviors which can be used to identify an appropriate semantic behavior for the third region of the computer program source code. | 4. The method as recited in claim 1 , further comprising: an act of the computer system receiving a third user interface gesture explicitly specifying a third region of the computer program source code this is to be associated with one or more of: a semantic behavior that specifies whether a type inference is applicable or not applicable to the corresponding first or second semantic region; a semantic behavior that specifies whether or not a variable needs to be declared prior to utilization of the variable within the corresponding first or second semantic region; a semantic behavior that specifies whether a particular type conversion is allowed or disallowed within the corresponding first or second semantic region; or a semantic behavior that specifies type safety for the corresponding first or second semantic region, the method further comprising: resolving proper semantics for the third region of the computer program source code using rules that define a hierarchy of semantic behaviors which can be used to identify an appropriate semantic behavior for the third region of the computer program source code. 10. The method as recited in claim 4 , wherein the third region of the computer program source code is associated with type safety, such that the method further comprises: generating an error when type safety is violated within the third region of the computer program source code. | 0.565015 |
10,088,972 | 1 | 7 | 1. A method comprising: under control of a service computing device configured with executable instructions, obtaining an input from a first user of a first computing device; determining, based at least in part on contextual information associated with at least one of the input or the first computing device: a first virtual assistant via the first computing device, the first virtual assistant being associated with the first user and a first set of attributes; a task to be performed associated with the user input or the first computing device; and a second virtual assistant that is associated with a second user further associated with performance of the task, the second virtual assistant being associated with a second set of attributes; sending a message on behalf of the first virtual assistant to the second virtual assistant requesting authorization for the first virtual assistant to communicate with the second virtual assistant; determining that the second user has authorized the first virtual assistant to communicate with the second virtual assistant; based at least in part on determining that the second user has authorized the first virtual assistant to communicate with the second virtual assistant, causing the first virtual assistant and the second virtual assistant to communicate; providing a first dialog representation in a conversation user interface via the first computing device; providing a second dialog representation in the conversation user interface via the second computing device, at least one of the first dialog representation or the second dialog representation including a natural language representation of a communication from at least one of the first virtual assistant or the second virtual assistant; providing the natural language representation of the communication to a virtual assistant trainer interface via a third computing device that is used by a trainer of the first or second virtual assistants to submit feedback on the communication; receiving the feedback submitted in the virtual assistant trainer interface at the service computing device; and reconfiguring the respective attributes of the first or second virtual assistant based on the feedback. | 1. A method comprising: under control of a service computing device configured with executable instructions, obtaining an input from a first user of a first computing device; determining, based at least in part on contextual information associated with at least one of the input or the first computing device: a first virtual assistant via the first computing device, the first virtual assistant being associated with the first user and a first set of attributes; a task to be performed associated with the user input or the first computing device; and a second virtual assistant that is associated with a second user further associated with performance of the task, the second virtual assistant being associated with a second set of attributes; sending a message on behalf of the first virtual assistant to the second virtual assistant requesting authorization for the first virtual assistant to communicate with the second virtual assistant; determining that the second user has authorized the first virtual assistant to communicate with the second virtual assistant; based at least in part on determining that the second user has authorized the first virtual assistant to communicate with the second virtual assistant, causing the first virtual assistant and the second virtual assistant to communicate; providing a first dialog representation in a conversation user interface via the first computing device; providing a second dialog representation in the conversation user interface via the second computing device, at least one of the first dialog representation or the second dialog representation including a natural language representation of a communication from at least one of the first virtual assistant or the second virtual assistant; providing the natural language representation of the communication to a virtual assistant trainer interface via a third computing device that is used by a trainer of the first or second virtual assistants to submit feedback on the communication; receiving the feedback submitted in the virtual assistant trainer interface at the service computing device; and reconfiguring the respective attributes of the first or second virtual assistant based on the feedback. 7. The method of claim 1 , wherein the first set of attributes and the second set of attributes contain at least one of a specific persona, a functionality, a visual appearance, or an audible manner of output. | 0.614391 |
8,023,626 | 1 | 11 | 1. A method of providing a language interpretation service, comprising: providing a language access telephone number that a caller speaking a first language and having a business need dials to place a telephone call to a language interpretation service to obtain language interpretation assistance; receiving a language access telephone call at the language interpretation service provider from the caller; identifying, after the telephone call is initiated, the first language from a plurality of languages with a voice recognition system so as to provide the caller with an interpreter that can translate between the first language and a second language, wherein the interpreter is associated with the language interpretation service provider; and permitting the interpreter to telephonically engage an agent representing a merchant that can serve the business need of the caller, wherein the agent speaks the second language and the interpreter translates a conversation between the caller and the agent. | 1. A method of providing a language interpretation service, comprising: providing a language access telephone number that a caller speaking a first language and having a business need dials to place a telephone call to a language interpretation service to obtain language interpretation assistance; receiving a language access telephone call at the language interpretation service provider from the caller; identifying, after the telephone call is initiated, the first language from a plurality of languages with a voice recognition system so as to provide the caller with an interpreter that can translate between the first language and a second language, wherein the interpreter is associated with the language interpretation service provider; and permitting the interpreter to telephonically engage an agent representing a merchant that can serve the business need of the caller, wherein the agent speaks the second language and the interpreter translates a conversation between the caller and the agent. 11. The method of claim 1 , wherein the conversation between the caller and the agent relates to a business transaction between the caller and the merchant. | 0.832258 |
10,152,695 | 13 | 14 | 13. The system of claim 12 , wherein each in the set of factor scores is associated with one of the data points. | 13. The system of claim 12 , wherein each in the set of factor scores is associated with one of the data points. 14. The system of claim 13 , wherein each factor score is based on, at least, a factor weight and a personal factor value, wherein the personal factor value is determined from a criteria calculation that is the same for all members, wherein the personal factor value of a member calculated at a first time (T1) is different from the personal factor value of that member calculated at a second time (T2). | 0.5 |
8,983,849 | 2 | 4 | 2. The method of claim 1 , wherein each update message further includes a priority level reflecting the priority of change events in the update message. | 2. The method of claim 1 , wherein each update message further includes a priority level reflecting the priority of change events in the update message. 4. The method of claim 2 , wherein the master language model is updated with any update messages having a high priority level before the master language model is updated with any update messages having a low priority level. | 0.508811 |
7,734,627 | 1 | 9 | 1. A method performed by a computer system, the method comprising: randomly sampling pairs of ordered terms from a particular document of a set of documents, to generate a cluster of pairs of ordered terms for the particular document, where the pairs of ordered terms include a first term and a second term and where, in at least some of the pairs of ordered terms, the second term occurs after one or more intervening terms occurring after the first term in the particular document, and where the random sampling is biased to have a higher chance of including a first ordered pair in the cluster than a second ordered pair, if the first ordered pair has fewer intervening terms that the second ordered pair, the randomly sampling being performed using one or more processors associated with the computer system; building, using one or more processors associated with the computer system, a similarity model that includes the cluster of pairs; comparing, using one or more processors associated with the computer system, a cluster of pairs from a target document to clusters of pairs from the similarity model; generating, using one or more processors associated with the computer system, similarity metrics that measure similarity between the target document and particular documents in the set of documents, the generating being based on the comparing; and outputting the generated similarity metrics. | 1. A method performed by a computer system, the method comprising: randomly sampling pairs of ordered terms from a particular document of a set of documents, to generate a cluster of pairs of ordered terms for the particular document, where the pairs of ordered terms include a first term and a second term and where, in at least some of the pairs of ordered terms, the second term occurs after one or more intervening terms occurring after the first term in the particular document, and where the random sampling is biased to have a higher chance of including a first ordered pair in the cluster than a second ordered pair, if the first ordered pair has fewer intervening terms that the second ordered pair, the randomly sampling being performed using one or more processors associated with the computer system; building, using one or more processors associated with the computer system, a similarity model that includes the cluster of pairs; comparing, using one or more processors associated with the computer system, a cluster of pairs from a target document to clusters of pairs from the similarity model; generating, using one or more processors associated with the computer system, similarity metrics that measure similarity between the target document and particular documents in the set of documents, the generating being based on the comparing; and outputting the generated similarity metrics. 9. The method of claim 1 , where the comparing includes: comparing pairs of ordered terms from the target document to the similarity model by enumerating pairs from the target document such that a first term and a second term in one of the enumerated pairs appears within a fixed distance of each other in the target document. | 0.5 |
8,024,178 | 1 | 6 | 1. A method, performed by one or more server or client devices, for completing fragments of text, the method comprising: obtaining, using one or more processors associated with the one or more server or client devices, a text fragment; identifying, using one or more processors associated with the one or more server or client devices, one or more documents based, at least in part on the text fragment; identifying, using one or more processors associated with the one or more server or client devices, sentences within the one or more documents that include the text fragment; determining, using one or more processors associated with the one or more server or client devices, sentence endings associated with the identified sentences; determining a measure of popularity for each of the sentence endings, where the measure of popularity for one of the sentence endings is based, at least in part, on a quantity of documents within a document corpus that includes the one of the sentence endings; ordering the sentence endings based, at least in part, on the determined measure of popularity for each of the sentence endings; and presenting, using one or more processors associated with the one or more server or client devices, the ordered sentence endings as potential completions for the text fragment. | 1. A method, performed by one or more server or client devices, for completing fragments of text, the method comprising: obtaining, using one or more processors associated with the one or more server or client devices, a text fragment; identifying, using one or more processors associated with the one or more server or client devices, one or more documents based, at least in part on the text fragment; identifying, using one or more processors associated with the one or more server or client devices, sentences within the one or more documents that include the text fragment; determining, using one or more processors associated with the one or more server or client devices, sentence endings associated with the identified sentences; determining a measure of popularity for each of the sentence endings, where the measure of popularity for one of the sentence endings is based, at least in part, on a quantity of documents within a document corpus that includes the one of the sentence endings; ordering the sentence endings based, at least in part, on the determined measure of popularity for each of the sentence endings; and presenting, using one or more processors associated with the one or more server or client devices, the ordered sentence endings as potential completions for the text fragment. 6. The method of claim 1 , where presenting the sentence endings comprises: assigning scores to the sentence endings based on the measure of popularity, ordering the sentence endings based, at least in part, on the scores, and presenting the ordered sentence endings as potential completions for the text fragment. | 0.516923 |
8,543,382 | 1 | 5 | 1. A computer-executable method of diacritizing a text, the method comprising: storing a Hidden Markov Model in computer memory for establishing a probability for associating a given diacritical mark with a given character of the text; inputting a sequence of individual characters of the text into a computer processor, the computer processor programmed for: analyzing the text to determine whether the text requires at least one diacritical mark, the text including a plurality of characters associated with an Arabic language; converting each character to an ASCII code; feeding each ASCII code in sequence to the Hidden Markov Model; applying an expectation-maximization process to each ASCII code starting at one end of the sequence; transitioning from one diacritical mark to another diacritical mark from a set of diacritical marks for each ASCII code; recording a probability for each diacritical mark when associated with each current ASCII code; changing a state of the Hidden Markov Model based on each probability over regularly spaced periods of time; wherein the Hidden Markov Model transitions from state q i at time t to a state q i at time t+1, where t=1, 2, 3, . . . M; and i, j=1, 2, . . . , N, and where M represents a number of the transitions and N represents a number of the states; wherein a transition probability a ij , representing a probability that diacritical mark q i appears directly after diacritical mark q i , equals an expected number of transitions from state q i to state q i divided by an expected number of transitions from state q i , finalizing a diacritical mark having the highest probability for the current ASCII code; processing each character in the sequence of the text, wherein the Hidden Markov Model bases the probability at least in part on the probability of a diacritical mark applied on one or more preceding characters of the sequence and on a context of the text for determining the probability of a diacritical mark on a given character; generating a sequence of the diacritical marks corresponding to the sequence of characters; matching the sequence of diacritical marks with the text to obtain the diacritized text; and displaying the diacritized text on an output device. | 1. A computer-executable method of diacritizing a text, the method comprising: storing a Hidden Markov Model in computer memory for establishing a probability for associating a given diacritical mark with a given character of the text; inputting a sequence of individual characters of the text into a computer processor, the computer processor programmed for: analyzing the text to determine whether the text requires at least one diacritical mark, the text including a plurality of characters associated with an Arabic language; converting each character to an ASCII code; feeding each ASCII code in sequence to the Hidden Markov Model; applying an expectation-maximization process to each ASCII code starting at one end of the sequence; transitioning from one diacritical mark to another diacritical mark from a set of diacritical marks for each ASCII code; recording a probability for each diacritical mark when associated with each current ASCII code; changing a state of the Hidden Markov Model based on each probability over regularly spaced periods of time; wherein the Hidden Markov Model transitions from state q i at time t to a state q i at time t+1, where t=1, 2, 3, . . . M; and i, j=1, 2, . . . , N, and where M represents a number of the transitions and N represents a number of the states; wherein a transition probability a ij , representing a probability that diacritical mark q i appears directly after diacritical mark q i , equals an expected number of transitions from state q i to state q i divided by an expected number of transitions from state q i , finalizing a diacritical mark having the highest probability for the current ASCII code; processing each character in the sequence of the text, wherein the Hidden Markov Model bases the probability at least in part on the probability of a diacritical mark applied on one or more preceding characters of the sequence and on a context of the text for determining the probability of a diacritical mark on a given character; generating a sequence of the diacritical marks corresponding to the sequence of characters; matching the sequence of diacritical marks with the text to obtain the diacritized text; and displaying the diacritized text on an output device. 5. The computer-executable method of claim 1 , wherein analyzing the text to determine whether the text requires at least one diacritical mark comprises scanning the text to determine whether each character of the plurality of characters comprises the at least one diacritical mark. | 0.633766 |
8,122,001 | 6 | 7 | 6. A non-transitory computer readable storage medium having computer executable instructions which are executed by a management computer coupled to at least one client computer and a plurality of search engines coupled to the client computer via a network, and which control the management computer to execute the steps of: collecting logs of access from the client computer; specifying a parameter transferred from the client computer to an access destination of the client computer by analyzing the collected logs of access; judging whether the specified parameter is a search query; judging that an access including the parameter judged as the search query is an access to a given search engine of the plurality of search engines; selecting a log of access to each of the plurality of search engines from the collected logs of access; extracting an address of the search engine and a search query from the selected log of access to the search engine; and storing, for each of the plurality of search engines, a correspondence between the extracted address of the search engine and the extracted search query in a search engine profile, wherein the program medium further controls the management computer to execute the steps of: receiving a search request of a search engine containing a search query from the client computer; specifying an address of the search engine corresponding to the search query contained in the received search request by referring to the search engine profile; and transmitting the specified address of the search engine to the client computer as a search result. | 6. A non-transitory computer readable storage medium having computer executable instructions which are executed by a management computer coupled to at least one client computer and a plurality of search engines coupled to the client computer via a network, and which control the management computer to execute the steps of: collecting logs of access from the client computer; specifying a parameter transferred from the client computer to an access destination of the client computer by analyzing the collected logs of access; judging whether the specified parameter is a search query; judging that an access including the parameter judged as the search query is an access to a given search engine of the plurality of search engines; selecting a log of access to each of the plurality of search engines from the collected logs of access; extracting an address of the search engine and a search query from the selected log of access to the search engine; and storing, for each of the plurality of search engines, a correspondence between the extracted address of the search engine and the extracted search query in a search engine profile, wherein the program medium further controls the management computer to execute the steps of: receiving a search request of a search engine containing a search query from the client computer; specifying an address of the search engine corresponding to the search query contained in the received search request by referring to the search engine profile; and transmitting the specified address of the search engine to the client computer as a search result. 7. The non-transitory computer readable storage medium according to claim 6 , wherein the step of judging whether the specified parameter is a search query includes judging based on a number of kinds, which is a number of values that do not overlap one another among values contained in the specified parameter. | 0.818976 |
9,229,800 | 11 | 15 | 11. One or more computer-readable storage devices or memory devices comprising device-readable instructions which, when executed by one or more processing devices, cause the one or more processing devices to perform acts comprising: identifying phrases in support tickets, wherein the support tickets include text describing troubleshooting steps taken by one or more network engineers to resolve one or more network problems; mapping the phrases in the support tickets to specific phrase classes, the specific phrase classes including an entity phrase class for phrases identifying network devices and an action phrase class for phrases identifying actions taken on the network devices; identifying sequences of the entity phrase class and the action phrase class in the text of the support tickets; inferring that the actions were taken on the network devices based on the identified sequences of the entity phrase class and the action phrase class in the text of the support tickets; and providing a user interface with representations of the actions that were taken on the network devices. | 11. One or more computer-readable storage devices or memory devices comprising device-readable instructions which, when executed by one or more processing devices, cause the one or more processing devices to perform acts comprising: identifying phrases in support tickets, wherein the support tickets include text describing troubleshooting steps taken by one or more network engineers to resolve one or more network problems; mapping the phrases in the support tickets to specific phrase classes, the specific phrase classes including an entity phrase class for phrases identifying network devices and an action phrase class for phrases identifying actions taken on the network devices; identifying sequences of the entity phrase class and the action phrase class in the text of the support tickets; inferring that the actions were taken on the network devices based on the identified sequences of the entity phrase class and the action phrase class in the text of the support tickets; and providing a user interface with representations of the actions that were taken on the network devices. 15. The one or more computer-readable storage devices or memory devices according to claim 11 , the acts further comprising: receiving, via the user interface, an input specifying a particular device model of the network devices and inferring that the actions were taken for the network devices of the particular device model. | 0.697588 |
9,916,286 | 9 | 15 | 9. A method for reformatting a set of target paragraphs with a format pattern of a set of sample paragraphs, the method comprising: generating a sample combination by dividing the set of sample paragraphs into sample groups according to their order and format pattern, each sample group includes adjacent sample paragraphs of the same format pattern; determining a plurality of all possible combinations of the set of target paragraphs and the sample groups, wherein each of the target paragraphs is assigned to only one sample group for each possible combination; calculating a degree of similarity between the sample combination and each of the possible combinations, the degree of similarity is the sum of the number of operations of removing a target paragraph from a sample group for each of the possible combinations and the number of operations of adding a target paragraph to a sample group for each of the possible combinations in order for each of the possible combination to match the sample combination; selecting two or more preferred combinations from the plurality of possible combinations based on the degree of similarity between the sample combination and each of the possible combinations, wherein the two or more preferred combinations comprises the smallest degree of similarity; selecting a matching combination from the two or more preferred candidate combinations, the single matching combination having the same combination of target paragraphs and sample groups as the sample combination; and applying the format pattern of the set of sample paragraphs to the set of target paragraphs based on the single matching combination. | 9. A method for reformatting a set of target paragraphs with a format pattern of a set of sample paragraphs, the method comprising: generating a sample combination by dividing the set of sample paragraphs into sample groups according to their order and format pattern, each sample group includes adjacent sample paragraphs of the same format pattern; determining a plurality of all possible combinations of the set of target paragraphs and the sample groups, wherein each of the target paragraphs is assigned to only one sample group for each possible combination; calculating a degree of similarity between the sample combination and each of the possible combinations, the degree of similarity is the sum of the number of operations of removing a target paragraph from a sample group for each of the possible combinations and the number of operations of adding a target paragraph to a sample group for each of the possible combinations in order for each of the possible combination to match the sample combination; selecting two or more preferred combinations from the plurality of possible combinations based on the degree of similarity between the sample combination and each of the possible combinations, wherein the two or more preferred combinations comprises the smallest degree of similarity; selecting a matching combination from the two or more preferred candidate combinations, the single matching combination having the same combination of target paragraphs and sample groups as the sample combination; and applying the format pattern of the set of sample paragraphs to the set of target paragraphs based on the single matching combination. 15. The method of claim 9 , wherein the possible combinations are determined using the equation: n=(N−1)/[(M−1)!(NM)!], where n is the number of possible combinations. | 0.81236 |
9,619,805 | 1 | 12 | 1. A computer-implemented method for query optimization, the computer-implemented method comprising executing instructions in a computer system to perform the operations of: receiving a request for a fact regarding a product offered for purchase in a product catalog from a client, wherein receiving the request for a fact includes receiving a product identifier associated with the product offered for purchase; in response to receiving the request for the fact regarding the product offered for purchase in the product catalog from the client, generating the fact regarding the product offered for purchase in the product catalog and returning the fact regarding the product offered for purchase in the product catalog to the client in response to the request; determining a probability that the client will request one or more additional facts regarding the product offered for purchase in the product catalog, wherein the probability that the client will request the one or more additional facts is determined at least in part on historical requests data describing a probability that the one or more additional facts will be requested following a request for the fact; determining an estimated cost of generating the one or more additional facts regarding the product offered for purchase in the product catalog, wherein the estimated cost is determined at least in part based upon historical cost data describing an actual historical cost to generate the one or more additional facts, the estimated cost comprising one or more of an estimated time, memory usage, processing capacity, or network bandwidth required to generate the one or more additional facts; speculatively generating the one or more additional facts regarding the product offered for purchase in the product catalog based upon the determined probability and the estimated cost of generating the one or more additional facts regarding the product offered for purchase in the product catalog; storing the speculatively generated one or more additional facts regarding the product offered for purchase in the product catalog for use in responding to a future fact request regarding the product offered for purchase in the product catalog from the client; updating the historical cost data with an actual cost to generate the one or more additional facts; receiving a request from the client for the one or more additional facts; and responding to the request for the one or more additional facts with the one or more additional facts. | 1. A computer-implemented method for query optimization, the computer-implemented method comprising executing instructions in a computer system to perform the operations of: receiving a request for a fact regarding a product offered for purchase in a product catalog from a client, wherein receiving the request for a fact includes receiving a product identifier associated with the product offered for purchase; in response to receiving the request for the fact regarding the product offered for purchase in the product catalog from the client, generating the fact regarding the product offered for purchase in the product catalog and returning the fact regarding the product offered for purchase in the product catalog to the client in response to the request; determining a probability that the client will request one or more additional facts regarding the product offered for purchase in the product catalog, wherein the probability that the client will request the one or more additional facts is determined at least in part on historical requests data describing a probability that the one or more additional facts will be requested following a request for the fact; determining an estimated cost of generating the one or more additional facts regarding the product offered for purchase in the product catalog, wherein the estimated cost is determined at least in part based upon historical cost data describing an actual historical cost to generate the one or more additional facts, the estimated cost comprising one or more of an estimated time, memory usage, processing capacity, or network bandwidth required to generate the one or more additional facts; speculatively generating the one or more additional facts regarding the product offered for purchase in the product catalog based upon the determined probability and the estimated cost of generating the one or more additional facts regarding the product offered for purchase in the product catalog; storing the speculatively generated one or more additional facts regarding the product offered for purchase in the product catalog for use in responding to a future fact request regarding the product offered for purchase in the product catalog from the client; updating the historical cost data with an actual cost to generate the one or more additional facts; receiving a request from the client for the one or more additional facts; and responding to the request for the one or more additional facts with the one or more additional facts. 12. The computer-implemented method as in claim 1 , wherein the request for the one or more additional facts further comprises one or more optimization hints comprising preferences regarding how the one or more additional facts are to be generated. | 0.549091 |
7,693,714 | 1 | 4 | 1. An electronic communication method comprising: transmitting a narrowband speech signal comprising a narrowband version of speech utterances of a speaker; transmitting speaker-dependent data comprising data correlating narrowband versions of the speech utterances of the speaker with corresponding wideband versions of the speech utterances of the speaker; receiving the narrowband speech signal and the speaker-dependent data; and using the narrowband speech signal and the speaker-dependent data to generate a wideband speech signal corresponding to a wideband version of the speech utterances of the speaker. | 1. An electronic communication method comprising: transmitting a narrowband speech signal comprising a narrowband version of speech utterances of a speaker; transmitting speaker-dependent data comprising data correlating narrowband versions of the speech utterances of the speaker with corresponding wideband versions of the speech utterances of the speaker; receiving the narrowband speech signal and the speaker-dependent data; and using the narrowband speech signal and the speaker-dependent data to generate a wideband speech signal corresponding to a wideband version of the speech utterances of the speaker. 4. The electronic communication method of claim 1 , where the transmission of the narrowband speech signal and the transmission of the speaker-dependent data take place over a single transmission channel. | 0.823834 |
7,533,107 | 1 | 10 | 1. A method for integrating a plurality of data sources comprising the steps of: obtaining semantic information from each of the plurality of data sources; creating a conceptual model for each of the plurality of data sources using said semantic information; accessing a secondary knowledge source having information that relates the different data sources; creating an integrated semantic model of the plurality of data sources using said conceptual models and said secondary knowledge source; wherein said semantic information comprises characterization of at least one of constraints that hold for subsets of data in the plurality of data sources and relationships that hold between the data; wherein said semantic information further comprises information expressing properties of the data that have not been explicitly encoded in an alphanumeric representation of the data or in a syntactic structure that holds together different data elements. | 1. A method for integrating a plurality of data sources comprising the steps of: obtaining semantic information from each of the plurality of data sources; creating a conceptual model for each of the plurality of data sources using said semantic information; accessing a secondary knowledge source having information that relates the different data sources; creating an integrated semantic model of the plurality of data sources using said conceptual models and said secondary knowledge source; wherein said semantic information comprises characterization of at least one of constraints that hold for subsets of data in the plurality of data sources and relationships that hold between the data; wherein said semantic information further comprises information expressing properties of the data that have not been explicitly encoded in an alphanumeric representation of the data or in a syntactic structure that holds together different data elements. 10. A method as in claim 1 wherein said integrated semantic model comprises logic statements, and wherein at least one of said logic statements comprises an instance declaration, at least one of said logic statements comprises a subclass declaration, and at least one of said logic statements comprises a method declaration. | 0.656051 |
9,911,407 | 1 | 7 | 1. A method for generating parameters in a speech synthesis system, wherein the system comprises a parameter generation module operatively coupled to a speech synthesis module, using a continuous feature stream, for provided text for use in speech synthesis, comprising the steps of: a) partitioning, by the parameter generation module, said provided text into a sequence of phrases; b) generating, by the parameter generation module, parameters in a continuous approximation for said sequence of phrases using a speech model; and c) processing, by the parameter generation module, the generated parameters to obtain an other set of parameters, wherein said other set of parameters comprise at least one clamped delta value and wherein said other set of parameters are utilized in speech synthesis for provided text by the speech synthesis module. | 1. A method for generating parameters in a speech synthesis system, wherein the system comprises a parameter generation module operatively coupled to a speech synthesis module, using a continuous feature stream, for provided text for use in speech synthesis, comprising the steps of: a) partitioning, by the parameter generation module, said provided text into a sequence of phrases; b) generating, by the parameter generation module, parameters in a continuous approximation for said sequence of phrases using a speech model; and c) processing, by the parameter generation module, the generated parameters to obtain an other set of parameters, wherein said other set of parameters comprise at least one clamped delta value and wherein said other set of parameters are utilized in speech synthesis for provided text by the speech synthesis module. 7. The method of claim 1 , wherein the partitioning of said provided text into a sequence of phrases further comprises the steps of: a) generating a vector based on predicted parameters, wherein said predicted parameters are determined as parameters that represent the text; b) determining a frame increment value; and c) determining state of a phrase, wherein i) if the phrase has started, determining if voicing has started and 1) if voicing has started, adjusting the vector based on parameters of voiced phonemes and restarting step (c); otherwise, 2) if voicing has ended, adjusting the vector based on parameters of unvoiced phonemes and restarting from step (c); ii) if the phrase has ended, smoothing the vector and performing a global variance adjustment. | 0.597471 |
9,436,741 | 13 | 14 | 13. A computer-readable, non-transitory storage medium storing computer executable instructions that, when executed by one or more computer systems, configure the one or more computer systems to perform operations comprising: receiving information identifying a plurality of stories of interest to a user; identifying a plurality of referents that are referenced in content of one or more of the plurality of stories of interest to the user; determining, from among the plurality of referents, a referent of potential interest to the user, wherein the referent of potential interest is determined based at least in part on a count of how many of the plurality of stories of interest to the user are associated with the referent of potential interest, wherein the referent of potential interest is determined based at least in part by identifying that the referent of potential interest is referenced in at least a portion of content of each of two or more of the plurality of stories of interest to the user; and determining a recommended story for the user, wherein the recommended story is determined based at least in part on a determination that the recommended story is associated with the referent of potential interest to the user. | 13. A computer-readable, non-transitory storage medium storing computer executable instructions that, when executed by one or more computer systems, configure the one or more computer systems to perform operations comprising: receiving information identifying a plurality of stories of interest to a user; identifying a plurality of referents that are referenced in content of one or more of the plurality of stories of interest to the user; determining, from among the plurality of referents, a referent of potential interest to the user, wherein the referent of potential interest is determined based at least in part on a count of how many of the plurality of stories of interest to the user are associated with the referent of potential interest, wherein the referent of potential interest is determined based at least in part by identifying that the referent of potential interest is referenced in at least a portion of content of each of two or more of the plurality of stories of interest to the user; and determining a recommended story for the user, wherein the recommended story is determined based at least in part on a determination that the recommended story is associated with the referent of potential interest to the user. 14. The computer-readable, non-transitory storage medium of claim 13 , wherein determining the recommended story for the user comprises determining a score for each of a plurality of stories based at least in part on a number of associations between each story and the referent of potential interest. | 0.5 |
7,974,938 | 11 | 20 | 11. A system for storing and using a set of observations of real-world events and actions, the system comprising: a cluster generator for generating clusters based on the set of observations, each cluster representing correlations between events and actions in a subset of observations in the set of observations; and a graph processor coupled to the cluster generator for processing the clusters into a first linked graph based on a set of rules to identify correlations between the events and actions, the first linked graph tagged to indicate the correlations between the events and the actions; wherein the cluster generator presupposes an event as reversible unless an observation contradicting reversibility of the event is obtained, and wherein the graph processor tags an edge associated with the irreversible event as having uncertain correlations with actions that are irreversible. | 11. A system for storing and using a set of observations of real-world events and actions, the system comprising: a cluster generator for generating clusters based on the set of observations, each cluster representing correlations between events and actions in a subset of observations in the set of observations; and a graph processor coupled to the cluster generator for processing the clusters into a first linked graph based on a set of rules to identify correlations between the events and actions, the first linked graph tagged to indicate the correlations between the events and the actions; wherein the cluster generator presupposes an event as reversible unless an observation contradicting reversibility of the event is obtained, and wherein the graph processor tags an edge associated with the irreversible event as having uncertain correlations with actions that are irreversible. 20. The system of claim 11 , wherein the subset of observations include one or two observations of the events and actions. | 0.873444 |
7,805,441 | 7 | 10 | 7. A system comprising: a server, comprising a processor and memory, for: parsing an original search query received from a user over a network through a query web page to obtain at least one query term; retrieving a plurality of keywords from a database related contextually to a category of said query web page and said at least one query term; generating a set of modified queries, each modified query further comprising said at least one query term and at least one keyword of said plurality of keywords; retrieving a plurality of advertising offers for each modified query and for said original query; removing, in a computer, any advertising offer that is not related contextually to said category of said query web page; and ranking, in a computer, the advertising offers not removed based on at least one predetermined parameter. | 7. A system comprising: a server, comprising a processor and memory, for: parsing an original search query received from a user over a network through a query web page to obtain at least one query term; retrieving a plurality of keywords from a database related contextually to a category of said query web page and said at least one query term; generating a set of modified queries, each modified query further comprising said at least one query term and at least one keyword of said plurality of keywords; retrieving a plurality of advertising offers for each modified query and for said original query; removing, in a computer, any advertising offer that is not related contextually to said category of said query web page; and ranking, in a computer, the advertising offers not removed based on at least one predetermined parameter. 10. The system according to claim 7 , wherein: said at least one predetermined parameter further comprises a “pay-per-click” (PPC) categorization parameter. | 0.786301 |
9,922,640 | 16 | 18 | 16. A system comprising: a memory for storing computer-readable instructions associated with speech detection; and a processor programmed to execute the computer-readable instructions to enable the operation of the speech detection, wherein when the computer-readable instructions are executed, the speech detection is programmed to: a) receive a continuous stream of input; b) generate at least one feature from the continuous stream of input; c) generate a plurality of continuous stream segments using the at least one feature; d) process the continuous stream segments using characteristics of the continuous stream to create a plurality of candidate segments; e) receive a discrete set of inputs, wherein the discrete set of inputs comprise at least one multimodal input from a user interface, wherein each input of the discrete set is associated with a discrete timing information; f) separate the candidate segments into at least one separated output, wherein the speech detection is programmed to separate the candidate segments into separated output by correlating at least one stream timing information associated with the candidate segments with at least one discrete timing information; and g) output the separated output, wherein the separated output represents a detected speech utterance, thereby selecting the detected speech utterance based on discrete timing information that is non-speech data. | 16. A system comprising: a memory for storing computer-readable instructions associated with speech detection; and a processor programmed to execute the computer-readable instructions to enable the operation of the speech detection, wherein when the computer-readable instructions are executed, the speech detection is programmed to: a) receive a continuous stream of input; b) generate at least one feature from the continuous stream of input; c) generate a plurality of continuous stream segments using the at least one feature; d) process the continuous stream segments using characteristics of the continuous stream to create a plurality of candidate segments; e) receive a discrete set of inputs, wherein the discrete set of inputs comprise at least one multimodal input from a user interface, wherein each input of the discrete set is associated with a discrete timing information; f) separate the candidate segments into at least one separated output, wherein the speech detection is programmed to separate the candidate segments into separated output by correlating at least one stream timing information associated with the candidate segments with at least one discrete timing information; and g) output the separated output, wherein the separated output represents a detected speech utterance, thereby selecting the detected speech utterance based on discrete timing information that is non-speech data. 18. The system of claim 16 , wherein the continuous stream of input includes a recording by an eye-tracking system of at least one object viewed by a user's eye. | 0.849813 |
9,177,320 | 1 | 5 | 1. A method to generate a data file related to a particular entity, the method comprising: performing a first search, by a processor, with use of a first search term related to the particular entity, wherein performance of the first search includes sending the first search term over a network; receiving, by the processor, first results from the first search, wherein the first results relate to the particular entity and wherein the first results include first data in a first structure; parsing, by the processor, the first data from the first structure to produce first pieces of unstructured data; receiving, by the processor, second results from the first search, wherein the second results relate to the particular entity and wherein the second results include second data in a second structure, wherein the second structure is different from the first structure; parsing, by the processor, the second data from the second structure to produce second pieces of unstructured data; identifying matching pieces of data from among the first and second pieces of unstructured data; combining the matching pieces of data to form combined data in a third structure different from the first structure and the second structure, wherein the third structure relates to the particular entity as opposed to other entities, and wherein the combined data in the third structure relates to the particular entity; storing the combined data in the third structure in a memory; receiving a second search query related to the entity; performing the second search of the combined data in the third structure, by the processor, with use of the second search query related to the entity; producing a data file by the processor based on the results of the second search, wherein the data file includes at least some of the first and second pieces of unstructured data; and storing the data file in the memory. | 1. A method to generate a data file related to a particular entity, the method comprising: performing a first search, by a processor, with use of a first search term related to the particular entity, wherein performance of the first search includes sending the first search term over a network; receiving, by the processor, first results from the first search, wherein the first results relate to the particular entity and wherein the first results include first data in a first structure; parsing, by the processor, the first data from the first structure to produce first pieces of unstructured data; receiving, by the processor, second results from the first search, wherein the second results relate to the particular entity and wherein the second results include second data in a second structure, wherein the second structure is different from the first structure; parsing, by the processor, the second data from the second structure to produce second pieces of unstructured data; identifying matching pieces of data from among the first and second pieces of unstructured data; combining the matching pieces of data to form combined data in a third structure different from the first structure and the second structure, wherein the third structure relates to the particular entity as opposed to other entities, and wherein the combined data in the third structure relates to the particular entity; storing the combined data in the third structure in a memory; receiving a second search query related to the entity; performing the second search of the combined data in the third structure, by the processor, with use of the second search query related to the entity; producing a data file by the processor based on the results of the second search, wherein the data file includes at least some of the first and second pieces of unstructured data; and storing the data file in the memory. 5. The method of claim 1 , wherein the third structure includes a database related to the particular entity. | 0.946322 |
9,020,924 | 4 | 5 | 4. The method of claim 1 , wherein identifying the plurality of second sequences of terms comprises: generating an expansion/contraction table that includes pairs of sequences of terms having highest respective similarity measures; and identifying a plurality of pairs of sequences of terms that include the first sequence of terms in the expansion/contraction table. | 4. The method of claim 1 , wherein identifying the plurality of second sequences of terms comprises: generating an expansion/contraction table that includes pairs of sequences of terms having highest respective similarity measures; and identifying a plurality of pairs of sequences of terms that include the first sequence of terms in the expansion/contraction table. 5. The method of claim 4 , wherein generating the expansion/contraction table comprises: determining a plurality of frequently occurring word sequences; and filtering out non-phrasal word sequences from the frequently occurring word sequences, wherein a non-phrasal word sequence is a word sequence that does not occur at a beginning or an end of at least a threshold number of queries in a collection of queries. | 0.5 |
7,770,111 | 9 | 10 | 9. The computer-readable storage medium of claim 8 , further comprising instructions for caching formatting information for the paragraph for utilization during the line-by-line layout of the paragraph. | 9. The computer-readable storage medium of claim 8 , further comprising instructions for caching formatting information for the paragraph for utilization during the line-by-line layout of the paragraph. 10. The computer-readable storage medium of claim 9 , wherein generating the at least one layout node comprises: identifying the at least one layout node in the node pool as the current node; and creating at least one new layout node from the current layout node by appending each possible variant of a next line of the text to the current layout node. | 0.5 |
8,542,950 | 1 | 12 | 1. A method comprising: receiving, at a computing device, a plurality of candidate images; using, via the computing device, a learned probabilistic composition model to divide each candidate image in the plurality of candidate images into a most probable rectangular object region and a background region, wherein the most probable rectangular object region has a maximal composition score from possible composition scores computed according to the composition model for possible divisions of the candidate image into object and background regions, each possible composition score is based upon at least one image feature cue computed over the object and background regions, and the composition model is trained on a set of images independent of the plurality of candidate images; ranking, via the computing device, the plurality of candidate images according to the maximal composition score of the most probable rectangular object region of each image determined using the learned probabilistic composition model; removing, via the computing device, non-discriminative images from the plurality of candidate images; clustering, via the computing device, a plurality of highest-ranked images from the plurality of candidate images ranked according to the maximal composition score of the most probable rectangular object region of each image determined using the learned probabilistic composition model to form a plurality of clusters, wherein each cluster includes a plurality of images selected from the plurality of highest-ranked images and having similar object regions according to a feature match score; selecting, via the computing device, a representative image from each cluster as an iconic image representative of an object category; and causing, via the computing device, display of the iconic image. | 1. A method comprising: receiving, at a computing device, a plurality of candidate images; using, via the computing device, a learned probabilistic composition model to divide each candidate image in the plurality of candidate images into a most probable rectangular object region and a background region, wherein the most probable rectangular object region has a maximal composition score from possible composition scores computed according to the composition model for possible divisions of the candidate image into object and background regions, each possible composition score is based upon at least one image feature cue computed over the object and background regions, and the composition model is trained on a set of images independent of the plurality of candidate images; ranking, via the computing device, the plurality of candidate images according to the maximal composition score of the most probable rectangular object region of each image determined using the learned probabilistic composition model; removing, via the computing device, non-discriminative images from the plurality of candidate images; clustering, via the computing device, a plurality of highest-ranked images from the plurality of candidate images ranked according to the maximal composition score of the most probable rectangular object region of each image determined using the learned probabilistic composition model to form a plurality of clusters, wherein each cluster includes a plurality of images selected from the plurality of highest-ranked images and having similar object regions according to a feature match score; selecting, via the computing device, a representative image from each cluster as an iconic image representative of an object category; and causing, via the computing device, display of the iconic image. 12. The method of claim 1 , wherein clustering comprises using a k-medoids clustering method based upon geometric blur features computed within the object regions to find similar-looking object regions from the plurality of highest-ranked images, and selecting the representative image from each cluster comprises selecting the medoid of the cluster. | 0.705882 |
8,813,046 | 8 | 9 | 8. A non-transitory computer readable storage medium having stored thereon data representing sequences of instructions, which when executed by at least one computing device, cause the at least one computing device to: analyze at least one source code file that processes character text data in an original format to determine compliance with a target locale neutral encoding format, the target locale neutral encoding format being selected from a plurality of locale neutral encoding formats based on a first estimation of the source code file's compliance with at least one of the plurality of locale neutral encoding formats and a second estimation of encoding conversions required to achieve compliance with the at least one of the plurality of locale neutral encoding formats; and transform the source code into a transformed source code that is capable of processing character text data in the target locale encoding format. | 8. A non-transitory computer readable storage medium having stored thereon data representing sequences of instructions, which when executed by at least one computing device, cause the at least one computing device to: analyze at least one source code file that processes character text data in an original format to determine compliance with a target locale neutral encoding format, the target locale neutral encoding format being selected from a plurality of locale neutral encoding formats based on a first estimation of the source code file's compliance with at least one of the plurality of locale neutral encoding formats and a second estimation of encoding conversions required to achieve compliance with the at least one of the plurality of locale neutral encoding formats; and transform the source code into a transformed source code that is capable of processing character text data in the target locale encoding format. 9. The computer readable medium of claim 8 , wherein the target locale neutral encoding format is a Unicode format. | 0.711055 |
9,336,457 | 1 | 3 | 1. A method for adaptive region prediction, comprising: receiving, by a processor, a set of exemplar images including annotated first landmarks; obtaining, by the processor, user definitions of first anatomical regions in the exemplar images; detecting, by the processor, second landmarks in a subject image; computing, by the processor, anatomical similarity scores between the subject image and the exemplar images based on the first and second landmarks; and predicting, by the processor, a second anatomical region in the subject image by adaptively combining coordinates of points representing the first anatomical regions weighted by coefficients derived from the anatomical similarity scores. | 1. A method for adaptive region prediction, comprising: receiving, by a processor, a set of exemplar images including annotated first landmarks; obtaining, by the processor, user definitions of first anatomical regions in the exemplar images; detecting, by the processor, second landmarks in a subject image; computing, by the processor, anatomical similarity scores between the subject image and the exemplar images based on the first and second landmarks; and predicting, by the processor, a second anatomical region in the subject image by adaptively combining coordinates of points representing the first anatomical regions weighted by coefficients derived from the anatomical similarity scores. 3. The method of claim 1 further comprising: annotating a set of the first landmarks in each exemplar image; training detectors for the annotated first landmarks, and using the trained detectors to automatically detect the second landmarks in the subject image. | 0.670455 |
7,840,076 | 1 | 8 | 1. A method comprising: receiving input that identifies an example image for use in querying a collection of digital images; using local and global feature descriptors to automatically rank the collection of digital images with respect to similarity to the example image, wherein each local feature descriptor represents a portion of an image based on a division of the image into multiple portions, and wherein each global feature descriptor represents an image as a whole; using a final classifier and multiple different intermediate classifiers to perform the automatic ranking, wherein: the different intermediate classifiers generate intermediate relevance metrics with respect to different modalities; the final classifier blends results from the intermediate classifiers into a final relevance metric to be used for displaying images in ranked order; after generating the final relevance metric, receiving input identifying a second example image for use in querying the collection of digital images; automatically determining at least one new intermediate classifier, based at least in part on the example image; automatically determining a new final classifier, based at least in part on the example image; and using the new intermediate classifier and the new final classifier to automatically re-rank the collection of digital images with respect to similarity to the example image. | 1. A method comprising: receiving input that identifies an example image for use in querying a collection of digital images; using local and global feature descriptors to automatically rank the collection of digital images with respect to similarity to the example image, wherein each local feature descriptor represents a portion of an image based on a division of the image into multiple portions, and wherein each global feature descriptor represents an image as a whole; using a final classifier and multiple different intermediate classifiers to perform the automatic ranking, wherein: the different intermediate classifiers generate intermediate relevance metrics with respect to different modalities; the final classifier blends results from the intermediate classifiers into a final relevance metric to be used for displaying images in ranked order; after generating the final relevance metric, receiving input identifying a second example image for use in querying the collection of digital images; automatically determining at least one new intermediate classifier, based at least in part on the example image; automatically determining a new final classifier, based at least in part on the example image; and using the new intermediate classifier and the new final classifier to automatically re-rank the collection of digital images with respect to similarity to the example image. 8. A method according to claim 1 , further comprising: automatically computing at least one set of feature descriptors for digital images in the collection dynamically, after receiving the input that identifies the example image. | 0.826778 |
8,872,677 | 27 | 28 | 27. The apparatus of claim 24 , wherein the at least one processor is further operative to: define a block of dictionary data symbols; select every M th symbol in the defined block of dictionary data symbols and identifying its corresponding location in the dictionary; calculate a hash value HV M for each selected M th symbol; and store the calculated hash value HV M and the corresponding dictionary location as an entry in the dictionary-index, wherein M≠N. | 27. The apparatus of claim 24 , wherein the at least one processor is further operative to: define a block of dictionary data symbols; select every M th symbol in the defined block of dictionary data symbols and identifying its corresponding location in the dictionary; calculate a hash value HV M for each selected M th symbol; and store the calculated hash value HV M and the corresponding dictionary location as an entry in the dictionary-index, wherein M≠N. 28. The apparatus of claim 27 , wherein N and M do not have a common factor value. | 0.5 |
9,720,907 | 1 | 3 | 1. A method comprising: analyzing a first natural language corpus to generate a latent representation for words in the first natural language corpus; calculating, for each word in the latent representation, a Euclidian distance between a left context of the each word and a right context of the each word, to yield a centroid of latent vectors for each word in the latent representation; analyzing a second natural language corpus having a target word, the target word being a word that is not in the first natural language corpus; and predicting, via a processor, a label for the target word based on the latent representation and the centroid of latent vectors for each word in the latent representation. | 1. A method comprising: analyzing a first natural language corpus to generate a latent representation for words in the first natural language corpus; calculating, for each word in the latent representation, a Euclidian distance between a left context of the each word and a right context of the each word, to yield a centroid of latent vectors for each word in the latent representation; analyzing a second natural language corpus having a target word, the target word being a word that is not in the first natural language corpus; and predicting, via a processor, a label for the target word based on the latent representation and the centroid of latent vectors for each word in the latent representation. 3. The method of claim 1 , wherein predicting the label for the target word is further based on a connectionist model. | 0.740088 |
10,102,278 | 16 | 20 | 16. A computerized method for providing recommendations from a recommendation engine using an implicit profile of a user which implicit profile is continuously modifiable, comprising: providing the user with access to the recommendation engine for providing recommended items responsive to a user inquiry on a specific topic and peers with expertise pertaining to the specific topic; storing an explicit profile and the implicit profile of the user in a profile database associated with the recommendation engine; providing the user with electronic access to the recommended items in response to the user inquiry, the recommended items comprising at least one of an interactive info-graphics chart or a document containing an interactive info-graphics chart, the recommended items being maintained in at least one of a private recommendation engine database and a remote server in communication with the recommendation engine; enabling user interaction with the info-graphics chart; electronically tracking and storing details of the user interaction with the info-graphics chart; prioritizing the details of the user interaction based on at least one of recency of the user interaction, significance of the user interaction, and depth of the user interaction; modifying the implicit profile based on the prioritized details of the user interaction; and serving one or more additional recommended items to the user based on the modified implicit profile, the one or more additional recommended items comprising one of an article, a chart, a graphic, a report, a web page, a peer recommendation, or a different document; wherein: the info-graphics chart comprises an X axis and a Y axis and an interactive graphics display enabling modification of one or more weights assigned to data elements represented in the info-graphics chart; the explicit profile comprises data provided by the user to the recommendation engine; the implicit profile in an unmodified state comprises information obtained from user behavior; the information obtained from the user behavior comprises one or more of key words used in key word searches, articles reviewed, web pages reviewed, articles purchased, discussions reviewed, discussions participated in, peer profiles selected for connection, articles or web pages saved or downloaded, and items clicked on in an info-graphics chart; the user interaction comprises at least modifying of the one or more weights assigned to the data elements in the info-graphics chart via the interactive graphics display; the implicit profile comprises a modifiable attributes field and a modifiable recency field; and the modifying of the implicit profile based on the prioritized details of the user interaction comprises modifying the attributes field to include the details of the user interaction and modifying the recency field to include a time stamp corresponding to a time of the user interaction. | 16. A computerized method for providing recommendations from a recommendation engine using an implicit profile of a user which implicit profile is continuously modifiable, comprising: providing the user with access to the recommendation engine for providing recommended items responsive to a user inquiry on a specific topic and peers with expertise pertaining to the specific topic; storing an explicit profile and the implicit profile of the user in a profile database associated with the recommendation engine; providing the user with electronic access to the recommended items in response to the user inquiry, the recommended items comprising at least one of an interactive info-graphics chart or a document containing an interactive info-graphics chart, the recommended items being maintained in at least one of a private recommendation engine database and a remote server in communication with the recommendation engine; enabling user interaction with the info-graphics chart; electronically tracking and storing details of the user interaction with the info-graphics chart; prioritizing the details of the user interaction based on at least one of recency of the user interaction, significance of the user interaction, and depth of the user interaction; modifying the implicit profile based on the prioritized details of the user interaction; and serving one or more additional recommended items to the user based on the modified implicit profile, the one or more additional recommended items comprising one of an article, a chart, a graphic, a report, a web page, a peer recommendation, or a different document; wherein: the info-graphics chart comprises an X axis and a Y axis and an interactive graphics display enabling modification of one or more weights assigned to data elements represented in the info-graphics chart; the explicit profile comprises data provided by the user to the recommendation engine; the implicit profile in an unmodified state comprises information obtained from user behavior; the information obtained from the user behavior comprises one or more of key words used in key word searches, articles reviewed, web pages reviewed, articles purchased, discussions reviewed, discussions participated in, peer profiles selected for connection, articles or web pages saved or downloaded, and items clicked on in an info-graphics chart; the user interaction comprises at least modifying of the one or more weights assigned to the data elements in the info-graphics chart via the interactive graphics display; the implicit profile comprises a modifiable attributes field and a modifiable recency field; and the modifying of the implicit profile based on the prioritized details of the user interaction comprises modifying the attributes field to include the details of the user interaction and modifying the recency field to include a time stamp corresponding to a time of the user interaction. 20. A method in accordance with claim 16 , wherein the implicit profile is modified at least one of: periodically at defined intervals; after each user interaction; after a defined number of user interactions; and after a number of user interactions supersede a predetermined threshold number of interactions. | 0.567227 |
9,507,824 | 1 | 5 | 1. A method implemented on a computer system, the method comprising, the computer system: for each table of a plurality of database tables and for each column of a plurality of columns within the each table, creating a profile for the each column by accessing and analyzing a subset of values stored in the column; establishing a join graph of nodes, wherein each node represents one of the plurality of database tables; for each pair of a plurality of pairs of a first table and a second table from the plurality of database tables, wherein the first table is different than the second table and wherein no defined relationship exists between the first table and the second table: for each pair of a plurality of pairs of a first column from the first table and a second column from the second table, calculating a joinability score representative of a predicted level of success in performing a join from the first table on the first column to the second table on the second column, wherein the score is determined based upon the profile for the first column and the profile for the second column, and for one pair of the plurality of pairs of the first column from the first table and the second column from the second table, adding, based on the joinability score, a directed edge to the join graph from a node representing the first table to a node representing the second table; receiving a selection of a subset of the plurality of database tables; creating a join tree comprising a subset of edges in the join graph that spans a subset of nodes in the join graph corresponding to the selected subset of the plurality of database tables; extracting a set of joins represented by the subset of edges; and providing the extracted set of joins as a result, wherein creating a profile for the each column comprises: processing the each column to create a set of m observables, with m being a positive integer constant greater than one, wherein each observable is a function of a set of elements in the each column, independent of replications, and including the set of m observables in the profile for the each column, and wherein calculating the joinability score comprises: combining the set of m observables included in the profile for the first column and the set of m observables included in the profile for the second column to create a combined set of m observables, wherein each observable in the combined set of m observables is a function of a set of elements in a union between the first column and the second column, independent of replications, computing an estimated cardinality of a union between the first column and the second column based on the combined set of m observables without creating a union between the first column and the second column, computing an estimated cardinality of an intersection between the first column and the second column by subtracting the estimated cardinality of the union from the sum of an estimated cardinality of the first column and an estimated cardinality of the second column, and dividing the estimated cardinality of the intersection by the estimated cardinality of the first column. | 1. A method implemented on a computer system, the method comprising, the computer system: for each table of a plurality of database tables and for each column of a plurality of columns within the each table, creating a profile for the each column by accessing and analyzing a subset of values stored in the column; establishing a join graph of nodes, wherein each node represents one of the plurality of database tables; for each pair of a plurality of pairs of a first table and a second table from the plurality of database tables, wherein the first table is different than the second table and wherein no defined relationship exists between the first table and the second table: for each pair of a plurality of pairs of a first column from the first table and a second column from the second table, calculating a joinability score representative of a predicted level of success in performing a join from the first table on the first column to the second table on the second column, wherein the score is determined based upon the profile for the first column and the profile for the second column, and for one pair of the plurality of pairs of the first column from the first table and the second column from the second table, adding, based on the joinability score, a directed edge to the join graph from a node representing the first table to a node representing the second table; receiving a selection of a subset of the plurality of database tables; creating a join tree comprising a subset of edges in the join graph that spans a subset of nodes in the join graph corresponding to the selected subset of the plurality of database tables; extracting a set of joins represented by the subset of edges; and providing the extracted set of joins as a result, wherein creating a profile for the each column comprises: processing the each column to create a set of m observables, with m being a positive integer constant greater than one, wherein each observable is a function of a set of elements in the each column, independent of replications, and including the set of m observables in the profile for the each column, and wherein calculating the joinability score comprises: combining the set of m observables included in the profile for the first column and the set of m observables included in the profile for the second column to create a combined set of m observables, wherein each observable in the combined set of m observables is a function of a set of elements in a union between the first column and the second column, independent of replications, computing an estimated cardinality of a union between the first column and the second column based on the combined set of m observables without creating a union between the first column and the second column, computing an estimated cardinality of an intersection between the first column and the second column by subtracting the estimated cardinality of the union from the sum of an estimated cardinality of the first column and an estimated cardinality of the second column, and dividing the estimated cardinality of the intersection by the estimated cardinality of the first column. 5. The method of claim 1 , wherein the profile for the each column further comprises a data type determined by accessing and analyzing a subset of values stored in the column, and wherein calculating the joinability score is performed in response to comparing the data type included in the profile for the first column with the data type included in the profile for the second column and determining that the data type included in the profile for the first column matches the data type included in the profile for the second column. | 0.5 |
8,983,805 | 2 | 8 | 2. The method of claim 1 , wherein the method further comprises: (a) computing a second rewriting rule that specifies: (i) a second part of the old input graph to be replaced, (ii) a part of the old output graph to replace the second part of the old input graph, and (iii) a second interface which is a part common to the second part of the old input graph and the part of the old output graph, the second rewriting rule corresponding to logical operations which are computed based on the old input graph and the old output graph, and (b) testing that the rewriting rule and the second rewriting rule are commutative before applying the rewriting rule to the old output graph. | 2. The method of claim 1 , wherein the method further comprises: (a) computing a second rewriting rule that specifies: (i) a second part of the old input graph to be replaced, (ii) a part of the old output graph to replace the second part of the old input graph, and (iii) a second interface which is a part common to the second part of the old input graph and the part of the old output graph, the second rewriting rule corresponding to logical operations which are computed based on the old input graph and the old output graph, and (b) testing that the rewriting rule and the second rewriting rule are commutative before applying the rewriting rule to the old output graph. 8. The method of claim 2 , wherein the method further comprises testing for 3D compatibility between a volume corresponding to the part of the new input graph and a volume corresponding to the part of the old output graph. | 0.697548 |
8,924,219 | 1 | 2 | 1. A method for a computing device, the method comprising: during a first speech detection mode, the computing device: capturing first audio, detecting first speech in the captured first audio, comparing the detected first speech to a plurality of activation phrases to identify any potential matches based on a first language model, wherein the plurality of activation phrases is associated with a plurality of applications on the computing device such that each application in the plurality of applications is associated with a respective activation phrase in the plurality of activation phrases, and wherein the first language model covers the plurality of activation phrases, and in response to identifying a matching activation phrase within a confidence threshold, invoking the application in the plurality of applications associated with the matching activation phrase and entering a second speech detection mode; and during the second speech detection mode, the computing device: in response to entering the second speech detection mode, reducing the confidence threshold, capturing second audio, detecting second speech in the captured second audio, obtaining a recognition result of the detected second speech based on a second language model, wherein the second language model has a wider coverage than the first language model, determining whether the recognition result is identified within the confidence threshold, and after determining that the recognition result is identified within the confidence threshold, providing the recognition result to the invoked application. | 1. A method for a computing device, the method comprising: during a first speech detection mode, the computing device: capturing first audio, detecting first speech in the captured first audio, comparing the detected first speech to a plurality of activation phrases to identify any potential matches based on a first language model, wherein the plurality of activation phrases is associated with a plurality of applications on the computing device such that each application in the plurality of applications is associated with a respective activation phrase in the plurality of activation phrases, and wherein the first language model covers the plurality of activation phrases, and in response to identifying a matching activation phrase within a confidence threshold, invoking the application in the plurality of applications associated with the matching activation phrase and entering a second speech detection mode; and during the second speech detection mode, the computing device: in response to entering the second speech detection mode, reducing the confidence threshold, capturing second audio, detecting second speech in the captured second audio, obtaining a recognition result of the detected second speech based on a second language model, wherein the second language model has a wider coverage than the first language model, determining whether the recognition result is identified within the confidence threshold, and after determining that the recognition result is identified within the confidence threshold, providing the recognition result to the invoked application. 2. The method of claim 1 , further comprising: the computing device detecting a trigger; and the computing device entering the first speech detection mode in response to the detected trigger. | 0.689935 |
8,898,064 | 12 | 19 | 12. Non-transitory computer-readable media storing computer-executable instructions that, when executed on a processor, cause the processor to perform acts comprising: decoding audio at least in part to determine a number of alternative character strings from the audio; and submitting, one at a time, the alternative character strings as a password until the earlier of authentication or until each of the number of alternative character strings has been submitted; wherein the decoding utilizes a language model, the acts further comprising: identifying a candidate character string of the alternative character strings that, upon submittal, results in authentication; and sending the candidate character string of the alternative character strings over a wireless network and to an entity to allow the entity to train the language model or another language model using the candidate character string of the alternative character strings. | 12. Non-transitory computer-readable media storing computer-executable instructions that, when executed on a processor, cause the processor to perform acts comprising: decoding audio at least in part to determine a number of alternative character strings from the audio; and submitting, one at a time, the alternative character strings as a password until the earlier of authentication or until each of the number of alternative character strings has been submitted; wherein the decoding utilizes a language model, the acts further comprising: identifying a candidate character string of the alternative character strings that, upon submittal, results in authentication; and sending the candidate character string of the alternative character strings over a wireless network and to an entity to allow the entity to train the language model or another language model using the candidate character string of the alternative character strings. 19. Non-transitory computer-readable media as recited in claim 12 , wherein the authentication is effective to grant access to an online account, a wireless network account, a wired network account, a computing device, an application, or a content item. | 0.669713 |
9,875,022 | 1 | 5 | 1. A handwriting input method of an electronic device using a touch pen, the handwriting input method comprising: displaying an execution screen of an application on a touch screen in response to the application being executed; overlapping a handwriting input layer, which is configured for a handwriting input, with the execution screen; in response to a handwriting image being input on the handwriting input layer by the touch pen, determining a data type for recognizing the handwriting image based on an area in which the handwriting image is input among a plurality of areas of the handwriting input layer, and recognizing the handwriting image as the determined data type; and applying the recognized handwriting image to the application differently according to the determined data type of the handwriting image, wherein, the determining comprises: determining the data type for recognizing the handwriting image as a text in response to the handwriting image being input in a first area among the plurality of areas of the handwriting input layer, and determining the data type for recognizing the handwriting image as an image in response to the handwriting image being input in a second area different from the first area among the plurality of areas of the handwriting input layer. | 1. A handwriting input method of an electronic device using a touch pen, the handwriting input method comprising: displaying an execution screen of an application on a touch screen in response to the application being executed; overlapping a handwriting input layer, which is configured for a handwriting input, with the execution screen; in response to a handwriting image being input on the handwriting input layer by the touch pen, determining a data type for recognizing the handwriting image based on an area in which the handwriting image is input among a plurality of areas of the handwriting input layer, and recognizing the handwriting image as the determined data type; and applying the recognized handwriting image to the application differently according to the determined data type of the handwriting image, wherein, the determining comprises: determining the data type for recognizing the handwriting image as a text in response to the handwriting image being input in a first area among the plurality of areas of the handwriting input layer, and determining the data type for recognizing the handwriting image as an image in response to the handwriting image being input in a second area different from the first area among the plurality of areas of the handwriting input layer. 5. The handwriting input method of claim 1 , wherein in response to the area of the handwriting input layer in which the handwriting image has been input corresponding to a number input field in the execution screen, the determining the data type for recognizing the handwriting image further comprises: recognizing the handwriting image as at least one of a number or a numerical formula, and the applying the recognized result of the determined data type to the application comprises: displaying a result calculated according to the recognized at least one number or the numerical formula in the number input field. | 0.601421 |
8,265,936 | 1 | 5 | 1. A method for creating and editing an XML-based speech synthesis document for input to a text-to-speech engine, the method comprising: recording voice utterances of a user reading a pre-selected text; parsing the recorded voice utterances into individual words and periods of silence; generating an XML-based speech synthesis document corresponding to the pre-selected text; recording a synthesized speech output generated by a text-to-speech engine based on the XML-based speech synthesis document, the synthesized speech output comprising an audible rendering of the pre-selected text; parsing the synthesized speech output into individual words and periods of silence; and annotating the XML-based speech synthesis document based upon a comparison of the recorded voice utterances and the recorded synthesized speech output, the annotating comprising inserting at least one of an XML emphasis element, an XML prosody element, or an XML break element into the XML-based speech synthesis document. | 1. A method for creating and editing an XML-based speech synthesis document for input to a text-to-speech engine, the method comprising: recording voice utterances of a user reading a pre-selected text; parsing the recorded voice utterances into individual words and periods of silence; generating an XML-based speech synthesis document corresponding to the pre-selected text; recording a synthesized speech output generated by a text-to-speech engine based on the XML-based speech synthesis document, the synthesized speech output comprising an audible rendering of the pre-selected text; parsing the synthesized speech output into individual words and periods of silence; and annotating the XML-based speech synthesis document based upon a comparison of the recorded voice utterances and the recorded synthesized speech output, the annotating comprising inserting at least one of an XML emphasis element, an XML prosody element, or an XML break element into the XML-based speech synthesis document. 5. The method of claim 1 , further comprising generating a plurality of partial phrase grammars and concatenating sequential segments of the synthesized speech output if the synthesized speech output fails to match the pre-selected text. | 0.794271 |
9,229,973 | 1 | 9 | 1. A method for associating documents and queries, comprising: receiving a first query submitted by a user; retrieving by said first query at least one reference to a document; displaying said at least one reference to a document; tracing at least one action of said user in relation to said at least one reference to a document; storing each of a) said first query as submitted by said user, and b) a relation between said first query and said retrieved at least one reference to a document based on said traced action; using at least in part a second query from any user to identify at least one stored query; and displaying to said any user said at least one reference to a document based on said relation to said at least one identified query; wherein a stored query comprises at least two query terms, and at least one query term comprises an expression containing more than one word. | 1. A method for associating documents and queries, comprising: receiving a first query submitted by a user; retrieving by said first query at least one reference to a document; displaying said at least one reference to a document; tracing at least one action of said user in relation to said at least one reference to a document; storing each of a) said first query as submitted by said user, and b) a relation between said first query and said retrieved at least one reference to a document based on said traced action; using at least in part a second query from any user to identify at least one stored query; and displaying to said any user said at least one reference to a document based on said relation to said at least one identified query; wherein a stored query comprises at least two query terms, and at least one query term comprises an expression containing more than one word. 9. A method according to claim 1 , comprising: after using said second query, receiving a third query; using said third query to identify at least one stored query containing at least one query term of said third query; using said at least one identified query to identify at least one search session to which it belongs; searching said at least one search session for at least one related reference to a document; retrieving said at least one related reference to document associated with said at least one identified query; and displaying said at least one related reference to a document. | 0.5 |
7,885,987 | 1 | 17 | 1. A method for managing a plurality of attributes in association with a plurality of electronic documents and a plurality of attribute types, implemented by a computer system, said method comprising at least one of sequential, non-sequential and sequence-independent steps in the computer system of: (A) providing, in the computer system, a first data storage having a group of a plurality of documents including at least one document; (B) accepting, from an input device, a user's selection of a plurality of attributes to be associated with a single pre-determined attribute type for the at least one document, the attribute type having parent and child attribute types, the selected attributes being predetermined and having different parent attributes, attribute types being predetermined and ordered in a predetermined tree-structure hierarchy; and (C) responsive to the selection of the attributes, automatically tagging, in the first data storage, the documents in the group including the at least one document, with the selected attributes, and with all attributes of all ancestors but not descendants or siblings according to the hierarchy of the selected attributes; and storing, in a second data storage, respective references in association with the selected attributes and the ancestor attributes, for later retrieval of individual documents in the group by searching the ancestor attributes instead of the selected attributes, the respective references uniquely indicating respective individual documents in the first data storage, wherein the at least one document is a data record including a plurality of fields, wherein the attribute and the attribute type are different from the fields in the document and contents of the fields. | 1. A method for managing a plurality of attributes in association with a plurality of electronic documents and a plurality of attribute types, implemented by a computer system, said method comprising at least one of sequential, non-sequential and sequence-independent steps in the computer system of: (A) providing, in the computer system, a first data storage having a group of a plurality of documents including at least one document; (B) accepting, from an input device, a user's selection of a plurality of attributes to be associated with a single pre-determined attribute type for the at least one document, the attribute type having parent and child attribute types, the selected attributes being predetermined and having different parent attributes, attribute types being predetermined and ordered in a predetermined tree-structure hierarchy; and (C) responsive to the selection of the attributes, automatically tagging, in the first data storage, the documents in the group including the at least one document, with the selected attributes, and with all attributes of all ancestors but not descendants or siblings according to the hierarchy of the selected attributes; and storing, in a second data storage, respective references in association with the selected attributes and the ancestor attributes, for later retrieval of individual documents in the group by searching the ancestor attributes instead of the selected attributes, the respective references uniquely indicating respective individual documents in the first data storage, wherein the at least one document is a data record including a plurality of fields, wherein the attribute and the attribute type are different from the fields in the document and contents of the fields. 17. The method of claim 1 , further comprising utilizing the attributes as criteria for at least one of searching, retrieving, reporting, and viewing the at least one document. | 0.744928 |
9,760,566 | 11 | 14 | 11. The computer-readable storage device of claim 10 , further comprising: determining whether the result is to be shared with the at least one second human user; and when determined that the result is to be shared with the at least one second human user, displaying the result associated with performing the agent action to the at least one second human user. | 11. The computer-readable storage device of claim 10 , further comprising: determining whether the result is to be shared with the at least one second human user; and when determined that the result is to be shared with the at least one second human user, displaying the result associated with performing the agent action to the at least one second human user. 14. The computer-readable storage device of claim 11 , wherein determining whether the result is to be shared with the at least one second human user comprises determining whether a prior result associated with performing the agent action has been previously shared with the at least one second human user. | 0.5 |
8,621,376 | 15 | 16 | 15. The method of claim 14 , further comprising: selecting a format for display of the results data by activating a control in the GUI. | 15. The method of claim 14 , further comprising: selecting a format for display of the results data by activating a control in the GUI. 16. The method of claim 15 , wherein the selected format is either an Extensible Markup Language (XML) format. | 0.5 |
8,301,514 | 1 | 3 | 1. A system for using purchase phrases to generate recommendations, the system comprising: a computer system comprising computer hardware, the computer system programmed to implement: a purchase phrase index builder configured to: identify purchase phrases used by a plurality of users of an electronic catalog system to purchase items represented in the electronic catalog system, and build a purchase phrase index by at least associating each of the purchased items with the purchase phrases used to purchase the items; a natural language processor configured to identify one or more similar purchase phrases that are at least similar to a purchase phrase created by a target user; and a purchase phrase recommender configured to generate recommendations for the target user by at least: using at least the identified one or more similar purchase phrases to identify one or more of the purchased items as candidate recommendations from the purchase phrase index, and selecting one or more items from the one or more candidate recommendations to present to the target user as the recommendations. | 1. A system for using purchase phrases to generate recommendations, the system comprising: a computer system comprising computer hardware, the computer system programmed to implement: a purchase phrase index builder configured to: identify purchase phrases used by a plurality of users of an electronic catalog system to purchase items represented in the electronic catalog system, and build a purchase phrase index by at least associating each of the purchased items with the purchase phrases used to purchase the items; a natural language processor configured to identify one or more similar purchase phrases that are at least similar to a purchase phrase created by a target user; and a purchase phrase recommender configured to generate recommendations for the target user by at least: using at least the identified one or more similar purchase phrases to identify one or more of the purchased items as candidate recommendations from the purchase phrase index, and selecting one or more items from the one or more candidate recommendations to present to the target user as the recommendations. 3. The system of claim 1 , wherein the purchase phrase recommender is further configured to generate the recommendations in response to a user adding an item to an electronic shopping cart with the purchase phrase created by the target user. | 0.5 |
8,291,468 | 7 | 11 | 7. A method comprising: receiving, with a network security device, network traffic from an endpoint device; in response to receiving the network traffic, requesting, with the network security device, authorization information from an intermediate storage device in accordance with Interface for Metadata Access Point (IF-MAP) protocol, wherein the intermediate storage device stores the authorization information in accordance with a first IF-MAP authorization data model that complies with an IF-MAP standard; receiving, with the network security device, the authorization information from the intermediate storage device; applying, with the network security device, an import translation policy to translate the authorization information stored in accordance the first IF-MAP authorization data model into translated authorization information that complies with a second proprietary authorization data model, wherein the second IF-MAP authorization data model is different from the first proprietary authorization data model; and forwarding, with the network security device, the network traffic based on the translated authorization information. | 7. A method comprising: receiving, with a network security device, network traffic from an endpoint device; in response to receiving the network traffic, requesting, with the network security device, authorization information from an intermediate storage device in accordance with Interface for Metadata Access Point (IF-MAP) protocol, wherein the intermediate storage device stores the authorization information in accordance with a first IF-MAP authorization data model that complies with an IF-MAP standard; receiving, with the network security device, the authorization information from the intermediate storage device; applying, with the network security device, an import translation policy to translate the authorization information stored in accordance the first IF-MAP authorization data model into translated authorization information that complies with a second proprietary authorization data model, wherein the second IF-MAP authorization data model is different from the first proprietary authorization data model; and forwarding, with the network security device, the network traffic based on the translated authorization information. 11. The method of claim 7 , wherein requesting the authorization information comprises: generating a search IF-MAP message that requests the authorization information corresponding to the endpoint device; and forwarding the search IF-MAP message to the intermediate storage device that implements the IF-MAP data model. | 0.843627 |
10,042,880 | 5 | 6 | 5. The computing system of claim 4 , the memory further comprises instructions that further configure the computing system to: determine a third plurality of features from the second block, the third plurality of features relating, at least in part, to the second title portion; provide the third plurality of features to a second classifier, the second classifier to identify whether the second block is likely to be where the hypothetical person would begin reading the first electronic document based on the second title portion; and determine, based on a third score output by the second classifier in response to the third plurality of features, that the second block is not likely to be where the hypothetical person would begin reading the first electronic document, wherein the third score is determined prior to the second plurality of features being provided to the first classifier. | 5. The computing system of claim 4 , the memory further comprises instructions that further configure the computing system to: determine a third plurality of features from the second block, the third plurality of features relating, at least in part, to the second title portion; provide the third plurality of features to a second classifier, the second classifier to identify whether the second block is likely to be where the hypothetical person would begin reading the first electronic document based on the second title portion; and determine, based on a third score output by the second classifier in response to the third plurality of features, that the second block is not likely to be where the hypothetical person would begin reading the first electronic document, wherein the third score is determined prior to the second plurality of features being provided to the first classifier. 6. The computing system of claim 5 , the memory further comprises instructions that further configure the computing system to: process each document in a training set of documents to determine training blocks, each training block constituting a logical entity within the training set; categorize a portion of each training block as being a title portion of the training block; determine a frequency of occurrence of each words appearing in title portions of the training blocks; rank the words based on their frequency of occurrence; and select a set of the words based on their ranking, wherein the instructions to determine the third plurality of features further configure the computing device to: determine which of the words in the set occur in the second title portion and which of the words in the set do not occur in the second title portion, wherein the third plurality of features include an indication of how many of the words in the set do occur in the second title portion, and how many of the words in the set do not occur in the second title portion. | 0.553646 |
9,378,755 | 11 | 14 | 11. An electronic device comprising: at least one microphone; and a Voice Activity Detector (VAD) to receive an acoustic signal from the at least one microphone and to determine whether a user's voice activity is detected in the acoustic signal, wherein the VAD includes: a speech features model generator to generate probabilistic models that respectively model features of speech dynamically over time, wherein the probabilistic models model each feature dependent on a past feature and a current state, wherein the features of speech include a nonstationary signal presence feature, a periodicity feature, and a sparsity feature, a noise suppressor to perform noise suppression on the acoustic signal to generate a nonstationary signal presence signal and a noise suppressed acoustic signal; a Linear Predictive Coding (LPC) module to perform residual analysis on the noise suppressed data signal to generate a periodicity signal and a sparsity signal; and an inference generator to receive the probabilistic models, to receive, in real-time, the nonstationary signal presence signal, the periodicity signal, and the sparsity signal, and to generate in real time an estimate of voice activity based on the probabilistic models, the nonstationary signal presence signal, the periodicity signal, and the sparsity signal. | 11. An electronic device comprising: at least one microphone; and a Voice Activity Detector (VAD) to receive an acoustic signal from the at least one microphone and to determine whether a user's voice activity is detected in the acoustic signal, wherein the VAD includes: a speech features model generator to generate probabilistic models that respectively model features of speech dynamically over time, wherein the probabilistic models model each feature dependent on a past feature and a current state, wherein the features of speech include a nonstationary signal presence feature, a periodicity feature, and a sparsity feature, a noise suppressor to perform noise suppression on the acoustic signal to generate a nonstationary signal presence signal and a noise suppressed acoustic signal; a Linear Predictive Coding (LPC) module to perform residual analysis on the noise suppressed data signal to generate a periodicity signal and a sparsity signal; and an inference generator to receive the probabilistic models, to receive, in real-time, the nonstationary signal presence signal, the periodicity signal, and the sparsity signal, and to generate in real time an estimate of voice activity based on the probabilistic models, the nonstationary signal presence signal, the periodicity signal, and the sparsity signal. 14. The electronic device in claim 11 , wherein the speech features model generator generates probabilistic models by applying a Dynamic Bayesian Network model at feature layers that models evolution of features jointly over time. | 0.673295 |
7,477,909 | 17 | 19 | 17. The method of claim 12 wherein said query is selected from a list of available queries on said wireless mobile device. | 17. The method of claim 12 wherein said query is selected from a list of available queries on said wireless mobile device. 19. The method of claim 17 wherein said list of available queries is generated based on the location of said wireless mobile device. | 0.574194 |
7,912,795 | 14 | 16 | 14. A non-transitory, tangible computer readable medium having stored thereon computer executable instructions that, if executed by a processor-based system for predicting a future event associated with a business based on historical data associated with a model of past events of the business, cause the processor-based system to perform operations comprising: performing, by the processor-based system, a transformation of a plurality of modeling variables to obtain a linear relationship of each of the plurality of modeling variables in relation to the dependent variable, wherein the plurality of modeling variables are associated with the model, and wherein a dependent variable is associated with the model and is dependent on the plurality of modeling variables, and wherein the historical data is associated with the model; selecting, by the processor-based system, a subset of the plurality of transformed modeling variables, wherein the selecting comprises applying a selecting rule based on a log-likelihood difference that comprises determining a difference between a first model-fit statistic derived by utilizing an intercept model and a second model-fit statistic derived by utilizing an intercept-plus-covariate model; determining, by the processor-based system, a set of prediction variables; and generating, by the processor-based system, a predictive model using the set of prediction variables, wherein the predictive model predicts the future event. | 14. A non-transitory, tangible computer readable medium having stored thereon computer executable instructions that, if executed by a processor-based system for predicting a future event associated with a business based on historical data associated with a model of past events of the business, cause the processor-based system to perform operations comprising: performing, by the processor-based system, a transformation of a plurality of modeling variables to obtain a linear relationship of each of the plurality of modeling variables in relation to the dependent variable, wherein the plurality of modeling variables are associated with the model, and wherein a dependent variable is associated with the model and is dependent on the plurality of modeling variables, and wherein the historical data is associated with the model; selecting, by the processor-based system, a subset of the plurality of transformed modeling variables, wherein the selecting comprises applying a selecting rule based on a log-likelihood difference that comprises determining a difference between a first model-fit statistic derived by utilizing an intercept model and a second model-fit statistic derived by utilizing an intercept-plus-covariate model; determining, by the processor-based system, a set of prediction variables; and generating, by the processor-based system, a predictive model using the set of prediction variables, wherein the predictive model predicts the future event. 16. The medium of claim 14 , wherein the transformation of the plurality of modeling variables is based on at least one of a correlation between a modeling variable of the plurality of modeling variables and the dependent variable, a proportion of range of the modeling variable utilized, and a proportion of range of the dependent variable that is explained by the modeling variable. | 0.5 |
8,543,532 | 18 | 19 | 18. A computer-readable storage medium of claim 15 , wherein the apparatus is caused to further perform: calculating a first appearance frequency value of each of the tags relative to an entirety of each of the plurality of information sources; calculating a second appearance frequency value of each of the tags relative to one or more pages of each of the plurality of information sources in which the tag appears; normalizing the first and second appearance frequency values of each tag to be in a predetermined range; and forming a metric for each of the tags with the first and second appearance frequency values as corresponding fields in the metric, wherein the probabilistic analysis includes, at least in part, analysis of the metric. | 18. A computer-readable storage medium of claim 15 , wherein the apparatus is caused to further perform: calculating a first appearance frequency value of each of the tags relative to an entirety of each of the plurality of information sources; calculating a second appearance frequency value of each of the tags relative to one or more pages of each of the plurality of information sources in which the tag appears; normalizing the first and second appearance frequency values of each tag to be in a predetermined range; and forming a metric for each of the tags with the first and second appearance frequency values as corresponding fields in the metric, wherein the probabilistic analysis includes, at least in part, analysis of the metric. 19. A computer-readable storage medium of claim 18 , wherein the apparatus is caused to further perform: calculating a correlation value between each of the one or more categories selected for inclusion in the model of the user's expertise and each of the tags extracted from the information sources; obtaining an overall correlation for each of the one or more categories by adding together the correlation values corresponding to each of the respective one or more categories; computing a weighting factor for the each of the one or more categories by averaging the metric of the each of the tags and the overall correlation value corresponding to each of the one or more categories; causing, at least in part, actions that result in sending the weighting factors to the user for confirmation; and adjusting the model of the user's expertise based upon the weighting factors as confirmed by the user. | 0.5 |
8,397,223 | 1 | 9 | 1. A computer-implemented method, comprising: receiving input files comprising markup language text for a graphical user interface; obtaining an application framework code; receiving web application business logic objects; organizing the application framework code and the web application business logic objects into web application source code; and binding the web application source code with the input files, the binding including compiling the input files; wherein the application framework code is configured to, at runtime, detect any change to web application screens and to re-compile the input files in response to detecting the any change. | 1. A computer-implemented method, comprising: receiving input files comprising markup language text for a graphical user interface; obtaining an application framework code; receiving web application business logic objects; organizing the application framework code and the web application business logic objects into web application source code; and binding the web application source code with the input files, the binding including compiling the input files; wherein the application framework code is configured to, at runtime, detect any change to web application screens and to re-compile the input files in response to detecting the any change. 9. The computer-implemented method of claim 1 , further comprising compiling the web application source code. | 0.915504 |
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