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9,706,265 | 23 | 26 | 23. The method of claim 22 , further comprising: querying, by a discovery agent running with the sandboxed program, the discovery service for the service information associated with the at least one of the networked device and the networked service, wherein the service information comprises at least one of a GUID, an alphanumeric name, a public address pair, and a private address pair. | 23. The method of claim 22 , further comprising: querying, by a discovery agent running with the sandboxed program, the discovery service for the service information associated with the at least one of the networked device and the networked service, wherein the service information comprises at least one of a GUID, an alphanumeric name, a public address pair, and a private address pair. 26. The method of claim 23 , further comprising: enforcing, by the discovery agent, a communications policy imposing an access restriction to the at least one of the networked device and the networked service. | 0.838235 |
8,892,488 | 1 | 4 | 1. A method for document classification, comprising: embedding n-grams from an input text in a latent space; embedding the input text in the latent space based on the embedded n-grams and weighting the n-grams according to a non-linear function q j = 1 Q ∑ k = 1 K sigmoid ( a k · j N + b k ) , using a mixture model on a relative position of the n-grams in the input text, where a k and b k are parameters to be learned, Q = ∑ j = 1 N q j and K specify a number of mixture quantities, sigmoid (•) is a non-linear transfer function, q j is the weight associated with a j th n-gram, j signifies the position of an n-gram in the input text, and N is the position of a final n-gram in the input text; classifying the document along one or more axes using a processor; and adjusting weights used to weight the n-grams based on the output of the classifying step. | 1. A method for document classification, comprising: embedding n-grams from an input text in a latent space; embedding the input text in the latent space based on the embedded n-grams and weighting the n-grams according to a non-linear function q j = 1 Q ∑ k = 1 K sigmoid ( a k · j N + b k ) , using a mixture model on a relative position of the n-grams in the input text, where a k and b k are parameters to be learned, Q = ∑ j = 1 N q j and K specify a number of mixture quantities, sigmoid (•) is a non-linear transfer function, q j is the weight associated with a j th n-gram, j signifies the position of an n-gram in the input text, and N is the position of a final n-gram in the input text; classifying the document along one or more axes using a processor; and adjusting weights used to weight the n-grams based on the output of the classifying step. 4. The method of claim 1 , wherein embedding the input text in the latent space further comprises: dividing the input text into sub-parts; forming an embedded representation of each of the sub-parts based on embedded n-grams in each respective sub-part; and concatenating the sub-parts to form an embedded representation of the full input text. | 0.5 |
9,992,344 | 15 | 18 | 15. A computer-readable storage device having instructions stored which, when executed by a computing device, result in the computing device performing operations comprising: presenting, via a client device, a notification of an incoming call to a subscriber; receiving, while presenting the notification, an input from the subscriber with regard to the incoming call, wherein the input is associated with instructions to: (1) transfer the incoming call to a voicemail system; and (2) when the incoming call results in a voicemail at the voicemail system, transcribe the voicemail into text; transferring, based on the input, the incoming call to the voicemail system to yield the voicemail; determining whether a current time is within a first time window associated with a first class of service or a second time window a second class of service to yield a determined class of service according to the current time; generating, based on the determined class of service according to the current time, a transcription of the voicemail, the transcription comprising text generated from the voicemail; and presenting the transcription on a device of the subscriber. | 15. A computer-readable storage device having instructions stored which, when executed by a computing device, result in the computing device performing operations comprising: presenting, via a client device, a notification of an incoming call to a subscriber; receiving, while presenting the notification, an input from the subscriber with regard to the incoming call, wherein the input is associated with instructions to: (1) transfer the incoming call to a voicemail system; and (2) when the incoming call results in a voicemail at the voicemail system, transcribe the voicemail into text; transferring, based on the input, the incoming call to the voicemail system to yield the voicemail; determining whether a current time is within a first time window associated with a first class of service or a second time window a second class of service to yield a determined class of service according to the current time; generating, based on the determined class of service according to the current time, a transcription of the voicemail, the transcription comprising text generated from the voicemail; and presenting the transcription on a device of the subscriber. 18. The computer-readable storage device of claim 15 , having additional instructions stored which, when executed by the computing device, cause the computing device to perform operations comprising: identifying a current transcription state of the voicemail for the subscriber; storing the current transcription state in a subscriber directory; and notifying the subscriber when the current transcription state changes. | 0.5 |
7,512,575 | 3 | 10 | 3. A computer implemented method for automating integration of terminological information into a knowledge base, said method comprising the steps of: receiving, into a computer, input terminology information comprising a plurality of input terms and at least one relationship indicator from a set of predetermined relationship indicators, each relationship indicator specifying an ontological relationship among at least two of said input terms; storing, in said computer, a knowledge base comprising a plurality of ontologies, each one of said ontologies comprising a plurality of nodes, each node representing a term, and comprising associations among said nodes that depict ontological relationships among respective terms; generating a logical structure of said input terms from said input terminology information using a mapping table comprising a mapping entry for each relationship indicator in said set of predetermined relationship indicators, each mapping entry comprising a mapping from a relationship indicator to a particular ontological relationship that is in a format compatible with said ontological relationships depicted in said knowledge base; and integrating said logical structure of said input terms into said knowledge base, said integrating comprising: determining whether at least one input term matches a node in said knowledge base; if so, extending said knowledge base by storing data that logically couples said logical structure of said input terms to a node that matches an input term; and if not, generating a new and independent ontology for said knowledge base comprising said logical structure of said input terms. | 3. A computer implemented method for automating integration of terminological information into a knowledge base, said method comprising the steps of: receiving, into a computer, input terminology information comprising a plurality of input terms and at least one relationship indicator from a set of predetermined relationship indicators, each relationship indicator specifying an ontological relationship among at least two of said input terms; storing, in said computer, a knowledge base comprising a plurality of ontologies, each one of said ontologies comprising a plurality of nodes, each node representing a term, and comprising associations among said nodes that depict ontological relationships among respective terms; generating a logical structure of said input terms from said input terminology information using a mapping table comprising a mapping entry for each relationship indicator in said set of predetermined relationship indicators, each mapping entry comprising a mapping from a relationship indicator to a particular ontological relationship that is in a format compatible with said ontological relationships depicted in said knowledge base; and integrating said logical structure of said input terms into said knowledge base, said integrating comprising: determining whether at least one input term matches a node in said knowledge base; if so, extending said knowledge base by storing data that logically couples said logical structure of said input terms to a node that matches an input term; and if not, generating a new and independent ontology for said knowledge base comprising said logical structure of said input terms. 10. The method as set forth in claim 3 , wherein: receiving input terminology information comprises receiving preferred term (“PTâ€) relationships among at least two input terms; storing a knowledge base comprises storing a canonical/alternate form index that indexes a canonical form from one or more alternative forms; and generating a logical structure comprises generating a canonical/alternate form index between terms comprising a preferred term (PT) relationship in said input terminological information. | 0.758004 |
8,145,582 | 1 | 2 | 1. A computer implemented method comprising: receiving a first set of data; receiving a second set of data different than the first set of data, wherein directly comparing the first set of data to the second set of data would be computationally explosive; organizing the first set of data into a first cohort; organizing the second set of data into a second cohort; processing the first cohort and the second cohort to generate a first synthetic event, wherein the first synthetic event comprises a third set of data representing a result of a mathematical computation defined by the operation S(p 1 )==>F(p 2 ), wherein S comprises a set of input facts with probability p 1 , wherein the set of input facts comprise the first cohort and the second cohort, wherein F comprises an inferred event with probability p 2 , wherein the term “event” means a particular set of data that represents, encodes, or records at least one of a thing or happening, and wherein each of the first set of data, the second set of data, the first cohort, the second cohort, and the first synthetic event all comprise different events; receiving a third set of data; organizing the third set of data into a third cohort; organizing the synthetic event into a fourth cohort; processing the first cohort, the second cohort, the third cohort, and the fourth cohort to generate a second synthetic event; processing the first synthetic event and the second synthetic event to generate a third synthetic event; and storing the first synthetic event, the second synthetic event, and the third synthetic event. | 1. A computer implemented method comprising: receiving a first set of data; receiving a second set of data different than the first set of data, wherein directly comparing the first set of data to the second set of data would be computationally explosive; organizing the first set of data into a first cohort; organizing the second set of data into a second cohort; processing the first cohort and the second cohort to generate a first synthetic event, wherein the first synthetic event comprises a third set of data representing a result of a mathematical computation defined by the operation S(p 1 )==>F(p 2 ), wherein S comprises a set of input facts with probability p 1 , wherein the set of input facts comprise the first cohort and the second cohort, wherein F comprises an inferred event with probability p 2 , wherein the term “event” means a particular set of data that represents, encodes, or records at least one of a thing or happening, and wherein each of the first set of data, the second set of data, the first cohort, the second cohort, and the first synthetic event all comprise different events; receiving a third set of data; organizing the third set of data into a third cohort; organizing the synthetic event into a fourth cohort; processing the first cohort, the second cohort, the third cohort, and the fourth cohort to generate a second synthetic event; processing the first synthetic event and the second synthetic event to generate a third synthetic event; and storing the first synthetic event, the second synthetic event, and the third synthetic event. 2. The computer implemented method of claim 1 wherein each corresponding event of the different events is represented as a corresponding pointer. | 0.865492 |
9,436,681 | 3 | 8 | 3. A computer-implemented method for translating a text string derived from natural language input to a machine-readable format, comprising: using one or more computing devices, generating an input graph for the text string, the input graph representing a plurality of different subsets of words in the text string; using the one or more computing devices, retrieving a template graph representing a plurality of translation templates, each translation template being for translating matching natural language input to the machine-readable format, and each translation template corresponding to at least one of a plurality of terminal nodes in the template graph, wherein each internal node of the template graph is labeled with an identifier of a highest priority template that can be reached from that point in the template graph; using the one or more computing devices, generating an intersection graph from the input graph and the template graph, each node in the intersection graph representing a tuple of corresponding nodes in the input and template graphs; using the one or more computing devices, processing the intersection graph according to template priorities associated with the nodes of the template graph thereby identifying one or more candidate terminal nodes of the intersection graph, the template priorities associated with the terminal nodes of the template graph causing processing of the intersection graph to proceed preferentially along edges of the intersection graph leading to terminal nodes of the intersection graph that represent terminal nodes of the template graph that correspond to the highest priority templates; using the one or more computing devices, selecting a terminal node in the template graph that corresponds to one of the one or more candidate terminal nodes of the intersection graph based at least in part on a probability associated with the selected terminal node of the template graph that the text string corresponds to an intended meaning of the natural language input, wherein a first translation template corresponds to the selected terminal node of the template graph; using the one or more computing devices, translating the text string using the first translation template thereby generating a machine-readable translation that accurately represents a semantic meaning of the natural language input; using the one or more computing devices, processing the machine-readable translation using structured data to generate a result; using the one or more computing devices, generating a natural language response using the result; and using the one or more computing devices, presenting the natural language response to a user on a client device. | 3. A computer-implemented method for translating a text string derived from natural language input to a machine-readable format, comprising: using one or more computing devices, generating an input graph for the text string, the input graph representing a plurality of different subsets of words in the text string; using the one or more computing devices, retrieving a template graph representing a plurality of translation templates, each translation template being for translating matching natural language input to the machine-readable format, and each translation template corresponding to at least one of a plurality of terminal nodes in the template graph, wherein each internal node of the template graph is labeled with an identifier of a highest priority template that can be reached from that point in the template graph; using the one or more computing devices, generating an intersection graph from the input graph and the template graph, each node in the intersection graph representing a tuple of corresponding nodes in the input and template graphs; using the one or more computing devices, processing the intersection graph according to template priorities associated with the nodes of the template graph thereby identifying one or more candidate terminal nodes of the intersection graph, the template priorities associated with the terminal nodes of the template graph causing processing of the intersection graph to proceed preferentially along edges of the intersection graph leading to terminal nodes of the intersection graph that represent terminal nodes of the template graph that correspond to the highest priority templates; using the one or more computing devices, selecting a terminal node in the template graph that corresponds to one of the one or more candidate terminal nodes of the intersection graph based at least in part on a probability associated with the selected terminal node of the template graph that the text string corresponds to an intended meaning of the natural language input, wherein a first translation template corresponds to the selected terminal node of the template graph; using the one or more computing devices, translating the text string using the first translation template thereby generating a machine-readable translation that accurately represents a semantic meaning of the natural language input; using the one or more computing devices, processing the machine-readable translation using structured data to generate a result; using the one or more computing devices, generating a natural language response using the result; and using the one or more computing devices, presenting the natural language response to a user on a client device. 8. The method of claim 3 , wherein the input graph includes probabilities associated with the text string, and the template graph includes probabilities associated with the translation templates. | 0.816384 |
8,358,747 | 18 | 19 | 18. A computer system comprising: a processor, a computer readable memory, and a computer readable non-transitory storage media; first program instructions to receive an initial caller voice input from a caller, wherein the initial caller voice input is the caller saying an unintelligible phrase caused by a speech limitation of the caller; second program instructions to determine that the initial caller voice input is a computer-unintelligible voice input that does not match any entry from a lexicon of known voice inputs; third program instructions to prompt the caller to transmit a secondary input to clarify the computer-unintelligible voice input, wherein the secondary input is a dual-tone multi-frequency (DTMF) phone keypad input from the caller that identifies a user-requested activity that the caller was requesting when saying the unintelligible phrase; fourth program instructions to utilize the secondary input to match the computer-unintelligible voice input with a specific known voice input from the lexicon of known voice inputs; and fifth program instructions to update the lexicon of known voice inputs with the computer-unintelligible voice input, wherein the computer-unintelligible voice input is matched to the specific known voice input from the lexicon of known voice inputs; sixth program instructions to match the unintelligible phrase to the user-requested activity in a database that is specific for the caller; seventh program instructions to receive the unintelligible phrase in a subsequent phone call from the user; and eighth program instructions to match the unintelligible phrase to the user-requested activity for the caller; and wherein the first, second, third, fourth, fifth, sixth, seventh, and eighth program instructions are stored on the computer readable storage media for execution by the processor via the computer readable memory. | 18. A computer system comprising: a processor, a computer readable memory, and a computer readable non-transitory storage media; first program instructions to receive an initial caller voice input from a caller, wherein the initial caller voice input is the caller saying an unintelligible phrase caused by a speech limitation of the caller; second program instructions to determine that the initial caller voice input is a computer-unintelligible voice input that does not match any entry from a lexicon of known voice inputs; third program instructions to prompt the caller to transmit a secondary input to clarify the computer-unintelligible voice input, wherein the secondary input is a dual-tone multi-frequency (DTMF) phone keypad input from the caller that identifies a user-requested activity that the caller was requesting when saying the unintelligible phrase; fourth program instructions to utilize the secondary input to match the computer-unintelligible voice input with a specific known voice input from the lexicon of known voice inputs; and fifth program instructions to update the lexicon of known voice inputs with the computer-unintelligible voice input, wherein the computer-unintelligible voice input is matched to the specific known voice input from the lexicon of known voice inputs; sixth program instructions to match the unintelligible phrase to the user-requested activity in a database that is specific for the caller; seventh program instructions to receive the unintelligible phrase in a subsequent phone call from the user; and eighth program instructions to match the unintelligible phrase to the user-requested activity for the caller; and wherein the first, second, third, fourth, fifth, sixth, seventh, and eighth program instructions are stored on the computer readable storage media for execution by the processor via the computer readable memory. 19. The computer system of claim 18 , wherein the lexicon of known voice inputs is exclusive to the caller. | 0.5 |
9,038,016 | 15 | 16 | 15. A system comprising: a memory storing an executable graphical model; and a processor configured to: receive a graphical specification of a parent class of graphical objects in a graphical modeling environment; receive a graphical specification of a first child class of graphical objects and a second child class of graphical objects in the graphical modeling environment, wherein: the first child class of graphical objects is a first subclass of the parent class of graphical objects in a hierarchy of classes of graphical objects, the second child class of graphical objects is a second subclass of the parent class of graphical objects in the hierarchy of classes of graphical objects, and the first child class of graphical objects and the second child class of graphical objects depend respectively on the parent class of graphical objects for common features that are shared by the first child class of graphical objects and the second child class of graphical objects; receive an instruction to incorporate an instance of the parent class of graphical objects into an executable graphical model in the graphical modeling environment; instantiate an instance of the first child class of graphical objects and an instance of the second child class of graphical objects in the executable graphical model; execute the executable graphical model; and dynamically switch between the instance of the first child class of graphical objects and the instance of the second child class of graphical objects during the execution of the executable graphical model. | 15. A system comprising: a memory storing an executable graphical model; and a processor configured to: receive a graphical specification of a parent class of graphical objects in a graphical modeling environment; receive a graphical specification of a first child class of graphical objects and a second child class of graphical objects in the graphical modeling environment, wherein: the first child class of graphical objects is a first subclass of the parent class of graphical objects in a hierarchy of classes of graphical objects, the second child class of graphical objects is a second subclass of the parent class of graphical objects in the hierarchy of classes of graphical objects, and the first child class of graphical objects and the second child class of graphical objects depend respectively on the parent class of graphical objects for common features that are shared by the first child class of graphical objects and the second child class of graphical objects; receive an instruction to incorporate an instance of the parent class of graphical objects into an executable graphical model in the graphical modeling environment; instantiate an instance of the first child class of graphical objects and an instance of the second child class of graphical objects in the executable graphical model; execute the executable graphical model; and dynamically switch between the instance of the first child class of graphical objects and the instance of the second child class of graphical objects during the execution of the executable graphical model. 16. The system of claim 15 , wherein the first child class of graphical objects and the second child class of graphical objects differ from each other such that the first child class of graphical objects and the second child class of graphical objects are variants of each other. | 0.682955 |
9,009,827 | 15 | 16 | 15. Non-transitory computer storage comprising instructions for causing one or more computing devices to share security information by: receiving security attack data from a first entity, the security attack data comprising information regarding one or more security attacks detected by the first entity, wherein an access control list is associated with at least a portion of the security attack data, the access control list indicating respective one or more entities permissioned to receive the portion of security attack data; transmitting the portion of security attack data to respective one or more entities, wherein said transmitting of the portion of security attack data to respective one or more entities is in accordance with the access control list; receiving a ruleset from a second entity, the ruleset based at least in part on the security attack data from the first entity, wherein the ruleset be comprises code instructions executable by a plurality of entities to detect one or more security attacks, and wherein execution of the code instructions of the ruleset identifies malicious behavior of one or more security attacks, and wherein execution of the code instructions of the ruleset accesses one or more data objects associated with respective entities to identify the malicious behavior associated with the respective entities, the one or more data objects comprising at least one of IP address data, proxy data, user login data, malware data, virtual private network data, hostname data, data associated with computing device behavior, or network data, and wherein the ruleset is associated with a ruleset access control list, the ruleset access control list indicating respective one or more entities permissioned to receive the ruleset; and transmitting the ruleset to respective one or more entities, wherein said transmitting of the ruleset to respective one or more entities is in accordance with the ruleset access control list. | 15. Non-transitory computer storage comprising instructions for causing one or more computing devices to share security information by: receiving security attack data from a first entity, the security attack data comprising information regarding one or more security attacks detected by the first entity, wherein an access control list is associated with at least a portion of the security attack data, the access control list indicating respective one or more entities permissioned to receive the portion of security attack data; transmitting the portion of security attack data to respective one or more entities, wherein said transmitting of the portion of security attack data to respective one or more entities is in accordance with the access control list; receiving a ruleset from a second entity, the ruleset based at least in part on the security attack data from the first entity, wherein the ruleset be comprises code instructions executable by a plurality of entities to detect one or more security attacks, and wherein execution of the code instructions of the ruleset identifies malicious behavior of one or more security attacks, and wherein execution of the code instructions of the ruleset accesses one or more data objects associated with respective entities to identify the malicious behavior associated with the respective entities, the one or more data objects comprising at least one of IP address data, proxy data, user login data, malware data, virtual private network data, hostname data, data associated with computing device behavior, or network data, and wherein the ruleset is associated with a ruleset access control list, the ruleset access control list indicating respective one or more entities permissioned to receive the ruleset; and transmitting the ruleset to respective one or more entities, wherein said transmitting of the ruleset to respective one or more entities is in accordance with the ruleset access control list. 16. The non-transitory computer storage of claim 15 , further comprising: detecting a pattern across security attack data from multiple entities, wherein a second ruleset indicates one or more attributes of the detected pattern that are usable in detecting similar security attacks by respective one or more entities with which the second ruleset is transmitted. | 0.5 |
10,049,032 | 18 | 19 | 18. The medium of claim 17 , wherein the attribute class comprises a string attribute class, a numeric attribute class with boundary constraints, or a numeric attribute class with relational constraints. | 18. The medium of claim 17 , wherein the attribute class comprises a string attribute class, a numeric attribute class with boundary constraints, or a numeric attribute class with relational constraints. 19. The medium of claim 18 , wherein determining the one or more attribute classes further comprises: parsing a set of paths of the structured diagram; and extracting the set of path predicates, a set of attribute constraints, and a set of attribute dependencies from the parsed set of paths. | 0.5 |
8,972,260 | 1 | 6 | 1. A method of generating language models for speech recognition comprising: identifying a plurality of utterances in training data corresponding to speech; generating a frequency count of each utterance in the plurality of utterances; generating a high-frequency plurality of utterances from the plurality of utterances having a frequency that exceeds a predetermined frequency threshold; generating a low-frequency plurality of utterances from the plurality of utterances having a frequency that is below the predetermined frequency threshold; generating with at least one processor a grammar-based language model using the high-frequency plurality of utterances as training data; storing the grammar based language model in a memory; generating with the at least one processor a statistical language model using the low-frequency plurality of utterances as training data; and storing the statistical language model in the memory. | 1. A method of generating language models for speech recognition comprising: identifying a plurality of utterances in training data corresponding to speech; generating a frequency count of each utterance in the plurality of utterances; generating a high-frequency plurality of utterances from the plurality of utterances having a frequency that exceeds a predetermined frequency threshold; generating a low-frequency plurality of utterances from the plurality of utterances having a frequency that is below the predetermined frequency threshold; generating with at least one processor a grammar-based language model using the high-frequency plurality of utterances as training data; storing the grammar based language model in a memory; generating with the at least one processor a statistical language model using the low-frequency plurality of utterances as training data; and storing the statistical language model in the memory. 6. The method of claim 1 further comprising: performing, by executing with the at least one processor program instructions stored in the memory, a speech recognition operation on speech data using the stored grammar-based language model to generate a first speech recognition result; performing, by executing with the at least one processor the program instructions stored in the memory, a speech recognition operation on the speech data using the stored statistical language model to generate a second speech recognition result; and identifying, by executing with the at least one processor the program instructions stored in the memory, a final speech recognition result as either of the first speech recognition result or the second speech recognition result when the first speech recognition result is equivalent to the second speech recognition result. | 0.538751 |
9,361,343 | 3 | 4 | 3. The method of claim 1 , wherein generating the set of the large event types is configured to determine a set of candidate event types, for which a total number of supporting customers is a preset minimum support or more, among one or more candidate event types to be the large event type set. | 3. The method of claim 1 , wherein generating the set of the large event types is configured to determine a set of candidate event types, for which a total number of supporting customers is a preset minimum support or more, among one or more candidate event types to be the large event type set. 4. The method of claim 3 , wherein: the candidate event types are extracted by processing the event file in parallel using a Map function, and supports of the candidate event types are calculated in parallel using a Reduce function. | 0.5 |
7,849,437 | 1 | 9 | 1. A computer-readable storage device having computer-executable components executable by a processing device to perform a method, the processing device accessing a local memory, comprising: (a) receiving a web-page including elements defined to access a plurality of resources in a JavaScript framework provided to the processing device in local memory, the resources including methods interpreted by a browser to create resource objects; (b) parsing the web-page to define instances of objects based on bindings to the resources defined by elements in the web-page; (c) downloading a base set of resources in a core framework library of the JavaScript framework to the local memory; (d) determining if additional resources not included in the local memory are needed based on the bindings declared in the web-page and if so, downloading the additional resources to a resource cache in local memory; (e) managing instances of the objects by maintaining a global list of binding declarations maintained by the JavaScript framework; and (f) displaying the web-page in the browser using one or more of the objects. | 1. A computer-readable storage device having computer-executable components executable by a processing device to perform a method, the processing device accessing a local memory, comprising: (a) receiving a web-page including elements defined to access a plurality of resources in a JavaScript framework provided to the processing device in local memory, the resources including methods interpreted by a browser to create resource objects; (b) parsing the web-page to define instances of objects based on bindings to the resources defined by elements in the web-page; (c) downloading a base set of resources in a core framework library of the JavaScript framework to the local memory; (d) determining if additional resources not included in the local memory are needed based on the bindings declared in the web-page and if so, downloading the additional resources to a resource cache in local memory; (e) managing instances of the objects by maintaining a global list of binding declarations maintained by the JavaScript framework; and (f) displaying the web-page in the browser using one or more of the objects. 9. The computer-readable storage device of claim 1 wherein the JavaScript framework includes a namespace for instance arguments. | 0.752896 |
9,122,683 | 6 | 10 | 6. One or more non-transitory computer-readable storage media storing computer executable instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising: receiving first descriptive information associated with an electronic version of particular media content; identifying, based at least in part on the first descriptive information, second descriptive information, wherein the second descriptive information is associated with a physical media product and includes information about the particular media content that is not included in the first descriptive information, and wherein the physical media product includes the particular media content; in response to identifying the second descriptive information, storing a data record relating the electronic version of the particular media product to the identified second descriptive information associated with the physical media product; receiving a request for information related to the particular media product; accessing the data record relating the electronic version of the particular media product to the second descriptive information associated with the physical media product; and providing at least a portion of the first descriptive information and at least a portion of the second descriptive information for presentation at least partly in response to the request. | 6. One or more non-transitory computer-readable storage media storing computer executable instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising: receiving first descriptive information associated with an electronic version of particular media content; identifying, based at least in part on the first descriptive information, second descriptive information, wherein the second descriptive information is associated with a physical media product and includes information about the particular media content that is not included in the first descriptive information, and wherein the physical media product includes the particular media content; in response to identifying the second descriptive information, storing a data record relating the electronic version of the particular media product to the identified second descriptive information associated with the physical media product; receiving a request for information related to the particular media product; accessing the data record relating the electronic version of the particular media product to the second descriptive information associated with the physical media product; and providing at least a portion of the first descriptive information and at least a portion of the second descriptive information for presentation at least partly in response to the request. 10. The one or more non-transitory computer-readable storage media of claim 6 , the operations further comprising: receiving a request to display information regarding the physical media product that stores the particular media content; and sending, at least partly in response to the request, access information to permit access to a portion of an electronic catalog associated with the electronic version of the particular media content. | 0.5 |
8,055,758 | 1 | 8 | 1. A method performed by a device embedded in an apparatus to report a state of the apparatus to a remote computer, the method comprising: detecting the state of the apparatus; generating a message that reports the state of the apparatus to the remote computer, the message comprising a HyperText Transfer Protocol (HTTP) command, the message using eXtensible Markup Language (XML) to report the state, and the message containing a code that is unique to the device or apparatus, wherein generating is performed periodically or in response to a deviation in the state; and sending the message comprising the HTTP command to the remote computer, the HTTP command comprising a command that is configured to report the state of the apparatus using XML; wherein the device is on an internal network and the remote computer is on an external network that is separate from the internal network, and wherein, as a result, the remote computer cannot communicate to the device to obtain the state of the apparatus; and wherein the state of the apparatus comprises values of two or more variables associated with the apparatus, one or more of the variables being flagged if one or more of the variables corresponds to an error condition associated with the apparatus. | 1. A method performed by a device embedded in an apparatus to report a state of the apparatus to a remote computer, the method comprising: detecting the state of the apparatus; generating a message that reports the state of the apparatus to the remote computer, the message comprising a HyperText Transfer Protocol (HTTP) command, the message using eXtensible Markup Language (XML) to report the state, and the message containing a code that is unique to the device or apparatus, wherein generating is performed periodically or in response to a deviation in the state; and sending the message comprising the HTTP command to the remote computer, the HTTP command comprising a command that is configured to report the state of the apparatus using XML; wherein the device is on an internal network and the remote computer is on an external network that is separate from the internal network, and wherein, as a result, the remote computer cannot communicate to the device to obtain the state of the apparatus; and wherein the state of the apparatus comprises values of two or more variables associated with the apparatus, one or more of the variables being flagged if one or more of the variables corresponds to an error condition associated with the apparatus. 8. The method of claim 1 , wherein the HTTP command comprises a POST command. | 0.881173 |
9,984,683 | 12 | 14 | 12. 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, by the one or more computers, audio data that describes an utterance; processing the audio data using a neural network that has been trained as an acoustic model, wherein the processing comprises: providing, as input to the neural network, input vectors having values describing the utterance, the values including values representing audio waveform features, wherein the audio waveform features are determined using a filterbank having parameters trained jointly with weights of the neural network, wherein the neural network has first memory blocks for time information and second memory blocks for frequency information, the first memory blocks being different from the second memory blocks; wherein the first memory blocks are time-LSTM blocks that each have a state, and wherein the second memory blocks are frequency-LSTM blocks that each have a state and a corresponding frequency step in a sequence of multiple frequency steps, wherein the states are determined for each of a sequence of multiple time steps; wherein, for each of at least some of the frequency-LSTM blocks, the frequency-LSTM block determines its state using the state of the time-LSTM block corresponding to the same frequency step at the previous time step; and wherein, for each of at least some of the time-LSTM blocks, the time-LSTM block determines its state using the state of the frequency-LSTM block corresponding to the same time step and the previous frequency step; receiving, as output of the neural network, one or more scores that each indicate a likelihood that a respective phonetic unit represents a portion of the utterance; determining, by the one or more computers, a transcription for the utterance based on the one or more scores; and providing, by the one or more computers, the determined transcription as output of an automated speech recognizer. | 12. 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, by the one or more computers, audio data that describes an utterance; processing the audio data using a neural network that has been trained as an acoustic model, wherein the processing comprises: providing, as input to the neural network, input vectors having values describing the utterance, the values including values representing audio waveform features, wherein the audio waveform features are determined using a filterbank having parameters trained jointly with weights of the neural network, wherein the neural network has first memory blocks for time information and second memory blocks for frequency information, the first memory blocks being different from the second memory blocks; wherein the first memory blocks are time-LSTM blocks that each have a state, and wherein the second memory blocks are frequency-LSTM blocks that each have a state and a corresponding frequency step in a sequence of multiple frequency steps, wherein the states are determined for each of a sequence of multiple time steps; wherein, for each of at least some of the frequency-LSTM blocks, the frequency-LSTM block determines its state using the state of the time-LSTM block corresponding to the same frequency step at the previous time step; and wherein, for each of at least some of the time-LSTM blocks, the time-LSTM block determines its state using the state of the frequency-LSTM block corresponding to the same time step and the previous frequency step; receiving, as output of the neural network, one or more scores that each indicate a likelihood that a respective phonetic unit represents a portion of the utterance; determining, by the one or more computers, a transcription for the utterance based on the one or more scores; and providing, by the one or more computers, the determined transcription as output of an automated speech recognizer. 14. The system of claim 12 , wherein processing the audio data using the neural network comprises processing audio data representing characteristics of multiple channels of audio describing the utterance. | 0.790123 |
7,891,125 | 1 | 6 | 1. An apparatus comprising: a. a token having a surface configured for imparting a personalized message relating to a source of an emotion triggering event; b. a frame detachably attached to the token, wherein the frame is configured for affixing a memento relating to the source; and c. a mount detachably attached to the frame. | 1. An apparatus comprising: a. a token having a surface configured for imparting a personalized message relating to a source of an emotion triggering event; b. a frame detachably attached to the token, wherein the frame is configured for affixing a memento relating to the source; and c. a mount detachably attached to the frame. 6. The apparatus of claim 1 , wherein the mount further comprises means, detachably attached to the mount, for displaying the memento. | 0.906555 |
9,164,671 | 9 | 14 | 9. A computer-implemented method comprising: receiving a user selection of a site mode, the site mode being associated with a web application installed on an associated client device; responsive to receiving the site mode selection, requesting a start URL, wherein the start URL is ascertained from a web application file that was created from information received from a website associated with the web application; receiving web resources associated with the start URL; rendering the web resources in a web application window; receiving a user interaction with respect to the resources rendered in the web application window; responsive to the user interaction being within boundaries, associated with a website domain or subdomain and defined by the web application file, rendering user interface customization within the boundaries in the web application window; and responsive to the user interaction not being within boundaries associated with a website domain or subdomain and defined by the web application file, rendering content associated with the user interaction in a default browser different from the web application window. | 9. A computer-implemented method comprising: receiving a user selection of a site mode, the site mode being associated with a web application installed on an associated client device; responsive to receiving the site mode selection, requesting a start URL, wherein the start URL is ascertained from a web application file that was created from information received from a website associated with the web application; receiving web resources associated with the start URL; rendering the web resources in a web application window; receiving a user interaction with respect to the resources rendered in the web application window; responsive to the user interaction being within boundaries, associated with a website domain or subdomain and defined by the web application file, rendering user interface customization within the boundaries in the web application window; and responsive to the user interaction not being within boundaries associated with a website domain or subdomain and defined by the web application file, rendering content associated with the user interaction in a default browser different from the web application window. 14. The method of claim 9 , wherein receiving the user interaction comprises receiving the user interaction from a desktop task bar, with respect to the resources rendered in the web application window. | 0.603922 |
9,263,059 | 1 | 8 | 1. A method for deep tagging a recording, the method comprising the steps of: a computer recording audio of a communication between a plurality of participants, wherein the audio comprises speech from one or more of the plurality of participants; the computer detecting a non-speech sound within the audio, wherein the non-speech sound is transmitted to the plurality of participants; the computer automatically determining that the non-speech sound corresponds to a type of sound, and in response, automatically associating a descriptive term with a time of occurrence of the non-speech sound within the recorded audio to form a searchable tag, wherein the descriptive term includes a phonetic translation of the non-speech sound; and the computer storing the searchable tag as metadata of the recorded audio. | 1. A method for deep tagging a recording, the method comprising the steps of: a computer recording audio of a communication between a plurality of participants, wherein the audio comprises speech from one or more of the plurality of participants; the computer detecting a non-speech sound within the audio, wherein the non-speech sound is transmitted to the plurality of participants; the computer automatically determining that the non-speech sound corresponds to a type of sound, and in response, automatically associating a descriptive term with a time of occurrence of the non-speech sound within the recorded audio to form a searchable tag, wherein the descriptive term includes a phonetic translation of the non-speech sound; and the computer storing the searchable tag as metadata of the recorded audio. 8. The method of claim 1 , further comprising the steps of: the computer receiving a search query; the computer matching the search query to the searchable tag; and the computer responding to the search query with the recorded audio and an indication of the time of occurrence of the non-speech sound. | 0.545317 |
8,712,759 | 9 | 10 | 9. A method of semantically parsing a natural language expression, comprising: constructing, by a processor, a first ambiguous meaning representation for a first natural language expression; and fully or partially disambiguating, by a processor, the first meaning representation by specializing it by replacing a first semantic descriptor in it by a second, more specific semantic descriptor; wherein: the constructed meaning representation comprises a first pointer to a first semantic descriptor that is logically organized in one or more specialization hierarchies; the disambiguating comprises changing the pointer to point to the second semantic descriptor; and the second semantic descriptor is a specialization of the first semantic descriptor according to a first specialization hierarchy. | 9. A method of semantically parsing a natural language expression, comprising: constructing, by a processor, a first ambiguous meaning representation for a first natural language expression; and fully or partially disambiguating, by a processor, the first meaning representation by specializing it by replacing a first semantic descriptor in it by a second, more specific semantic descriptor; wherein: the constructed meaning representation comprises a first pointer to a first semantic descriptor that is logically organized in one or more specialization hierarchies; the disambiguating comprises changing the pointer to point to the second semantic descriptor; and the second semantic descriptor is a specialization of the first semantic descriptor according to a first specialization hierarchy. 10. The method of claim 9 , further comprising including in the meaning representation a second pointer to a second semantic descriptor that is not a generalization or specialization of the first semantic descriptor in the first specialization hierarchy, and treating the first and second pointer disjunctively. | 0.5 |
7,533,172 | 1 | 15 | 1. A peer-to-peer network system, comprising: a plurality of peers, wherein each peer comprises a network node configured to communicate with one or more other ones of said peers over one or more networks; a plurality of peer services or content provided by one or more of said peers; a service or content advertisement for each of said services or content, wherein each service or content advertisement comprises an identification of a corresponding service or content and an indication of how to access the corresponding service or content; a peer advertisement for each of said peers, wherein each peer advertisement comprises an identification of a corresponding one of said peers and communication address for the corresponding one of said peers, wherein one or more of said peer advertisements further comprises an indication of a service or a content provided by the peer corresponding to that peer advertisement; wherein one or more of said peers are configured to publish their corresponding peer advertisements and one or more of said service or content advertisements in the peer-to-peer network system to be discoverable by other peers; and wherein each advertisement is a separate programming language independent metadata document. | 1. A peer-to-peer network system, comprising: a plurality of peers, wherein each peer comprises a network node configured to communicate with one or more other ones of said peers over one or more networks; a plurality of peer services or content provided by one or more of said peers; a service or content advertisement for each of said services or content, wherein each service or content advertisement comprises an identification of a corresponding service or content and an indication of how to access the corresponding service or content; a peer advertisement for each of said peers, wherein each peer advertisement comprises an identification of a corresponding one of said peers and communication address for the corresponding one of said peers, wherein one or more of said peer advertisements further comprises an indication of a service or a content provided by the peer corresponding to that peer advertisement; wherein one or more of said peers are configured to publish their corresponding peer advertisements and one or more of said service or content advertisements in the peer-to-peer network system to be discoverable by other peers; and wherein each advertisement is a separate programming language independent metadata document. 15. The peer-to-peer network system as recited in claim 1 , wherein one or more of said service or content advertisements comprises a time-to-live indicator, wherein the corresponding advertisement is deleted or invalidated when the time-to-live indicator expires. | 0.752809 |
8,736,553 | 25 | 26 | 25. The text input method as claimed in claim 24 , wherein the key-related graphical user interface component allows the forward movement and the backward movement of the focus even if the first touch movement track is separated from the key-related graphical user interface component. | 25. The text input method as claimed in claim 24 , wherein the key-related graphical user interface component allows the forward movement and the backward movement of the focus even if the first touch movement track is separated from the key-related graphical user interface component. 26. The text input method as claimed in claim 25 , wherein the key-related graphical user interface component is displayed in response to an instance of long press on the selected key during the operation period. | 0.5 |
9,524,279 | 13 | 17 | 13. A system, comprising: one or more processors; and a memory that includes one or more software components that are executable by the one or more processors to: parse a text instruction in a text-based document of an application to identify a noun that corresponds to a user interface (UI) element of the application and a related verb that corresponds to an operation action performed on the UI element; in response to being unable to parse the text instruction for the related verb, prompt a user, via a user interface (UI), to manually enter the related verb or to select the operation action that corresponds to the related verb; receive, via the user interface (UI), one of the manual entry of the related verb or a selection of the operation action; generate, based at least in part on the noun and the related verb, an operation record from the text-based help document of the application, the operation record including data for animating the operation action that is able to be performed on the user interface (UI) element of the application as described in the text-based help document; modify the text-based help document to generate an enhanced help document including a control that loads the operation record; play an animation of the operation action that is able to be performed on the UI element upon activation of the control in the enhanced help document, the animation including a visualization of one or more steps for completing the operation action; and present an option menu to enable a user to, upon completion of playing the animation of the operation action, retain the operation action performed and undo the operation action performed. | 13. A system, comprising: one or more processors; and a memory that includes one or more software components that are executable by the one or more processors to: parse a text instruction in a text-based document of an application to identify a noun that corresponds to a user interface (UI) element of the application and a related verb that corresponds to an operation action performed on the UI element; in response to being unable to parse the text instruction for the related verb, prompt a user, via a user interface (UI), to manually enter the related verb or to select the operation action that corresponds to the related verb; receive, via the user interface (UI), one of the manual entry of the related verb or a selection of the operation action; generate, based at least in part on the noun and the related verb, an operation record from the text-based help document of the application, the operation record including data for animating the operation action that is able to be performed on the user interface (UI) element of the application as described in the text-based help document; modify the text-based help document to generate an enhanced help document including a control that loads the operation record; play an animation of the operation action that is able to be performed on the UI element upon activation of the control in the enhanced help document, the animation including a visualization of one or more steps for completing the operation action; and present an option menu to enable a user to, upon completion of playing the animation of the operation action, retain the operation action performed and undo the operation action performed. 17. The system of claim 13 , wherein the one or more components are further executable to generate one of the operation record at least by: traversing a user interface (UI) element tree of the application to obtain a hierarchic path from a main window to a UI element of the application; and representing the user interface (UI) element of the application in the operation record via a corresponding hierarchic structure that includes hierarchical strings that lead from the main window of the application to the user interface (UI) element. | 0.590772 |
7,747,651 | 8 | 12 | 8. A system comprising: at least one computing device; a metadata model generator configured in the at least one computing device to provide a metadata model including model objects representing a data source, the metadata model having a query layer and a package layer, the query layer providing a business view of the data in the data source, the query layer including query subjects, wherein the query subjects directly describe actual physical data within an underlying database, are used in creating reports, and are abstracted and separate from the underlying database that includes physical data from one or more data sources, and wherein the package layer includes packages having direct references to the query subjects; a query specification interface configured in the at least one computing device to allow a client application to generate a query specification based on a user input, the query specification is not in a form applicable to the data source directly; and a translator configured in the at least one computing device to translate the generated query specification into a query applicable to the data source based on the query subjects referred to by the packages in the package layer in the metadata model, wherein the query is executed using a data source specification language of the data source, and wherein the query includes data query language statements that are embedded within the query subjects in the metadata model; and a sender configured in the at least one computing device to provide the query to the data source for execution. | 8. A system comprising: at least one computing device; a metadata model generator configured in the at least one computing device to provide a metadata model including model objects representing a data source, the metadata model having a query layer and a package layer, the query layer providing a business view of the data in the data source, the query layer including query subjects, wherein the query subjects directly describe actual physical data within an underlying database, are used in creating reports, and are abstracted and separate from the underlying database that includes physical data from one or more data sources, and wherein the package layer includes packages having direct references to the query subjects; a query specification interface configured in the at least one computing device to allow a client application to generate a query specification based on a user input, the query specification is not in a form applicable to the data source directly; and a translator configured in the at least one computing device to translate the generated query specification into a query applicable to the data source based on the query subjects referred to by the packages in the package layer in the metadata model, wherein the query is executed using a data source specification language of the data source, and wherein the query includes data query language statements that are embedded within the query subjects in the metadata model; and a sender configured in the at least one computing device to provide the query to the data source for execution. 12. The system of claim 8 , wherein the metadata model generator directly changes definitions of the query subjects in the metadata model without reliance on a separate modeling layer. | 0.5 |
8,180,630 | 5 | 6 | 5. The method of automated dictionary population, as recited in claim 1 , wherein the storing the new words includes preserving the generated statistical information. | 5. The method of automated dictionary population, as recited in claim 1 , wherein the storing the new words includes preserving the generated statistical information. 6. The method of automated dictionary population, as recited in claim 5 , wherein the generated statistical information includes at least one of word usage frequency, recency, or likelihood of use. | 0.5 |
8,700,395 | 7 | 9 | 7. A system for providing a formatted transcription from an original transcription related to patient data, the system comprising: at least one computer-readable storage medium to store the original transcription; and at least one computer capable of accessing the at least one computer-readable storage medium, the at least one computer programmed to: identify at least one trigger phrase in the original transcription; determine whether text located subsequent to the at least one trigger phrase includes data of a data type corresponding to the at least one trigger phrase; and format the at least one trigger phrase and/or the data when the data is determined to be of the data type corresponding to the at least one trigger phrase to produce, at least in part, the formatted transcription. | 7. A system for providing a formatted transcription from an original transcription related to patient data, the system comprising: at least one computer-readable storage medium to store the original transcription; and at least one computer capable of accessing the at least one computer-readable storage medium, the at least one computer programmed to: identify at least one trigger phrase in the original transcription; determine whether text located subsequent to the at least one trigger phrase includes data of a data type corresponding to the at least one trigger phrase; and format the at least one trigger phrase and/or the data when the data is determined to be of the data type corresponding to the at least one trigger phrase to produce, at least in part, the formatted transcription. 9. The system of claim 7 , wherein the at least one trigger phrase is associated with at least one field in a table, and wherein the at least one computer is configured to include the at least one field in the formatted transcription in association with the data determined to be of the data type corresponding to the at least one trigger phrase. | 0.560914 |
8,977,644 | 1 | 4 | 1. A computer-implemented method comprising: receiving a submission of a first resource locator for a first alternative search result by a first user using a first user device, wherein the submission by the first user comprises a user selection by the first user of a first subpopulation of users, among a plurality of subpopulations of users of a search engine, to receive the first alternative search result in addition to search results generated by the search engine for a first query; associating the first query with the first alternative search result and the first subpopulation of users selected by the first user; receiving a second query submitted from a second user device by a second user different from the first user; obtaining, from the search engine, second search results responsive to the second query; determining that the first query matches the second query and that the second user is a member of the first subpopulation of users selected by the first user to receive the first alternative search result in addition to search results generated by the search engine for the first query; in response to the determining, generating a search results page that includes the first alternative search result in addition to one or more of the second search results; and providing the search results page to the second user device for presentation to the second user. | 1. A computer-implemented method comprising: receiving a submission of a first resource locator for a first alternative search result by a first user using a first user device, wherein the submission by the first user comprises a user selection by the first user of a first subpopulation of users, among a plurality of subpopulations of users of a search engine, to receive the first alternative search result in addition to search results generated by the search engine for a first query; associating the first query with the first alternative search result and the first subpopulation of users selected by the first user; receiving a second query submitted from a second user device by a second user different from the first user; obtaining, from the search engine, second search results responsive to the second query; determining that the first query matches the second query and that the second user is a member of the first subpopulation of users selected by the first user to receive the first alternative search result in addition to search results generated by the search engine for the first query; in response to the determining, generating a search results page that includes the first alternative search result in addition to one or more of the second search results; and providing the search results page to the second user device for presentation to the second user. 4. The method of claim 1 , wherein the submission comprises additional content for the first alternative search result, the additional content being content submitted by the first user, the additional content comprising an image, additional text, or an additional resource locator, and wherein the search results page includes the first alternative search result and the additional content submitted by the first user. | 0.5 |
8,489,605 | 9 | 10 | 9. An apparatus comprising: web indexing application logic communicating with a computer processor to receive a hypertext markup language (HTML) page at a computer and identify HTML page elements, wherein the HTML page elements comprising parent nodes, the parent nodes comprising child nodes, and process each of the HTML page elements, wherein wherein the computer processor is configured for: grouping the child nodes by parent node into a group of child nodes; detecting patterns in the group of child nodes in response to the grouping; reducing the group of child nodes to text strings in response to the detecting; and storing the text strings as text values in the parent nodes; and the web indexing application logic further configured to generate a unique identifier (ID) of the HTML page in response to the processing; wherein the HTML page is a Web 2.0 page, the Web 2.0 page comprising content, the content being generated dynamically and wherein the web indexing application logic is further configured to filter HTML page elements in response to the identifying, the filtering removing the child nodes and the parent nodes that meet filter criteria, the filter criteria comprising: extensible markup language path language instructions; regular expression (reqex) instructions; and a list of html nodes. | 9. An apparatus comprising: web indexing application logic communicating with a computer processor to receive a hypertext markup language (HTML) page at a computer and identify HTML page elements, wherein the HTML page elements comprising parent nodes, the parent nodes comprising child nodes, and process each of the HTML page elements, wherein wherein the computer processor is configured for: grouping the child nodes by parent node into a group of child nodes; detecting patterns in the group of child nodes in response to the grouping; reducing the group of child nodes to text strings in response to the detecting; and storing the text strings as text values in the parent nodes; and the web indexing application logic further configured to generate a unique identifier (ID) of the HTML page in response to the processing; wherein the HTML page is a Web 2.0 page, the Web 2.0 page comprising content, the content being generated dynamically and wherein the web indexing application logic is further configured to filter HTML page elements in response to the identifying, the filtering removing the child nodes and the parent nodes that meet filter criteria, the filter criteria comprising: extensible markup language path language instructions; regular expression (reqex) instructions; and a list of html nodes. 10. The apparatus of claim 9 , wherein the processing further comprising: sorting the group of child nodes in response to the reducing. | 0.712766 |
9,087,053 | 16 | 17 | 16. The method of claim 6 , wherein the document data is automatically retrieved based on the field value when a user begins performing an operation associated with the operation identification. | 16. The method of claim 6 , wherein the document data is automatically retrieved based on the field value when a user begins performing an operation associated with the operation identification. 17. The method of claim 16 , further comprising performing an enabler application configuration operation, wherein the configuration operation identifies a location of the interface field to the enabler application, wherein the enabler application uses the location to capture the field value. | 0.5 |
8,041,729 | 18 | 22 | 18. A volatile or non-volatile computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform: associating a category to a set of nodes of a graph by: determining a first node that represents a first term that is in the category; locating a second node associated with the first node based at least in part on a first degree of cross-reference between the first node and the second node, the second node representing a second term, wherein the first degree of cross-reference is based at least in part on a frequency by which the first term appears in a set of documents with the second term; locating a third node associated with the second node based at least in part on a second degree of cross-reference between the second node and the third node, the third node representing a third term, wherein the second degree of cross-reference is based at least in part on a frequency by which the second term appears in a set of documents with the third term; based at least in part on both (a) the first degree of cross-reference between the first node and the second node, and (b) the second degree of cross-reference between the second node and the third node, determining whether or not the third term is in the category; in response to determining that the third term is in the category, storing information that indicates the third term is in the category. | 18. A volatile or non-volatile computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform: associating a category to a set of nodes of a graph by: determining a first node that represents a first term that is in the category; locating a second node associated with the first node based at least in part on a first degree of cross-reference between the first node and the second node, the second node representing a second term, wherein the first degree of cross-reference is based at least in part on a frequency by which the first term appears in a set of documents with the second term; locating a third node associated with the second node based at least in part on a second degree of cross-reference between the second node and the third node, the third node representing a third term, wherein the second degree of cross-reference is based at least in part on a frequency by which the second term appears in a set of documents with the third term; based at least in part on both (a) the first degree of cross-reference between the first node and the second node, and (b) the second degree of cross-reference between the second node and the third node, determining whether or not the third term is in the category; in response to determining that the third term is in the category, storing information that indicates the third term is in the category. 22. The volatile or non-volatile computer-readable storage medium of claim 18 , wherein the one or more sequences of instructions, when executed by the one or more processors, further cause the one or more processors to perform: determining that the first node is a domain node matching the category; wherein storing the information that indicates the third term is in the category comprises storing a full-strength link between the third node and the domain node. | 0.721489 |
9,317,406 | 9 | 11 | 9. A computer program product for generating a test script from a pre-existing script, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code embodied therewith and the computer-readable program code configured to perform a method, the method comprising: parsing a pre-existing test script for a graphical user interface (GUI) to identify, using keywords and associated parameters, a first GUI action in a first line of the pre-existing test script; parsing a model associated with the GUI to identify GUI actions and associated elements for the GUI actions in the model; identifying a corresponding element for the first GUI action in a second line, following the first line, in the pre-existing test script that match a GUI action and associated element of the GUI actions and associated elements for the GUI actions in the model; identifying GUI actions in the pre-existing test script that match GUI actions in the model; and generating a new test script by adding the first GUI action and corresponding element to the pre-existing test script. | 9. A computer program product for generating a test script from a pre-existing script, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code embodied therewith and the computer-readable program code configured to perform a method, the method comprising: parsing a pre-existing test script for a graphical user interface (GUI) to identify, using keywords and associated parameters, a first GUI action in a first line of the pre-existing test script; parsing a model associated with the GUI to identify GUI actions and associated elements for the GUI actions in the model; identifying a corresponding element for the first GUI action in a second line, following the first line, in the pre-existing test script that match a GUI action and associated element of the GUI actions and associated elements for the GUI actions in the model; identifying GUI actions in the pre-existing test script that match GUI actions in the model; and generating a new test script by adding the first GUI action and corresponding element to the pre-existing test script. 11. The computer program product of claim 9 the method further comprising, wherein if no matching GUI elements are found or none of the elements in the list are suitable then the process is halted so that a user can input the appropriate action the script needs to take. | 0.640957 |
9,239,875 | 1 | 10 | 1. A method comprising: in response to receiving a search query from an end user device: searching, by a node of a system, a set of candidate records including co-occurring features to identify one or more candidate records matching one or more extracted features, wherein an extracted feature that matches a candidate record is a primary feature, wherein the node comprises a main memory hosting an in-memory database, wherein the in-memory database stores a knowledge base of clusters, each cluster comprises a disambiguated primary feature with a unique identifier (“unique ID”), and a set of associated secondary features; associating, by the node, each of the extracted features with one or more machine-generated topic identifiers (“topic IDs”); disambiguating, by the node, each of the primary features from one another based on relatedness of topic IDs; identifying, by the node, a set of secondary features associated with each primary feature based upon the relatedness of topic IDs; disambiguating, by the node, each of the primary features from each of the secondary features in the associated set of secondary features based on relatedness of topic IDs; linking, by the node, in real-time, as data is retrieved from the knowledgebase from the in-memory database, each primary feature to the associated set of secondary features to form a new cluster; determining, by a disambiguation module of the in-memory database of the node, whether each of the new cluster matches an existing knowledgebase cluster by assignment of relative matching scores to existing knowledge clusters with disambiguated primary features, wherein, when there is a match, determining, an existing unique ID corresponding to each matching primary feature in the existing knowledgebase cluster and updating the existing knowledgebase cluster to include the new cluster; when there is no match, creating, a new knowledgebase cluster and assigning a new unique ID to the primary feature of the new knowledgebase cluster; and transmitting, one of the existing unique ID and the new unique ID for the primary feature to the user device. | 1. A method comprising: in response to receiving a search query from an end user device: searching, by a node of a system, a set of candidate records including co-occurring features to identify one or more candidate records matching one or more extracted features, wherein an extracted feature that matches a candidate record is a primary feature, wherein the node comprises a main memory hosting an in-memory database, wherein the in-memory database stores a knowledge base of clusters, each cluster comprises a disambiguated primary feature with a unique identifier (“unique ID”), and a set of associated secondary features; associating, by the node, each of the extracted features with one or more machine-generated topic identifiers (“topic IDs”); disambiguating, by the node, each of the primary features from one another based on relatedness of topic IDs; identifying, by the node, a set of secondary features associated with each primary feature based upon the relatedness of topic IDs; disambiguating, by the node, each of the primary features from each of the secondary features in the associated set of secondary features based on relatedness of topic IDs; linking, by the node, in real-time, as data is retrieved from the knowledgebase from the in-memory database, each primary feature to the associated set of secondary features to form a new cluster; determining, by a disambiguation module of the in-memory database of the node, whether each of the new cluster matches an existing knowledgebase cluster by assignment of relative matching scores to existing knowledge clusters with disambiguated primary features, wherein, when there is a match, determining, an existing unique ID corresponding to each matching primary feature in the existing knowledgebase cluster and updating the existing knowledgebase cluster to include the new cluster; when there is no match, creating, a new knowledgebase cluster and assigning a new unique ID to the primary feature of the new knowledgebase cluster; and transmitting, one of the existing unique ID and the new unique ID for the primary feature to the user device. 10. The method according to claim 1 , further comprising performing, by a node, a fuzzy key search of the set of candidate records. | 0.854444 |
8,595,030 | 12 | 20 | 12. A method for managing form-generated data related to a patient encounter, the method comprising: receiving location information related to a form that has designated information fields in different locations on the form, wherein the location information is generated in response to a user writing on the form in one of the designated information fields; translating the location information to a contextualized data element, wherein the contextualized data element comprises contextual information that is associated with the user writing, wherein translating the location information to a contextualized data element comprises using the location information to identify a label by comparing the location information to a mapping data set that maps user areas on the form to labels that are associated with the designated information fields, and wherein the contextualized data element comprises the label; and wherein the label in the contextualized data element is utilized by an EMR/EHR application to perform a function related to the user writing on the form, wherein the contextualized data element is distributed to the EMR/EHR application via a publish/subscribe protocol in which the EMR/EHR application subscribes to a specific contextualized data element by identifying the label associated with the contextualized data element. | 12. A method for managing form-generated data related to a patient encounter, the method comprising: receiving location information related to a form that has designated information fields in different locations on the form, wherein the location information is generated in response to a user writing on the form in one of the designated information fields; translating the location information to a contextualized data element, wherein the contextualized data element comprises contextual information that is associated with the user writing, wherein translating the location information to a contextualized data element comprises using the location information to identify a label by comparing the location information to a mapping data set that maps user areas on the form to labels that are associated with the designated information fields, and wherein the contextualized data element comprises the label; and wherein the label in the contextualized data element is utilized by an EMR/EHR application to perform a function related to the user writing on the form, wherein the contextualized data element is distributed to the EMR/EHR application via a publish/subscribe protocol in which the EMR/EHR application subscribes to a specific contextualized data element by identifying the label associated with the contextualized data element. 20. The method of claim 12 , wherein the labels include taxonomic information. | 0.891365 |
8,370,740 | 11 | 15 | 11. A computer system comprising: a processor; and system memory storing executable instructions which, when executed by the processor, perform the following functions: creating an enhanced native control object by wrapping a native control object with a wrapper that defines one or more extension methods, the one or more extension methods being configured to be called by an application level add-in to add and remove document level controls from the native control object for documents opened or created in the application, the application level add-in being configured to act under a first root object corresponding to an add-in class; determining, by the application level add-in, that a particular document has been opened or created in the application; determining, by the application level add-in, a class of documents for which document level controls should be added; determining, by the application level add-in, that the particular document that has been opened or created is in the class of documents; based on determining that the particular document is in the class of documents, the computer system determining, by the application level add-in, and that a particular document level control is to be added to the particular document; adding, by the application level add-in, the particular document level control to the particular document by calling the one or more extension methods of the enhanced native control object, the document level control being configured to act under a different second root object representing the particular document; monitoring, by the application level add-in, the particular document to determine when the particular document is closed; and upon the application level add-in determining that the particular document has been closed, the application level add-in removing the particular document level control to prevent the particular document level control from further executing by calling the one or more extension methods of the enhanced native control object. | 11. A computer system comprising: a processor; and system memory storing executable instructions which, when executed by the processor, perform the following functions: creating an enhanced native control object by wrapping a native control object with a wrapper that defines one or more extension methods, the one or more extension methods being configured to be called by an application level add-in to add and remove document level controls from the native control object for documents opened or created in the application, the application level add-in being configured to act under a first root object corresponding to an add-in class; determining, by the application level add-in, that a particular document has been opened or created in the application; determining, by the application level add-in, a class of documents for which document level controls should be added; determining, by the application level add-in, that the particular document that has been opened or created is in the class of documents; based on determining that the particular document is in the class of documents, the computer system determining, by the application level add-in, and that a particular document level control is to be added to the particular document; adding, by the application level add-in, the particular document level control to the particular document by calling the one or more extension methods of the enhanced native control object, the document level control being configured to act under a different second root object representing the particular document; monitoring, by the application level add-in, the particular document to determine when the particular document is closed; and upon the application level add-in determining that the particular document has been closed, the application level add-in removing the particular document level control to prevent the particular document level control from further executing by calling the one or more extension methods of the enhanced native control object. 15. The computer system of claim 11 , wherein determining, from the add-in, a class of documents for which document level controls should be added comprises determining the role of a user opening or creating the particular document. | 0.543307 |
9,787,836 | 5 | 6 | 5. The system of claim 4 , wherein the instructions further cause the processor to determine whether the message content satisfies a particular criteria, and in response to determining that the message content satisfies the particular criteria, transmit the command for generating the audio recording. | 5. The system of claim 4 , wherein the instructions further cause the processor to determine whether the message content satisfies a particular criteria, and in response to determining that the message content satisfies the particular criteria, transmit the command for generating the audio recording. 6. The system of claim 5 , wherein the particular criteria is that the message content is free of malicious data. | 0.5 |
7,712,092 | 8 | 9 | 8. The method of claim 1 further comprising executing instruction sequences of the target binary during translation to implement dynamic binary translation. | 8. The method of claim 1 further comprising executing instruction sequences of the target binary during translation to implement dynamic binary translation. 9. The method of claim 8 further comprising executing the target binary after translating an entire executable to implement static binary translation. | 0.5 |
8,532,333 | 1 | 2 | 1. A computer-implemented method, comprising: recognizing one or more text strings in each of a plurality of geo-tagged images, each of the plurality of geo-tagged images indicating a geographical location of its corresponding geo-tagged image; identifying one or more establishments near the geographical locations of the plurality of geo-tagged images; for each specific establishment of the one or more establishments: (i) extracting one or more phrases from information associated with the specific establishment; (ii) comparing the one or more text strings recognized in the plurality of geo-tagged images with the extracted one or more phrases to derive one or more matches; (iii) determining one or more image-establishment pairs based on the one or more matches, each image-establishment pair pairing a specific geo-tagged image with the specific establishment; and (iv) selecting a representative geo-tagged image for the specific establishment from among the plurality of geo-tagged images based on the one or more image-establishment pairs. | 1. A computer-implemented method, comprising: recognizing one or more text strings in each of a plurality of geo-tagged images, each of the plurality of geo-tagged images indicating a geographical location of its corresponding geo-tagged image; identifying one or more establishments near the geographical locations of the plurality of geo-tagged images; for each specific establishment of the one or more establishments: (i) extracting one or more phrases from information associated with the specific establishment; (ii) comparing the one or more text strings recognized in the plurality of geo-tagged images with the extracted one or more phrases to derive one or more matches; (iii) determining one or more image-establishment pairs based on the one or more matches, each image-establishment pair pairing a specific geo-tagged image with the specific establishment; and (iv) selecting a representative geo-tagged image for the specific establishment from among the plurality of geo-tagged images based on the one or more image-establishment pairs. 2. The method of claim 1 , wherein determining one or more image-establishment pairs comprises scoring each of the one or more image-establishment pairs to obtain a corresponding score, wherein selecting the representative geo-tagged image for the specific establishment is further based on the corresponding scores. | 0.572973 |
9,472,182 | 15 | 17 | 15. A tangible computer storage medium containing computer executable instructions which, when executed by a computer, perform a method of generating a multi-voice font for rendering text as computer-generated speech, the method comprising the acts of: obtaining the text to be rendered as the computer-generated speech; loading the source voice fonts; predicting duration values, voiced/unvoiced probability values, f0 values, and spectral trajectory values for the text for each source voice font using at least one characteristic prediction model associated with each source voice font; assigning a duration weight, a f0 weight, a spectrum weight to each source voice font; merging the duration values predicted with each source voice font with the duration weight to produce interpolated duration values, the duration weight for each source voice font representing the percentage that the source voice font contributes to the interpolated duration values; merging the f0 values predicted with each source voice font with the f0 weight given to that source voice font to produce interpolated f0 values, the f0 weight for each source voice font representing the percentage that the source voice font contributes to the interpolated f0 values; merging the voiced/unvoiced decision values and the spectral trajectory values predicted with each source voice font with the spectrum weight given to that source voice font to produce interpolated voiced/unvoiced probability values and interpolated spectral trajectory values, the spectrum weight for each source voice font representing the percentage that the source voice font contributes to the interpolated voiced/unvoiced probability values and interpolated spectral trajectory values; and rendering the text as computer-generated speech having the interpolated duration values, interpolated f0 values, interpolated voiced/unvoiced probability values, and interpolated spectral trajectory values. | 15. A tangible computer storage medium containing computer executable instructions which, when executed by a computer, perform a method of generating a multi-voice font for rendering text as computer-generated speech, the method comprising the acts of: obtaining the text to be rendered as the computer-generated speech; loading the source voice fonts; predicting duration values, voiced/unvoiced probability values, f0 values, and spectral trajectory values for the text for each source voice font using at least one characteristic prediction model associated with each source voice font; assigning a duration weight, a f0 weight, a spectrum weight to each source voice font; merging the duration values predicted with each source voice font with the duration weight to produce interpolated duration values, the duration weight for each source voice font representing the percentage that the source voice font contributes to the interpolated duration values; merging the f0 values predicted with each source voice font with the f0 weight given to that source voice font to produce interpolated f0 values, the f0 weight for each source voice font representing the percentage that the source voice font contributes to the interpolated f0 values; merging the voiced/unvoiced decision values and the spectral trajectory values predicted with each source voice font with the spectrum weight given to that source voice font to produce interpolated voiced/unvoiced probability values and interpolated spectral trajectory values, the spectrum weight for each source voice font representing the percentage that the source voice font contributes to the interpolated voiced/unvoiced probability values and interpolated spectral trajectory values; and rendering the text as computer-generated speech having the interpolated duration values, interpolated f0 values, interpolated voiced/unvoiced probability values, and interpolated spectral trajectory values. 17. The tangible computer storage medium of claim 15 wherein the method performed by the computer executable instructions further comprises the act of parsing the text into a sequence of phonemes with each phoneme dividable into frames and: the act of merging the duration values predicted with each source voice font with the duration weight given to that source voice font to produce interpolated duration values further comprises the act of, for each phoneme, summing the products of the duration value predicted by each source voice font and the corresponding duration weight; the act of merging the f0 values predicted with each source voice font with the f0 weight given to that source voice font to produce interpolated f0 values further comprises the acts of: for each frame, summing the products of the f0 values predicted by each source voice font and the corresponding f0 weight; and normalizing the interpolated f0 values; and the act of merging the voiced/unvoiced probability values and the spectral trajectory values predicted with each source voice font with the spectrum weight given to that source voice font to produce interpolated voiced/unvoiced probability values and interpolated spectral trajectory values further comprises the acts of: for each phoneme, summing the products of the voiced/unvoiced probability values predicted by each source voice font and the corresponding spectrum weight; determining whether each phoneme is voiced using the interpolated voiced/unvoiced probability value for each phoneme; and for each frame, summing the products of the spectral trajectory values predicted by each source voice font and the corresponding spectrum weight. | 0.5 |
7,757,225 | 1 | 5 | 1. A method comprising: examining, by a computing device, a program code that includes at least procedural programming language statements; detecting, by the computing device, a first non-procedural programming language statement in the program code, the first non-procedural programming language statement having multiple implementations one of which is selected based on a presence or absence of a second non-procedural language statement in the program code; in response to detecting the first non-procedural programming language statement, determining, by the computing device, if the program code includes the second non-procedural programming language statement defining a context specific implementation of the first non-procedural programming language statement; in response to determining that the program code includes the second non-procedural programming language statement, introducing into the program code, by the computing device, a declaration for an undefined variable to flag the presence of the second non-procedural programming language statement, the undefined variable being declared using an extern keyword; inserting, by the computing device, function calls compliant with an Open Database Connectivity (ODBC) standard; compiling, by the computing device, the program code; at link-time, selecting, by the computing device, one of a plurality of alternative object modules based on whether the compiled program code includes the undefined variable, a first of the alternative object modules providing a definition of the undefined variable and the context specific implementation of the first non-procedural programming language statement being selected if the compiled program code includes the undefined variable, and a second of the alternative object modules providing a second implementation of the first non-procedural programming language statement being selected if the compiled program code does not include the undefined variable; and building, by the computing device, an executable program corresponding to the program code by linking-in the selected alternative object module. | 1. A method comprising: examining, by a computing device, a program code that includes at least procedural programming language statements; detecting, by the computing device, a first non-procedural programming language statement in the program code, the first non-procedural programming language statement having multiple implementations one of which is selected based on a presence or absence of a second non-procedural language statement in the program code; in response to detecting the first non-procedural programming language statement, determining, by the computing device, if the program code includes the second non-procedural programming language statement defining a context specific implementation of the first non-procedural programming language statement; in response to determining that the program code includes the second non-procedural programming language statement, introducing into the program code, by the computing device, a declaration for an undefined variable to flag the presence of the second non-procedural programming language statement, the undefined variable being declared using an extern keyword; inserting, by the computing device, function calls compliant with an Open Database Connectivity (ODBC) standard; compiling, by the computing device, the program code; at link-time, selecting, by the computing device, one of a plurality of alternative object modules based on whether the compiled program code includes the undefined variable, a first of the alternative object modules providing a definition of the undefined variable and the context specific implementation of the first non-procedural programming language statement being selected if the compiled program code includes the undefined variable, and a second of the alternative object modules providing a second implementation of the first non-procedural programming language statement being selected if the compiled program code does not include the undefined variable; and building, by the computing device, an executable program corresponding to the program code by linking-in the selected alternative object module. 5. The method of claim 1 , wherein the second alternative object module includes a second undefined variable. | 0.665644 |
9,384,735 | 8 | 11 | 8. A non-transitory computer-readable medium having stored thereon a computer-executable component configured to execute in one or more processors of a computing device, the computer-executable component being further configured to: receive first audio data comprising first speech; transcribe the first speech using a first language model to generate a first transcription; provide the first transcription to a first client device; receive feedback on the first transcription from the first client device; based at least in part on the feedback on the first transcription, update the first language model; select a second language model; and based at least in part on the feedback on the transcription, update the second language model, wherein the second language model is not used to generate the first transcription. | 8. A non-transitory computer-readable medium having stored thereon a computer-executable component configured to execute in one or more processors of a computing device, the computer-executable component being further configured to: receive first audio data comprising first speech; transcribe the first speech using a first language model to generate a first transcription; provide the first transcription to a first client device; receive feedback on the first transcription from the first client device; based at least in part on the feedback on the first transcription, update the first language model; select a second language model; and based at least in part on the feedback on the transcription, update the second language model, wherein the second language model is not used to generate the first transcription. 11. The non-transitory computer-readable medium of claim 8 , wherein the first audio data comprising first speech is received from the first client device. | 0.729965 |
10,140,978 | 1 | 17 | 1. A method comprising: obtaining, by one or more computers, acoustic data for an utterance; determining, by the one or more computers, speech recognition candidates for the utterance based on the acoustic data; obtaining, by the one or more computers, a ranking of the speech recognition candidates determined by a speech recognizer; selecting, by the one or more computers, a transcription for the acoustic data from among the speech recognition candidates; determining, by the one or more computers, feature scores from the ranking of the speech recognition candidates; generating, by the one or more computers, a classifier output for each of at least some of the speech recognition candidates, wherein each of the classifier outputs is an output that a trained machine learning classifier provided in response to receiving at least one of the feature scores as input; selecting, by the one or more computers, a subset of the speech recognition candidates based on the classifier outputs of the trained machine learning classifier; and providing, by the one or more computers and for display at a client device, data indicating (i) the transcription for the utterance and (ii) the subset of the speech recognition candidates as a set of alternative transcriptions for the utterance, wherein the one or more computers are configured to provide different quantities of alternative transcriptions for different utterances. | 1. A method comprising: obtaining, by one or more computers, acoustic data for an utterance; determining, by the one or more computers, speech recognition candidates for the utterance based on the acoustic data; obtaining, by the one or more computers, a ranking of the speech recognition candidates determined by a speech recognizer; selecting, by the one or more computers, a transcription for the acoustic data from among the speech recognition candidates; determining, by the one or more computers, feature scores from the ranking of the speech recognition candidates; generating, by the one or more computers, a classifier output for each of at least some of the speech recognition candidates, wherein each of the classifier outputs is an output that a trained machine learning classifier provided in response to receiving at least one of the feature scores as input; selecting, by the one or more computers, a subset of the speech recognition candidates based on the classifier outputs of the trained machine learning classifier; and providing, by the one or more computers and for display at a client device, data indicating (i) the transcription for the utterance and (ii) the subset of the speech recognition candidates as a set of alternative transcriptions for the utterance, wherein the one or more computers are configured to provide different quantities of alternative transcriptions for different utterances. 17. The method of claim 1 , wherein generating the score for each of at least some of the speech recognition candidates comprises providing, as input to the trained machine learning classifier, a value indicating a phone edit distance between alternate and a particular word of the transcription of the utterance. | 0.823761 |
9,805,292 | 9 | 10 | 9. The system of claim 8 , wherein receiving the data identifying the selected image acquisition template is based on at least one of a selection by a user and automatic selection of the selected image acquisition template by the mobile device based on identifying a general category of the particular type of the object. | 9. The system of claim 8 , wherein receiving the data identifying the selected image acquisition template is based on at least one of a selection by a user and automatic selection of the selected image acquisition template by the mobile device based on identifying a general category of the particular type of the object. 10. The system of claim 9 , wherein the operations further comprise: receiving, at the mobile device, a selection of a specific category of the particular type of the object in response to the client device identifying the general category of the particular type of the object. | 0.5 |
9,002,772 | 1 | 2 | 1. A system for scalable, rule-based processing, the system comprising: a processor; and storage coupled to the processor, wherein the storage stores computer program instructions, and wherein the computer program instructions are executed by the processor to: construct a plurality of automatons corresponding to a plurality of trigger rules and a plurality of word lists that are employed by the trigger rules; evaluate any of the plurality of trigger rules with respect to an input document by: selecting any of the automatons to evaluate a given one of the plurality of trigger rules, parsing the input document using the selected automatons, determining whether conditions of the given trigger rule are met, identifying any actions that are associated with the given trigger rule, and display in a rule tracing any of the plurality of trigger rules that are evaluated, together with indicia for different portions of the displayed trigger rules indicating an evaluation result of each of the different portions. | 1. A system for scalable, rule-based processing, the system comprising: a processor; and storage coupled to the processor, wherein the storage stores computer program instructions, and wherein the computer program instructions are executed by the processor to: construct a plurality of automatons corresponding to a plurality of trigger rules and a plurality of word lists that are employed by the trigger rules; evaluate any of the plurality of trigger rules with respect to an input document by: selecting any of the automatons to evaluate a given one of the plurality of trigger rules, parsing the input document using the selected automatons, determining whether conditions of the given trigger rule are met, identifying any actions that are associated with the given trigger rule, and display in a rule tracing any of the plurality of trigger rules that are evaluated, together with indicia for different portions of the displayed trigger rules indicating an evaluation result of each of the different portions. 2. The system of claim 1 where the input document includes a set of textual content fields, where each of the textual content fields are either single-valued or multi-valued. | 0.876596 |
8,756,278 | 1 | 2 | 1. A computer-implemented method for communication in a social networking system, the method comprising: receiving a content item from an author, the author being a user of a social networking system; receiving a definition of a first audience from the author, the first audience comprising one or more users of the social networking system; receiving a content item tag from the author, the content item tag indicating an association between the content item and a tagged user, wherein the tagged user is a user of the social networking system different than the author; receiving a definition of a second audience from the tagged user, the second audience comprising one or more users of the social networking system, wherein at least one user in the second audience is not in the first audience; determining if a viewing user is a member of a union of the first audience and the second audience; and sending the content item and the content item tag for display to the viewing user if the viewing user is a member of a union of the first audience and the second audience. | 1. A computer-implemented method for communication in a social networking system, the method comprising: receiving a content item from an author, the author being a user of a social networking system; receiving a definition of a first audience from the author, the first audience comprising one or more users of the social networking system; receiving a content item tag from the author, the content item tag indicating an association between the content item and a tagged user, wherein the tagged user is a user of the social networking system different than the author; receiving a definition of a second audience from the tagged user, the second audience comprising one or more users of the social networking system, wherein at least one user in the second audience is not in the first audience; determining if a viewing user is a member of a union of the first audience and the second audience; and sending the content item and the content item tag for display to the viewing user if the viewing user is a member of a union of the first audience and the second audience. 2. The method of claim 1 , wherein the content item comprises an image or video uploaded to the social networking system by the author. | 0.893196 |
6,023,701 | 9 | 10 | 9. A storage medium including machine readable indicia, said storage medium being selectively coupled to a reading device, said reading device being selectively coupled to processing circuitry within a computer system, said reading device being selectively operable to read said machine readable indicia and provide program signals representative thereof, said program signals being effective to cause said computer system to selectively provide a listing of hyperlinks assembled from a designated network page, said program signals being selectively operable to effect an accomplishment of the steps of: presenting a selection screen to the user whereby the user may make a selection to provide a listing of hyperlinks included on at least one designated network page; displaying said listing of hyperlinks to the user; enabling the user to select at least one of said displayed hyperlinks for activation; enabling a user to select a level value representative of a level of linked pages to be perused in providing said listing of hyperlinks; and retrieving a network page associated with a selected one of said displayed hyperlinks. | 9. A storage medium including machine readable indicia, said storage medium being selectively coupled to a reading device, said reading device being selectively coupled to processing circuitry within a computer system, said reading device being selectively operable to read said machine readable indicia and provide program signals representative thereof, said program signals being effective to cause said computer system to selectively provide a listing of hyperlinks assembled from a designated network page, said program signals being selectively operable to effect an accomplishment of the steps of: presenting a selection screen to the user whereby the user may make a selection to provide a listing of hyperlinks included on at least one designated network page; displaying said listing of hyperlinks to the user; enabling the user to select at least one of said displayed hyperlinks for activation; enabling a user to select a level value representative of a level of linked pages to be perused in providing said listing of hyperlinks; and retrieving a network page associated with a selected one of said displayed hyperlinks. 10. The storage medium as set forth in claim 9 wherein said steps further include: enabling a user to change said selected level value to a new value after said display of said hyperlinks; assembling a new set of hyperlinks in accordance with said changed level value; and displaying a new listing of hyperlinks to the user based upon a changed level value. | 0.5 |
8,774,705 | 1 | 3 | 1. A learning support system which includes a computer including a processor and a memory, comprising: a question database in which a question to be asked of an answerer is stored; a learning history database in which a result of giving an answer to the question asked of the answerer, is recorded; a question generator for generating a question relating to a word changed in accordance with perturbation that changes a word, and stores the question in the question database; a question presentation module for asking the answerer the question extracted from the question database; an answer acquisition module for receiving the answer to the asked question; a scoring module for determining correctness or wrongness of the answer, by referring to the question database; and an achievement degree estimator for storing a result of the determination in the learning history database, wherein: the achievement degree estimator compares a number of types of perturbation within the questions to which a correct answer has been given with a predetermined learning threshold value; and the question generator generates a word changed in accordance with the perturbation of a different type in a case where the number of types of perturbation within the questions to which the correct answer has been given is smaller than the predetermined learning threshold value, and sets a question relating to the generated word as a candidate for the question. | 1. A learning support system which includes a computer including a processor and a memory, comprising: a question database in which a question to be asked of an answerer is stored; a learning history database in which a result of giving an answer to the question asked of the answerer, is recorded; a question generator for generating a question relating to a word changed in accordance with perturbation that changes a word, and stores the question in the question database; a question presentation module for asking the answerer the question extracted from the question database; an answer acquisition module for receiving the answer to the asked question; a scoring module for determining correctness or wrongness of the answer, by referring to the question database; and an achievement degree estimator for storing a result of the determination in the learning history database, wherein: the achievement degree estimator compares a number of types of perturbation within the questions to which a correct answer has been given with a predetermined learning threshold value; and the question generator generates a word changed in accordance with the perturbation of a different type in a case where the number of types of perturbation within the questions to which the correct answer has been given is smaller than the predetermined learning threshold value, and sets a question relating to the generated word as a candidate for the question. 3. The learning support system according to claim 1 , further comprising a perturbation rule database that includes at least any two of the types of perturbation being a synonym, an antonym, and a modification rule, wherein the question generator is configured to, in order to set the question relating to the word changed in accordance with the perturbation of the different type as the candidate for the question: select the word to be the question from the perturbation rule database; select modification of the selected word from the perturbation rule database by a random number; and generate the question relating to the word subjected to the selected modification. | 0.646842 |
9,623,119 | 21 | 28 | 21. A system for processing search results, comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving documents responsive to a query, each document having a respective associated score indicative of the document's relevance to the query, wherein: each respective associated score is based on a plurality of different score factors; each score factor for a given associated score corresponds to a different criterion than each other score factor for the given associated score; and each score factor has a respective trustworthiness score that indicates a respective reliability of values determined based on the score factor; determining, based on the trustworthiness scores for the score factors of a plurality of the associated scores of a plurality of different documents, a value-based distribution of the trustworthiness scores for each of the different score factors of the plurality of associated scores along a dimension based on relative values of the trustworthiness scores, the distribution including trustworthiness scores for multiple score factors for each of the different documents; adjusting the value of at least one score factor of a given associated score by an amount that is determined based on: a relative position of the at least one score factor's respective trustworthiness score in the distribution with respect to other trustworthiness scores in the distribution; and a measure of how widely the trustworthiness scores for each of the different score factors of the plurality of associated scores are distributed in the distribution; adjusting the given associated score based on the adjusted value of the at least one score factor; and ranking the documents to account for the adjusted score. | 21. A system for processing search results, comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving documents responsive to a query, each document having a respective associated score indicative of the document's relevance to the query, wherein: each respective associated score is based on a plurality of different score factors; each score factor for a given associated score corresponds to a different criterion than each other score factor for the given associated score; and each score factor has a respective trustworthiness score that indicates a respective reliability of values determined based on the score factor; determining, based on the trustworthiness scores for the score factors of a plurality of the associated scores of a plurality of different documents, a value-based distribution of the trustworthiness scores for each of the different score factors of the plurality of associated scores along a dimension based on relative values of the trustworthiness scores, the distribution including trustworthiness scores for multiple score factors for each of the different documents; adjusting the value of at least one score factor of a given associated score by an amount that is determined based on: a relative position of the at least one score factor's respective trustworthiness score in the distribution with respect to other trustworthiness scores in the distribution; and a measure of how widely the trustworthiness scores for each of the different score factors of the plurality of associated scores are distributed in the distribution; adjusting the given associated score based on the adjusted value of the at least one score factor; and ranking the documents to account for the adjusted score. 28. The system of claim 21 , wherein the trustworthiness score of a particular score factor is based on a language of the score factor's associated document. | 0.709259 |
7,526,731 | 13 | 20 | 13. A method for designing a customized user interface, comprising: categorizing a user population into at least two groups, describing the categorized groups, and modeling the described groups using qualitative and quantitative models, the categorizing, describing and modeling being based upon Categorize-Describe-Model (CDM) methodology; applying the models to interface design by analyzing screen flow including a prototypical screen flow; and creating the quantitative models based upon the screen flow analysis. | 13. A method for designing a customized user interface, comprising: categorizing a user population into at least two groups, describing the categorized groups, and modeling the described groups using qualitative and quantitative models, the categorizing, describing and modeling being based upon Categorize-Describe-Model (CDM) methodology; applying the models to interface design by analyzing screen flow including a prototypical screen flow; and creating the quantitative models based upon the screen flow analysis. 20. The method of claim 13 , further comprising iteratively testing the design. | 0.808252 |
5,559,898 | 3 | 5 | 3. The method of claim 2, wherein all of the retrieved templates are excluded during the classification process except for a single non-excluded template. | 3. The method of claim 2, wherein all of the retrieved templates are excluded during the classification process except for a single non-excluded template. 5. The method of claim 3, wherein the initial cumulative bound for each correlation calculation (s,t.sub.i) is high and forms an upper bound which progresses downward toward the final bound. | 0.5 |
9,946,765 | 1 | 2 | 1. A method implemented by an information handling system that includes a memory and a processor, the method comprising: ingesting a first set of traditional sources that comprise a plurality of first terms; ingesting a second set of crowd-based sources that comprise a plurality of second terms and a plurality of crowd-based metadata; calculating one or more weightings pertaining to one or more of the first terms based on the crowd-based metadata, wherein the calculating further comprises: matching one or more of the first terms to one or more of the plurality of second terms; and applying a plurality of crowd-based definition rankings corresponding to the matched one or more second terms to a plurality of first definitions corresponding to the matched one or more first terms, wherein the plurality of crowd-based definition rankings correspond to the one or more weightings; receiving a question that includes one or more question terms; and identifying an answer to the question based on one or more of the weightings corresponding to one or more of the first terms that are relevant to one or more of the question terms. | 1. A method implemented by an information handling system that includes a memory and a processor, the method comprising: ingesting a first set of traditional sources that comprise a plurality of first terms; ingesting a second set of crowd-based sources that comprise a plurality of second terms and a plurality of crowd-based metadata; calculating one or more weightings pertaining to one or more of the first terms based on the crowd-based metadata, wherein the calculating further comprises: matching one or more of the first terms to one or more of the plurality of second terms; and applying a plurality of crowd-based definition rankings corresponding to the matched one or more second terms to a plurality of first definitions corresponding to the matched one or more first terms, wherein the plurality of crowd-based definition rankings correspond to the one or more weightings; receiving a question that includes one or more question terms; and identifying an answer to the question based on one or more of the weightings corresponding to one or more of the first terms that are relevant to one or more of the question terms. 2. The method of claim 1 further comprising: creating a crowd enhanced domain dictionary that comprises the one or more first terms, the plurality of first definitions, and the plurality of crowd-based definition rankings. | 0.661585 |
8,930,182 | 12 | 17 | 12. A system for voice transformation comprising: a processor; a voice transformation component for transforming a source speech of a person using transformation parameters, wherein the transforming comprises modifying the source speech to sound as if the source speech were spoken by a different person; and a steganography component for encoding information on the transformation parameters in an output speech using steganography; wherein the source speech can be reconstructed using the output speech and the information on the transformation parameters. | 12. A system for voice transformation comprising: a processor; a voice transformation component for transforming a source speech of a person using transformation parameters, wherein the transforming comprises modifying the source speech to sound as if the source speech were spoken by a different person; and a steganography component for encoding information on the transformation parameters in an output speech using steganography; wherein the source speech can be reconstructed using the output speech and the information on the transformation parameters. 17. The system as claimed in claim 12 , including: a compiling component for compiling the information on the transformation parameters by training inverse parameters to convert a transformed speech into a source speech. | 0.803922 |
9,623,119 | 1 | 6 | 1. A computer-implemented method, comprising: receiving documents responsive to a query, each document having a respective associated score indicative of the document's relevance to the query, wherein: each respective associated score is based on a respective value for each of a plurality of different score factors; each score factor for a given associated score corresponds to a different criterion than each other score factor for the given associated score; and each score factor has a respective trustworthiness score that indicates a respective reliability of values determined based on the score factor; determining, based on the trustworthiness scores for the score factors of a plurality of the associated scores of a plurality of different documents, a value-based distribution of the trustworthiness scores for each of the different score factors of the plurality of associated scores along a dimension based on relative values of the trustworthiness scores, the distribution including trustworthiness scores for multiple score factors for each of the different documents; adjusting the value of at least one score factor of a given associated score by an amount that is determined based on: a relative position of the at least one score factor's respective trustworthiness score in the distribution with respect to other trustworthiness scores in the distribution; and a measure of how widely the trustworthiness scores for each of the different score factors of the plurality of associated scores are distributed in the distribution; adjusting the given associated score based on the adjusted value of the at least one score factor; and ranking the documents to account for the adjusted associated score. | 1. A computer-implemented method, comprising: receiving documents responsive to a query, each document having a respective associated score indicative of the document's relevance to the query, wherein: each respective associated score is based on a respective value for each of a plurality of different score factors; each score factor for a given associated score corresponds to a different criterion than each other score factor for the given associated score; and each score factor has a respective trustworthiness score that indicates a respective reliability of values determined based on the score factor; determining, based on the trustworthiness scores for the score factors of a plurality of the associated scores of a plurality of different documents, a value-based distribution of the trustworthiness scores for each of the different score factors of the plurality of associated scores along a dimension based on relative values of the trustworthiness scores, the distribution including trustworthiness scores for multiple score factors for each of the different documents; adjusting the value of at least one score factor of a given associated score by an amount that is determined based on: a relative position of the at least one score factor's respective trustworthiness score in the distribution with respect to other trustworthiness scores in the distribution; and a measure of how widely the trustworthiness scores for each of the different score factors of the plurality of associated scores are distributed in the distribution; adjusting the given associated score based on the adjusted value of the at least one score factor; and ranking the documents to account for the adjusted associated score. 6. The method of claim 1 , further comprising providing the ranked documents to a client. | 0.917593 |
8,560,456 | 1 | 2 | 1. A computer-implemented method for exchanging private data, comprising the steps of: registering a plurality of data providers and a plurality of data buyers; building, by the computer, a searchable index for each of the data buyers based on at least one index of private data for sale provided from one or more of the data providers; for each searchable index of each of the data buyers, encrypting and decrypting the searchable index by a public key unique to a respective one of the data buyers, when building the searchable index; receiving a query that identifies a data buyer of the plurality of data buyers and defines a purchase request for the private data; retrieving the searchable index associated with the data buyer; determining, by the computer, whether at least one of the data providers maintains the private data requested by the data buyer by comparing the purchase request to the searchable index associated with the data buyer; encrypting and decrypting the searchable index associated with the data buyer by a private key unique to the data buyer, when determining whether at least one of the data providers maintains the private data requested; and in response to determining that at least one of the data providers maintains the private data requested, arranging an exchange of the private data requested with the data buyer. | 1. A computer-implemented method for exchanging private data, comprising the steps of: registering a plurality of data providers and a plurality of data buyers; building, by the computer, a searchable index for each of the data buyers based on at least one index of private data for sale provided from one or more of the data providers; for each searchable index of each of the data buyers, encrypting and decrypting the searchable index by a public key unique to a respective one of the data buyers, when building the searchable index; receiving a query that identifies a data buyer of the plurality of data buyers and defines a purchase request for the private data; retrieving the searchable index associated with the data buyer; determining, by the computer, whether at least one of the data providers maintains the private data requested by the data buyer by comparing the purchase request to the searchable index associated with the data buyer; encrypting and decrypting the searchable index associated with the data buyer by a private key unique to the data buyer, when determining whether at least one of the data providers maintains the private data requested; and in response to determining that at least one of the data providers maintains the private data requested, arranging an exchange of the private data requested with the data buyer. 2. The computer-implemented method of claim 1 , wherein the private data requested comprises at least one of an address, a telephone number, and a social security number. | 0.882759 |
9,390,630 | 7 | 14 | 7. A method, comprising: detecting a change in position of a body member of a performer relative to a performance element of a performance object with which an event is to be performed; generating a signal dependent on the detected change in position of the body member; and recording the signal so that a first sensory cue can be determined to indicate the change in position of the body member relative to the performance element of the performance object during a learning session; determining the first sensory cue dependent on the recorded signal; and applying the first sensory cue to indicate to a user learning session the learning session the change in position of the body member of the performer during the performance of the event,the first sensory cue being effective for stimulating a first processing center of the user; wherein the first sensory cue is one of a haptic, auditory and visual sensory cue effective for stimulating a first processing center of a brain of the user; and generating a visual sensory cue capable of being displayed to the user on a video display device, the visual sensory cue providing a virtual visual indication to the user of the change in position of the body member of the performer during the performance, the visual sensory cue being effective for stimulating the visual processing center of the brain of the user, the visual sensory cue being synchronized with the first sensory cue so that the change in position of body member of the performer is virtually visually indicated in synchronization with the first sensory cue and so that the visual processing center is stimulated with a visual sensory cue in synchronization, stimulating the first processing center, wherein thesnthat 5 iDimuiatio of the first processing center and the visual processing center is effective for teaching the user to perform a version of the event. | 7. A method, comprising: detecting a change in position of a body member of a performer relative to a performance element of a performance object with which an event is to be performed; generating a signal dependent on the detected change in position of the body member; and recording the signal so that a first sensory cue can be determined to indicate the change in position of the body member relative to the performance element of the performance object during a learning session; determining the first sensory cue dependent on the recorded signal; and applying the first sensory cue to indicate to a user learning session the learning session the change in position of the body member of the performer during the performance of the event,the first sensory cue being effective for stimulating a first processing center of the user; wherein the first sensory cue is one of a haptic, auditory and visual sensory cue effective for stimulating a first processing center of a brain of the user; and generating a visual sensory cue capable of being displayed to the user on a video display device, the visual sensory cue providing a virtual visual indication to the user of the change in position of the body member of the performer during the performance, the visual sensory cue being effective for stimulating the visual processing center of the brain of the user, the visual sensory cue being synchronized with the first sensory cue so that the change in position of body member of the performer is virtually visually indicated in synchronization with the first sensory cue and so that the visual processing center is stimulated with a visual sensory cue in synchronization, stimulating the first processing center, wherein thesnthat 5 iDimuiatio of the first processing center and the visual processing center is effective for teaching the user to perform a version of the event. 14. A method according to claim 7 : further comprising detecting electrical impulses of the user during the learning session. | 0.899679 |
10,026,506 | 15 | 16 | 15. The server device of claim 7 , wherein the one or more memory sources further store a plurality of past events, each of the plurality of past events including a plurality of input attributes and a quantifiable outcome; and the controller further configured to: train a neural network model (NNM) to generate the trained model, wherein the training of the NNM includes: performing pre-processing on the plurality of input attributes for each of the plurality of past events to generate a plurality of input data sets; dividing the plurality of past events into a first set of training data and a second set of validation data; iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the training data; and validating the NNM based upon the second set of validation data. | 15. The server device of claim 7 , wherein the one or more memory sources further store a plurality of past events, each of the plurality of past events including a plurality of input attributes and a quantifiable outcome; and the controller further configured to: train a neural network model (NNM) to generate the trained model, wherein the training of the NNM includes: performing pre-processing on the plurality of input attributes for each of the plurality of past events to generate a plurality of input data sets; dividing the plurality of past events into a first set of training data and a second set of validation data; iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the training data; and validating the NNM based upon the second set of validation data. 16. The server device of claim 15 , wherein: the NNM includes an input layer, output layer, and a plurality of hidden layers with a plurality of hidden neurons; and each of the plurality of hidden neurons includes an activation function, the activation function is one of: (1) the sigmoid function f(x)=1/(1+e −x ); (2) the hyperbolic tangent function f(x)=(e 2x −1)/(e 2x +1); and (3) a linear function f(x)=x, wherein x is a summation of input neurons biased by the synoptic weights. | 0.601806 |
9,141,607 | 1 | 6 | 1. A computer-implemented method comprising: performing a first optical character recognition process with an optical character recognition engine on one or more pages of a document to generate a first output response for each of multiple writing systems that each comprise a character set that is associated with one or more natural languages, wherein the first optical character recognition process uses a first configuration of the optical character recognition engine, and wherein, the first configuration of the optical character recognition engine configures the optical character recognition engine to recognize, for each of the writing systems, a limited subset of characters from the character set of the writing system; using a trained classifier to identify, from among the multiple writing systems, a dominant writing system of the document based on the first output responses generated by the first optical character recognition process; after identifying the dominant writing system of the document and before selecting a dominant natural language of the document, reconfiguring the optical character recognition engine from the first configuration to a second configuration that is different from the first configuration, wherein the second configuration of the optical character recognition engine configures the optical character recognition engine to recognize the full character set that is associated with the one or more natural languages associated with the dominant writing system of the document; performing a second optical character recognition process with the reconfigured optical character recognition engine on one or more pages of the document to generate a second output response, wherein the second optical character recognition process uses the second configuration of the optical character recognition engine; and selecting the dominant natural language from the one or more natural languages associated with the dominant writing system by applying one or more statistical language models to the second output response. | 1. A computer-implemented method comprising: performing a first optical character recognition process with an optical character recognition engine on one or more pages of a document to generate a first output response for each of multiple writing systems that each comprise a character set that is associated with one or more natural languages, wherein the first optical character recognition process uses a first configuration of the optical character recognition engine, and wherein, the first configuration of the optical character recognition engine configures the optical character recognition engine to recognize, for each of the writing systems, a limited subset of characters from the character set of the writing system; using a trained classifier to identify, from among the multiple writing systems, a dominant writing system of the document based on the first output responses generated by the first optical character recognition process; after identifying the dominant writing system of the document and before selecting a dominant natural language of the document, reconfiguring the optical character recognition engine from the first configuration to a second configuration that is different from the first configuration, wherein the second configuration of the optical character recognition engine configures the optical character recognition engine to recognize the full character set that is associated with the one or more natural languages associated with the dominant writing system of the document; performing a second optical character recognition process with the reconfigured optical character recognition engine on one or more pages of the document to generate a second output response, wherein the second optical character recognition process uses the second configuration of the optical character recognition engine; and selecting the dominant natural language from the one or more natural languages associated with the dominant writing system by applying one or more statistical language models to the second output response. 6. The method of claim 1 , wherein performing the first optical character recognition process with the optical character recognition engine on one or more pages of the document to generate the multiple first output responses, and performing the second optical character recognition process with the reconfigured optical character recognition engine on one or more pages of the document to generate the second output response comprises: performing the first optical character recognition process and the second optical character recognition process on the same one or more pages of the document. | 0.59919 |
7,716,190 | 15 | 16 | 15. The method as in claim 10 , further comprising: (iv) carrying out at least one post-processing task on said data set, generated at step (iii), comprising data elements structured according to said second predefined hierarchical metadata structure. | 15. The method as in claim 10 , further comprising: (iv) carrying out at least one post-processing task on said data set, generated at step (iii), comprising data elements structured according to said second predefined hierarchical metadata structure. 16. The method as in claim 15 , wherein said at least one post-processing task comprises performing a semantic translation of terms contained in said data set generated at step (iii). | 0.5 |
8,351,075 | 1 | 21 | 1. A print mediator comprising: an interface module which serves as an interface for a document to be submitted for printing by an associated printing infrastructure, the interface module communicating with the associated printing infrastructure for acquiring color rendering information for a print queue of the printing infrastructure which is predicted to be used for printing the document, the print queue including at least one printing device, the acquired color rendering information including information acquired from the at least one printing device; a first reviewing application capable of communicating with the interface module for receiving the acquired color rendering information, the first reviewing application generating a visual representation of a document to be submitted for printing based on the color rendering information communicated by the interface module, the first reviewing application detecting problems relating to color rendering by the printing infrastructure and proposing corrections for the problems to a first user, the first reviewing application being configured for displaying the visual representation on a first display and for receiving, as input, the first user's annotations to the document and for receiving the first user's decision on the proposed corrections, the first user's annotations including a textual description of at least one of a problem observed by the user and a required correction; and at least one of the first reviewing application and a second reviewing application capable of communicating with the interface module for enabling a second user to review the annotated document on a second display after it has been submitted to the interface module, including reviewing the first user's annotations. | 1. A print mediator comprising: an interface module which serves as an interface for a document to be submitted for printing by an associated printing infrastructure, the interface module communicating with the associated printing infrastructure for acquiring color rendering information for a print queue of the printing infrastructure which is predicted to be used for printing the document, the print queue including at least one printing device, the acquired color rendering information including information acquired from the at least one printing device; a first reviewing application capable of communicating with the interface module for receiving the acquired color rendering information, the first reviewing application generating a visual representation of a document to be submitted for printing based on the color rendering information communicated by the interface module, the first reviewing application detecting problems relating to color rendering by the printing infrastructure and proposing corrections for the problems to a first user, the first reviewing application being configured for displaying the visual representation on a first display and for receiving, as input, the first user's annotations to the document and for receiving the first user's decision on the proposed corrections, the first user's annotations including a textual description of at least one of a problem observed by the user and a required correction; and at least one of the first reviewing application and a second reviewing application capable of communicating with the interface module for enabling a second user to review the annotated document on a second display after it has been submitted to the interface module, including reviewing the first user's annotations. 21. The print mediator of claim 1 , wherein the first reviewing application provides for the first user to select, as the annotation, a problem category within a predefined taxonomy to enter a textual description. | 0.674312 |
10,062,031 | 1 | 5 | 1. A computer-implementable method for performing cognitive computing operations comprising: receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph, the data enriching performing sentiment analysis, geotagging and entity detection operations on the streams of data from the plurality of data sources, the processing being performed by a cognitive inference and learning system, the cognitive inference and learning system executing on a hardware processor of an information processing system and interacting with the plurality of data sources, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a cognitive engine, the cognitive engine processing the streams of data from the plurality of data sources; incorporating enriched data resulting from the performing of the data enriching into the cognitive graph as nodes within the cognitive graph; defining a cognitive persona within the cognitive graph, the cognitive persona corresponding to an archetype user model, the defining comprising associating attributes with respective nodes of a set of nodes in the cognitive graph; associating a user with the cognitive persona; and, defining a cognitive profile within the cognitive graph, the cognitive profile comprising an instance of the cognitive persona that references personal data associated with the user, the personal data associated with the user being stored as at least one node within the cognitive graph; associating the user with the cognitive profile; and, performing a cognitive computing operation based upon the cognitive profile associated with the user, the cognitive computing operation comprising at least one of performing a spatial navigation operation, a machine vision operation and a pattern recognition operation on at least some of the streams of data from the plurality of data sources. | 1. A computer-implementable method for performing cognitive computing operations comprising: receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph, the data enriching performing sentiment analysis, geotagging and entity detection operations on the streams of data from the plurality of data sources, the processing being performed by a cognitive inference and learning system, the cognitive inference and learning system executing on a hardware processor of an information processing system and interacting with the plurality of data sources, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a cognitive engine, the cognitive engine processing the streams of data from the plurality of data sources; incorporating enriched data resulting from the performing of the data enriching into the cognitive graph as nodes within the cognitive graph; defining a cognitive persona within the cognitive graph, the cognitive persona corresponding to an archetype user model, the defining comprising associating attributes with respective nodes of a set of nodes in the cognitive graph; associating a user with the cognitive persona; and, defining a cognitive profile within the cognitive graph, the cognitive profile comprising an instance of the cognitive persona that references personal data associated with the user, the personal data associated with the user being stored as at least one node within the cognitive graph; associating the user with the cognitive profile; and, performing a cognitive computing operation based upon the cognitive profile associated with the user, the cognitive computing operation comprising at least one of performing a spatial navigation operation, a machine vision operation and a pattern recognition operation on at least some of the streams of data from the plurality of data sources. 5. The method of claim 1 , wherein: the cognitive profile represents a plurality of attributes associated with the user; and each of the plurality of attributes corresponds to a node within the set of nodes in the cognitive graph. | 0.5 |
7,599,924 | 7 | 8 | 7. A computer-readable storage medium containing a program which, when executed by a processor, performs a process of creating queries querying physical data logically represented by a data abstraction model, the process comprising: receiving an abstract query including logical fields against physical data in a database, the logical fields contained in the abstract query defined by the data abstraction model; determining that the data abstraction model includes logical links associated with the logical fields contained in the abstract query, wherein the logical links define relationships between the associated logical fields and other logical fields; and then: retrieving the associated logical links; and transforming the abstract query into an executable query capable of being executed formatted for execution against the physical data, wherein the transforming is done using the data abstraction model and the retrieved associated logical links. | 7. A computer-readable storage medium containing a program which, when executed by a processor, performs a process of creating queries querying physical data logically represented by a data abstraction model, the process comprising: receiving an abstract query including logical fields against physical data in a database, the logical fields contained in the abstract query defined by the data abstraction model; determining that the data abstraction model includes logical links associated with the logical fields contained in the abstract query, wherein the logical links define relationships between the associated logical fields and other logical fields; and then: retrieving the associated logical links; and transforming the abstract query into an executable query capable of being executed formatted for execution against the physical data, wherein the transforming is done using the data abstraction model and the retrieved associated logical links. 8. The computer-readable medium of claim 7 , wherein determining whether the data abstraction model includes the associated logical links comprises: determining, for each result field of the abstract query, whether a corresponding logical field specification includes a logical link. | 0.503509 |
4,697,209 | 1 | 10 | 1. A method of identifying a signal received by an audience member's television receiver for determining audience ratings comprising the steps of: detecting the occurence of a first event in the functional operations of he audience namber's televison receiver; detecting the occurence of a second event after the detected first event in the signal to be identified; extracting a signature from a single frame of the video signal to be identified after the occurrence of the second event; storing the signature and the time of occurence thereof; and comparing the stored signature with reference signatures that occurred at approximately the same time as the stored signature. | 1. A method of identifying a signal received by an audience member's television receiver for determining audience ratings comprising the steps of: detecting the occurence of a first event in the functional operations of he audience namber's televison receiver; detecting the occurence of a second event after the detected first event in the signal to be identified; extracting a signature from a single frame of the video signal to be identified after the occurrence of the second event; storing the signature and the time of occurence thereof; and comparing the stored signature with reference signatures that occurred at approximately the same time as the stored signature. 10. The method recited in claim 1 wherein said signal is a television signal and said step of extracting said signature includes the step of low pass filtering a video signal of the televison signal to provide a video envelope signal, periodically sampling said video envelope signal to provide a series of video samples, and digitizing said video samples to provide said signature. | 0.5 |
9,839,735 | 1 | 12 | 1. A dialysis system, comprising: a dialysis machine; an authentication component configured to determine that a source of a voice command received by the dialysis system is an authorized user of the dialysis system; a processor configured to carry out a function determined based on the voice command; and a user interface component configured to cause a user interface to display an arrangement of user interface elements, wherein the voice command comprises a command to rearrange the user interface elements that causes a first user interface element and a second user interface element to be combined into a single user interface element that, when interacted with, causes the dialysis system to perform at least one action associated with the first user interface element and at least one action associated with the second user interface element. | 1. A dialysis system, comprising: a dialysis machine; an authentication component configured to determine that a source of a voice command received by the dialysis system is an authorized user of the dialysis system; a processor configured to carry out a function determined based on the voice command; and a user interface component configured to cause a user interface to display an arrangement of user interface elements, wherein the voice command comprises a command to rearrange the user interface elements that causes a first user interface element and a second user interface element to be combined into a single user interface element that, when interacted with, causes the dialysis system to perform at least one action associated with the first user interface element and at least one action associated with the second user interface element. 12. The dialysis system of claim 1 , wherein the authentication component is configured to receive input from a non-voice interface of the dialysis system and determine that the input is received from an authorized user. | 0.543568 |
8,712,774 | 1 | 10 | 1. A method of creating a hybrid text string, wherein the method is performed by a computing system having a processor and a memory, the method comprising: receiving a first time-indexed text string generated from an audio input by a first automated speech recognition system; receiving a second time-indexed text string generated from the audio input by a second automated speech recognition system; identifying at least one anchor word, wherein the at least one anchor word is associated with an anchor word point, wherein the at least one anchor word is a word having a high probability of being correctly recognized by both the first and second automated speech recognition systems, and, wherein the anchor word point is located at a time location t 1 in the first time-indexed text string and at a time location t 2 in the second time-indexed text string, wherein time locations t 1 and t 2 are different; calculating a match vector, wherein the match vector is associated with a variance between the time location t 1 in the first time-indexed text string and the time location t 2 in the second time-indexed text string; identifying at least one matched word, wherein the at least one matched word is located at a time location t 3 in the first time-indexed text string and at a time location t 4 in the second time-indexed text string, wherein time location t 4 is located within a second time range in the second time-indexed text string, wherein the second time range is generated by combining at least the time location t 3 , the match vector, and a margin of error; and creating a hybrid text string, wherein the hybrid text string includes the at least one anchor word and the at least one matched word, and wherein the hybrid text string includes at least one word from the first time-indexed text string that has a higher probability of being correctly recognized than a corresponding word in the second time-indexed text string. | 1. A method of creating a hybrid text string, wherein the method is performed by a computing system having a processor and a memory, the method comprising: receiving a first time-indexed text string generated from an audio input by a first automated speech recognition system; receiving a second time-indexed text string generated from the audio input by a second automated speech recognition system; identifying at least one anchor word, wherein the at least one anchor word is associated with an anchor word point, wherein the at least one anchor word is a word having a high probability of being correctly recognized by both the first and second automated speech recognition systems, and, wherein the anchor word point is located at a time location t 1 in the first time-indexed text string and at a time location t 2 in the second time-indexed text string, wherein time locations t 1 and t 2 are different; calculating a match vector, wherein the match vector is associated with a variance between the time location t 1 in the first time-indexed text string and the time location t 2 in the second time-indexed text string; identifying at least one matched word, wherein the at least one matched word is located at a time location t 3 in the first time-indexed text string and at a time location t 4 in the second time-indexed text string, wherein time location t 4 is located within a second time range in the second time-indexed text string, wherein the second time range is generated by combining at least the time location t 3 , the match vector, and a margin of error; and creating a hybrid text string, wherein the hybrid text string includes the at least one anchor word and the at least one matched word, and wherein the hybrid text string includes at least one word from the first time-indexed text string that has a higher probability of being correctly recognized than a corresponding word in the second time-indexed text string. 10. The method of claim 1 , wherein the first automated speech recognition system and the second automated speech recognition system comprise different automated speech recognition systems. | 0.907715 |
4,866,778 | 6 | 7 | 6. A speech recognition system as described in claim 1, wherein: said control-input means includes means for enabling an operator to input a string of one or more selected alphabetic letters as said string; said alphabetic filtering means includes means for responding to a string of one or more alphabetic letters input through said control-input means by selecting a sub-vocabulary for use as said second recognition vocabulary which includes an increased percent of vocabulary words which start with that string said control-input means includes means for enabling an operator to add one or more selected additional alphabetic letters to the end of a string of one or more letters input by the operator after that string has been input, and said re-recognition means has caused its alphabetic filtering means to select a first sub-vocabulary based on that string, and has caused said recognition means to start performing said second recognition using said first sub-vocabulary as said second recognition vocabulary; and said re-recognition means includes means for responding to the input of additional letters to said string through said control-input means by causing said alphabetic filtering means to select a second sub-vocabulary including an increased percentage of words starting with the new string formed by adding said one or more additional letters to the said string, and for causing said recognition means to start to perform an additional recognition of said portion of speech using said second sub-vocabulary as a third recognition vocabulary. | 6. A speech recognition system as described in claim 1, wherein: said control-input means includes means for enabling an operator to input a string of one or more selected alphabetic letters as said string; said alphabetic filtering means includes means for responding to a string of one or more alphabetic letters input through said control-input means by selecting a sub-vocabulary for use as said second recognition vocabulary which includes an increased percent of vocabulary words which start with that string said control-input means includes means for enabling an operator to add one or more selected additional alphabetic letters to the end of a string of one or more letters input by the operator after that string has been input, and said re-recognition means has caused its alphabetic filtering means to select a first sub-vocabulary based on that string, and has caused said recognition means to start performing said second recognition using said first sub-vocabulary as said second recognition vocabulary; and said re-recognition means includes means for responding to the input of additional letters to said string through said control-input means by causing said alphabetic filtering means to select a second sub-vocabulary including an increased percentage of words starting with the new string formed by adding said one or more additional letters to the said string, and for causing said recognition means to start to perform an additional recognition of said portion of speech using said second sub-vocabulary as a third recognition vocabulary. 7. A speech recognition system as described in claim 6, wherein said re-recognition means further includes means for causing said recognition means to abort said second recognition using said first sub-vocabulary before it causes said recognition means to start performing said additional recognition using said second sub-vocabulary. | 0.5 |
9,594,828 | 8 | 9 | 8. A non-transitory computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more computing devices, cause the computing devices to perform actions including: transmitting a pilot query to an unstructured data store that stores records in JSON-based format; responsive to transmitting the pilot query, receiving a plurality of records generated by execution of the pilot query against the unstructured data store, each record including one or more pairs of field names and values in JSON-based format; automatically identifying field names and data types from the pairs in the plurality of records generated by execution of the pilot query against the unstructured data store that stores records in JSON-based format and defining a first set of fields that corresponds to the identified field names and data types; receiving, at a query converter, a structured query whose portions all correspond to a structured query language; converting, by the query converter, the structured query into an unstructured query in an unstructured query language associated with searching the records in the unstructured data store, the unstructured query referencing at least one field in the first set of fields; and transmitting an instruction to the unstructured data store requesting that the unstructured data store execute the unstructured query against the records. | 8. A non-transitory computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more computing devices, cause the computing devices to perform actions including: transmitting a pilot query to an unstructured data store that stores records in JSON-based format; responsive to transmitting the pilot query, receiving a plurality of records generated by execution of the pilot query against the unstructured data store, each record including one or more pairs of field names and values in JSON-based format; automatically identifying field names and data types from the pairs in the plurality of records generated by execution of the pilot query against the unstructured data store that stores records in JSON-based format and defining a first set of fields that corresponds to the identified field names and data types; receiving, at a query converter, a structured query whose portions all correspond to a structured query language; converting, by the query converter, the structured query into an unstructured query in an unstructured query language associated with searching the records in the unstructured data store, the unstructured query referencing at least one field in the first set of fields; and transmitting an instruction to the unstructured data store requesting that the unstructured data store execute the unstructured query against the records. 9. The computer-readable storage medium of claim 8 , wherein as a result of initiating a search on the unstructured data store using the unstructured query, the unstructured query causes one or more values for one or more fields included in the unstructured query to be extracted from the unstructured data store, further including receiving the extracted values for the fields included in the unstructured query from the unstructured data store. | 0.5 |
9,949,096 | 9 | 10 | 9. The method according to claim 1 , wherein the setting the place obtained on basis of the place-associated information to a destination by the navigation device includes: querying whether the place obtained on basis of the place-associated information is to be set to a destination. | 9. The method according to claim 1 , wherein the setting the place obtained on basis of the place-associated information to a destination by the navigation device includes: querying whether the place obtained on basis of the place-associated information is to be set to a destination. 10. The method according to claim 9 , wherein the setting the place obtained on the basis of the place-associated information to the destination by the navigation device further includes: determining whether the navigation device is in a navigation state indicating a path guiding state; if the navigation device is in the navigation state, determining whether a legacy destination is different from the place obtained on basis of the place-associated information; if the legacy destination is different from the place obtained on basis of the place-associated information, deleting the legacy destination, and querying whether or not the place obtained on the basis of the place-associated information is set to the destination. | 0.5 |
9,563,704 | 8 | 9 | 8. A system for presenting suggestions of related media content, the system comprising: a hardware processor that is configured to: generate a transcript of a first media content item, wherein the transcript indicates spoken words in audio content of the first media content item; receive one or more social network posts associated with one or more other media content items; compute one or more correlations between text in the one or more social network posts and the transcript, wherein the correlations indicate overlap between the spoken words in the audio content of the first media content item and the text in the one or more social network posts; rank the social network posts based at least in part on the correlations to produce rankings; determine that the first media content item is being presented on a user device; and in response to determining that the first media content item is being presented on the user device, cause one or more suggestions to view the one or more other media content items associated with the one or more social network posts based at least in part on the rankings to be presented. | 8. A system for presenting suggestions of related media content, the system comprising: a hardware processor that is configured to: generate a transcript of a first media content item, wherein the transcript indicates spoken words in audio content of the first media content item; receive one or more social network posts associated with one or more other media content items; compute one or more correlations between text in the one or more social network posts and the transcript, wherein the correlations indicate overlap between the spoken words in the audio content of the first media content item and the text in the one or more social network posts; rank the social network posts based at least in part on the correlations to produce rankings; determine that the first media content item is being presented on a user device; and in response to determining that the first media content item is being presented on the user device, cause one or more suggestions to view the one or more other media content items associated with the one or more social network posts based at least in part on the rankings to be presented. 9. The system of claim 8 , wherein the one or more social network posts contain links to the one or more other media content items. | 0.670854 |
10,108,723 | 1 | 7 | 1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising travel related information; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device. | 1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising travel related information; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device. 7. The method of claim 1 , in which the two-way communication channel comprises at least one of short message service (SMS), click-to-voice, interactive voice response (IVR), e-mail, phone, Internet protocol, message board, social media, digital communication, or a combination thereof. | 0.584302 |
8,515,030 | 4 | 5 | 4. The non-transitory computer-readable storage medium of claim 1 , comprising computer instructions which, responsive to being executed by the processor, cause the processor to perform operations comprising: detecting from the actionable field a callback identification and a location coordinate. | 4. The non-transitory computer-readable storage medium of claim 1 , comprising computer instructions which, responsive to being executed by the processor, cause the processor to perform operations comprising: detecting from the actionable field a callback identification and a location coordinate. 5. The non-transitory computer-readable storage medium of claim 4 , comprising computer instructions which, responsive to being executed by the processor, cause the processor to perform operations comprising: receiving at an user interface of the first end user device a geographic instructions request for the location coordinate, wherein the request is responsive to an interactive voice response prompt generated by the first end user device; creating a map associated with the location coordinate; and presenting the map at the first end user device, wherein the map indicates the location coordinate. | 0.5 |
8,082,215 | 1 | 9 | 1. A system, comprising: an inference data acquisition module configured to acquire inference data including a first inference data that indicates an inferred mental state of a first authoring user in connection with a particular item of an electronic message and a second inference data that indicates an inferred mental state of a second authoring user in connection with the particular item of the electronic message; and an inference data association module configured to associate the first inference data and the second inference data with the particular item. | 1. A system, comprising: an inference data acquisition module configured to acquire inference data including a first inference data that indicates an inferred mental state of a first authoring user in connection with a particular item of an electronic message and a second inference data that indicates an inferred mental state of a second authoring user in connection with the particular item of the electronic message; and an inference data association module configured to associate the first inference data and the second inference data with the particular item. 9. The system of claim 1 , wherein said inference data acquisition module configured to acquire inference data including a first inference data that indicates an inferred mental state of a first authoring user in connection with a particular item of an electronic message and a second inference data that indicates an inferred mental state of a second authoring user in connection with the particular item of the electronic message comprises: a mental state inference module configured to infer a mental state of the first authoring user based, at least in part, on one or more observed physical characteristics of the first authoring user, and to infer a mental state of the second authoring user based, at least in part, on one or more observed physical characteristics of the second authoring user. | 0.5 |
9,946,790 | 1 | 5 | 1. A non-transitory computer-readable medium embodying a program executable in at least one computing device, wherein, when executed, the program causes the at least one computing device to at least: generate a user interface to facilitate creation of a plurality of user-created item lists via a plurality of client devices; receive the plurality of user-created item lists from the plurality of client devices via the user interface rendered on the plurality of client devices; maintain the plurality of user-created item lists in an item list registry, individual user-created item lists of the plurality of user-created item lists including a plurality of items available for purchase, lease, or download via an electronic commerce system, and the individual user-created item lists being identified by a respective item list title that includes a title term; for the individual user-created item lists, compare the title term with a plurality of predefined keywords; for the individual user-created item lists, assign a keyword tag associated with a respective predefined keyword of the plurality of predefined keywords to individual items of the plurality of items included in a respective user-created item list of the plurality of user-created item lists in response to the respective predefined keyword matching the title term; create an item category associated with the respective predefined keyword; and populate the item category with at least one of the plurality of items when a number of keyword tags corresponding to the respective predefined keyword and assigned to the at least one of the plurality of items reaches a predefined category threshold. | 1. A non-transitory computer-readable medium embodying a program executable in at least one computing device, wherein, when executed, the program causes the at least one computing device to at least: generate a user interface to facilitate creation of a plurality of user-created item lists via a plurality of client devices; receive the plurality of user-created item lists from the plurality of client devices via the user interface rendered on the plurality of client devices; maintain the plurality of user-created item lists in an item list registry, individual user-created item lists of the plurality of user-created item lists including a plurality of items available for purchase, lease, or download via an electronic commerce system, and the individual user-created item lists being identified by a respective item list title that includes a title term; for the individual user-created item lists, compare the title term with a plurality of predefined keywords; for the individual user-created item lists, assign a keyword tag associated with a respective predefined keyword of the plurality of predefined keywords to individual items of the plurality of items included in a respective user-created item list of the plurality of user-created item lists in response to the respective predefined keyword matching the title term; create an item category associated with the respective predefined keyword; and populate the item category with at least one of the plurality of items when a number of keyword tags corresponding to the respective predefined keyword and assigned to the at least one of the plurality of items reaches a predefined category threshold. 5. The non-transitory computer-readable medium of claim 1 , wherein a first item of the plurality of items in a first item list of the plurality of user-created item lists is the same as a second item of the plurality of items in a second item list of the plurality of user-created item lists. | 0.590782 |
7,739,354 | 1 | 5 | 1. A computer implemented method for embedding data in a text page at a proxy, the proxy comprising an intermediary between a resource and a request for the resource, the method comprising: receiving the request for the resource at the proxy from a client machine, wherein the proxy is configured to accept requests from a plurality of client machines, wherein the client machines are independent of the proxy; extracting, at the proxy, a resource identifier from the request and storing said the resource identifier, wherein the resource identifier identifies the resource being requested; forwarding the request for said resource from the proxy to a location having the resource; receiving the resource in response to forwarding the request; thereafter, at the proxy, parsing the received resource based at least partially on the resource identifier to match the received resource with the previously stored resource identifier; analyzing a plurality of templates using at least the previously stored resource identifier to obtain a template associated with the resource; using the template associated with the received resource identified by the previously stored resource identifier to identify one or more recognized elements in the received resource; and embedding data into the received resource based on the one or more recognized elements. | 1. A computer implemented method for embedding data in a text page at a proxy, the proxy comprising an intermediary between a resource and a request for the resource, the method comprising: receiving the request for the resource at the proxy from a client machine, wherein the proxy is configured to accept requests from a plurality of client machines, wherein the client machines are independent of the proxy; extracting, at the proxy, a resource identifier from the request and storing said the resource identifier, wherein the resource identifier identifies the resource being requested; forwarding the request for said resource from the proxy to a location having the resource; receiving the resource in response to forwarding the request; thereafter, at the proxy, parsing the received resource based at least partially on the resource identifier to match the received resource with the previously stored resource identifier; analyzing a plurality of templates using at least the previously stored resource identifier to obtain a template associated with the resource; using the template associated with the received resource identified by the previously stored resource identifier to identify one or more recognized elements in the received resource; and embedding data into the received resource based on the one or more recognized elements. 5. The computer implemented method of claim 1 wherein said proxy comprises a web browser and/or an extension to one. | 0.797909 |
6,021,384 | 9 | 10 | 9. An apparatus that determines superwords, comprising: a database of observed sequences of words, symbols and/or sounds, the database having a perplexity value based on a language model; generating means for generating candidate phrases from the database; input means for incorporating one of the candidate phrases from the database into the language model; analyzing means for analyzing how the perplexity value of the database, which is based on the language model, is affected by the incorporated candidate phrase, the analyzing means producing an output; and determining means for determining if the candidate phrase is a superword from the output of the analyzing means. | 9. An apparatus that determines superwords, comprising: a database of observed sequences of words, symbols and/or sounds, the database having a perplexity value based on a language model; generating means for generating candidate phrases from the database; input means for incorporating one of the candidate phrases from the database into the language model; analyzing means for analyzing how the perplexity value of the database, which is based on the language model, is affected by the incorporated candidate phrase, the analyzing means producing an output; and determining means for determining if the candidate phrase is a superword from the output of the analyzing means. 10. The apparatus of claim 9, wherein the determining means decides based on the output of the analyzing means that if the perplexity value for the database decreases, the candidate phrase is added to the database as a superword. | 0.5 |
7,904,439 | 11 | 12 | 11. The display-building system of claim 7 , wherein the formatting component formats a display by increasing expected utility, subject to a size of the display and a relation of the size and an amount of embedded content placed in a montage. | 11. The display-building system of claim 7 , wherein the formatting component formats a display by increasing expected utility, subject to a size of the display and a relation of the size and an amount of embedded content placed in a montage. 12. The display-building system of claim 11 , further comprising at least one of a knapsack algorithm, box fitting algorithm and a geometric analysis to fit a plurality of display clippings into the montage. | 0.5 |
8,731,176 | 1 | 2 | 1. An operator evaluation support device comprising: a speech recording unit configured to record speech times of a customer and an operator during a telephone call from the customer to which the operator attends; a superposition identifying unit configured to refer to the speech recording unit and identify a superposition speech, which is a speech of the operator that has started during a speech of the customer, during the telephone call; a problematic superposition identifying unit configured to identify a problematic superposition speech among the superposition speeches, by comparing a sound quality of a speech of the customer that has started after the superposition speech with a standard sound quality; a call information creating unit configured to refer to the speech recording unit and create telephone call information indicating the identified problematic superposition speech during the telephone call; and a sending unit configured to send the created telephone call information to an administrator terminal used by an administrator who is evaluating the operator. | 1. An operator evaluation support device comprising: a speech recording unit configured to record speech times of a customer and an operator during a telephone call from the customer to which the operator attends; a superposition identifying unit configured to refer to the speech recording unit and identify a superposition speech, which is a speech of the operator that has started during a speech of the customer, during the telephone call; a problematic superposition identifying unit configured to identify a problematic superposition speech among the superposition speeches, by comparing a sound quality of a speech of the customer that has started after the superposition speech with a standard sound quality; a call information creating unit configured to refer to the speech recording unit and create telephone call information indicating the identified problematic superposition speech during the telephone call; and a sending unit configured to send the created telephone call information to an administrator terminal used by an administrator who is evaluating the operator. 2. The operator evaluation support device according to claim 1 , wherein the speech recording unit is configured to record an average-volume-in-speech in association with the speech time of the customer, and the problematic superposition identifying unit is configured to acquire, from the speech recording unit, the average-volume-in-speech of the speech of the customer that has started after the superposition speech, compare the average-volume-in-speech of the speech of the customer that has started after the superposition speech with a standard average volume, and identify the superposition speech as the problematic superposition speech when the average-volume-in-speech of the speech of the customer that has started after the superposition speech is higher than the standard average volume. | 0.5 |
8,738,376 | 1 | 6 | 1. A computer-implemented method for speech recognition, the computer-implemented method comprising: accessing acoustic data of a first speaker, the acoustic data of the first speaker being a collection of recorded utterances spoken by the first speaker; accessing a baseline acoustic speech model of an automated speech recognition system, the baseline acoustic speech model having a plurality of acoustic parameters used in converting spoken words to text; estimating, using a maximum a posteriori probability process, statistical changes to acoustic parameters of the baseline acoustic speech model that improve speech recognition accuracy of the acoustic model when executing speech recognition on utterances of the first speaker, wherein using the maximum a posteriori probability process includes comparing an analysis of the acoustic data of the first speaker to the plurality of acoustic parameters of the baseline acoustic speech model, wherein using the maximum a posteriori probability process includes restricting estimation of statistical changes such that an amount of acoustic parameters from the baseline acoustic speech model that have an estimated statistical change is less than a total number of acoustic parameters included in the baseline acoustic speech model; and storing changes to a set of acoustic parameters corresponding to acoustic parameters from the baseline acoustic speech model that have an estimated statistical change, the changes being stored as acoustic parameter adaptation data linked to the first speaker. | 1. A computer-implemented method for speech recognition, the computer-implemented method comprising: accessing acoustic data of a first speaker, the acoustic data of the first speaker being a collection of recorded utterances spoken by the first speaker; accessing a baseline acoustic speech model of an automated speech recognition system, the baseline acoustic speech model having a plurality of acoustic parameters used in converting spoken words to text; estimating, using a maximum a posteriori probability process, statistical changes to acoustic parameters of the baseline acoustic speech model that improve speech recognition accuracy of the acoustic model when executing speech recognition on utterances of the first speaker, wherein using the maximum a posteriori probability process includes comparing an analysis of the acoustic data of the first speaker to the plurality of acoustic parameters of the baseline acoustic speech model, wherein using the maximum a posteriori probability process includes restricting estimation of statistical changes such that an amount of acoustic parameters from the baseline acoustic speech model that have an estimated statistical change is less than a total number of acoustic parameters included in the baseline acoustic speech model; and storing changes to a set of acoustic parameters corresponding to acoustic parameters from the baseline acoustic speech model that have an estimated statistical change, the changes being stored as acoustic parameter adaptation data linked to the first speaker. 6. The computer-implemented method of claim 1 , further comprising: in response to receiving a spoken utterance of the first speaker as speech recognition input, modifying the baseline acoustic speech model using the acoustic parameter adaptation data linked to the first speaker; and executing speech recognition on the spoken utterance of the first speaker using the baseline acoustic speech model modified by the acoustic parameter adaptation data. | 0.5 |
8,290,777 | 11 | 12 | 11. A method for synchronizing the playing and displaying of digital content in an electronic device, comprising: rendering a first portion of digital content; displaying the rendered first portion of digital content on the electronic device; playing a segment of digital content as audio using a text-to-speech engine while the rendered first portion of digital content is displayed on the electronic device; and rendering a second portion of digital content for display when a current bookmark that is associated with a particular position in the digital content is greater than a last position in the rendered first portion of digital content. | 11. A method for synchronizing the playing and displaying of digital content in an electronic device, comprising: rendering a first portion of digital content; displaying the rendered first portion of digital content on the electronic device; playing a segment of digital content as audio using a text-to-speech engine while the rendered first portion of digital content is displayed on the electronic device; and rendering a second portion of digital content for display when a current bookmark that is associated with a particular position in the digital content is greater than a last position in the rendered first portion of digital content. 12. The method of claim 11 , further comprising inserting bookmarks into the segment of digital content that is to be played by the text-to-speech engine. | 0.801546 |
9,965,561 | 17 | 18 | 17. The computing device of claim 15 , wherein the search record includes the code corresponding to the instance of execution of the browser-executable component, and wherein the browser-executable component is configured to request a resource corresponding to the browser from the computing device. | 17. The computing device of claim 15 , wherein the search record includes the code corresponding to the instance of execution of the browser-executable component, and wherein the browser-executable component is configured to request a resource corresponding to the browser from the computing device. 18. The computing device of claim 17 , wherein the resource corresponding to the browser is unique to the instance of execution of the browser-executable component and the code is used to name the search record such that a local copy of the search record is not available in a cache of the browser. | 0.5 |
8,762,358 | 11 | 12 | 11. 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: determining an interface language associated with a user interface through which one or more query terms of a search query are received; identifying respective languages that are associated with search result selections for a plurality of previously submitted search queries, each of the previously submitted search queries matching the search query; determining, for each identified language and based on the respective languages that are associated with the search result selections, a respective likelihood that the search query is in the language; and selecting a likelihood for a language that matches the interface language as a likelihood that one or more of the query terms are in the interface language; determining, for each of the query terms, a vector corresponding to a set of likelihoods that the respective query term is in a plurality of languages; multiplying together the vectors corresponding to the sets of likelihoods that the query terms are in the plurality of languages to generate a resultant vector; multiplying the resultant vector by the likelihood that one or more of the query terms are in the determined interface language to generate a query probability vector; and selecting a query language of the query based at least on the query probability vector. | 11. 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: determining an interface language associated with a user interface through which one or more query terms of a search query are received; identifying respective languages that are associated with search result selections for a plurality of previously submitted search queries, each of the previously submitted search queries matching the search query; determining, for each identified language and based on the respective languages that are associated with the search result selections, a respective likelihood that the search query is in the language; and selecting a likelihood for a language that matches the interface language as a likelihood that one or more of the query terms are in the interface language; determining, for each of the query terms, a vector corresponding to a set of likelihoods that the respective query term is in a plurality of languages; multiplying together the vectors corresponding to the sets of likelihoods that the query terms are in the plurality of languages to generate a resultant vector; multiplying the resultant vector by the likelihood that one or more of the query terms are in the determined interface language to generate a query probability vector; and selecting a query language of the query based at least on the query probability vector. 12. The system of claim 11 , further comprising: identifying, based on a language-specific mapping, a respective common form of the one or more query terms for one or more of the one or more query terms; identifying, for the one or more query terms, a respective synonym based on the respective common forms of the one or more query terms; and revising the search query based on the one or more identified synonyms. | 0.5 |
7,844,464 | 18 | 19 | 18. The method of claim 1 , wherein the step (A) comprises a step of: (A) (1) deriving the likelihood from a confidence measure representing a degree of confidence that the region of the document correctly represents the content in the corresponding region of the spoken audio stream, wherein the confidence measure is provided by an automatic transcription system that produced the region of the document based on the region of the spoken audio stream. | 18. The method of claim 1 , wherein the step (A) comprises a step of: (A) (1) deriving the likelihood from a confidence measure representing a degree of confidence that the region of the document correctly represents the content in the corresponding region of the spoken audio stream, wherein the confidence measure is provided by an automatic transcription system that produced the region of the document based on the region of the spoken audio stream. 19. The method of claim 18 , wherein the step (A)(1) comprises a step of deriving the estimate of the likelihood from the confidence measure, a prior likelihood of correctness of the region of the document, and a feature of the spoken audio stream. | 0.5 |
9,785,304 | 1 | 6 | 1. A system for providing a contextual search tool that improves search results presented to a user, the system comprising: one or more memory devices; and one or more processing devices operatively coupled to the one or more memory devices, wherein the one or more processing devices are configured to execute computer-readable program code to: receive a search from the user; determine the search results to display in a contextual search interface based on the search; display the search results in a search results section of the contextual search interface, wherein the search results comprise a list of the search results; display content in a content section of the contextual search interface, wherein the content is for at least one of the search results from the search results section, and wherein the content section and the search results section are different sections; determine dynamic contextual information based on the search from the user and a household profile of the user, wherein the dynamic contextual information comprises household profile information associated with the household profile of the user, wherein the household profile information comprises profile information for a first individual and a second individual other than the user, wherein the profile information comprises at least a role of the first individual and a role of the second individual, the first individual and the second individual having different roles within the household profile, and wherein determining the search results and the dynamic contextual information is further based on the roles of the first and second individuals; display the dynamic contextual information in a dynamic contextual information section of the contextual search interface, wherein the dynamic contextual information section is different from the search results section and the content section; receive, via the dynamic contextual information section of the contextual search interface, contextual information input from the user, wherein the contextual information input comprises at least an edit to household profile information within the household profile used to update the search; determine the user would like to communicate with an advisor based on the search or contextual information input from the user; identify favorite advisors of at least one individual in the household profile; determine an advisor to present to the user based on the favorite advisors of the at least one individual in the household profile; initiate communication between the advisor and the user; provide the advisor with access to the search; receive input from the advisor related to the search; determine updated search results, updated content, and updated dynamic contextual information based on the contextual information input from the user and the input from the advisor; and display the updated search results in the search results section, the updated content in the content section, and the updated dynamic contextual information in the dynamic contextual information section. | 1. A system for providing a contextual search tool that improves search results presented to a user, the system comprising: one or more memory devices; and one or more processing devices operatively coupled to the one or more memory devices, wherein the one or more processing devices are configured to execute computer-readable program code to: receive a search from the user; determine the search results to display in a contextual search interface based on the search; display the search results in a search results section of the contextual search interface, wherein the search results comprise a list of the search results; display content in a content section of the contextual search interface, wherein the content is for at least one of the search results from the search results section, and wherein the content section and the search results section are different sections; determine dynamic contextual information based on the search from the user and a household profile of the user, wherein the dynamic contextual information comprises household profile information associated with the household profile of the user, wherein the household profile information comprises profile information for a first individual and a second individual other than the user, wherein the profile information comprises at least a role of the first individual and a role of the second individual, the first individual and the second individual having different roles within the household profile, and wherein determining the search results and the dynamic contextual information is further based on the roles of the first and second individuals; display the dynamic contextual information in a dynamic contextual information section of the contextual search interface, wherein the dynamic contextual information section is different from the search results section and the content section; receive, via the dynamic contextual information section of the contextual search interface, contextual information input from the user, wherein the contextual information input comprises at least an edit to household profile information within the household profile used to update the search; determine the user would like to communicate with an advisor based on the search or contextual information input from the user; identify favorite advisors of at least one individual in the household profile; determine an advisor to present to the user based on the favorite advisors of the at least one individual in the household profile; initiate communication between the advisor and the user; provide the advisor with access to the search; receive input from the advisor related to the search; determine updated search results, updated content, and updated dynamic contextual information based on the contextual information input from the user and the input from the advisor; and display the updated search results in the search results section, the updated content in the content section, and the updated dynamic contextual information in the dynamic contextual information section. 6. The system of claim 1 , wherein the one or more processing devices are further configured to execute computer-readable program code to: receive user profile information, and wherein determining the search results or the updated search results is based in part on the user profile information; and receive user account information, and wherein determining the search results or the updated search results is based in part on the user account information. | 0.5 |
6,038,668 | 34 | 40 | 34. A method for organizing information comprising the steps of storing at a remote location data including organizational terms; scanning said remote location for the existence of the organizational terms; retrieving from said remote location the data as based on said organizational terms. | 34. A method for organizing information comprising the steps of storing at a remote location data including organizational terms; scanning said remote location for the existence of the organizational terms; retrieving from said remote location the data as based on said organizational terms. 40. The method according to claim 34, wherein the organizational terms include at least one descriptor. | 0.682099 |
10,104,221 | 1 | 2 | 1. A first device comprising: at least one computer memory that is not a transitory signal and that comprises instructions executable by at least one processor to: responsive to wireless text communication between the first device and a second device, the first and second devices being associated with respective first and second users with a history of past wireless text communication with each other, establish a first language setting for the first device responsive to the first language setting being a language setting of the first device in a most recent past communication between the first and second users; responsive to wireless text communication between the first device and a third device of the respective first user and a third user with no history of past wireless text communication with each other, and responsive to an indication that the first and third users have no language in common with each other, establish a first language setting on the first device according to a first user-indicated language preference; responsive to wireless text communication between the first and third devices of the respective first and third users with no history of past wireless text communication with each other, and responsive to an indication that the first and third users have one and only one language in common with each other, establish language settings on the first device according to the one and only one language; responsive to wireless text communication between the first and third devices of the respective first and third users with no history of past wireless text communication with each other, and responsive to an indication that the first and third users have at least first and second languages in common with each other, establish language settings on the first device according to which of the first and second languages is used more frequently than the other on the first device. | 1. A first device comprising: at least one computer memory that is not a transitory signal and that comprises instructions executable by at least one processor to: responsive to wireless text communication between the first device and a second device, the first and second devices being associated with respective first and second users with a history of past wireless text communication with each other, establish a first language setting for the first device responsive to the first language setting being a language setting of the first device in a most recent past communication between the first and second users; responsive to wireless text communication between the first device and a third device of the respective first user and a third user with no history of past wireless text communication with each other, and responsive to an indication that the first and third users have no language in common with each other, establish a first language setting on the first device according to a first user-indicated language preference; responsive to wireless text communication between the first and third devices of the respective first and third users with no history of past wireless text communication with each other, and responsive to an indication that the first and third users have one and only one language in common with each other, establish language settings on the first device according to the one and only one language; responsive to wireless text communication between the first and third devices of the respective first and third users with no history of past wireless text communication with each other, and responsive to an indication that the first and third users have at least first and second languages in common with each other, establish language settings on the first device according to which of the first and second languages is used more frequently than the other on the first device. 2. The first device of claim 1 , wherein the instructions are executable to: responsive to the first and third users having no language in common, present a warning user interface (UI) on the first device indicating no common language. | 0.662356 |
9,720,961 | 10 | 11 | 10. The system of claim 9 , wherein assigning unique union identifiers for union tuples defined by the union relation comprises generating a union id relation for the union relation, wherein the union id relation defines uniquely identified union tuples that each assign a unique number to each of the union tuples. | 10. The system of claim 9 , wherein assigning unique union identifiers for union tuples defined by the union relation comprises generating a union id relation for the union relation, wherein the union id relation defines uniquely identified union tuples that each assign a unique number to each of the union tuples. 11. The system of claim 10 , wherein generating respective injector relations for each of the alternative subtypes comprises matching domain identifiers in a domain id relation for the alternative subtype to domain identifiers in a union id relation for the algebraic data type. | 0.5 |
8,131,740 | 18 | 19 | 18. A system, comprising: a processor; and a memory containing a program, which when executed by the processor is configured to respond to a search request for non-avatar virtual objects present in an immersive virtual environment by performing the steps of: receiving, from a user, a search query for non-avatar virtual objects of the virtual environment, wherein the search query includes one or more attribute condition describing characteristics of at least one non-avatar virtual object of the virtual environment, and wherein the search query includes at least one interaction condition identifying the user's past interaction with non-avatar virtual objects of the virtual environment; determining a collection of non-avatar virtual objects present in the virtual environment satisfying the one or more attribute conditions of the search query; filtering the collection of non-avatar virtual objects, based on the one or more interaction conditions, to produce a set of search results responsive to the search query; and returning the set of search results to the user. | 18. A system, comprising: a processor; and a memory containing a program, which when executed by the processor is configured to respond to a search request for non-avatar virtual objects present in an immersive virtual environment by performing the steps of: receiving, from a user, a search query for non-avatar virtual objects of the virtual environment, wherein the search query includes one or more attribute condition describing characteristics of at least one non-avatar virtual object of the virtual environment, and wherein the search query includes at least one interaction condition identifying the user's past interaction with non-avatar virtual objects of the virtual environment; determining a collection of non-avatar virtual objects present in the virtual environment satisfying the one or more attribute conditions of the search query; filtering the collection of non-avatar virtual objects, based on the one or more interaction conditions, to produce a set of search results responsive to the search query; and returning the set of search results to the user. 19. The system of claim 18 , wherein the collection of virtual objects is filtered to include virtual objects with which a user avatar has interacted while interacting with the virtual environment. | 0.740106 |
9,093,073 | 1 | 7 | 1. A computer implemented method, comprising: collecting an utterance; analyzing the utterance; creating a tagging strategy; providing the tagging strategy to a tagging server; predicting a potential tag for the utterance; assigning a tag to the analyzed utterance; assigning a context category to the tagged utterance; and combining identical tag assignments, wherein the utterance is compressed and expanded by a tagging server. | 1. A computer implemented method, comprising: collecting an utterance; analyzing the utterance; creating a tagging strategy; providing the tagging strategy to a tagging server; predicting a potential tag for the utterance; assigning a tag to the analyzed utterance; assigning a context category to the tagged utterance; and combining identical tag assignments, wherein the utterance is compressed and expanded by a tagging server. 7. The computer implemented method of claim 1 , further comprising: correlating tagged utterances according to a semantic meaning. | 0.539007 |
7,869,989 | 1 | 6 | 1. A method for representing text in a language independent format of multiple bits and fields of bits comprising: referencing a word from a set of words into a dictionary of representations of words, in said format of multiple bits and fields of bits, to find a corresponding dictionary term, the set of words derived from a natural language usage defining a natural language context of the words in the set of words; mapping the referenced dictionary term to at least one definition element indicative of usage of the word in at least one context, the definition elements defining a record of fields; comparing the mapped definition element to the corresponding fields in the definition elements of other words in the set of words, the comparison operative to identify similar contexts between the definition elements; disambiguating the referenced words by analyzing each of the definition elements of the referenced words with the definition elements of the other referenced words, the analysis operable to determine a particular definition for each of the referenced words in the context of the set of words, disambiguating including: performing, with a processor, bitwise operations on at least a subset of the fields in the definition elements with corresponding fields in the other definition elements in the set of words, each of the definition elements indicative of a particular context, the operations for identifying a particular definition element based on the context in the set of words, the definition elements including bit fields of class, method, and category in the high bits, the method field determining a structure of fields in lower order bits, wherein disambiguating is performed on definition elements of equal category and method fields; and identifying, from the comparing, a definition element corresponding to the usage of the word in a context of the set of words. | 1. A method for representing text in a language independent format of multiple bits and fields of bits comprising: referencing a word from a set of words into a dictionary of representations of words, in said format of multiple bits and fields of bits, to find a corresponding dictionary term, the set of words derived from a natural language usage defining a natural language context of the words in the set of words; mapping the referenced dictionary term to at least one definition element indicative of usage of the word in at least one context, the definition elements defining a record of fields; comparing the mapped definition element to the corresponding fields in the definition elements of other words in the set of words, the comparison operative to identify similar contexts between the definition elements; disambiguating the referenced words by analyzing each of the definition elements of the referenced words with the definition elements of the other referenced words, the analysis operable to determine a particular definition for each of the referenced words in the context of the set of words, disambiguating including: performing, with a processor, bitwise operations on at least a subset of the fields in the definition elements with corresponding fields in the other definition elements in the set of words, each of the definition elements indicative of a particular context, the operations for identifying a particular definition element based on the context in the set of words, the definition elements including bit fields of class, method, and category in the high bits, the method field determining a structure of fields in lower order bits, wherein disambiguating is performed on definition elements of equal category and method fields; and identifying, from the comparing, a definition element corresponding to the usage of the word in a context of the set of words. 6. The method of claim 1 wherein each of the definition elements identifies a particular definition of a word in the context defined by proximate words, each definition element having a context specific definition. | 0.821963 |
9,235,381 | 9 | 10 | 9. The device as claimed in claim 8 , wherein the programming tool is configured to: generate the target project code for the target project to be executed on the programmable logic controller in a corresponding high-level language; implement program organization unit (POU) entities contained in the target project code with POUs for the IEC61131-3 applications; and load the POU entries from the programming tool to the programmable logic controller. | 9. The device as claimed in claim 8 , wherein the programming tool is configured to: generate the target project code for the target project to be executed on the programmable logic controller in a corresponding high-level language; implement program organization unit (POU) entities contained in the target project code with POUs for the IEC61131-3 applications; and load the POU entries from the programming tool to the programmable logic controller. 10. The device as claimed in claim 9 , comprising: a POU unit, which is configured to implement the POU entries with C-Code-based applications. | 0.5 |
7,801,896 | 1 | 16 | 1. A computer interface method comprising: defining a displayed interface at a client device for interacting with objects having associated resource locators; receiving an input at the client device and sending a search communication from the client device to a network element based at least in part on the input, the search communication comprising a search for objects from a set of indexed objects; receiving at the client device from the network element at least respective resource locators of objects corresponding to the input; displaying a hierarchal object comprising at least three levels of a taxonomic hierarchy each containing at least one of the respective resource locators of the objects received from the network element corresponding to the input, and at least one level having at least two of the objects, the objects being taxonomically organized based on a content associated with a respective object; and presenting within the taxonomic hierarchy a set of separate objects, separate from the objects received from the network element corresponding to the input, wherein the levels of the taxonomic hierarchy contain at least one separate object corresponding to the input, the set of separate objects providing a basis for a third party subsidy. | 1. A computer interface method comprising: defining a displayed interface at a client device for interacting with objects having associated resource locators; receiving an input at the client device and sending a search communication from the client device to a network element based at least in part on the input, the search communication comprising a search for objects from a set of indexed objects; receiving at the client device from the network element at least respective resource locators of objects corresponding to the input; displaying a hierarchal object comprising at least three levels of a taxonomic hierarchy each containing at least one of the respective resource locators of the objects received from the network element corresponding to the input, and at least one level having at least two of the objects, the objects being taxonomically organized based on a content associated with a respective object; and presenting within the taxonomic hierarchy a set of separate objects, separate from the objects received from the network element corresponding to the input, wherein the levels of the taxonomic hierarchy contain at least one separate object corresponding to the input, the set of separate objects providing a basis for a third party subsidy. 16. The method according to claim 1 , further comprising defining a user profile, for modifying the selection of objects corresponding to the input. | 0.762058 |
9,234,763 | 1 | 2 | 1. A host server for providing geographic information comprising: one or more computing devices associated with the host server, the one or more computing devices being configured to perform operations comprising: receiving a search query from a client device, the client device having been associated with user account; in response to the received search query, identifying a point of interest for display on a map; and obtaining a plurality of candidate waypoints from a database, each of the candidate waypoints having been identified by analyzing data associated with the user account; determining a score for each of the candidate waypoints using a scoring formula, wherein the scoring formula includes scoring components that are based at least in part on a first category defining the type of entity of each candidate waypoint being scored and a second category defining the type of entity of the point of interest, and wherein the scoring formula includes a distance scaling component, the distance scaling component comprising a weighted distance divided by a distance between the candidate waypoint being scored and the point of interest, the weighted distance providing a number based on the second category associated with the point of interest; and selecting, based on the determined scores, at least one of the candidate waypoints for presentation on the map displaying the point of interest. | 1. A host server for providing geographic information comprising: one or more computing devices associated with the host server, the one or more computing devices being configured to perform operations comprising: receiving a search query from a client device, the client device having been associated with user account; in response to the received search query, identifying a point of interest for display on a map; and obtaining a plurality of candidate waypoints from a database, each of the candidate waypoints having been identified by analyzing data associated with the user account; determining a score for each of the candidate waypoints using a scoring formula, wherein the scoring formula includes scoring components that are based at least in part on a first category defining the type of entity of each candidate waypoint being scored and a second category defining the type of entity of the point of interest, and wherein the scoring formula includes a distance scaling component, the distance scaling component comprising a weighted distance divided by a distance between the candidate waypoint being scored and the point of interest, the weighted distance providing a number based on the second category associated with the point of interest; and selecting, based on the determined scores, at least one of the candidate waypoints for presentation on the map displaying the point of interest. 2. The host server of claim 1 , wherein the client device is signed into the user account. | 0.880319 |
8,719,259 | 17 | 20 | 17. A system comprising: computer memory storing instructions that are executable; and one or more processing devices to execute the instructions to implement a keyword matching engine, an auction engine, and a geographic matching engine; the keyword matching engine to perform operations comprising: comparing words in an input query received from a computing device to keywords, the keywords being associated with content items that can be provided to computing devices; and generating, based on the comparing, matching scores indicating how well the input query matches keywords for different content items; the geographic matching engine to perform operations comprising: obtaining geographies associated with the computing device and associated with the content items that can be provided to computing devices; and identifying geographic matches between the computing device and at least some of the content items; the auction engine to perform operations comprising: including, in an auction, content items having matching scores that exceed a threshold and that match a geography of the computing device, the auction for receiving bids from content providers to determine which of the content items in the auction to output in response to the input query; the geographic matching engine to perform operations comprising: determining, based at least in part on bids provided in the auction, candidate content items for output in response to the input query; obtaining geographic areas associated with the candidate content items; and selecting a candidate content item having a smallest geographic area; and an output engine to perform operation comprising outputting the selected candidate content item in response to the input query. | 17. A system comprising: computer memory storing instructions that are executable; and one or more processing devices to execute the instructions to implement a keyword matching engine, an auction engine, and a geographic matching engine; the keyword matching engine to perform operations comprising: comparing words in an input query received from a computing device to keywords, the keywords being associated with content items that can be provided to computing devices; and generating, based on the comparing, matching scores indicating how well the input query matches keywords for different content items; the geographic matching engine to perform operations comprising: obtaining geographies associated with the computing device and associated with the content items that can be provided to computing devices; and identifying geographic matches between the computing device and at least some of the content items; the auction engine to perform operations comprising: including, in an auction, content items having matching scores that exceed a threshold and that match a geography of the computing device, the auction for receiving bids from content providers to determine which of the content items in the auction to output in response to the input query; the geographic matching engine to perform operations comprising: determining, based at least in part on bids provided in the auction, candidate content items for output in response to the input query; obtaining geographic areas associated with the candidate content items; and selecting a candidate content item having a smallest geographic area; and an output engine to perform operation comprising outputting the selected candidate content item in response to the input query. 20. The system of claim 17 , wherein a geographic area of a first candidate content item is larger than the geographic area of a second candidate content item, the second candidate content item being the selected candidate. | 0.523504 |
8,504,984 | 1 | 7 | 1. A computer-implemented method, comprising: selecting a first grammar rule in a computer programming language grammar that is used for determining a syntax validity of a computer programming code statement in a text editor, wherein the first grammar rule includes a first symbol that is syntactically correct for the computer programming code statement; creating a second grammar rule and linking the second grammar rule within the grammar chain adjacent to the first grammar rule for the computer programming language grammar, the second grammar rule substantially identical to the first grammar rule but replaces the first symbol with an a second symbol, wherein the second symbol is syntactically incorrect for the computer programming code statement and the first and second symbols are associated with keys on a keyboard; and associating an action with the computer programming language grammar, the action comprising executable code that, when executed, causes the second, syntactically incorrect symbol in the computer programming code statement in the text editor to be replaced with the first syntactically correct symbol; and after or during editing of programming language statements within a document in the text editor, invoking a parser to check the syntax of the programming language statements based on the modified computer programming language grammar, wherein the parser, upon encountering a condition that satisfies the second grammar rule, invokes the action to replace the second symbol with the first symbol. | 1. A computer-implemented method, comprising: selecting a first grammar rule in a computer programming language grammar that is used for determining a syntax validity of a computer programming code statement in a text editor, wherein the first grammar rule includes a first symbol that is syntactically correct for the computer programming code statement; creating a second grammar rule and linking the second grammar rule within the grammar chain adjacent to the first grammar rule for the computer programming language grammar, the second grammar rule substantially identical to the first grammar rule but replaces the first symbol with an a second symbol, wherein the second symbol is syntactically incorrect for the computer programming code statement and the first and second symbols are associated with keys on a keyboard; and associating an action with the computer programming language grammar, the action comprising executable code that, when executed, causes the second, syntactically incorrect symbol in the computer programming code statement in the text editor to be replaced with the first syntactically correct symbol; and after or during editing of programming language statements within a document in the text editor, invoking a parser to check the syntax of the programming language statements based on the modified computer programming language grammar, wherein the parser, upon encountering a condition that satisfies the second grammar rule, invokes the action to replace the second symbol with the first symbol. 7. The method of claim 1 , further comprising determining a type of computer programming language of the computer programming language grammar and the computer programming code statement, wherein the type of computer programming language is utilized to identify the first and second symbols. | 0.539557 |
9,087,033 | 1 | 2 | 1. A non-transitory computer-readable medium having instructions stored thereon that, responsive to being executed by a client device, cause the client device to perform operations comprising: rendering an interface for a resource application of a client device that includes a display area configured to present a resource; presenting the resource in the display area; exposing an annotation portion in the interface that is configured to enable input of annotations for the presented resource in accordance with a location setting of the client device associated with a user profile, the presented resource unmodified by the annotations, wherein the user profile is one of at least two user profiles associated with a user of the client device, and wherein each of the at least two user profiles is associated with a different location of the client device; receiving an annotation input via the annotation portion; and communicating with an annotation server, separate from the client device, to store the annotation for the presented resource at predetermined times in response to the receiving the annotation. | 1. A non-transitory computer-readable medium having instructions stored thereon that, responsive to being executed by a client device, cause the client device to perform operations comprising: rendering an interface for a resource application of a client device that includes a display area configured to present a resource; presenting the resource in the display area; exposing an annotation portion in the interface that is configured to enable input of annotations for the presented resource in accordance with a location setting of the client device associated with a user profile, the presented resource unmodified by the annotations, wherein the user profile is one of at least two user profiles associated with a user of the client device, and wherein each of the at least two user profiles is associated with a different location of the client device; receiving an annotation input via the annotation portion; and communicating with an annotation server, separate from the client device, to store the annotation for the presented resource at predetermined times in response to the receiving the annotation. 2. The non-transitory computer-readable medium of claim 1 , wherein the communicating comprises communicating according to a scheduled interval. | 0.872114 |
7,516,125 | 15 | 18 | 15. An apparatus for indexing a corpus of documents, wherein the words in the corpus of documents include a set of words having a characteristic to be subject of queries, comprising: a data processor arranged to parse documents in the corpus of documents to identify words found in the documents and locations of the words in the documents, and to create an index structure including entries representing words found in the corpus of documents mapping entries in the index structure to locations of the words in documents in the corpus memory storing the index structure writable and readable by the data processor; wherein the data processor includes an indexing processor which indentifies words in a set of words having a characteristic represent d by a mark in a set of marks, and add entries in the index structure representing marks for the identified the set mapping the marks to the locations of the identified words, and wherein one or more entries representing marks include fewer, if any, than all of the characters of the corresponding identified words; wherein entries in the index structure representing the marks comprise tokens coalesced with prefixes of respective marked words, the prefixes comprising one or more leading characters of the respective marked words. | 15. An apparatus for indexing a corpus of documents, wherein the words in the corpus of documents include a set of words having a characteristic to be subject of queries, comprising: a data processor arranged to parse documents in the corpus of documents to identify words found in the documents and locations of the words in the documents, and to create an index structure including entries representing words found in the corpus of documents mapping entries in the index structure to locations of the words in documents in the corpus memory storing the index structure writable and readable by the data processor; wherein the data processor includes an indexing processor which indentifies words in a set of words having a characteristic represent d by a mark in a set of marks, and add entries in the index structure representing marks for the identified the set mapping the marks to the locations of the identified words, and wherein one or more entries representing marks include fewer, if any, than all of the characters of the corresponding identified words; wherein entries in the index structure representing the marks comprise tokens coalesced with prefixes of respective marked words, the prefixes comprising one or more leading characters of the respective marked words. 18. The apparatus of claim 15 , wherein the index structure comprises a dictionary and a reverse index including said entries. | 0.86031 |
9,633,483 | 1 | 5 | 1. A system for filtering, segmenting and recognizing objects, comprising: one or more processors and a memory, the memory having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of: down sampling a three-dimensional (3D) point cloud having a plurality of data points in 3D space to generate a down-sampled 3D point cloud P with reduced data points in the 3D space; identifying and removing a ground plane in the down-sampled 3D point cloud to leave non-ground data points in the down-sampled 3D point cloud; generating a set of 3D candidate blobs by clustering the non-ground data points to generate a plurality of 3D blobs, each of the 3D blobs having a cluster size; extracting features from each 3D candidate blob, the features being vectors that represent morphological characteristics of each 3D candidate blob; and classifying at least one of the 3D candidate blobs as a pre-defined object class based on the extracted features by assigning a semantic meaning to a segmented real-world individual object. | 1. A system for filtering, segmenting and recognizing objects, comprising: one or more processors and a memory, the memory having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of: down sampling a three-dimensional (3D) point cloud having a plurality of data points in 3D space to generate a down-sampled 3D point cloud P with reduced data points in the 3D space; identifying and removing a ground plane in the down-sampled 3D point cloud to leave non-ground data points in the down-sampled 3D point cloud; generating a set of 3D candidate blobs by clustering the non-ground data points to generate a plurality of 3D blobs, each of the 3D blobs having a cluster size; extracting features from each 3D candidate blob, the features being vectors that represent morphological characteristics of each 3D candidate blob; and classifying at least one of the 3D candidate blobs as a pre-defined object class based on the extracted features by assigning a semantic meaning to a segmented real-world individual object. 5. The system as set forth in claim 1 , wherein identifying and removing the ground plane further comprises operations of: randomly selecting three non-collinear unique points from the down-sampled 3D point cloud P; computing plane model coefficients from the three non-collinear unique points; computing distances from all points in the down-sampled 3D point cloud P to the plane model; and determining a number of points p* belonging to the down-sampled 3D point cloud P whose distance to the plane model falls between a pre-defined range; and designating a plane with a largest number of points p* that fall within the pre-defined range as the ground plane; and removing the points p* that are included in the ground plane from the down-sampled 3D point cloud P data to leave non-ground data points. | 0.5 |
7,672,932 | 16 | 17 | 16. A method as recited in claim 1 , wherein: the search query is not-yet-submitted; the step of determining the search result comprises determining a speculative search result based on the not-yet-submitted search query; and the method further comprising providing a suggestion to said client node to improve said not-yet submitted search query prior to receiving an indication from said client node that said search query is completely formed. | 16. A method as recited in claim 1 , wherein: the search query is not-yet-submitted; the step of determining the search result comprises determining a speculative search result based on the not-yet-submitted search query; and the method further comprising providing a suggestion to said client node to improve said not-yet submitted search query prior to receiving an indication from said client node that said search query is completely formed. 17. A computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 16 . | 0.5 |
9,773,271 | 9 | 10 | 9. The apparatus according to claim 3 , wherein the processing circuitry is further configured to store information indicating a relationship with a predetermined feature quantity, wherein when a predetermined word is selected from words displayed in a format different from the other words as a word having served for the selection of the item, the control section is configured to select the feature quantities related to the selected predetermined word on the basis of the information stored in the other storage section, and is configured to display a list of the selected feature quantities. | 9. The apparatus according to claim 3 , wherein the processing circuitry is further configured to store information indicating a relationship with a predetermined feature quantity, wherein when a predetermined word is selected from words displayed in a format different from the other words as a word having served for the selection of the item, the control section is configured to select the feature quantities related to the selected predetermined word on the basis of the information stored in the other storage section, and is configured to display a list of the selected feature quantities. 10. The apparatus according to claim 9 , wherein when the item to be selected is music, the feature quantity is an artist, and when the item to be selected is a movie, the feature quantity is a director. | 0.770362 |
8,126,888 | 1 | 7 | 1. A computer-implemented method for digital search with task-enhanced search results, the method comprising: generating a search query based on interaction with a user; sending the search query to a search engine for an initial search; receiving initial search results from the search engine; predicting a current task being performed by the user at the time of the initial search, the current task being an action that the user is intending to perform at the time the search query is generated, wherein the current task corresponds to the search query without being specified by the search query, wherein predicting the current task includes associating with each of a plurality of tasks a probability that the current task is a particular task from among the plurality of tasks, wherein the associating uses past event records and associated task identifiers stored in a database, the associated task identifiers identifying the plurality of tasks from which the current task is predicted, and wherein the predicted current task is the particular task having a highest probability; computing task-related information from the predicted current task; filtering and ranking the initial search results based on the computed task-related information to produce enhanced search results; and presenting the enhanced search results to the user. | 1. A computer-implemented method for digital search with task-enhanced search results, the method comprising: generating a search query based on interaction with a user; sending the search query to a search engine for an initial search; receiving initial search results from the search engine; predicting a current task being performed by the user at the time of the initial search, the current task being an action that the user is intending to perform at the time the search query is generated, wherein the current task corresponds to the search query without being specified by the search query, wherein predicting the current task includes associating with each of a plurality of tasks a probability that the current task is a particular task from among the plurality of tasks, wherein the associating uses past event records and associated task identifiers stored in a database, the associated task identifiers identifying the plurality of tasks from which the current task is predicted, and wherein the predicted current task is the particular task having a highest probability; computing task-related information from the predicted current task; filtering and ranking the initial search results based on the computed task-related information to produce enhanced search results; and presenting the enhanced search results to the user. 7. The method of claim 1 wherein the ranking comprises calculating for each resource referenced in the initial search results a likelihood that the resource is associated with the current task being performed by the user. | 0.566667 |
9,443,008 | 1 | 3 | 1. A method, comprising: clustering a plurality of documents to obtain one or more first sets of clusters, wherein a first cluster of the one or more first sets of clusters comprises at least two first individual documents of the plurality of documents; accessing a search query after the clustering the plurality of documents; identifying a search result in response to the search query, wherein the search result comprises the at least two first individual documents of the plurality of documents; and clustering the search result to obtain a second set of clusters, wherein second individual documents of the search result belong to one second cluster of the second set of clusters, the clustering the search result comprising: for a unique pair of the second individual documents, computing a similarity measure for the second individual documents with respect to the search query based, at least in part, on the one or more first sets of clusters, wherein the similarity measure for the second individual documents is computed based, at least in part, on a weighted sum of a clustering similarity between the second individual documents with respect to the one or more first sets of clusters and a query-based similarity between the second individual documents with respect to the search query; and clustering the second individual documents based, at least in part, on the similarity measure; wherein the query-based similarity between the second individual documents is based, at least in part, on a fraction of a sum of: a textual match between the search query and the second individual documents to the textual match between the query, and the intersection of the documents; and wherein the clustering similarity between the second individual documents with respect to the one or more first sets of clusters is based, at least in part, on a weighted combination of agreements between the one or more first sets of clusters and the second individual documents. | 1. A method, comprising: clustering a plurality of documents to obtain one or more first sets of clusters, wherein a first cluster of the one or more first sets of clusters comprises at least two first individual documents of the plurality of documents; accessing a search query after the clustering the plurality of documents; identifying a search result in response to the search query, wherein the search result comprises the at least two first individual documents of the plurality of documents; and clustering the search result to obtain a second set of clusters, wherein second individual documents of the search result belong to one second cluster of the second set of clusters, the clustering the search result comprising: for a unique pair of the second individual documents, computing a similarity measure for the second individual documents with respect to the search query based, at least in part, on the one or more first sets of clusters, wherein the similarity measure for the second individual documents is computed based, at least in part, on a weighted sum of a clustering similarity between the second individual documents with respect to the one or more first sets of clusters and a query-based similarity between the second individual documents with respect to the search query; and clustering the second individual documents based, at least in part, on the similarity measure; wherein the query-based similarity between the second individual documents is based, at least in part, on a fraction of a sum of: a textual match between the search query and the second individual documents to the textual match between the query, and the intersection of the documents; and wherein the clustering similarity between the second individual documents with respect to the one or more first sets of clusters is based, at least in part, on a weighted combination of agreements between the one or more first sets of clusters and the second individual documents. 3. The method recited in claim 1 , further comprising: accessing a new document; and determining whether the new document belongs to a cluster from the first set of clusters; in response to determining that the new document belongs to the cluster from the first set of clusters, adding the new document to the cluster from first set of clusters; and in response to determining that the new document does not belong to any cluster from the first set of clusters, creating a new cluster, adding the new document to the new cluster, and adding the new cluster to the first set of clusters. | 0.5 |
8,577,912 | 1 | 2 | 1. A computer-implemented method comprising: under control of one or more computer systems configured with executable instructions, generating a set of signatures for a plurality of electronic documents by at least in part determining one or more codes each of which represents a unique portion of a selected content from the plurality of electronic documents, wherein the unique portion of the selected content is associated with a particular set of characters different from other sets of characters in the selected content, and wherein each of the one or more codes includes a hash that is generated based on utilizing the unique portion of the selected content as input in order to output the hash, the hash being generated as an identifier for the unique portion of the selected content, wherein each signature in the set of signatures is formed at least in part by a set of hashes; generating an index that associates the one or more codes for a signature with a document location unique to a corresponding electronic document; and upon determining that a received document location and a received signature for a requested electronic document is invalid: identifying two or more electronic documents from the plurality of electronic documents, each of the two or more identified electronic documents having a respective plural number of codes that match at least a subset of a plural number of codes of the received signature; and providing two or more links each specifying a respective location corresponding to a respective one of the two or more identified electronic documents, the respective location being determined according to the index and being different than the received document location, the two or more links being ordered according to a percentage of matching between the respective plural number of codes of each identified document and the plural number of codes of the received signature, the percentage indicating an estimate of an adjustable threshold amount of content associated with the received signature that will be included with each of the two or more links. | 1. A computer-implemented method comprising: under control of one or more computer systems configured with executable instructions, generating a set of signatures for a plurality of electronic documents by at least in part determining one or more codes each of which represents a unique portion of a selected content from the plurality of electronic documents, wherein the unique portion of the selected content is associated with a particular set of characters different from other sets of characters in the selected content, and wherein each of the one or more codes includes a hash that is generated based on utilizing the unique portion of the selected content as input in order to output the hash, the hash being generated as an identifier for the unique portion of the selected content, wherein each signature in the set of signatures is formed at least in part by a set of hashes; generating an index that associates the one or more codes for a signature with a document location unique to a corresponding electronic document; and upon determining that a received document location and a received signature for a requested electronic document is invalid: identifying two or more electronic documents from the plurality of electronic documents, each of the two or more identified electronic documents having a respective plural number of codes that match at least a subset of a plural number of codes of the received signature; and providing two or more links each specifying a respective location corresponding to a respective one of the two or more identified electronic documents, the respective location being determined according to the index and being different than the received document location, the two or more links being ordered according to a percentage of matching between the respective plural number of codes of each identified document and the plural number of codes of the received signature, the percentage indicating an estimate of an adjustable threshold amount of content associated with the received signature that will be included with each of the two or more links. 2. The computer-implemented method of claim 1 , wherein the determining of the one or more codes comprises calculating at least one code for each possible substring of a number of characters of the selected content. | 0.538627 |
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