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8,868,425 | 8 | 11 | 8. A method for facilitating allocation of conversational resources among a mobile device and at least one server, the method comprising: in response to receiving a request for a conversational service, determining at the mobile device, based at least in part on available conversational resources at the mobile device, which one of the following three ways is to be used for processing the requested conversational service: (1) processing the requested conversational service locally using the mobile device, (2) processing the requested conversational service remotely using the at least one server, and (3) processing the requested conversational service at least in part locally using the mobile device and at least in part remotely using the at least one server, wherein the determining is performed without evaluating whether processing the requested conversational service locally produces acceptable results; and automatically communicating, via a network, with the at least one server in furtherance of processing of the requested conversational service, when it is determined that the requested conversational service is to be processed partially or fully using the at least one server. | 8. A method for facilitating allocation of conversational resources among a mobile device and at least one server, the method comprising: in response to receiving a request for a conversational service, determining at the mobile device, based at least in part on available conversational resources at the mobile device, which one of the following three ways is to be used for processing the requested conversational service: (1) processing the requested conversational service locally using the mobile device, (2) processing the requested conversational service remotely using the at least one server, and (3) processing the requested conversational service at least in part locally using the mobile device and at least in part remotely using the at least one server, wherein the determining is performed without evaluating whether processing the requested conversational service locally produces acceptable results; and automatically communicating, via a network, with the at least one server in furtherance of processing of the requested conversational service, when it is determined that the requested conversational service is to be processed partially or fully using the at least one server. 11. The method of claim 8 , wherein when it is determined that the requested conversational service is to be processed at least in part using the mobile device, the method further comprises: processing the requested conversational service at least in part by using conversational resources at the mobile device. | 0.58971 |
10,033,533 | 7 | 9 | 7. A client computing device associated with a signer, the client computing device comprising: a processor; and memory coupled to the processor and storing instructions that, when executed by the processor, cause the signer's client computing device to perform operations comprising: receiving an electronic signature document from a client computing device associated with a sender, wherein the electronic signature document is received from the client computing device of the sender by the client computing device of the signer independently of an electronic signature service; installing a code module on the signer's client computing device, the code module received from the electronic signature service and configured to transmit the electronic signature document received by an email client of the signer's client computing device to the electronic signature service in response to a first input received from the signer, transmitting, by the signer's client computing device, the received electronic signature document to the electronic signature service for storage in the electronic signature service; accessing, by the singer's client computing device, the electronic signature document stored in the electronic signature service, wherein accessing the electronic signature document includes signing the stored electronic signature document in response to a second input from the signer; and causing, by the signer's client computing device, the electronic signature service to transmit an email attached with a copy of the stored electronic signature document to the client computing device of the sender using the code module. | 7. A client computing device associated with a signer, the client computing device comprising: a processor; and memory coupled to the processor and storing instructions that, when executed by the processor, cause the signer's client computing device to perform operations comprising: receiving an electronic signature document from a client computing device associated with a sender, wherein the electronic signature document is received from the client computing device of the sender by the client computing device of the signer independently of an electronic signature service; installing a code module on the signer's client computing device, the code module received from the electronic signature service and configured to transmit the electronic signature document received by an email client of the signer's client computing device to the electronic signature service in response to a first input received from the signer, transmitting, by the signer's client computing device, the received electronic signature document to the electronic signature service for storage in the electronic signature service; accessing, by the singer's client computing device, the electronic signature document stored in the electronic signature service, wherein accessing the electronic signature document includes signing the stored electronic signature document in response to a second input from the signer; and causing, by the signer's client computing device, the electronic signature service to transmit an email attached with a copy of the stored electronic signature document to the client computing device of the sender using the code module. 9. The client computing device associated with the signer of claim 7 , wherein the signer's client computing device is a smart phone or a tablet computer. | 0.660793 |
10,163,170 | 1 | 4 | 1. A method for generating product configurations, the method comprising: storing a plurality of option structures defined by a user; outputting, by a computer processor, in an authoring environment respective representations of the stored plurality of option structures, wherein: the authoring environment is user-interactable for: input by the user of an instruction in response to which the processor is configured to generate a plurality of state structures and display respective visual representations of the generated state structures; respective selection and dragging by the user of the representations of the plurality of option structures into respective ones of the displayed visual representations of the generated state structures, for respective associations between the option structures and the respective state structures into which they were dragged; and definition by the user of transition rules between the state structures, in response to which definition the processor displays respective edges between the visual representations of the generated states connected by the respective transition rules, wherein each transition rule defines a source state, a target state, one or more triggers, one or more operators, a mode of transition, and a priority, wherein the source state is a state or sub-state on which the transition rule can be evaluated for activation, where if defined conditions are met, the transition rule can be activated for a responsive display of a target screen or sub-screen corresponding to the defined target state of the transition rule, wherein each trigger is a condition defined for an option, each operator being a logical connective which connects the triggers; based on the state structures, the respective options associated with the state structures, and the defined transition rules, a product configuration user interface is generatable, which product configuration user interface includes a plurality of display screens that (a) correspond to respective ones of the state structures, (b) display respective sets of options corresponding to the respective option structures with which the corresponding state structures have been associated, and (c) are displayed in a sequence dictated by the defined transition rules, when conditions are satisfied for multiple transition rules creating a conflict amongst such multiple transition rules, such transition rules are ranked according to their priority defined in such rule and a mode of transition corresponding to and defined by only the top ranked transition rule is executed. | 1. A method for generating product configurations, the method comprising: storing a plurality of option structures defined by a user; outputting, by a computer processor, in an authoring environment respective representations of the stored plurality of option structures, wherein: the authoring environment is user-interactable for: input by the user of an instruction in response to which the processor is configured to generate a plurality of state structures and display respective visual representations of the generated state structures; respective selection and dragging by the user of the representations of the plurality of option structures into respective ones of the displayed visual representations of the generated state structures, for respective associations between the option structures and the respective state structures into which they were dragged; and definition by the user of transition rules between the state structures, in response to which definition the processor displays respective edges between the visual representations of the generated states connected by the respective transition rules, wherein each transition rule defines a source state, a target state, one or more triggers, one or more operators, a mode of transition, and a priority, wherein the source state is a state or sub-state on which the transition rule can be evaluated for activation, where if defined conditions are met, the transition rule can be activated for a responsive display of a target screen or sub-screen corresponding to the defined target state of the transition rule, wherein each trigger is a condition defined for an option, each operator being a logical connective which connects the triggers; based on the state structures, the respective options associated with the state structures, and the defined transition rules, a product configuration user interface is generatable, which product configuration user interface includes a plurality of display screens that (a) correspond to respective ones of the state structures, (b) display respective sets of options corresponding to the respective option structures with which the corresponding state structures have been associated, and (c) are displayed in a sequence dictated by the defined transition rules, when conditions are satisfied for multiple transition rules creating a conflict amongst such multiple transition rules, such transition rules are ranked according to their priority defined in such rule and a mode of transition corresponding to and defined by only the top ranked transition rule is executed. 4. The method of claim 1 , wherein one and only one of the state structures is a starting state structure and the product configuration user interface displays the screen associated with the starting state structure before display of any screen associated with any of the other state structures. | 0.513201 |
8,712,926 | 6 | 8 | 6. A computer program product comprising a non-transitory computer useable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: identify a dictionary of frequently used terms in a text data set U, wherein identifying the dictionary comprises representing each document of U as a vector of weighted frequencies of the document features, the document features being words and phrases contained in the document, wherein the vector is normalized to have unit Euclidean norm; create a feature space that identifies the dictionary term occurrences in each document of U; apply a rule induction algorithm to the feature space over U to identify rules that classify documents into categories based on a subset V of U, wherein the rule induction algorithm utilizes an entropy function that favors splitting the set U into categories, and wherein the rule induction algorithm creates a separate category for the documents in V and also the documents in U that are not in V; use feature based antecedents of each rule to describe events; and display the events using positive and negative antecedents, wherein the subset V comprises documents of U that were written within a specific time period, and the subset V provides an indication of emerging trends in the set U of documents that occur at a higher frequency during the specific time period than outside the specific time period. | 6. A computer program product comprising a non-transitory computer useable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: identify a dictionary of frequently used terms in a text data set U, wherein identifying the dictionary comprises representing each document of U as a vector of weighted frequencies of the document features, the document features being words and phrases contained in the document, wherein the vector is normalized to have unit Euclidean norm; create a feature space that identifies the dictionary term occurrences in each document of U; apply a rule induction algorithm to the feature space over U to identify rules that classify documents into categories based on a subset V of U, wherein the rule induction algorithm utilizes an entropy function that favors splitting the set U into categories, and wherein the rule induction algorithm creates a separate category for the documents in V and also the documents in U that are not in V; use feature based antecedents of each rule to describe events; and display the events using positive and negative antecedents, wherein the subset V comprises documents of U that were written within a specific time period, and the subset V provides an indication of emerging trends in the set U of documents that occur at a higher frequency during the specific time period than outside the specific time period. 8. The computer program product of claim 6 , wherein: creating the feature space comprises indexing the documents of U by their feature occurrences using the vector of weighted frequencies of the document features. | 0.612319 |
9,727,743 | 16 | 17 | 16. The one or more non-transitory computer-readable storage media of claim 15 , wherein the list comprises discrete values representing the range of the field. | 16. The one or more non-transitory computer-readable storage media of claim 15 , wherein the list comprises discrete values representing the range of the field. 17. The one or more non-transitory computer-readable storage media of claim 16 , wherein the list is updated based at least in part on a threshold of changes that have been made to the field. | 0.5 |
9,430,445 | 11 | 12 | 11. A method for editing a document to display status and metadata for an object embedded in the document, the method comprising: opening the document in a user interface of an editor; identifying the object embedded in the document, an identifier of the object, and an address of a remote service corresponding to the object according to an object definition; communicating with the remote service using the identifier and the address to exchange object information with the remote service, wherein the object information includes the status and the metadata; providing the object information to the user interface; and displaying the status and the metadata of the object together with the object in the user interface, at least one of the status and the metadata being displayed with a special effect indicating a change detected in the status or the metadata of the object; determining whether the remote service is available or unavailable; wherein the user interface is configured to display the object as plain text if said document editor determines the remote service to be unavailable; wherein the object information is exchanged by periodically communicating with the remote service, the special effect indicating the change detected since a previous periodic communication with the remote service. | 11. A method for editing a document to display status and metadata for an object embedded in the document, the method comprising: opening the document in a user interface of an editor; identifying the object embedded in the document, an identifier of the object, and an address of a remote service corresponding to the object according to an object definition; communicating with the remote service using the identifier and the address to exchange object information with the remote service, wherein the object information includes the status and the metadata; providing the object information to the user interface; and displaying the status and the metadata of the object together with the object in the user interface, at least one of the status and the metadata being displayed with a special effect indicating a change detected in the status or the metadata of the object; determining whether the remote service is available or unavailable; wherein the user interface is configured to display the object as plain text if said document editor determines the remote service to be unavailable; wherein the object information is exchanged by periodically communicating with the remote service, the special effect indicating the change detected since a previous periodic communication with the remote service. 12. The method of claim 11 , wherein the object definition comprises one or more of the following: an object type, a label for display in the document, the address of the remote service corresponding to the object, the identifier, the metadata, and specified content of the object to be displayed in the editor. | 0.585333 |
9,875,302 | 1 | 7 | 1. A method comprising: identifying a concept-unit from a multi-language document corpus, the concept-unit including a set of documents in different languages describing a particular concept; modeling the concept-unit identified from the multi-language document corpus to create a generative model, wherein the generative model represents at least: (a) a plurality of universal topics, individual ones of the plurality of universal topics being defined by a plurality of topic word distributions corresponding respectively to the different languages; and (b) a universal topic distribution associated with the concept-unit, the universal topic distribution identifying: (i) two or more universal topics for which documents describing the concept unit are to contain; and (ii) a relative significance of individual universal topics of the two or more universal topics within the concept unit; and classifying a set of documents of an unclassified document corpus using the generative model, the classifying comprising: obtaining the universal topic distribution for the set of documents of the generative model, obtaining a topic distribution of the set of documents of the unclassified document corpus, comparing the universal topic distribution to the topic distribution of the set of documents of the unclassified document corpus; and based on the comparing, classifying one or more documents of the set of documents of the unclassified document corpus. | 1. A method comprising: identifying a concept-unit from a multi-language document corpus, the concept-unit including a set of documents in different languages describing a particular concept; modeling the concept-unit identified from the multi-language document corpus to create a generative model, wherein the generative model represents at least: (a) a plurality of universal topics, individual ones of the plurality of universal topics being defined by a plurality of topic word distributions corresponding respectively to the different languages; and (b) a universal topic distribution associated with the concept-unit, the universal topic distribution identifying: (i) two or more universal topics for which documents describing the concept unit are to contain; and (ii) a relative significance of individual universal topics of the two or more universal topics within the concept unit; and classifying a set of documents of an unclassified document corpus using the generative model, the classifying comprising: obtaining the universal topic distribution for the set of documents of the generative model, obtaining a topic distribution of the set of documents of the unclassified document corpus, comparing the universal topic distribution to the topic distribution of the set of documents of the unclassified document corpus; and based on the comparing, classifying one or more documents of the set of documents of the unclassified document corpus. 7. A method as recited in claim 1 , further comprising generating a recommendation using the generative model, the generating the recommendation comprising: obtaining a topic distribution of a reference document of a first language; obtaining a topic distributions of a plurality of documents of a second language; comparing the topic distributions between the reference document of the first language and the plurality of documents of the second language to identify topics of the documents; and recommending at least a first document of the second language that is related to the reference document based on its identified topics. | 0.5 |
8,630,856 | 18 | 27 | 18. Hardware having stored thereon, a computer program having a plurality of code sections, said code sections executable by a machine for causing the machine to perform the steps of: determining at least two possible meanings for a language input; determining a first probability that a first possible meaning of the at least two possible meanings is a correct interpretation of said language input; determining a second probability that a second possible meaning of the at least two possible meanings is a correct interpretation of said language input; computing at least one relative delta computation comprising a value derived at least in part on a difference between at least the first probability and the second probability, the difference divided by a denominator based on the first probability; detecting at least one irregularity within said language input based upon said relative delta computation; and performing at least one programmatic action responsive to detecting said irregularity. | 18. Hardware having stored thereon, a computer program having a plurality of code sections, said code sections executable by a machine for causing the machine to perform the steps of: determining at least two possible meanings for a language input; determining a first probability that a first possible meaning of the at least two possible meanings is a correct interpretation of said language input; determining a second probability that a second possible meaning of the at least two possible meanings is a correct interpretation of said language input; computing at least one relative delta computation comprising a value derived at least in part on a difference between at least the first probability and the second probability, the difference divided by a denominator based on the first probability; detecting at least one irregularity within said language input based upon said relative delta computation; and performing at least one programmatic action responsive to detecting said irregularity. 27. The hardware of claim 18 , wherein said at least two possible meanings comprise at least three possible meanings, said method further comprises ordering said possible meanings according to the first and second probabilities and a third probability that a third possible meaning of the at least three possible meanings is a correct interpretation of said language input, wherein said computing step further comprises the steps of: computing a first relative delta computation based upon two sequentially ordered ones of said possible meanings; and computing a second relative delta computation based upon two different sequentially ordered ones of said ordered meanings, wherein said detecting of said irregularity is based upon said first relative delta computation and said second relative delta computation. | 0.5 |
8,086,038 | 11 | 13 | 11. The method of claim 9 , further comprising: extracting an additional invisible junction feature descriptor; and adding a second feature point to the quantization tree for the additional invisible junction feature descriptor. | 11. The method of claim 9 , further comprising: extracting an additional invisible junction feature descriptor; and adding a second feature point to the quantization tree for the additional invisible junction feature descriptor. 13. The method of claim 11 , wherein the feature point is added to the quantization tree by adding a leaf node including a page ID and coordinates (x, y) of the feature point. | 0.5 |
9,977,801 | 20 | 31 | 20. The one or more computer-readable media of claim 18 , wherein a string value in a given value block of the one or more value blocks is represented using segments that further comprise: a fourth segment specifying zero or more logical pointers to large string pages. | 20. The one or more computer-readable media of claim 18 , wherein a string value in a given value block of the one or more value blocks is represented using segments that further comprise: a fourth segment specifying zero or more logical pointers to large string pages. 31. The one or more non-transitory computer-readable media of claim 20 , the segments further comprising a fifth segment specifying a number of the logical pointers in the fourth segment. | 0.5 |
7,970,721 | 15 | 16 | 15. A method for analyzing objects of interest within a context comprising: controlling one or more processors configured with executable instructions for: projecting objects of interest onto a web graph to produce one or more sub graphs, wherein producing the sub graphs comprises: ordering sets of connected nodes by size; identifying a shortest path between a largest set of connected nodes, from among the ordered sets, to a second largest set of connected nodes, from among the ordered sets; adding, according to the shortest path, connection nodes and edges between the largest set of connected nodes and the second largest set of connected nodes; and repeating the step of identifying the shortest path and the step of adding connection nodes while more sets of nodes are available in the sets of connected nodes ordered by size; and making an inference related to at least one of the context or objects of interest based upon graphical properties of the one or more sub graphs. | 15. A method for analyzing objects of interest within a context comprising: controlling one or more processors configured with executable instructions for: projecting objects of interest onto a web graph to produce one or more sub graphs, wherein producing the sub graphs comprises: ordering sets of connected nodes by size; identifying a shortest path between a largest set of connected nodes, from among the ordered sets, to a second largest set of connected nodes, from among the ordered sets; adding, according to the shortest path, connection nodes and edges between the largest set of connected nodes and the second largest set of connected nodes; and repeating the step of identifying the shortest path and the step of adding connection nodes while more sets of nodes are available in the sets of connected nodes ordered by size; and making an inference related to at least one of the context or objects of interest based upon graphical properties of the one or more sub graphs. 16. The method of claim 15 , wherein the web graph comprises nodes representing objects from a data store and edges between nodes representing associations between objects. | 0.726115 |
7,644,391 | 13 | 17 | 13. A method for providing real-time information via a secondary user interface of a stand-alone gadget application within a host user interface of a host application, said host user interface being rendered on a display, said method comprising: providing a first pointer, the first pointer indicating a file path to a location of one of a file or a directory; searching the location indicated by the first pointer for a pointer file containing at least a second pointer indicating the location of at least one of a definition file and a script file, the definition file containing computer code defining an object model corresponding to the secondary user interface associated with the stand-alone gadget application, said gadget application providing real-time information via the secondary user interface, said secondary user interface being rendered within the host user interface, and the script file containing computer code defining actions corresponding to the secondary user interface; creating a representation of an object model corresponding to the definition file if the second pointer indicates the location of the definition file; providing real-time display of information via the secondary user interface, wherein providing the display comprises: providing a loader component module passing the definition file to a program parser module and passing the script data file to a script engine; the program parser module performing the steps of: a) creating the object model of the secondary user interface based on the definition file; b) converting the object model into the secondary user interface for display; and c) passing the object model to the script engine module; providing a settings module, accessible to the script engine module, for customization of the secondary user interface; and the script engine module performing the steps of: a) parsing the script data file; b) creating and executing a script based on the parsing of the script data file, accessible configuration settings, and received object model; and c) configuring the display of the secondary user interface based on the execution of the script. | 13. A method for providing real-time information via a secondary user interface of a stand-alone gadget application within a host user interface of a host application, said host user interface being rendered on a display, said method comprising: providing a first pointer, the first pointer indicating a file path to a location of one of a file or a directory; searching the location indicated by the first pointer for a pointer file containing at least a second pointer indicating the location of at least one of a definition file and a script file, the definition file containing computer code defining an object model corresponding to the secondary user interface associated with the stand-alone gadget application, said gadget application providing real-time information via the secondary user interface, said secondary user interface being rendered within the host user interface, and the script file containing computer code defining actions corresponding to the secondary user interface; creating a representation of an object model corresponding to the definition file if the second pointer indicates the location of the definition file; providing real-time display of information via the secondary user interface, wherein providing the display comprises: providing a loader component module passing the definition file to a program parser module and passing the script data file to a script engine; the program parser module performing the steps of: a) creating the object model of the secondary user interface based on the definition file; b) converting the object model into the secondary user interface for display; and c) passing the object model to the script engine module; providing a settings module, accessible to the script engine module, for customization of the secondary user interface; and the script engine module performing the steps of: a) parsing the script data file; b) creating and executing a script based on the parsing of the script data file, accessible configuration settings, and received object model; and c) configuring the display of the secondary user interface based on the execution of the script. 17. The method of claim 13 further comprising hooking up the script file to the representation of the object model. | 0.5 |
7,873,228 | 15 | 17 | 15. The system of claim 14 , wherein the operations associated with the cluster module further comprising forming a plurality of clusters, each cluster of the plurality of clusters including one or more similar scanned images, and wherein the operations associated with the prototype module further comprising creating the plurality of prototypes based on processing the one or more similar scanned images of the plurality of clusters. | 15. The system of claim 14 , wherein the operations associated with the cluster module further comprising forming a plurality of clusters, each cluster of the plurality of clusters including one or more similar scanned images, and wherein the operations associated with the prototype module further comprising creating the plurality of prototypes based on processing the one or more similar scanned images of the plurality of clusters. 17. The system of claim 15 , wherein the operations associated with the cluster module further comprising labelling the clusters of similar scanned images as a type character or ligature based on optical character recognition (OCR) data associated with the scanned document. | 0.569182 |
8,897,486 | 21 | 24 | 21. One or more non-transitory computer-readable storage media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising: identifying a plurality of named entities within a written work; for a named entity of the plurality of named entities: identifying one or more textual strings associated with the named entity; calculating a significance value based at least in part on a location of the one or more textual strings associated with the named entity within the written work; determining that the named entity is included in other written works; updating the significance value for the named entity based at least in part on the inclusion of the named entity in the other written works; and selecting a primary textual string from the one or more textual strings associated with the named entity; and providing a list of at least a portion of the plurality of named entities, the list including the primary textual string and other primary textual strings for the at least the portion of the plurality of named entities and the list being sorted based at least in part on the significance value. | 21. One or more non-transitory computer-readable storage media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising: identifying a plurality of named entities within a written work; for a named entity of the plurality of named entities: identifying one or more textual strings associated with the named entity; calculating a significance value based at least in part on a location of the one or more textual strings associated with the named entity within the written work; determining that the named entity is included in other written works; updating the significance value for the named entity based at least in part on the inclusion of the named entity in the other written works; and selecting a primary textual string from the one or more textual strings associated with the named entity; and providing a list of at least a portion of the plurality of named entities, the list including the primary textual string and other primary textual strings for the at least the portion of the plurality of named entities and the list being sorted based at least in part on the significance value. 24. The one or more non-transitory computer-readable storage media of claim 21 , wherein the acts further comprise: causing a display of the list of the at least the portion of the plurality of named entities on a device associated with a user; receiving, from the device associated with the user, feedback indicating that at least one named entity is more or less significant compared to a position within the list; and updating at least one significance value of the at least one named entity based on the feedback. | 0.5 |
9,213,684 | 1 | 10 | 1. A method for rendering a document, the method comprising: converting a plurality of resources in a document file into a plurality of files that are native to a browser; creating a style sheet based on the document file, wherein an aggregate of the plurality of files together with the style sheet are configured to cause the browser to render an appearance of the document file; and generating, based on the document file, an invisible layer to be laid on the appearance, wherein the invisible layer enables actions to be performed on the document file. | 1. A method for rendering a document, the method comprising: converting a plurality of resources in a document file into a plurality of files that are native to a browser; creating a style sheet based on the document file, wherein an aggregate of the plurality of files together with the style sheet are configured to cause the browser to render an appearance of the document file; and generating, based on the document file, an invisible layer to be laid on the appearance, wherein the invisible layer enables actions to be performed on the document file. 10. The method of claim 1 , wherein the actions to be performed on the document file includes one or more of text selecting, text copying, text cutting, text pasting, text searching, text filling, and hyperlinking. | 0.682493 |
7,568,171 | 28 | 29 | 28. The computer program product of claim 25 , wherein moving the element comprises: determining a set of candidate positions for which a projection of the axis onto screen space is located on the screen space from the starting point to the ending point of the stroke; and selecting a candidate position from the set based at least in part on the direction of the stroke. | 28. The computer program product of claim 25 , wherein moving the element comprises: determining a set of candidate positions for which a projection of the axis onto screen space is located on the screen space from the starting point to the ending point of the stroke; and selecting a candidate position from the set based at least in part on the direction of the stroke. 29. The computer program product of claim 28 , wherein the position of the element in scene space is further definable by an orientation about the axis, and positioning the element further comprises: determining the orientation of the element based at least in part on the curvature of the stroke. | 0.5 |
8,270,733 | 14 | 20 | 14. A system, comprising: a video input source configured to provide image data; a processor; and a memory containing a program, which, when executed on the processor is configured to perform an operation that identifies anomaly object types during classification of image data captured by a video camera, the operation comprising: receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object within the image data; classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters based on the micro-feature vector; computing a probability density function for the object type clusters; computing a probability density value for the micro-feature vector; evaluating a rareness measure of the micro-feature vector, wherein the rareness measure estimates a likelihood of observing the micro-feature vector, based on the probability density function and the probability density value, and identifying the foreground object as an anomaly object type when the rareness measure is below a specified threshold. | 14. A system, comprising: a video input source configured to provide image data; a processor; and a memory containing a program, which, when executed on the processor is configured to perform an operation that identifies anomaly object types during classification of image data captured by a video camera, the operation comprising: receiving a micro-feature vector including multiple micro-feature values, each micro-feature value based on at least one pixel-level characteristic of a foreground patch that depicts a foreground object within the image data; classifying the foreground object as depicting a first object type corresponding to a first object type cluster of the object type clusters based on the micro-feature vector; computing a probability density function for the object type clusters; computing a probability density value for the micro-feature vector; evaluating a rareness measure of the micro-feature vector, wherein the rareness measure estimates a likelihood of observing the micro-feature vector, based on the probability density function and the probability density value, and identifying the foreground object as an anomaly object type when the rareness measure is below a specified threshold. 20. The system of claim 14 , further comprising adding a new object type cluster when the micro-feature vector does not correspond to any of the object type clusters for the image data. | 0.649621 |
9,842,101 | 5 | 9 | 5. The method of claim 3 , further comprising: receiving, from the user, a selection of a candidate word string from the displayed portion of the plurality of candidate word strings; and displaying the selected candidate word string in a text field of the electronic device. | 5. The method of claim 3 , further comprising: receiving, from the user, a selection of a candidate word string from the displayed portion of the plurality of candidate word strings; and displaying the selected candidate word string in a text field of the electronic device. 9. The method of claim 5 , further comprising: determining a predicted word of the second symbolic system based on a probability of occurrence of a sequence of words in the obtained corpus of text, the sequence of words comprising the selected candidate word string and the predicted text; and displaying the predicted word adjacent to the selected candidate word string in the text field. | 0.5 |
9,792,906 | 15 | 16 | 15. The system of claim 12 , wherein the background environment classification comprises one of office, airport, street, vehicle, train and home. | 15. The system of claim 12 , wherein the background environment classification comprises one of office, airport, street, vehicle, train and home. 16. The system of claim 15 , wherein the background environment is classified based on two levels comprising a first level from the listing of background environments and a second, finer, level based on specific geographic location. | 0.5 |
5,543,818 | 18 | 19 | 18. The system of claim 9, wherein the processor includes means for communicating with a passenger information and entertainment network. | 18. The system of claim 9, wherein the processor includes means for communicating with a passenger information and entertainment network. 19. The system of claim 18, wherein the processor includes means for sending alphanumeric text that has been entered into said computer system to the passenger information and entertainment network. | 0.5 |
8,464,147 | 6 | 8 | 6. A method for validation of structured documents, the method comprising: receiving a first request for validating a first structured document; responsive to receiving the first request: identifying a first subset of fast parsers from a group of fast parsers, searching the first subset of fast parsers for a fast parser corresponding to an instance of the first structured document, determining that no fast parser that corresponds to an instance of the first structured document is available within the first subset of fast parsers, and responsive to determining that no fast parser that corresponds to an instance of the first structured document is available within the first subset of fast parsers, performing in parallel: (a) parsing the first instance of the first structured document using a generic parser, wherein an Abstract Syntax Tree (AST) for the first structured document is being generated while the parsing is in progression, and (b) generating a fast parser for the first structured document, wherein the fast parser being generated is based on (i) the structure of the first structured document, and (ii) the AST that is concurrently being generated for the first structured document while the first instance of the first structured document is being parsed, and adding the generated fast parser to the group of fast parsers for processing subsequently received structured documents. | 6. A method for validation of structured documents, the method comprising: receiving a first request for validating a first structured document; responsive to receiving the first request: identifying a first subset of fast parsers from a group of fast parsers, searching the first subset of fast parsers for a fast parser corresponding to an instance of the first structured document, determining that no fast parser that corresponds to an instance of the first structured document is available within the first subset of fast parsers, and responsive to determining that no fast parser that corresponds to an instance of the first structured document is available within the first subset of fast parsers, performing in parallel: (a) parsing the first instance of the first structured document using a generic parser, wherein an Abstract Syntax Tree (AST) for the first structured document is being generated while the parsing is in progression, and (b) generating a fast parser for the first structured document, wherein the fast parser being generated is based on (i) the structure of the first structured document, and (ii) the AST that is concurrently being generated for the first structured document while the first instance of the first structured document is being parsed, and adding the generated fast parser to the group of fast parsers for processing subsequently received structured documents. 8. The method of claim 6 , wherein the generating comprises using Left-to-right Rightmost derivation with k-token look ahead (LR (k)) parse tables. | 0.918514 |
9,997,161 | 4 | 5 | 4. The speech recognition device of claim 1 wherein the normalization circuitry executes a polynomial-based mapping generating the mapped speech recognition confidence classifier score that equally or more accurately satisfies the recognition acceptance condition than the first speech recognition confidence classifier score. | 4. The speech recognition device of claim 1 wherein the normalization circuitry executes a polynomial-based mapping generating the mapped speech recognition confidence classifier score that equally or more accurately satisfies the recognition acceptance condition than the first speech recognition confidence classifier score. 5. The speech recognition device of claim 4 wherein the normalization circuitry executes the polynomial-based mapping by collecting a set of acceptance metrics from the first confidence classifier and a set of acceptance metrics from the second confidence classifier, sampling the sets of acceptance metrics at a specified sampling interval to obtain a sampled set of confidence threshold for the first confidence classifier and a sampled set of confidence thresholds for the first confidence classifier, and learning a polynomial that represents a set of confidence thresholds for the first and second confidence classifiers with a preset resolution. | 0.5 |
9,066,135 | 11 | 12 | 11. The system of claim 9 , wherein, prior to the identification of links, the processing device executes logical instructions to rank the identified keywords by frequency of occurrence in the subtitle data and the identification of links is carried out only on a highest ranked portion of the identified keywords. | 11. The system of claim 9 , wherein, prior to the identification of links, the processing device executes logical instructions to rank the identified keywords by frequency of occurrence in the subtitle data and the identification of links is carried out only on a highest ranked portion of the identified keywords. 12. The system of claim 11 , wherein the rank of an identified keyword that matches a word in metadata for the video content is increased. | 0.5 |
8,793,311 | 1 | 4 | 1. A communication method, comprising: providing a communications platform capable of handling multiple types of communications with multiple users, comprising: a browser for a communications network, the browser configured to interact with and organize information about the multiple communication types and the multiple users; a server for handling communications between the platform and user devices that are external to the platform; a database configured to store user data comprising a preferred communication mode and contact protocol for a given user; a speech engine for converting text to speech, for converting speech to text, or both; a mining engine configured to analyze the communication data flowing through and present in the platform; and a network interface; connecting a communications network to the network interface of the communications platform; using the browser to provide an interface between the communication platform and the communications network, the interface configured for the platform to interact with various types of user devices and communication modes; connecting first and second user devices from first user and second users to the communications network at the same time using each users' preferred contact protocol; determining a communication mode for the first user and the second user based on the user data in the database, wherein the first user employs a first communication mode with first mode components and the second user employs a second communication mode with second mode components that is different than the first communication mode; allowing the first and second users to communicate with each other in parallel using the platform and each user's respective communication mode while each user is connected to the communications network; synchronizing the communication between the first user and the second user by matching the mode components from the first communication mode to the mode components of the second communication mode to minimize the time needed by both the first and second user for the communication; and using the mining engine to optimize operation of the platform. | 1. A communication method, comprising: providing a communications platform capable of handling multiple types of communications with multiple users, comprising: a browser for a communications network, the browser configured to interact with and organize information about the multiple communication types and the multiple users; a server for handling communications between the platform and user devices that are external to the platform; a database configured to store user data comprising a preferred communication mode and contact protocol for a given user; a speech engine for converting text to speech, for converting speech to text, or both; a mining engine configured to analyze the communication data flowing through and present in the platform; and a network interface; connecting a communications network to the network interface of the communications platform; using the browser to provide an interface between the communication platform and the communications network, the interface configured for the platform to interact with various types of user devices and communication modes; connecting first and second user devices from first user and second users to the communications network at the same time using each users' preferred contact protocol; determining a communication mode for the first user and the second user based on the user data in the database, wherein the first user employs a first communication mode with first mode components and the second user employs a second communication mode with second mode components that is different than the first communication mode; allowing the first and second users to communicate with each other in parallel using the platform and each user's respective communication mode while each user is connected to the communications network; synchronizing the communication between the first user and the second user by matching the mode components from the first communication mode to the mode components of the second communication mode to minimize the time needed by both the first and second user for the communication; and using the mining engine to optimize operation of the platform. 4. The method of claim 1 , wherein the mode components include the type of user device, the software operating on the user device, and the communication channel for the user and wherein the communication mode is determined using these three mode components. | 0.5 |
8,219,406 | 2 | 4 | 2. The computer-implemented interface of claim 1 , wherein the first modality is a speech modality, and the environmental data identifies environmental noise. | 2. The computer-implemented interface of claim 1 , wherein the first modality is a speech modality, and the environmental data identifies environmental noise. 4. The computer-implemented interface of claim 2 , wherein the response manager is further configured to prompt for re-engagement of the speech modality as the primary modality when the environmental data changes. | 0.5 |
9,904,709 | 15 | 16 | 15. A wireless device, comprising: a memory and a processor that are respectively adapted to store and execute instructions that implement operations, the operations comprising: computing a location of the wireless device; predicting a future activity of a user based on user context and the computed location; determining a direction in which the wireless device is being pointed; determining a field of view associated with the direction; retrieving, from at least one network resource, information regarding at least one business within the determined field of view that relates to the predicted future activity, the retrieving being based at least on the computed location, the determined field of view, and the predicted future activity, and wherein the at least one network resource includes information identifying a type of business for each of the at least one business; accessing user preferences; inferring that at least a portion of the retrieved information may be of potential interest to the user, wherein the inferring is based upon: the user preferences, a satiation model that employs an amount of time since the user stopped at a location associated a particular type of business, and availability of the particular type of business in the direction in which the location aware wireless device is being pointed; and presenting at least the inferred portion of the retrieved information to the user. | 15. A wireless device, comprising: a memory and a processor that are respectively adapted to store and execute instructions that implement operations, the operations comprising: computing a location of the wireless device; predicting a future activity of a user based on user context and the computed location; determining a direction in which the wireless device is being pointed; determining a field of view associated with the direction; retrieving, from at least one network resource, information regarding at least one business within the determined field of view that relates to the predicted future activity, the retrieving being based at least on the computed location, the determined field of view, and the predicted future activity, and wherein the at least one network resource includes information identifying a type of business for each of the at least one business; accessing user preferences; inferring that at least a portion of the retrieved information may be of potential interest to the user, wherein the inferring is based upon: the user preferences, a satiation model that employs an amount of time since the user stopped at a location associated a particular type of business, and availability of the particular type of business in the direction in which the location aware wireless device is being pointed; and presenting at least the inferred portion of the retrieved information to the user. 16. The wireless device of claim 15 , wherein the operations further comprise: computing another direction in which the wireless device is moving. | 0.756667 |
8,924,414 | 3 | 4 | 3. The method according to claim 1 , wherein the step of modifying at least a portion of the metadata comprises incorporating additional information in the metadata. | 3. The method according to claim 1 , wherein the step of modifying at least a portion of the metadata comprises incorporating additional information in the metadata. 4. The method according to claim 3 , wherein enhancing the metadata with additional information comprises creating additional fields and allowing the additional fields to co-exist with the metadata. | 0.5 |
9,535,895 | 1 | 8 | 1. A computer-implemented method, comprising: under control of a device comprising one or more processors configured with executable instructions, receiving at a graphical user interface of the device, user selection of a sample electronic text; identifying multiple sample n-grams of the sample electronic text; for a first language: identifying a first set of n-grams that occur in a first language reference corresponding to the first language; calculating a first set of Bayesian probabilities, including calculating a first Bayesian probability based at least in part on a frequency of occurrence, in the first set of n-grams, of a first sample n-gram of the multiple sample n-grams; and calculating a first average of the first set of Bayesian probabilities; for a second language: identifying a second set of n-grams that occur in the second language reference corresponding to the second language; calculating a second set of Bayesian probabilities, including calculating a second Bayesian probability based at least in part on a frequency of occurrence, in the second set of n-grams, of a second sample n-gram of the multiple sample n-grams; and calculating a second average of the second set of Bayesian probabilities; comparing at least the first average and the second average; determine a language of the sample electronic text based at least in part on the comparing at least the first average and the second average; determining a meaning of a word of the sample electronic text in a dictionary of the language; and presenting the meaning of the word on a display of the device. | 1. A computer-implemented method, comprising: under control of a device comprising one or more processors configured with executable instructions, receiving at a graphical user interface of the device, user selection of a sample electronic text; identifying multiple sample n-grams of the sample electronic text; for a first language: identifying a first set of n-grams that occur in a first language reference corresponding to the first language; calculating a first set of Bayesian probabilities, including calculating a first Bayesian probability based at least in part on a frequency of occurrence, in the first set of n-grams, of a first sample n-gram of the multiple sample n-grams; and calculating a first average of the first set of Bayesian probabilities; for a second language: identifying a second set of n-grams that occur in the second language reference corresponding to the second language; calculating a second set of Bayesian probabilities, including calculating a second Bayesian probability based at least in part on a frequency of occurrence, in the second set of n-grams, of a second sample n-gram of the multiple sample n-grams; and calculating a second average of the second set of Bayesian probabilities; comparing at least the first average and the second average; determine a language of the sample electronic text based at least in part on the comparing at least the first average and the second average; determining a meaning of a word of the sample electronic text in a dictionary of the language; and presenting the meaning of the word on a display of the device. 8. The computer-implemented method of claim 1 , wherein calculating the first Bayesian probability comprises calculating the Bayesian probability P(A|B) of the first sample n-gram corresponding to the first language based at least in part on: P ( B ❘ A ) P ( A ) P ( B ) where: P(B|A) is a first frequency with which the first sample n-gram occurs in the first set of n-grams, relative to other n-grams that occur in the first set of n-grams; P(B) is a second frequency with which the first sample n-gram occurs in a combined collection of language references that include the first language reference and the second language reference, relative to other n-grams that occur in the combined collection of language references; and P(A) is a number of short words of the first language that occur in the sample electronic text. | 0.5 |
9,288,321 | 10 | 12 | 10. A method comprising: determining, using one or more hardware processors, interactive voice response (IVR) flow information from each webpage element of a plurality of webpage elements in a webpage, wherein the IVR flow information comprises a sequence of audio outputs for presentation of the plurality of webpage elements to a user using a device; transmitting a software component library to the device for processing on a user initiation to present an IVR interface to the user using the IVR flow information, wherein the user utilizes the IVR interface to enter at least one input to at least one of the plurality of webpage elements in response to the sequence of the audio outputs; and receiving the at least one input from the user. | 10. A method comprising: determining, using one or more hardware processors, interactive voice response (IVR) flow information from each webpage element of a plurality of webpage elements in a webpage, wherein the IVR flow information comprises a sequence of audio outputs for presentation of the plurality of webpage elements to a user using a device; transmitting a software component library to the device for processing on a user initiation to present an IVR interface to the user using the IVR flow information, wherein the user utilizes the IVR interface to enter at least one input to at least one of the plurality of webpage elements in response to the sequence of the audio outputs; and receiving the at least one input from the user. 12. The method of claim 10 , wherein the IVR flow information comprises custom HTML tags corresponding to the plurality of webpage elements. | 0.536424 |
8,620,872 | 1 | 2 | 1. A system for generating content-matching feedback on author-generated documents, the system comprising: a content matching engine comprising computer hardware configured to: provide a user interface for authors to submit documents for content matching feedback to avoid accidental plagiarism, receive a document from an author over a communications medium, the document generated by the author, compare at least a portion of the document to at least a portion of one or more first publications using one or more pattern-matching techniques, generate content-matching feedback responsive to the comparison, the content-matching feedback comprising suggestions for correcting attribution errors in the document to thereby assist the author in avoiding accidental plagiarism, generate a certificate certifying that the document was checked for plagiarism, thereby enabling the author to indicate to a reviewing entity that the document was checked for plagiarism, and output the content-matching feedback for display to the author; and a research recommender in communication with the content matching engine, the research recommender configured to: detect publication associations between a plurality of second publications based at least in part on citations in text of the plurality of second publications, generate one or more research recommendations based at least in part on cited publications in the text of the document and on the publication associations, the one or more research recommendations comprising one or more of the second publications that have publication associations with one or more of the cited publications in the text of the document, and output a subset of the one or more research recommendations for display to the author. | 1. A system for generating content-matching feedback on author-generated documents, the system comprising: a content matching engine comprising computer hardware configured to: provide a user interface for authors to submit documents for content matching feedback to avoid accidental plagiarism, receive a document from an author over a communications medium, the document generated by the author, compare at least a portion of the document to at least a portion of one or more first publications using one or more pattern-matching techniques, generate content-matching feedback responsive to the comparison, the content-matching feedback comprising suggestions for correcting attribution errors in the document to thereby assist the author in avoiding accidental plagiarism, generate a certificate certifying that the document was checked for plagiarism, thereby enabling the author to indicate to a reviewing entity that the document was checked for plagiarism, and output the content-matching feedback for display to the author; and a research recommender in communication with the content matching engine, the research recommender configured to: detect publication associations between a plurality of second publications based at least in part on citations in text of the plurality of second publications, generate one or more research recommendations based at least in part on cited publications in the text of the document and on the publication associations, the one or more research recommendations comprising one or more of the second publications that have publication associations with one or more of the cited publications in the text of the document, and output a subset of the one or more research recommendations for display to the author. 2. The system of claim 1 , wherein the content-matching feedback comprises suggested citations to at least some of the one or more first publications. | 0.784483 |
7,565,489 | 2 | 3 | 2. The method as recited in claim 1 , wherein the act of referring to a relevance threshold comprises an act of referring to a relevance threshold that defines a cacheable data perimeter around the requested data entity, the cacheable data perimeter indicating the number of links from the requested data entity that can be followed before the configurable relevance rules indicate that data is no longer relevant to the requested data entity. | 2. The method as recited in claim 1 , wherein the act of referring to a relevance threshold comprises an act of referring to a relevance threshold that defines a cacheable data perimeter around the requested data entity, the cacheable data perimeter indicating the number of links from the requested data entity that can be followed before the configurable relevance rules indicate that data is no longer relevant to the requested data entity. 3. The method as recited in claim 2 , wherein the act of determining that the other data entity satisfies the relevance threshold with respect to the requested data entity comprises an act of determining that the other data entity is within the cacheable data perimeter. | 0.5 |
8,024,173 | 4 | 5 | 4. A text analysis method relating to nominalizations, the method comprising: ascertaining automatically whether or not at least one sign is present in text, the sign relating to a possible writing problem, the sign comprising a characteristic of nominalizations; when the sign is present in the text, determining whether the sign is part of a word or word group that is present in a database of false positives; when it is determined that the sign is not part of a word or word group that is present in the database of false positives, determining an edit to propose to a user, the edit comprising changing the text to remove a nominalization; and comparing a selected sign with a first list of false positives and if the selected sign does not match a false positive, determining if the first four words preceding the selected sign include more than one of a “to be” verb and/or a verb from a verb pool; said method performed by a computer system that comprises one or more computers. | 4. A text analysis method relating to nominalizations, the method comprising: ascertaining automatically whether or not at least one sign is present in text, the sign relating to a possible writing problem, the sign comprising a characteristic of nominalizations; when the sign is present in the text, determining whether the sign is part of a word or word group that is present in a database of false positives; when it is determined that the sign is not part of a word or word group that is present in the database of false positives, determining an edit to propose to a user, the edit comprising changing the text to remove a nominalization; and comparing a selected sign with a first list of false positives and if the selected sign does not match a false positive, determining if the first four words preceding the selected sign include more than one of a “to be” verb and/or a verb from a verb pool; said method performed by a computer system that comprises one or more computers. 5. The method of claim 4 , wherein the edit comprises selecting the verb closest to the selected sign and deleting that verb along with any article and any occurrence of the term “in” between the verb and the selected sign. | 0.5 |
8,590,011 | 1 | 6 | 1. A method of providing controlled, electronic access to variable domain data stored in a data processing system, the method comprising: performing using a computer system: receiving information from a principal that includes information identifying the principal; performing one or more logical relationship operations on a data security model and a variable domain data model using security attributes of the data security model to determine a level of resource data access to be granted to the principal, wherein the variable domain model comprises a product configuration model, and performing the one or more logical operations comprises executing a configuration engine to perform an intersection between configuration spaces defined by a data security model and a product configuration model, wherein data included in any overlap of the configuration spaces is used to determine a level of resource data access to be granted to the principal; and granting the principal access to the resource data in accordance with the determined level of resource data access to be granted to the principal, wherein the principal comprises an entity that has controlled access to the resource data. | 1. A method of providing controlled, electronic access to variable domain data stored in a data processing system, the method comprising: performing using a computer system: receiving information from a principal that includes information identifying the principal; performing one or more logical relationship operations on a data security model and a variable domain data model using security attributes of the data security model to determine a level of resource data access to be granted to the principal, wherein the variable domain model comprises a product configuration model, and performing the one or more logical operations comprises executing a configuration engine to perform an intersection between configuration spaces defined by a data security model and a product configuration model, wherein data included in any overlap of the configuration spaces is used to determine a level of resource data access to be granted to the principal; and granting the principal access to the resource data in accordance with the determined level of resource data access to be granted to the principal, wherein the principal comprises an entity that has controlled access to the resource data. 6. The method of claim 1 wherein the level of resource data access granted to the principal comprises rights to modify at least one member of the group consisting of: the resource data, variable domain data model attributes, and logical relationships used to describe the resource data. | 0.675 |
5,414,423 | 2 | 5 | 2. A method as in claim 1 wherein the compressing of data includes an arithmetic coding decision using a given model context assigned an index A which is expanded into a context A, N, where N is the number of previous decisions for said given model context used in the conditioning of the arithmetic coding decision. | 2. A method as in claim 1 wherein the compressing of data includes an arithmetic coding decision using a given model context assigned an index A which is expanded into a context A, N, where N is the number of previous decisions for said given model context used in the conditioning of the arithmetic coding decision. 5. A method as in claim 2 wherein said model context has M bits of coding and N bits of history decision with value H(A), and the coding context is assigned an index (2**N)A+H(A), if the N bits were the low order bits of the coding context index. | 0.5 |
8,627,260 | 1 | 5 | 1. A computer-implemented method for design analysis comprising: obtaining a high-level design which includes a word-level representation; obtaining a bit-level representation of the high-level design; determining, using one or more processors, a map between the word-level representation and the bit-level representation; optimizing the bit-level representation; and lifting results from the optimizing of the bit-level representation and including the results in the word-level representation based on the map wherein the lifting includes creating an isomorph of the bit-level representation within the word-level representation and where the creating an isomorph includes mapping bit-level nets to corresponding nets within the word-level representation where the bit-level nets are merged, identifying nets from the bit-level representation that are merged, and merging the corresponding nets within the word-level representation. | 1. A computer-implemented method for design analysis comprising: obtaining a high-level design which includes a word-level representation; obtaining a bit-level representation of the high-level design; determining, using one or more processors, a map between the word-level representation and the bit-level representation; optimizing the bit-level representation; and lifting results from the optimizing of the bit-level representation and including the results in the word-level representation based on the map wherein the lifting includes creating an isomorph of the bit-level representation within the word-level representation and where the creating an isomorph includes mapping bit-level nets to corresponding nets within the word-level representation where the bit-level nets are merged, identifying nets from the bit-level representation that are merged, and merging the corresponding nets within the word-level representation. 5. The method of claim 1 wherein the creating the isomorph comprises taking constants from the bit-level representation and using those constants in the word-level representation. | 0.523936 |
8,447,599 | 1 | 2 | 1. A computer-implemented method comprising: receiving, by a speech recognition model training system and from a first computing device, (i) a request for an update to a speaker-dependent speech recognition model associated with the first computing device, and (ii) recorded speech; generating, based on processing the recorded speech from the first computing device and by the speech recognition model training system, the update to the speaker-dependent speech recognition model associated with the first computing device; generating, based on processing the recorded speech from the first computing device and by the speech recognition model training system, an update for a speaker-independent speech recognition model associated with a second computing device; transmitting, based on receiving the request and by the speech recognition model training system, the update to the speaker-dependent speech recognition model to the first computing device; determining, by the speech recognition model training system, that a predetermined period of time has elapsed since the speaker-independent speech recognition model associated with the second computing device was last updated; and transmitting, based on determining that the predetermined period of time has elapsed since the speaker-independent speech recognition model associated with the second computing device was last updated and by the speech recognition model training system, the update to the speaker-independent speech recognition model to the second computing device. | 1. A computer-implemented method comprising: receiving, by a speech recognition model training system and from a first computing device, (i) a request for an update to a speaker-dependent speech recognition model associated with the first computing device, and (ii) recorded speech; generating, based on processing the recorded speech from the first computing device and by the speech recognition model training system, the update to the speaker-dependent speech recognition model associated with the first computing device; generating, based on processing the recorded speech from the first computing device and by the speech recognition model training system, an update for a speaker-independent speech recognition model associated with a second computing device; transmitting, based on receiving the request and by the speech recognition model training system, the update to the speaker-dependent speech recognition model to the first computing device; determining, by the speech recognition model training system, that a predetermined period of time has elapsed since the speaker-independent speech recognition model associated with the second computing device was last updated; and transmitting, based on determining that the predetermined period of time has elapsed since the speaker-independent speech recognition model associated with the second computing device was last updated and by the speech recognition model training system, the update to the speaker-independent speech recognition model to the second computing device. 2. The method of claim 1 , further comprising receiving, by the speech recognition model training system, a transcription of the recorded speech. | 0.603825 |
8,555,269 | 16 | 17 | 16. The non-transitory computer-readable medium of claim 12 , further comprising the step of: inserting statements into, or modifying statements within, said software application to secure vulnerabilities which are identified as a result of said verifying step. | 16. The non-transitory computer-readable medium of claim 12 , further comprising the step of: inserting statements into, or modifying statements within, said software application to secure vulnerabilities which are identified as a result of said verifying step. 17. The non-transitory computer-readable medium of claim 16 , wherein said step of inserting or modifying statements further comprises the step of: inserting at least one of said statements at each location associated with an insecure variable. | 0.515873 |
7,624,368 | 21 | 25 | 21. A computer-readable e storage medium having a computer-readable program embodied therein for directing operation of a computer system including a processor and at least one input device, wherein the computer-readable program includes instructions for operating the computer system for optimizing a digital circuit design in accordance with the following: receiving a first representation of the digital circuit design from the at least one input device; translating the first representation of the digital circuit design to a second representation of the digital circuit design, the second representation comprising a plurality of syntactic expressions that admit a representation of a higher-order function of base Boolean values; and executing and syntactically manipulating the plurality of syntactic expressions to form a third representation of the digital circuit design, wherein manipulating step comprises reducing a number of nodes within the syntactic expressions. | 21. A computer-readable e storage medium having a computer-readable program embodied therein for directing operation of a computer system including a processor and at least one input device, wherein the computer-readable program includes instructions for operating the computer system for optimizing a digital circuit design in accordance with the following: receiving a first representation of the digital circuit design from the at least one input device; translating the first representation of the digital circuit design to a second representation of the digital circuit design, the second representation comprising a plurality of syntactic expressions that admit a representation of a higher-order function of base Boolean values; and executing and syntactically manipulating the plurality of syntactic expressions to form a third representation of the digital circuit design, wherein manipulating step comprises reducing a number of nodes within the syntactic expressions. 25. The computer-readable storage medium having the computer readable program embodied therein for directing operation of the computer system including the processor and at least one input device as recited in claim 21 , wherein manipulating the plurality of syntactic expressions comprises removing logical inversions within the syntactic expressions. | 0.65692 |
9,600,833 | 16 | 23 | 16. A system, comprising: a data store storing sets of duplicate keywords for an advertising account, each set of duplicate keywords including keywords having common matching text for the set; and a duplicate keyword selection system comprising one or more processors configured to: identify two or more different sets of duplicate keywords including duplicate keywords that satisfy a selection criterion; receive a single action request to modify an attribute value of a proper subset of duplicate keywords in each of the identified two or more different sets of duplicate keywords, the proper subset of duplicate keywords in each of the two or more different sets of duplicate keywords being only those keywords that satisfy the selection criterion; and modify, within each particular set of the two or more different sets of duplicate keywords, attribute values for the proper subset of duplicate keywords in the particular set, the modification being performed in response to the single action request. | 16. A system, comprising: a data store storing sets of duplicate keywords for an advertising account, each set of duplicate keywords including keywords having common matching text for the set; and a duplicate keyword selection system comprising one or more processors configured to: identify two or more different sets of duplicate keywords including duplicate keywords that satisfy a selection criterion; receive a single action request to modify an attribute value of a proper subset of duplicate keywords in each of the identified two or more different sets of duplicate keywords, the proper subset of duplicate keywords in each of the two or more different sets of duplicate keywords being only those keywords that satisfy the selection criterion; and modify, within each particular set of the two or more different sets of duplicate keywords, attribute values for the proper subset of duplicate keywords in the particular set, the modification being performed in response to the single action request. 23. The system of claim 16 , wherein the duplicate keyword selection system is further configured to highlight the proper subset of duplicate keywords in each of the two or more different sets of duplicate keywords. | 0.754005 |
9,348,899 | 1 | 6 | 1. A computer-implemented method of automatically classifying digital content, the method comprising: creating, by an auto-classification system embodied on non-transitory computer memory, a classification model for classifying digital content, wherein creating the classification model comprises: prompting a user via a user interface to enter a name and description for the classification model, and responsive to user selection of one or more documents via the user interface, adding or importing, from a document source into a container, the selected one or more documents as exemplars for a category of digital content within the classification model; subsequent to a user selecting a plurality of documents, the auto-classification system classifying the plurality of documents using characteristics of the exemplars; during the classifying, after the classifying is complete, or both during the classifying and after the classifying is complete, the auto-classification system determining performance metrics representing accuracy of the classification model in classifying the plurality of documents, the performance metrics including precision, recall, match, noise, and silence metrics; the auto-classification system determining one or more recommended actions based on the performance metrics; the auto-classification system displaying the performance metrics including the precision, recall, match, noise, and silence metrics for a number of the plurality of documents that have been classified using the classification model; the auto-classification system displaying a user feedback guide that presents the one or more recommended actions to improve the accuracy of the classification model, wherein the one or more recommended actions comprise adding one or more exemplars from the number of the plurality of documents that have been classified using the classification model or specifying how to meet one or more associated content classification rules; and providing a user interface element associated with the user feedback guide that, in response to receipt of user input, causes a recommended action to be performed on the classification model. | 1. A computer-implemented method of automatically classifying digital content, the method comprising: creating, by an auto-classification system embodied on non-transitory computer memory, a classification model for classifying digital content, wherein creating the classification model comprises: prompting a user via a user interface to enter a name and description for the classification model, and responsive to user selection of one or more documents via the user interface, adding or importing, from a document source into a container, the selected one or more documents as exemplars for a category of digital content within the classification model; subsequent to a user selecting a plurality of documents, the auto-classification system classifying the plurality of documents using characteristics of the exemplars; during the classifying, after the classifying is complete, or both during the classifying and after the classifying is complete, the auto-classification system determining performance metrics representing accuracy of the classification model in classifying the plurality of documents, the performance metrics including precision, recall, match, noise, and silence metrics; the auto-classification system determining one or more recommended actions based on the performance metrics; the auto-classification system displaying the performance metrics including the precision, recall, match, noise, and silence metrics for a number of the plurality of documents that have been classified using the classification model; the auto-classification system displaying a user feedback guide that presents the one or more recommended actions to improve the accuracy of the classification model, wherein the one or more recommended actions comprise adding one or more exemplars from the number of the plurality of documents that have been classified using the classification model or specifying how to meet one or more associated content classification rules; and providing a user interface element associated with the user feedback guide that, in response to receipt of user input, causes a recommended action to be performed on the classification model. 6. The method as claimed in claim 1 wherein creating the classification model further comprises defining at least one classification rule, wherein the rule comprises a rule priority determining an order in which rule is applied, a confidence level to be applied to a document when the document satisfies a condition specified by the rule and an applied classification that is to be applied to the document. | 0.5 |
9,298,702 | 26 | 27 | 26. The medium of claim 15 wherein the NLP system includes the functionality of entity recognition and entity type assignment, and further wherein these are provided by a wiki that includes the ability to receive requested pages for word sequences and provide corresponding response data. | 26. The medium of claim 15 wherein the NLP system includes the functionality of entity recognition and entity type assignment, and further wherein these are provided by a wiki that includes the ability to receive requested pages for word sequences and provide corresponding response data. 27. The medium of claim 26 wherein the wiki includes the ability to receive a request for a list of categories or types for any pages found responsive to the page request and provide corresponding response data. | 0.5 |
7,703,003 | 16 | 17 | 16. A tangible computer readable storage medium, storing program instructions, that when executed by one or more processors, cause the one or more processors to implement: a service store comprising a plurality of service objects each corresponding to a different service, wherein each service object has a hierarchical structure specifying a plurality of activities and one or more service elements for each activity, wherein each activity is a procedure to be carried out in providing the corresponding service, wherein each service object includes activity data identifying the service object's respective plurality of activities and specifying the one or more service elements for each activity, wherein each service element represents a component of the corresponding activity and specifies a representation for inclusion in a document to be used in performing the service; wherein a particular service element of an activity of a service object stored in the service store comprises at least one of: a description of the activity, an input for the activity, an output or deliverable from the activity, an assumption for the activity, information regarding responsibility for the activity, a subsidiary activity of the activity, or a sub-work plan of the activity; a document template store comprising one or more document templates, wherein each document template specifies an organization of a type of document to be used in performing a service; and a document provider configured to: identify, for a given service selected by a user, and for a given document template selected by the user, one or more service elements of one or more activities of a service object corresponding to the given service, wherein representations of the one or more service elements are to be included in a document organized in accordance with the given document template; and generate a document including representations of the one or more service elements, wherein the document is organized in accordance with the given document template, wherein the representations of the one or more service elements are included in the document without the user having to identify each representation separately. | 16. A tangible computer readable storage medium, storing program instructions, that when executed by one or more processors, cause the one or more processors to implement: a service store comprising a plurality of service objects each corresponding to a different service, wherein each service object has a hierarchical structure specifying a plurality of activities and one or more service elements for each activity, wherein each activity is a procedure to be carried out in providing the corresponding service, wherein each service object includes activity data identifying the service object's respective plurality of activities and specifying the one or more service elements for each activity, wherein each service element represents a component of the corresponding activity and specifies a representation for inclusion in a document to be used in performing the service; wherein a particular service element of an activity of a service object stored in the service store comprises at least one of: a description of the activity, an input for the activity, an output or deliverable from the activity, an assumption for the activity, information regarding responsibility for the activity, a subsidiary activity of the activity, or a sub-work plan of the activity; a document template store comprising one or more document templates, wherein each document template specifies an organization of a type of document to be used in performing a service; and a document provider configured to: identify, for a given service selected by a user, and for a given document template selected by the user, one or more service elements of one or more activities of a service object corresponding to the given service, wherein representations of the one or more service elements are to be included in a document organized in accordance with the given document template; and generate a document including representations of the one or more service elements, wherein the document is organized in accordance with the given document template, wherein the representations of the one or more service elements are included in the document without the user having to identify each representation separately. 17. The computer readable storage medium as recited in claim 16 , wherein the document provider comprises a service selector configured to: provide an interface to select a service from a set of services for which service objects are stored in the service store; and in response to input received from a user via the interface, select the given service. | 0.63832 |
7,958,448 | 1 | 9 | 1. A method of activating a font, comprising: receiving, by a computing system, input requesting activation of a font; determining, by the computing system, that the font does not exist in a font management vault; upon determining that the font does not exist in the font management vault, identifying the font in one multi-font suitcase file of a plurality of multi-font suitcase files, each multi-font suitcase file of the plurality including a similarly named version of the font, separating the font from the multi-font suitcase file, and saving the separated font in the font management vault; and activating, by the computing system, the font from the font management vault. | 1. A method of activating a font, comprising: receiving, by a computing system, input requesting activation of a font; determining, by the computing system, that the font does not exist in a font management vault; upon determining that the font does not exist in the font management vault, identifying the font in one multi-font suitcase file of a plurality of multi-font suitcase files, each multi-font suitcase file of the plurality including a similarly named version of the font, separating the font from the multi-font suitcase file, and saving the separated font in the font management vault; and activating, by the computing system, the font from the font management vault. 9. A method of activating a font according to claim 1 , wherein data for generating the input requesting activation of the font is stored as a portion of a personal computer operating system. | 0.598739 |
10,146,822 | 9 | 16 | 9. A computer-implemented method executed on a computing device for generating a computer-executable script for processing a plurality of storage system data files, the method comprising the steps of: selecting, by the computing device, selected storage system data files from the plurality of storage system data files based on one or more data structure description files; generating, by the computing device, one or more computer-executable statements for loading the selected storage system data files into storage system data tables; selecting, by the computing device, one or more storage system data fields from the storage system data tables based on the one or more data structure description files; generating, by the computing device, one or more computer-executable statements for generating filtered storage system data tables by filtering the storage system data tables based on the selected storage system data fields; determining, by the computing device, a join sequence for joining the filtered storage system data tables based on one or more properties of the filtered storage system data tables; generating, by the computing device, one or more computer-executable statements for joining the filtered storage system data tables into a joined table based on the join sequence; formatting, by the computing device, the joined table based on the one or more data structure description files; generating, by the computing device, one or more computer-executable statements for creating an output based on the formatted joined table; generating, by the computing device, the computer-executable script based on the one or more computer-executable statements for loading the selected storage system data files into storage system data tables, the one or more computer-executable statements for filtering the storage system data tables, the one or more computer-executable statements for joining the filtered storage system data tables into the joined table based on the join sequence, and the one or more computer-executable statements for creating the output based on the formatted joined table; and committing, by the computing device, the computer-executable script to a file system in communication with the computing device, the one or more properties of the filtered storage system data tables including complexities of the respective filtered storage system data tables, the complexities including lesser complexities and greater complexities, a predetermined amount of memory and processor resources of the computing device being required to determine the join sequence based on the complexities of the respective filtered storage system data tables, wherein determining the join sequence includes joining, in the join sequence, the filtered storage system data tables having the lesser complexities before joining, in the join sequence, the filtered storage system data tables having the greater complexities to reduce the required predetermined amount of memory and processor resources of the computing device. | 9. A computer-implemented method executed on a computing device for generating a computer-executable script for processing a plurality of storage system data files, the method comprising the steps of: selecting, by the computing device, selected storage system data files from the plurality of storage system data files based on one or more data structure description files; generating, by the computing device, one or more computer-executable statements for loading the selected storage system data files into storage system data tables; selecting, by the computing device, one or more storage system data fields from the storage system data tables based on the one or more data structure description files; generating, by the computing device, one or more computer-executable statements for generating filtered storage system data tables by filtering the storage system data tables based on the selected storage system data fields; determining, by the computing device, a join sequence for joining the filtered storage system data tables based on one or more properties of the filtered storage system data tables; generating, by the computing device, one or more computer-executable statements for joining the filtered storage system data tables into a joined table based on the join sequence; formatting, by the computing device, the joined table based on the one or more data structure description files; generating, by the computing device, one or more computer-executable statements for creating an output based on the formatted joined table; generating, by the computing device, the computer-executable script based on the one or more computer-executable statements for loading the selected storage system data files into storage system data tables, the one or more computer-executable statements for filtering the storage system data tables, the one or more computer-executable statements for joining the filtered storage system data tables into the joined table based on the join sequence, and the one or more computer-executable statements for creating the output based on the formatted joined table; and committing, by the computing device, the computer-executable script to a file system in communication with the computing device, the one or more properties of the filtered storage system data tables including complexities of the respective filtered storage system data tables, the complexities including lesser complexities and greater complexities, a predetermined amount of memory and processor resources of the computing device being required to determine the join sequence based on the complexities of the respective filtered storage system data tables, wherein determining the join sequence includes joining, in the join sequence, the filtered storage system data tables having the lesser complexities before joining, in the join sequence, the filtered storage system data tables having the greater complexities to reduce the required predetermined amount of memory and processor resources of the computing device. 16. The method of claim 9 further comprising configuring, by the computing device, the computer-executable script to process storage system data files stored in a Hadoop Distributed File System. | 0.772834 |
9,226,047 | 18 | 23 | 18. The method of claim 17 further comprising locating the specified media object within video content through scanning an online site containing user-generated content; and wherein restricting access to playback of video content containing the media object in accordance with the allowed actions comprises notifying a content distributor of the video content that the content is copyright protected and who the identified content owner is. | 18. The method of claim 17 further comprising locating the specified media object within video content through scanning an online site containing user-generated content; and wherein restricting access to playback of video content containing the media object in accordance with the allowed actions comprises notifying a content distributor of the video content that the content is copyright protected and who the identified content owner is. 23. The method of claim 18 , wherein notifying a content distributor of the video content that the content is copyright protected and who the identified content owner is comprises issuing a take down notice to stop distribution of the video content. | 0.5 |
8,308,539 | 8 | 10 | 8. A method for conducting a letter placement game, comprising: providing a puzzle grid having a plurality of cells, a plurality of horizontal rows, a plurality of vertical rows, and a plurality of subgrids, each subgrid, each horizontal row, and each vertical row having an equal number of cells; providing a predetermined set of characters, the puzzle grid comprising filled cells, each filled cell comprising one of the characters, and the puzzle grid comprising empty cells, each empty cell associated with a predetermined solution character; providing a set of character singularity rules requiring that (i) each horizontal row contains one set of the predetermined characters having one instance of each character, (ii) each vertical row contains one set of the predetermined characters having one instance of each character, and (iii) each subgrid contains one set of the predetermined characters having one instance of each character; receiving clue input comprising a plurality of clues for a puzzle grid and a request to generate a puzzle grid based on the plurality of clues, each clue having a solution to each clue based on the predetermined set of characters, each clue associated with a predetermined location in the puzzle grid, each predetermined location associated with a predetermined number of the cells, each predetermined location having a contiguous association of the cells of each predetermined location, each clue solution having a set of solution characters corresponding to the predetermined number of cells associated with each clue, each clue solution having a semantic meaning; generating the puzzle grid based on the clue input and a puzzle solution for the puzzle grid, each character in each set of solution characters conforming to the character singularity rules, and each solution character for each empty cell conforming to the character singularity rules; and providing the puzzle grid and the puzzle solution in response to the request to generate the puzzle grid. | 8. A method for conducting a letter placement game, comprising: providing a puzzle grid having a plurality of cells, a plurality of horizontal rows, a plurality of vertical rows, and a plurality of subgrids, each subgrid, each horizontal row, and each vertical row having an equal number of cells; providing a predetermined set of characters, the puzzle grid comprising filled cells, each filled cell comprising one of the characters, and the puzzle grid comprising empty cells, each empty cell associated with a predetermined solution character; providing a set of character singularity rules requiring that (i) each horizontal row contains one set of the predetermined characters having one instance of each character, (ii) each vertical row contains one set of the predetermined characters having one instance of each character, and (iii) each subgrid contains one set of the predetermined characters having one instance of each character; receiving clue input comprising a plurality of clues for a puzzle grid and a request to generate a puzzle grid based on the plurality of clues, each clue having a solution to each clue based on the predetermined set of characters, each clue associated with a predetermined location in the puzzle grid, each predetermined location associated with a predetermined number of the cells, each predetermined location having a contiguous association of the cells of each predetermined location, each clue solution having a set of solution characters corresponding to the predetermined number of cells associated with each clue, each clue solution having a semantic meaning; generating the puzzle grid based on the clue input and a puzzle solution for the puzzle grid, each character in each set of solution characters conforming to the character singularity rules, and each solution character for each empty cell conforming to the character singularity rules; and providing the puzzle grid and the puzzle solution in response to the request to generate the puzzle grid. 10. The method of claim 8 , wherein the predetermined set of characters is based on an alphabet, and the plurality of clues are based on words, each clue comprising a sequence of words. | 0.752011 |
8,069,467 | 11 | 13 | 11. A computerized method which reduces the risk that an online identifier will disclose information about an online user's offline identity, the method comprising: a computer system receiving electronically a question regarding a proposed online identifier, which is a username or an avatar, said question causing an inquiry whether at least a portion of said proposed online identifier's content coincides with a set of the online user's offline identity; and the computer system answering the question to help maintain the privacy of offline identities of online users. | 11. A computerized method which reduces the risk that an online identifier will disclose information about an online user's offline identity, the method comprising: a computer system receiving electronically a question regarding a proposed online identifier, which is a username or an avatar, said question causing an inquiry whether at least a portion of said proposed online identifier's content coincides with a set of the online user's offline identity; and the computer system answering the question to help maintain the privacy of offline identities of online users. 13. The method of claim 11 , further comprising proposing an online identifier. | 0.924474 |
9,300,986 | 13 | 14 | 13. A non-transitory computer-accessible storage medium storing program instructions executable by one or more processors to: communicate with a plurality of content provider systems to import a set of media productions from each content provider system; create a respective normalized media document representing each of the media productions that are imported; and identify a potential match between at least three media productions, wherein at least two media productions of the three media productions are identified as instances of a same content provided by different content provider systems, and wherein at least one media production of the three media productions is identified as not being an instance of the at least two media productions, but is identified as being related to the at least two media productions; maintain a mapping table comprising a plurality of entries, wherein each entry is configured to identify a potential match between at least two media productions and a score that represents a level of confidence in said potential match; create a canonical object that corresponds to the at least three media productions, wherein the canonical object identifies each of the three media productions; maintain a canonical object table comprising a plurality of entries, wherein each entry is configured to identify a canonical object; and maintain a canonical version table configured to identify multiple instances of a media production. | 13. A non-transitory computer-accessible storage medium storing program instructions executable by one or more processors to: communicate with a plurality of content provider systems to import a set of media productions from each content provider system; create a respective normalized media document representing each of the media productions that are imported; and identify a potential match between at least three media productions, wherein at least two media productions of the three media productions are identified as instances of a same content provided by different content provider systems, and wherein at least one media production of the three media productions is identified as not being an instance of the at least two media productions, but is identified as being related to the at least two media productions; maintain a mapping table comprising a plurality of entries, wherein each entry is configured to identify a potential match between at least two media productions and a score that represents a level of confidence in said potential match; create a canonical object that corresponds to the at least three media productions, wherein the canonical object identifies each of the three media productions; maintain a canonical object table comprising a plurality of entries, wherein each entry is configured to identify a canonical object; and maintain a canonical version table configured to identify multiple instances of a media production. 14. The non-transitory computer-accessible storage medium of claim 13 , wherein a first media production of the at least three media productions is a first instance of a given movie, television program or video from a first content provider system, and wherein a second media production of the at least three media productions is a second instance of the movie, television program or video from a second content provider system that is different from the first content provider system. | 0.5 |
9,659,097 | 9 | 16 | 9. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, at a computer system, a request to classify a first query; obtaining, by the computer system, session data for the first query, wherein: the session data for the first query identifies a plurality of sessions in which the first query occurs, each session of the plurality of sessions includes (i) one or more queries submitted in succession by a single respective user for the session and (ii) one or more search entities returned in response to executing the one or more queries, and each search entity is assigned one or more classifications; selecting, by the computer system from the session data for the first query, a first plurality of search entities, wherein the first plurality of search entities are the search entities that frequently occur in the plurality of query sessions in response to executing the first query; identifying, by the computer system, one or more potential classifications for the first query; for each potential classification of the one or more potential classifications, determining a first measure of how many of the first plurality of search entities have been assigned the potential classification; determining that the first measure have been assigned the classification satisfies a classification threshold; and in response to determining that the first measure have been assigned the classification satisfies the classification threshold, assigning the potential classification to the first query, determining that a second query occurs in the same first session with the first query; and assigning the potential classification to the second query. | 9. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, at a computer system, a request to classify a first query; obtaining, by the computer system, session data for the first query, wherein: the session data for the first query identifies a plurality of sessions in which the first query occurs, each session of the plurality of sessions includes (i) one or more queries submitted in succession by a single respective user for the session and (ii) one or more search entities returned in response to executing the one or more queries, and each search entity is assigned one or more classifications; selecting, by the computer system from the session data for the first query, a first plurality of search entities, wherein the first plurality of search entities are the search entities that frequently occur in the plurality of query sessions in response to executing the first query; identifying, by the computer system, one or more potential classifications for the first query; for each potential classification of the one or more potential classifications, determining a first measure of how many of the first plurality of search entities have been assigned the potential classification; determining that the first measure have been assigned the classification satisfies a classification threshold; and in response to determining that the first measure have been assigned the classification satisfies the classification threshold, assigning the potential classification to the first query, determining that a second query occurs in the same first session with the first query; and assigning the potential classification to the second query. 16. The system of claim 9 , wherein determining the first measure of how many of the first plurality of search entities have been assigned the potential classification comprises weighting a classification assigned to each of the first plurality of search entities according to user behavior data relative to the first query. | 0.562162 |
8,078,963 | 43 | 45 | 43. A non-transitory computer readable medium storing a computer program which when executed by at least one processor inserts content into a document, the computer program comprising sets of instructions for: displaying a page of the document, the page comprising (i) a first layer containing in-line text content and (ii) a second layer containing floating content; performing a drag operation on the floating content to move the floating content from a first location to a second location over the text content; while the floating content moves over the text content, adjusting the text content to wrap around the floating content in real time, in order to provide feedback regarding positioning of the text content around the floating content; receiving input to drop the floating content at the second location; and inserting the floating content into the floating layer at the second location and wrapping the text content around the floating content at the second location. | 43. A non-transitory computer readable medium storing a computer program which when executed by at least one processor inserts content into a document, the computer program comprising sets of instructions for: displaying a page of the document, the page comprising (i) a first layer containing in-line text content and (ii) a second layer containing floating content; performing a drag operation on the floating content to move the floating content from a first location to a second location over the text content; while the floating content moves over the text content, adjusting the text content to wrap around the floating content in real time, in order to provide feedback regarding positioning of the text content around the floating content; receiving input to drop the floating content at the second location; and inserting the floating content into the floating layer at the second location and wrapping the text content around the floating content at the second location. 45. The non-transitory computer readable medium of claim 43 , wherein said floating content is occludable by other content in said second layer. | 0.5 |
8,538,759 | 1 | 6 | 1. A speech recognition system installed in a terminal coupled to a server via a network, wherein: the terminal holds map data including a landmark; the speech recognition system manages recognition data including a word corresponding to a name of the landmark included in the held map data, and sends update area information indicating an area of the map data to be updated and update data on the area indicated by the update area information to the server; the server is configured to: generate, in a case of which recognition data of the area indicated by the update area information sent from the terminal has been changed, after a time indicated by the update data sent from the terminal, difference data between latest recognition data and recognition data corresponding to the area indicated by the update area information at a time indicated by the update data; and send the generated difference data to the terminal along with map data on the area indicated by the update area information; the terminal updates the map data held in the terminal based on the map data sent from the server; and the speech recognition system updates the recognition data managed by the terminal based on the difference data sent from the server; wherein: the recognition data includes confusion information, the confusion information including a confusion word having a tendency to cause a recognition error with the word corresponding to a name of the landmark, and a confusion score which represents the tendency of the confusion word to cause recognition error; the server sends the difference data including the confusion information to the terminal; and the speech recognition system updates the confusion information included in the recognition data held by the terminal based on the confusion information sent from the server. | 1. A speech recognition system installed in a terminal coupled to a server via a network, wherein: the terminal holds map data including a landmark; the speech recognition system manages recognition data including a word corresponding to a name of the landmark included in the held map data, and sends update area information indicating an area of the map data to be updated and update data on the area indicated by the update area information to the server; the server is configured to: generate, in a case of which recognition data of the area indicated by the update area information sent from the terminal has been changed, after a time indicated by the update data sent from the terminal, difference data between latest recognition data and recognition data corresponding to the area indicated by the update area information at a time indicated by the update data; and send the generated difference data to the terminal along with map data on the area indicated by the update area information; the terminal updates the map data held in the terminal based on the map data sent from the server; and the speech recognition system updates the recognition data managed by the terminal based on the difference data sent from the server; wherein: the recognition data includes confusion information, the confusion information including a confusion word having a tendency to cause a recognition error with the word corresponding to a name of the landmark, and a confusion score which represents the tendency of the confusion word to cause recognition error; the server sends the difference data including the confusion information to the terminal; and the speech recognition system updates the confusion information included in the recognition data held by the terminal based on the confusion information sent from the server. 6. The speech recognition system according to claim 1 , wherein: the server extracts confusion information on a word having a tendency to cause a recognition error with a word included in the difference data, and sends the extracted confusion information as confusion subject information to the terminal; and the speech recognition system updates the confusion information managed by the terminal based on the confusion subject information sent from the server. | 0.506424 |
8,566,154 | 20 | 24 | 20. A computer system for online re-targeted advertisement selection, comprising: a storage device configured to store advertising content associated with a Web site; a communications device configured to communicate with the Web site and a user; and an advertisement server configured to: receive a description of online activities for a collection of online identities for users accessing one or more affiliate web sites; identify a desired behavior, the desired behavior describing user interaction that indicates that an online identity demonstrating the desired behavior is more likely to be responsive to a re-targeted advertisement; analyze the description of online activities to determine whether a particular online identity appearing in the description of the online activities demonstrates the desired behavior; generate a watch list of users to receive re-targeted advertisements based on analyzing the description of online activities; monitor, in real-time and from the one or more affiliate web sites, information related to user interaction with the one or more affiliate web sites; receive, from within the monitored information, a request to display advertising content to a user; determine that the user appears in the watch list of users to receive re-targeted advertisements; and select, in response to the request, advertising content for display based upon determining that the user appears in the watch list of users to receive re-targeted advertisements. | 20. A computer system for online re-targeted advertisement selection, comprising: a storage device configured to store advertising content associated with a Web site; a communications device configured to communicate with the Web site and a user; and an advertisement server configured to: receive a description of online activities for a collection of online identities for users accessing one or more affiliate web sites; identify a desired behavior, the desired behavior describing user interaction that indicates that an online identity demonstrating the desired behavior is more likely to be responsive to a re-targeted advertisement; analyze the description of online activities to determine whether a particular online identity appearing in the description of the online activities demonstrates the desired behavior; generate a watch list of users to receive re-targeted advertisements based on analyzing the description of online activities; monitor, in real-time and from the one or more affiliate web sites, information related to user interaction with the one or more affiliate web sites; receive, from within the monitored information, a request to display advertising content to a user; determine that the user appears in the watch list of users to receive re-targeted advertisements; and select, in response to the request, advertising content for display based upon determining that the user appears in the watch list of users to receive re-targeted advertisements. 24. The system according to claim 20 , wherein the description of online activities is a tag included in a redirect message from the Web site, the tag identifying a specific Web page and indicating a prior activity of the user at the Web site. | 0.5 |
8,700,641 | 11 | 14 | 11. A system, comprising: a processor, communicatively coupled to a memory that stores computer-executable instructions, that executes or facilitates execution of the computer-executable instructions to perform operations comprising: a social application server: determine a first match M h between a first audio descriptor representing a first recording at a first time step in an environment and a first reference descriptor, the first match M h having-a first confidence score C h indicative of a confidence of the first match, the first time step having a time step length l; determine a second match M 0 between a second audio descriptor representing a second recording at a second time step in the environment and a second reference descriptor, the second match M 0 having a second confidence score C 0 indicative of a confidence of the second match, the second time step having the time step length l, wherein the first time step is temporally prior to the second time step, and the first match M h and the second match M 0 are non-identity matches determined using a direct or locality sensitive hashing function and a validation process to select a most accurate match out of a plurality of candidate matches, wherein the first confidence score C h and the second confidence score C 0 are based upon a log-likelihood function given by an audio fingerprinting process; discount the first confidence score C h by a discount value l/L to generate a discounted first confidence score C h −l/L, where L is an expected dwell time between a channel change; in response to the discounted first confidence score C h −l/L being greater than the second confidence score C 0 , employ the first reference descriptor associated with the first match M h for selecting related content; and in response to the discounted first confidence score C h −l/L not being greater than the second confidence score C 0 , employ the second reference descriptor associated with the second match M 0 for selecting the related content. | 11. A system, comprising: a processor, communicatively coupled to a memory that stores computer-executable instructions, that executes or facilitates execution of the computer-executable instructions to perform operations comprising: a social application server: determine a first match M h between a first audio descriptor representing a first recording at a first time step in an environment and a first reference descriptor, the first match M h having-a first confidence score C h indicative of a confidence of the first match, the first time step having a time step length l; determine a second match M 0 between a second audio descriptor representing a second recording at a second time step in the environment and a second reference descriptor, the second match M 0 having a second confidence score C 0 indicative of a confidence of the second match, the second time step having the time step length l, wherein the first time step is temporally prior to the second time step, and the first match M h and the second match M 0 are non-identity matches determined using a direct or locality sensitive hashing function and a validation process to select a most accurate match out of a plurality of candidate matches, wherein the first confidence score C h and the second confidence score C 0 are based upon a log-likelihood function given by an audio fingerprinting process; discount the first confidence score C h by a discount value l/L to generate a discounted first confidence score C h −l/L, where L is an expected dwell time between a channel change; in response to the discounted first confidence score C h −l/L being greater than the second confidence score C 0 , employ the first reference descriptor associated with the first match M h for selecting related content; and in response to the discounted first confidence score C h −l/L not being greater than the second confidence score C 0 , employ the second reference descriptor associated with the second match M 0 for selecting the related content. 14. The system of claim 11 , wherein the second reference descriptor has a highest confidence score of a plurality of reference descriptors that match the second audio descriptor. | 0.718553 |
5,428,737 | 5 | 6 | 5. The method recited in claim 4, wherein the database is relational and the text format query uses a structured query language. | 5. The method recited in claim 4, wherein the database is relational and the text format query uses a structured query language. 6. The method recited in claim 5, wherein the graphical format query is depicted on a video display screen using windows for one or more query clause relationships. | 0.5 |
6,076,059 | 1 | 15 | 1. A computerized method for aligning text segments of a text file with audio segments of an audio file, comprising the steps of: generating a vocabulary and language model from the text file, generation of said model involving determination of relative probabilities of all one, two, and three word sequences in all unaligned text segments of the text file based upon frequencies of occurrences of said sequences in said unaligned text segments, all of said text segments being initially classified as unaligned text segments; recognizing a word list from the audio segments using the vocabulary and language model but without considering the text file; aligning the word list with the text segments based upon respective scores for all possible alignments of words in the word list with the text segments, each respective score being weighted to increase each respective score by a relatively greater amount if a respective alignment associated with the respective score involves relatively longer sequences of correctly aligned words; choosing corresponding anchors in the word list and text segments in accordance with the respective scores; partitioning the text and the audio segments into unaligned and aligned text and audio segments according to the anchors; and repeating the generating, recognizing, aligning, choosing, and partitioning steps with the unaligned text and audio segments until a termination condition is reached. | 1. A computerized method for aligning text segments of a text file with audio segments of an audio file, comprising the steps of: generating a vocabulary and language model from the text file, generation of said model involving determination of relative probabilities of all one, two, and three word sequences in all unaligned text segments of the text file based upon frequencies of occurrences of said sequences in said unaligned text segments, all of said text segments being initially classified as unaligned text segments; recognizing a word list from the audio segments using the vocabulary and language model but without considering the text file; aligning the word list with the text segments based upon respective scores for all possible alignments of words in the word list with the text segments, each respective score being weighted to increase each respective score by a relatively greater amount if a respective alignment associated with the respective score involves relatively longer sequences of correctly aligned words; choosing corresponding anchors in the word list and text segments in accordance with the respective scores; partitioning the text and the audio segments into unaligned and aligned text and audio segments according to the anchors; and repeating the generating, recognizing, aligning, choosing, and partitioning steps with the unaligned text and audio segments until a termination condition is reached. 15. The method of claim 1 wherein the vocabulary and language model is rebuilt from the unaligned segments during repeated iterations of the generating step. | 0.595361 |
7,734,620 | 1 | 3 | 1. An apparatus comprising: at least one processor; a memory coupled to the at least one processor containing a database of records; a query residing in the memory that includes a Select statement with a Fetch First n Rows Only clause, where n is an integer variable; a query optimizer residing in the memory and executed by the at least one processor, wherein the query optimizer: determines that the query can be optimized and generates for the query an optimized access plan that eliminates records defined by a Where clause prior to ordering the records and then returns a first n rows; determines that the query contains a Group By clause and that an index exists for a leftmost column but not all the columns of the Group By clause; and generates an access plan that eliminates records prior to grouping by fetching n rows from the index over the leftmost column and remaining rows until a unique value of the index is encountered. | 1. An apparatus comprising: at least one processor; a memory coupled to the at least one processor containing a database of records; a query residing in the memory that includes a Select statement with a Fetch First n Rows Only clause, where n is an integer variable; a query optimizer residing in the memory and executed by the at least one processor, wherein the query optimizer: determines that the query can be optimized and generates for the query an optimized access plan that eliminates records defined by a Where clause prior to ordering the records and then returns a first n rows; determines that the query contains a Group By clause and that an index exists for a leftmost column but not all the columns of the Group By clause; and generates an access plan that eliminates records prior to grouping by fetching n rows from the index over the leftmost column and remaining rows until a unique value of the index is encountered. 3. The apparatus of claim 1 wherein the query optimizer further determines the query contains an Order By clause, that an index exists for each predicate in the Where clause, and that a field of the Order By clause exists in each index; and wherein the access plan eliminates records prior to a sort by fetching only n rows from each index and then returning n rows after sorting a set of records that includes the n rows from each index. | 0.5 |
9,087,272 | 1 | 9 | 1. A method for enhanced optical character recognition, the method comprising: identifying a sample character in a textual context to be optically recognized; comparing the sample character with a template character, wherein the sample characters is scaled into a first grid and the template character is scaled into a second grid; identifying one or more pixels in the sample character within the first grid and one or more pixels in the template character in the second grid, wherein the one or more pixels are identified as belonging to a foreground category in the textual content, a foreground pixel having at least N gradients corresponding to edges of the foreground pixel that are juxtaposed to a neighbor pixel, wherein a contour foreground pixel has at least one gradient that is neighbored by a background pixel in the textual context; identifying one or more template contour pixels in the template character that correspond to at least one sample contour pixel in the sample character, mapping the at least one sample contour pixel to the corresponding template contour pixels such that one or more distances are calculated between the at least one sample contour pixel and the respective one or more template contour pixels; and determining that the sample contour character and the template contour character are a match based on an analysis of the one or more distances. | 1. A method for enhanced optical character recognition, the method comprising: identifying a sample character in a textual context to be optically recognized; comparing the sample character with a template character, wherein the sample characters is scaled into a first grid and the template character is scaled into a second grid; identifying one or more pixels in the sample character within the first grid and one or more pixels in the template character in the second grid, wherein the one or more pixels are identified as belonging to a foreground category in the textual content, a foreground pixel having at least N gradients corresponding to edges of the foreground pixel that are juxtaposed to a neighbor pixel, wherein a contour foreground pixel has at least one gradient that is neighbored by a background pixel in the textual context; identifying one or more template contour pixels in the template character that correspond to at least one sample contour pixel in the sample character, mapping the at least one sample contour pixel to the corresponding template contour pixels such that one or more distances are calculated between the at least one sample contour pixel and the respective one or more template contour pixels; and determining that the sample contour character and the template contour character are a match based on an analysis of the one or more distances. 9. The method of claim 1 , further comprising performing a reverse mapping from the template character to the sample character to further enhance the accuracy of the optical character recognition based on a calculation of distances between corresponding pixels identified in the reverse mapping. | 0.5 |
7,610,199 | 7 | 14 | 7. The method of claim 6 , wherein said endpointing search comprises the steps of: locating at least a first speech endpoint in said audio signal using a first Hidden Markov Model; and locating a second speech endpoint in said audio signal, such that at least a portion of said audio signal located between said first speech endpoint and said second speech endpoint represents speech. | 7. The method of claim 6 , wherein said endpointing search comprises the steps of: locating at least a first speech endpoint in said audio signal using a first Hidden Markov Model; and locating a second speech endpoint in said audio signal, such that at least a portion of said audio signal located between said first speech endpoint and said second speech endpoint represents speech. 14. The method of claim 7 , wherein said step of locating at least a first speech endpoint comprises: identifying a most likely word in said audio signal; and determining whether a duration of said most likely word is long enough to indicate that said most likely word represents said first speech endpoint. | 0.5 |
9,875,239 | 12 | 13 | 12. A system for implementing an online document sharing community in a network environment including a plurality of participant computers operated by participants in the online document sharing community and a storage system storing documents, comprising: a processor; a computer database having participant information for a plurality of participants in the online document sharing community, wherein the participant information for at least one of the participants is associated with document information in the computer database for at least one document owned by the participant, wherein the document information identifies a document in the storage system, an owner of the document, a public/private status flag indicating whether the document is public or private, a public description providing a description of the document that does not include all content of the document, a provide public description field indicating whether the public description is to be provided to requesting participants not in a group of participants allowed access to the document, and wherein the document information for at least one document indicated as private indicates the group of participants allowed to access the document; a computer readable storage medium including code executed by the processor to perform operations, the operations comprising: receiving a request for a page from a requesting participant computer, wherein the requesting participant computer comprises one of the participant computers operated by a requesting participant comprising one of the participants in the online document sharing community; determining a document to include in the page; determining whether the public/private status flag indicates whether the document is public or private; including in the page an access element to provide access to the content of the document in response to the determining that the public/private status flag indicates that that the document is public; determining whether the requesting participant is a member of the group of participants allowed access to the document in response to determining that the public/private status flag indicates that the document is private; determining whether the provide public description field indicates that the public description is to be provided in response to the determining that the requesting participant is not a member of the group of participants allowed to access the document; including in the page access to the public description for the document in response to the determining that the public/private status flag indicates that the document is private, in response to the determining that the requesting participant is not a member of the group of participants allowed to access the document, and in response to the determining that the provide public description field indicates that the public description is to be provided; including in the page the access element to provide access to the content of the determined document in response to the determining that the public/private status flag indicates that the document is private and in response to the determining that the requesting participant is a member of the group of participants allowed to access the document; and returning the page to the requesting participant computer. | 12. A system for implementing an online document sharing community in a network environment including a plurality of participant computers operated by participants in the online document sharing community and a storage system storing documents, comprising: a processor; a computer database having participant information for a plurality of participants in the online document sharing community, wherein the participant information for at least one of the participants is associated with document information in the computer database for at least one document owned by the participant, wherein the document information identifies a document in the storage system, an owner of the document, a public/private status flag indicating whether the document is public or private, a public description providing a description of the document that does not include all content of the document, a provide public description field indicating whether the public description is to be provided to requesting participants not in a group of participants allowed access to the document, and wherein the document information for at least one document indicated as private indicates the group of participants allowed to access the document; a computer readable storage medium including code executed by the processor to perform operations, the operations comprising: receiving a request for a page from a requesting participant computer, wherein the requesting participant computer comprises one of the participant computers operated by a requesting participant comprising one of the participants in the online document sharing community; determining a document to include in the page; determining whether the public/private status flag indicates whether the document is public or private; including in the page an access element to provide access to the content of the document in response to the determining that the public/private status flag indicates that that the document is public; determining whether the requesting participant is a member of the group of participants allowed access to the document in response to determining that the public/private status flag indicates that the document is private; determining whether the provide public description field indicates that the public description is to be provided in response to the determining that the requesting participant is not a member of the group of participants allowed to access the document; including in the page access to the public description for the document in response to the determining that the public/private status flag indicates that the document is private, in response to the determining that the requesting participant is not a member of the group of participants allowed to access the document, and in response to the determining that the provide public description field indicates that the public description is to be provided; including in the page the access element to provide access to the content of the determined document in response to the determining that the public/private status flag indicates that the document is private and in response to the determining that the requesting participant is a member of the group of participants allowed to access the document; and returning the page to the requesting participant computer. 13. The system of claim 12 , wherein the operations further comprise: maintaining, in the computer database, comments from participants and owners of the documents about the documents for which document information is maintained in the computer database; determining the comments associated with the document in response to the requesting participant being a member of the group allowed to access the document; and including in the page the determined comments for the document. | 0.616372 |
5,416,898 | 20 | 21 | 20. The method of claim 19, wherein said processing is accomplished according to a finite state machine. | 20. The method of claim 19, wherein said processing is accomplished according to a finite state machine. 21. The method of claim 20, wherein said second glyph is said first glyph kerned in two axes. | 0.731214 |
8,624,898 | 1 | 2 | 1. A method of defining a component including a plurality of digital assets, the method comprising: receiving a component request specifying at least one requested graph link type; receiving a typed dependency graph representing digital assets, wherein the typed dependency graph includes graph nodes corresponding with the digital assets and includes typed graph links connecting the graph nodes, wherein each typed graph link includes one of a plurality of graph link types representing a type of digital asset relationship between at least two of the digital assets; adding at least one of the graph nodes to a graph node processing set; selecting and removing a first graph node from the graph node processing set; comparing, by operation of one or more computer processors, at least a first typed graph link connected with the first graph node with the requested graph link type; determining if a first graph link type included in the first typed graph link is consistent with the requested graph link type; adding a second graph node connected with the first typed graph link to the graph node processing set in response to the determination that the first graph link type included in the first typed graph link is consistent with the requested graph link type; wherein each digital asset is associated with a type mapping that is selected from a group consisting of reference-based mapping, structure-based mapping, and software tool-based mapping; and wherein each graph link type is selected from a group consisting of one-way digital asset dependencies, two-way digital asset dependencies, digital asset version relationships, digital asset aggregation relationships, and tool/digital asset dependencies. | 1. A method of defining a component including a plurality of digital assets, the method comprising: receiving a component request specifying at least one requested graph link type; receiving a typed dependency graph representing digital assets, wherein the typed dependency graph includes graph nodes corresponding with the digital assets and includes typed graph links connecting the graph nodes, wherein each typed graph link includes one of a plurality of graph link types representing a type of digital asset relationship between at least two of the digital assets; adding at least one of the graph nodes to a graph node processing set; selecting and removing a first graph node from the graph node processing set; comparing, by operation of one or more computer processors, at least a first typed graph link connected with the first graph node with the requested graph link type; determining if a first graph link type included in the first typed graph link is consistent with the requested graph link type; adding a second graph node connected with the first typed graph link to the graph node processing set in response to the determination that the first graph link type included in the first typed graph link is consistent with the requested graph link type; wherein each digital asset is associated with a type mapping that is selected from a group consisting of reference-based mapping, structure-based mapping, and software tool-based mapping; and wherein each graph link type is selected from a group consisting of one-way digital asset dependencies, two-way digital asset dependencies, digital asset version relationships, digital asset aggregation relationships, and tool/digital asset dependencies. 2. The method of claim 1 , wherein selecting the first graph node from the graph node processing set comprises: selecting the first graph node from the graph node processing set in a breadth-first order. | 0.804432 |
7,685,512 | 37 | 45 | 37. A computer readable medium for use in validating an eXtensible Markup Language (XML) message, said computer readable medium containing computer-executable instructions which, when performed by a processor in a computing device, cause the computing device to: from a custom XML schema based message model having at least one logical model extension, generate an XML schema fragment for use in validating said XML message at a node which lacks said custom XML schema based message model, wherein said custom XML schema based message model comprises a logical model of said XML message that complies with a standard XML schema, and wherein said custom XML schema based message model comprises a physical model that customizes said message using a logical model extension that is unsupported by said standard XML schema, wherein said custom XML schema based message model has an original group representing at least a portion of said XML message, said logical model extension being associated with said original group, and wherein said original group has an original sequence declaration containing a set of original subordinate entities, and wherein generating said XML schema fragment comprises: generating a new group; generating a sequence declaration within said new group, said sequence declaration having a minimum occurrence attribute with a value equivalent to a total number of said original subordinate entities in said set; and generating a choice declaration within said sequence declaration, said choice declaration containing said set of original subordinate entities. | 37. A computer readable medium for use in validating an eXtensible Markup Language (XML) message, said computer readable medium containing computer-executable instructions which, when performed by a processor in a computing device, cause the computing device to: from a custom XML schema based message model having at least one logical model extension, generate an XML schema fragment for use in validating said XML message at a node which lacks said custom XML schema based message model, wherein said custom XML schema based message model comprises a logical model of said XML message that complies with a standard XML schema, and wherein said custom XML schema based message model comprises a physical model that customizes said message using a logical model extension that is unsupported by said standard XML schema, wherein said custom XML schema based message model has an original group representing at least a portion of said XML message, said logical model extension being associated with said original group, and wherein said original group has an original sequence declaration containing a set of original subordinate entities, and wherein generating said XML schema fragment comprises: generating a new group; generating a sequence declaration within said new group, said sequence declaration having a minimum occurrence attribute with a value equivalent to a total number of said original subordinate entities in said set; and generating a choice declaration within said sequence declaration, said choice declaration containing said set of original subordinate entities. 45. The computer readable medium of claim 37 , wherein generating said XML schema fragment comprises: generating a new group; generating a sequence declaration within said new group; and generating an “any” element declaration within said sequence declaration, said “any” element declaration having a process content attribute specifying lax validation, a minimum occurrence attribute with a value of zero, and a maximum occurrence attribute with a value of “unbounded”. | 0.681572 |
7,665,063 | 17 | 18 | 17. The method of claim 15 , further comprising parsing said procedures for automated generation of dependency networks and associated hash tables. | 17. The method of claim 15 , further comprising parsing said procedures for automated generation of dependency networks and associated hash tables. 18. The method of claim 17 , further comprising utilizing an ATN (Augmented Transition Network) based parser to perform said parsing step. | 0.5 |
8,032,550 | 11 | 15 | 11. A system comprising: data storage to store a main search index; and a main server coupled to the main search index to receive a search request including a keyword, hash the keyword with a plurality of hash functions to obtain a sequence of hash values, perform a search based on the main search index and a sequence of hash values, and return a search result to include identifiers of documents that contain the keyword. | 11. A system comprising: data storage to store a main search index; and a main server coupled to the main search index to receive a search request including a keyword, hash the keyword with a plurality of hash functions to obtain a sequence of hash values, perform a search based on the main search index and a sequence of hash values, and return a search result to include identifiers of documents that contain the keyword. 15. The system of claim 11 , wherein the plurality of indexing servers are owned by autonomous cooperating entities. | 0.847368 |
9,262,938 | 7 | 8 | 7. A method comprising: automatically identifying a question lexical answer type (LAT) for a question in a question-answering system, using a computerized device; automatically generating a candidate answer to said question, using said computerized device; automatically determining preliminary types for said candidate answer using first components of said computerized device, said first components using different resources to produce said preliminary types, and each of said first components producing a preliminary type for said candidate answer; automatically scoring a match between said preliminary type and said question LAT using said first components of said computerized device and producing a first type-score for each preliminary type based on an amount that said preliminary type corresponds to said question LAT, said first components using different resources to produce said first type-score for said preliminary type, and said scoring being differentiated based on which of said first components produced said preliminary type; each of second components automatically evaluating each said preliminary type and said first type-score from each of said first components, using said computerized device, each of said second components producing a second score for said preliminary type for said candidate answer based on a combination of said first type-score and a measure of degree that said preliminary type matches said question LAT; and automatically outputting a final score based on said second score from each of said second components, said final score representing a degree of confidence that said candidate answer is a type that matches said question LAT, using said computerized device. | 7. A method comprising: automatically identifying a question lexical answer type (LAT) for a question in a question-answering system, using a computerized device; automatically generating a candidate answer to said question, using said computerized device; automatically determining preliminary types for said candidate answer using first components of said computerized device, said first components using different resources to produce said preliminary types, and each of said first components producing a preliminary type for said candidate answer; automatically scoring a match between said preliminary type and said question LAT using said first components of said computerized device and producing a first type-score for each preliminary type based on an amount that said preliminary type corresponds to said question LAT, said first components using different resources to produce said first type-score for said preliminary type, and said scoring being differentiated based on which of said first components produced said preliminary type; each of second components automatically evaluating each said preliminary type and said first type-score from each of said first components, using said computerized device, each of said second components producing a second score for said preliminary type for said candidate answer based on a combination of said first type-score and a measure of degree that said preliminary type matches said question LAT; and automatically outputting a final score based on said second score from each of said second components, said final score representing a degree of confidence that said candidate answer is a type that matches said question LAT, using said computerized device. 8. The method according to claim 7 , further comprising: receiving a question into said computerized device; and performing automated query analysis to determine said question LAT, using said computerized device. | 0.787149 |
8,527,487 | 12 | 18 | 12. A system for automatic construction of information organization structure, comprising: a processor that controls the system to implement: an input that receives input of a search term; an object retrieving unit that retrieves search results related to the search term; a topic extraction unit that extracts topics related to the search term; an existing structure identification unit that searches a website related to the search term for topics related to the search term among the extracted topics to identify other websites related to the search term; and a matched structure obtaining unit that selects a matching website from the identified other websites by comparing the search term with each of the identified other websites and sets a structure of the matching website as a structure for organizing the search results related to the search term. | 12. A system for automatic construction of information organization structure, comprising: a processor that controls the system to implement: an input that receives input of a search term; an object retrieving unit that retrieves search results related to the search term; a topic extraction unit that extracts topics related to the search term; an existing structure identification unit that searches a website related to the search term for topics related to the search term among the extracted topics to identify other websites related to the search term; and a matched structure obtaining unit that selects a matching website from the identified other websites by comparing the search term with each of the identified other websites and sets a structure of the matching website as a structure for organizing the search results related to the search term. 18. The system according to claim 12 , wherein the matched structure obtaining unit obtains a plurality of matching website candidates from the identified other websites by comparing the search term with each of the identified other websites, and the system further comprises: a structure integration that integrates the plurality of matching website candidates to obtain a final matched structure. | 0.589691 |
10,146,768 | 19 | 20 | 19. A non-transitory computer readable medium having stored thereon software instructions that, when executed by a processor, cause the processor to automatically provide suggested content by performing operations including: detecting an image posted within a first message by a first user; programmatically analyzing the image to determine a feature vector representative of the image; programmatically generating one or more suggested responses to the first message based on the feature vector, the one or more suggested responses each being a conversational reply to the first message, wherein programmatically generating the one or more suggested responses includes: determining probabilities associated with word sequences for the feature vector using a model trained with previous responses to previous images, wherein the previous responses are selected from a larger set of responses to the previous images, wherein the previous responses are more specific to particular content of the previous images than other previous responses of the larger set of responses; selecting one or more word sequences of the word sequences based on the probabilities associated with the word sequences, wherein the one or more suggested responses are determined based on the selected one or more word sequences; and outputting the one or more suggested responses to be rendered in a messaging application as one or more suggestions to a user. | 19. A non-transitory computer readable medium having stored thereon software instructions that, when executed by a processor, cause the processor to automatically provide suggested content by performing operations including: detecting an image posted within a first message by a first user; programmatically analyzing the image to determine a feature vector representative of the image; programmatically generating one or more suggested responses to the first message based on the feature vector, the one or more suggested responses each being a conversational reply to the first message, wherein programmatically generating the one or more suggested responses includes: determining probabilities associated with word sequences for the feature vector using a model trained with previous responses to previous images, wherein the previous responses are selected from a larger set of responses to the previous images, wherein the previous responses are more specific to particular content of the previous images than other previous responses of the larger set of responses; selecting one or more word sequences of the word sequences based on the probabilities associated with the word sequences, wherein the one or more suggested responses are determined based on the selected one or more word sequences; and outputting the one or more suggested responses to be rendered in a messaging application as one or more suggestions to a user. 20. The computer readable medium of claim 19 wherein the model is a conditioned language model that is conditioned by the feature vector received as an input, wherein the conditioned language model uses a long-short term memory (LSTM) network. | 0.5 |
9,443,164 | 18 | 19 | 18. A system for object instance localization in an image comprising: memory which, for each of a set of labeled reference images, stores a global descriptor and a keypoint descriptor for each of a set of keypoints detected in the reference image, each of the reference images comprising an object instance; a keypoint detection component which detects keypoints in a target image; a keypoint description component which describes each of the detected keypoints in the target image with a local descriptor; a keypoint matching component which matches keypoints in the target image to keypoints in the reference images based on their local descriptors; a candidate region detector which locates candidate regions in the target image, based on the matched descriptors; a feature extraction component which computes global descriptors for the located candidate regions; a recognition component which computes similarity measures between the global descriptors computed for the located candidate regions and the global descriptors for the reference images and assigns labels to at least some of the candidate regions based on the computed similarity measures; and a processor which implements the keypoint detection component, keypoint description component, keypoint matching component, candidate region detector, feature extraction component, and recognition component. | 18. A system for object instance localization in an image comprising: memory which, for each of a set of labeled reference images, stores a global descriptor and a keypoint descriptor for each of a set of keypoints detected in the reference image, each of the reference images comprising an object instance; a keypoint detection component which detects keypoints in a target image; a keypoint description component which describes each of the detected keypoints in the target image with a local descriptor; a keypoint matching component which matches keypoints in the target image to keypoints in the reference images based on their local descriptors; a candidate region detector which locates candidate regions in the target image, based on the matched descriptors; a feature extraction component which computes global descriptors for the located candidate regions; a recognition component which computes similarity measures between the global descriptors computed for the located candidate regions and the global descriptors for the reference images and assigns labels to at least some of the candidate regions based on the computed similarity measures; and a processor which implements the keypoint detection component, keypoint description component, keypoint matching component, candidate region detector, feature extraction component, and recognition component. 19. The system of claim 18 , further comprising a filtering component which filters the labeled candidate regions to remove at least some overlapping candidate regions. | 0.5 |
9,477,937 | 14 | 24 | 14. A computer system, comprising one or more processors and a memory, operatively interconnected to one another and configured to implement steps for analyzing a control-flow in a business process, comprising: invoking a representation of the business process as an acyclic workflow graph containing AND-, XOR- and IOR-types of nodes and edges linking nodes of the graph; labeling edges of the graph such that a label assigned to a labeled edge comprises a plurality of edge identifiers identifying respective edges, each of the edges identified being an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing any one of the identified edges ensures that the first edge will be executed; and automatically checking the labels for a deadlock using a processor, while labeling the edges of the graph, wherein a deadlock is found if a condition for relaxed soundness is true. | 14. A computer system, comprising one or more processors and a memory, operatively interconnected to one another and configured to implement steps for analyzing a control-flow in a business process, comprising: invoking a representation of the business process as an acyclic workflow graph containing AND-, XOR- and IOR-types of nodes and edges linking nodes of the graph; labeling edges of the graph such that a label assigned to a labeled edge comprises a plurality of edge identifiers identifying respective edges, each of the edges identified being an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing any one of the identified edges ensures that the first edge will be executed; and automatically checking the labels for a deadlock using a processor, while labeling the edges of the graph, wherein a deadlock is found if a condition for relaxed soundness is true. 24. The computer system of claim 14 , wherein the step of labeling is performed based on a maximum prefix of the acyclic workflow graph that does not contain a lack of synchronization. | 0.807933 |
8,458,164 | 33 | 41 | 33. A non-transitory computer storage medium readable by a computer and embodying one or more instructions executable by the computer, the computer program providing a query assist tool for assisting a user in creating and/or editing a query statement and further comprising: program instructions for visually displaying a search condition of a query statement in a first display area of the user interface; and program instructions for visually selecting two or more predicates of the displayed search condition for grouping; program instructions for visually resolving column references and value expression datatypes in the query statement comprising: syntactic parsing of an input into an internal model form, semantic resolving of SQL expression tables and columns by associating the SQL expression tables and columns with table and column entities provided in an information catalog, and resolving remaining value expression datatypes; and program instructions for visually indicating the grouping in the first display area in response to selection of the two or more predicates. | 33. A non-transitory computer storage medium readable by a computer and embodying one or more instructions executable by the computer, the computer program providing a query assist tool for assisting a user in creating and/or editing a query statement and further comprising: program instructions for visually displaying a search condition of a query statement in a first display area of the user interface; and program instructions for visually selecting two or more predicates of the displayed search condition for grouping; program instructions for visually resolving column references and value expression datatypes in the query statement comprising: syntactic parsing of an input into an internal model form, semantic resolving of SQL expression tables and columns by associating the SQL expression tables and columns with table and column entities provided in an information catalog, and resolving remaining value expression datatypes; and program instructions for visually indicating the grouping in the first display area in response to selection of the two or more predicates. 41. The non-transitory computer storage medium of manufacture of claim 33 , further comprising program instructions for confirming selection of the two or more predicates for grouping. | 0.8107 |
9,215,207 | 1 | 4 | 1. A non-transitory computer readable storage medium having a computer program stored therein, wherein the program, when executed by a processor of a computer, causes the computer to execute the steps comprising: providing a dictionary for referencing during analysis of an electronic communication in real time, said dictionary comprising a Hash-table store of expressions, wherein the Hash-table store for each expression comprises: at least one section, corresponding to one of a plurality of grammatical functions; and at least one subject corresponding to one of a plurality of categories of expressions; a weighted classification score associated with each expression; an alert level assigned to each expression or plurality of expressions based on the weighted score; and computer readable instructions for determining an array of expressions of the electronic communication to be matched against the Hash-table store of expressions; wherein the computer readable instructions, when operated on by the processor of the computer, are adapted to: a) load a plurality of the expressions and their respective alert levels in the Hash-table, based on their respective subjects; b) sample the electronic communication with the use of electronic filtering means; c) form an array of data representing expressions from at least part of the sampled electronic communication; d) search, using data processing means, for matches between expressions from the array of data and expressions stored in the Hash-table; e) determine an aggregate alert level with regard to context and intent for the sampled communication based on the sum of the respective alert levels of the expressions stored in the Hash-table that match expressions in the array of data, and f) automatically transmit an alert message to a remote terminal via a telecommunications network, in response to the aggregated alert level reaching or exceeding a threshold; wherein in response to the alert message, taking a plurality of actions that include: displaying an alert message at the user terminal; terminating the electronic communication; and shutting down the user terminal, and wherein said step of shutting down the user terminal comprises responding to no response to the alert message being received from the remote terminal within a predetermined period of time. | 1. A non-transitory computer readable storage medium having a computer program stored therein, wherein the program, when executed by a processor of a computer, causes the computer to execute the steps comprising: providing a dictionary for referencing during analysis of an electronic communication in real time, said dictionary comprising a Hash-table store of expressions, wherein the Hash-table store for each expression comprises: at least one section, corresponding to one of a plurality of grammatical functions; and at least one subject corresponding to one of a plurality of categories of expressions; a weighted classification score associated with each expression; an alert level assigned to each expression or plurality of expressions based on the weighted score; and computer readable instructions for determining an array of expressions of the electronic communication to be matched against the Hash-table store of expressions; wherein the computer readable instructions, when operated on by the processor of the computer, are adapted to: a) load a plurality of the expressions and their respective alert levels in the Hash-table, based on their respective subjects; b) sample the electronic communication with the use of electronic filtering means; c) form an array of data representing expressions from at least part of the sampled electronic communication; d) search, using data processing means, for matches between expressions from the array of data and expressions stored in the Hash-table; e) determine an aggregate alert level with regard to context and intent for the sampled communication based on the sum of the respective alert levels of the expressions stored in the Hash-table that match expressions in the array of data, and f) automatically transmit an alert message to a remote terminal via a telecommunications network, in response to the aggregated alert level reaching or exceeding a threshold; wherein in response to the alert message, taking a plurality of actions that include: displaying an alert message at the user terminal; terminating the electronic communication; and shutting down the user terminal, and wherein said step of shutting down the user terminal comprises responding to no response to the alert message being received from the remote terminal within a predetermined period of time. 4. The computer-readable medium as claimed in claim 1 , wherein the computer readable instructions are adapted to include the aggregate alert level in said alert message. | 0.761905 |
9,632,997 | 6 | 11 | 6. The system according to claim 1 , further comprising a parser component executable by the at least one processor and configured to generate the plurality of elements by executing at least one parse of the transcription information. | 6. The system according to claim 1 , further comprising a parser component executable by the at least one processor and configured to generate the plurality of elements by executing at least one parse of the transcription information. 11. The system according to claim 6 , wherein the at least one parse includes a plurality of parses and each parse of the plurality of parses is associated with a score. | 0.644958 |
10,146,939 | 2 | 4 | 2. The method of claim 1 , wherein the anomaly detection score is determined by applying a content anomaly detection model to the received input dataset. | 2. The method of claim 1 , wherein the anomaly detection score is determined by applying a content anomaly detection model to the received input dataset. 4. The method of claim 2 , wherein the content anomaly detection model is a binary-based detection model that determines a number of distinct n-grams in the input dataset and a total number of n-grams contained in the input dataset. | 0.584229 |
8,880,402 | 1 | 9 | 1. A speech recognition method comprising the steps of: (a) receiving input speech from a user; (b) processing said input speech to obtain at least one parameter value; and (c) determining the user's level of experience with using automatic speech recognition, using the at least one parameter value. | 1. A speech recognition method comprising the steps of: (a) receiving input speech from a user; (b) processing said input speech to obtain at least one parameter value; and (c) determining the user's level of experience with using automatic speech recognition, using the at least one parameter value. 9. The speech recognition method of claim 1 , wherein said at least one parameter value includes at least one of: speech volume, vocal pitch, vocal speed, energy profiles, steadiness, or intonation. | 0.766509 |
9,405,379 | 5 | 6 | 5. The system of claim 1 , wherein the classification of the touch contact is determined based at least in part on whether the touch contact is within a predetermined portion of the touch device. | 5. The system of claim 1 , wherein the classification of the touch contact is determined based at least in part on whether the touch contact is within a predetermined portion of the touch device. 6. The system of claim 5 , wherein: the predetermined portion of the touch device includes first and second portions; and the classification of the touch contact indicates a degree of unintentional contact of the touch contact, the degree of unintentional contact being based, at least in part, on whether the touch contact is within the first portion or the second portion. | 0.5 |
8,621,629 | 20 | 21 | 20. The non-transitory computer-readable media according to claim 17 , further configured for forwarding data associated with the unauthorized intrusion to a device that replicates a fake network so that an intruder is not aware that the intrusion has been detected. | 20. The non-transitory computer-readable media according to claim 17 , further configured for forwarding data associated with the unauthorized intrusion to a device that replicates a fake network so that an intruder is not aware that the intrusion has been detected. 21. The non-transitory computer-readable media according to claim 20 , further configured for recording and using the data sent to the device that replicates the fake network to augment an existing algorithm for identifying an attack mode. | 0.5 |
8,170,865 | 1 | 6 | 1. A speech recognition device, adapted to recognize a Chinese word composed of a plurality of Chinese characters, comprising: a lexicon model, keeping a plurality of words, wherein each word is composed of at least one Chinese character; a speech recognition module, performing a speech recognition processing on a voice signal that conforms to a syntax structure of a Chinese word description, wherein the speech recognition processing searches words related to the Chinese word description from the lexicon model according to a feature of the Chinese word description, and produces a literal word series in digital data form by referring a syntax combination probability; a language model, based on the syntax structure of the Chinese word description for providing the referred syntax combination probability according to the connection relations between the searched words; and a parsing module, analyzing a syntax structure of the literal word series, and retrieving the Chinese word from the literal word series. | 1. A speech recognition device, adapted to recognize a Chinese word composed of a plurality of Chinese characters, comprising: a lexicon model, keeping a plurality of words, wherein each word is composed of at least one Chinese character; a speech recognition module, performing a speech recognition processing on a voice signal that conforms to a syntax structure of a Chinese word description, wherein the speech recognition processing searches words related to the Chinese word description from the lexicon model according to a feature of the Chinese word description, and produces a literal word series in digital data form by referring a syntax combination probability; a language model, based on the syntax structure of the Chinese word description for providing the referred syntax combination probability according to the connection relations between the searched words; and a parsing module, analyzing a syntax structure of the literal word series, and retrieving the Chinese word from the literal word series. 6. The speech recognition device according to claim 1 , wherein the feature is a part of speech of the Chinese word. | 0.686486 |
4,882,759 | 2 | 3 | 2. A method of synthesizing word baseforms for words not spoken during a training session, wherein each synthesized baseform represents a series of output-related models and wherein each output-related model correlates to an output generatable by an acoustic processor, the method comprising the steps of: (a) representing each of N words by a respective sequence of phonetic models, the positioning of a subject phonetic model relative to other phonetic models forming a phonetic context for the subject phonetic model; (b) representing M words spoken during a training session by a series of output-related models, where the M words form a subset of the N words; (c) for at least one subject word, aligning the phonetic models for the subject word against the output-related models for the subject word, the subject word having been spoken during the training session; (d) from the alignment of output-related models and phonetic models, associating a string of output-related models with each of at least one respective phonetic model in a given context; and (e) constructing an output-related model baseform for a word not spoken during the training session including the steps of (i) correlating a piece of said word not spoken during the training session to a phonetic model in a defined context; (ii) determining if the phonetic model in said defined context corresponds to a similarly contexted phonetic model that has an associated string of output-related models; and (iii) representing said word piece by said associated string. | 2. A method of synthesizing word baseforms for words not spoken during a training session, wherein each synthesized baseform represents a series of output-related models and wherein each output-related model correlates to an output generatable by an acoustic processor, the method comprising the steps of: (a) representing each of N words by a respective sequence of phonetic models, the positioning of a subject phonetic model relative to other phonetic models forming a phonetic context for the subject phonetic model; (b) representing M words spoken during a training session by a series of output-related models, where the M words form a subset of the N words; (c) for at least one subject word, aligning the phonetic models for the subject word against the output-related models for the subject word, the subject word having been spoken during the training session; (d) from the alignment of output-related models and phonetic models, associating a string of output-related models with each of at least one respective phonetic model in a given context; and (e) constructing an output-related model baseform for a word not spoken during the training session including the steps of (i) correlating a piece of said word not spoken during the training session to a phonetic model in a defined context; (ii) determining if the phonetic model in said defined context corresponds to a similarly contexted phonetic model that has an associated string of output-related models; and (iii) representing said word piece by said associated string. 3. The method of claim 2 comprising the further step of: (f) identifying the context of a phonetic model based on the positioning of the phonetic model relative to n other phonetic models, where n is an integer greater than or equal to zero; wherein the associated string of step (e)(iii) is the string of output-related models associated with the similarly contexted phonetic model having the highest n value. | 0.5 |
8,374,913 | 14 | 15 | 14. The system of claim 11 , wherein the digitized text is created by converting the audio-visual advertisement into digital text by taking screen shots of key portions of the audio-visual advertisement. | 14. The system of claim 11 , wherein the digitized text is created by converting the audio-visual advertisement into digital text by taking screen shots of key portions of the audio-visual advertisement. 15. The system of claim 14 , wherein the digital text represents portions of the audio-visual advertisement. | 0.6625 |
8,806,401 | 14 | 15 | 14. A system for reasonable functional verification of a design of an integrated circuit (IC), the system comprising: a processing unit; a storage coupled to the processing unit; and, a memory coupled to the processing unit, the memory containing instructions that when executed by the processing unit: retrieve from storage a description of the design of at least a portion of the IC; bring the at least a portion of the IC in the received description to a state close to a suspected point of failure respective of a setup for failure (SFF) property; execute a set of instructions by functional verification of the at least a portion of the design of the IC from the suspected point of failure respective of at least a trigger for failure (TFF) property; and, providing a report respective of a result of execution of the functional verification. | 14. A system for reasonable functional verification of a design of an integrated circuit (IC), the system comprising: a processing unit; a storage coupled to the processing unit; and, a memory coupled to the processing unit, the memory containing instructions that when executed by the processing unit: retrieve from storage a description of the design of at least a portion of the IC; bring the at least a portion of the IC in the received description to a state close to a suspected point of failure respective of a setup for failure (SFF) property; execute a set of instructions by functional verification of the at least a portion of the design of the IC from the suspected point of failure respective of at least a trigger for failure (TFF) property; and, providing a report respective of a result of execution of the functional verification. 15. The system of claim 14 , wherein the processing unit comprises one or more central processing units. | 0.846154 |
8,401,252 | 16 | 17 | 16. The method of claim 1 , further comprising: displaying a co-occurrence of two human faces in said plurality of video frames as a link graph on said electronic display, wherein said link graph includes a plurality of nodes, and wherein each node in said link graph represents a different detected human face in said plurality of video frames regardless of identification status of said detected human face. | 16. The method of claim 1 , further comprising: displaying a co-occurrence of two human faces in said plurality of video frames as a link graph on said electronic display, wherein said link graph includes a plurality of nodes, and wherein each node in said link graph represents a different detected human face in said plurality of video frames regardless of identification status of said detected human face. 17. The method of claim 16 , wherein said link graph includes a plurality of dimensionally-weighted links, wherein each link connects a pair of nodes from said plurality of nodes, and wherein weighting of each said link is proportional to the amount of interaction between two humans represented as nodes connected by said link. | 0.5 |
9,792,527 | 17 | 18 | 17. The computer program product of claim 16 , wherein the computer readable program code instructions for execution by the processor further cause the processor to generate the similarity confidence scores for each of the respective ones of the plurality of slides as the functions of the weighted averages of the text content confidence scores and the graphic element content confidence scores generated for the respective ones of the plurality of slides by: generating first weighted averages of the graphic element content confidence scores and the text content confidence scores for each of the plurality of slides of the each slides as functions of a first differential weighting of the graphic element content confidence scores relative to the text content confidence scores; comparing the graphic element content confidence scores of the plurality of slides to an image content confidence threshold value that indicates a strength of match of an attribute of the graphic content of the input slide to a corresponding attribute of the graphic content of the plurality of slides; for each of the plurality of slides having a compared graphic element content confidence score that meets the image content confidence threshold value, generating second weighted averages of the graphic element content confidence scores and the text content confidence scores as functions of a second differential weighting of the graphic element content confidence scores relative to the text content confidence scores, wherein the second differential weighting increases a weighting of the graphic element content confidence score relative to the text content confidence score more than the first differential weighting; and selecting higher value ones of the first weighted averages and the second weighted averages as the similarity confidence scores for each of the respective ones of the plurality of slides. | 17. The computer program product of claim 16 , wherein the computer readable program code instructions for execution by the processor further cause the processor to generate the similarity confidence scores for each of the respective ones of the plurality of slides as the functions of the weighted averages of the text content confidence scores and the graphic element content confidence scores generated for the respective ones of the plurality of slides by: generating first weighted averages of the graphic element content confidence scores and the text content confidence scores for each of the plurality of slides of the each slides as functions of a first differential weighting of the graphic element content confidence scores relative to the text content confidence scores; comparing the graphic element content confidence scores of the plurality of slides to an image content confidence threshold value that indicates a strength of match of an attribute of the graphic content of the input slide to a corresponding attribute of the graphic content of the plurality of slides; for each of the plurality of slides having a compared graphic element content confidence score that meets the image content confidence threshold value, generating second weighted averages of the graphic element content confidence scores and the text content confidence scores as functions of a second differential weighting of the graphic element content confidence scores relative to the text content confidence scores, wherein the second differential weighting increases a weighting of the graphic element content confidence score relative to the text content confidence score more than the first differential weighting; and selecting higher value ones of the first weighted averages and the second weighted averages as the similarity confidence scores for each of the respective ones of the plurality of slides. 18. The computer program product of claim 17 , wherein the computer readable program code instructions for execution by the processor further cause the processor to generate at least one of the text content confidence scores and the graphic element content confidence scores by: generating raw confidence scores as a function of comparing slide content; and generating content confidence scores by normalizing the raw confidence scores as a function of lowest and highest generated raw score values. | 0.751988 |
7,644,360 | 20 | 21 | 20. A method for displaying patent claims, the method steps comprising: selecting at least part of a patent claims series; importing the at least part of a patent claims series; parsing the at least part of a patent claims series into the claims hierarchy of at least part of a patent claims series; displaying the parsed at least part of a patent claims series in an interactive format that is operable to dynamically expand and compress the at least part of a patent claims series, including both the graphical claim structure and the fully included textual claim content, according to the hierarch of the at least part of a patent claims series, the textual content of each claim ending with a numerical representation of how many claims directly depend on that claim; wherein the graphical claim structure comprises multiple geometric outlines, each outline operable to fully contain the textual claim content of one claim and at least one line directly connecting the outlines to each other according the hierarch of the at least part of a patent claims series; wherein at least one of the multiple geometric outlines further has a visual emphasis, the visual emphasis indicating whether there are additional levels of the hierarchy of the at least part of a patent claims series directly connected to the at least one of the multiple geometric outlines that has a visual emphasis. | 20. A method for displaying patent claims, the method steps comprising: selecting at least part of a patent claims series; importing the at least part of a patent claims series; parsing the at least part of a patent claims series into the claims hierarchy of at least part of a patent claims series; displaying the parsed at least part of a patent claims series in an interactive format that is operable to dynamically expand and compress the at least part of a patent claims series, including both the graphical claim structure and the fully included textual claim content, according to the hierarch of the at least part of a patent claims series, the textual content of each claim ending with a numerical representation of how many claims directly depend on that claim; wherein the graphical claim structure comprises multiple geometric outlines, each outline operable to fully contain the textual claim content of one claim and at least one line directly connecting the outlines to each other according the hierarch of the at least part of a patent claims series; wherein at least one of the multiple geometric outlines further has a visual emphasis, the visual emphasis indicating whether there are additional levels of the hierarchy of the at least part of a patent claims series directly connected to the at least one of the multiple geometric outlines that has a visual emphasis. 21. The method of claim 20 , wherein the at least part of a claim series include an independent claim. | 0.662252 |
8,682,574 | 2 | 3 | 2. The machine-readable, non-transitory storage medium of claim 1 , the process further comprising: determining a driver ID for the current driving session; and restricting the plurality of conditional variant models associated with the target attribute to include only conditional variant models associated with the driver ID. | 2. The machine-readable, non-transitory storage medium of claim 1 , the process further comprising: determining a driver ID for the current driving session; and restricting the plurality of conditional variant models associated with the target attribute to include only conditional variant models associated with the driver ID. 3. The machine-readable, non-transitory storage medium of claim 2 , wherein the determining a driver ID for the current driving session comprises at least one selected from a group consisting of: receiving login criteria associated with the driver ID; detecting an ignition key associated with the driver ID; detecting a preferred seat setting associated with the driver ID; detecting a preferred mirror setting associated with the driver ID; and detecting a driver weight associated with the driver ID. | 0.5 |
9,471,567 | 13 | 15 | 13. A system, comprising: a digital display; a microphone integrated within or interfaced to the digital display; and memory having a language translator that processes as executable instructions on one or more processors of the digital display; wherein the language translator is configured to detect voice conversations in proximity to the microphone and resolve a spoken language for the voice conversations and in response thereto translating original written information presented on the digital display in an original language to a target language for the spoken language and presenting the written information and written communication in the target language on the digital display, wherein the voice conversations are obtained from one of: a single person conversing on a phone in proximity to the microphone and multiple people conversing with one another in proximity to the microphone, and wherein the language translator is further configured to modify an original image presented on the digital display with a new image that is appropriate for a cultural of the spoken language. | 13. A system, comprising: a digital display; a microphone integrated within or interfaced to the digital display; and memory having a language translator that processes as executable instructions on one or more processors of the digital display; wherein the language translator is configured to detect voice conversations in proximity to the microphone and resolve a spoken language for the voice conversations and in response thereto translating original written information presented on the digital display in an original language to a target language for the spoken language and presenting the written information and written communication in the target language on the digital display, wherein the voice conversations are obtained from one of: a single person conversing on a phone in proximity to the microphone and multiple people conversing with one another in proximity to the microphone, and wherein the language translator is further configured to modify an original image presented on the digital display with a new image that is appropriate for a cultural of the spoken language. 15. The system of claim 13 , wherein the system is a digital sign. | 0.899696 |
9,098,872 | 24 | 25 | 24. The method of claim 23 wherein the first and second personalized digital content are different. | 24. The method of claim 23 wherein the first and second personalized digital content are different. 25. The method of claim 24 wherein the selected personalized digital content comprises: selecting a second advertisement for the sender, based on the first sharing activity of the first link to the first recipient. | 0.5 |
9,202,171 | 29 | 31 | 29. The apparatus of claim 27 , wherein the artificial intelligence engine is further configured with instructions to translate game states and game outcomes into virtual human behaviors and virtual human speech of a virtual human bystander, and wherein the apparatus is further configured with instructions to present the virtual human bystander with the virtual assistant behaviors and virtual assistant speech as the electronic game executes, the behaviors and speech based on at least the game states. | 29. The apparatus of claim 27 , wherein the artificial intelligence engine is further configured with instructions to translate game states and game outcomes into virtual human behaviors and virtual human speech of a virtual human bystander, and wherein the apparatus is further configured with instructions to present the virtual human bystander with the virtual assistant behaviors and virtual assistant speech as the electronic game executes, the behaviors and speech based on at least the game states. 31. The apparatus of claim 29 , further comprising a customizable billboard, background, foreground, or artifact to be displayed with the virtual human bystander. | 0.726351 |
7,479,011 | 3 | 8 | 3. The system recited in claim 2 , wherein the scoring means comprises a code segment in the software comprising a set of rules for calculating a score for each attempt. | 3. The system recited in claim 2 , wherein the scoring means comprises a code segment in the software comprising a set of rules for calculating a score for each attempt. 8. The system recited in claim 3 , wherein the rule set comprises a rule wherein the at least partial credit is disallowed if the attempt further includes a predetermined error. | 0.79932 |
9,100,326 | 14 | 17 | 14. The non-transitory computer readable medium of claim 13 , further comprising: grouping the plurality of slices that are divided from each of the plurality of conversations using the field delimiter of the protocol, into a slice-set for each of the plurality of conversations, wherein the plurality of conversations correspond to a plurality of slice-sets; extracting, based on a pre-determined key-value delimiter selection criterion, a plurality of longest common prefixes each shared across a portion of the plurality of slice-sets; and extracting a common trailing token in the plurality of longest common prefixes as the key-value delimiter of the protocol. | 14. The non-transitory computer readable medium of claim 13 , further comprising: grouping the plurality of slices that are divided from each of the plurality of conversations using the field delimiter of the protocol, into a slice-set for each of the plurality of conversations, wherein the plurality of conversations correspond to a plurality of slice-sets; extracting, based on a pre-determined key-value delimiter selection criterion, a plurality of longest common prefixes each shared across a portion of the plurality of slice-sets; and extracting a common trailing token in the plurality of longest common prefixes as the key-value delimiter of the protocol. 17. The non-transitory computer readable medium of claim 14 , further comprising: removing the common trailing token from the plurality of longest common prefixes to identify a keyword of the protocol. | 0.801775 |
9,754,014 | 13 | 15 | 13. The computer program product of claim 10 , further comprising program instructions executable by a computer to generate a second binary decision model by training the binary classifier using the plurality of training documents and the confirmation or the negation of the classification label of the most relevant example of the classified test documents. | 13. The computer program product of claim 10 , further comprising program instructions executable by a computer to generate a second binary decision model by training the binary classifier using the plurality of training documents and the confirmation or the negation of the classification label of the most relevant example of the classified test documents. 15. The computer program product of claim 13 , wherein the confirmation or the negation of the classification label of the most relevant example of the classified test documents is the single example of user input used in generating the second binary decision model. | 0.5 |
8,195,795 | 22 | 28 | 22. A method of detecting unwanted conversational media sessions, said method comprising: utilizing a fake conversation client in a manner to cause an originator of real-time CMS data to setup and initiate a real-time CMS with the fake conversation client; and generating a reference data item based upon at least a media portion of the real-time CMS data received the fake conversation client; generating a traffic control rule based upon signaling data associated with the real-time CMS established with the fake conversation client; and publishing the reference data and the CMS traffic control rule in a manner to enable a client in receipt of the reference data item and the CMS traffic control rule to control real-time CMS data received at the client in accordance with the CMS traffic control rule and/or using the reference data item. | 22. A method of detecting unwanted conversational media sessions, said method comprising: utilizing a fake conversation client in a manner to cause an originator of real-time CMS data to setup and initiate a real-time CMS with the fake conversation client; and generating a reference data item based upon at least a media portion of the real-time CMS data received the fake conversation client; generating a traffic control rule based upon signaling data associated with the real-time CMS established with the fake conversation client; and publishing the reference data and the CMS traffic control rule in a manner to enable a client in receipt of the reference data item and the CMS traffic control rule to control real-time CMS data received at the client in accordance with the CMS traffic control rule and/or using the reference data item. 28. The method according to claim 22 , wherein the CMS traffic control rule corresponds to one or more of the following: a network address of an originator of real-time CMS; a range of network addresses which includes a network address of an originator of real-time CMS; a username associated with an originator of a real-time CMS. | 0.636264 |
9,043,198 | 2 | 3 | 2. The method of claim 1 , comprising: normalizing the received text item, including tokenizing and synonymizing the text item; and determining the text strings based on the normalized text item. | 2. The method of claim 1 , comprising: normalizing the received text item, including tokenizing and synonymizing the text item; and determining the text strings based on the normalized text item. 3. The method of claim 2 , wherein determining the plurality of test strings includes submitting at least a portion of the normalized text item as a search query, receiving an ordered group of search results, and selecting the plurality of text strings from the search results. | 0.5 |
10,133,920 | 11 | 16 | 11. An information handling device, comprising: an input device; a display device; an audio capture device; a processor; a memory device that stores instructions executable by the processor to: receive handwriting input; receive voice input, wherein to receive voice input comprises receiving voice input at a time associated with receipt of the handwriting input; generate at least one first word based on the handwriting input and a probability associated with the at least one first word; generate at least one second word based on the voice input and a probability associated with the at least one second word, wherein the probability is based on a context of the voice input; determine a highest probability word based on the at least one first word and the at least one second word, wherein to determines comprises using at least one of: the at least one first word and the at least one second word to modify a probability of the other of: the at least one first word and the at least one second word; and provide the determined highest probability word. | 11. An information handling device, comprising: an input device; a display device; an audio capture device; a processor; a memory device that stores instructions executable by the processor to: receive handwriting input; receive voice input, wherein to receive voice input comprises receiving voice input at a time associated with receipt of the handwriting input; generate at least one first word based on the handwriting input and a probability associated with the at least one first word; generate at least one second word based on the voice input and a probability associated with the at least one second word, wherein the probability is based on a context of the voice input; determine a highest probability word based on the at least one first word and the at least one second word, wherein to determines comprises using at least one of: the at least one first word and the at least one second word to modify a probability of the other of: the at least one first word and the at least one second word; and provide the determined highest probability word. 16. The information handling device of claim 11 , further comprising: storing, on the memory device, a recording of the voice input. | 0.871845 |
7,672,846 | 2 | 5 | 2. The voice recognition system according to claim 1 , wherein the priority determining unit uses as the predetermined priority criterion at least one selected from (1) a length of the detected voice section, (2) a power or an S/N ratio of the detected voice section, and (3) a chronological order of the detected voice section. | 2. The voice recognition system according to claim 1 , wherein the priority determining unit uses as the predetermined priority criterion at least one selected from (1) a length of the detected voice section, (2) a power or an S/N ratio of the detected voice section, and (3) a chronological order of the detected voice section. 5. The voice recognition system according to claim 2 , wherein (1) the length of the detected voice section is used as the predetermined priority criterion, and the priority determining unit selects a predetermined number of detected voice sections in decreasing order of their length under a condition that a sum of the lengths of the voice sections does not exceed a predetermined time period. | 0.671927 |
8,752,005 | 22 | 27 | 22. One or more non-transitory computer-readable storage media storing a data structure, the data structure comprising: one or more concepts associated with a software system; one or more relationship types, respective of the relationship types having one or more terms; for respective of the relationship types, one or more role definitions associated with the one or more terms of the respective relationship type, respective of the role definitions defining the permissible concepts and/or concept instances that can represent the term associated with the respective role definition for the respective relationship type; and one or more relationships based on the one or more concepts and/or the one or more concept instances, the one or more relationships comprising a cross-artifact relationship between a first concept instance of the one or more concept instances and a second concept instance of the one or more concept instances, wherein the cross-artifact relationship specifies a relationship type of the one or more relationship types that relates a concept of the one or more concepts to the first concept instance and the second concept instance. | 22. One or more non-transitory computer-readable storage media storing a data structure, the data structure comprising: one or more concepts associated with a software system; one or more relationship types, respective of the relationship types having one or more terms; for respective of the relationship types, one or more role definitions associated with the one or more terms of the respective relationship type, respective of the role definitions defining the permissible concepts and/or concept instances that can represent the term associated with the respective role definition for the respective relationship type; and one or more relationships based on the one or more concepts and/or the one or more concept instances, the one or more relationships comprising a cross-artifact relationship between a first concept instance of the one or more concept instances and a second concept instance of the one or more concept instances, wherein the cross-artifact relationship specifies a relationship type of the one or more relationship types that relates a concept of the one or more concepts to the first concept instance and the second concept instance. 27. The one or more non-transitory computer-readable storage media of claim 22 , wherein the data structure further comprises: one or more pluralities of viewing concept instances, the pluralities of viewing concepts instances representing recovered architectures of the software system, respective of the viewing concept instances of the one or more viewing concept instances pluralities having a layer index; and one or more pluralities of viewing concept instances relationships, respective of the viewing concept instances relationships pluralities associated with one of the one or more viewing concept instances pluralities. | 0.5 |
7,590,729 | 15 | 16 | 15. An article comprising: a storage medium; said storage medium including stored instructions that, when executed by a processor, result in parsing a document having transaction information, creating a document object using said transaction information, parsing pattern information of a pattern for one or more elements according to a predefined pattern object data structure, placing said elements in appropriate blocks within said pattern object data structure, creating a pattern object from said pattern information, making a switching decision for a message based upon a comparison of said document object with said pattern object, and evaluating at least one expression contained in said pattern object for a match with said document object. | 15. An article comprising: a storage medium; said storage medium including stored instructions that, when executed by a processor, result in parsing a document having transaction information, creating a document object using said transaction information, parsing pattern information of a pattern for one or more elements according to a predefined pattern object data structure, placing said elements in appropriate blocks within said pattern object data structure, creating a pattern object from said pattern information, making a switching decision for a message based upon a comparison of said document object with said pattern object, and evaluating at least one expression contained in said pattern object for a match with said document object. 16. The article of claim 15 , wherein the stored instructions, when executed by a processor, further result in receiving a message from a network, receiving said switching decision, and routing or switching the received message to one of a plurality of processing nodes to process said message based upon said switching decision. | 0.5 |
9,191,456 | 1 | 2 | 1. A method implemented on a machine having at least one processor, storage, and a communication platform connected to a network for establishing or maintaining a personalized trusted social network for a user, the method comprising: identifying a plurality of online services, wherein the plurality of online services comprise a first online service of which a given user is a subscriber and a second online service of which the given user is not a subscriber; identifying one or more trusted sources for the given user and microcontent associated with the plurality of online services by the one or more trusted sources; creating a trusted social network based on the identified one or more trusted sources; receiving a search query from the given user; retrieving a search report in response to the search query; discovering the microcontent in the search report; and annotating the microcontent in the search report with one or more links to the plurality of online services, an indication of the one or more trusted sources from the trusted social network, wherein at least one of the one or more trusted sources is related to the second online service of which the given user is not a subscriber, and one or more expressions each of which describes an origin of a corresponding trusted source in the one or more trusted sources, wherein each of the one or more expressions is determined based on a type of the corresponding trusted source. | 1. A method implemented on a machine having at least one processor, storage, and a communication platform connected to a network for establishing or maintaining a personalized trusted social network for a user, the method comprising: identifying a plurality of online services, wherein the plurality of online services comprise a first online service of which a given user is a subscriber and a second online service of which the given user is not a subscriber; identifying one or more trusted sources for the given user and microcontent associated with the plurality of online services by the one or more trusted sources; creating a trusted social network based on the identified one or more trusted sources; receiving a search query from the given user; retrieving a search report in response to the search query; discovering the microcontent in the search report; and annotating the microcontent in the search report with one or more links to the plurality of online services, an indication of the one or more trusted sources from the trusted social network, wherein at least one of the one or more trusted sources is related to the second online service of which the given user is not a subscriber, and one or more expressions each of which describes an origin of a corresponding trusted source in the one or more trusted sources, wherein each of the one or more expressions is determined based on a type of the corresponding trusted source. 2. The method of claim 1 wherein the trusted social network is created in a user profile automatically without input from the given user. | 0.713389 |
8,417,521 | 4 | 5 | 4. The method according to claim 3 , further comprising detecting, by the media resource processing device, a speech recognition process according to an instruction of the media resource control device, and feeding back a detection result to the media resource control device. | 4. The method according to claim 3 , further comprising detecting, by the media resource processing device, a speech recognition process according to an instruction of the media resource control device, and feeding back a detection result to the media resource control device. 5. The method according to claim 4 , further comprising feeding back, by the media resource processing device, a corresponding error code to the media resource control device when detecting the abnormal event during the speech recognition. | 0.5 |
10,133,921 | 10 | 12 | 10. The method of claim 1 , further comprising: determining the pixels within the bounding region that correspond to the candidate features; defining an origin of the graphical object based on a rule; and determining coordinates for each of the pixels relative to the origin. | 10. The method of claim 1 , further comprising: determining the pixels within the bounding region that correspond to the candidate features; defining an origin of the graphical object based on a rule; and determining coordinates for each of the pixels relative to the origin. 12. The method of claim 10 , wherein each of the one or more features detectible within the one or more graphical objects comprises text characters, alphanumeric characters, or both. | 0.672662 |
9,401,987 | 1 | 10 | 1. A computer-implemented method comprising: associating, in a computing apparatus, a plurality of call-in references of a plurality of different regions with respective different language preferences, wherein each of the call-in references is local to a first one of the regions but not to at least a second one of the regions; identifying, by the computing apparatus, regions in which callers reside; providing, by the computing apparatus, listings of callees, the listing presented to the callers in different regions including: different call-in references for receiving calls from the callers; and different user inputs about services provided by the callees in prior calls filtered according to regions in which the callers reside; receiving, in the computing apparatus, the calls via the call-in references from the callers; customizing, by the computing apparatus, interactive voice response prompts presented during one or more of the calls to one or more respective callers based at least in part on the language preferences associated with the call-in references via which the calls are received; and connecting the calls to the callees based on responses to the interactive voice response prompts. | 1. A computer-implemented method comprising: associating, in a computing apparatus, a plurality of call-in references of a plurality of different regions with respective different language preferences, wherein each of the call-in references is local to a first one of the regions but not to at least a second one of the regions; identifying, by the computing apparatus, regions in which callers reside; providing, by the computing apparatus, listings of callees, the listing presented to the callers in different regions including: different call-in references for receiving calls from the callers; and different user inputs about services provided by the callees in prior calls filtered according to regions in which the callers reside; receiving, in the computing apparatus, the calls via the call-in references from the callers; customizing, by the computing apparatus, interactive voice response prompts presented during one or more of the calls to one or more respective callers based at least in part on the language preferences associated with the call-in references via which the calls are received; and connecting the calls to the callees based on responses to the interactive voice response prompts. 10. The method of claim 1 , further comprising: identifying, by the computing apparatus, locations of callers of the calls to callees of the calls, in granularity determined in accordance with preferences of the callers. | 0.775967 |
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