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9,894,138 | 1 | 4 | 1. A method for natural language management of online social network connections, comprising: receiving natural language data associated with a user's social network interactions; analyzing, by a processor, the natural language data associated with the user's social network interactions; determining, by the processor, features used in the user's social network interactions based on the analysis; assigning, by the processor, point values to the features used in the user's social network interactions; creating, by the processor, a fingerprint of the user based on the features and the point values; comparing, by the processor, the fingerprint with information associated with online communities; and based on the comparing, recommending, by the processor, one or more of the online communities as the user's potential social network connections, wherein the features comprise a sequence of a number of items from the natural language data associated with the user's social network interactions, wherein the fingerprint represents one or more of words the user uses and a context in which the user uses the one or more of words, wherein the fingerprint is used to connect to an online community or a web site, or a combination thereof, the method further comprising augmenting the fingerprint with superimposed features, wherein fingerprints are compared to look for common features that overlap and can be linked together, the fingerprint that is superimposed used in the recommending of the one or more of the online communities as the user's potential social network connections. | 1. A method for natural language management of online social network connections, comprising: receiving natural language data associated with a user's social network interactions; analyzing, by a processor, the natural language data associated with the user's social network interactions; determining, by the processor, features used in the user's social network interactions based on the analysis; assigning, by the processor, point values to the features used in the user's social network interactions; creating, by the processor, a fingerprint of the user based on the features and the point values; comparing, by the processor, the fingerprint with information associated with online communities; and based on the comparing, recommending, by the processor, one or more of the online communities as the user's potential social network connections, wherein the features comprise a sequence of a number of items from the natural language data associated with the user's social network interactions, wherein the fingerprint represents one or more of words the user uses and a context in which the user uses the one or more of words, wherein the fingerprint is used to connect to an online community or a web site, or a combination thereof, the method further comprising augmenting the fingerprint with superimposed features, wherein fingerprints are compared to look for common features that overlap and can be linked together, the fingerprint that is superimposed used in the recommending of the one or more of the online communities as the user's potential social network connections. 4. The method of claim 1 , wherein the online communities comprise a predefined list of online communities. | 0.891919 |
9,552,412 | 1 | 2 | 1. A method of refining Boolean queries, the method comprising: obtaining, with one or more processors, a query provided by a user via a user's computing device; searching, with one or more processors, a corpus of documents based on the query to identify responsive documents, the corpus having more than 2,000 documents; selecting, with one or more processors, narrowing terms that pertain to respective subsets of the responsive documents; selecting, with one or more processors, broadening terms related to the query; instructing, with one or more processors, the user's computing device to present a graphical user interface comprising: graphical representations of the narrowing terms; graphical representations of the broadening terms; and one or more user inputs by which the user refines the query by adding a selected narrowing term or a selected broadening term; obtaining, with one or more processors, a user selection of a broadening term or a narrowing term; forming, with one or more processors, a refined query based on the user selection; searching, with one or more processors, at least part of the corpus based on the refined query to identify refined responsive documents; and instructing, with one or more processors, the user's computing device to present an updated graphical user interface with information about the refined responsive documents, wherein the updated graphical user interface comprises a query entry input having graphical regions representing query constituent components and user-selectable inputs for each of the components by which the respective component is removed from the query. | 1. A method of refining Boolean queries, the method comprising: obtaining, with one or more processors, a query provided by a user via a user's computing device; searching, with one or more processors, a corpus of documents based on the query to identify responsive documents, the corpus having more than 2,000 documents; selecting, with one or more processors, narrowing terms that pertain to respective subsets of the responsive documents; selecting, with one or more processors, broadening terms related to the query; instructing, with one or more processors, the user's computing device to present a graphical user interface comprising: graphical representations of the narrowing terms; graphical representations of the broadening terms; and one or more user inputs by which the user refines the query by adding a selected narrowing term or a selected broadening term; obtaining, with one or more processors, a user selection of a broadening term or a narrowing term; forming, with one or more processors, a refined query based on the user selection; searching, with one or more processors, at least part of the corpus based on the refined query to identify refined responsive documents; and instructing, with one or more processors, the user's computing device to present an updated graphical user interface with information about the refined responsive documents, wherein the updated graphical user interface comprises a query entry input having graphical regions representing query constituent components and user-selectable inputs for each of the components by which the respective component is removed from the query. 2. The method of claim 1 , wherein the graphical user interface comprises: a plurality of graphical regions, each graphical region corresponding to one of the narrowing terms, wherein a spatial dimension of each graphical region is selected based on an amount of the responsive documents responsive to a refined query including the respective term as a conjunctive addition to the query. | 0.813763 |
8,849,034 | 9 | 10 | 9. A method for triggering a sub-word unit recognition comprising: drawing one or more strokes of a desired sub-word unit using a stylus on a touch screen, wherein one of the drawn one or more strokes is a first head-line stroke and is a last drawn stroke in the drawn one or more strokes of the desired sub-word unit; inputting an associated data of the drawn one or more strokes via the touch screen into a handwriting recognition engine; computing stroke recognition characteristics of each of the drawn one or more strokes with reference to a horizontal reference line, wherein the stroke recognition characteristics are selected from the group comprising aspect ratio and slope; determining a first trigger stroke in the drawn one or more strokes of the desired sub-word unit that can be used to trigger the sub-word unit recognition based as a function of the computed stroke recognition characteristics of each of the multiple drawn strokes, wherein the first trigger stroke is the first head-line stroke which is drawn substantially parallel to the horizontal reference line; and triggering sub-word unit recognition for the drawn one or more strokes by the handwriting recognition engine upon determining the first trigger stroke. | 9. A method for triggering a sub-word unit recognition comprising: drawing one or more strokes of a desired sub-word unit using a stylus on a touch screen, wherein one of the drawn one or more strokes is a first head-line stroke and is a last drawn stroke in the drawn one or more strokes of the desired sub-word unit; inputting an associated data of the drawn one or more strokes via the touch screen into a handwriting recognition engine; computing stroke recognition characteristics of each of the drawn one or more strokes with reference to a horizontal reference line, wherein the stroke recognition characteristics are selected from the group comprising aspect ratio and slope; determining a first trigger stroke in the drawn one or more strokes of the desired sub-word unit that can be used to trigger the sub-word unit recognition based as a function of the computed stroke recognition characteristics of each of the multiple drawn strokes, wherein the first trigger stroke is the first head-line stroke which is drawn substantially parallel to the horizontal reference line; and triggering sub-word unit recognition for the drawn one or more strokes by the handwriting recognition engine upon determining the first trigger stroke. 10. The method of claim 9 , further comprising: producing a first candidate sub-word unit upon triggering the sub-word unit recognition by the handwriting recognition engine; and outputting the first candidate sub-word unit. | 0.878788 |
10,089,639 | 1 | 11 | 1. A computer implemented method for capturing user data across interactions using a unique user identification, the method comprising: providing a processor for implementing a user management module, said user management module receiving a request for a service from the user; said user management module creating a plurality of linkages across a plurality of channels of interaction and a plurality of devices and within a session and across a plurality of sessions, wherein the plurality of linkages is made probabilistically based on machine learning and statistical models driven by behavior and attributes of said user's journeys on the plurality of channels including a web-based channel, a voice-based channel, and a text-based channel; said user management module either querying said user for at least one personal identifier or automatically identifying at least one of a plurality of unique user identifiers when said user interacts with said user management module, wherein the plurality of unique user identifiers is created, captured, and passed among the plurality of channels of interaction and across a plurality of organizations with which said user interacts; said user management module generating a unique ID for said user when said user management module receives said at least one personal identifier from the user or identifies the at least one unique user identifier, wherein said unique ID is common for said user across all of the plurality of channels of interaction; said user management module querying said user regarding the service that said user is requesting once said user has been identified; said user management module tracking said user's interaction journey by using the plurality of unique user identifiers to facilitate collecting data across a plurality of said channels and from a plurality of data sources, and storing relevant information of said interaction journey including any of a time of interaction, a channel of interaction, content or nature of interaction, or user location of interaction in a database; said user management module using said relevant information of said interaction journey for any of building, updating, and modifying a continuously generated user profile that is linked to said unique ID; evaluating said relevant information of said interaction journey stored in said user profile to personalize the service or access to the service by the user based on the user profile that is linked to said unique ID; and said user management module routing the user to the service over one of said plurality of channels based on the user profile that is linked to said unique ID. | 1. A computer implemented method for capturing user data across interactions using a unique user identification, the method comprising: providing a processor for implementing a user management module, said user management module receiving a request for a service from the user; said user management module creating a plurality of linkages across a plurality of channels of interaction and a plurality of devices and within a session and across a plurality of sessions, wherein the plurality of linkages is made probabilistically based on machine learning and statistical models driven by behavior and attributes of said user's journeys on the plurality of channels including a web-based channel, a voice-based channel, and a text-based channel; said user management module either querying said user for at least one personal identifier or automatically identifying at least one of a plurality of unique user identifiers when said user interacts with said user management module, wherein the plurality of unique user identifiers is created, captured, and passed among the plurality of channels of interaction and across a plurality of organizations with which said user interacts; said user management module generating a unique ID for said user when said user management module receives said at least one personal identifier from the user or identifies the at least one unique user identifier, wherein said unique ID is common for said user across all of the plurality of channels of interaction; said user management module querying said user regarding the service that said user is requesting once said user has been identified; said user management module tracking said user's interaction journey by using the plurality of unique user identifiers to facilitate collecting data across a plurality of said channels and from a plurality of data sources, and storing relevant information of said interaction journey including any of a time of interaction, a channel of interaction, content or nature of interaction, or user location of interaction in a database; said user management module using said relevant information of said interaction journey for any of building, updating, and modifying a continuously generated user profile that is linked to said unique ID; evaluating said relevant information of said interaction journey stored in said user profile to personalize the service or access to the service by the user based on the user profile that is linked to said unique ID; and said user management module routing the user to the service over one of said plurality of channels based on the user profile that is linked to said unique ID. 11. The method of claim 1 , further comprising: said user management module operatively communicating with a customer care center of an organization from which said user intends to request services. | 0.740157 |
8,761,513 | 1 | 5 | 1. A method for translating a video feed in real-time augmented reality from a first language to a second language using a mobile device comprising a video camera, a processor, a memory, and a display, the method comprising the steps of: (a) capturing a frame in real-time from the video feed of one or more words in the first language which need to be translated using the video camera to produce a captured frame; (b) cropping the captured frame to fit inside an image processing bounding box to produce a cropped frame; (c) pre-processing the cropped frame to produce a pre-processed frame; (d) performing character segment recognition on the pre-processed frame to produce a plurality of character segments; (e) performing character merging on the character segments to produce a plurality of merged character segments; (f) performing character recognition on the merged character segments to produce a recognized frame having a plurality of recognized characters; (g) processing the recognized frame through a translation engine to produce a translation of the recognized characters in the first language into one or more words of the second language to produce a translated frame, while also calculating a translation quality representing how well the recognized characters have been translated for each translated frame; (h) storing the translated frame to the memory as a current translated frame, wherein a previous translated frame and a previous translation quality is also stored in the memory; (i) checking that the bounding box has stayed on a same set of characters for the current translated frame and the previous translated frame by determining a fraction of similar characters that are overlapping between the current translated frame and the previous translated frame, wherein a higher fraction indicates that the bounding box has stayed on the same set of characters for the current translated frame and the previous translated frame; (j) comparing the translation quality determined by the translation engine for the current translated frame to the previous translation quality for the previous translated frame; (k) selecting one of the previous translated frame and the current translated frame to be removed from the memory based on a frame having a lower translation quality; and (l) displaying an optimal translated frame from the previous translated frame and the current translated frame, the optimal translated frame having a higher translation quality, wherein the words of the second language are overlaid over or next to the words in the first language which is being translated in an augmented reality on the display of the mobile device. | 1. A method for translating a video feed in real-time augmented reality from a first language to a second language using a mobile device comprising a video camera, a processor, a memory, and a display, the method comprising the steps of: (a) capturing a frame in real-time from the video feed of one or more words in the first language which need to be translated using the video camera to produce a captured frame; (b) cropping the captured frame to fit inside an image processing bounding box to produce a cropped frame; (c) pre-processing the cropped frame to produce a pre-processed frame; (d) performing character segment recognition on the pre-processed frame to produce a plurality of character segments; (e) performing character merging on the character segments to produce a plurality of merged character segments; (f) performing character recognition on the merged character segments to produce a recognized frame having a plurality of recognized characters; (g) processing the recognized frame through a translation engine to produce a translation of the recognized characters in the first language into one or more words of the second language to produce a translated frame, while also calculating a translation quality representing how well the recognized characters have been translated for each translated frame; (h) storing the translated frame to the memory as a current translated frame, wherein a previous translated frame and a previous translation quality is also stored in the memory; (i) checking that the bounding box has stayed on a same set of characters for the current translated frame and the previous translated frame by determining a fraction of similar characters that are overlapping between the current translated frame and the previous translated frame, wherein a higher fraction indicates that the bounding box has stayed on the same set of characters for the current translated frame and the previous translated frame; (j) comparing the translation quality determined by the translation engine for the current translated frame to the previous translation quality for the previous translated frame; (k) selecting one of the previous translated frame and the current translated frame to be removed from the memory based on a frame having a lower translation quality; and (l) displaying an optimal translated frame from the previous translated frame and the current translated frame, the optimal translated frame having a higher translation quality, wherein the words of the second language are overlaid over or next to the words in the first language which is being translated in an augmented reality on the display of the mobile device. 5. The method of claim 1 , further comprising: utilizing a conversion table for converting traditional Chinese characters to simplified Chinese characters before translating the first language into the second language. | 0.629252 |
7,552,864 | 1 | 3 | 1. A method for testing the authenticity of documents wherein a document is tested by authenticity criteria, comprising providing at least two different authenticity classes each with one or more authenticity criteria for a valid document, the individual authenticity classes differing in at least one authenticity criterion, so that requirements for authenticity vary in strictness depending on the authenticity class, determining fitness or denomination of the documents; selecting an authenticity class from the different authenticity classes in dependence on the determined fitness or denomination of the document, testing the document by the authenticity criteria of the selected authenticity class, and assigning the document the selected authenticity class if the document meets the authenticity criteria thereof; wherein if the authenticity criteria of the selected authenticity class are not met, a further authenticity class with lower requirements for authenticity is selected and the authenticity testing repeated by the authenticity criteria of the selected further authenticity class. | 1. A method for testing the authenticity of documents wherein a document is tested by authenticity criteria, comprising providing at least two different authenticity classes each with one or more authenticity criteria for a valid document, the individual authenticity classes differing in at least one authenticity criterion, so that requirements for authenticity vary in strictness depending on the authenticity class, determining fitness or denomination of the documents; selecting an authenticity class from the different authenticity classes in dependence on the determined fitness or denomination of the document, testing the document by the authenticity criteria of the selected authenticity class, and assigning the document the selected authenticity class if the document meets the authenticity criteria thereof; wherein if the authenticity criteria of the selected authenticity class are not met, a further authenticity class with lower requirements for authenticity is selected and the authenticity testing repeated by the authenticity criteria of the selected further authenticity class. 3. The method according to claim 1 , wherein individual documents are sorted in accordance with the particular assigned authenticity class. | 0.784161 |
8,356,046 | 1 | 6 | 1. A computer-implemented method comprising: identifying a process context associated with a business process, wherein the process context is represented by a process context data object including at least one variable representing values relevant to the business process, and wherein the at least one variable is linked to at least one data field displayed in a user interface; determining the variable type of each variable of the at least one variable in the process context data object, wherein the determined variable type includes inherent semantic information reflecting the meaning of the variable or logical relationships associated with the variable; selecting a particular variable of the at least one variable in the process context data object to use for searching a back-end repository for additional information associated with the business process; dynamically determining, by operation of a computer, a suggested entry for the at least one data field in the user interface based on at least search results obtained from the search; and presenting the suggested entry to a user through the user interface. | 1. A computer-implemented method comprising: identifying a process context associated with a business process, wherein the process context is represented by a process context data object including at least one variable representing values relevant to the business process, and wherein the at least one variable is linked to at least one data field displayed in a user interface; determining the variable type of each variable of the at least one variable in the process context data object, wherein the determined variable type includes inherent semantic information reflecting the meaning of the variable or logical relationships associated with the variable; selecting a particular variable of the at least one variable in the process context data object to use for searching a back-end repository for additional information associated with the business process; dynamically determining, by operation of a computer, a suggested entry for the at least one data field in the user interface based on at least search results obtained from the search; and presenting the suggested entry to a user through the user interface. 6. The computer-implemented method of claim 1 , wherein search results include an object graph depicting logical connections between data objects and the at least one variable associated with the particular process context data object. | 0.642857 |
8,941,589 | 1 | 18 | 1. A system comprising: a plurality of tags affixed to a plurality of objects, wherein the plurality of tags include a plurality of features such that each tag comprises at least one feature; a plurality of sensors, wherein a location of the plurality of sensors defines a spatial operating environment (SOE) that includes the plurality of objects, wherein the plurality of sensors detect the plurality of features; and an adaptive tracking component (ATC) running on a processor, wherein the ATC receives from each sensor of the plurality of sensors feature data corresponding to each object of the plurality of objects detected by the respective sensor, wherein the ATC generates and maintains a coherent model of relationships between the plurality of objects and the SOE by integrating the feature data from the plurality of sensors. | 1. A system comprising: a plurality of tags affixed to a plurality of objects, wherein the plurality of tags include a plurality of features such that each tag comprises at least one feature; a plurality of sensors, wherein a location of the plurality of sensors defines a spatial operating environment (SOE) that includes the plurality of objects, wherein the plurality of sensors detect the plurality of features; and an adaptive tracking component (ATC) running on a processor, wherein the ATC receives from each sensor of the plurality of sensors feature data corresponding to each object of the plurality of objects detected by the respective sensor, wherein the ATC generates and maintains a coherent model of relationships between the plurality of objects and the SOE by integrating the feature data from the plurality of sensors. 18. The system of claim 1 , wherein a projective image of a tag includes labeling. | 0.908482 |
8,843,370 | 1 | 4 | 1. A method of adjusting model parameters in a speech recognition system comprising: in a speech recognition system executed by computer system that combines recognition outputs from a plurality of parallel speech recognition processes that operate on a given speech input word sequence and produce different competing recognition outputs which are then combined to determine a final recognition output; wherein the speech recognition processes are complementary so as to produce different recognition errors on a given same speech input; performing a discriminative adjustment process including: i. selecting at least one acoustic model of the system, and ii. adjusting a plurality of model parameters of the selected acoustic model based on a joint discriminative criterion over a plurality of complementary acoustic models to lower combined recognition WER in the system over only independently discriminatively trained recognition systems. | 1. A method of adjusting model parameters in a speech recognition system comprising: in a speech recognition system executed by computer system that combines recognition outputs from a plurality of parallel speech recognition processes that operate on a given speech input word sequence and produce different competing recognition outputs which are then combined to determine a final recognition output; wherein the speech recognition processes are complementary so as to produce different recognition errors on a given same speech input; performing a discriminative adjustment process including: i. selecting at least one acoustic model of the system, and ii. adjusting a plurality of model parameters of the selected acoustic model based on a joint discriminative criterion over a plurality of complementary acoustic models to lower combined recognition WER in the system over only independently discriminatively trained recognition systems. 4. A method according to claim 1 , wherein the speech recognition system combines recognition outputs based on a Recognizer Output Voting for Error Reduction (ROVER) approach. | 0.532086 |
8,924,374 | 1 | 9 | 1. A computer-implemented method, comprising: at a computer having memory and one or more processors: receiving one or more search keywords from a user; selecting a plurality of candidate document identifiers in accordance with the one or more search keywords, each candidate document identifier corresponding to a respective document at a respective data source; for a respective candidate document identifier of the plurality of candidate document identifiers: retrieving a document corresponding to the respective candidate document identifier from a data source, wherein the document has a structure type; converting the document into a node stream, wherein the document conversion is initiated immediately after retrieving a portion of the document; generating a customized data model for the document using the node stream in accordance with the structure type of the document; identifying one or more candidate chunks within the customized data model in accordance with a set of heuristic rules associated with the structure type; and selecting one or more chunks of the candidate chunks that satisfy the one or more search keywords; and providing at least one of the selected one or more chunks for display to the user. | 1. A computer-implemented method, comprising: at a computer having memory and one or more processors: receiving one or more search keywords from a user; selecting a plurality of candidate document identifiers in accordance with the one or more search keywords, each candidate document identifier corresponding to a respective document at a respective data source; for a respective candidate document identifier of the plurality of candidate document identifiers: retrieving a document corresponding to the respective candidate document identifier from a data source, wherein the document has a structure type; converting the document into a node stream, wherein the document conversion is initiated immediately after retrieving a portion of the document; generating a customized data model for the document using the node stream in accordance with the structure type of the document; identifying one or more candidate chunks within the customized data model in accordance with a set of heuristic rules associated with the structure type; and selecting one or more chunks of the candidate chunks that satisfy the one or more search keywords; and providing at least one of the selected one or more chunks for display to the user. 9. The method of claim 1 , wherein the document is a plain-text document. | 0.748276 |
9,721,068 | 1 | 2 | 1. A method, comprising: conducting, by a device, a conversation with a patient; capturing, by the device, therapy interaction data during the conversation, the therapy interaction data including speech uttered by the patient; parsing, by the device, the captured therapy interaction data to identify one or more word occurrences; determining, by the device, one or more associations among the one or more word occurrences using a fuzzy association analysis and deep belief networks; determining, by the device and using fuzzy association propensity survival scoring, a propensity of the one or more associations based on a probability analysis that is based on patient data relating to a plurality of other patients, the patient data including control data that includes: data relating to one or more healthy patients, and data relating to one or more patients that have one or more diseases or health conditions; and determining, by the device and based on the propensity of the one or more associations, whether the patient has the one or more diseases or health conditions. | 1. A method, comprising: conducting, by a device, a conversation with a patient; capturing, by the device, therapy interaction data during the conversation, the therapy interaction data including speech uttered by the patient; parsing, by the device, the captured therapy interaction data to identify one or more word occurrences; determining, by the device, one or more associations among the one or more word occurrences using a fuzzy association analysis and deep belief networks; determining, by the device and using fuzzy association propensity survival scoring, a propensity of the one or more associations based on a probability analysis that is based on patient data relating to a plurality of other patients, the patient data including control data that includes: data relating to one or more healthy patients, and data relating to one or more patients that have one or more diseases or health conditions; and determining, by the device and based on the propensity of the one or more associations, whether the patient has the one or more diseases or health conditions. 2. The method of claim 1 , further comprising: determining the propensity of the one or more associations further based on a probability that the one or more word occurrences imply a determined meaning. | 0.82735 |
8,793,271 | 5 | 6 | 5. The computer implemented method of claim 4 , wherein the plurality of documents further includes: (v) a fifth document having, inside the specified context, no occurrences of any of the first and second search terms and, outside the specified context, respective occurrences of the first and second search terms; and (vi) a sixth document having, inside the specified context, an occurrence of the second search term and no occurrences of the first search term and, outside the specified context, an occurrence of the first search term and no occurrences of the second search term. | 5. The computer implemented method of claim 4 , wherein the plurality of documents further includes: (v) a fifth document having, inside the specified context, no occurrences of any of the first and second search terms and, outside the specified context, respective occurrences of the first and second search terms; and (vi) a sixth document having, inside the specified context, an occurrence of the second search term and no occurrences of the first search term and, outside the specified context, an occurrence of the first search term and no occurrences of the second search term. 6. The computer implemented method of claim 5 , wherein the identified set of documents does not include the first document, does not include the second document, and does include the third, fourth, fifth, and sixth documents. | 0.5 |
9,412,392 | 81 | 107 | 81. A electronic device comprising: one or more input devices; one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for: receiving user input; in response to receiving user input, recording at least a portion of a voice command; storing contextual information of the electronic device, the contextual information related to a state of the electronic device at a time the at least a portion of the voice command is recorded; and after recording the at least a portion of the voice command and storing the contextual information, transmitting the at least a portion of the recorded voice command and the stored contextual information from the electronic device to remote computing equipment; and receiving, from the remote computing equipment, data related to processing the voice command. | 81. A electronic device comprising: one or more input devices; one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for: receiving user input; in response to receiving user input, recording at least a portion of a voice command; storing contextual information of the electronic device, the contextual information related to a state of the electronic device at a time the at least a portion of the voice command is recorded; and after recording the at least a portion of the voice command and storing the contextual information, transmitting the at least a portion of the recorded voice command and the stored contextual information from the electronic device to remote computing equipment; and receiving, from the remote computing equipment, data related to processing the voice command. 107. The electronic device of claim 81 , wherein the contextual information comprises information from a business productivity application of the electronic device. | 0.763689 |
8,019,793 | 1 | 19 | 1. A method of populating a database, comprising: a) providing a plurality of information files, wherein each information file includes an object, each object including a type, an object property and an object explicit relationship; b) providing a plurality of rule files, wherein each rule file includes a relation between at least two object types, each relation including a relation property and an explicit relationship definition; c) validating the object properties and the object explicit relationships based in part on the relation properties, the explicit relationship definitions, or both; d) determining at least one implicit relationship based on at least one of the explicit relationship definitions and at least one of the object explicit relationships; e) generating at least one Structured Query Language (SQL) command representative of the at least one implicit relationship; f) executing the generated at least one SQL command on a database to store an implicit relationship definition in the form of a SQL statement; g) selecting an object from the information files; and h) generating a document by populating a presentation template with the object property of the selected object, the document having at least one hypertext link to a second document, the link based on the stored implicit relationship definition. | 1. A method of populating a database, comprising: a) providing a plurality of information files, wherein each information file includes an object, each object including a type, an object property and an object explicit relationship; b) providing a plurality of rule files, wherein each rule file includes a relation between at least two object types, each relation including a relation property and an explicit relationship definition; c) validating the object properties and the object explicit relationships based in part on the relation properties, the explicit relationship definitions, or both; d) determining at least one implicit relationship based on at least one of the explicit relationship definitions and at least one of the object explicit relationships; e) generating at least one Structured Query Language (SQL) command representative of the at least one implicit relationship; f) executing the generated at least one SQL command on a database to store an implicit relationship definition in the form of a SQL statement; g) selecting an object from the information files; and h) generating a document by populating a presentation template with the object property of the selected object, the document having at least one hypertext link to a second document, the link based on the stored implicit relationship definition. 19. The method of claim 1 , further comprising generating a relationship database that includes each object explicit relationship included in the information files. | 0.885475 |
8,365,072 | 1 | 5 | 1. A computer readable medium storing a computer program which when executed by at least one processor analyzes a document, the computer program comprising sets of instructions for: receiving a document that comprises a plurality of primitive graphic elements, each primitive graphic element defined as a single object in the document, the document having a drawing order that indicates the order in which the primitive graphic elements are drawn when the document is displayed; calculating, for a first primitive graphic element and a second primitive graphic element that is subsequent to the first in the drawing order, a size of a single element that comprises the first and second primitive graphic elements; and based on the size of the single element compared to a plurality of different size calculations for additional primitive graphic elements that are subsequent in the drawing order, defining a single structural graphic element within the document from the first and second primitive graphic elements. | 1. A computer readable medium storing a computer program which when executed by at least one processor analyzes a document, the computer program comprising sets of instructions for: receiving a document that comprises a plurality of primitive graphic elements, each primitive graphic element defined as a single object in the document, the document having a drawing order that indicates the order in which the primitive graphic elements are drawn when the document is displayed; calculating, for a first primitive graphic element and a second primitive graphic element that is subsequent to the first in the drawing order, a size of a single element that comprises the first and second primitive graphic elements; and based on the size of the single element compared to a plurality of different size calculations for additional primitive graphic elements that are subsequent in the drawing order, defining a single structural graphic element within the document from the first and second primitive graphic elements. 5. The computer readable medium of claim 1 , wherein the document is a portable document format (PDF) document. | 0.928295 |
9,123,004 | 10 | 14 | 10. A computer program product for predicting that an event identified in a first topic map meta-model will have an effect on at least one asset identified in a second topic map meta-model, the computer program product comprising: one or more non-transitory computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices, to create a third topic map meta-model which maps at least one asset from the second topic map meta-model to an event from the first topic map meta-model, the third topic map meta-model comprising: a topic map representation of assets of the second topic map meta-model, the second topic map meta-model further comprising a topic map based index and instance ontology of a meta-model of assets; and a topic map representation of events of the first topic map meta-model, the first topic map meta-model further comprising a topic map based index and instance ontology of a meta-model of events; program instructions, stored on at least one of the one or more storage devices, to receive a query input from a user identifying an event; program instructions, stored on at least one of the one or more storage devices, to identify at least one asset mapped to an event corresponding to the event identified in the query input in the third topic map meta-model, wherein the program instructions to identify the at least one asset determine that the at least one asset has greater than a threshold probability of being affected by the event identified in the query input; and program instructions, stored on at least one of the one or more storage devices, to cause the display of a probability that the event corresponding to the event identified in the query input will affect the mapped at least one asset. | 10. A computer program product for predicting that an event identified in a first topic map meta-model will have an effect on at least one asset identified in a second topic map meta-model, the computer program product comprising: one or more non-transitory computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices, to create a third topic map meta-model which maps at least one asset from the second topic map meta-model to an event from the first topic map meta-model, the third topic map meta-model comprising: a topic map representation of assets of the second topic map meta-model, the second topic map meta-model further comprising a topic map based index and instance ontology of a meta-model of assets; and a topic map representation of events of the first topic map meta-model, the first topic map meta-model further comprising a topic map based index and instance ontology of a meta-model of events; program instructions, stored on at least one of the one or more storage devices, to receive a query input from a user identifying an event; program instructions, stored on at least one of the one or more storage devices, to identify at least one asset mapped to an event corresponding to the event identified in the query input in the third topic map meta-model, wherein the program instructions to identify the at least one asset determine that the at least one asset has greater than a threshold probability of being affected by the event identified in the query input; and program instructions, stored on at least one of the one or more storage devices, to cause the display of a probability that the event corresponding to the event identified in the query input will affect the mapped at least one asset. 14. The computer program product of claim 10 , further comprising: program instructions, stored on at least one of the one or more storage devices, to store the third topic map meta-model in a repository. | 0.768707 |
8,954,455 | 6 | 15 | 6. A method comprising: generating, at a social networking system, a structured query based on information received from a user, the structured query including a focal search object and a connected search object; updating a reverse index to include an identifier for structured query, the reverse index storing a plurality of structured query identifiers based on focal search objects and the connected search objects in the structured queries; receiving an action; accessing the reverse index to determine whether the received action causes an object maintained by the social networking system to match the structured query; and responsive to determining that the received action causes the object to match the structured query, storing a link to the object in association with the structured query. | 6. A method comprising: generating, at a social networking system, a structured query based on information received from a user, the structured query including a focal search object and a connected search object; updating a reverse index to include an identifier for structured query, the reverse index storing a plurality of structured query identifiers based on focal search objects and the connected search objects in the structured queries; receiving an action; accessing the reverse index to determine whether the received action causes an object maintained by the social networking system to match the structured query; and responsive to determining that the received action causes the object to match the structured query, storing a link to the object in association with the structured query. 15. The method of claim 6 , further comprising: presenting the object to the user after storing the link to the object. | 0.775472 |
8,812,556 | 1 | 3 | 1. A method of managing a set of artifacts, the method comprising: generating a proxy agent for a framework agent at a first at least one computing device, wherein the framework agent manages a set of modifications to the data for an artifact in the set of artifacts, wherein the proxy agent receives messages communicated from one of a user interface or the framework agent for processing by the other of the user interface or the framework agent, processes the messages, and forwards the messages to the other of the user interface or the framework agent, and wherein the artifact implements a service in a computer infrastructure; receiving a modification message for processing by the framework agent for the artifact from the user interface at the proxy agent executing on the first at least one computing device; capturing modification data based on the modification message using the proxy agent, wherein the modification data is distinct from the modification message; storing the modification data using the proxy agent, wherein the stored modification data is configured to recreate the modification message without the user interface when executed by a computing device; and forwarding the modification message from the proxy agent for processing by the framework agent executing on a second at least one computing device. | 1. A method of managing a set of artifacts, the method comprising: generating a proxy agent for a framework agent at a first at least one computing device, wherein the framework agent manages a set of modifications to the data for an artifact in the set of artifacts, wherein the proxy agent receives messages communicated from one of a user interface or the framework agent for processing by the other of the user interface or the framework agent, processes the messages, and forwards the messages to the other of the user interface or the framework agent, and wherein the artifact implements a service in a computer infrastructure; receiving a modification message for processing by the framework agent for the artifact from the user interface at the proxy agent executing on the first at least one computing device; capturing modification data based on the modification message using the proxy agent, wherein the modification data is distinct from the modification message; storing the modification data using the proxy agent, wherein the stored modification data is configured to recreate the modification message without the user interface when executed by a computing device; and forwarding the modification message from the proxy agent for processing by the framework agent executing on a second at least one computing device. 3. The method of claim 1 , the storing including serializing the modification data in an XML-based document. | 0.804348 |
7,668,791 | 3 | 4 | 3. The method of claim 2 , further comprising tagging words of the factual descriptions with their parts of speech. | 3. The method of claim 2 , further comprising tagging words of the factual descriptions with their parts of speech. 4. The method of claim 3 , wherein tagging words of the factual descriptions with their parts of speech comprises applying a noun tag when a word may be either a verb or a noun. | 0.5 |
8,341,081 | 1 | 11 | 1. A computer-implemented method for identifying an on-line bank account utilized for business purposes, the method comprising: a computer receiving or determining a name of an on-line bank account entered by an account holder; the computer parsing the name into a plurality of name segments; the computer applying a first set of rules to each of the plurality of name segments individually, and a second set of rules to groups of multiple name segments; and the computer determining whether the on-line bank account is utilized for business purposes based at least in part upon respective scores generated by applying the first and second sets of rules. | 1. A computer-implemented method for identifying an on-line bank account utilized for business purposes, the method comprising: a computer receiving or determining a name of an on-line bank account entered by an account holder; the computer parsing the name into a plurality of name segments; the computer applying a first set of rules to each of the plurality of name segments individually, and a second set of rules to groups of multiple name segments; and the computer determining whether the on-line bank account is utilized for business purposes based at least in part upon respective scores generated by applying the first and second sets of rules. 11. The method of claim 1 , at least one rule of the first set of rules specifying a score assigned to a name segment based at least in part upon one or more of a tense of the name segment, whether the name segment is singular or plural, and whether the name segment is possessive. | 0.776984 |
8,271,473 | 1 | 6 | 1. A computer-implemented method for search engine optimization of career listings on a network, the method comprising: establishing base information for each of a plurality of careers at a company; creating at least one category webpage based on the base information for display over the network, the category webpage associated with a career listing category identifying the category webpage; creating a job listing page for each of the plurality of careers based on the respective base information for each career; associating at least one job listing page with a category webpage, the category webpage including a network link to each associated job listing page; and displaying the category webpage over the network to an end user job searcher; wherein the category webpage is persistent, such that at least one characteristic of the category webpage remains static on the network regardless of any change in job listing pages associated with the category webpage. | 1. A computer-implemented method for search engine optimization of career listings on a network, the method comprising: establishing base information for each of a plurality of careers at a company; creating at least one category webpage based on the base information for display over the network, the category webpage associated with a career listing category identifying the category webpage; creating a job listing page for each of the plurality of careers based on the respective base information for each career; associating at least one job listing page with a category webpage, the category webpage including a network link to each associated job listing page; and displaying the category webpage over the network to an end user job searcher; wherein the category webpage is persistent, such that at least one characteristic of the category webpage remains static on the network regardless of any change in job listing pages associated with the category webpage. 6. The computer-implemented method of claim 1 , wherein the category webpage and associated job listing pages include same similar core job information. | 0.697211 |
4,516,260 | 38 | 40 | 38. A talking electronic apparatus as set forth in claim 32, wherein said means responsive to said digital control data and said operator response is effective to initiate a second selected audible request in human speech via said speech synthesizer means if said operator response to the first selected audible request conforms to the appropriate operator response corresponding thereto. | 38. A talking electronic apparatus as set forth in claim 32, wherein said means responsive to said digital control data and said operator response is effective to initiate a second selected audible request in human speech via said speech synthesizer means if said operator response to the first selected audible request conforms to the appropriate operator response corresponding thereto. 40. A talking electronic apparatus according to claim 38, wherein said operator input means comprises a keyboard. | 0.79529 |
7,636,656 | 10 | 11 | 10. A system, comprising: one or more processors; a memory coupled to the processor and comprising a localization database comprising text strings and associated translations of the text strings in a different language, wherein the memory further comprises program instructions executable by the one or more processors to: extract localizable text strings from one or more localizable files associated with a product to be localized for the different language, wherein said extracting comprises masking each localizable file with a corresponding shade file, wherein each shade file comprises content of the corresponding localizable file with the localizable text strings removed, and wherein the localization mechanism is configured to operate on the localizable files independent of the particular file formats of the localizable files; and initiate an iteration of a localization process, wherein, in said localization process, the program instructions are further executable to: search the localization database for translations of the extracted localizable text strings; if a translation for a localizable text string is not found in the localization database, generate an entry in a translation kit for the localizable text string, wherein the translation kit is formatted in accordance with a canonical translation kit format, wherein the same canonical format is used for the translation kit regardless of the format of the localizable files, and wherein the translation kit is separate from the localizable files; and if a translation for a localizable text string is found in the localization database, record the translation for the localizable text string. | 10. A system, comprising: one or more processors; a memory coupled to the processor and comprising a localization database comprising text strings and associated translations of the text strings in a different language, wherein the memory further comprises program instructions executable by the one or more processors to: extract localizable text strings from one or more localizable files associated with a product to be localized for the different language, wherein said extracting comprises masking each localizable file with a corresponding shade file, wherein each shade file comprises content of the corresponding localizable file with the localizable text strings removed, and wherein the localization mechanism is configured to operate on the localizable files independent of the particular file formats of the localizable files; and initiate an iteration of a localization process, wherein, in said localization process, the program instructions are further executable to: search the localization database for translations of the extracted localizable text strings; if a translation for a localizable text string is not found in the localization database, generate an entry in a translation kit for the localizable text string, wherein the translation kit is formatted in accordance with a canonical translation kit format, wherein the same canonical format is used for the translation kit regardless of the format of the localizable files, and wherein the translation kit is separate from the localizable files; and if a translation for a localizable text string is found in the localization database, record the translation for the localizable text string. 11. The system as recited in claim 10 , wherein, to record the translation for the localizable text string, the program instructions are further executable to write the translation for the localizable text string into a localized version of the localizable file at a location from which the corresponding localizable string was extracted, such that the localized version matches an original file structure of the localizable file. | 0.798689 |
7,496,511 | 21 | 27 | 21. An apparatus that recognizes voice input, comprising: a receiving mechanism configured to receive a document that includes a specification of a datatype for which there exists a predefined grammar, wherein the document that includes the specification of the datatype is a Multi-channel extensible Markup Language (MXML) document; a generation mechanism configured to generate a Voice eXtensible Markup Language (VoiceXML) document from the MXML document; wherein the receiving mechanism is additionally configured to obtain a locale attribute for the datatype, wherein the locale attribute identifies a version of a language that is spoken in a locale; a lookup mechanism configured to use the locale attribute to lookup a locale-specific grammar for the datatype; and a communication mechanism configured to communicate the locale-specific grammar to a speech recognition engine, wherein the locale-specific grammar comprises a gateway-specific transformer that is produced by a gateway driver, wherein the gateway driver is incorporated into a transformation framework, thereby allowing the speech recognition engine to use the locale-specific grammar in recognizing a voice input for the datatype; wherein communicating the locale-specific grammar fully specifies the set of phrases that can be recognized for the datatype. | 21. An apparatus that recognizes voice input, comprising: a receiving mechanism configured to receive a document that includes a specification of a datatype for which there exists a predefined grammar, wherein the document that includes the specification of the datatype is a Multi-channel extensible Markup Language (MXML) document; a generation mechanism configured to generate a Voice eXtensible Markup Language (VoiceXML) document from the MXML document; wherein the receiving mechanism is additionally configured to obtain a locale attribute for the datatype, wherein the locale attribute identifies a version of a language that is spoken in a locale; a lookup mechanism configured to use the locale attribute to lookup a locale-specific grammar for the datatype; and a communication mechanism configured to communicate the locale-specific grammar to a speech recognition engine, wherein the locale-specific grammar comprises a gateway-specific transformer that is produced by a gateway driver, wherein the gateway driver is incorporated into a transformation framework, thereby allowing the speech recognition engine to use the locale-specific grammar in recognizing a voice input for the datatype; wherein communicating the locale-specific grammar fully specifies the set of phrases that can be recognized for the datatype. 27. The apparatus of claim 21 , wherein the locale-specific grammar associates a phrase that can be spoken with a corresponding semantic meaning. | 0.765372 |
10,102,189 | 2 | 3 | 2. The method of claim 1 , further comprising: ranking each grapheme representation to produce a ranked list, wherein the ranking is based on a likelihood that a grapheme representation sounds similar to a pronunciation sound of the string of characters; and filtering the ranked list to produce a subset of grapheme representations. | 2. The method of claim 1 , further comprising: ranking each grapheme representation to produce a ranked list, wherein the ranking is based on a likelihood that a grapheme representation sounds similar to a pronunciation sound of the string of characters; and filtering the ranked list to produce a subset of grapheme representations. 3. The method of claim 2 , further comprising determining a first composite weight for the one or more phonetic representations based on the first data structure. | 0.5 |
7,937,389 | 10 | 11 | 10. The system of claim 9 , further comprising: means for preprocessing a plurality of initial documents; and means for computing a data model representing the plurality of initial documents; and wherein the document vector for the document is compared to at least document vector for the data model to determine the similarity of the document to the documents forming the data model. | 10. The system of claim 9 , further comprising: means for preprocessing a plurality of initial documents; and means for computing a data model representing the plurality of initial documents; and wherein the document vector for the document is compared to at least document vector for the data model to determine the similarity of the document to the documents forming the data model. 11. The system of claim 10 , wherein the document vector for the document is compared to a document vector for the data model without updating the data model until a second plurality of documents have been received and processed by the computer. | 0.801459 |
8,630,841 | 10 | 11 | 10. A computer based system having instructions stored on a tangible medium for identifying a pattern, which, when executed, perform the steps of: receiving a textual input; accessing a rule associated with a desired pattern of the textual input; parsing the rule into a rule structure having multiple nodes that are connected through logical operations, wherein a first one of the nodes corresponds to a first segment bit and a second one of the nodes corresponds to a second segment bit; associating the first and second segment bits with the textual input; identifying first and second entries in a lexical data structure based at least in part on the first and second segment bits, wherein the first segment bit is used to identify the first entry and the second segment bit is used to identify the second entry; identifying first and second textual segments from the first and second entries in the lexical data structure; utilizing a computer processor that is a component of a computer to compare the multiple nodes with the first and second textual segments identified from the first and second entries; and based on said comparison, providing an output signal indicative of whether the textual input matched the desired pattern. | 10. A computer based system having instructions stored on a tangible medium for identifying a pattern, which, when executed, perform the steps of: receiving a textual input; accessing a rule associated with a desired pattern of the textual input; parsing the rule into a rule structure having multiple nodes that are connected through logical operations, wherein a first one of the nodes corresponds to a first segment bit and a second one of the nodes corresponds to a second segment bit; associating the first and second segment bits with the textual input; identifying first and second entries in a lexical data structure based at least in part on the first and second segment bits, wherein the first segment bit is used to identify the first entry and the second segment bit is used to identify the second entry; identifying first and second textual segments from the first and second entries in the lexical data structure; utilizing a computer processor that is a component of a computer to compare the multiple nodes with the first and second textual segments identified from the first and second entries; and based on said comparison, providing an output signal indicative of whether the textual input matched the desired pattern. 11. The system of claim 10 , wherein the rule and the lexical data structure are configured to be edited utilizing a text editor. | 0.712054 |
8,874,525 | 1 | 2 | 1. A method for capturing a workflow history of an electronic document, the method comprising: storing an array having a plurality of slots that each corresponds to a different cell in a plurality of cells, wherein each cell in the plurality of cells is associated with a different spatial subdivision of an image represented by the electronic document and includes one or more pixels included in the image; receiving an event generated by an application that is configured to modify at least one pixel corresponding to at least one cell in the plurality of cells; generating a data object that includes information related to the event; capturing a digital image that reflects a state of the document at a point in the workflow history of the document corresponding to when the application generated the event; storing the data object and the digital image in a memory; and storing a pointer to the data object in each slot in the array that corresponds to a cell of the document that is modified as a result of the event. | 1. A method for capturing a workflow history of an electronic document, the method comprising: storing an array having a plurality of slots that each corresponds to a different cell in a plurality of cells, wherein each cell in the plurality of cells is associated with a different spatial subdivision of an image represented by the electronic document and includes one or more pixels included in the image; receiving an event generated by an application that is configured to modify at least one pixel corresponding to at least one cell in the plurality of cells; generating a data object that includes information related to the event; capturing a digital image that reflects a state of the document at a point in the workflow history of the document corresponding to when the application generated the event; storing the data object and the digital image in a memory; and storing a pointer to the data object in each slot in the array that corresponds to a cell of the document that is modified as a result of the event. 2. The method of claim 1 , wherein the document is divided into a plurality of different portions. | 0.901804 |
9,983,849 | 4 | 5 | 4. The mobile device news reader of claim 1 , further comprising: a display screen coupled to the at least one processor; and wherein the program instructions are further configured to cause the at least one processor to provide a visual display of voice commands available to the user on the display screen. | 4. The mobile device news reader of claim 1 , further comprising: a display screen coupled to the at least one processor; and wherein the program instructions are further configured to cause the at least one processor to provide a visual display of voice commands available to the user on the display screen. 5. The mobile device news reader of claim 4 , wherein the visual display identifies active voice commands available to the user at a current stage within the program instructions. | 0.5 |
7,734,556 | 1 | 2 | 1. A method for discovering knowledge from a set of text documents using a processor, the method comprising: extracting semi-structured meta-data from the set of text documents using a meta-data extractor, the semi-structured meta-data comprising a plurality of concepts and a plurality of relations between the concepts; filtering the semi-structured meta-data to identify a set of key concepts and a corresponding set of key relations between the key concepts, the set of key concepts corresponding to the plurality of concepts; deriving at least one set of sub-concepts corresponding to the set of key concepts based upon data within a domain knowledge base, using a meta-data transformer; formulating a plurality of training samples, each training sample including a vector representing a sub-concept and a vector representing a key concept; and analyzing the plurality of training samples using an associative discoverer to derive a set of associations between a set of vectors representing a sub-concept and at least one vector representing a key concept, wherein neither the set of text documents nor the semi-structured meta-data mention the set of associations, and wherein the set of associations corresponds to discovered knowledge that is extractable by a knowledge interpreter. | 1. A method for discovering knowledge from a set of text documents using a processor, the method comprising: extracting semi-structured meta-data from the set of text documents using a meta-data extractor, the semi-structured meta-data comprising a plurality of concepts and a plurality of relations between the concepts; filtering the semi-structured meta-data to identify a set of key concepts and a corresponding set of key relations between the key concepts, the set of key concepts corresponding to the plurality of concepts; deriving at least one set of sub-concepts corresponding to the set of key concepts based upon data within a domain knowledge base, using a meta-data transformer; formulating a plurality of training samples, each training sample including a vector representing a sub-concept and a vector representing a key concept; and analyzing the plurality of training samples using an associative discoverer to derive a set of associations between a set of vectors representing a sub-concept and at least one vector representing a key concept, wherein neither the set of text documents nor the semi-structured meta-data mention the set of associations, and wherein the set of associations corresponds to discovered knowledge that is extractable by a knowledge interpreter. 2. The method as in claim 1 , wherein extracting semi-structured meta-data from the set of text documents comprises extracting text content from documents containing at least one type of text, image, audio, and video information. | 0.593972 |
9,547,647 | 1 | 14 | 1. A method for searching for media items using a voice-based digital assistant, comprising: at an electronic device with a processor and memory storing instructions for execution by the processor: providing multiple media items wherein at least some of the media items are each associated with a respective tag comprising at least one of a time tag, a date tag, or a geo-code tag; providing a natural language text string corresponding to a search query for one or more media items, wherein the search query includes one or more query terms; searching at least one information source to identify at least one parameter associated with at least one of the one or more query terms, wherein the at least one parameter comprises at least one of a time parameter, a date parameter, or a geo-code parameter, wherein the at least one information source comprises user-specific descriptive information, and wherein the at least one parameter is not provided in the search query; comparing the respective tags to the at least one parameter to identify at least one media item whose tag matches the identified parameter; and facilitating the presentation of the at least one media item to a user. | 1. A method for searching for media items using a voice-based digital assistant, comprising: at an electronic device with a processor and memory storing instructions for execution by the processor: providing multiple media items wherein at least some of the media items are each associated with a respective tag comprising at least one of a time tag, a date tag, or a geo-code tag; providing a natural language text string corresponding to a search query for one or more media items, wherein the search query includes one or more query terms; searching at least one information source to identify at least one parameter associated with at least one of the one or more query terms, wherein the at least one parameter comprises at least one of a time parameter, a date parameter, or a geo-code parameter, wherein the at least one information source comprises user-specific descriptive information, and wherein the at least one parameter is not provided in the search query; comparing the respective tags to the at least one parameter to identify at least one media item whose tag matches the identified parameter; and facilitating the presentation of the at least one media item to a user. 14. The method of claim 1 , wherein the time parameter comprises a time range surrounding a time specified in the one or more query terms. | 0.830467 |
9,311,386 | 19 | 23 | 19. A non-transitory computer readable medium embodying instructions for network resource classification, the instructions when executed by a processor comprising functionality for: obtaining a hierarchy of categories for classifying a plurality of network resources, where each category is assigned a text item describing the category; obtaining a plurality of resource description data collections corresponding to the plurality of network resources, wherein the plurality of resource description data collections comprise a first resource description data collection corresponding to a first network resource of the plurality of network resources; generating, using a semantic correlation algorithm, a first category score vector of the first network resource by comparing the first resource description data collection to the text item assigned to each category in the hierarchy of categories, wherein the first category score vector comprises a category score for each category in the hierarchy of categories, wherein the category score is determined based on at least a semantic correlation measure between the first resource description data collection and the text item assigned to a corresponding category, wherein the plurality of network resources are classified based at least on the category score analyzing a network trace associated with a user to identify the plurality of network resources accessed by the user; generating, based on a pre-determined criterion, a relationship graph comprising: a plurality of nodes representing the plurality of network resources, and a plurality of edges representing a measure of cross-references between the plurality of resource description data collections; and adjusting, based on the relationship graph, the first category score vector to generate a first adjusted category score vector using at least another category score vector of another network resource of the plurality of network resources. | 19. A non-transitory computer readable medium embodying instructions for network resource classification, the instructions when executed by a processor comprising functionality for: obtaining a hierarchy of categories for classifying a plurality of network resources, where each category is assigned a text item describing the category; obtaining a plurality of resource description data collections corresponding to the plurality of network resources, wherein the plurality of resource description data collections comprise a first resource description data collection corresponding to a first network resource of the plurality of network resources; generating, using a semantic correlation algorithm, a first category score vector of the first network resource by comparing the first resource description data collection to the text item assigned to each category in the hierarchy of categories, wherein the first category score vector comprises a category score for each category in the hierarchy of categories, wherein the category score is determined based on at least a semantic correlation measure between the first resource description data collection and the text item assigned to a corresponding category, wherein the plurality of network resources are classified based at least on the category score analyzing a network trace associated with a user to identify the plurality of network resources accessed by the user; generating, based on a pre-determined criterion, a relationship graph comprising: a plurality of nodes representing the plurality of network resources, and a plurality of edges representing a measure of cross-references between the plurality of resource description data collections; and adjusting, based on the relationship graph, the first category score vector to generate a first adjusted category score vector using at least another category score vector of another network resource of the plurality of network resources. 23. The non-transitory computer readable medium of claim 19 , wherein the first network resource comprises a network hostname, wherein obtaining the plurality of resource description data collections comprises: obtaining a plurality of search results using the hostname as a search phrase of a pre-determined search engine, wherein the first resource description data collection comprises the plurality of search results. | 0.686289 |
8,788,270 | 1 | 24 | 1. A method for determining an emotion state of a speaker, comprising: providing an acoustic space having one or more dimensions, wherein each dimension of the one or more dimensions of the acoustic space corresponds to at least one baseline acoustic characteristic; receiving a subject utterance of speech by a speaker; measuring, via one or more processors, one or more acoustic characteristics of the subject utterance of speech; comparing, via the one or more processors, each acoustic characteristic of the one or more acoustic characteristics of the subject utterance of speech to a corresponding one or more baseline acoustic characteristic; and determining, via the one or more processors, an emotion state of the speaker based on the comparison, wherein determining the emotion state of the speaker based on the comparison occurs within one day of receiving the subject utterance of speech by the speaker. | 1. A method for determining an emotion state of a speaker, comprising: providing an acoustic space having one or more dimensions, wherein each dimension of the one or more dimensions of the acoustic space corresponds to at least one baseline acoustic characteristic; receiving a subject utterance of speech by a speaker; measuring, via one or more processors, one or more acoustic characteristics of the subject utterance of speech; comparing, via the one or more processors, each acoustic characteristic of the one or more acoustic characteristics of the subject utterance of speech to a corresponding one or more baseline acoustic characteristic; and determining, via the one or more processors, an emotion state of the speaker based on the comparison, wherein determining the emotion state of the speaker based on the comparison occurs within one day of receiving the subject utterance of speech by the speaker. 24. The method according to claim 1 , wherein determining the emotion state of the speaker based on the comparison occurs within 15 seconds of receiving the subject utterance of speech by the speaker. | 0.856734 |
7,663,628 | 5 | 7 | 5. The apparatus according to claim 1 wherein said behavior elements of said aspect comprise at least one chosen from a list comprising: geometric; orientation; and appearance elements. | 5. The apparatus according to claim 1 wherein said behavior elements of said aspect comprise at least one chosen from a list comprising: geometric; orientation; and appearance elements. 7. The apparatus according to claim 5 wherein said orientation behavior element comprises changes to said character resulting from transformations to vertices of said polygonal surfaces. | 0.5 |
7,756,930 | 88 | 89 | 88. The apparatus of claim 85 , wherein the step of computing the reputation score comprises the steps of: determining an output score based on the individual score for each list in the two or more lists. | 88. The apparatus of claim 85 , wherein the step of computing the reputation score comprises the steps of: determining an output score based on the individual score for each list in the two or more lists. 89. The apparatus of claim 88 , wherein the step of determining the output score comprises the steps of: determining a normalized score based on the aggregate score; and determining the output score based on the normalized score. | 0.5 |
9,547,423 | 22 | 24 | 22. One or more non-transitory computer-readable media including stored thereon instructions that when executed by a computing device cause the computing device to perform operations comprising: storing in a memory an executable graphical model including a plurality of components including hierarchically arranged child components disposed within parent components, the child components configured to send and receive messages during execution of the graphical model in an order, the messages persist for determined time intervals between a model execution start time and a model execution end time, and have payloads that remain fixed for a given send-receive interaction; identifying, by a processor coupled to the memory: the child components configured to send and receive messages, and the determined time intervals of the messages; automatically generating, by the processor, a message view window for the graphical model, the message view window including: an execution time scale corresponding to a time of execution of the graphical model, parent lifelines corresponding to the parent components, the parent lifelines extending across the execution time scale, and a plurality of first graphical affordances associated with the parent lifelines, the plurality of first graphical affordances representing the messages, the plurality of first graphical affordances arranged in the order of the messages; and expanding a given parent lifeline corresponding to a given parent component of the executable graphical model to show child lifelines corresponding to at least two of the child components of the given parent component. | 22. One or more non-transitory computer-readable media including stored thereon instructions that when executed by a computing device cause the computing device to perform operations comprising: storing in a memory an executable graphical model including a plurality of components including hierarchically arranged child components disposed within parent components, the child components configured to send and receive messages during execution of the graphical model in an order, the messages persist for determined time intervals between a model execution start time and a model execution end time, and have payloads that remain fixed for a given send-receive interaction; identifying, by a processor coupled to the memory: the child components configured to send and receive messages, and the determined time intervals of the messages; automatically generating, by the processor, a message view window for the graphical model, the message view window including: an execution time scale corresponding to a time of execution of the graphical model, parent lifelines corresponding to the parent components, the parent lifelines extending across the execution time scale, and a plurality of first graphical affordances associated with the parent lifelines, the plurality of first graphical affordances representing the messages, the plurality of first graphical affordances arranged in the order of the messages; and expanding a given parent lifeline corresponding to a given parent component of the executable graphical model to show child lifelines corresponding to at least two of the child components of the given parent component. 24. The one or more non-transitory computer-readable media of claim 22 wherein the operations performed by the computing device further comprise: moving, in response to user interaction with the message view window, at least one of the parent lifelines from a first location on the message view window to a second location on the message view window, where the plurality of first graphical affordances remain associated with the at least one of the parent lifelines moved to the second location. | 0.5 |
8,682,924 | 1 | 7 | 1. A method for providing recommendations to improve a query, comprising: receiving a query with query keywords and selected categories; and in response to determining that the selected categories are ranked high with reference to query relevance indicator values for each of the selected categories, determining whether a lowest category level has been reached in the selected categories; in response to determining that the lowest category level has been reached, ranking individual services that are at the lowest category levels; and providing one or more high ranked services from the ranked individual services; and in response to determining that the lowest category level has not been reached, calculating a keyword relevance indicator of each keyword in the query for each subcategory of each of the selected categories, wherein the keyword relevance indicator for a keyword is calculated using a keyword frequency of the keyword and an inverse service frequency of a subcategory; calculating a query relevance indicator of the query with each subcategory using the retrieved keyword relevance indicators, wherein the query relevance indicator is generated based on a keyword relevance indicator of a keyword specified in the query and a keyword relevance indicator of a keyword in the subcategory that is not specified in the query; ranking each subcategory based on the query relevance indicators; and providing the ranked subcategories for use in selecting new categories to be submitted with the query. | 1. A method for providing recommendations to improve a query, comprising: receiving a query with query keywords and selected categories; and in response to determining that the selected categories are ranked high with reference to query relevance indicator values for each of the selected categories, determining whether a lowest category level has been reached in the selected categories; in response to determining that the lowest category level has been reached, ranking individual services that are at the lowest category levels; and providing one or more high ranked services from the ranked individual services; and in response to determining that the lowest category level has not been reached, calculating a keyword relevance indicator of each keyword in the query for each subcategory of each of the selected categories, wherein the keyword relevance indicator for a keyword is calculated using a keyword frequency of the keyword and an inverse service frequency of a subcategory; calculating a query relevance indicator of the query with each subcategory using the retrieved keyword relevance indicators, wherein the query relevance indicator is generated based on a keyword relevance indicator of a keyword specified in the query and a keyword relevance indicator of a keyword in the subcategory that is not specified in the query; ranking each subcategory based on the query relevance indicators; and providing the ranked subcategories for use in selecting new categories to be submitted with the query. 7. The method of claim 1 , further comprising: executing the query in a current form; and providing a list of one or more services. | 0.868209 |
7,506,302 | 11 | 12 | 11. A computer-performed method of providing closed-loop analysis of a business process, the method performed by a process management subsystem of an enterprise system using a plurality of commercial off-the-shelf software (COTS) products including at least one flowcharting tool, at least one simulator, at least one project management tool, and at least one workflow management tool, the method comprising: in response to user input, selectively communicating with each of the COTS products via application programming interface (API) between each COTS product and the process management subsystem; providing a plurality of modeling stencils on a graphical display via the at least one flowcharting tool, the stencils selectable by the user to compose and display extended modeling diagrams describing requirements for and activities of the business process, and to extend at least one activity diagram of the modeling diagrams to obtain a simulation model for delivery to the at least one simulator to simulate at least part of the business process, the modeling diagrams extended so as to be independent of proprietary tools and protocols of the plurality of COTS products; based on user input, using at least one activity diagram selected by the user to specify a workflow model of the business process, generating activity duration data based on simulation output received from the at least one COSTS simulator, sending the duration data to one of the at least one COSTS project management tool, and specify a project management model of the business process using open architectures of the products to provide for semantic integration of models specified by the user; and providing abstractions of workflow and project management for use in one or more user-selected implementations of the selected activity diagram via the at least one COSTS workflow management tool and via the at least one COSTS project management tool. | 11. A computer-performed method of providing closed-loop analysis of a business process, the method performed by a process management subsystem of an enterprise system using a plurality of commercial off-the-shelf software (COTS) products including at least one flowcharting tool, at least one simulator, at least one project management tool, and at least one workflow management tool, the method comprising: in response to user input, selectively communicating with each of the COTS products via application programming interface (API) between each COTS product and the process management subsystem; providing a plurality of modeling stencils on a graphical display via the at least one flowcharting tool, the stencils selectable by the user to compose and display extended modeling diagrams describing requirements for and activities of the business process, and to extend at least one activity diagram of the modeling diagrams to obtain a simulation model for delivery to the at least one simulator to simulate at least part of the business process, the modeling diagrams extended so as to be independent of proprietary tools and protocols of the plurality of COTS products; based on user input, using at least one activity diagram selected by the user to specify a workflow model of the business process, generating activity duration data based on simulation output received from the at least one COSTS simulator, sending the duration data to one of the at least one COSTS project management tool, and specify a project management model of the business process using open architectures of the products to provide for semantic integration of models specified by the user; and providing abstractions of workflow and project management for use in one or more user-selected implementations of the selected activity diagram via the at least one COSTS workflow management tool and via the at least one COSTS project management tool. 12. The method of claim 11 , further comprising semantically integrating two or more models specified by the user, the integrating performed by extending an information model associated with one of the products. | 0.689706 |
8,522,156 | 1 | 12 | 1. A method for supporting input of one or more search parameters of a search engine in an input field, comprising: receiving input of a text character string including one or more search parameters in the input field displayed on a display device, wherein said text character string represents a search condition; determining a selection type in response to a user selection of a part of the text character string; displaying on the display device one or more search options of the search parameters depending on the determined selection type; and in response to the user selection of a desired search option, transforming said text character string to include the desired search option selected and displaying the transformed text character string on the display device; wherein said search options include search field specification, wild card, fuzzy search, proximity search, range search, search term boosting, Boolean operator, grouping and field grouping. | 1. A method for supporting input of one or more search parameters of a search engine in an input field, comprising: receiving input of a text character string including one or more search parameters in the input field displayed on a display device, wherein said text character string represents a search condition; determining a selection type in response to a user selection of a part of the text character string; displaying on the display device one or more search options of the search parameters depending on the determined selection type; and in response to the user selection of a desired search option, transforming said text character string to include the desired search option selected and displaying the transformed text character string on the display device; wherein said search options include search field specification, wild card, fuzzy search, proximity search, range search, search term boosting, Boolean operator, grouping and field grouping. 12. The method according to claim 1 , wherein labels of said one or more search options that can be selected are displayed on the display device. | 0.769108 |
4,499,553 | 23 | 39 | 23. A digital data processing means for locating from a plurality of digital coded candidate words at least one which is both an acceptable misspelling and an acceptable inflection of a digital coded query word, the query word and each of plural ones of the candidate words comprising plural characters, the means comprising: means for determining the characters forming a stem portion and an ending portion of such query word; means for forming a suffix class indication for any one of a plurality of classes in which the query word may be included; means for comparing the characters of the stem portion of the query word with characters in the beginning of such candidate words for finding candidate words with acceptable misspelling matches and candidate words with nonacceptable misspelling matches; means for determining characters forming an ending portion, if any, in each of individual ones of the candidate words; means for utilizing the suffix class indication to select from among other suffixes a representation of characters forming at least one acceptable suffix for the candidate words; and means for comparing character by character the characters of said at least one selected acceptable suffix with the characters in the ending portion in each of the individual ones of the candidate words for finding acceptable ending portions, the first and second recited means thereby locating candidate words which are both an acceptable misspelling and an acceptable inflection of the query word. | 23. A digital data processing means for locating from a plurality of digital coded candidate words at least one which is both an acceptable misspelling and an acceptable inflection of a digital coded query word, the query word and each of plural ones of the candidate words comprising plural characters, the means comprising: means for determining the characters forming a stem portion and an ending portion of such query word; means for forming a suffix class indication for any one of a plurality of classes in which the query word may be included; means for comparing the characters of the stem portion of the query word with characters in the beginning of such candidate words for finding candidate words with acceptable misspelling matches and candidate words with nonacceptable misspelling matches; means for determining characters forming an ending portion, if any, in each of individual ones of the candidate words; means for utilizing the suffix class indication to select from among other suffixes a representation of characters forming at least one acceptable suffix for the candidate words; and means for comparing character by character the characters of said at least one selected acceptable suffix with the characters in the ending portion in each of the individual ones of the candidate words for finding acceptable ending portions, the first and second recited means thereby locating candidate words which are both an acceptable misspelling and an acceptable inflection of the query word. 39. Means according to claim 23 comprising means for stripping the ending portion from the stem portion of the query word, and wherein the means for comparing the characters of the stem portion compare the stem portion from which the ending has been stripped. | 0.894715 |
5,469,355 | 1 | 7 | 1. A synonymous term generating method implemented by a computer for generating synonymous terms of a target character string by retrieving a synonymous term file based on the target character string, said synonymous term file defining synonymous terms for one or a plurality of words, said synonymous term generating method comprising the steps of: (a) retrieving the synonymous term file using words which form the target character string as keys, and extracting synonymous terms which are defined for each of the words used as the keys as the synonymous terms for each of the words forming the target character string; (b) forming a synonymous term group from each of the words forming the target character string and the synonymous terms so as to form a plurality of such synonymous term groups, and selecting at least one of the words and synonymous terms from each of the synonymous term groups; and (c) generating the synonymous terms of the target character string by combining at least one of words and synonymous terms obtained by said step (b) in an order which is different from the order of the words forming the target character string. | 1. A synonymous term generating method implemented by a computer for generating synonymous terms of a target character string by retrieving a synonymous term file based on the target character string, said synonymous term file defining synonymous terms for one or a plurality of words, said synonymous term generating method comprising the steps of: (a) retrieving the synonymous term file using words which form the target character string as keys, and extracting synonymous terms which are defined for each of the words used as the keys as the synonymous terms for each of the words forming the target character string; (b) forming a synonymous term group from each of the words forming the target character string and the synonymous terms so as to form a plurality of such synonymous term groups, and selecting at least one of the words and synonymous terms from each of the synonymous term groups; and (c) generating the synonymous terms of the target character string by combining at least one of words and synonymous terms obtained by said step (b) in an order which is different from the order of the words forming the target character string. 7. The synonymous term generating method as claimed in claim 1, which further comprises the step of: (d) dividing the target character string into the words forming the target character string if the target character string is not yet divided into such words by using the words, the synonymous terms of which are defined in the synonymous term file, prior to the retrieval by said step (a). | 0.613095 |
8,620,659 | 33 | 35 | 33. The method of claim 23 , further comprising: determining a most likely context for the natural language utterances; comparing one or more text combinations against one or more grammar expression entries in a context description grammar to identify one or more contexts that completely or partially match the one or more text combinations; providing a relevance score for each of identified matching contexts; selecting the matching context having a highest score as the most likely context for the natural language utterance, wherein the domain agent is associated with the selected context; communicating the request to the domain agent associated with the selected context; and generating the response to the request using content gathered as a result of the domain agent processing the request, wherein the response arranges the content in an order based on the relevance scores for the identified matching contexts. | 33. The method of claim 23 , further comprising: determining a most likely context for the natural language utterances; comparing one or more text combinations against one or more grammar expression entries in a context description grammar to identify one or more contexts that completely or partially match the one or more text combinations; providing a relevance score for each of identified matching contexts; selecting the matching context having a highest score as the most likely context for the natural language utterance, wherein the domain agent is associated with the selected context; communicating the request to the domain agent associated with the selected context; and generating the response to the request using content gathered as a result of the domain agent processing the request, wherein the response arranges the content in an order based on the relevance scores for the identified matching contexts. 35. The method of claim 33 , further comprising: determining a personality based on the identified matching contexts, the domain agent processing the request, or a user profile associated with the user; and formatting the response based on the personality using a personality module. | 0.5 |
9,613,125 | 2 | 14 | 2. The method of claim 1 , further comprising: storing in the database a plurality of annotations in addition to the first annotation and the second annotation, the plurality of annotations comprising a plurality of semantic labels that each indicates a semantic classification among a plurality of semantic classifications, at least some of the plurality of semantic classifications indicating a classification of meaning, each annotation of the plurality of annotations comprising a semantic label from among the plurality of semantic labels, the plurality of semantic classifications comprising the semantic classification of the first content and the semantic classification of the second content, wherein the storing the plurality of annotations in the database comprises storing each of the plurality of semantic labels in the first table. | 2. The method of claim 1 , further comprising: storing in the database a plurality of annotations in addition to the first annotation and the second annotation, the plurality of annotations comprising a plurality of semantic labels that each indicates a semantic classification among a plurality of semantic classifications, at least some of the plurality of semantic classifications indicating a classification of meaning, each annotation of the plurality of annotations comprising a semantic label from among the plurality of semantic labels, the plurality of semantic classifications comprising the semantic classification of the first content and the semantic classification of the second content, wherein the storing the plurality of annotations in the database comprises storing each of the plurality of semantic labels in the first table. 14. The method of claim 2 , wherein: the at least one data store stores, separate from the first table and the second table, a set of all semantic classifications that are available for inclusion in annotations as semantic labels indicating semantic classification of content of annotations; and the plurality of semantic classifications for the plurality of annotations are a subset of the set of semantic classifications. | 0.666929 |
9,292,799 | 1 | 12 | 1. A method for predicting failures in an artificial lift system, the method comprising: extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems; identifying pre-failure signatures based at least in part on a moving window of operational data in the extracted features preceding a known failure; forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system; and predicting one or more failures in an artificial lift system based on the learning model. | 1. A method for predicting failures in an artificial lift system, the method comprising: extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems; identifying pre-failure signatures based at least in part on a moving window of operational data in the extracted features preceding a known failure; forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system; and predicting one or more failures in an artificial lift system based on the learning model. 12. The method of claim 1 , further comprising periodically updating the learning model with a refreshed dataset including time sampled performance of the plurality of artificial lift systems. | 0.70997 |
9,569,698 | 3 | 5 | 3. The method of classification of claim 1 , wherein said first modality is textual, and said second modality is visual, the test object being a test image associated with textual tags, said dictionary according to the first modality being a textual dictionary W T and said dictionary according to the second modality being a visual dictionary W v . | 3. The method of classification of claim 1 , wherein said first modality is textual, and said second modality is visual, the test object being a test image associated with textual tags, said dictionary according to the first modality being a textual dictionary W T and said dictionary according to the second modality being a visual dictionary W v . 5. The method of classification of claim 3 , comprising a sequence of at least the following steps performed online: recoding of each textual tag of the test image on the multimedia dictionary, W m , for generating a recoded matrix Z; aggregating the recoded matrix Z and generating a multimedia signature BoMW for the test image. | 0.691589 |
9,594,802 | 1 | 10 | 1. A method comprising: accessing a database query statement, the database query statement being an executable instruction expressing an action to be performed with respect to a database; generating, using one or more processors, an abstract syntax tree (AST) corresponding to the database query statement, the AST being a data structure representing a syntactic structure of the database query statement; generating a domain model using the AST, the domain model describing elements of the database query statement and roles and relationships of each element; generating a graphical model of the database query statement using the domain model, the graphical model being a graphical representation of the structure of the database query statement; and causing presentation of the graphical model of the database query statement on a client device. | 1. A method comprising: accessing a database query statement, the database query statement being an executable instruction expressing an action to be performed with respect to a database; generating, using one or more processors, an abstract syntax tree (AST) corresponding to the database query statement, the AST being a data structure representing a syntactic structure of the database query statement; generating a domain model using the AST, the domain model describing elements of the database query statement and roles and relationships of each element; generating a graphical model of the database query statement using the domain model, the graphical model being a graphical representation of the structure of the database query statement; and causing presentation of the graphical model of the database query statement on a client device. 10. The method of claim 1 , wherein the domain model is an Extensible Markup Language (XML) model. | 0.884434 |
7,836,057 | 1 | 8 | 1. A computer implemented method for reconstructing a first search query corresponding to a user, and inferring user preferences manifested as query parameters, said computer implemented method comprising: receiving a plurality of search results, said plurality of search results equivalent to a first output derived from executing said first search query; determining a plurality of query parameters based on said plurality of search results, wherein said plurality of query parameters corresponds to user preferences, said plurality of search results includes an ordered results list, items from said ordered results list each have values for one or more specific criteria, and said values are associated with said items of said ordered results list; generating, based on said ordered search results list, specific parameter values for said plurality of query parameters; constructing a second search query based on said plurality of search results, said plurality of query parameters, and said specific parameter values; wherein said second search query is equivalent to and a reconstruction of said first search query in that a second output derived from executing said second search query results in said plurality of search results, whereby said first search query is reconstructed and said user preferences are inferred via said plurality of search results without further knowledge of said first search query. | 1. A computer implemented method for reconstructing a first search query corresponding to a user, and inferring user preferences manifested as query parameters, said computer implemented method comprising: receiving a plurality of search results, said plurality of search results equivalent to a first output derived from executing said first search query; determining a plurality of query parameters based on said plurality of search results, wherein said plurality of query parameters corresponds to user preferences, said plurality of search results includes an ordered results list, items from said ordered results list each have values for one or more specific criteria, and said values are associated with said items of said ordered results list; generating, based on said ordered search results list, specific parameter values for said plurality of query parameters; constructing a second search query based on said plurality of search results, said plurality of query parameters, and said specific parameter values; wherein said second search query is equivalent to and a reconstruction of said first search query in that a second output derived from executing said second search query results in said plurality of search results, whereby said first search query is reconstructed and said user preferences are inferred via said plurality of search results without further knowledge of said first search query. 8. The computer implemented method for reconstructing a first search query as recited in claim 1 , further comprising displaying said inferred user preference to said user in a graphical format; allowing said user to adjust said inferred user preferences through a graphical user interface; and generating a third search query in response to said user adjusting said inferred user preferences. | 0.5 |
9,043,397 | 3 | 7 | 3. The method of claim 2 , wherein the associations represent a number of the subset of connected accounts associated with visits to the website of the website-account pair, and wherein the method further comprises: calculating, for each of the plurality of website-account pairs, a connection score based on the number, wherein providing the account suggestions is based on the calculated connection scores. | 3. The method of claim 2 , wherein the associations represent a number of the subset of connected accounts associated with visits to the website of the website-account pair, and wherein the method further comprises: calculating, for each of the plurality of website-account pairs, a connection score based on the number, wherein providing the account suggestions is based on the calculated connection scores. 7. The method of claim 3 , wherein each of the plurality of verified accounts has a number of core followers exceeding a predefined minimum, and wherein each of the plurality of verified accounts has a number of followees less than a predefined maximum. | 0.688424 |
9,940,318 | 1 | 2 | 1. A computer-implemented method, comprising: grouping a corpus of outgoing communications sent by a user using one or more computing devices into a plurality of clusters, wherein each outgoing communication of the corpus is grouped into a cluster based on one or more attributes of a context of the user when the outgoing communication was sent, and wherein the one or more attributes of the context of the user when the outgoing communication was sent are determined based on one or more signals provided by a computing device operated by the user when the outgoing communication was sent; classifying one or more segments of each outgoing communication of a particular cluster as fixed in response to a determination that a count of occurrences of the one or more segments across the particular cluster satisfies a criterion; classifying one or more remaining segments of each outgoing communication of the particular cluster as transient; generating, based on sequences of classified segments associated with each communication of the particular cluster, an outgoing communication template that is usable to automatically populate at least a portion of one or more draft outgoing communications being prepared by the user; determining, based on one or more interactions between the user and the same computing device or a different computing device, that the user is preparing a subsequent outgoing communication; selecting a particular outgoing communication template from a plurality of outgoing communication templates, wherein the selecting is based on one or more attributes of a current context of the user that are determined based on one or more signals provided by the same computing device or by the different computing device; and applying the particular outgoing communication template to automatically populate at least a portion of the subsequent outgoing communication being prepared by the user with content from the particular outgoing communication template. | 1. A computer-implemented method, comprising: grouping a corpus of outgoing communications sent by a user using one or more computing devices into a plurality of clusters, wherein each outgoing communication of the corpus is grouped into a cluster based on one or more attributes of a context of the user when the outgoing communication was sent, and wherein the one or more attributes of the context of the user when the outgoing communication was sent are determined based on one or more signals provided by a computing device operated by the user when the outgoing communication was sent; classifying one or more segments of each outgoing communication of a particular cluster as fixed in response to a determination that a count of occurrences of the one or more segments across the particular cluster satisfies a criterion; classifying one or more remaining segments of each outgoing communication of the particular cluster as transient; generating, based on sequences of classified segments associated with each communication of the particular cluster, an outgoing communication template that is usable to automatically populate at least a portion of one or more draft outgoing communications being prepared by the user; determining, based on one or more interactions between the user and the same computing device or a different computing device, that the user is preparing a subsequent outgoing communication; selecting a particular outgoing communication template from a plurality of outgoing communication templates, wherein the selecting is based on one or more attributes of a current context of the user that are determined based on one or more signals provided by the same computing device or by the different computing device; and applying the particular outgoing communication template to automatically populate at least a portion of the subsequent outgoing communication being prepared by the user with content from the particular outgoing communication template. 2. The computer-implemented method of claim 1 , wherein the corpus includes outgoing communications sent by one or more additional users, and wherein each outgoing communication of the corpus is grouped into a cluster based on one or more attributes of a context of a user that sent the outgoing communication. | 0.565826 |
9,305,092 | 11 | 17 | 11. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a search query initial input from a user; receiving a plurality of query auto-completions based on the search query initial input; receiving social graph data, the social graph data being specific to a social graph of the user; for each query auto-completion of the plurality of query auto-completions, determining a ranking score, the ranking score being determined at least partially based on the social graph data and degree of separation between the user and other members in the social graph; transmitting instructions to display the plurality of query auto-completions to the user in a rank order that is determined based on ranking scores, receiving a selection from the user of a particular query auto-completion from the plurality of query auto-completions; and providing the particular query auto-completion to a search system as a search query, the search system providing search results based on the search query. | 11. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a search query initial input from a user; receiving a plurality of query auto-completions based on the search query initial input; receiving social graph data, the social graph data being specific to a social graph of the user; for each query auto-completion of the plurality of query auto-completions, determining a ranking score, the ranking score being determined at least partially based on the social graph data and degree of separation between the user and other members in the social graph; transmitting instructions to display the plurality of query auto-completions to the user in a rank order that is determined based on ranking scores, receiving a selection from the user of a particular query auto-completion from the plurality of query auto-completions; and providing the particular query auto-completion to a search system as a search query, the search system providing search results based on the search query. 17. The computer storage medium of claim 11 , wherein operations further comprise: identifying the query auto-completion as a highest ranking query auto-completion, wherein the selection is an automatic selection in response to the query auto-completion being the highest ranking query auto-completion; and transmitting instructions to display the search results as preliminary search results. | 0.5 |
8,676,732 | 1 | 11 | 1. A computer-implemented method of searching for content in a target set of content based on a reference set of content, a reference semantic network representing knowledge associated with the reference set of content, and a target semantic network representing knowledge associated with the target set of content, the method comprising: receiving a user-specified search query; obtaining, by using at least one processor executing stored program instructions, at least one concept semantically relevant to the user-specified search query by using the target semantic network and the reference semantic network; constructing a second search query by augmenting the first search query with one or more terms associated with the at least one obtained concept; providing, to the at least one user, content associated with search results obtained based at least in part on searching the target set of content by using the second search query, wherein any concept in the semantic network is represented by a data structure storing data associated with a node in the semantic network. | 1. A computer-implemented method of searching for content in a target set of content based on a reference set of content, a reference semantic network representing knowledge associated with the reference set of content, and a target semantic network representing knowledge associated with the target set of content, the method comprising: receiving a user-specified search query; obtaining, by using at least one processor executing stored program instructions, at least one concept semantically relevant to the user-specified search query by using the target semantic network and the reference semantic network; constructing a second search query by augmenting the first search query with one or more terms associated with the at least one obtained concept; providing, to the at least one user, content associated with search results obtained based at least in part on searching the target set of content by using the second search query, wherein any concept in the semantic network is represented by a data structure storing data associated with a node in the semantic network. 11. The computer-implemented method of claim 1 , wherein the target semantic network is represented by a data structure embodying a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node is associated with a concept and an edge between two nodes represents a relationship between the two corresponding concepts. | 0.732253 |
7,752,207 | 15 | 20 | 15. A computer-implemented method for crawling structured application data of an application by a search engine, the method comprising: generating, with one or more processors associated with one or more computer systems hosting a search engine, a first request for application data as a seed link sourcing a business object associated with the application; sending, from the one or more computer systems hosting the search engine, the first request for the sourced business object associated with the application; receiving, at the one or more computer systems, a control feed generated in response to the seed link and a crawlable definition for the business object associated with the application, the crawlable definition including information specifying a query selecting one or more attributes of the business object associated with the application, the control feed comprising information dividing a data feed associated with a crawlable document into a set of transferable batches according to predetermined criteria and one or more re-entry links for each transferable batches in the set of transferable batches; crawling, with the one or more processors associated with the one or more computer systems, the control feed to push the one or more re-entry links onto a job queue maintained by the one or more computer systems; generating, with the one or more processors associated with the one or more computer systems, a second request for structured application data as a re-entry link associated with the feed document, the second request being for a re-entry link of the one or more re-entry links in the job queue; sending, from the one or more computer system, the second request for structured application data; receiving, at the one or more computer systems, a crawlable batch from the data feed where the batch is requested in the re-entry link as a batch link; receiving, at the one or more computer systems, a crawlable related document where the crawlable related document is requested in the re-entry link as a related document link; and receiving, at the one or more computer systems, a crawlable dependent document where the crawlable related document is requested in the re-entry link as a dependent document link. | 15. A computer-implemented method for crawling structured application data of an application by a search engine, the method comprising: generating, with one or more processors associated with one or more computer systems hosting a search engine, a first request for application data as a seed link sourcing a business object associated with the application; sending, from the one or more computer systems hosting the search engine, the first request for the sourced business object associated with the application; receiving, at the one or more computer systems, a control feed generated in response to the seed link and a crawlable definition for the business object associated with the application, the crawlable definition including information specifying a query selecting one or more attributes of the business object associated with the application, the control feed comprising information dividing a data feed associated with a crawlable document into a set of transferable batches according to predetermined criteria and one or more re-entry links for each transferable batches in the set of transferable batches; crawling, with the one or more processors associated with the one or more computer systems, the control feed to push the one or more re-entry links onto a job queue maintained by the one or more computer systems; generating, with the one or more processors associated with the one or more computer systems, a second request for structured application data as a re-entry link associated with the feed document, the second request being for a re-entry link of the one or more re-entry links in the job queue; sending, from the one or more computer system, the second request for structured application data; receiving, at the one or more computer systems, a crawlable batch from the data feed where the batch is requested in the re-entry link as a batch link; receiving, at the one or more computer systems, a crawlable related document where the crawlable related document is requested in the re-entry link as a related document link; and receiving, at the one or more computer systems, a crawlable dependent document where the crawlable related document is requested in the re-entry link as a dependent document link. 20. The method of claim 15 , further comprising: indexing, with the one or more computer systems, a content of the received crawlable document according to an indexing rule set out in metadata of the received document. | 0.542017 |
9,552,213 | 1 | 3 | 1. A method for facilitating software interface localization comprising: receiving a software module to be localized, the software module having a first graphical user interface comprising at least one control label displaying a plurality of first graphemes in a first language, wherein the plurality of first graphemes correspond to text comprising one or more words in the first language; providing at least one look up table having at least some of the first graphemes and a plurality of second graphemes in a second language associated therewith, said association being based on a phonetic similarly between the first and second graphemes when the first graphemes are vocalized in the first language and the second graphemes are vocalized in the second language; and generating a second graphical user interface of the software module based on the first graphical user interface, including searching the at least one look up table for at least one second grapheme corresponding to at least one of a plurality of portions of the first graphemes, determining a plurality at least a first matching grapheme and a second matching grapheme among the plurality of second graphemes, wherein the first matching grapheme corresponds to a first portion of the plurality of portions of first graphemes, wherein the second matching grapheme corresponds to a second portion of the plurality of portions of first graphemes, wherein the first portion has more text characters than the second portion, and replacing at least one of the first graphemes in the at least one control label of the first graphical user interface with the associated second graphemes such that the second graphical user interface displays the second graphemes in the second language, the second graphemes being understandable in the first language when the second graphemes are vocalized, wherein the replacing of the at least one of the first graphemes comprises determining the first portion has more text characters than the second portion, and replacing the first portion with the first matching grapheme before replacing the second portion with the second matching grapheme based at least in part on a number of text characters comprised in each of the first portion and the second portion. | 1. A method for facilitating software interface localization comprising: receiving a software module to be localized, the software module having a first graphical user interface comprising at least one control label displaying a plurality of first graphemes in a first language, wherein the plurality of first graphemes correspond to text comprising one or more words in the first language; providing at least one look up table having at least some of the first graphemes and a plurality of second graphemes in a second language associated therewith, said association being based on a phonetic similarly between the first and second graphemes when the first graphemes are vocalized in the first language and the second graphemes are vocalized in the second language; and generating a second graphical user interface of the software module based on the first graphical user interface, including searching the at least one look up table for at least one second grapheme corresponding to at least one of a plurality of portions of the first graphemes, determining a plurality at least a first matching grapheme and a second matching grapheme among the plurality of second graphemes, wherein the first matching grapheme corresponds to a first portion of the plurality of portions of first graphemes, wherein the second matching grapheme corresponds to a second portion of the plurality of portions of first graphemes, wherein the first portion has more text characters than the second portion, and replacing at least one of the first graphemes in the at least one control label of the first graphical user interface with the associated second graphemes such that the second graphical user interface displays the second graphemes in the second language, the second graphemes being understandable in the first language when the second graphemes are vocalized, wherein the replacing of the at least one of the first graphemes comprises determining the first portion has more text characters than the second portion, and replacing the first portion with the first matching grapheme before replacing the second portion with the second matching grapheme based at least in part on a number of text characters comprised in each of the first portion and the second portion. 3. The method of claim 1 , wherein the step of replacing the first graphemes is automated. | 0.914449 |
7,523,076 | 1 | 7 | 1. A method of selecting a profile model for use in examining a structure formed on a semiconductor wafer using optical metrology, the method comprising: a) obtaining an initial profile model having a set of profile parameters that characterize the structure to be examined; b) training a machine learning system using the initial profile model; c) generating a simulated diffraction signal for an optimized profile model using the trained machine learning system, wherein the optimized profile model has a set of profile parameters with the same or fewer profile parameters than the initial profile model; and d) modifying the optimized profile model by eliminating at least one profile parameter or fixing to a value at least one profile parameter and iterating steps c) and d) using the modified optimized profile model and the same trained machine learning system until one or more termination criteria are met. | 1. A method of selecting a profile model for use in examining a structure formed on a semiconductor wafer using optical metrology, the method comprising: a) obtaining an initial profile model having a set of profile parameters that characterize the structure to be examined; b) training a machine learning system using the initial profile model; c) generating a simulated diffraction signal for an optimized profile model using the trained machine learning system, wherein the optimized profile model has a set of profile parameters with the same or fewer profile parameters than the initial profile model; and d) modifying the optimized profile model by eliminating at least one profile parameter or fixing to a value at least one profile parameter and iterating steps c) and d) using the modified optimized profile model and the same trained machine learning system until one or more termination criteria are met. 7. The method of claim 1 , wherein the one or more termination criteria includes a sensitivity determined for a profile parameter of the optimized profile model. | 0.810142 |
8,745,094 | 18 | 31 | 18. A method for tokenization of sensitive strings, the method comprising: receiving a sensitive string of characters; selecting a substring of the sensitive string of characters; forming an intermediate tokenized string of characters, by a processor, by replacing the selected substring of the sensitive string of characters with a first token; selecting a substring of the intermediate tokenized string of characters, the selected substring of the intermediate tokenized string of characters including at least one character replaced by the first token; and forming a final tokenized string of characters, by the processor, by replacing the selected substring of the intermediate tokenized string of characters with a second token, the second token being different from the first token. | 18. A method for tokenization of sensitive strings, the method comprising: receiving a sensitive string of characters; selecting a substring of the sensitive string of characters; forming an intermediate tokenized string of characters, by a processor, by replacing the selected substring of the sensitive string of characters with a first token; selecting a substring of the intermediate tokenized string of characters, the selected substring of the intermediate tokenized string of characters including at least one character replaced by the first token; and forming a final tokenized string of characters, by the processor, by replacing the selected substring of the intermediate tokenized string of characters with a second token, the second token being different from the first token. 31. The method of claim 18 , wherein one or more of the first token and the second token are randomly selected from a plurality of tokens. | 0.903631 |
8,327,458 | 7 | 8 | 7. The method of claim 1 , wherein accessing the access mechanism comprises accessing a conditional update program that defines how the particular part is to be modified if the content of the particular part is inaccessible when attempting to present the modular document. | 7. The method of claim 1 , wherein accessing the access mechanism comprises accessing a conditional update program that defines how the particular part is to be modified if the content of the particular part is inaccessible when attempting to present the modular document. 8. The method of claim 7 , further comprising: executing the conditional update program to modify the content of the particular part; and presenting the modular document with the modified content of the particular part. | 0.5 |
7,720,674 | 14 | 15 | 14. The method of claim 11 , the method further comprising: identifying at least one term having a semantic relationship with the at least one semantic token generated from the natural language query. | 14. The method of claim 11 , the method further comprising: identifying at least one term having a semantic relationship with the at least one semantic token generated from the natural language query. 15. The method of claim 14 , wherein identifying data in a knowledge base comprises identifying data in a knowledge base using the at least one semantic token and the at least one semantically-related term. | 0.5 |
7,571,183 | 5 | 6 | 5. A physical computer-readable medium having computer executable instructions stored thereon for generating media object playlists from a set of coordinate vectors derived from a sparse graph of music object similarities, comprising: identifying a set of media objects available to a local client computer; querying a remote server computer to retrieve a set of coordinate vectors corresponding to each of the music objects available to the local client computer; recursively forming clusters of at least one of the retrieved coordinate vectors as a function of computed distances between the retrieved coordinate vectors in multidimensional space; and wherein recursively forming clusters comprises: initially placing each retrieved coordinate vector into a unique cluster, identifying a coordinate vector having a largest number of neighboring coordinate vectors within a first minimum distance threshold, and forming a new cluster from the identified coordinate vector and the identified neighboring coordinate vectors, removing any coordinate vectors already added to a new cluster from further cluster considerations, and repeating the formation of new clusters and removing of coordinate vectors from further cluster consideration until all coordinate vectors have been assigned to a new cluster of at least one coordinate vector. | 5. A physical computer-readable medium having computer executable instructions stored thereon for generating media object playlists from a set of coordinate vectors derived from a sparse graph of music object similarities, comprising: identifying a set of media objects available to a local client computer; querying a remote server computer to retrieve a set of coordinate vectors corresponding to each of the music objects available to the local client computer; recursively forming clusters of at least one of the retrieved coordinate vectors as a function of computed distances between the retrieved coordinate vectors in multidimensional space; and wherein recursively forming clusters comprises: initially placing each retrieved coordinate vector into a unique cluster, identifying a coordinate vector having a largest number of neighboring coordinate vectors within a first minimum distance threshold, and forming a new cluster from the identified coordinate vector and the identified neighboring coordinate vectors, removing any coordinate vectors already added to a new cluster from further cluster considerations, and repeating the formation of new clusters and removing of coordinate vectors from further cluster consideration until all coordinate vectors have been assigned to a new cluster of at least one coordinate vector. 6. The computer-readable medium of claim 5 wherein the first minimum distance threshold is adjustable. | 0.791837 |
8,311,967 | 20 | 23 | 20. The computer-readable storage device of claim 19 , the operations further comprising: determining an output type for predictive output for the client-subscriber computing system, wherein: if the output type is included in the request, then determining the output type comprises receiving the output type in the request; and if the output type is not included in the request, then determining the output type comprises determining from the information that describes each of the trained predictive models that one or more models included in the predictive model repository are compatible with the one or more input types determined from the request to input types and that the one or more models can generate output of one or more output types, and selecting the output type from the one or more output types. | 20. The computer-readable storage device of claim 19 , the operations further comprising: determining an output type for predictive output for the client-subscriber computing system, wherein: if the output type is included in the request, then determining the output type comprises receiving the output type in the request; and if the output type is not included in the request, then determining the output type comprises determining from the information that describes each of the trained predictive models that one or more models included in the predictive model repository are compatible with the one or more input types determined from the request to input types and that the one or more models can generate output of one or more output types, and selecting the output type from the one or more output types. 23. The computer-readable storage device of claim 20 , wherein determining from the information that describes each of the trained predictive models that one or more models included in the predictive model repository match the request from the client-subscriber computing system is further based on a comparison of the output type determined for the request to output types included in the information that describes the trained predictive models. | 0.5 |
8,713,445 | 1 | 12 | 1. A computer-implemented method of generating a customized user interface representing a contract, comprising: receiving, over an electronic network, a contract description message including description information corresponding to a computer device being used to display the contract to a user; retrieving, by using a processor, the description information from the contract description message; comparing the description information with information stored in a template library and adaptor library to identify a generic user interface and a corresponding adaptor module; ranking the identified generic user interface and the identified adaptor module, wherein: the identified generic user interface is ranked when the comparison provides a plurality of potential generic user interfaces for generation of the customized user interface; and the identified adaptor module is ranked when the comparison provides a plurality of potential adaptor modules for generation of the customized user interface; and generating a contract response message based on a result of the comparison and the ranking, the response message including an instruction to generate, on the computer device, the customized user interface based on the identified generic user interface and corresponding adaptor module when the generic user interface and the corresponding adaptor module are identified. | 1. A computer-implemented method of generating a customized user interface representing a contract, comprising: receiving, over an electronic network, a contract description message including description information corresponding to a computer device being used to display the contract to a user; retrieving, by using a processor, the description information from the contract description message; comparing the description information with information stored in a template library and adaptor library to identify a generic user interface and a corresponding adaptor module; ranking the identified generic user interface and the identified adaptor module, wherein: the identified generic user interface is ranked when the comparison provides a plurality of potential generic user interfaces for generation of the customized user interface; and the identified adaptor module is ranked when the comparison provides a plurality of potential adaptor modules for generation of the customized user interface; and generating a contract response message based on a result of the comparison and the ranking, the response message including an instruction to generate, on the computer device, the customized user interface based on the identified generic user interface and corresponding adaptor module when the generic user interface and the corresponding adaptor module are identified. 12. The method of claim 1 , further comprising: generating a search message when the comparison result does not identify at least one of the generic user interface and adaptor; and sending the search message to a repository to search for at least one of the generic user interface and adaptor module. | 0.748322 |
10,013,414 | 21 | 22 | 21. A management entity comprising: a memory comprising instructions; and a processor in communication with the memory wherein the processor executes the instructions to: parse a request to collect data about a communications system for an entity in the communications system, the parsing to produce a parsed request and dependency information, generate sets of model elements in accordance with context tokens and content tokens derived from the parsed request, the content tokens including extrinsic metadata and intrinsic metadata of the parsed request, wherein the processor executing the instructions to generate the sets of model elements comprises the processor executing the instructions to: evaluate content of the request to collect the data to produce links between context tokens and the content tokens, and map the links into a model element graph, analyze context metadata tokens and content metadata tokens in accordance with the dependency information, analyze the model element graph in accordance with the dependency information, modify the model element graph for each dependency and dependency type in the dependency information, generate a platform-neutral description of results of the request from the model element graph derived from the sets of model elements, the platform-neutral description being independent of a protocol used by the management entity to collect the data about the communications system, and execute the request to collect the data as requested in accordance with the platform-neutral description; and store the data as collected in the memory. | 21. A management entity comprising: a memory comprising instructions; and a processor in communication with the memory wherein the processor executes the instructions to: parse a request to collect data about a communications system for an entity in the communications system, the parsing to produce a parsed request and dependency information, generate sets of model elements in accordance with context tokens and content tokens derived from the parsed request, the content tokens including extrinsic metadata and intrinsic metadata of the parsed request, wherein the processor executing the instructions to generate the sets of model elements comprises the processor executing the instructions to: evaluate content of the request to collect the data to produce links between context tokens and the content tokens, and map the links into a model element graph, analyze context metadata tokens and content metadata tokens in accordance with the dependency information, analyze the model element graph in accordance with the dependency information, modify the model element graph for each dependency and dependency type in the dependency information, generate a platform-neutral description of results of the request from the model element graph derived from the sets of model elements, the platform-neutral description being independent of a protocol used by the management entity to collect the data about the communications system, and execute the request to collect the data as requested in accordance with the platform-neutral description; and store the data as collected in the memory. 22. The management entity of claim 21 , wherein the processor executes the instructions to process the data as collected to produce processed data, and to display the processed data. | 0.80131 |
8,131,715 | 7 | 8 | 7. A computer, comprising: means for assigning initial scores to a plurality of documents, where the initial score assigned to one of the documents, that has been previously added to a list of bookmarks by a user, is higher than the initial score assigned to another one of the documents, that has not been previously added to the list of bookmarks by the user; means for calculating a score for a particular one of the documents based on the initial scores assigned to the documents that contain a link pointing to the particular one of the documents; and means for storing the score for the particular one of the documents. | 7. A computer, comprising: means for assigning initial scores to a plurality of documents, where the initial score assigned to one of the documents, that has been previously added to a list of bookmarks by a user, is higher than the initial score assigned to another one of the documents, that has not been previously added to the list of bookmarks by the user; means for calculating a score for a particular one of the documents based on the initial scores assigned to the documents that contain a link pointing to the particular one of the documents; and means for storing the score for the particular one of the documents. 8. The computer of claim 7 , where the means for calculating the score for the particular one of the documents includes: means for calculating the score for the particular one of the documents based on a sum of the initial scores assigned to the documents that contain a link pointing to the particular one of the documents. | 0.616114 |
8,140,515 | 31 | 34 | 31. A computer program product with instructions recorded on a non-transitory computer readable storage medium, which, when executed by a processor, carry out a method for building a user profile for a user, the instructions comprising: instructions for labeling and storing, with a computing device, user registration information in a database as a set of demographic nouns; instructions for analyzing, with a computing device, author-generated classification information regarding at least one document of a set of documents and assigning a set of first taxonomic nouns to characterize the user based upon the author-generated classification information; instructions for examining, with a computing device, a user-generated tag from a client computer and characterizing the user of at least one document of the set of documents and assigning a set of second taxonomic nouns to characterize the user based upon the user-generated tag characterization; instructions for identifying, with a computing device, a method by which the user accessed at least one document of the set of documents from a content provider and assigning at set of third taxonomic nouns to characterize the user based upon the method of access; instructions for evaluating, with a computing device, attributes that are related to the method of access and assigning a set of fourth taxonomic nouns to characterize the user based upon the attributes related to the method of access; instructions for processing, with a computing device, at least one document of the set of documents to extract a set of fifth taxonomic nouns to characterize the user; instructions for aggregating, with a computing device, the set of first taxonomic nouns, the set of second taxonomic nouns, the set of third taxonomic nouns, the set of fourth taxonomic nouns, and the set of fifth taxonomic nouns into a composite set of taxonomic nouns; instructions for building, with a computing device, a user profile based upon the composite set of taxonomic nouns, the author-generated classification information, and at least one of the demographic nouns; instructions for comparing, with a computing device, the composite set of taxonomic nouns with taxonomic nouns associated with a plurality of other user profiles corresponding to a plurality of other users; and instructions for modifying, with a computing device, the user profile based on the comparison. | 31. A computer program product with instructions recorded on a non-transitory computer readable storage medium, which, when executed by a processor, carry out a method for building a user profile for a user, the instructions comprising: instructions for labeling and storing, with a computing device, user registration information in a database as a set of demographic nouns; instructions for analyzing, with a computing device, author-generated classification information regarding at least one document of a set of documents and assigning a set of first taxonomic nouns to characterize the user based upon the author-generated classification information; instructions for examining, with a computing device, a user-generated tag from a client computer and characterizing the user of at least one document of the set of documents and assigning a set of second taxonomic nouns to characterize the user based upon the user-generated tag characterization; instructions for identifying, with a computing device, a method by which the user accessed at least one document of the set of documents from a content provider and assigning at set of third taxonomic nouns to characterize the user based upon the method of access; instructions for evaluating, with a computing device, attributes that are related to the method of access and assigning a set of fourth taxonomic nouns to characterize the user based upon the attributes related to the method of access; instructions for processing, with a computing device, at least one document of the set of documents to extract a set of fifth taxonomic nouns to characterize the user; instructions for aggregating, with a computing device, the set of first taxonomic nouns, the set of second taxonomic nouns, the set of third taxonomic nouns, the set of fourth taxonomic nouns, and the set of fifth taxonomic nouns into a composite set of taxonomic nouns; instructions for building, with a computing device, a user profile based upon the composite set of taxonomic nouns, the author-generated classification information, and at least one of the demographic nouns; instructions for comparing, with a computing device, the composite set of taxonomic nouns with taxonomic nouns associated with a plurality of other user profiles corresponding to a plurality of other users; and instructions for modifying, with a computing device, the user profile based on the comparison. 34. The computer program product for building a user profile of claim 31 , wherein the attributes related to the method of access include at least one of a network type used to access the document, a Web site from which the document was accessed, an electronic correspondence to which the document was associated, a time of day the document was accessed, a referrer who directed the user to the document, and a user category used by a database storing the document to describe the user. | 0.5 |
9,454,602 | 12 | 13 | 12. The method of claim 10 , further comprising: calculating an average similarity score between terms included in the term cluster and text portions included in the text portion cluster; determining that the average similarity score satisfies a third threshold; and providing an indication that the term cluster and the text portion cluster are related based on determining that the average similarity score satisfying the third threshold. | 12. The method of claim 10 , further comprising: calculating an average similarity score between terms included in the term cluster and text portions included in the text portion cluster; determining that the average similarity score satisfies a third threshold; and providing an indication that the term cluster and the text portion cluster are related based on determining that the average similarity score satisfying the third threshold. 13. The method of claim 12 , further comprising: modifying the third threshold; and providing, based on modifying the modified third threshold, at least one of: an indication that the term cluster and the text portion are no longer related, or an indication that the term cluster and an additional text portion, included in the set of text portions, are related. | 0.5 |
10,089,295 | 10 | 16 | 10. A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: store a plurality of page templates, each page template including one or more regions, each region configured to present one or more content items, one or more of the plurality of page templates including one or more regions, each region associated with a height that is based on a width of a display area; receive a request from a client device to present one or more content items from one or more sources in the digital magazine to a user; retrieve information describing content items associated with the digital magazine; retrieve information describing user interaction with one or more content items associated with the digital magazine; identify one or more page templates previously associated with the digital magazine from the one or more page templates; determine weights associated with characteristics of page templates based at least in part on characteristics of the identified one or more page templates previously associated with the digital magazine; select one or more candidate page templates by applying the determined weights to one or more selected from a group consisting of: the identified one or more page templates previously associated with the digital magazine, one or more characteristics of the content items associated with the digital magazine, the user interaction with the one or more content items associated with the digital magazine, and any combination thereof; generate a score associated with each of the one or more candidate page templates, where a score associated with a candidate page template is based on a number of the content items, characteristics of the one or more content items, and a number of regions in the page template; select a display page template based on the scores associated with the one or more candidate page templates; and generate a section of the digital magazine for presentation via the client device, the section including one or more regions each presenting one or more content items placed in positions specified by the one or more regions of the display page template. | 10. A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: store a plurality of page templates, each page template including one or more regions, each region configured to present one or more content items, one or more of the plurality of page templates including one or more regions, each region associated with a height that is based on a width of a display area; receive a request from a client device to present one or more content items from one or more sources in the digital magazine to a user; retrieve information describing content items associated with the digital magazine; retrieve information describing user interaction with one or more content items associated with the digital magazine; identify one or more page templates previously associated with the digital magazine from the one or more page templates; determine weights associated with characteristics of page templates based at least in part on characteristics of the identified one or more page templates previously associated with the digital magazine; select one or more candidate page templates by applying the determined weights to one or more selected from a group consisting of: the identified one or more page templates previously associated with the digital magazine, one or more characteristics of the content items associated with the digital magazine, the user interaction with the one or more content items associated with the digital magazine, and any combination thereof; generate a score associated with each of the one or more candidate page templates, where a score associated with a candidate page template is based on a number of the content items, characteristics of the one or more content items, and a number of regions in the page template; select a display page template based on the scores associated with the one or more candidate page templates; and generate a section of the digital magazine for presentation via the client device, the section including one or more regions each presenting one or more content items placed in positions specified by the one or more regions of the display page template. 16. The computer program product of claim 10 , wherein the user interaction with the one or more content items associated with the digital magazine is based at least in part on a frequency with which the user interacted with types of content item and types associated with the retrieved content items. | 0.831467 |
9,317,491 | 1 | 8 | 1. A method of generating an adaptable and interactive network document, comprising: extracting a visual layout of an interactive network document having a plurality of discrete interactive elements and published in a network accessible storage to be available to a plurality of web browsers installed on a plurality of client terminals; automatically calculating for said extracted visual layout, plurality of relative arrangement rules ranked according to an hierarchical order each one of said plurality of relative arrangement rules defining a relation between a layout parameter of one of said plurality of discrete interactive elements and a respective layout parameter of another of said plurality of discrete interactive elements; storing said plurality of relative arrangement rules in association with said interactive network document; receiving instructions to change said visual layout; and applying said instructions to change said visual layout of said interactive network document for generating accordingly a layout adjusted interactive network document having an adapted version of said visual layout wherein layout parameters of said plurality of discrete interactive elements are adapted according to said instructions such that said plurality of discrete interactive elements comply with said plurality of relative arrangement rules in said hierarchical order; and replacing said interactive network document with said layout adjusted interactive network document in said network accessible storage such that said layout adjusted interactive network document is available to said plurality of web browsers. | 1. A method of generating an adaptable and interactive network document, comprising: extracting a visual layout of an interactive network document having a plurality of discrete interactive elements and published in a network accessible storage to be available to a plurality of web browsers installed on a plurality of client terminals; automatically calculating for said extracted visual layout, plurality of relative arrangement rules ranked according to an hierarchical order each one of said plurality of relative arrangement rules defining a relation between a layout parameter of one of said plurality of discrete interactive elements and a respective layout parameter of another of said plurality of discrete interactive elements; storing said plurality of relative arrangement rules in association with said interactive network document; receiving instructions to change said visual layout; and applying said instructions to change said visual layout of said interactive network document for generating accordingly a layout adjusted interactive network document having an adapted version of said visual layout wherein layout parameters of said plurality of discrete interactive elements are adapted according to said instructions such that said plurality of discrete interactive elements comply with said plurality of relative arrangement rules in said hierarchical order; and replacing said interactive network document with said layout adjusted interactive network document in said network accessible storage such that said layout adjusted interactive network document is available to said plurality of web browsers. 8. The method of claim 1 , wherein said automatically calculating is performed according to the size of said plurality of discrete interactive elements in said interactive network document in relation to the size of said another of said plurality of discrete interactive elements in said interactive network document. | 0.524024 |
8,568,144 | 10 | 13 | 10. The system of claim 1 further comprising a main training unit capable of being coupled to the computing device over a communications path wherein the main training unit further comprises a main game logic unit that cooperates with the game logic portion executed by the computing device to generate the reading comprehension challenge and adjust a difficulty level of the next reading comprehension challenge. | 10. The system of claim 1 further comprising a main training unit capable of being coupled to the computing device over a communications path wherein the main training unit further comprises a main game logic unit that cooperates with the game logic portion executed by the computing device to generate the reading comprehension challenge and adjust a difficulty level of the next reading comprehension challenge. 13. The system of claim 10 , wherein the main training unit further comprises one or more server computers. | 0.894477 |
9,148,458 | 17 | 20 | 17. The processor-based method of claim 16 , further including determining a number of multi-dimensional behavioral vectors for behavioral indicator values the multi-dimensional behavioral vector including a vector indicative of at least one of traveling activity, movement activity, music consumption activity, or sleeping activity. | 17. The processor-based method of claim 16 , further including determining a number of multi-dimensional behavioral vectors for behavioral indicator values the multi-dimensional behavioral vector including a vector indicative of at least one of traveling activity, movement activity, music consumption activity, or sleeping activity. 20. The processor-based method of claim 17 , further including triggering an alarm on the basis of a triggering condition relative to a comparison result between two behavioral vectors of behavioral indicator values or relative to a calculation of a new behavioral indicator. | 0.714137 |
9,817,813 | 5 | 7 | 5. The method of claim 1 , wherein the semantic group comprises a plurality of terms, and wherein the first term is replaced with the second term from the plurality of terms of the semantic group, the second term being different from the first term. | 5. The method of claim 1 , wherein the semantic group comprises a plurality of terms, and wherein the first term is replaced with the second term from the plurality of terms of the semantic group, the second term being different from the first term. 7. The method of claim 5 , wherein the second term corresponds to the semantic group comprising the plurality of terms. | 0.887947 |
9,940,397 | 8 | 12 | 8. A non-transitory computer-readable storage medium storing instructions that, when executed by a mobile client device executing a social media messaging program, cause the mobile client device to perform the operations of: receiving, in the social media messaging program, a search request including one or more search terms; in accordance with a predefined search type hierarchy, searching content of a first set of content types of a plurality of content types to produce first search results, wherein: the search is based on the search request; and the predefined search type hierarchy specifies an order with which content types in the plurality of content types are to be searched; determining a count of the first search results; when the count of first search results is greater than or equal to a predefined number, displaying the first search results and affordances for searching content of one or more other content types in the plurality of content types; when the count of first search results is less than the predefined number: in accordance with the predefined search type hierarchy, searching content of a second set of content types of the plurality of content types to produce second search results; and displaying the second search results. | 8. A non-transitory computer-readable storage medium storing instructions that, when executed by a mobile client device executing a social media messaging program, cause the mobile client device to perform the operations of: receiving, in the social media messaging program, a search request including one or more search terms; in accordance with a predefined search type hierarchy, searching content of a first set of content types of a plurality of content types to produce first search results, wherein: the search is based on the search request; and the predefined search type hierarchy specifies an order with which content types in the plurality of content types are to be searched; determining a count of the first search results; when the count of first search results is greater than or equal to a predefined number, displaying the first search results and affordances for searching content of one or more other content types in the plurality of content types; when the count of first search results is less than the predefined number: in accordance with the predefined search type hierarchy, searching content of a second set of content types of the plurality of content types to produce second search results; and displaying the second search results. 12. The non-transitory computer-readable storage medium of claim 8 , wherein the predefined number is one. | 0.892495 |
9,922,655 | 2 | 4 | 2. The system of claim 1 , wherein the interruption determining circuit determines an allowable time for causing the computer speech output unit to output the computer speech based on the priority setting and the status of the human conversation. | 2. The system of claim 1 , wherein the interruption determining circuit determines an allowable time for causing the computer speech output unit to output the computer speech based on the priority setting and the status of the human conversation. 4. The system of claim 2 , wherein the interruption determining circuit sets the allowable time for interruption as a pause time in the human conversation. | 0.90227 |
10,056,076 | 2 | 4 | 2. The method of claim 1 , wherein said plurality of indices comprise at least one first feature index of a plurality of feature indices, at least one second feature index of said plurality of feature indices, at least one first Gaussian index of a plurality of Gaussian indices, and at least one second Gaussian index of a plurality of Gaussian indices. | 2. The method of claim 1 , wherein said plurality of indices comprise at least one first feature index of a plurality of feature indices, at least one second feature index of said plurality of feature indices, at least one first Gaussian index of a plurality of Gaussian indices, and at least one second Gaussian index of a plurality of Gaussian indices. 4. The method of claim 2 , wherein said modifying comprises setting some of said plurality of second covariance values equal to zero when said at least one first feature index is not equal to said at least one second feature index and said at least one first Gaussian index is not equal to said at least one second Gaussian index. | 0.600484 |
7,603,427 | 13 | 14 | 13. The method of claim 8 further comprising generating an estimated frequency of notifications, the estimated frequency of notifications corresponding to a particular contextual profile having a particular corresponding scalar. | 13. The method of claim 8 further comprising generating an estimated frequency of notifications, the estimated frequency of notifications corresponding to a particular contextual profile having a particular corresponding scalar. 14. The method of claim 13 further comprising logging a history of notifications for the user, the generating act comprising ascertaining the estimated frequency of notifications based at least in part on the history. | 0.5 |
7,870,087 | 59 | 67 | 59. A method of processing queries of databases, the method comprising: receiving a problem; parsing the problem into subproblems of suitable size to be processed by an analog processor; for each of the subproblems, setting a number of parameters of the analog processor to embed the subproblem into the analog processor; determining a subanswer to the subproblem from a final state of the analog processor. | 59. A method of processing queries of databases, the method comprising: receiving a problem; parsing the problem into subproblems of suitable size to be processed by an analog processor; for each of the subproblems, setting a number of parameters of the analog processor to embed the subproblem into the analog processor; determining a subanswer to the subproblem from a final state of the analog processor. 67. The method of claim 59 , further comprising: determining whether an answer to a problem is adequate; and resubmitting the problem for processing by the analog processor if the answer is determined to be inadequate. | 0.771488 |
9,779,171 | 1 | 8 | 1. A business networking system comprising: a computer processor configured to: receive from a user a first search query comprising search criteria; execute the first search query to retrieve information relating to members of the business networking system based on the search criteria and to retrieve additional information relating to one or more facet values of the business networking system based on the search criteria and a scoring of a relevance of the facet values to the user; execute a second search query using the one or more retrieved facet values to determine a count of documents that satisfy the search criteria and that include the one or more facet values; select a portion of the facet values for transmission to the user; and display on a user device the information relating to the members, the one or more facet values, and the counts for the one or more facet values; wherein the computer processor is configured to generate one or more posting lists, each posting list comprising documents having a particular facet value; wherein the computer processor is configured to terminate the second search query prior to completion of the second search query; and to estimate the count for each of the facet values; and wherein the count for each of the facet values is estimated, the estimation comprising multiplying a total number of documents in a particular posting list by a number of documents retrieved from the particular posting list that satisfy the search criteria at the time of the termination of the second search query, thereby generating a result, and dividing the result by an ordered position in the particular posting list of a last retrieved document in the particular posting list that satisfies the search criteria. | 1. A business networking system comprising: a computer processor configured to: receive from a user a first search query comprising search criteria; execute the first search query to retrieve information relating to members of the business networking system based on the search criteria and to retrieve additional information relating to one or more facet values of the business networking system based on the search criteria and a scoring of a relevance of the facet values to the user; execute a second search query using the one or more retrieved facet values to determine a count of documents that satisfy the search criteria and that include the one or more facet values; select a portion of the facet values for transmission to the user; and display on a user device the information relating to the members, the one or more facet values, and the counts for the one or more facet values; wherein the computer processor is configured to generate one or more posting lists, each posting list comprising documents having a particular facet value; wherein the computer processor is configured to terminate the second search query prior to completion of the second search query; and to estimate the count for each of the facet values; and wherein the count for each of the facet values is estimated, the estimation comprising multiplying a total number of documents in a particular posting list by a number of documents retrieved from the particular posting list that satisfy the search criteria at the time of the termination of the second search query, thereby generating a result, and dividing the result by an ordered position in the particular posting list of a last retrieved document in the particular posting list that satisfies the search criteria. 8. The business networking system of claim 1 , wherein the scoring of the facet values is based on one or more of a browsing history of the user, a connection of the user, a following by the user, and a count of the search criteria in retrieved documents associated with the facet value. | 0.66472 |
8,458,194 | 1 | 7 | 1. A computer-implemented method for categorizing documents, comprising: receiving, at a computer system, topic information for a source document, the information including at least one topic and a weight for each topic, where the topic relates to content of the source document, and the weight represents how strongly the topic is associated with the source document; generating similarity scores based on a weight of each topic in the source document and the weight of the same topic in each document within one or more sets of documents, where each document in the one or more sets of documents comprises topic information; generating, based on the similarity scores, a confidence score for each of the document sets with respect to the source document; comparing the confidence score for each of the document sets to a threshold confidence score; categorizing the document sets into classes based on the comparison of confidence scores for each of the document sets; selecting one or more of the classes of document sets; determining one or more filing attributes for the source document based on the selected one or more classes of document sets; and outputting the filing attributes. | 1. A computer-implemented method for categorizing documents, comprising: receiving, at a computer system, topic information for a source document, the information including at least one topic and a weight for each topic, where the topic relates to content of the source document, and the weight represents how strongly the topic is associated with the source document; generating similarity scores based on a weight of each topic in the source document and the weight of the same topic in each document within one or more sets of documents, where each document in the one or more sets of documents comprises topic information; generating, based on the similarity scores, a confidence score for each of the document sets with respect to the source document; comparing the confidence score for each of the document sets to a threshold confidence score; categorizing the document sets into classes based on the comparison of confidence scores for each of the document sets; selecting one or more of the classes of document sets; determining one or more filing attributes for the source document based on the selected one or more classes of document sets; and outputting the filing attributes. 7. The method of claim 1 , wherein the threshold confidence score comprises a varying threshold confidence score generated using an automatic threshold detection process. | 0.628821 |
7,953,591 | 13 | 16 | 13. A computer program product embodied in a non-transitory computer readable storage medium for automatically identifying unique language independent keys comprising the programming steps of: extracting language independent keys and associated text strings from resource files; inserting said extracted language independent keys and associated text strings in a file; receiving a first value of a first locale; searching for language independent keys in said file associated with said received first value of said first locale; identifying a plurality of language independent keys associated with said received first value of said first locale; identifying a second locale to narrow a number of said plurality of language independent keys; receiving a second value of a second locale; searching for language independent keys out of said plurality of language independent keys that are associated with said received first value of said first locale and associated with said received second value of said second locale; and identifying one or more of said plurality of language independent keys that are associated with said first value of said first locale and associated with said second value of said second locale. | 13. A computer program product embodied in a non-transitory computer readable storage medium for automatically identifying unique language independent keys comprising the programming steps of: extracting language independent keys and associated text strings from resource files; inserting said extracted language independent keys and associated text strings in a file; receiving a first value of a first locale; searching for language independent keys in said file associated with said received first value of said first locale; identifying a plurality of language independent keys associated with said received first value of said first locale; identifying a second locale to narrow a number of said plurality of language independent keys; receiving a second value of a second locale; searching for language independent keys out of said plurality of language independent keys that are associated with said received first value of said first locale and associated with said received second value of said second locale; and identifying one or more of said plurality of language independent keys that are associated with said first value of said first locale and associated with said second value of said second locale. 16. The computer program product as recited in claim 13 , wherein if more than one of said or more of said plurality of language independent keys is identified, then the computer program product further comprises the programming step of: identifying a third locale. | 0.523381 |
7,912,715 | 1 | 13 | 1. A method comprising: comparing by a processor a first feature vector in a sequence of feature vectors formed from a digitized incoming signal to be recognized, with a first number of templates from a set of templates representing candidate patterns, based on said comparison, selecting by a processor in response to a control signal, a second number of templates from said template set, the second number being smaller than the first number, comparing by a processor a second feature vector only with said selected templates, and generating by a processor a signal corresponding to a recognized pattern of said digitized incoming signal as a result of said comparing said second feature vector only with said selected templates. | 1. A method comprising: comparing by a processor a first feature vector in a sequence of feature vectors formed from a digitized incoming signal to be recognized, with a first number of templates from a set of templates representing candidate patterns, based on said comparison, selecting by a processor in response to a control signal, a second number of templates from said template set, the second number being smaller than the first number, comparing by a processor a second feature vector only with said selected templates, and generating by a processor a signal corresponding to a recognized pattern of said digitized incoming signal as a result of said comparing said second feature vector only with said selected templates. 13. A method according to claim 1 , wherein said number of successive feature vectors is static. | 0.808765 |
9,779,728 | 11 | 17 | 11. A system for modifying a voice file comprising a plurality of words, the system comprising: a silence-detection module configured to detect silences in the voice file and to divide the voice file into multiple segments based on at least the detected silences, an identification module configured to: apply a language model to the voice file as a whole, the language model including a plurality of feature units, a preliminary punctuation state, and a preliminary weight of the preliminary punctuation state, each of the feature units including a word or phrase, a part of speech or sentence element of the word or phrase, the application of the language model to the voice file as a whole identifying in the voice file as a whole one or more first feature units of the plurality of feature units; and identifying in the segments one or more second feature units of the plurality of feature units; and a punctuation-addition module configured to: generate a first aggregate weight R1 based on a combination of the preliminary weights of the preliminary punctuation states corresponding to the identified one or more first feature units; apply the language model to the segments, the application of the language model to the segments to generate a second aggregate weight R2 including a combination of the preliminary weights of the preliminary punctuation states corresponding to the identified one or more second feature units; generate a third aggregate weight R3 determined according to R3=a×R1+(1−a)×R2 where 0<a<1; and modifying the voice file so as to include one or more final punctuations based on at least the third aggregate weight R3. | 11. A system for modifying a voice file comprising a plurality of words, the system comprising: a silence-detection module configured to detect silences in the voice file and to divide the voice file into multiple segments based on at least the detected silences, an identification module configured to: apply a language model to the voice file as a whole, the language model including a plurality of feature units, a preliminary punctuation state, and a preliminary weight of the preliminary punctuation state, each of the feature units including a word or phrase, a part of speech or sentence element of the word or phrase, the application of the language model to the voice file as a whole identifying in the voice file as a whole one or more first feature units of the plurality of feature units; and identifying in the segments one or more second feature units of the plurality of feature units; and a punctuation-addition module configured to: generate a first aggregate weight R1 based on a combination of the preliminary weights of the preliminary punctuation states corresponding to the identified one or more first feature units; apply the language model to the segments, the application of the language model to the segments to generate a second aggregate weight R2 including a combination of the preliminary weights of the preliminary punctuation states corresponding to the identified one or more second feature units; generate a third aggregate weight R3 determined according to R3=a×R1+(1−a)×R2 where 0<a<1; and modifying the voice file so as to include one or more final punctuations based on at least the third aggregate weight R3. 17. The system of claim 11 wherein the one or more second feature units include an aggregation of one or more third feature units of the segments. | 0.91978 |
9,389,729 | 9 | 10 | 9. The non-transitory computer readable storage medium of claim 7 , wherein the executable program instructions further cause the electronic device to: upon determining that the electronic device is in the first proximity state, display information on the touch-sensitive display in a first display state, wherein the first display state is a regular power mode; and upon determining that the electronic device is in the second proximity state, cease display of information on the touch-sensitive display in a second display state different from the first display state, wherein the second display state is a low-power mode. | 9. The non-transitory computer readable storage medium of claim 7 , wherein the executable program instructions further cause the electronic device to: upon determining that the electronic device is in the first proximity state, display information on the touch-sensitive display in a first display state, wherein the first display state is a regular power mode; and upon determining that the electronic device is in the second proximity state, cease display of information on the touch-sensitive display in a second display state different from the first display state, wherein the second display state is a low-power mode. 10. The non-transitory computer readable storage medium of claim 9 , wherein the executable program instructions further cause the electronic device to: in response to a change in the received proximity data, change the display state of the electronic device. | 0.693128 |
8,375,312 | 1 | 10 | 1. A computer-implemented method for classifying digital content, the method comprising: displaying one or more poster frames in a user interface, wherein a poster frame corresponds to an item of digital content; in response to receiving an input, displaying a plurality of first level classification panes adjacent to a poster frame corresponding to an item of digital content, wherein each of the first level classification panes is associated with a corresponding keyword; detecting a selection and positioning of the poster frame at an at least partially common location with a classification pane of the plurality of first level classification panes; and in response to the detecting, associating the item of digital content to which the selected poster frame corresponds with a keyword associated with the first level classification pane on which the selected poster frame that corresponds to the item of digital content is positioned. | 1. A computer-implemented method for classifying digital content, the method comprising: displaying one or more poster frames in a user interface, wherein a poster frame corresponds to an item of digital content; in response to receiving an input, displaying a plurality of first level classification panes adjacent to a poster frame corresponding to an item of digital content, wherein each of the first level classification panes is associated with a corresponding keyword; detecting a selection and positioning of the poster frame at an at least partially common location with a classification pane of the plurality of first level classification panes; and in response to the detecting, associating the item of digital content to which the selected poster frame corresponds with a keyword associated with the first level classification pane on which the selected poster frame that corresponds to the item of digital content is positioned. 10. The method of claim 1 further comprising: in response to receiving another input, displaying a plurality of second level classification panes adjacent to the first level classification pane on which the selected poster frame that corresponds to the item of digital content is positioned, wherein each second level classification pane is a further classification of the first level classification pane and each second level classification pane is associated with a second keyword; detecting a positioning of the poster frame that was previously positioned at the first level classification pane, at an at least partially common location with a second level classification pane; and associating the item of digital content to which the selected poster frame corresponds with a second keyword associated with the second level classification pane on which the poster frame that corresponds to the item of digital content is positioned. | 0.5 |
8,510,656 | 1 | 2 | 1. A computer-based interactive storybook system, comprising: a processor executing a storybook program; a memory storing story data and image data associated with at least one story, each story including at least one user-modifiable scene; said story data including, for each user-modifiable scene, a static text portion and a plurality of alternative, user selectable text elements; said system having a first, word-based mode of operation wherein each user selectable text element is selectable by an end user to modify the story and alter the storyline while the story is in progress; said image data including, for each user-modifiable scene, a static image portion and a plurality of provisional image elements, wherein each of said provisional image elements is associated with a corresponding one of said user selectable text elements; said storybook program having a second, picture-based mode of operation wherein each of the plurality of image elements is selectable by the end user to modify the story and alter the storyline while the story is in progress; an input device for selecting from among said plurality of user selectable text elements; a display for displaying said story data and said image data; the storybook program configured to respond to the user selectable text elements selected via the first mode of operation and to cause the display of the static text portion in combination with the user selected provisional text elements and to cause the display to display the static image portion in combination with the provisional image element associated with the selected one of the user selectable provisional text elements; and the storybook program configured, responsive to one of the plurality of image elements being selected in the second mode of operation, to cause the display to display the static image portion in combination with the selected one of the provisional image elements and to cause the display to display the static text portion in combination with the user selectable text element that is associated with the selected one of the plurality of provisional image elements. | 1. A computer-based interactive storybook system, comprising: a processor executing a storybook program; a memory storing story data and image data associated with at least one story, each story including at least one user-modifiable scene; said story data including, for each user-modifiable scene, a static text portion and a plurality of alternative, user selectable text elements; said system having a first, word-based mode of operation wherein each user selectable text element is selectable by an end user to modify the story and alter the storyline while the story is in progress; said image data including, for each user-modifiable scene, a static image portion and a plurality of provisional image elements, wherein each of said provisional image elements is associated with a corresponding one of said user selectable text elements; said storybook program having a second, picture-based mode of operation wherein each of the plurality of image elements is selectable by the end user to modify the story and alter the storyline while the story is in progress; an input device for selecting from among said plurality of user selectable text elements; a display for displaying said story data and said image data; the storybook program configured to respond to the user selectable text elements selected via the first mode of operation and to cause the display of the static text portion in combination with the user selected provisional text elements and to cause the display to display the static image portion in combination with the provisional image element associated with the selected one of the user selectable provisional text elements; and the storybook program configured, responsive to one of the plurality of image elements being selected in the second mode of operation, to cause the display to display the static image portion in combination with the selected one of the provisional image elements and to cause the display to display the static text portion in combination with the user selectable text element that is associated with the selected one of the plurality of provisional image elements. 2. The system of claim 1 , further comprising: for each user-modifiable scene, an on-screen object selectable to display said list of said plurality of user selectable text elements. | 0.553922 |
8,392,150 | 1 | 5 | 1. A computer implemented method for manipulating a graphical representation of a real-world object in a computer drawing application comprising: (a) defining, using a computer, a semantic behavior for the real-world object, wherein: (i) the semantic behavior comprises a collision rule, an orientation rule, an affinity rule, and an attachment rule; (ii) the collision rule defines an intersection between geometries or bounding boxes of a subject object and a host object; (iii) the orientation rule defines an alignment of the subject object with a geometry of the host object; (iv) the affinity rule defines placement rules that determine where the subject object can be legally placed in a drawing; (v) the attachment rule defines an attachment between the subject object and the host object wherein both the subject object and the host object move if either the host object or subject object is moved; (vi) the affinity rule has procedural priority over the orientation rule; (vii) the attachment rule has procedural priority over the affinity rule and the orientation rule; and (viii) the collision rule has procedural priority over the attachment rule, the affinity rule, and the orientation rule; (b) obtaining, using a computer, a graphical representation of the real-world object, wherein the graphical representation is referred to as the subject object; (c) assigning, using a computer, the semantic behavior to the subject object, wherein: (i) the semantic behavior defines a behavioral rule for placement of the subject object into the drawing; (ii) the behavioral rule specifies a host object type that specifies a type of object that the behavioral rule applies to; (iii) the behavioral rule specifies an exclusion host identifier that identifies a particular host object for which an application of the behavioral rule will be excluded; and (d) placing, using a computer, the subject object into the drawing using the computer-drawing application, wherein the subject object automatically, without additional user input, places itself into the drawing based on the semantic behavior. | 1. A computer implemented method for manipulating a graphical representation of a real-world object in a computer drawing application comprising: (a) defining, using a computer, a semantic behavior for the real-world object, wherein: (i) the semantic behavior comprises a collision rule, an orientation rule, an affinity rule, and an attachment rule; (ii) the collision rule defines an intersection between geometries or bounding boxes of a subject object and a host object; (iii) the orientation rule defines an alignment of the subject object with a geometry of the host object; (iv) the affinity rule defines placement rules that determine where the subject object can be legally placed in a drawing; (v) the attachment rule defines an attachment between the subject object and the host object wherein both the subject object and the host object move if either the host object or subject object is moved; (vi) the affinity rule has procedural priority over the orientation rule; (vii) the attachment rule has procedural priority over the affinity rule and the orientation rule; and (viii) the collision rule has procedural priority over the attachment rule, the affinity rule, and the orientation rule; (b) obtaining, using a computer, a graphical representation of the real-world object, wherein the graphical representation is referred to as the subject object; (c) assigning, using a computer, the semantic behavior to the subject object, wherein: (i) the semantic behavior defines a behavioral rule for placement of the subject object into the drawing; (ii) the behavioral rule specifies a host object type that specifies a type of object that the behavioral rule applies to; (iii) the behavioral rule specifies an exclusion host identifier that identifies a particular host object for which an application of the behavioral rule will be excluded; and (d) placing, using a computer, the subject object into the drawing using the computer-drawing application, wherein the subject object automatically, without additional user input, places itself into the drawing based on the semantic behavior. 5. The method of claim 1 , wherein the semantic behavior comprises an affinity that defines placement rules that determine where the subject object can be legally placed in the drawing. | 0.601293 |
8,781,080 | 1 | 10 | 1. A method comprising: receiving an audio message from a first user; generating a text-based representation of the audio message; generating one or more identification tags using the text-based representation of the audio message, wherein at least one of the one or more identification tags includes a subject of the audio message; and presenting at least one of the text-based representation of the audio message or the one or more identification tags to a second user using a graphical user interface. | 1. A method comprising: receiving an audio message from a first user; generating a text-based representation of the audio message; generating one or more identification tags using the text-based representation of the audio message, wherein at least one of the one or more identification tags includes a subject of the audio message; and presenting at least one of the text-based representation of the audio message or the one or more identification tags to a second user using a graphical user interface. 10. The method of claim 1 , wherein the one or more identification tags includes a priority status tag, wherein the method further comprises prioritizing the text-based representation of the audio message presented to the second user with respect to other text-based representations of audio messages that are presented to the second user using the graphical user interface, and wherein the prioritization is based on the priority status tag associated with the text-based representation of the audio message and on other priority status tags associated with other text-based representations of audio messages. | 0.50727 |
8,671,280 | 1 | 2 | 1. A non-transitory computer-readable medium having recorded thereon an electronic document management program that causes a computer to manage document information relating to an electronic bond generated from electronic information, the program causing the computer to perform a process comprising: acquiring a plurality of pieces of part identification information respectively identifiably expressing a plurality of parts of document information and a digital signature corresponding to the document information; acquiring a preparation type, a preparer's name and a time and date of preparation of the document information as tracing information of the document information, the preparation type indicating at least one of new issuance of an electronic bond, transfer of a whole electronic bond, issuance of divided electronic bonds, and transfer of one or more than one divided electronic bonds; managing the part identification information and the digital signature acquired in acquiring the plurality of pieces of part identification information and the digital signature and the tracing information acquired in acquiring the preparation type, the preparer's name and the time and date of preparation of the document information, in association with each other, managing, when the preparation type indicates the issuance of divided electronic bonds, the part identification information identifiably expressing amounts of the divided electronic bonds and attached with an electronic signature of the preparer's name of the issuance of the divided electronic bonds; and presenting information relating to the tracing information to the user in response to a request from the user, and causing the acquiring of the plurality of pieces of part identification information and the digital signature and acquiring of the preparation type, the preparer's name and the time and date of preparation of the document information to be executed in response to a directive from the user, wherein the part identification information acquired in acquiring the plurality of pieces of part identification information is acquired as hash information generated by using the parts of the document information, and the hash information is generated by adding the time information to the information of each of the parts of the document information. | 1. A non-transitory computer-readable medium having recorded thereon an electronic document management program that causes a computer to manage document information relating to an electronic bond generated from electronic information, the program causing the computer to perform a process comprising: acquiring a plurality of pieces of part identification information respectively identifiably expressing a plurality of parts of document information and a digital signature corresponding to the document information; acquiring a preparation type, a preparer's name and a time and date of preparation of the document information as tracing information of the document information, the preparation type indicating at least one of new issuance of an electronic bond, transfer of a whole electronic bond, issuance of divided electronic bonds, and transfer of one or more than one divided electronic bonds; managing the part identification information and the digital signature acquired in acquiring the plurality of pieces of part identification information and the digital signature and the tracing information acquired in acquiring the preparation type, the preparer's name and the time and date of preparation of the document information, in association with each other, managing, when the preparation type indicates the issuance of divided electronic bonds, the part identification information identifiably expressing amounts of the divided electronic bonds and attached with an electronic signature of the preparer's name of the issuance of the divided electronic bonds; and presenting information relating to the tracing information to the user in response to a request from the user, and causing the acquiring of the plurality of pieces of part identification information and the digital signature and acquiring of the preparation type, the preparer's name and the time and date of preparation of the document information to be executed in response to a directive from the user, wherein the part identification information acquired in acquiring the plurality of pieces of part identification information is acquired as hash information generated by using the parts of the document information, and the hash information is generated by adding the time information to the information of each of the parts of the document information. 2. The non-transitory computer-readable medium according to claim 1 , wherein the process further comprises presenting the tracing information in a predetermined format to the user and supporting the verification by the user of the properness of the document information. | 0.5 |
10,061,863 | 9 | 12 | 9. A device, comprising: a memory to store instructions; and a processor to execute the instructions to: automatically process an asset including content and metadata; automatically select a data map to perform transforming of the metadata, wherein the transforming includes converting the metadata to a target metadata format; automatically perform the transforming of a validated metadata based on the data map, wherein the transforming includes converting the metadata to the target metadata format, and wherein the target metadata format includes one or more extendible fields and values that correspond to one or more fields and values included in a source metadata format of the metadata and not provided by a standard of a metadata format on which the target metadata format is based and the target metadata format includes each of the fields and values provided by the standard of the metadata format on which the target metadata format is based, and wherein the transforming includes using the one or more extendible fields when one or more fields of the validated metadata do not have corresponding one or more fields afforded by the standard of the metadata format; and automatically store a target metadata based on the automatically performing. | 9. A device, comprising: a memory to store instructions; and a processor to execute the instructions to: automatically process an asset including content and metadata; automatically select a data map to perform transforming of the metadata, wherein the transforming includes converting the metadata to a target metadata format; automatically perform the transforming of a validated metadata based on the data map, wherein the transforming includes converting the metadata to the target metadata format, and wherein the target metadata format includes one or more extendible fields and values that correspond to one or more fields and values included in a source metadata format of the metadata and not provided by a standard of a metadata format on which the target metadata format is based and the target metadata format includes each of the fields and values provided by the standard of the metadata format on which the target metadata format is based, and wherein the transforming includes using the one or more extendible fields when one or more fields of the validated metadata do not have corresponding one or more fields afforded by the standard of the metadata format; and automatically store a target metadata based on the automatically performing. 12. The device of claim 9 , wherein the target metadata format includes each of the fields and values provided by an Asset Distribution Interface (ADI)-standard metadata format. | 0.840253 |
9,116,877 | 8 | 9 | 8. The method of claim 5 , further comprising: weighting each c i . | 8. The method of claim 5 , further comprising: weighting each c i . 9. The method of claim 8 wherein the weighting is according to a simulated annealing algorithm. | 0.5 |
8,762,369 | 10 | 16 | 10. A method of estimating a result of a continuous query in a data stream management system to reduce data stream processing times therein, the data stream management system being operable configured to execute the continuous query against data items received via at least one input data stream to produce at least one output data stream, the method comprising: classifying a set of at least one data item received via the at least one input data stream into one of a plurality of input data groups; storing in a data store, in association with each of the input data groups, a respective rule for estimating a result of executing the continuous query against a data item belonging to the input data group; and selecting a rule from the rules stored in the data store on the basis of the classification; and applying the selected rule to the received set of data items to generate an estimate for a result of executing the continuous query against the received set of data items. | 10. A method of estimating a result of a continuous query in a data stream management system to reduce data stream processing times therein, the data stream management system being operable configured to execute the continuous query against data items received via at least one input data stream to produce at least one output data stream, the method comprising: classifying a set of at least one data item received via the at least one input data stream into one of a plurality of input data groups; storing in a data store, in association with each of the input data groups, a respective rule for estimating a result of executing the continuous query against a data item belonging to the input data group; and selecting a rule from the rules stored in the data store on the basis of the classification; and applying the selected rule to the received set of data items to generate an estimate for a result of executing the continuous query against the received set of data items. 16. The method according to claim 10 , wherein each of the rules comprises a decision tree whose branches can be navigated to arrive at the estimate of the result of executing the continuous query based on values of the received set of data items. | 0.751509 |
9,916,303 | 17 | 20 | 17. A computer program product providing an answer to a question containing at least one time-sensitive word or at least one time-sensitive phrase using natural language processing (NLP), the computer program product comprising: one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices, the program instructions executable by a processor, the program instructions comprising: program instructions to create and maintaining an online T-Word Dictionary, wherein creating and maintaining the online T-Word Dictionary comprises: program instructions to determine a relationship between a plurality of T-Words and a plurality of corresponding values, wherein the plurality of corresponding values include a plurality of related lookup phrases and a plurality of concept terms; program instructions to mapping the plurality of T-Words to the plurality of corresponding values based on the determine relationship; and program instructions to store the mapped plurality of T-Words to the plurality of corresponding values in the online T-Word Dictionary; program instructions to receive the input question, wherein the input question is entered by a user via a graphical user interface associated with a first computer; program instructions to perform natural language processing (NLP) analysis on the input question to extract a required value phrase; program instructions to form at least one mathematical equation based on the extracted required value phrase, wherein forming the at least one mathematical equation comprises: program instructions to identify the at least one time-sensitive word or the at least one time-sensitive phrase contained in the received input question, wherein a value associated with the identified at least one time-sensitive word or the at least one time-sensitive phrase varies according to a particular point in time, and wherein the identifying comprises communicating online with a second computer to access the online T-Word Dictionary; and program instructions to resolve the identified at least one time-sensitive word or the at least one time-sensitive phrase contained in the received input question, wherein the resolving comprises communicating online with the second computer to access the online T-Word Dictionary and recursively mapping a plurality of variables associated with the identified at least one time-sensitive word or the at least one time-sensitive phrase to at least one formula contained in the T-Word Dictionary; program instructions to determine the answer to the input question in natural language based on the solved at least one mathematical equation; and program instructions to narrate the answer to the input question in natural language based on the solved at least one mathematical equation. | 17. A computer program product providing an answer to a question containing at least one time-sensitive word or at least one time-sensitive phrase using natural language processing (NLP), the computer program product comprising: one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices, the program instructions executable by a processor, the program instructions comprising: program instructions to create and maintaining an online T-Word Dictionary, wherein creating and maintaining the online T-Word Dictionary comprises: program instructions to determine a relationship between a plurality of T-Words and a plurality of corresponding values, wherein the plurality of corresponding values include a plurality of related lookup phrases and a plurality of concept terms; program instructions to mapping the plurality of T-Words to the plurality of corresponding values based on the determine relationship; and program instructions to store the mapped plurality of T-Words to the plurality of corresponding values in the online T-Word Dictionary; program instructions to receive the input question, wherein the input question is entered by a user via a graphical user interface associated with a first computer; program instructions to perform natural language processing (NLP) analysis on the input question to extract a required value phrase; program instructions to form at least one mathematical equation based on the extracted required value phrase, wherein forming the at least one mathematical equation comprises: program instructions to identify the at least one time-sensitive word or the at least one time-sensitive phrase contained in the received input question, wherein a value associated with the identified at least one time-sensitive word or the at least one time-sensitive phrase varies according to a particular point in time, and wherein the identifying comprises communicating online with a second computer to access the online T-Word Dictionary; and program instructions to resolve the identified at least one time-sensitive word or the at least one time-sensitive phrase contained in the received input question, wherein the resolving comprises communicating online with the second computer to access the online T-Word Dictionary and recursively mapping a plurality of variables associated with the identified at least one time-sensitive word or the at least one time-sensitive phrase to at least one formula contained in the T-Word Dictionary; program instructions to determine the answer to the input question in natural language based on the solved at least one mathematical equation; and program instructions to narrate the answer to the input question in natural language based on the solved at least one mathematical equation. 20. The computer program product of claim 17 , wherein an output from the natural language processing (NLP) is stored in a temporary repository or held in memory. | 0.672065 |
9,143,603 | 1 | 16 | 1. A method employing a portable user device having at least one microphone that captures audio, and at least one image sensor for capturing imagery, the method comprising the acts: (a) capturing imagery with the image sensor, the captured image depicting one or more physical subjects within an environment of said user, and capturing user speech with the microphone; (b) sending, to a speech recognition module, audio data corresponding to the user speech, and receiving recognized user speech data corresponding thereto; (c) applying a computer-implemented cognition process to the imagery, said cognition process also employing information from the recognized user speech data as a clue to help identify a physical subject within the captured imagery that is of interest to said user; and (d) presenting a set of plural response options to the user, for user selection therebetween; wherein the set of plural response options presented to the user varies based on said identified physical subject. | 1. A method employing a portable user device having at least one microphone that captures audio, and at least one image sensor for capturing imagery, the method comprising the acts: (a) capturing imagery with the image sensor, the captured image depicting one or more physical subjects within an environment of said user, and capturing user speech with the microphone; (b) sending, to a speech recognition module, audio data corresponding to the user speech, and receiving recognized user speech data corresponding thereto; (c) applying a computer-implemented cognition process to the imagery, said cognition process also employing information from the recognized user speech data as a clue to help identify a physical subject within the captured imagery that is of interest to said user; and (d) presenting a set of plural response options to the user, for user selection therebetween; wherein the set of plural response options presented to the user varies based on said identified physical subject. 16. The method of claim 1 in which act (c) includes employing information from the recognized user speech data as a clue to help identify a sub-part within the captured imagery that is of user interest, said cognition process yielding output data relating to a subject at said identified sub-part of the imagery. | 0.654867 |
9,189,652 | 1 | 7 | 1. A computer-implemented method comprising: characterizing multiple items of content accessed over a network, each item of content associated with a content descriptor and characterized with a set of multiple depersonalized keywords, the set comprising words submitted to an online search engine by a population comprising multiple entities in the past to locate the content; recording, at a keyword mapping system, a consumption history of a specified entity, the consumption history comprising at least two different content descriptors, each content descriptor associated with a respective item of content accessed over the network by the specified entity; selecting, from the recorded consumption history, at least two content descriptors which have been stored for longer than a specified timeframe; converting the recorded consumption history to a protected consumption history by representing each selected content descriptor with the set of multiple depersonalized keywords which characterize the respective selected content descriptor's item of content; removing the selected content descriptors from the protected consumption history; receiving a bid request from an advertising exchange, the bid request comprising an identifier of the specified entity; determining a frequency of each specified keyword from a list of specified keywords in the depersonalized keywords of the protected consumption history; configuring a bid response by assessing the suitability of the specified entity to receive advertising content based at least in part on the frequencies; and sending the bid response to the advertising exchange. | 1. A computer-implemented method comprising: characterizing multiple items of content accessed over a network, each item of content associated with a content descriptor and characterized with a set of multiple depersonalized keywords, the set comprising words submitted to an online search engine by a population comprising multiple entities in the past to locate the content; recording, at a keyword mapping system, a consumption history of a specified entity, the consumption history comprising at least two different content descriptors, each content descriptor associated with a respective item of content accessed over the network by the specified entity; selecting, from the recorded consumption history, at least two content descriptors which have been stored for longer than a specified timeframe; converting the recorded consumption history to a protected consumption history by representing each selected content descriptor with the set of multiple depersonalized keywords which characterize the respective selected content descriptor's item of content; removing the selected content descriptors from the protected consumption history; receiving a bid request from an advertising exchange, the bid request comprising an identifier of the specified entity; determining a frequency of each specified keyword from a list of specified keywords in the depersonalized keywords of the protected consumption history; configuring a bid response by assessing the suitability of the specified entity to receive advertising content based at least in part on the frequencies; and sending the bid response to the advertising exchange. 7. The method of claim 1 wherein: the specified timeframe is configured based on a jurisdiction of the specified entity. | 0.684211 |
8,050,908 | 12 | 13 | 12. The method of claim 11 , wherein dynamically expanding the third finite-state automaton based on an input string to be recognized by the third finite-state automaton comprises: inputting the input string; selecting a symbol of the input string; determining, based on the selected symbol, whether expanding of the third finite-state automaton is desirable; and if expanding the third finite-state automaton is desirable, replacing an edge of the third finite-state automaton that is labeled with the selected symbol with one of the at least one second finite-state automaton that contains the selected symbol. | 12. The method of claim 11 , wherein dynamically expanding the third finite-state automaton based on an input string to be recognized by the third finite-state automaton comprises: inputting the input string; selecting a symbol of the input string; determining, based on the selected symbol, whether expanding of the third finite-state automaton is desirable; and if expanding the third finite-state automaton is desirable, replacing an edge of the third finite-state automaton that is labeled with the selected symbol with one of the at least one second finite-state automaton that contains the selected symbol. 13. The method of claim 12 , wherein replacing the edge of the third finite-state automaton that is labeled with the selected symbol with one of the at least one second finite-state automaton that contains the selected symbol comprises: modifying that second finite-state automaton into a new automaton that accepts the selected symbol; and substituting that edge with the new automaton. | 0.5 |
5,392,428 | 16 | 18 | 16. The system of claim 1 wherein the output means further comprises: a) text search and retrieval query means for locating each occurrence of a string of characters in at least one of the first and second bodies of text; and b) text search and retrieval report means comprising: i) occurrence report means for listing the location in at least one of the first and second bodies of text of all occurrences of the string of characters; and ii) context report means for reporting each string of characters as it occurs in a range of surrounding text of a size determined by a user. | 16. The system of claim 1 wherein the output means further comprises: a) text search and retrieval query means for locating each occurrence of a string of characters in at least one of the first and second bodies of text; and b) text search and retrieval report means comprising: i) occurrence report means for listing the location in at least one of the first and second bodies of text of all occurrences of the string of characters; and ii) context report means for reporting each string of characters as it occurs in a range of surrounding text of a size determined by a user. 18. The system of claim 16 wherein: a) the text search and retrieval query means further comprises topic query means for enabling a user to define queries to text occurring in records previously associated with at least one topic; and b) the text search and retrieval report means further comprises record report means for reporting each record in which the string occurs. | 0.5 |
9,213,946 | 4 | 6 | 4. The method of claim 3 , wherein: applying the first generative model to the observation includes applying the first generative model to one or more observable variables of the observation to identify, as the first assessment, a first proper subset of clusters of the first set of clusters; applying the second generative model to the observation includes applying the second generative model to the one or more observable variables of the observation to identify, as the second assessment, a second proper subset of clusters of the second set of clusters; and determining the similarity score between the first and second assessments includes determining a similarity between the first proper subset and the second proper subset. | 4. The method of claim 3 , wherein: applying the first generative model to the observation includes applying the first generative model to one or more observable variables of the observation to identify, as the first assessment, a first proper subset of clusters of the first set of clusters; applying the second generative model to the observation includes applying the second generative model to the one or more observable variables of the observation to identify, as the second assessment, a second proper subset of clusters of the second set of clusters; and determining the similarity score between the first and second assessments includes determining a similarity between the first proper subset and the second proper subset. 6. The method of claim 4 , wherein determining a similarity between the first proper subset and the second proper subset includes: mapping the first proper subset of clusters to a first smooth distribution; mapping the second proper subset of clusters to a second smooth distribution; and determining the similarity based on the first smooth distribution and the second smooth distribution. | 0.639556 |
10,025,779 | 1 | 6 | 1. A method for predicting an optimal machine translation system for a user comprising: for each of a set of users, providing a respective user profile which includes rankings for at least some machine translation systems from a set of machine translation systems, the set of users including a first user and a plurality of other users, wherein the rankings are pairwise rankings for pairs of machine translation systems; updating the user profile of the first user based on the user profiles of at least a subset of the other users, the updating including generating at least one missing ranking, the updating including: for each of a subset of the other users whose user profiles include a pairwise ranking for a selected pair of the machine translation systems, computing a similarity between the first user's user profile and the respective other user's user profile; identifying, based on the computed similarities, a set of the other users as nearest neighbors to the first user, the identifying comprising applying at least one criterion for inclusion of the other users in the nearest neighbors; and computing a pairwise ranking for the pair of the machine translation systems as a function of the pairwise rankings of the nearest neighbors for the selected pair of machine translation systems, wherein when a number of the nearest neighbors does not meet a threshold number of nearest neighbors, the pairwise ranking for the pair of the machine translation systems is computed as a function of the pairwise rankings of at least one other of the users in addition to the nearest neighbors; predicting an optimal machine translation system for the first user from the set of machine translation systems based on the pairwise rankings for the pairs of machine translation systems in the updated user profile computed for the first user for translation of source text in a source language to target text in a target language; outputting a machine translation of source text in the target language for the first user, based on the prediction; and wherein at least one of the updating and the predicting of the optimal translation system is performed with a processor. | 1. A method for predicting an optimal machine translation system for a user comprising: for each of a set of users, providing a respective user profile which includes rankings for at least some machine translation systems from a set of machine translation systems, the set of users including a first user and a plurality of other users, wherein the rankings are pairwise rankings for pairs of machine translation systems; updating the user profile of the first user based on the user profiles of at least a subset of the other users, the updating including generating at least one missing ranking, the updating including: for each of a subset of the other users whose user profiles include a pairwise ranking for a selected pair of the machine translation systems, computing a similarity between the first user's user profile and the respective other user's user profile; identifying, based on the computed similarities, a set of the other users as nearest neighbors to the first user, the identifying comprising applying at least one criterion for inclusion of the other users in the nearest neighbors; and computing a pairwise ranking for the pair of the machine translation systems as a function of the pairwise rankings of the nearest neighbors for the selected pair of machine translation systems, wherein when a number of the nearest neighbors does not meet a threshold number of nearest neighbors, the pairwise ranking for the pair of the machine translation systems is computed as a function of the pairwise rankings of at least one other of the users in addition to the nearest neighbors; predicting an optimal machine translation system for the first user from the set of machine translation systems based on the pairwise rankings for the pairs of machine translation systems in the updated user profile computed for the first user for translation of source text in a source language to target text in a target language; outputting a machine translation of source text in the target language for the first user, based on the prediction; and wherein at least one of the updating and the predicting of the optimal translation system is performed with a processor. 6. The method of claim 1 , further comprising translating source text from a source language into target text in a target language with the predicted machine translation system. | 0.788278 |
10,146,751 | 13 | 14 | 13. The computer-implemented method of claim 10 , wherein the text content comprises an article, a comment, an email or message containing user feedback or discussion related to a product or service, wherein the first information type refers to a problem being reported or a request for information, and the second information type refers to a sub-type of the problem or a sub-type of the request for information, wherein the sub-type of the problem or the sub-type of the request for information is related to at least an operating procedure, a product feature or function, or a price. | 13. The computer-implemented method of claim 10 , wherein the text content comprises an article, a comment, an email or message containing user feedback or discussion related to a product or service, wherein the first information type refers to a problem being reported or a request for information, and the second information type refers to a sub-type of the problem or a sub-type of the request for information, wherein the sub-type of the problem or the sub-type of the request for information is related to at least an operating procedure, a product feature or function, or a price. 14. The method of claim 13 , wherein text unit representing the request for information includes a question sentence. | 0.820552 |
5,452,238 | 6 | 7 | 6. The method of claim 1, said building step including developing a unique plan fragment for each geometric entity and constraint in the system. | 6. The method of claim 1, said building step including developing a unique plan fragment for each geometric entity and constraint in the system. 7. The method of claim 6, including storing the developed plan fragments as subroutines in computer memory and calling the subroutines for use in building said assembly plan. | 0.611607 |
9,471,282 | 1 | 7 | 1. A method to create a custom JavaServer Faces (JSF) component, the method comprising: receiving annotated source code that defines a component class for the custom JSF component of a JSF application, the annotated source code being Java code of a Java class that defines behavior of a user interface element in a web page, the custom JSF component being callable by a custom tag in a markup language document at least in part defining the web page, the annotated source code including a Java annotation designated by an @ symbol prefix corresponding to a renderer; and creating the custom JSF component, at least in part, by: identifying the annotation in the received source code; and in response to identifying the annotation, automatically generating a default decode method of the renderer that interprets inputs related to the custom JSF component and associating the renderer with the custom JSF component in a faces-config.xml file of the JSF application without manually adding a reference to the custom JSF component to the faces-config.xml file of the JSF application. | 1. A method to create a custom JavaServer Faces (JSF) component, the method comprising: receiving annotated source code that defines a component class for the custom JSF component of a JSF application, the annotated source code being Java code of a Java class that defines behavior of a user interface element in a web page, the custom JSF component being callable by a custom tag in a markup language document at least in part defining the web page, the annotated source code including a Java annotation designated by an @ symbol prefix corresponding to a renderer; and creating the custom JSF component, at least in part, by: identifying the annotation in the received source code; and in response to identifying the annotation, automatically generating a default decode method of the renderer that interprets inputs related to the custom JSF component and associating the renderer with the custom JSF component in a faces-config.xml file of the JSF application without manually adding a reference to the custom JSF component to the faces-config.xml file of the JSF application. 7. The method of claim 1 , wherein creating the custom JSF component comprises: performing steps for automatically generating a framework for the custom JSF component. | 0.882889 |
8,811,742 | 12 | 16 | 12. A server system, for processing a visual query, comprising: one or more central processing units for executing programs; memory storing one or more programs be executed by the one or more central processing units; the one or more programs comprising instructions for: receiving a visual query from a client system, the visual query including an image; performing optical character recognition (OCR) on the visual query to produce text recognition data representing textual characters including a plurality of textual characters in a contiguous region of the image of the visual query, and structural information associated with the plurality of textual characters in the contiguous region of the image of the visual query, the structural information specifying a position of at least one of the plurality of textual characters with respect to one or more reference point elements in the image of the visual query; scoring each textual character in the plurality of textual characters; identifying, in accordance with the scoring, one or more high quality textual strings, each comprising a plurality of high quality textual characters from among the plurality of textual characters in the contiguous region of the image of the visual query; retrieving, using the one or more high quality textual strings and the structural information, a canonical document that includes the one or more high quality textual strings at a location in the canonical document that is consistent with the structural information; and sending at least a portion of the canonical document to the client system. | 12. A server system, for processing a visual query, comprising: one or more central processing units for executing programs; memory storing one or more programs be executed by the one or more central processing units; the one or more programs comprising instructions for: receiving a visual query from a client system, the visual query including an image; performing optical character recognition (OCR) on the visual query to produce text recognition data representing textual characters including a plurality of textual characters in a contiguous region of the image of the visual query, and structural information associated with the plurality of textual characters in the contiguous region of the image of the visual query, the structural information specifying a position of at least one of the plurality of textual characters with respect to one or more reference point elements in the image of the visual query; scoring each textual character in the plurality of textual characters; identifying, in accordance with the scoring, one or more high quality textual strings, each comprising a plurality of high quality textual characters from among the plurality of textual characters in the contiguous region of the image of the visual query; retrieving, using the one or more high quality textual strings and the structural information, a canonical document that includes the one or more high quality textual strings at a location in the canonical document that is consistent with the structural information; and sending at least a portion of the canonical document to the client system. 16. The server system of claim 12 , wherein the portion of the canonical document is a machine readable text segment of the canonical document. | 0.836758 |
9,924,033 | 10 | 18 | 10. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for processing user data, the operations comprising: establishing a communication session between a user device of a user and an agent device of an agent to discuss content provided by a client, wherein the user and the agent are matched based on a user profile of the user and an agent profile of the agent; detecting at a server a first interactive event occurred during the communication session between the user device and the agent device; in response to the first interactive event, identifying a first data collection package that is associated with the first interactive event, wherein the first data collection package includes a plurality of queries, each query being associated with one of a plurality of workflow stages of a data collection workflow; for each of the queries in one of the workflow stages, examining a data collection rule corresponding to a current workflow stage to determine whether the query should be sent to the user, in response to the data collection rule indicating that the query should be sent to the user, transmitting the query to the user device of the user, and receiving a user response from the user device in response to the query; and updating at least one of the user profile associated with the user and the agent profile associated with the agent based on user responses received from the user device, wherein the updated user profile and the agent profile are used for subsequent matching for the user and the agent. | 10. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for processing user data, the operations comprising: establishing a communication session between a user device of a user and an agent device of an agent to discuss content provided by a client, wherein the user and the agent are matched based on a user profile of the user and an agent profile of the agent; detecting at a server a first interactive event occurred during the communication session between the user device and the agent device; in response to the first interactive event, identifying a first data collection package that is associated with the first interactive event, wherein the first data collection package includes a plurality of queries, each query being associated with one of a plurality of workflow stages of a data collection workflow; for each of the queries in one of the workflow stages, examining a data collection rule corresponding to a current workflow stage to determine whether the query should be sent to the user, in response to the data collection rule indicating that the query should be sent to the user, transmitting the query to the user device of the user, and receiving a user response from the user device in response to the query; and updating at least one of the user profile associated with the user and the agent profile associated with the agent based on user responses received from the user device, wherein the updated user profile and the agent profile are used for subsequent matching for the user and the agent. 18. The non-transitory machine-readable medium of claim 10 , wherein the first interactive event indicates starting of the communication session between the user device and the agent device, and wherein the method further comprises: receiving a second interactive event indicating an end of the communication session; in response to the second interactive event, identifying a second data collection package associated with the second interactive event; and transmitting one or more queries of the second data collection package to at least one of the user device of the user or the agent device of the agent. | 0.5 |
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