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9,159,024 | 19 | 20 | 19. The PI platform of claim 14 , further comprising storing the types in a type schema comprising both a relational data store and non-relational data store, wherein data identities and data relationships are modeled in the relational data store, extended instance properties are modeled in a non-relational data store. | 19. The PI platform of claim 14 , further comprising storing the types in a type schema comprising both a relational data store and non-relational data store, wherein data identities and data relationships are modeled in the relational data store, extended instance properties are modeled in a non-relational data store. 20. The PI platform of claim 19 , further comprising segmenting the type schema for extended type-level properties by domains to enable complete removal of domain-specific properties/attributes while maintaining validity of anonymized data entity events and relationships. | 0.931278 |
9,792,914 | 1 | 6 | 1. A computer-implemented method comprising: obtaining, by a first computing device that is configured to respond to voice commands while in a locked state upon receipt of a particular, predefined hotword, a value for a setting that indicates that the first computing device is permitted to provide speaker verification data to other computing devices; receiving, by the first computing device, audio data that corresponds to an utterance of a voice command that is preceded by the particular, predefined hotword, the audio data being received while the first computing device is in a locked state and is co-located with a second computing device that is also configured to respond to voice commands that are preceded by the particular, predefined hotword; while the first computing device is in the locked state, and based on the obtained value for the setting that indicates that the first computing device is permitted to share speaker verification data with other computing devices, transmitting, by the first computing device, a message to the second computing device that (i) is co-located with the first computing device and (ii) is configured to respond to voice commands that are preceded by the particular, predefined hotword; and determining, by the first computing device, to remain in the locked state and not respond to the voice command despite receiving the audio data that corresponds to the utterance of the voice command that is preceded by the particular, predefined hotword. | 1. A computer-implemented method comprising: obtaining, by a first computing device that is configured to respond to voice commands while in a locked state upon receipt of a particular, predefined hotword, a value for a setting that indicates that the first computing device is permitted to provide speaker verification data to other computing devices; receiving, by the first computing device, audio data that corresponds to an utterance of a voice command that is preceded by the particular, predefined hotword, the audio data being received while the first computing device is in a locked state and is co-located with a second computing device that is also configured to respond to voice commands that are preceded by the particular, predefined hotword; while the first computing device is in the locked state, and based on the obtained value for the setting that indicates that the first computing device is permitted to share speaker verification data with other computing devices, transmitting, by the first computing device, a message to the second computing device that (i) is co-located with the first computing device and (ii) is configured to respond to voice commands that are preceded by the particular, predefined hotword; and determining, by the first computing device, to remain in the locked state and not respond to the voice command despite receiving the audio data that corresponds to the utterance of the voice command that is preceded by the particular, predefined hotword. 6. The method of claim 1 , comprising: determining, by the first computing device, that the second computing device is co-located with the first computing device, wherein transmitting the message to the second computing device is responsive to determining that the second computing device is co-located with the first computing device. | 0.825521 |
8,738,355 | 7 | 8 | 7. The method as recited in claim 1 , further comprising: with said at least one computing device: identifying first information to be translated based, at least in part, on said request for translation information. | 7. The method as recited in claim 1 , further comprising: with said at least one computing device: identifying first information to be translated based, at least in part, on said request for translation information. 8. The method as recited in claim 7 , wherein said request for translation information comprises at least one of: text information, audio information, and/or image information. | 0.935907 |
9,110,501 | 10 | 14 | 10. A computer program product for detecting and classifying talking segments of a face in a visual cue, the product comprising: an integrated circuit further comprising at least one processor; at least one memory having a computer program code within the integrated circuit; the at least one memory and the computer program product configured to, with the at least one processor, cause the product to: normalize and localize a face region for each frame of the visual cue; obtain a histogram of structure descriptive features for the frame in the visual cue; derive an integrated gradient histogram (IGH) from the descriptive features for the frame in the visual cue; compute entropy of the IGH for the frame in the visual cue; perform segmentation of the IGH to detect talking segments for the face in the visual cue; and analyze the segments for the frame in the visual cue for inferring emotions. | 10. A computer program product for detecting and classifying talking segments of a face in a visual cue, the product comprising: an integrated circuit further comprising at least one processor; at least one memory having a computer program code within the integrated circuit; the at least one memory and the computer program product configured to, with the at least one processor, cause the product to: normalize and localize a face region for each frame of the visual cue; obtain a histogram of structure descriptive features for the frame in the visual cue; derive an integrated gradient histogram (IGH) from the descriptive features for the frame in the visual cue; compute entropy of the IGH for the frame in the visual cue; perform segmentation of the IGH to detect talking segments for the face in the visual cue; and analyze the segments for the frame in the visual cue for inferring emotions. 14. The computer program product of claim 10 , wherein the entropy of the IGH is computed for determining an amount of an uncertainty involved in talking segments in the visual cue. | 0.562802 |
9,338,202 | 12 | 15 | 12. A method of managing a collaborative space with an electronic device, the method comprising: identifying, within a collaborative space, a recipient of a message; obtaining recipient context information that, based on information about the recipient, defines a context for communication with that specific recipient; and changing an operation of at least one functionality of the collaborative space based on the obtained recipient context information. | 12. A method of managing a collaborative space with an electronic device, the method comprising: identifying, within a collaborative space, a recipient of a message; obtaining recipient context information that, based on information about the recipient, defines a context for communication with that specific recipient; and changing an operation of at least one functionality of the collaborative space based on the obtained recipient context information. 15. The method of claim 12 , in which obtaining the recipient context information comprises inferring recipient context information based on data in a repository. | 0.661088 |
8,601,079 | 1 | 8 | 1. A network device, comprising: a storage device for storing one or more files that are associated with a user of at least one client device; and a processor for enabling actions, the actions comprising: receiving one or more message file attachments, wherein the one or more message file attachments are associated with the user; automatically determining one or more automatic tags that are associated with the one or more message file attachments, wherein each of the one or more message file attachments is associated with at least one of the one or more automatic tags; automatically generating a personalized hierarchical structure of tags (“PHST”) from the one or more automatic tags; receiving, from the user, one or more custom tags for at least one of the one or more message file attachments; associating the at least one of the one or more message file attachments with the one or more custom tags; automatically generating at least one custom generated tag using the one or more custom tags; automatically modifying the PHST to include the at least one custom generated tag within the hierarchy; and displaying the PHST which separately indicates the one or more automatic tags and the at least one custom generated tag to the user such that the user is enabled to select at least one tag which in turn selects one or more files associated with the selected tag for attachment to a message. | 1. A network device, comprising: a storage device for storing one or more files that are associated with a user of at least one client device; and a processor for enabling actions, the actions comprising: receiving one or more message file attachments, wherein the one or more message file attachments are associated with the user; automatically determining one or more automatic tags that are associated with the one or more message file attachments, wherein each of the one or more message file attachments is associated with at least one of the one or more automatic tags; automatically generating a personalized hierarchical structure of tags (“PHST”) from the one or more automatic tags; receiving, from the user, one or more custom tags for at least one of the one or more message file attachments; associating the at least one of the one or more message file attachments with the one or more custom tags; automatically generating at least one custom generated tag using the one or more custom tags; automatically modifying the PHST to include the at least one custom generated tag within the hierarchy; and displaying the PHST which separately indicates the one or more automatic tags and the at least one custom generated tag to the user such that the user is enabled to select at least one tag which in turn selects one or more files associated with the selected tag for attachment to a message. 8. The network device of claim 1 , wherein the one or more automatic tags are determined from an application that is utilized to access or execute an associated file. | 0.848263 |
9,626,455 | 1 | 6 | 1. A method for providing for display an estimated relevance indicator for a result set of documents, the method comprising: receiving, at a computer comprising a processor and a memory component, a search query, wherein the search query includes a plurality of query terms; searching a database using the search query to identify the result set of documents, wherein the result set of documents are identified based on the search query; calculating an estimated relevance score for the result set of documents, wherein the estimated relevance score is indicative of a degree to which the result set of documents are relevant to the search query; providing for display on a graphical user interface a results feedback element and an estimated relevance element comprising the estimated relevance indicator based on the estimated relevance score, wherein the estimated relevance indicator provides a visual indication of the degree to which the result set of documents are relevant to the search query; updating the results feedback element based on the result set of documents; and updating the estimated relevance element to display the estimated relevance indicator based on the estimated relevance score. | 1. A method for providing for display an estimated relevance indicator for a result set of documents, the method comprising: receiving, at a computer comprising a processor and a memory component, a search query, wherein the search query includes a plurality of query terms; searching a database using the search query to identify the result set of documents, wherein the result set of documents are identified based on the search query; calculating an estimated relevance score for the result set of documents, wherein the estimated relevance score is indicative of a degree to which the result set of documents are relevant to the search query; providing for display on a graphical user interface a results feedback element and an estimated relevance element comprising the estimated relevance indicator based on the estimated relevance score, wherein the estimated relevance indicator provides a visual indication of the degree to which the result set of documents are relevant to the search query; updating the results feedback element based on the result set of documents; and updating the estimated relevance element to display the estimated relevance indicator based on the estimated relevance score. 6. The method of claim 1 , further comprising determining one or more results characteristics indicative of a characteristic of the result set of documents, wherein the estimated relevance score is calculated based on the one or more results characteristics. | 0.930757 |
3,945,482 | 2 | 3 | 2. A keyboard, as in claim 1, comprising letter keys arranged as follows: on the left hand side of the keyboard as viewed by the operator: a vowel assigned to the first thumb key; and on the right hand side of the keyboard as viewed by the operator: a consonant assigned to the fourth thumb key, a second consonant assigned to the fifth lower letter key, a third consonant assigned to the sixth lower letter key, a fourth consonant assigned to the seventh lower letter key, and a fifth consonant assigned to the eighth lower letter key; wherein each of these five consonants is a different letter. | 2. A keyboard, as in claim 1, comprising letter keys arranged as follows: on the left hand side of the keyboard as viewed by the operator: a vowel assigned to the first thumb key; and on the right hand side of the keyboard as viewed by the operator: a consonant assigned to the fourth thumb key, a second consonant assigned to the fifth lower letter key, a third consonant assigned to the sixth lower letter key, a fourth consonant assigned to the seventh lower letter key, and a fifth consonant assigned to the eighth lower letter key; wherein each of these five consonants is a different letter. 3. A keyboard, as in claim 2, comprising letter and control keys arranged as follows: on the left hand side of the keyboard as viewed by the operator: a vowel assigned to the first thumb key, and the carriage return key assigned to the fourth thumb key; and on the right hand side of the keyboard as viewed by the operator: a consonant on the fourth thumb key, and another consonant on the sixth thumb key. | 0.962525 |
7,519,566 | 33 | 37 | 33. A computer program product as recited in claim 32 , wherein the contacting entities are potential or current customers and the answering entities are sales or service agents in communication with the potential or current customers. | 33. A computer program product as recited in claim 32 , wherein the contacting entities are potential or current customers and the answering entities are sales or service agents in communication with the potential or current customers. 37. A computer program product as recited in claim 33 , wherein the interactive server is part of a telephone service center. | 0.977734 |
7,493,499 | 6 | 9 | 6. The apparatus of claim 1 further comprising a publishing tool that accepts plaintext documents, encrypts the plaintext documents and generates document identifiers for each encrypted document. | 6. The apparatus of claim 1 further comprising a publishing tool that accepts plaintext documents, encrypts the plaintext documents and generates document identifiers for each encrypted document. 9. The apparatus of claim 6 wherein the publishing tool computes a document identifier for each encrypted document from the encrypted document content and a text string embedded in the publishing tool code. | 0.931425 |
8,782,050 | 12 | 18 | 12. A method of sorting a plurality of documents to be accessed by a plurality of readers or groups of readers, said method comprising the steps of developing a list of interest areas represented by said plurality of readers, developing a hierarchical index of subject matter referred to in said plurality of documents, each entry in said hierarchical index having at least one of an index term and an associated code, assigning a limited number of index terms and associated codes of said hierarchical index to each document of said plurality of documents, assigning at least one of said interest areas to each document of said plurality of documents, and assembling a plurality of hierarchical indices of subject matter for respective interest areas from index terms and associated codes assigned to documents in each of said interest areas, sorting respective documents of said plurality of documents in accordance with a respective one of said plurality of hierarchical indices for respective interest areas, and at least one of browsing said index terms and selecting an index term at a less refined hierarchical level as a context for another index term for purposes of a search, and sorting search results based on an index term and an interest area in accordance with components of said documents. | 12. A method of sorting a plurality of documents to be accessed by a plurality of readers or groups of readers, said method comprising the steps of developing a list of interest areas represented by said plurality of readers, developing a hierarchical index of subject matter referred to in said plurality of documents, each entry in said hierarchical index having at least one of an index term and an associated code, assigning a limited number of index terms and associated codes of said hierarchical index to each document of said plurality of documents, assigning at least one of said interest areas to each document of said plurality of documents, and assembling a plurality of hierarchical indices of subject matter for respective interest areas from index terms and associated codes assigned to documents in each of said interest areas, sorting respective documents of said plurality of documents in accordance with a respective one of said plurality of hierarchical indices for respective interest areas, and at least one of browsing said index terms and selecting an index term at a less refined hierarchical level as a context for another index term for purposes of a search, and sorting search results based on an index term and an interest area in accordance with components of said documents. 18. The method as recited in claim 12 , including the further step of designating a document as a key article. | 0.668675 |
10,079,014 | 29 | 32 | 29. A data processing system comprising: an input device for receiving a speech input from a user; a set of one or more processors coupled to the input device; and a memory coupled to the set of one or more processors, the memory storing a phonetic dictionary for speech recognition and storing an extended phonetic dictionary for the user; wherein the extended phonetic dictionary is produced by: responsive to detecting a change in a user's set of one or more databases, processing, using the set of one or more processors, words in the user's set of one or more databases using a set of one or more pronunciation guessers, and wherein the set of one or more processors performs speech recognition on the speech input to determine phonemes in the speech input and to determine a best match using the determined phonemes, the phonetic dictionary, and the extended phonetic dictionary. | 29. A data processing system comprising: an input device for receiving a speech input from a user; a set of one or more processors coupled to the input device; and a memory coupled to the set of one or more processors, the memory storing a phonetic dictionary for speech recognition and storing an extended phonetic dictionary for the user; wherein the extended phonetic dictionary is produced by: responsive to detecting a change in a user's set of one or more databases, processing, using the set of one or more processors, words in the user's set of one or more databases using a set of one or more pronunciation guessers, and wherein the set of one or more processors performs speech recognition on the speech input to determine phonemes in the speech input and to determine a best match using the determined phonemes, the phonetic dictionary, and the extended phonetic dictionary. 32. The system of claim 29 , wherein the user's set of one or more databases comprises a contacts database and wherein the change in the user's set of one or more databases comprises an addition of a name of a contact to the contacts database. | 0.833333 |
8,739,129 | 28 | 29 | 28. One or more non-transitory computer-readable media storing computer-executable instructions executable by a processor, the media storing one or more instructions for: identifying a call chain of methods that are called during an execution of a graphical model, the graphical model under control of a multi-domain unified debugger that enables the debugging of the graphical model, where: the graphical model includes: a first entity associated with a first modeling domain of a plurality of different types of modeling domains, and a second entity associated with a second modeling domain of the plurality of different types of modeling domains, the different types of modeling domains include a statechart domain, a time-based block diagram domain, a physical system domain, a data flow diagram domain, a unified modeling language domain, a discrete event modeling domain, or a compiled code domain, the call chain indicates an execution order of the methods associated with the first entity and the second entity, the first entity and the second entity are associated with a programming interface, and the programming interface allows access to information associated with the first entity and the second entity; transferring information associated with the first entity via the programming interface to the multi-domain unified debugger after executing the first entity; generating a first domain-specific debugger view of the graphical model, the generated first domain-specific debugger view consistent with the first modeling domain, the generating first domain-specific debugger view being based on the transferring information associated with the first entity; transferring information associated with the second entity via the programming interface to the debugger after executing the second entity; generating a second domain-specific debugger view of the graphical model, the generated second domain-specific debugger view consistent with the second modeling domain, the generating the second domain-specific debugger being based on the transferring information associated with the second entity; displaying the first domain-specific debugger view on a display device, where the first domain-specific debugger view is displayed: after the first entity is executed, concurrently with the identified call chain, and using a first user interface element; automatically transitioning to an updated domain display, the transitioning occurring when the second entity is executing; and displaying the second domain-specific debugger view on the display device, where the second domain-specific debugger view is displayed: based on the transitioning, after the second entity is executed, concurrently with the identified call chain, and using a second user interface element. | 28. One or more non-transitory computer-readable media storing computer-executable instructions executable by a processor, the media storing one or more instructions for: identifying a call chain of methods that are called during an execution of a graphical model, the graphical model under control of a multi-domain unified debugger that enables the debugging of the graphical model, where: the graphical model includes: a first entity associated with a first modeling domain of a plurality of different types of modeling domains, and a second entity associated with a second modeling domain of the plurality of different types of modeling domains, the different types of modeling domains include a statechart domain, a time-based block diagram domain, a physical system domain, a data flow diagram domain, a unified modeling language domain, a discrete event modeling domain, or a compiled code domain, the call chain indicates an execution order of the methods associated with the first entity and the second entity, the first entity and the second entity are associated with a programming interface, and the programming interface allows access to information associated with the first entity and the second entity; transferring information associated with the first entity via the programming interface to the multi-domain unified debugger after executing the first entity; generating a first domain-specific debugger view of the graphical model, the generated first domain-specific debugger view consistent with the first modeling domain, the generating first domain-specific debugger view being based on the transferring information associated with the first entity; transferring information associated with the second entity via the programming interface to the debugger after executing the second entity; generating a second domain-specific debugger view of the graphical model, the generated second domain-specific debugger view consistent with the second modeling domain, the generating the second domain-specific debugger being based on the transferring information associated with the second entity; displaying the first domain-specific debugger view on a display device, where the first domain-specific debugger view is displayed: after the first entity is executed, concurrently with the identified call chain, and using a first user interface element; automatically transitioning to an updated domain display, the transitioning occurring when the second entity is executing; and displaying the second domain-specific debugger view on the display device, where the second domain-specific debugger view is displayed: based on the transitioning, after the second entity is executed, concurrently with the identified call chain, and using a second user interface element. 29. The non-transitory computer-readable media of claim 28 wherein the displayed call chain includes one or more names associated with the called methods. | 0.503226 |
10,050,913 | 1 | 2 | 1. A method for sending and receiving emails using international multilingual mailboxes, comprising the steps of: 1) setting an X-alternate-address field in the header of an email message in an international multilingual mailbox to record a substitute English email address corresponding to the international multilingual mailbox; and setting an X-address-language field in the header to set a predetermined language to describe email sender's email address; 2) before sending an email generated by a sending terminal that supports international multilingual mailbox, checking whether the receiving terminal of the email supports international multilingual mailbox; 3) if the receiving terminal supports emails of international multilingual mailbox, directly sending the email with the field of X-alternate-address in the email header; extracting the X-address-language field from the header of the email by the receiving terminal, determining the corresponding predetermined language, and sending a prompt in English or in the predetermined language to the email recipient; and 4) if the receiving terminal does not support emails of international multilingual mailbox, sending the email according to the English email address in the X-alternate-address field; and receiving the email by the receiving terminal. | 1. A method for sending and receiving emails using international multilingual mailboxes, comprising the steps of: 1) setting an X-alternate-address field in the header of an email message in an international multilingual mailbox to record a substitute English email address corresponding to the international multilingual mailbox; and setting an X-address-language field in the header to set a predetermined language to describe email sender's email address; 2) before sending an email generated by a sending terminal that supports international multilingual mailbox, checking whether the receiving terminal of the email supports international multilingual mailbox; 3) if the receiving terminal supports emails of international multilingual mailbox, directly sending the email with the field of X-alternate-address in the email header; extracting the X-address-language field from the header of the email by the receiving terminal, determining the corresponding predetermined language, and sending a prompt in English or in the predetermined language to the email recipient; and 4) if the receiving terminal does not support emails of international multilingual mailbox, sending the email according to the English email address in the X-alternate-address field; and receiving the email by the receiving terminal. 2. The method according to claim 1 , wherein the sender's email address is named using a language specified in UTF-8 mailbox. | 0.813433 |
8,413,069 | 1 | 10 | 1. A method for specifying a written character, comprising: receiving a selection of a first character shape; identifying a first plurality of characters containing said first character shape; displaying at least one of said first plurality of characters; receiving a selection of a subtraction mode; receiving a selection of a second character shape; and in response to said selection of a second character shape, modifying said first plurality of characters to obtain a second plurality of characters, wherein said second plurality of characters includes only characters containing said first selected shape but not said second selected shape; and displaying at least one of said second plurality of identified characters. | 1. A method for specifying a written character, comprising: receiving a selection of a first character shape; identifying a first plurality of characters containing said first character shape; displaying at least one of said first plurality of characters; receiving a selection of a subtraction mode; receiving a selection of a second character shape; and in response to said selection of a second character shape, modifying said first plurality of characters to obtain a second plurality of characters, wherein said second plurality of characters includes only characters containing said first selected shape but not said second selected shape; and displaying at least one of said second plurality of identified characters. 10. The method of claim 1 , wherein said method is implemented by a computational component comprising a logic circuit. | 0.87931 |
9,495,955 | 6 | 14 | 6. A computer-implemented method, comprising: under control of one or more computing devices configured with specific computer-executable instructions, receiving an identification of a desired training utterance having a first portion and a second portion, wherein the desired training utterance is not included in a corpus; selecting, from the corpus, a first utterance comprising at least a first portion of the desired training utterance; extracting, from the first utterance, a portion of the first utterance comprising the first portion of the desired training utterance, wherein the first portion of the desired training utterance and at least the portion of the first utterance are associated with a first desired characteristic; selecting, from the corpus, a second utterance comprising at least a second portion of the desired training utterance; extracting, from the second utterance, a portion of the second utterance comprising the second portion of the desired training utterance, wherein the second portion of the desired training utterance and at least the portion of the second utterance are associated with a second desired characteristic; concatenating at least the portion of the first utterance and the portion of the second utterance to generate the desired training utterance; and training an acoustic model using the desired training utterance that is generated. | 6. A computer-implemented method, comprising: under control of one or more computing devices configured with specific computer-executable instructions, receiving an identification of a desired training utterance having a first portion and a second portion, wherein the desired training utterance is not included in a corpus; selecting, from the corpus, a first utterance comprising at least a first portion of the desired training utterance; extracting, from the first utterance, a portion of the first utterance comprising the first portion of the desired training utterance, wherein the first portion of the desired training utterance and at least the portion of the first utterance are associated with a first desired characteristic; selecting, from the corpus, a second utterance comprising at least a second portion of the desired training utterance; extracting, from the second utterance, a portion of the second utterance comprising the second portion of the desired training utterance, wherein the second portion of the desired training utterance and at least the portion of the second utterance are associated with a second desired characteristic; concatenating at least the portion of the first utterance and the portion of the second utterance to generate the desired training utterance; and training an acoustic model using the desired training utterance that is generated. 14. The computer-implemented method of claim 6 , wherein the first desired characteristic is one of a gender, an age category, or an accent. | 0.927159 |
7,536,370 | 2 | 5 | 2. The method of claim 1 , wherein said analyses perform Bayesian inferential logic of types selected from the group consisting of derivative list diagnostic inference analysis, derivative list causal inference analysis, and derivative list intercausal analysis. | 2. The method of claim 1 , wherein said analyses perform Bayesian inferential logic of types selected from the group consisting of derivative list diagnostic inference analysis, derivative list causal inference analysis, and derivative list intercausal analysis. 5. The method of claim 2 , wherein said derived list diagnostic inference analysis performed by said rules engine identifies potential root causes for known effects, said effects being selected from the group consisting of error events, fault events, and chargeable events. | 0.936482 |
9,607,279 | 14 | 20 | 14. A method of detecting pre-determined phrases to determine compliance quality, comprising: storing a plurality of pre-determined phrases in association with an event; receiving an audible input, from communication devices of a caller and an agent, in a communication over a communications network; determining, by a processor of a computer facilitating the communication, an occurrence of the event based on the communication; determining, by the processor, whether a pre-determined phrase of the plurality of pre-determined phrases associated with the event is present in the audible input received; and determining, by the processor, a compliance rating of the agent, based on a presence of the pre-determined phrase associated with the event and the determination of the occurrence of the event, for evaluating the agent. | 14. A method of detecting pre-determined phrases to determine compliance quality, comprising: storing a plurality of pre-determined phrases in association with an event; receiving an audible input, from communication devices of a caller and an agent, in a communication over a communications network; determining, by a processor of a computer facilitating the communication, an occurrence of the event based on the communication; determining, by the processor, whether a pre-determined phrase of the plurality of pre-determined phrases associated with the event is present in the audible input received; and determining, by the processor, a compliance rating of the agent, based on a presence of the pre-determined phrase associated with the event and the determination of the occurrence of the event, for evaluating the agent. 20. The method of claim 14 , wherein the event is an attribute included in account information of the caller. | 0.890562 |
9,900,392 | 1 | 5 | 1. A method comprising: maintaining one or more groups at an online system, each group including one or more users of the online system, with each user associated with a location; determining a location associated with each of the one or more groups, the location associated with a group based at least in part on locations associated with users in the group, comprising: determining a centroid of the locations associated with users in the group; determining distances between locations associated with each user in the group and the centroid; generating a histogram of the determined distances; and determining the centroid as the location associated with the group in response to at least a threshold percentile of the determined distances from the histogram being less than or equal to a threshold distance; receiving a request from a requesting user of a social networking system to identify one or more groups maintained by the social networking system; determining a plurality of candidate groups for the requesting user from the one or more groups based at least in part on a distance between a location associated with the requesting user and locations associated with each of the one or more groups, a candidate group associated with a location within a threshold distance of the location associated with the user; including the plurality of candidate groups in one or more selection processes selecting a set of groups; and communicating information identifying the selected set of groups to a client device associated with the requesting user for presentation. | 1. A method comprising: maintaining one or more groups at an online system, each group including one or more users of the online system, with each user associated with a location; determining a location associated with each of the one or more groups, the location associated with a group based at least in part on locations associated with users in the group, comprising: determining a centroid of the locations associated with users in the group; determining distances between locations associated with each user in the group and the centroid; generating a histogram of the determined distances; and determining the centroid as the location associated with the group in response to at least a threshold percentile of the determined distances from the histogram being less than or equal to a threshold distance; receiving a request from a requesting user of a social networking system to identify one or more groups maintained by the social networking system; determining a plurality of candidate groups for the requesting user from the one or more groups based at least in part on a distance between a location associated with the requesting user and locations associated with each of the one or more groups, a candidate group associated with a location within a threshold distance of the location associated with the user; including the plurality of candidate groups in one or more selection processes selecting a set of groups; and communicating information identifying the selected set of groups to a client device associated with the requesting user for presentation. 5. The method of claim 1 , wherein determining the plurality of candidate groups for the requesting user from the one or more groups based at least in part on the distance between the location associated with the requesting user and locations associated with each of the one or more groups comprises: identifying a geographic region including the location associated with the requesting user, the geographic region including locations within a specified geographic area; and identifying groups associated with locations included in the identified geographic region; and selecting identified groups satisfying at least a threshold number of criteria as candidate groups. | 0.732827 |
7,522,075 | 5 | 6 | 5. The method of claim 1 , wherein: the line feed command characters divide the sequence into portions that are visually displayable when received by a user's device, as two or more lines of equal length. | 5. The method of claim 1 , wherein: the line feed command characters divide the sequence into portions that are visually displayable when received by a user's device, as two or more lines of equal length. 6. The method of claim 5 , wherein: each line initiates and terminates with one or more special marker characters. | 0.933566 |
8,407,169 | 1 | 2 | 1. A system for representation of a real world problem situation said system comprising: a. a computer software process acquiring or capable of accepting a set of input data comprising: seed facts, said set of input data representing real world objects pertaining to a real-world problem situation; b. the computer software process generating new data consisting of additional not-previously-known facts about said real-world problem situation, said additional not-previously known facts comprising acquired facts and reasoned facts; c. the computer software process utilizing: a fact structured representation method representing a first group of facts about a problem situation; a rule structured representation method for representing a first group of rules about a class of problem situations; d. said computer software process representing a plurality of causal features of said problem situation such that a reasoning process results; e. said reasoning process further characterized as performing some elements of deep reasoning. | 1. A system for representation of a real world problem situation said system comprising: a. a computer software process acquiring or capable of accepting a set of input data comprising: seed facts, said set of input data representing real world objects pertaining to a real-world problem situation; b. the computer software process generating new data consisting of additional not-previously-known facts about said real-world problem situation, said additional not-previously known facts comprising acquired facts and reasoned facts; c. the computer software process utilizing: a fact structured representation method representing a first group of facts about a problem situation; a rule structured representation method for representing a first group of rules about a class of problem situations; d. said computer software process representing a plurality of causal features of said problem situation such that a reasoning process results; e. said reasoning process further characterized as performing some elements of deep reasoning. 2. The system according to claim 1 wherein said facts representing real world objects further comprise attributes of said real world objects. | 0.825495 |
8,838,517 | 1 | 15 | 1. A method of assessing a person's personal taste, comprising: causing an electronic device to output a user interface to a person; receiving, via the user interface, a rating set comprising the person's ratings for each of a plurality of rated consumable items, wherein at least a portion of the received ratings comprise item ratings rather than ratings of item characteristics; and by one or more processors, developing a preference model for the person based on the received ratings by: accessing a database comprising values of characteristics for a plurality of candidate consumable items, identifying the candidate consumable items in the database that correspond to the rated consumable items, using the received item ratings and the values of characteristics for the candidate consumable items in the database that correspond to the rated consumable items to determine the preference model. | 1. A method of assessing a person's personal taste, comprising: causing an electronic device to output a user interface to a person; receiving, via the user interface, a rating set comprising the person's ratings for each of a plurality of rated consumable items, wherein at least a portion of the received ratings comprise item ratings rather than ratings of item characteristics; and by one or more processors, developing a preference model for the person based on the received ratings by: accessing a database comprising values of characteristics for a plurality of candidate consumable items, identifying the candidate consumable items in the database that correspond to the rated consumable items, using the received item ratings and the values of characteristics for the candidate consumable items in the database that correspond to the rated consumable items to determine the preference model. 15. The method of claim 1 , further comprising: receiving a request for a recommendation of a consumable item, wherein the request comprises a pairing criterion; retrieving from the database one or more consumable items that have characteristics that satisfy the pairing criterion; selecting one or more of the retrieved items having characteristics that, based on the person's preference profile, the person is expected to like; and causing the user interface to present a recommendation of the one or more selected consumable items to the person. | 0.786438 |
9,626,651 | 1 | 2 | 1. A method for automated social networking for e-meetings, the method comprising: monitoring content provided by different participants to an e-meeting managed by an e-meeting server executing in memory of a host computer by speech recognizing audio provided to the e-meeting into textual content and monitoring the textual content speech recognized from the audio; detecting a name in the monitored content; comparing the detected name to names in a contact list for a social networking system executing externally to the e-meeting, the social networking system suggesting introductions of new contacts to end users of the social networking system based upon existing relationships of the end users; and, triggering generation of a social networking introduction for the name to each of the different participants to the e-meeting in a social networking system to which each of the different participants belong, in response to matching the detected name to a name in the contact list. | 1. A method for automated social networking for e-meetings, the method comprising: monitoring content provided by different participants to an e-meeting managed by an e-meeting server executing in memory of a host computer by speech recognizing audio provided to the e-meeting into textual content and monitoring the textual content speech recognized from the audio; detecting a name in the monitored content; comparing the detected name to names in a contact list for a social networking system executing externally to the e-meeting, the social networking system suggesting introductions of new contacts to end users of the social networking system based upon existing relationships of the end users; and, triggering generation of a social networking introduction for the name to each of the different participants to the e-meeting in a social networking system to which each of the different participants belong, in response to matching the detected name to a name in the contact list. 2. The method of claim 1 , wherein comparing the detected name to names in a contact list for a social networking system executing externally to the e-meeting, comprises comparing the detected name to names in different contact lists for respectively different social networking systems each executing externally to the e-meeting. | 0.794007 |
8,583,563 | 1 | 7 | 1. An apparatus, comprising: a server that includes a processor and a memory, the server being configured to interface with one or more end users and to manage information related to one or more of the end users, wherein the server is further configured to determine a personality type for one or more end users from a particular set of personality types and to match end users based on at least one end-user designated preference and on relationship rules of the personality types in the particular set of personality types, the rules outlining compatibilities between the personality types in the particular set of personality types, wherein the personality type of a user is determined from the particular set of personality types, and the determination of personality type from the particular set of personality types is based, at least in part, on: answers from the end users to questions provided by a central website including questions prompting end users for responses to pictures of human smiles, each response to a picture of a human smile including an end user assessment of whether the pictured smile is one of genuine or artificial, wherein the responses to questions prompting end users for responses to pictures of human smiles are used to infer hormonal levels of the respective end user and the determination of personality type from the particular set of personality types is further based on the inferred hormonal levels of the respective end user; word choices from the end users as found in the answers; and a frequency of word usage from the end users in the answers. | 1. An apparatus, comprising: a server that includes a processor and a memory, the server being configured to interface with one or more end users and to manage information related to one or more of the end users, wherein the server is further configured to determine a personality type for one or more end users from a particular set of personality types and to match end users based on at least one end-user designated preference and on relationship rules of the personality types in the particular set of personality types, the rules outlining compatibilities between the personality types in the particular set of personality types, wherein the personality type of a user is determined from the particular set of personality types, and the determination of personality type from the particular set of personality types is based, at least in part, on: answers from the end users to questions provided by a central website including questions prompting end users for responses to pictures of human smiles, each response to a picture of a human smile including an end user assessment of whether the pictured smile is one of genuine or artificial, wherein the responses to questions prompting end users for responses to pictures of human smiles are used to infer hormonal levels of the respective end user and the determination of personality type from the particular set of personality types is further based on the inferred hormonal levels of the respective end user; word choices from the end users as found in the answers; and a frequency of word usage from the end users in the answers. 7. The apparatus of claim 1 , wherein the particular set of personality types includes Director, Negotiator, Explorer, and Builder personality types. | 0.847959 |
8,949,132 | 16 | 19 | 16. The computer-readable storage device of claim 15 , having additional instructions stored which, when executed by the computing device, result in operations further comprising extracting a linguistic term from the website. | 16. The computer-readable storage device of claim 15 , having additional instructions stored which, when executed by the computing device, result in operations further comprising extracting a linguistic term from the website. 19. The computer-readable storage device of claim 16 , wherein the level of incorporation is based on the linguistic item. | 0.93857 |
9,350,791 | 8 | 13 | 8. A system comprising: a processor; and a memory, the memory storing instructions that, when executed by the processor, cause the processor to: provide a platform server and a plurality of intermediate servers, wherein each of the plurality of intermediate servers connects and maintains a persistent connection to the platform server, wherein the plurality of intermediate servers communicate and maintain a plurality of persistent connections with a plurality of edge servers; receive, by a port at an intermediate server among the plurality of intermediate servers, a service request from a given edge server of the plurality of edge servers over a first persistent connection; insert, by the processor at the intermediate server, a given state identifier to the service request, wherein the given state identifier is associated to a connection identity of the first persistent connection, and wherein the association is stored in memory at the intermediate server; transmit, at the intermediate server, the service request to the platform server over a second persistent connection; receive, at the intermediate server, a response message over the second persistent connection, the response message having been generated by the platform server in response to the service request, wherein the response message includes the given state identifier; retrieve, at the intermediate server, the connection identity of the first persistent connection using the given state identifier, wherein the given state identifier is the same state identifier transmitted within the service request; and route, at the intermediate server, the response message to a selected connection of the plurality of persistent connections with the plurality of edge servers, wherein the selected connection is based on the retrieved connection identity. | 8. A system comprising: a processor; and a memory, the memory storing instructions that, when executed by the processor, cause the processor to: provide a platform server and a plurality of intermediate servers, wherein each of the plurality of intermediate servers connects and maintains a persistent connection to the platform server, wherein the plurality of intermediate servers communicate and maintain a plurality of persistent connections with a plurality of edge servers; receive, by a port at an intermediate server among the plurality of intermediate servers, a service request from a given edge server of the plurality of edge servers over a first persistent connection; insert, by the processor at the intermediate server, a given state identifier to the service request, wherein the given state identifier is associated to a connection identity of the first persistent connection, and wherein the association is stored in memory at the intermediate server; transmit, at the intermediate server, the service request to the platform server over a second persistent connection; receive, at the intermediate server, a response message over the second persistent connection, the response message having been generated by the platform server in response to the service request, wherein the response message includes the given state identifier; retrieve, at the intermediate server, the connection identity of the first persistent connection using the given state identifier, wherein the given state identifier is the same state identifier transmitted within the service request; and route, at the intermediate server, the response message to a selected connection of the plurality of persistent connections with the plurality of edge servers, wherein the selected connection is based on the retrieved connection identity. 13. The system of claim 8 , wherein the given state identifier is inserted into a header portion of the service request. | 0.926199 |
9,589,579 | 14 | 19 | 14. A system comprising: one or more processors; and memory communicatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: evaluating a natural language processing system by causing the natural language processing system to process test user input that has been previously associated by a user with a particular result; and in the event that the evaluation indicates that the processing does not satisfy one or more criteria, presenting a user interface with an icon for the test user input to enable at least one of the user or another user to modify the natural language processing system, the icon indicating that the processing of the test user input does not satisfy the one or more criteria. | 14. A system comprising: one or more processors; and memory communicatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: evaluating a natural language processing system by causing the natural language processing system to process test user input that has been previously associated by a user with a particular result; and in the event that the evaluation indicates that the processing does not satisfy one or more criteria, presenting a user interface with an icon for the test user input to enable at least one of the user or another user to modify the natural language processing system, the icon indicating that the processing of the test user input does not satisfy the one or more criteria. 19. The system of claim 14 , wherein the operations further comprise: causing a virtual assistant to be presented to facilitate a user conversation; receiving user input during the conversation; causing the natural language processing system to process the user input to formulate a response to the user input; and causing the virtual assistant to provide the response. | 0.784965 |
9,378,517 | 1 | 2 | 1. A method for providing a third-party content provider with search queries that may be targeted by a given keyword, comprising: receiving, at a processor of a data processing system from a content provider computing device, a request to identify a plurality of search queries that match a keyword; determining, by the processor, a raw form of the keyword by arranging tokens corresponding to the keyword in alphabetical order and performing one or more of correcting misspelt words, replacing plural words with singular words, and replacing uppercase letters with lowercase letters; retrieving, by the processor, from a table corresponding to the raw form of the keyword, a plurality of potential search queries with which a content item was selected for display responsive to a bid for a keyword that corresponds to the raw form of the keyword; providing, by the processor to the content provider computing device, the plurality of potential search queries; and receiving, at the processor from the content provider computing device, a selection of a search query of the plurality of potential search queries and a bid associated with the search query. | 1. A method for providing a third-party content provider with search queries that may be targeted by a given keyword, comprising: receiving, at a processor of a data processing system from a content provider computing device, a request to identify a plurality of search queries that match a keyword; determining, by the processor, a raw form of the keyword by arranging tokens corresponding to the keyword in alphabetical order and performing one or more of correcting misspelt words, replacing plural words with singular words, and replacing uppercase letters with lowercase letters; retrieving, by the processor, from a table corresponding to the raw form of the keyword, a plurality of potential search queries with which a content item was selected for display responsive to a bid for a keyword that corresponds to the raw form of the keyword; providing, by the processor to the content provider computing device, the plurality of potential search queries; and receiving, at the processor from the content provider computing device, a selection of a search query of the plurality of potential search queries and a bid associated with the search query. 2. The method of claim 1 , wherein the table includes one or more of targeting information, a query frequency, a keyword frequency with which a particular keyword was used to select a third-party content item showed, and a spell-corrected query. | 0.830097 |
5,548,681 | 12 | 16 | 12. A speech dialogue system, comprising: microphone means for receiving a speech input uttered by a human speaker and outputting microphone output signals; speech recognition means for receiving input signals and recognizing the speech input received by the microphone means; synthetic speech response generation means for generating a synthetic speech response appropriate for the speech input recognized by the speech recognition means; loudspeaker means for outputting the synthetic speech response to the human speaker; and synthetic speech response cancellation means for cancelling the synthetic speech response, which is outputted from the loudspeaker means and then received by the microphone means, from the microphone output signals, to obtain input signals to be supplied to the speech recognition means from which the speech recognition means recognizes the speech input, the synthetic speech response cancellation means further comprising: adaptive filter means for adapting the synthetic speech response generated by the synthetic speech response generation means, to obtain an adaptive filter output; convolution means for calculating a convolution of the adaptive filter output obtained by the adaptive filter means and the synthetic speech response generated by the synthetic speech response generation means; subtractor means for subtracting the convolution calculated by the convolution means from the microphone output signals, to obtain the input signals supplied to the speech recognition means; at least one smoothing filter means for smoothing the synthetic speech response generated by the synthetic speech response generation means; and switching means for controlling an operation of the adaptive filter means according to an output power level of the smoothing filter means. | 12. A speech dialogue system, comprising: microphone means for receiving a speech input uttered by a human speaker and outputting microphone output signals; speech recognition means for receiving input signals and recognizing the speech input received by the microphone means; synthetic speech response generation means for generating a synthetic speech response appropriate for the speech input recognized by the speech recognition means; loudspeaker means for outputting the synthetic speech response to the human speaker; and synthetic speech response cancellation means for cancelling the synthetic speech response, which is outputted from the loudspeaker means and then received by the microphone means, from the microphone output signals, to obtain input signals to be supplied to the speech recognition means from which the speech recognition means recognizes the speech input, the synthetic speech response cancellation means further comprising: adaptive filter means for adapting the synthetic speech response generated by the synthetic speech response generation means, to obtain an adaptive filter output; convolution means for calculating a convolution of the adaptive filter output obtained by the adaptive filter means and the synthetic speech response generated by the synthetic speech response generation means; subtractor means for subtracting the convolution calculated by the convolution means from the microphone output signals, to obtain the input signals supplied to the speech recognition means; at least one smoothing filter means for smoothing the synthetic speech response generated by the synthetic speech response generation means; and switching means for controlling an operation of the adaptive filter means according to an output power level of the smoothing filter means. 16. The speech dialogue system of claim 12, further comprising speech segment detection means for detecting a speech segment in the input signals, wherein the speech recognition means recognize the speech input from the input signals only at the speech segment detected by the speech segment detection means. | 0.607143 |
8,126,887 | 11 | 14 | 11. A method implemented on a computer, comprising: storing a plurality of reports in a repository, wherein each report includes information automatically retrieved from a data source, where the information is structured in accordance with a report schema that specifies the form in which the information should be presented, wherein the report schema defines separate report elements as structural components found inside a report, the report interpreting the information from the data source and performs calculations based on at least one calculation model; extracting, from each report of the plurality of reports in the report repository, report element instance context metadata and report element instance context data to define indexed fields, wherein the report element instance context metadata specifies metadata that affects evaluation of a report element instance according to the at least one calculation model including context with information used to calculate a report element instance and the report element instance context data specifies data that affects evaluation of the report element instance; receiving a search query; applying the search query against the indexed fields; and compiling search query results to produce a list of relevant report element instances, wherein each report element instance is a single occurrence of a report element in a report and reports are ranked based on a composite ranking factor, the composite ranking factor being compiled from two or more ranking methods including a method based on a report element instance's level of hierarchy in a report or sub-report. | 11. A method implemented on a computer, comprising: storing a plurality of reports in a repository, wherein each report includes information automatically retrieved from a data source, where the information is structured in accordance with a report schema that specifies the form in which the information should be presented, wherein the report schema defines separate report elements as structural components found inside a report, the report interpreting the information from the data source and performs calculations based on at least one calculation model; extracting, from each report of the plurality of reports in the report repository, report element instance context metadata and report element instance context data to define indexed fields, wherein the report element instance context metadata specifies metadata that affects evaluation of a report element instance according to the at least one calculation model including context with information used to calculate a report element instance and the report element instance context data specifies data that affects evaluation of the report element instance; receiving a search query; applying the search query against the indexed fields; and compiling search query results to produce a list of relevant report element instances, wherein each report element instance is a single occurrence of a report element in a report and reports are ranked based on a composite ranking factor, the composite ranking factor being compiled from two or more ranking methods including a method based on a report element instance's level of hierarchy in a report or sub-report. 14. The method according to claim 11 further comprising storing a reference to at least one data source for each report element instance. | 0.63369 |
8,074,168 | 12 | 13 | 12. The computer-readable storage medium of claim 11 , wherein determining that graphical aspects of the proximate graphical items will interfere with a user's view of the text includes analyzing one or more of the following: a size, a resolution, or a level-of-detail associated with the graphical representation of the text; a size, a resolution, or a level-of-detail associated with a proximate graphical item in the graphical environment; perspective magnification, tilting, or fog effects related to distance and viewpoint for the proximate graphical item and the graphical representation of the text; variations in brightness, color, hue, saturation, virtual lighting, or virtual reflectivity related to the proximate graphical item, the graphical representation of the text, and virtual lighting in the graphical environment; and shadows, texture, scattering, absorption, reflections, or other effects related to interactions among the the proximate graphical item, the graphical representation of the text, and virtual lighting in the graphical environment. | 12. The computer-readable storage medium of claim 11 , wherein determining that graphical aspects of the proximate graphical items will interfere with a user's view of the text includes analyzing one or more of the following: a size, a resolution, or a level-of-detail associated with the graphical representation of the text; a size, a resolution, or a level-of-detail associated with a proximate graphical item in the graphical environment; perspective magnification, tilting, or fog effects related to distance and viewpoint for the proximate graphical item and the graphical representation of the text; variations in brightness, color, hue, saturation, virtual lighting, or virtual reflectivity related to the proximate graphical item, the graphical representation of the text, and virtual lighting in the graphical environment; and shadows, texture, scattering, absorption, reflections, or other effects related to interactions among the the proximate graphical item, the graphical representation of the text, and virtual lighting in the graphical environment. 13. The computer-readable storage medium of claim 12 , wherein modifying the graphical representation of the text involves one or more of the following: modifying one or more parameters for the graphical environment; modifying one or more parameters associated with the graphical representation of the text; changing a typeface for the graphical representation of the text; changing a color for the graphical representation of the text based on the appearance of a surface on or near the graphical representation of the text; and moving or tilting the graphical representation of the text. | 0.753763 |
9,411,886 | 17 | 18 | 17. The system as recited in claim 16 , wherein calculating the probability p(w|Q) further includes, calculating a probability that w generates q(p(q|w)) based on the translation probability t(q|w), calculating a probability of the internet query Q given the word w(p(Q|w)) as a product for all q of all the probabilities p(q|w), calculating a probability of the word w(p(w)) as the sum for all s in S of probabilities of w given the ad materials s(p(w|s)) times a probability of the ad materials s(p(s)), and calculating the probability p(w|Q) as proportional to the probability p(w) times the probability p(Q|w). | 17. The system as recited in claim 16 , wherein calculating the probability p(w|Q) further includes, calculating a probability that w generates q(p(q|w)) based on the translation probability t(q|w), calculating a probability of the internet query Q given the word w(p(Q|w)) as a product for all q of all the probabilities p(q|w), calculating a probability of the word w(p(w)) as the sum for all s in S of probabilities of w given the ad materials s(p(w|s)) times a probability of the ad materials s(p(s)), and calculating the probability p(w|Q) as proportional to the probability p(w) times the probability p(Q|w). 18. The system as recited in claim 17 , wherein calculating the probability p(q|w) further includes making p(q|w) equal to the probability t(q|w). | 0.937978 |
10,146,749 | 13 | 14 | 13. An apparatus for dynamically tracking of JavaScript actions in different document object models, the apparatus comprising: a communications fabric; a memory connected to the communications fabric, wherein the memory contains computer executable program code; a communications unit connected to the communications fabric; an input/output unit connected to the communications fabric; a display connected to the communications fabric; and a processor unit connected to the communications fabric, wherein the processor unit executes the computer executable program code to direct the apparatus to: receive a document object model (DOM) representative of a particular page of an application at a particular time; analyze the DOM received to identify each JavaScript action on the particular page; calculate, for each JavaScript action identified, a JavaScript action characteristics ID based on one or more JavaScript action attribute names and one or more respective computed node characteristics identifiers; store to memory, the JavaScript action characteristics ID and associated JavaScript action; determine whether multiple instances of a same JavaScript action exist, using the JavaScript action characteristics ID; responsive to a determination of multiple instances of the same JavaScript action exist, for each of the JavaScript action characteristics ID corresponding to a multiple JavaScript action, collecting a list of the JavaScript actions corresponding to a current JavaScript action characteristics ID; remove from memory, JavaScript action entries for the multiple instances of the same JavaScript action characteristics ID and a same neighbor influence; compute the neighbor influence, for members of the list of JavaScript actions to uniquely distinguish between the JavaScript actions comprising the list of JavaScript actions, wherein the processor unit executes the computer executable program code to compute the neighbor influence for a member of the list of JavaScript actions remaining further directs the apparatus to: identify a node as being the node to which is initiated searching for a closest neighbor that will distinguish between the JavaScript actions; construct a search tree rooted in the node identified using a current DOM tree; use the search tree to perform a breadth first search, until locating a correct element, wherein each time an element is selected, a same path is traversed as in the current DOM tree for all of the JavaScript action entries that are identical to determine whether the element selected uniquely identifies all of the JavaScript action entries that have a same JavaScript action characteristics ID and wherein a stop condition for the breadth first search is one of: completion of traversing the search tree and not locating an element to uniquely identify the JavaScript action entries in which case the JavaScript action entries are considered to be the same; a predefined depth threshold in the search tree is reached in which case the JavaScript action entries are considered to be the same in which case the breadth first search does not proceed; and an element that helps distinguish the JavaScript action entries from each other is located; store to memory, the JavaScript action characteristics ID and the associated JavaScript action wherein the JavaScript action characteristics ID is calculated for the members of the list of JavaScript actions remaining; determine whether there are more multiple JavaScript actions; and responsive to a determination there are no more multiple JavaScript actions, return all of the JavaScript action characteristics ID and the associated JavaScript action stored; receive a second DOM representative of a second particular page of the application at a second particular time; and compare the returned JavaScript action characteristics IDs and the associated JavaScript actions of the first DOM with the JavaScript action characteristics IDs and the associated JavaScript actions of the second DOM and outputting matching JavaScript actions based on the comparison. | 13. An apparatus for dynamically tracking of JavaScript actions in different document object models, the apparatus comprising: a communications fabric; a memory connected to the communications fabric, wherein the memory contains computer executable program code; a communications unit connected to the communications fabric; an input/output unit connected to the communications fabric; a display connected to the communications fabric; and a processor unit connected to the communications fabric, wherein the processor unit executes the computer executable program code to direct the apparatus to: receive a document object model (DOM) representative of a particular page of an application at a particular time; analyze the DOM received to identify each JavaScript action on the particular page; calculate, for each JavaScript action identified, a JavaScript action characteristics ID based on one or more JavaScript action attribute names and one or more respective computed node characteristics identifiers; store to memory, the JavaScript action characteristics ID and associated JavaScript action; determine whether multiple instances of a same JavaScript action exist, using the JavaScript action characteristics ID; responsive to a determination of multiple instances of the same JavaScript action exist, for each of the JavaScript action characteristics ID corresponding to a multiple JavaScript action, collecting a list of the JavaScript actions corresponding to a current JavaScript action characteristics ID; remove from memory, JavaScript action entries for the multiple instances of the same JavaScript action characteristics ID and a same neighbor influence; compute the neighbor influence, for members of the list of JavaScript actions to uniquely distinguish between the JavaScript actions comprising the list of JavaScript actions, wherein the processor unit executes the computer executable program code to compute the neighbor influence for a member of the list of JavaScript actions remaining further directs the apparatus to: identify a node as being the node to which is initiated searching for a closest neighbor that will distinguish between the JavaScript actions; construct a search tree rooted in the node identified using a current DOM tree; use the search tree to perform a breadth first search, until locating a correct element, wherein each time an element is selected, a same path is traversed as in the current DOM tree for all of the JavaScript action entries that are identical to determine whether the element selected uniquely identifies all of the JavaScript action entries that have a same JavaScript action characteristics ID and wherein a stop condition for the breadth first search is one of: completion of traversing the search tree and not locating an element to uniquely identify the JavaScript action entries in which case the JavaScript action entries are considered to be the same; a predefined depth threshold in the search tree is reached in which case the JavaScript action entries are considered to be the same in which case the breadth first search does not proceed; and an element that helps distinguish the JavaScript action entries from each other is located; store to memory, the JavaScript action characteristics ID and the associated JavaScript action wherein the JavaScript action characteristics ID is calculated for the members of the list of JavaScript actions remaining; determine whether there are more multiple JavaScript actions; and responsive to a determination there are no more multiple JavaScript actions, return all of the JavaScript action characteristics ID and the associated JavaScript action stored; receive a second DOM representative of a second particular page of the application at a second particular time; and compare the returned JavaScript action characteristics IDs and the associated JavaScript actions of the first DOM with the JavaScript action characteristics IDs and the associated JavaScript actions of the second DOM and outputting matching JavaScript actions based on the comparison. 14. The apparatus of claim 13 wherein the processor unit executes the computer executable program code to calculate, for each JavaScript action identified, a JavaScript action characteristics ID further directs the apparatus to: receive a name of an element, wherein the name is also known as a markup language tag name; loop though a list of stable attributes to identify a set of attributes corresponding to attributes associated with the element; and generate the JavaScript action characteristics ID as a string of a predefined format comprising an identifier associated with the particular JavaScript action identified on a web page in combination with one or more JavaScript action attribute names and the one or more respective computed node characteristics identifiers, in which elements of the string are separated by predefined delimiters. | 0.651764 |
9,704,054 | 1 | 2 | 1. A computer-implemented method comprising: performing a first feature transformation on a first plurality of inputs by a first classifier using at least one computer processor, wherein each of the first plurality of inputs comprises at least one of a plurality of images, and wherein the first classifier is configured to associate labels with images; receiving a first plurality of outputs from the first feature transformation using the at least one computer processor, wherein each of the first plurality of outputs comprises at least one of a plurality of labels identified as associated with one of the plurality of images; defining a plurality of clusters of the plurality of labels based at least in part on the first plurality of outputs using the at least one computer processor, wherein each of the plurality of clusters is a subset of the plurality of labels commonly associated with at least one of the plurality of images; assigning a pseudolabel to each of the plurality of clusters using the at least one computer processor; performing a second feature transformation on a first input by a second classifier using the at least one computer processor, wherein the first input comprises a first image, and wherein the second classifier is configured to associate pseudolabels with images; receiving a first output from the second feature transformation using the at least one computer processor, wherein the first output comprises a first pseudolabel identified as associated with the first image; identifying a first cluster based at least in part on the first pseudolabel using the at least one computer processor, wherein the first cluster comprises a first subset of the plurality of labels; selecting one of the labels of the first subset; and storing information regarding the selected one of the labels of the first subset in association with the first image in at least one data store. | 1. A computer-implemented method comprising: performing a first feature transformation on a first plurality of inputs by a first classifier using at least one computer processor, wherein each of the first plurality of inputs comprises at least one of a plurality of images, and wherein the first classifier is configured to associate labels with images; receiving a first plurality of outputs from the first feature transformation using the at least one computer processor, wherein each of the first plurality of outputs comprises at least one of a plurality of labels identified as associated with one of the plurality of images; defining a plurality of clusters of the plurality of labels based at least in part on the first plurality of outputs using the at least one computer processor, wherein each of the plurality of clusters is a subset of the plurality of labels commonly associated with at least one of the plurality of images; assigning a pseudolabel to each of the plurality of clusters using the at least one computer processor; performing a second feature transformation on a first input by a second classifier using the at least one computer processor, wherein the first input comprises a first image, and wherein the second classifier is configured to associate pseudolabels with images; receiving a first output from the second feature transformation using the at least one computer processor, wherein the first output comprises a first pseudolabel identified as associated with the first image; identifying a first cluster based at least in part on the first pseudolabel using the at least one computer processor, wherein the first cluster comprises a first subset of the plurality of labels; selecting one of the labels of the first subset; and storing information regarding the selected one of the labels of the first subset in association with the first image in at least one data store. 2. The computer-implemented method of claim 1 , wherein each of the first plurality of outputs further comprises at least one degree of confidence in an association of the at least one of the plurality of labels with the one of the plurality of images. | 0.869023 |
8,655,658 | 7 | 12 | 7. A system comprising: a processor; and a computer-readable storage device having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: receiving unconstrained input speech from a user; converting, via a processor, only the unconstrained input speech corresponding to single digits into a string of words, wherein each word in the string of words is modeled using a three segment structure comprising a plurality of heads and a plurality of tails; converting the string of words into a sequence of digits using classes of rules and according to an acoustic model database in which Markov models characterize acoustic features of numeric words; comparing the sequence of digits to a plurality of valid sequences of digits, to yield validity information; and providing the validity information to a device associated with the user. | 7. A system comprising: a processor; and a computer-readable storage device having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: receiving unconstrained input speech from a user; converting, via a processor, only the unconstrained input speech corresponding to single digits into a string of words, wherein each word in the string of words is modeled using a three segment structure comprising a plurality of heads and a plurality of tails; converting the string of words into a sequence of digits using classes of rules and according to an acoustic model database in which Markov models characterize acoustic features of numeric words; comparing the sequence of digits to a plurality of valid sequences of digits, to yield validity information; and providing the validity information to a device associated with the user. 12. The system of claim 7 , wherein the classes of rules varies depending upon one of a city and a country. | 0.82459 |
8,413,072 | 1 | 4 | 1. An apparatus, comprising: a processor to display a user interface on a display device, wherein the user interface includes a data entry menu having one or more menu selections, wherein the processor to activate one of the one or more menu selections to display a data entry method, wherein the data entry method is context specific to the activated menu selection by displaying only one or more virtual keys that are necessary for a user to enter data required by the activated menu selection, wherein the data entry method is displayed directly below the activated menu selection on the user interface wherein the data entry method is language specific to the activated menu selection by switching between different languages, wherein the different languages include English, Chinese and Russian, wherein only a virtual keyboard enabled to receive alphabetic input is displayed in the data entry method on the display device when alphabetic user input is required by the activated user selection, and only a virtual keypad enabled to receive numeric input is displayed in the data entry method on the display device when numeric user input is required by the activated user selection. | 1. An apparatus, comprising: a processor to display a user interface on a display device, wherein the user interface includes a data entry menu having one or more menu selections, wherein the processor to activate one of the one or more menu selections to display a data entry method, wherein the data entry method is context specific to the activated menu selection by displaying only one or more virtual keys that are necessary for a user to enter data required by the activated menu selection, wherein the data entry method is displayed directly below the activated menu selection on the user interface wherein the data entry method is language specific to the activated menu selection by switching between different languages, wherein the different languages include English, Chinese and Russian, wherein only a virtual keyboard enabled to receive alphabetic input is displayed in the data entry method on the display device when alphabetic user input is required by the activated user selection, and only a virtual keypad enabled to receive numeric input is displayed in the data entry method on the display device when numeric user input is required by the activated user selection. 4. The apparatus of claim 1 , wherein the one or more menu selections comprise one or more data entry boxes. | 0.753425 |
7,499,916 | 15 | 16 | 15. An image retrieval system comprising: a processor means; a means executed on the processor for handling keyword-based queries having one or more search keywords; a means for identifying at least one of (1) first images having keywords that match the search keywords from a keyword-based query, and (2) extracting low-level features from the first images and identifying second images using pattern match having low-level features similar to the first images, wherein the low-level features comprise color, shape and texture, and the low-level features do not match the keyword-based or the content-based, wherein the second image is an example image; a means for presenting to a user images identified by the feature and semantic matcher to a user, user interface allowing the user to indicate whether the first and example images are relevant to the query; and a means for training the image retrieval system based on user feedback as to relevancy, wherein the feedback analyzer is configured to assign a first weight to an association between the query keyword and the first images deemed relevant by the user and/or the feedback analyzer is configured to assign a second weight to an association between the query keyword and the example image, in order to rank the images, wherein the first weight is greater than the second weight. | 15. An image retrieval system comprising: a processor means; a means executed on the processor for handling keyword-based queries having one or more search keywords; a means for identifying at least one of (1) first images having keywords that match the search keywords from a keyword-based query, and (2) extracting low-level features from the first images and identifying second images using pattern match having low-level features similar to the first images, wherein the low-level features comprise color, shape and texture, and the low-level features do not match the keyword-based or the content-based, wherein the second image is an example image; a means for presenting to a user images identified by the feature and semantic matcher to a user, user interface allowing the user to indicate whether the first and example images are relevant to the query; and a means for training the image retrieval system based on user feedback as to relevancy, wherein the feedback analyzer is configured to assign a first weight to an association between the query keyword and the first images deemed relevant by the user and/or the feedback analyzer is configured to assign a second weight to an association between the query keyword and the example image, in order to rank the images, wherein the first weight is greater than the second weight. 16. An image retrieval system as recited in claim 15 , further comprising: a means for presenting to the user the images identified by the feature and semantic matcher to a user, the user interface allowing the user to identify an example image; and the means for indentifying is configured to identify additional images that contain low-level features similar to those of the example image. | 0.590147 |
8,606,774 | 1 | 5 | 1. A computer implemented method for 3D shape retrieval comprising: determining, by one or more processors, a similarity between a query 3D model and one or more 3D models using a composite distance function based on a plurality of features of the query 3D model and corresponding features of the one or more 3D models, wherein a feature distance is computed between the query 3D model and the one or more 3D models for each feature, and the composite distance function is computed as an average of each feature distance or an aggregate of each feature distance according to a pre-determined weighting; retrieving, by the one or more processors, one or more similar 3D models based on the determining step; ranking, by the one or more processors, the one or more similar 3D models based on the similarity; computing a second feature distance between the query 3D model and the one or more 3D models for each feature, from a second set of features, associated with the query 3D model and the one or more 3D models; evaluating a second composite distance function using each second feature distance; and re-ranking the one or more query 3D models based on results of the evaluated second composite distance function. | 1. A computer implemented method for 3D shape retrieval comprising: determining, by one or more processors, a similarity between a query 3D model and one or more 3D models using a composite distance function based on a plurality of features of the query 3D model and corresponding features of the one or more 3D models, wherein a feature distance is computed between the query 3D model and the one or more 3D models for each feature, and the composite distance function is computed as an average of each feature distance or an aggregate of each feature distance according to a pre-determined weighting; retrieving, by the one or more processors, one or more similar 3D models based on the determining step; ranking, by the one or more processors, the one or more similar 3D models based on the similarity; computing a second feature distance between the query 3D model and the one or more 3D models for each feature, from a second set of features, associated with the query 3D model and the one or more 3D models; evaluating a second composite distance function using each second feature distance; and re-ranking the one or more query 3D models based on results of the evaluated second composite distance function. 5. The method of claim 1 , further comprising: constructing a retrieval structure, using each of the one or more 3D models, for each feature associated with the one or more 3D models; and searching the retrieval structure, for each feature, to determine 3D models similar to the query 3D model. | 0.501695 |
9,128,980 | 1 | 2 | 1. A method of accessing data, including: accessing a data model structure, the data model structure comprising: a root object query that, when executed, returns a set of time stamped events in a data store on a computing device, each event including a portion of unstructured data; a model schema that references fields that can be extracted, by an extraction rule or regular expression, from the unstructured data in the time stamped events without modifying the unstructured data; and one or more submodels; each of the submodels comprising: a child object that provides for narrower search criteria than the root object query such that, when the child object query is executed against the time stamped events, the child object query returns a subset of the set of time stamped events that is smaller than the set; a submodel schema that inherits one or more fields referenced in the model schema; and the submodel schema further references additional fields that can be extracted, by an extraction rule or regular expression, from the unstructured data in the time stamped events without modifying the unstructured data; receiving electronically a data request comprising reference to a submodel selected from the data model structure and a query to be performed against the subset referenced by the selected submodel; and identifying responsive events, including extracting values from at least some of the events in the subset at query time using the extraction rule or regular expression in the submodel schema without modifying the unstructured event and matching the extracted values to the query; returning at least some values from or derived from the fields in the responsive events referenced by the submodel schema. | 1. A method of accessing data, including: accessing a data model structure, the data model structure comprising: a root object query that, when executed, returns a set of time stamped events in a data store on a computing device, each event including a portion of unstructured data; a model schema that references fields that can be extracted, by an extraction rule or regular expression, from the unstructured data in the time stamped events without modifying the unstructured data; and one or more submodels; each of the submodels comprising: a child object that provides for narrower search criteria than the root object query such that, when the child object query is executed against the time stamped events, the child object query returns a subset of the set of time stamped events that is smaller than the set; a submodel schema that inherits one or more fields referenced in the model schema; and the submodel schema further references additional fields that can be extracted, by an extraction rule or regular expression, from the unstructured data in the time stamped events without modifying the unstructured data; receiving electronically a data request comprising reference to a submodel selected from the data model structure and a query to be performed against the subset referenced by the selected submodel; and identifying responsive events, including extracting values from at least some of the events in the subset at query time using the extraction rule or regular expression in the submodel schema without modifying the unstructured event and matching the extracted values to the query; returning at least some values from or derived from the fields in the responsive events referenced by the submodel schema. 2. The method of claim 1 , wherein the values derived from the fields in the responsive events are results of aggregate calculations from the values in the fields of responsive events. | 0.862687 |
9,613,033 | 15 | 16 | 15. A system for providing emotionally relevant content to users, comprising: one or more processors; and memory comprising instructions that when executed by at least one of the one or more processors, implement: a labeling component configured to: receive, from a first user, a user emotion label for content; and label the content based upon the user emotion label to create labeled content, the labeled content having a first emotional content type; and a content provider component configured to: define an emotional transition trigger for a second user; and responsive to a triggering of the emotional transition trigger based upon the second user having a threshold amount of exposure to a second emotional content type of content that is different than the first emotional content type, provide the labeled content to the second user. | 15. A system for providing emotionally relevant content to users, comprising: one or more processors; and memory comprising instructions that when executed by at least one of the one or more processors, implement: a labeling component configured to: receive, from a first user, a user emotion label for content; and label the content based upon the user emotion label to create labeled content, the labeled content having a first emotional content type; and a content provider component configured to: define an emotional transition trigger for a second user; and responsive to a triggering of the emotional transition trigger based upon the second user having a threshold amount of exposure to a second emotional content type of content that is different than the first emotional content type, provide the labeled content to the second user. 16. The system of claim 15 , the labeling component configured to: provide an emotion labeling interface comprising a set of emotions for selection by the first user; and receive a selection of an emotion by the first user, from the emotion labeling interface, as the user emotion label for the content. | 0.644366 |
6,163,869 | 1 | 2 | 1. Method for repeating data transmitted incorrectly (ARQ), in which method for the purpose of transmission between subscribers (T1, T2) having in each case at least one transmit section, and at least one receive section, a data stream at the transmitting subscriber's end is subdivided into data words having a predetermined length and is combined to form data words and the individual data words are temporarily stored in a transmit buffer, are transmitted, if necessary via a transmit unit, and are received by the receiving subscriber, if necessary in a receive unit and, after temporary storage in a receive buffer are output, in which arrangement an error which has occurred during the transmission is detected and a command for retransmission of the data word transmitted incorrectly is issued via the transmit section of the receiving subscriber and the receive section of the transmitting subscriber to the transmit section of the transmitting subscriber, the ARQ retransmissions of the data transmitted incorrectly being carried out via a predetermined proportion, which is constant in the time average, of the number of data words transmitted within the averaged period of time, characterized in that, if the proportion provided for the retransmission is exceeded by incorrect data words, the incorrect data words are output from the receive buffer in a form not corresponding to their original information. | 1. Method for repeating data transmitted incorrectly (ARQ), in which method for the purpose of transmission between subscribers (T1, T2) having in each case at least one transmit section, and at least one receive section, a data stream at the transmitting subscriber's end is subdivided into data words having a predetermined length and is combined to form data words and the individual data words are temporarily stored in a transmit buffer, are transmitted, if necessary via a transmit unit, and are received by the receiving subscriber, if necessary in a receive unit and, after temporary storage in a receive buffer are output, in which arrangement an error which has occurred during the transmission is detected and a command for retransmission of the data word transmitted incorrectly is issued via the transmit section of the receiving subscriber and the receive section of the transmitting subscriber to the transmit section of the transmitting subscriber, the ARQ retransmissions of the data transmitted incorrectly being carried out via a predetermined proportion, which is constant in the time average, of the number of data words transmitted within the averaged period of time, characterized in that, if the proportion provided for the retransmission is exceeded by incorrect data words, the incorrect data words are output from the receive buffer in a form not corresponding to their original information. 2. Method according to claim 1, characterized in that the proportion of the retransmissions carried out in the time average is selected to be between 2 and 7%. | 0.684524 |
7,783,640 | 12 | 18 | 12. A system, comprising: a processor; a memory; and an interface connecting the processor, the memory, and one or more logics, the logics including: a term and sentence logic to identify one or more terms in a document and to identify one or more sentences in the document, to produce a term score related to a uniqueness value for a term, and to produce a sentence score related to a potential topic relevancy value for a sentence, where the term score is computed according to:
S ( t )=( a−b ( sf t /N−c ) 2 )* f ( ng ) S(t) being the term score; a, b, and c being pre-determined, configurable constants; sf t being the number of sentences in which term t occurs; N being the total number of sentences; and f(ng) being a function that returns a penalizing value for terms having a less than a first number of uni-grams, a linearly increasing value for terms having between the first number of uni-grams and a second number of uni-grams, and a constant value for terms having more than the second number of uni-grams; a term-sentence matrix logic to produce a term-sentence matrix from the one or more terms and the one or more sentences, where an entry in the term-sentence matrix relates a term to a sentence based, at least in part, on a term score for the term and a sentence score for the sentence; a dominant topic logic to determine a dominant topic for the document based, at least in part, on iterative Single Value Decomposition (SVD) based hybrid LSI-HITS (Latent Semantic Index, Hyperlink Induced Topic Search) processing; and a summary item selection logic to select one or more summary items based, at least in part, on a simultaneous ranking of terms and sentences related to the dominant topic. | 12. A system, comprising: a processor; a memory; and an interface connecting the processor, the memory, and one or more logics, the logics including: a term and sentence logic to identify one or more terms in a document and to identify one or more sentences in the document, to produce a term score related to a uniqueness value for a term, and to produce a sentence score related to a potential topic relevancy value for a sentence, where the term score is computed according to:
S ( t )=( a−b ( sf t /N−c ) 2 )* f ( ng ) S(t) being the term score; a, b, and c being pre-determined, configurable constants; sf t being the number of sentences in which term t occurs; N being the total number of sentences; and f(ng) being a function that returns a penalizing value for terms having a less than a first number of uni-grams, a linearly increasing value for terms having between the first number of uni-grams and a second number of uni-grams, and a constant value for terms having more than the second number of uni-grams; a term-sentence matrix logic to produce a term-sentence matrix from the one or more terms and the one or more sentences, where an entry in the term-sentence matrix relates a term to a sentence based, at least in part, on a term score for the term and a sentence score for the sentence; a dominant topic logic to determine a dominant topic for the document based, at least in part, on iterative Single Value Decomposition (SVD) based hybrid LSI-HITS (Latent Semantic Index, Hyperlink Induced Topic Search) processing; and a summary item selection logic to select one or more summary items based, at least in part, on a simultaneous ranking of terms and sentences related to the dominant topic. 18. The system of claim 12 , the first number of uni-grams being 1, the second number of uni-grams being 4. | 0.938435 |
8,000,956 | 2 | 3 | 2. The method of claim 1 , wherein when the entity is a named entity and the at least one attribute context being compared is a single attribute context, the comparison includes determining whether the entity context and attribute context are compatible. | 2. The method of claim 1 , wherein when the entity is a named entity and the at least one attribute context being compared is a single attribute context, the comparison includes determining whether the entity context and attribute context are compatible. 3. The method of claim 2 , wherein when the named entity context and the at least one attribute context are determined not to be compatible, the comparison identifies a conflict. | 0.925647 |
9,146,993 | 11 | 13 | 11. A system comprising: a data processing apparatus; and a memory coupled to the data processing apparatus having instructions stored thereon which, when executed by the data processing apparatus cause the data processing apparatus to perform operations comprising: identifying a first video content item and one or more additional content items that were displayed in association with the first video content item, the one or more additional content items forming a first set; compiling user interaction statistics for the one or more additional content items in the first set, at least some of the one or more additional content items being associated with one or more keywords, wherein the user interaction statistics are used to determine one or more top rated ones of the one or more additional content items based on a number of user interactions with a respective one of the one or more additional content items, wherein the user interactions are selected from the group comprising click through or conversion after presentation and click through; based on the interaction statistics, associating the first video content item with at least some of the keywords associated with one or more top rated content items based at least on the compiling, wherein the associating includes storing an association between the first video content item and the some of the keywords; identifying a second different video content item; determining one or more attributes associated with the first video content item, wherein an attribute is selected from the group comprising a respective source of the first video content item, a respective content channel of the first video content item, a respective search query associated in a search system with the first video content item, or a respective media document topic of the first video content item, wherein the media document topic is identified based on non-textual content of the first video content item and non-textual content of the second different video content item; determining one or more attributes associated with the second different video content item, wherein an attribute is selected from the group comprising a respective source of the second different video content item, a respective content channel of the second different video content item, a respective search query associated in a search system with the second different video content item, or a respective media document topic of the second different video content item; comparing the first and second different video content items including identifying one or more common attributes of the first video content item and the second different video content item, wherein the one or more common attributes of the first and second different video content items includes one or more of a respective source of the first and second different video content items, a respective content channel of the first and second different video content items, a respective search query associated in a search system with the first and second the video content items, or a respective media document topic of the first and second different video content items; and based on the identified one or more common attributes of the first video content item and the second different video content item, using at least some of the keywords assigned to the first video content item as keywords for the second different video content item, wherein the first video content item and the second different video content item will include one or more keywords in common; and providing additional content when displaying the second different video content based on the one or more keywords in common. | 11. A system comprising: a data processing apparatus; and a memory coupled to the data processing apparatus having instructions stored thereon which, when executed by the data processing apparatus cause the data processing apparatus to perform operations comprising: identifying a first video content item and one or more additional content items that were displayed in association with the first video content item, the one or more additional content items forming a first set; compiling user interaction statistics for the one or more additional content items in the first set, at least some of the one or more additional content items being associated with one or more keywords, wherein the user interaction statistics are used to determine one or more top rated ones of the one or more additional content items based on a number of user interactions with a respective one of the one or more additional content items, wherein the user interactions are selected from the group comprising click through or conversion after presentation and click through; based on the interaction statistics, associating the first video content item with at least some of the keywords associated with one or more top rated content items based at least on the compiling, wherein the associating includes storing an association between the first video content item and the some of the keywords; identifying a second different video content item; determining one or more attributes associated with the first video content item, wherein an attribute is selected from the group comprising a respective source of the first video content item, a respective content channel of the first video content item, a respective search query associated in a search system with the first video content item, or a respective media document topic of the first video content item, wherein the media document topic is identified based on non-textual content of the first video content item and non-textual content of the second different video content item; determining one or more attributes associated with the second different video content item, wherein an attribute is selected from the group comprising a respective source of the second different video content item, a respective content channel of the second different video content item, a respective search query associated in a search system with the second different video content item, or a respective media document topic of the second different video content item; comparing the first and second different video content items including identifying one or more common attributes of the first video content item and the second different video content item, wherein the one or more common attributes of the first and second different video content items includes one or more of a respective source of the first and second different video content items, a respective content channel of the first and second different video content items, a respective search query associated in a search system with the first and second the video content items, or a respective media document topic of the first and second different video content items; and based on the identified one or more common attributes of the first video content item and the second different video content item, using at least some of the keywords assigned to the first video content item as keywords for the second different video content item, wherein the first video content item and the second different video content item will include one or more keywords in common; and providing additional content when displaying the second different video content based on the one or more keywords in common. 13. The system of claim 11 wherein the user interaction statistics comprise at least one of a click-through rate of the content item when the content item is displayed in association with the first video content item and a conversion rate of the content item when the content item is displayed in association with the first video content item. | 0.52095 |
8,340,426 | 1 | 4 | 1. An apparatus for recognizing characters, the apparatus comprising: a display unit to display an image in which a region of interest or an error region is indicated, and a character recognition result; a region-of-interest setting unit to set the region of interest in the image displayed on the display unit; a recognition unit to perform character recognition on the region of interest or the error region and to provide the character recognition result to the display unit; and an error correction unit to set the error region in the image displayed on the display region, perform image copying on the set error region according to a user input, and provide a handwriting input using the image copying to the recognition unit, wherein the recognition unit provides information corresponding to the character recognition result to the display unit by searching and obtaining information corresponding to a recognized character in a dictionary included in a mobile device, and wherein the recognition unit performs a search using the character recognition result as a query word in the search after character recognition is performed on the error region, and provides the search result to the display unit. | 1. An apparatus for recognizing characters, the apparatus comprising: a display unit to display an image in which a region of interest or an error region is indicated, and a character recognition result; a region-of-interest setting unit to set the region of interest in the image displayed on the display unit; a recognition unit to perform character recognition on the region of interest or the error region and to provide the character recognition result to the display unit; and an error correction unit to set the error region in the image displayed on the display region, perform image copying on the set error region according to a user input, and provide a handwriting input using the image copying to the recognition unit, wherein the recognition unit provides information corresponding to the character recognition result to the display unit by searching and obtaining information corresponding to a recognized character in a dictionary included in a mobile device, and wherein the recognition unit performs a search using the character recognition result as a query word in the search after character recognition is performed on the error region, and provides the search result to the display unit. 4. The apparatus of claim 1 , wherein the display unit, the region-of-interest setting unit, the recognition unit, and the error correction unit are integrated into a mobile device. | 0.65458 |
10,133,275 | 1 | 5 | 1. A system for implementing a control algorithm for an autonomous vehicle, the system comprising: one or more processors; and one or more computer readable storage media communicatively coupled to the one or more processors and storing instructions that are executable by the one or more processors to: receive one or more symbols, the one or more symbols including at least one static symbol or at least one dynamic symbol; determine, based at least in part on the one or more symbols, one or more features; determine, based at least in part on the one or more symbols or the one or more features, one or more predicates; determine, based at least in part on the one or more symbols, one or more features, or one or more predicates, one or more linear temporal logic (LTL) formulas; determine, based at least in part on the one or more LTL formulas, one or more automaton; utilize a Monte Carlo Tree Search (MCTS) to generate one or more candidate trajectories; evaluate the one or more candidate trajectories using the one or more automaton, wherein the one or more automaton verifies that the one or more candidate trajectories satisfies the one or more LTL formulas associated with the one or more automaton; determine, based at least in part on a cost function, a cost associated with a trajectory of the one or more candidate trajectories; select, as a selected trajectory, the trajectory of the one or more candidate trajectories based at least in part on the cost; and control the autonomous vehicle based at least in part on the selected trajectory. | 1. A system for implementing a control algorithm for an autonomous vehicle, the system comprising: one or more processors; and one or more computer readable storage media communicatively coupled to the one or more processors and storing instructions that are executable by the one or more processors to: receive one or more symbols, the one or more symbols including at least one static symbol or at least one dynamic symbol; determine, based at least in part on the one or more symbols, one or more features; determine, based at least in part on the one or more symbols or the one or more features, one or more predicates; determine, based at least in part on the one or more symbols, one or more features, or one or more predicates, one or more linear temporal logic (LTL) formulas; determine, based at least in part on the one or more LTL formulas, one or more automaton; utilize a Monte Carlo Tree Search (MCTS) to generate one or more candidate trajectories; evaluate the one or more candidate trajectories using the one or more automaton, wherein the one or more automaton verifies that the one or more candidate trajectories satisfies the one or more LTL formulas associated with the one or more automaton; determine, based at least in part on a cost function, a cost associated with a trajectory of the one or more candidate trajectories; select, as a selected trajectory, the trajectory of the one or more candidate trajectories based at least in part on the cost; and control the autonomous vehicle based at least in part on the selected trajectory. 5. The system of claim 1 , the instructions further executable by the one or more processors to add at least one node to the MCTS based at least in part on a machine learning algorithm comprising a neural network. | 0.745823 |
8,051,073 | 3 | 4 | 3. The method according to claim 1 , wherein the act of evaluating the set of results further comprises the act of generating a first sampled set. | 3. The method according to claim 1 , wherein the act of evaluating the set of results further comprises the act of generating a first sampled set. 4. The method according to claim 3 , further comprising the acts of: analyzing the first sampled set to obtain a statistical distribution of at least one identifying characteristic within the sampled set; and determining the measure of distinctiveness relative to the statistical distributions for the sampled set. | 0.898905 |
9,609,127 | 1 | 6 | 1. A method comprising: detecting, by a system including a processor, input incompatible with an original script for an interactive communication over a communication network, wherein the detecting is performed during the interactive communication; modifying, by the system, the original script during the interactive communication into a dynamically updated script different from the original script in accordance with a type of the incompatible input; and providing, by the system over the communication network, information to a device participating in the interactive communication in accordance with the dynamically updated script, wherein at least a portion of a remainder of the interactive communication is conducted in accordance with the dynamically updated script. | 1. A method comprising: detecting, by a system including a processor, input incompatible with an original script for an interactive communication over a communication network, wherein the detecting is performed during the interactive communication; modifying, by the system, the original script during the interactive communication into a dynamically updated script different from the original script in accordance with a type of the incompatible input; and providing, by the system over the communication network, information to a device participating in the interactive communication in accordance with the dynamically updated script, wherein at least a portion of a remainder of the interactive communication is conducted in accordance with the dynamically updated script. 6. The method of claim 1 , wherein the incompatible input comprises unrecognized speech. | 0.824701 |
9,026,442 | 1 | 3 | 1. A method comprising: identifying an acoustic model, wherein the acoustic model is trained on native speech in a target dialect; identifying a class of a speaker; and replacing a phoneme in the acoustic model with a modified phoneme, wherein the modified phoneme is a weighted sum of plausible phonemes in a lattice of plausible phonemes associated with the class of the speaker. | 1. A method comprising: identifying an acoustic model, wherein the acoustic model is trained on native speech in a target dialect; identifying a class of a speaker; and replacing a phoneme in the acoustic model with a modified phoneme, wherein the modified phoneme is a weighted sum of plausible phonemes in a lattice of plausible phonemes associated with the class of the speaker. 3. The method of claim 1 , wherein the native speech represents the class of the speaker. | 0.857827 |
10,095,696 | 19 | 20 | 19. The method of claim 18 , further comprising: obtaining the edited version of the digital media content from the first client computing platform associated with the selected post-capture user; and effectuate transmission of the edited version of the digital media content from the first client computing platform to a second client computing platform associated with the content capture user and/or the end user. | 19. The method of claim 18 , further comprising: obtaining the edited version of the digital media content from the first client computing platform associated with the selected post-capture user; and effectuate transmission of the edited version of the digital media content from the first client computing platform to a second client computing platform associated with the content capture user and/or the end user. 20. The method of claim 19 , further comprising: receiving feedback information from the second client computing platform associated with the content capture user for the selected post-capture user based upon the edited version of the digital media content. | 0.935686 |
9,311,917 | 11 | 14 | 11. A computer program product, comprising a computer readable storage device having computer readable code stored therein, said code containing instructions configured to be executed by a processor to implement a method for teaching an object of a deictic reference to a machine, said machine having a conversational system, said machine comprising the processor and the storage device, said machine selected from the group consisting of an automobile and a robot, said method comprising: said processor teaching the object of the deictic reference to the machine which results in the machine learning the object, said teaching comprising: said processor receiving, from a user, a voice command that declares a name of the object and analyzing a physical pointing gesture, by the user, that points to the object; in response to said analyzing the physical pointing gesture, said processor finding an item in a region indicated by the pointing gesture and determining that a reference describing the item is not in a dictionary of the machine; in response to said determining that a reference describing the item is not in the dictionary of the machine, said processor shining a laser light on the item and in response, said processor receiving a negative spoken indication from the user that the item shined on by the laser light is not the object; in response to the negative spoken indication from the user, said processor interacting with the user in an iterative procedure, wherein during each iteration of the procedure, said processor shining the laser light on a portion of the item or on something thing that is physically coupled to the item and in response, said processor receiving from the user (i) a first spoken indication that the laser light has shined closer to the object than in the previous iteration, (ii) a second spoken indication that the laser light has shined further from the object than in the previous iteration, or (iii) a third spoken indication that the laser light has shined on the object, wherein the machine learns the object in a final iteration of the procedure in which the processor receives the third spoken indication from the user; said processor storing the learned object and the voice command in a storage repository, and said processor executing a task with respect to the learned object. | 11. A computer program product, comprising a computer readable storage device having computer readable code stored therein, said code containing instructions configured to be executed by a processor to implement a method for teaching an object of a deictic reference to a machine, said machine having a conversational system, said machine comprising the processor and the storage device, said machine selected from the group consisting of an automobile and a robot, said method comprising: said processor teaching the object of the deictic reference to the machine which results in the machine learning the object, said teaching comprising: said processor receiving, from a user, a voice command that declares a name of the object and analyzing a physical pointing gesture, by the user, that points to the object; in response to said analyzing the physical pointing gesture, said processor finding an item in a region indicated by the pointing gesture and determining that a reference describing the item is not in a dictionary of the machine; in response to said determining that a reference describing the item is not in the dictionary of the machine, said processor shining a laser light on the item and in response, said processor receiving a negative spoken indication from the user that the item shined on by the laser light is not the object; in response to the negative spoken indication from the user, said processor interacting with the user in an iterative procedure, wherein during each iteration of the procedure, said processor shining the laser light on a portion of the item or on something thing that is physically coupled to the item and in response, said processor receiving from the user (i) a first spoken indication that the laser light has shined closer to the object than in the previous iteration, (ii) a second spoken indication that the laser light has shined further from the object than in the previous iteration, or (iii) a third spoken indication that the laser light has shined on the object, wherein the machine learns the object in a final iteration of the procedure in which the processor receives the third spoken indication from the user; said processor storing the learned object and the voice command in a storage repository, and said processor executing a task with respect to the learned object. 14. The computer program product of claim 11 , said method further comprising: after the machine has learned the object, said processor directing the machine to move to the item and pick up the object to develop: a larger database of the object, a measure of a weight of the object, and surface friction presented by the object. | 0.50152 |
9,135,346 | 8 | 9 | 8. A system, comprising: a data processing apparatus; and a non-transitory computer readable storage medium storing instructions executable by the data processing apparatus and that upon execution cause the data processing apparatus to perform operations comprising: determining, for a native application that generates an application environment for display on a user device within the native application, the native application operating independent of a browser application that can operate on the user device, a set of environment instances of the native application, the determining comprising receiving a set of uniform resource identifiers for the native application from a publisher of the native application, each uniform resource identifier in the set of uniform resource identifiers corresponding to a corresponding environment instance in the set of environment instances; for each environment instance: determining textual data describing features of the corresponding environment instance, the textual data being data that is not rendered to be visible when the native application renders the environment instance on a user device display, the determining of textual data comprising: instantiating a virtual machine emulating an operating system of a user device; instantiating, within the virtual machine, the native application; accessing, within the virtual machine, the uniform resource identifier to which the environment instance corresponds, and in response to accessing each uniform resource identifier: generating in the virtual machine the environment instance that corresponds to the uniform resource identifier; and extracting the textual data provided to a rendering process of the native application, wherein the textual data is identified as invisible textual data for the rendering process so that the textual data is not rendered to be visible when the native application renders the environment instance on a user device display; generating, from the textual data, native application environment instance data describing content of the environment instance; and indexing the native application environment instance data for the native application in an index that is searchable by a search engine. | 8. A system, comprising: a data processing apparatus; and a non-transitory computer readable storage medium storing instructions executable by the data processing apparatus and that upon execution cause the data processing apparatus to perform operations comprising: determining, for a native application that generates an application environment for display on a user device within the native application, the native application operating independent of a browser application that can operate on the user device, a set of environment instances of the native application, the determining comprising receiving a set of uniform resource identifiers for the native application from a publisher of the native application, each uniform resource identifier in the set of uniform resource identifiers corresponding to a corresponding environment instance in the set of environment instances; for each environment instance: determining textual data describing features of the corresponding environment instance, the textual data being data that is not rendered to be visible when the native application renders the environment instance on a user device display, the determining of textual data comprising: instantiating a virtual machine emulating an operating system of a user device; instantiating, within the virtual machine, the native application; accessing, within the virtual machine, the uniform resource identifier to which the environment instance corresponds, and in response to accessing each uniform resource identifier: generating in the virtual machine the environment instance that corresponds to the uniform resource identifier; and extracting the textual data provided to a rendering process of the native application, wherein the textual data is identified as invisible textual data for the rendering process so that the textual data is not rendered to be visible when the native application renders the environment instance on a user device display; generating, from the textual data, native application environment instance data describing content of the environment instance; and indexing the native application environment instance data for the native application in an index that is searchable by a search engine. 9. The system of claim 8 wherein: determining a set of uniform resource identifiers for a native application comprises receiving the uniform resource identifiers from a publisher of the native application; and determining textual data describing features of the corresponding environment instance comprises receiving from the publisher and for each of the uniform resource identifiers textual data that is identified by the publisher as describing features of the environment instance corresponding to the uniform resource identifier. | 0.500935 |
8,204,999 | 53 | 55 | 53. An access server, comprising: a communication interface; one or more storage devices; and one or more processors in communication with said one or more storage devices and said communication interface, said one or more processors programmed to perform a method comprising the steps of: providing for creation of a first access rule for one or more resources, storing said first access rule at said access server, receiving and storing a set of one or more query variables to be associated with said first access rule, and receiving and storing one or more values for said one or more query variables, said one or more values uniquely identify said first access rule from a plurality of access rules; and determining whether access to said one or more resources is authorized based on said identification of said one or more resources without granting authorization to said one or more resources, said determining including accessing said first access rule for said one or more resources and accessing an identity profile for a first user to determine whether at least a portion of said first access rule is satisfied based on information in said identity profile, wherein said first access rule is not part of said identity profile. | 53. An access server, comprising: a communication interface; one or more storage devices; and one or more processors in communication with said one or more storage devices and said communication interface, said one or more processors programmed to perform a method comprising the steps of: providing for creation of a first access rule for one or more resources, storing said first access rule at said access server, receiving and storing a set of one or more query variables to be associated with said first access rule, and receiving and storing one or more values for said one or more query variables, said one or more values uniquely identify said first access rule from a plurality of access rules; and determining whether access to said one or more resources is authorized based on said identification of said one or more resources without granting authorization to said one or more resources, said determining including accessing said first access rule for said one or more resources and accessing an identity profile for a first user to determine whether at least a portion of said first access rule is satisfied based on information in said identity profile, wherein said first access rule is not part of said identity profile. 55. The access server according to claim 53 , wherein: said step of storing one or more query variables includes storing an order of said one or more query variables. | 0.882102 |
9,875,234 | 1 | 6 | 1. A method programmed in a non-transitory memory of a device comprising: a. capturing social networking information, including the social networking information of a user, from a social networking system; b. analyzing the social networking information of the user, including: i. determining interests of the user; and ii. storing the interests of the user in a data structure; c. processing the social networking information, including parsing the social networking information into parsed information; d. fact checking, with the device, the social networking information by comparing the parsed information with source information to generate fact checking results, including indicating when the parsed information is factually accurate and indicating when the parsed information is factually inaccurate; e. summarizing the social networking information to generate a summary of the social networking information, including determining total strengths of sentences within the social networking information, wherein a total strength of a sentence is based on a strength of a lexical chain, the interests of the user, and factual accuracy of the sentence based on the fact checking results generated by fact checking the social networking information, wherein summarizing the social networking information utilizes the interests of the user stored in the data structure by increasing the strength of the lexical chain when a word in the lexical chain matches at least one of the interests in the data structure, wherein lexical chaining includes selecting a set of candidate words or phrases, finding an appropriate chain using relatedness criteria among members of chains for each candidate word, and inserting a word in the lexical chain when the word is found, wherein the total strength of the sentence is increased when the sentence is factually accurate, wherein the summary of the social networking information includes the fact checking results; and f. providing the summary of the social networking information in real-time. | 1. A method programmed in a non-transitory memory of a device comprising: a. capturing social networking information, including the social networking information of a user, from a social networking system; b. analyzing the social networking information of the user, including: i. determining interests of the user; and ii. storing the interests of the user in a data structure; c. processing the social networking information, including parsing the social networking information into parsed information; d. fact checking, with the device, the social networking information by comparing the parsed information with source information to generate fact checking results, including indicating when the parsed information is factually accurate and indicating when the parsed information is factually inaccurate; e. summarizing the social networking information to generate a summary of the social networking information, including determining total strengths of sentences within the social networking information, wherein a total strength of a sentence is based on a strength of a lexical chain, the interests of the user, and factual accuracy of the sentence based on the fact checking results generated by fact checking the social networking information, wherein summarizing the social networking information utilizes the interests of the user stored in the data structure by increasing the strength of the lexical chain when a word in the lexical chain matches at least one of the interests in the data structure, wherein lexical chaining includes selecting a set of candidate words or phrases, finding an appropriate chain using relatedness criteria among members of chains for each candidate word, and inserting a word in the lexical chain when the word is found, wherein the total strength of the sentence is increased when the sentence is factually accurate, wherein the summary of the social networking information includes the fact checking results; and f. providing the summary of the social networking information in real-time. 6. The method of claim 1 further comprising generating advertisement information based on the summary of the social networking information, further wherein keywords of the lexical chain are analyzed to generate the advertisement information. | 0.865363 |
9,223,473 | 14 | 17 | 14. A method, comprising: generating, by at least one computing device, an interface that indicates at least a plurality of items of content including a first item of content, wherein the plurality of items of content are associated with a plurality of services including a first service, and wherein the first item of content is scheduled on the first service; receiving, by the at least one computing device, a first user input to highlight the first item of content; responsive to the first user input, determining, by the at least one computing device, a second service not indicated by the interface, the second service being based at least in part on the first item of content; and generating, by the at least one computing device, an updated interface to indicate the second service simultaneously with the plurality of items of content. | 14. A method, comprising: generating, by at least one computing device, an interface that indicates at least a plurality of items of content including a first item of content, wherein the plurality of items of content are associated with a plurality of services including a first service, and wherein the first item of content is scheduled on the first service; receiving, by the at least one computing device, a first user input to highlight the first item of content; responsive to the first user input, determining, by the at least one computing device, a second service not indicated by the interface, the second service being based at least in part on the first item of content; and generating, by the at least one computing device, an updated interface to indicate the second service simultaneously with the plurality of items of content. 17. The method of claim 14 , further comprising: receiving a second user input selecting an indication in the interface of the second service, wherein the generating the updated interface comprises generating for display the updated interface, after the receiving of the second user input, to comprise a grid of the plurality of items of content, wherein the grid is configured such that the plurality of services and the second service are arranged along a first axis and a window of time is arranged along a different second axis. | 0.500938 |
7,729,901 | 9 | 10 | 9. The method of claim 1 further comprising determining an immediate small relative context to moderate a long context. | 9. The method of claim 1 further comprising determining an immediate small relative context to moderate a long context. 10. The method of claim 9 further comprising storing a probability value for the long context only if sufficiently different from the small context. | 0.938385 |
10,068,016 | 25 | 28 | 25. A system for providing information, comprising: a network interface device to communicatively couple to a communication network; one or more processors configured to: receive a natural language query, receive one or more first electronic messages that include an answer to the natural language query, and metadata corresponding to the answer, the metadata separate from the answer and including information to enable construction by a computing device, using the metadata, of a syntactically correct natural language sentence or statement that recites and/or describes the answer, wherein the information to enable construction comprises information (i) indicating how the natural language query was interpreted in determining the answer and (ii) not included in the query; and generate the syntactically correct natural language sentence or statement that recites and/or describes the answer using the metadata in the one or more first electronic messages. | 25. A system for providing information, comprising: a network interface device to communicatively couple to a communication network; one or more processors configured to: receive a natural language query, receive one or more first electronic messages that include an answer to the natural language query, and metadata corresponding to the answer, the metadata separate from the answer and including information to enable construction by a computing device, using the metadata, of a syntactically correct natural language sentence or statement that recites and/or describes the answer, wherein the information to enable construction comprises information (i) indicating how the natural language query was interpreted in determining the answer and (ii) not included in the query; and generate the syntactically correct natural language sentence or statement that recites and/or describes the answer using the metadata in the one or more first electronic messages. 28. The system of claim 25 , wherein: the metadata includes information to enable construction by the computing device, using the metadata, of the natural language sentence or statement so that the natural language sentence or statement further rephrases the query. | 0.712581 |
8,620,842 | 1 | 6 | 1. An active learning system for classifying documents in a document collection as a member of a plurality of classes, the system comprising: a processor being adapted to: select a document from the document collection for which a user coding decision is to be received; calculate at least two predicted classifiers, each predicted classifier being calculated by incrementally updating a current classifier using a document information profile for the selected document and a different coding decision selected from a set of possible user coding decisions; determine a processing order for a subset of documents in the document collection that indicates an order in which the documents of the subset are to be scored; for each one of the predicted classifiers, calculate a set of scores for one or more documents in the document collection, at least in part, according to the processing order, wherein each score is generated for a document by utilizing the corresponding predicted classifier and a document information profile of the document to be scored; in response to receiving a user coding decision for the selected document, select a set of scores from the calculated sets of scores based on the predicted classifier that corresponds to the received user coding decision; classify a set of documents in the document collection using the selected scores or user coding decisions; and select a further document from the document collection using the selected scores and repeat the steps of calculating predicted classifiers, determining a processing order, and calculating a set of scores. | 1. An active learning system for classifying documents in a document collection as a member of a plurality of classes, the system comprising: a processor being adapted to: select a document from the document collection for which a user coding decision is to be received; calculate at least two predicted classifiers, each predicted classifier being calculated by incrementally updating a current classifier using a document information profile for the selected document and a different coding decision selected from a set of possible user coding decisions; determine a processing order for a subset of documents in the document collection that indicates an order in which the documents of the subset are to be scored; for each one of the predicted classifiers, calculate a set of scores for one or more documents in the document collection, at least in part, according to the processing order, wherein each score is generated for a document by utilizing the corresponding predicted classifier and a document information profile of the document to be scored; in response to receiving a user coding decision for the selected document, select a set of scores from the calculated sets of scores based on the predicted classifier that corresponds to the received user coding decision; classify a set of documents in the document collection using the selected scores or user coding decisions; and select a further document from the document collection using the selected scores and repeat the steps of calculating predicted classifiers, determining a processing order, and calculating a set of scores. 6. The system of claim 1 , wherein the processor is further adapted to manage priority queues for ranking the one or more documents of the document collection. | 0.689453 |
8,842,821 | 2 | 3 | 2. The method of claim 1 , wherein the contact handling system further includes: an agent workstation, which allows the agent to interact with customers, media, and the contact handling system; a media server, which handles connection paths for media; and an automatic contact distributor, which tracks the state of contacts and agents, the automatic contact distributor supplying available agents with waiting media. | 2. The method of claim 1 , wherein the contact handling system further includes: an agent workstation, which allows the agent to interact with customers, media, and the contact handling system; a media server, which handles connection paths for media; and an automatic contact distributor, which tracks the state of contacts and agents, the automatic contact distributor supplying available agents with waiting media. 3. The method of claim 2 , wherein the contact handling system further includes: an interactive voice response unit, which provides pre-recorded audio prompts to incoming voice media. | 0.935427 |
9,947,320 | 4 | 5 | 4. The method of claim 1 , wherein the query comprises a weight associated with at least one of the at least two key terms. | 4. The method of claim 1 , wherein the query comprises a weight associated with at least one of the at least two key terms. 5. The method of claim 4 , wherein determining the relevance score comprises: determining at least one group of key terms, wherein a key term in the at least one group appears in the at least one interaction, and the key term is connected to another key term in the at least one group if the key term and the other key term appear in one constraint complied with by the interaction; determining a score for the at least one group as a function of weights associated with key terms that belong to the at least one group; and determining the relevance score as a maximal relevance score associated with any group of the at least one group. | 0.839385 |
8,407,242 | 8 | 11 | 8. One or more computer-readable storage media storing information that when processed by a computer causes the computer to perform a process, the process comprising: maintaining a semantic data store that stores graphs comprised of interrelated facts of different fact types, each fact type comprising a subject data type, predicate data type, and object data type, each fact comprising a statement comprised of values of the data types of its fact type, respectively, wherein the semantic data store is configured to execute semantic queries; and the maintaining including adding new facts and logically deleting existing facts, wherein each adding of a new fact comprises automatically adding to the new fact a valid-from time value indicating a time before which the new fact was invalid and after which the new fact was valid, and wherein each logically deleting an existing fact comprises adding a valid-to time value indicating a time before which the existing fact was valid and after which the existing fact became invalid. | 8. One or more computer-readable storage media storing information that when processed by a computer causes the computer to perform a process, the process comprising: maintaining a semantic data store that stores graphs comprised of interrelated facts of different fact types, each fact type comprising a subject data type, predicate data type, and object data type, each fact comprising a statement comprised of values of the data types of its fact type, respectively, wherein the semantic data store is configured to execute semantic queries; and the maintaining including adding new facts and logically deleting existing facts, wherein each adding of a new fact comprises automatically adding to the new fact a valid-from time value indicating a time before which the new fact was invalid and after which the new fact was valid, and wherein each logically deleting an existing fact comprises adding a valid-to time value indicating a time before which the existing fact was valid and after which the existing fact became invalid. 11. One or more computer-readable storage media according to claim 8 , wherein a same query that specifies a time of facts to be retrieved, when run at different query times, returns a same set of facts that match the query and that have valid-from time values that precede the specified time and that have no valid-to time values that precede or match the specified time, wherein the same facts are in the returned set regardless of when the query time is executed including at a time when some of the facts in the set have been logically deleted. | 0.500911 |
7,552,098 | 1 | 2 | 1. A method for applying a model for an interactive voice response system comprising: a) receiving a training data set at a first computing unit; b) sorting classes of the training data set by frequency distribution at the first computing unit; c) distributing the sorted classes as a plurality of S groups across a plurality of S processors using a round robin partition, wherein each group includes classes different from classes in each other group, and each group is distributed to a different processor of the plurality of S processors, each of the S processors being located within a different computing unit; d) for each processor, processing the distributed group of sorted classes to produce learning data; e) for each processor, distributing the learning data to each of the other processors; f) merging results of the processing into a model at a second computing unit; and g) outputting the model to cache operatively connected to the second computing unit; and h) applying the model to an interactive voice response system. | 1. A method for applying a model for an interactive voice response system comprising: a) receiving a training data set at a first computing unit; b) sorting classes of the training data set by frequency distribution at the first computing unit; c) distributing the sorted classes as a plurality of S groups across a plurality of S processors using a round robin partition, wherein each group includes classes different from classes in each other group, and each group is distributed to a different processor of the plurality of S processors, each of the S processors being located within a different computing unit; d) for each processor, processing the distributed group of sorted classes to produce learning data; e) for each processor, distributing the learning data to each of the other processors; f) merging results of the processing into a model at a second computing unit; and g) outputting the model to cache operatively connected to the second computing unit; and h) applying the model to an interactive voice response system. 2. The method of claim 1 , wherein prior to b), the method further comprises determining if at least two training data in the training data set are identical, and merging identical data. | 0.527919 |
9,311,362 | 1 | 6 | 1. A computer implemented method comprising: receiving, at an Internet search system, a search query; receiving multiple search results, each of the search results identifying an Internet resource indexed by the search system that satisfies the query; determining that the search query matches a name of a user that submitted the search query; providing, in response to the search query, a ranking of one or more of the search results and a personal knowledge panel comprising one or more items of user-provided information about the user, wherein the personal knowledge panel includes multiple input fields for updating the user-provided information of the knowledge panel; receiving updated user information that was provided using the input fields of the personal knowledge panel; and associating the updated user information with an account of the user. | 1. A computer implemented method comprising: receiving, at an Internet search system, a search query; receiving multiple search results, each of the search results identifying an Internet resource indexed by the search system that satisfies the query; determining that the search query matches a name of a user that submitted the search query; providing, in response to the search query, a ranking of one or more of the search results and a personal knowledge panel comprising one or more items of user-provided information about the user, wherein the personal knowledge panel includes multiple input fields for updating the user-provided information of the knowledge panel; receiving updated user information that was provided using the input fields of the personal knowledge panel; and associating the updated user information with an account of the user. 6. The method of claim 1 , further comprising: receiving, at the Internet search system, a second occurrence of the search query from a second user; receiving multiple second search results, each of the second search results identifying an Internet resource indexed by the search system that satisfies the query; determining that the search query matches a name of the user and that the user is a contact of the second user; and providing to the second user, in response to the search query, a second ranking of the one or more second search results and a second personal knowledge panel comprising one or more items of the updated user information provided by the user. | 0.61975 |
8,972,324 | 9 | 13 | 9. A system comprising: a synthetic character further comprising a sensor configured to create sensor data based on conditions of a local environment or interactions with a user; an animation module configured to control the interactions of the synthetic character based on instructions from a traversable script; a first communication module configured to send the sensor data to, and receive a traversable script update from, a second communication module of an analysis platform; a modification module configured to implement the traversable script update; and a first memory configured to store the sensor data, traversable script, animation module, modification module, and first communication module; the analysis platform further comprising a second communication module configured to receive the sensor data from, and send the traversable script update to, the first communication module of the synthetic character; an analytics engine configured to generate an activity index, wherein the activity index is a set of script analytics, wherein the analytics engine calculates normative variations in the set of script analytics compared to an aggregated data set, and further wherein the traversable script update is generated based on the normative variations; and a second memory configured to store the sensor data received by the second communications module, the second communication module, and the analytics engine, wherein the traversable script update is generated by automatically generating new traversable behavior based on the normative variations in the activity index, and enabling manual updates by generating a script editor that allows the user to modify the traversable script. | 9. A system comprising: a synthetic character further comprising a sensor configured to create sensor data based on conditions of a local environment or interactions with a user; an animation module configured to control the interactions of the synthetic character based on instructions from a traversable script; a first communication module configured to send the sensor data to, and receive a traversable script update from, a second communication module of an analysis platform; a modification module configured to implement the traversable script update; and a first memory configured to store the sensor data, traversable script, animation module, modification module, and first communication module; the analysis platform further comprising a second communication module configured to receive the sensor data from, and send the traversable script update to, the first communication module of the synthetic character; an analytics engine configured to generate an activity index, wherein the activity index is a set of script analytics, wherein the analytics engine calculates normative variations in the set of script analytics compared to an aggregated data set, and further wherein the traversable script update is generated based on the normative variations; and a second memory configured to store the sensor data received by the second communications module, the second communication module, and the analytics engine, wherein the traversable script update is generated by automatically generating new traversable behavior based on the normative variations in the activity index, and enabling manual updates by generating a script editor that allows the user to modify the traversable script. 13. The system of claim 9 , wherein the sensor is adapted to detect one or more of speech patterns, movement in a local environment, movement of a physical object in relation to the synthetic character, and local environmental conditions. | 0.518219 |
8,526,739 | 53 | 54 | 53. The method as recited in claim 52 , wherein the updating comprises correcting one or more OCR errors. | 53. The method as recited in claim 52 , wherein the updating comprises correcting one or more OCR errors. 54. The method as recited in claim 53 , wherein the one or more OCR errors comprise one or more of incorrectly identified characters and unidentified characters. | 0.927215 |
9,773,182 | 8 | 13 | 8. A non-transitory computer readable storage medium including instructions that, when executed by at least one processing device, cause the at least one processing device to perform operations comprising: receiving, by the at least one processing device, an electronic document having a first portion, a second portion, and a third portion; determining, by the at least one processing device, a first content-to-noise ratio for the first portion; determining, by the at least one processing device, a second content-to-noise ratio for the second portion; determining, by the at least one processing device, a third content-to-noise ratio for the third portion; sort a list of the first portion, the second portion, and the third portion in order from a highest content-to-noise ratio to a lowest content-to-noise ratio using the first content-to-noise ratio, the second content-to-noise ratio, and the third content-to-noise ratio; and removing, from the electronic document, a predetermined number of the first portion, the second portion, and the third portion starting with the highest content-to-noise ratio in the list. | 8. A non-transitory computer readable storage medium including instructions that, when executed by at least one processing device, cause the at least one processing device to perform operations comprising: receiving, by the at least one processing device, an electronic document having a first portion, a second portion, and a third portion; determining, by the at least one processing device, a first content-to-noise ratio for the first portion; determining, by the at least one processing device, a second content-to-noise ratio for the second portion; determining, by the at least one processing device, a third content-to-noise ratio for the third portion; sort a list of the first portion, the second portion, and the third portion in order from a highest content-to-noise ratio to a lowest content-to-noise ratio using the first content-to-noise ratio, the second content-to-noise ratio, and the third content-to-noise ratio; and removing, from the electronic document, a predetermined number of the first portion, the second portion, and the third portion starting with the highest content-to-noise ratio in the list. 13. The non-transitory computer readable storage medium of claim 8 , wherein determining the content-to-noise ratio for the first portion further comprises: determining substantive content metrics and noise metrics of the electronic document; calculating a substantive content score for the first portion using a ratio of substantive content metrics of the first portion to corresponding substantive content metrics of the electronic document; calculating a noise score for the first portion using a ratio of noise metrics of the first portion to corresponding noise metrics of the electronic document; and calculating a ratio of the noise score to the substantive content score. | 0.500735 |
8,850,384 | 23 | 26 | 23. An apparatus comprising: a processing device; a database to store a service-oriented architecture (SOA) service model; and a modeling tool, executable by the processing device and coupled to the database, to: receive a user request for an action concerning a service candidate associated with the SOA service model, wherein the SOA service model specifies a plurality of service-oriented candidates and defines relationships between the plurality of service-oriented candidates, wherein each of the plurality of service-oriented candidates comprises at least one of a service candidate, a composition candidate comprising an aggregate of service candidates, or an operation candidate, track the relationships between the plurality of service-oriented candidates, determine a provider count indicating a number of times a service-oriented candidate is reused by other composition candidates in view of the tracked relationships, display a user interface corresponding to the requested action and the provider count, receive user input for the service candidate via the user interface, and update the SOA service model in view of the user input for the service candidate and the requested action. | 23. An apparatus comprising: a processing device; a database to store a service-oriented architecture (SOA) service model; and a modeling tool, executable by the processing device and coupled to the database, to: receive a user request for an action concerning a service candidate associated with the SOA service model, wherein the SOA service model specifies a plurality of service-oriented candidates and defines relationships between the plurality of service-oriented candidates, wherein each of the plurality of service-oriented candidates comprises at least one of a service candidate, a composition candidate comprising an aggregate of service candidates, or an operation candidate, track the relationships between the plurality of service-oriented candidates, determine a provider count indicating a number of times a service-oriented candidate is reused by other composition candidates in view of the tracked relationships, display a user interface corresponding to the requested action and the provider count, receive user input for the service candidate via the user interface, and update the SOA service model in view of the user input for the service candidate and the requested action. 26. The apparatus of claim 23 , wherein the user interface comprises: a main workspace having a service candidate area with one or more service candidate fields; an inventory sidebar located adjacent the main workspace, to display a service candidate inventory from the SOA service model; and a profile window to display profile information for the service candidate. | 0.599345 |
9,697,071 | 17 | 21 | 17. A system comprising: wrapping code coupled to an error handler of a routine, in which the routine produces an error and the wrapping code wraps the error with relevant information to provide a wrapped exception instance, including to use an exception type hierarchy to preserve information in the wrapped exception instance including an exception type; and an exception manager that receives the wrapped exception instance and determines one or more actions to take based upon the exception type of the wrapped exception instance, wherein the exception manager takes the one or more actions, including to present an interactive dialog associated with the exception type and wherein the dialog includes an interactive input mechanism, which when selected, results in the exception manager taking further action to navigate back to a prior location, or retry an operation that caused the error. | 17. A system comprising: wrapping code coupled to an error handler of a routine, in which the routine produces an error and the wrapping code wraps the error with relevant information to provide a wrapped exception instance, including to use an exception type hierarchy to preserve information in the wrapped exception instance including an exception type; and an exception manager that receives the wrapped exception instance and determines one or more actions to take based upon the exception type of the wrapped exception instance, wherein the exception manager takes the one or more actions, including to present an interactive dialog associated with the exception type and wherein the dialog includes an interactive input mechanism, which when selected, results in the exception manager taking further action to navigate back to a prior location, or retry an operation that caused the error. 21. The system of claim 17 wherein the interactive input mechanism, when selected, is further interactive to allow the exception manager to take further action to navigate to a location in a program that contains the routine, in which the location is determined based upon information in the wrapped exception type. | 0.675258 |
8,364,694 | 13 | 18 | 13. A non-transitory computer readable storage medium storing one or more programs configured for execution by a server system, the one or more programs comprising instructions for: receiving a search hints request, the search hints request including at least a character string including at least one character; determining a set of search hints based on the character string, each search hint in the set of search hints being associated with one or more digital media assets available from the online media store and each digital media asset having an associated media type; for each respective search hint in the determined set of search hints: determining whether the client device supports the media type of at least one of the one or more digital media assets associated with the respective search hint in the determined set of search hints; in accordance with a determination that the client device does not support the media type associated with any of the one or more digital media assets associated with the respective search hint, removing the respective search hint from the determined set of search hints; obtaining a media popularity indication for each of a plurality of the search hints in the set of search hints; prioritizing each of the plurality of the search hints in the set of search hints based on the media popularity indication, the media popularity indication based on purchase data for the digital media assets; and selecting a subset of the search hints having the highest media popularity indications; and sending the subset of the search hints. | 13. A non-transitory computer readable storage medium storing one or more programs configured for execution by a server system, the one or more programs comprising instructions for: receiving a search hints request, the search hints request including at least a character string including at least one character; determining a set of search hints based on the character string, each search hint in the set of search hints being associated with one or more digital media assets available from the online media store and each digital media asset having an associated media type; for each respective search hint in the determined set of search hints: determining whether the client device supports the media type of at least one of the one or more digital media assets associated with the respective search hint in the determined set of search hints; in accordance with a determination that the client device does not support the media type associated with any of the one or more digital media assets associated with the respective search hint, removing the respective search hint from the determined set of search hints; obtaining a media popularity indication for each of a plurality of the search hints in the set of search hints; prioritizing each of the plurality of the search hints in the set of search hints based on the media popularity indication, the media popularity indication based on purchase data for the digital media assets; and selecting a subset of the search hints having the highest media popularity indications; and sending the subset of the search hints. 18. The non-transitory computer readable storage medium of claim 13 , wherein the search hints request is issued by a client device, wherein the client device supports one or more media types, and wherein the instructions for determining of the set of search hints further comprise instructions for: obtaining a country in which the client device is located; and eliminating from the set of search hints those of the search hints in the set of search hints that are associated with a country other than the country associated with the client device. | 0.500909 |
9,251,137 | 25 | 28 | 25. The computer system of claim 21 , said method further comprising: said processor receiving selection rules for selecting text; said processor selecting text in accordance with the received selection rules; and before said receiving first text entered by the user, said processor indexing the selected text to generate multiple indexed segments of text which include the plurality of indexed segments of text. | 25. The computer system of claim 21 , said method further comprising: said processor receiving selection rules for selecting text; said processor selecting text in accordance with the received selection rules; and before said receiving first text entered by the user, said processor indexing the selected text to generate multiple indexed segments of text which include the plurality of indexed segments of text. 28. The computer system of claim 25 , wherein the selection rules for selecting text are based on the number of words in segments of the text. | 0.977008 |
8,275,613 | 7 | 12 | 7. The voice recording recited in claim 1 is transcribed/proofread by any source or system so a subsequent transcript can be submitted by the means recited computer processing for use by the dictationbase system comprising steps of: Including: a) creating the dictationbase dictation application for one or more documents wherein the tagged subject identifiers are stored in the dictationbase by the means computer processing from preformatted word processing templates or list to be used to assemble documents including matching dictated subject captions together with prose, type once data subject identifiers for subsequent transcript processing, including a plurality of tagged subject identifiers comprised of each subject, document element, database field and prescribed connections to resource systems, type once data fields, industry standard identification or staff assigned document data subject/elements; b) the dictation user designated for each application assigns and enters familiar (one or more words) comprising subject identification for dictated subject captions in their dictation routine or uses the subject identifiers absent any assigned entry as the dictated subject captions; c) the dictationbase application produces the reminder list of dictation subject captions to guide the user for the voice recording technique recited in claim 1 comprised of uttering the dictation subject captions with or without modifiers followed by free expression information relevant to said subjects and at least one term indicating an end of said information “end-it”; d) the voice recording dictated in claim 1 is transcribed so the subsequent transcript text file with dictated subject captions including prose can be processed by submitting for computer processing including dictated subject captions review for user dictated modifiers and assembly functions for preformatted documents as well as storing each captured dictated subject caption with dictated prose in the dictationbase by all identifications. e) the computer processing recited in claim 1 comprised of assembling one or more documents while capturing and storing a plurality of dictated subject captions with prose as computable data in the dictationbase including executing prescribed connection updates for resource database fields. | 7. The voice recording recited in claim 1 is transcribed/proofread by any source or system so a subsequent transcript can be submitted by the means recited computer processing for use by the dictationbase system comprising steps of: Including: a) creating the dictationbase dictation application for one or more documents wherein the tagged subject identifiers are stored in the dictationbase by the means computer processing from preformatted word processing templates or list to be used to assemble documents including matching dictated subject captions together with prose, type once data subject identifiers for subsequent transcript processing, including a plurality of tagged subject identifiers comprised of each subject, document element, database field and prescribed connections to resource systems, type once data fields, industry standard identification or staff assigned document data subject/elements; b) the dictation user designated for each application assigns and enters familiar (one or more words) comprising subject identification for dictated subject captions in their dictation routine or uses the subject identifiers absent any assigned entry as the dictated subject captions; c) the dictationbase application produces the reminder list of dictation subject captions to guide the user for the voice recording technique recited in claim 1 comprised of uttering the dictation subject captions with or without modifiers followed by free expression information relevant to said subjects and at least one term indicating an end of said information “end-it”; d) the voice recording dictated in claim 1 is transcribed so the subsequent transcript text file with dictated subject captions including prose can be processed by submitting for computer processing including dictated subject captions review for user dictated modifiers and assembly functions for preformatted documents as well as storing each captured dictated subject caption with dictated prose in the dictationbase by all identifications. e) the computer processing recited in claim 1 comprised of assembling one or more documents while capturing and storing a plurality of dictated subject captions with prose as computable data in the dictationbase including executing prescribed connection updates for resource database fields. 12. The steps of claim 7 consisting of submitting one or more documents nested in their prescribed formats adding subject identifiers as well as entry of dictated subject captions wherein one reminder list eliminates duplication of dictated subject captions for one dictation session. | 0.959739 |
8,412,509 | 10 | 11 | 10. The method of claim 1 wherein the translating the input information from the first language to information in a second language comprises: querying a first source for the information in the second language, wherein the first source is a different from a second source for which a document is being displayed in the primary window. | 10. The method of claim 1 wherein the translating the input information from the first language to information in a second language comprises: querying a first source for the information in the second language, wherein the first source is a different from a second source for which a document is being displayed in the primary window. 11. The method of claim 10 wherein the first source is a translation dictionary database accessible through the Internet. | 0.932327 |
8,024,178 | 15 | 17 | 15. A system comprising: one or more servers to: receive a text fragment; perform a search, using the text fragment, to identify one or more documents; locate sentences, within the one or more documents, that contain the text fragment; identify sentence endings included in the located sentences; assign scores to the sentence endings based, at least in part, on a location within the located sentences at which the text fragment occurs; and present the identified sentence endings as potential completions for the text fragment based, at least in part, on the scores. | 15. A system comprising: one or more servers to: receive a text fragment; perform a search, using the text fragment, to identify one or more documents; locate sentences, within the one or more documents, that contain the text fragment; identify sentence endings included in the located sentences; assign scores to the sentence endings based, at least in part, on a location within the located sentences at which the text fragment occurs; and present the identified sentence endings as potential completions for the text fragment based, at least in part, on the scores. 17. The system of claim 15 , where the one or more servers are further to: identify two or more of the sentence endings that have text in common; and merge the identified two or more sentence endings into a merged sentence ending, and, when presenting the identified sentence endings, the one or more servers are further to: present the merged sentence ending as a potential completion. | 0.818779 |
8,926,672 | 25 | 28 | 25. In combination a spinal implant anchor adapted for connection to a bone fixation structural member and a closure for capturing the structural member in the anchor, the closure comprising: a substantially cylindrical outer member having a helically wound flange form thereon, the flange form having a pitch of between 0.045 and 0.055 inches, and having a load flank adjacent a splay control ramp, a substantial portion of the splay control ramp being at an oblique angle with respect to the load flank, the splay control ramp being in spaced relation to both a root and a crest of the flange form, the splay control ramp terminating at a rounded surface that remains unloaded during use wherein the splay control ramp oblique angle is greater than seventy degrees. | 25. In combination a spinal implant anchor adapted for connection to a bone fixation structural member and a closure for capturing the structural member in the anchor, the closure comprising: a substantially cylindrical outer member having a helically wound flange form thereon, the flange form having a pitch of between 0.045 and 0.055 inches, and having a load flank adjacent a splay control ramp, a substantial portion of the splay control ramp being at an oblique angle with respect to the load flank, the splay control ramp being in spaced relation to both a root and a crest of the flange form, the splay control ramp terminating at a rounded surface that remains unloaded during use wherein the splay control ramp oblique angle is greater than seventy degrees. 28. The combination of claim 25 wherein the load flank slopes in a radial direction away from a top surface of the closure. | 0.967478 |
9,055,148 | 15 | 29 | 15. The method of claim 11 , further comprising: after said anchor is built, categorizing said chat data into said team/department names. | 15. The method of claim 11 , further comprising: after said anchor is built, categorizing said chat data into said team/department names. 29. The method of claim 15 , further comprising: including the agent's names as a variable for use in evaluating the agents' performance. | 0.945935 |
9,171,326 | 13 | 14 | 13. A system for user profiling comprising one or more processors and one or more memory devices operably coupled to the one or more processors, the one or more memory devices storing executable and operational data effective to cause the one or more processors to: evaluate a plurality of textual actions of a user, the plurality of textual actions comprising online social media activities wherein the user interacts with text or generates textual information; for each textual action of the plurality of textual actions, identify one or more concepts by: identifying at least one entity corresponding to the each textual action of the plurality of textual actions, by using natural language processing, to identify parts of speech performed by the at least one entity; identifying two or more concept candidates for at least one of the identified at least one entity; and selecting one of the identified two or more concept candidates as one of the identified one or more concepts according to a user profile of the user; and generate an interest profile for the user according to the identified one or more concepts; wherein the executable and operational data are further effective to cause the one or more processors to select the one of the identified two or more concept candidates as one of the identified one or more concepts by: assigning first scores to the two or more concept candidates according to the user profile; if a first one of the first scores assigned to a first one of the two or more concept candidates exceeds a first threshold criteria, selecting the first one of the two or more concept candidates corresponding to the first one of the first scores as one of the identified one or more concepts; if the first one of the first scores assigned to the first one of the two or more concept candidates does not exceed the first threshold criteria, updating the first one of the first scores for the first one of the two or more concept candidates according to one or more close friend profiles of close friends of the user to obtain a second score for the first one of the two or more concept candidates; if the second score assigned to the first one of the two or more concept candidates exceeds a second threshold criteria, selecting the first one of the two or more concept candidates corresponding to the second score as the one of the identified one or more concepts; if the second score assigned to the first one of the two or more concept candidates does not exceed the second threshold criteria, updating the second score for the first one of the two or more concept candidates according to one or more non-close friend profiles of the user to obtain a third score; if the third score assigned to the first one of the two or more concept candidates exceeds a third threshold criteria, selecting the first one of the two or more concept candidates corresponding to the third score as the one of the identified one or more concepts; if the third score assigned to the first one of the two or more concept candidates does not exceed the third threshold criteria, updating the third score for the first one of the two or more concept candidates according to a current global popularity of the first one of the two or more concept candidates to obtain a fourth score; and if the fourth score assigned to the first one of the two or more concept candidates, as updated according to the current global popularity of the first one of the two or more concept candidates, exceeds a fourth threshold criteria, selecting the first one of the two or more concept candidates corresponding to the fourth score as the one of the identified one or more concepts. | 13. A system for user profiling comprising one or more processors and one or more memory devices operably coupled to the one or more processors, the one or more memory devices storing executable and operational data effective to cause the one or more processors to: evaluate a plurality of textual actions of a user, the plurality of textual actions comprising online social media activities wherein the user interacts with text or generates textual information; for each textual action of the plurality of textual actions, identify one or more concepts by: identifying at least one entity corresponding to the each textual action of the plurality of textual actions, by using natural language processing, to identify parts of speech performed by the at least one entity; identifying two or more concept candidates for at least one of the identified at least one entity; and selecting one of the identified two or more concept candidates as one of the identified one or more concepts according to a user profile of the user; and generate an interest profile for the user according to the identified one or more concepts; wherein the executable and operational data are further effective to cause the one or more processors to select the one of the identified two or more concept candidates as one of the identified one or more concepts by: assigning first scores to the two or more concept candidates according to the user profile; if a first one of the first scores assigned to a first one of the two or more concept candidates exceeds a first threshold criteria, selecting the first one of the two or more concept candidates corresponding to the first one of the first scores as one of the identified one or more concepts; if the first one of the first scores assigned to the first one of the two or more concept candidates does not exceed the first threshold criteria, updating the first one of the first scores for the first one of the two or more concept candidates according to one or more close friend profiles of close friends of the user to obtain a second score for the first one of the two or more concept candidates; if the second score assigned to the first one of the two or more concept candidates exceeds a second threshold criteria, selecting the first one of the two or more concept candidates corresponding to the second score as the one of the identified one or more concepts; if the second score assigned to the first one of the two or more concept candidates does not exceed the second threshold criteria, updating the second score for the first one of the two or more concept candidates according to one or more non-close friend profiles of the user to obtain a third score; if the third score assigned to the first one of the two or more concept candidates exceeds a third threshold criteria, selecting the first one of the two or more concept candidates corresponding to the third score as the one of the identified one or more concepts; if the third score assigned to the first one of the two or more concept candidates does not exceed the third threshold criteria, updating the third score for the first one of the two or more concept candidates according to a current global popularity of the first one of the two or more concept candidates to obtain a fourth score; and if the fourth score assigned to the first one of the two or more concept candidates, as updated according to the current global popularity of the first one of the two or more concept candidates, exceeds a fourth threshold criteria, selecting the first one of the two or more concept candidates corresponding to the fourth score as the one of the identified one or more concepts. 14. The system of claim 13 , wherein the executable and operational data are further effective to cause the one or more processors to: select products corresponding to the interest profile; and transmit a gift recommendation including the selected products for display. | 0.843605 |
9,904,768 | 9 | 14 | 9. An apparatus comprising: at least one processor; and at least one memory storing processor-executable instructions that, when executed by the at least one processor, perform a method of analyzing a text documenting a patient encounter, the method comprising: analyzing the text to identify a set of one or more features of at least a portion of the text; correlating the set of one or more features to a set of alternative hypotheses for an abstract semantic concept representing an intended semantic meaning of the at least a portion of the text; computing, using at least one statistical model implemented using at least one processor and for each of at least some of the alternative hypotheses of the set, one or more measures of estimated likelihood that the respective alternative hypothesis accurately represents the intended semantic meaning of the at least a portion of the text; identifying hypotheses, including some or all of the at least some of the alternative hypotheses, to be presented to a user, wherein identifying the hypotheses comprises, for each of the at least some of the alternative hypotheses of the set: in response to determining that the one or more measures of estimated likelihood for the alternative hypothesis satisfy one or more criteria, identifying that the alternative hypothesis is to be presented to the user; and presenting the identified hypotheses, to the user documenting the patient encounter, as alternative hypotheses for a medical fact that could be extracted from the text. | 9. An apparatus comprising: at least one processor; and at least one memory storing processor-executable instructions that, when executed by the at least one processor, perform a method of analyzing a text documenting a patient encounter, the method comprising: analyzing the text to identify a set of one or more features of at least a portion of the text; correlating the set of one or more features to a set of alternative hypotheses for an abstract semantic concept representing an intended semantic meaning of the at least a portion of the text; computing, using at least one statistical model implemented using at least one processor and for each of at least some of the alternative hypotheses of the set, one or more measures of estimated likelihood that the respective alternative hypothesis accurately represents the intended semantic meaning of the at least a portion of the text; identifying hypotheses, including some or all of the at least some of the alternative hypotheses, to be presented to a user, wherein identifying the hypotheses comprises, for each of the at least some of the alternative hypotheses of the set: in response to determining that the one or more measures of estimated likelihood for the alternative hypothesis satisfy one or more criteria, identifying that the alternative hypothesis is to be presented to the user; and presenting the identified hypotheses, to the user documenting the patient encounter, as alternative hypotheses for a medical fact that could be extracted from the text. 14. The apparatus of claim 9 , wherein: the identified hypotheses comprise a first hypothesis and a second hypothesis of the at least some of the alternative hypotheses, and the presenting comprises: presenting the first hypothesis to the user; and in response to a selection by the user of the presented first hypothesis, presenting the second hypothesis to the user. | 0.552311 |
8,027,832 | 9 | 15 | 9. A method of identifying the natural language of text comprising: receiving a text sample written in an unidentified natural language; counting a total number of features in the text sample; determining occurrences of each of a plurality of unique features in at least one window of characters in the text sample, each feature being an individual character, and wherein each character in the at least one window is examined; calculating probability values for occurrences of each unique feature based on the total number of features of the text sample, stored expected probability values for each unique feature for each of a plurality of candidate languages, and the occurrences of each unique feature in the text sample; calculating language scores for the plurality of candidate languages using a computer, said calculation comprising estimating a joint probability of seeing all unique features in the text sample by multiplying the probability values calculated for occurrences of each unique feature; and identifying at least one language for the text sample from among the plurality of candidate languages based on the language scores. | 9. A method of identifying the natural language of text comprising: receiving a text sample written in an unidentified natural language; counting a total number of features in the text sample; determining occurrences of each of a plurality of unique features in at least one window of characters in the text sample, each feature being an individual character, and wherein each character in the at least one window is examined; calculating probability values for occurrences of each unique feature based on the total number of features of the text sample, stored expected probability values for each unique feature for each of a plurality of candidate languages, and the occurrences of each unique feature in the text sample; calculating language scores for the plurality of candidate languages using a computer, said calculation comprising estimating a joint probability of seeing all unique features in the text sample by multiplying the probability values calculated for occurrences of each unique feature; and identifying at least one language for the text sample from among the plurality of candidate languages based on the language scores. 15. The method of claim 9 , wherein calculating language scores comprises negative scoring a candidate language when the current count for the at least one feature falls outside a variance of the obtained expected probability information, wherein the at least one feature comprises a character. | 0.830058 |
10,042,506 | 8 | 13 | 8. A method for use by an interactive story development system having a memory and a processor, the method comprising: receiving, using the processor, a story state including an attribute state of each of a plurality of items present in a storyline, wherein the plurality of items are stored in the memory and include a plurality of objects of the storyline and a plurality of characters of the storyline; creating a story web using the processor, wherein the story web includes a node for every possible interaction between the plurality of objects and the plurality of characters in the storyline; calculating, using the processor, a narrative value for each of the nodes of the story web by determining a cost of transitioning from one node of the nodes to each of the other nodes using a cost function, wherein the cost function uses at least one of a pagerank feature, an inverse pagerank feature and a min-cut feature, wherein the pagerank feature is a likelihood of the one node being selected in a random exploration of the story web, wherein the inverse pagerank feature is an unlikelihood of the one node being selected in a random exploration of the story web, and wherein the min-cut feature is a measure of a minimum number of transition lines to be removed from the story web in order to sever all paths from the one node to another node of the nodes; receiving, using the processor, a first input from a user selecting user criteria including at least one of a story telling option of the storyline and a sentiment selection; determining, using the processor, based on the narrative value and the user criteria, a plurality of candidate nodes of the story web; and displaying, on a display, the storyline generated using the determined plurality of candidate nodes of the story web. | 8. A method for use by an interactive story development system having a memory and a processor, the method comprising: receiving, using the processor, a story state including an attribute state of each of a plurality of items present in a storyline, wherein the plurality of items are stored in the memory and include a plurality of objects of the storyline and a plurality of characters of the storyline; creating a story web using the processor, wherein the story web includes a node for every possible interaction between the plurality of objects and the plurality of characters in the storyline; calculating, using the processor, a narrative value for each of the nodes of the story web by determining a cost of transitioning from one node of the nodes to each of the other nodes using a cost function, wherein the cost function uses at least one of a pagerank feature, an inverse pagerank feature and a min-cut feature, wherein the pagerank feature is a likelihood of the one node being selected in a random exploration of the story web, wherein the inverse pagerank feature is an unlikelihood of the one node being selected in a random exploration of the story web, and wherein the min-cut feature is a measure of a minimum number of transition lines to be removed from the story web in order to sever all paths from the one node to another node of the nodes; receiving, using the processor, a first input from a user selecting user criteria including at least one of a story telling option of the storyline and a sentiment selection; determining, using the processor, based on the narrative value and the user criteria, a plurality of candidate nodes of the story web; and displaying, on a display, the storyline generated using the determined plurality of candidate nodes of the story web. 13. The method of claim 8 , wherein the memory includes a story web generator. | 0.937898 |
9,152,713 | 16 | 18 | 16. The computer-readable medium of claim 15 , where the one or more instructions that cause the one or more processors to calculate the score include: one or more instructions that cause the one or more processors to: generate a similarity vector for the text segment, the similarity vector for the text segment being generated based on a frequency of a term of the one or more particular terms; generate a similarity vector for the document, the similarity vector for the document being generated based on a frequency of a term in the document; and calculate the score based on the similarity vector for the text segment and the similarity vector for the document. | 16. The computer-readable medium of claim 15 , where the one or more instructions that cause the one or more processors to calculate the score include: one or more instructions that cause the one or more processors to: generate a similarity vector for the text segment, the similarity vector for the text segment being generated based on a frequency of a term of the one or more particular terms; generate a similarity vector for the document, the similarity vector for the document being generated based on a frequency of a term in the document; and calculate the score based on the similarity vector for the text segment and the similarity vector for the document. 18. The computer-readable medium of claim 16 , where the one or more instructions that cause the one or more processors to generate the similarity vector for the text segment include: one or more instructions that cause the one or more processors to: assign a weight to each term of the one or more particular terms in the text segment; and generate the similarity vector for the text segment based on the weight assigned to each term of the one or more particular terms in the text segment. | 0.871735 |
8,255,218 | 8 | 9 | 8. The method of claim 1 , further comprising: during the receiving of the vocal input, receiving a selection by the user of a third input field of the GUI displayed on the display after receiving the selection by the user of the second input field; identifying vocal input received after the selection of the third input field as third vocal input intended for the third input field; graphically inputting a third text portion in the third input field, wherein the third text portion is based upon conversion of the third vocal input into textual characters by the ASR engine. | 8. The method of claim 1 , further comprising: during the receiving of the vocal input, receiving a selection by the user of a third input field of the GUI displayed on the display after receiving the selection by the user of the second input field; identifying vocal input received after the selection of the third input field as third vocal input intended for the third input field; graphically inputting a third text portion in the third input field, wherein the third text portion is based upon conversion of the third vocal input into textual characters by the ASR engine. 9. The method of claim 8 , wherein graphically inputting the first text portion in the first input field occurs after receiving the selection by the user of the third input field. | 0.941234 |
5,446,653 | 12 | 15 | 12. A document generation system in accordance with claim 4 comprising third memory means for storing said insurance policy clauses and fourth memory means for storing said endorsement clauses, wherein said assembling means comprise a print system for retrieving the insurance policy and endorsement clauses set forth on said list from said third and fourth memory means, respectively, and formatting them for printing. | 12. A document generation system in accordance with claim 4 comprising third memory means for storing said insurance policy clauses and fourth memory means for storing said endorsement clauses, wherein said assembling means comprise a print system for retrieving the insurance policy and endorsement clauses set forth on said list from said third and fourth memory means, respectively, and formatting them for printing. 15. A document generation system in accordance with claim 12 wherein: said user interface, said computer processor and said first and second memory means comprise a first computer for providing said list of clauses and endorsements; and said print system comprises a second computer coupled to said second and third memory means for retrieving and formatting said clauses. | 0.892111 |
8,347,202 | 1 | 13 | 1. A computer-implemented method for tagging place names with geographic location coordinates, the method comprising: at a server system having one or more processors and memory storing programs executed by the one or more processors to perform the method: retrieving a first fact from a fact repository, the first fact having an attribute and a value, wherein the first fact is associated with a first object, the fact repository includes a plurality of objects and a plurality of facts associated with the plurality of objects, a respective fact in the fact repository includes a respective attribute and a respective value, the respective attribute is a text string, and the attribute of the first fact and plurality of values are extracted from free text in a plurality of web documents; determining that the attribute of the first fact indicates that the value of the first fact is a potential place name; and in response to the determining: identifying a first potential place name corresponding to the value of the first fact; determining geographic location coordinates for the first potential place name, including examining frequency with which the geographic location coordinates are associated with variations of the first potential place name; and storing the determined geographic location coordinates in the fact repository, the storing including associating the determined geographic location coordinates with the first fact. | 1. A computer-implemented method for tagging place names with geographic location coordinates, the method comprising: at a server system having one or more processors and memory storing programs executed by the one or more processors to perform the method: retrieving a first fact from a fact repository, the first fact having an attribute and a value, wherein the first fact is associated with a first object, the fact repository includes a plurality of objects and a plurality of facts associated with the plurality of objects, a respective fact in the fact repository includes a respective attribute and a respective value, the respective attribute is a text string, and the attribute of the first fact and plurality of values are extracted from free text in a plurality of web documents; determining that the attribute of the first fact indicates that the value of the first fact is a potential place name; and in response to the determining: identifying a first potential place name corresponding to the value of the first fact; determining geographic location coordinates for the first potential place name, including examining frequency with which the geographic location coordinates are associated with variations of the first potential place name; and storing the determined geographic location coordinates in the fact repository, the storing including associating the determined geographic location coordinates with the first fact. 13. The method of claim 1 , wherein the determining geographic location coordinates for the first potential place name comprises comparing potential geographic location coordinates for the first potential place name with the geographic location coordinates for an identified place name from a same source document as the source document containing the first potential place name. | 0.721324 |
7,873,643 | 1 | 3 | 1. A computer implemented method of predicting, comprising the steps of: receiving a pre-existing classification structure; receiving an instance to be predicted, comprising at least one attribute to be predicted; determining a best host for the instance to be predicted; optionally placing the instance to be predicted into a location relative to at least one child of the best host within the pre-existing classification structure; determining a confidence level for an occurrence of an at least one possible value for the at least one attribute to be predicted; querying each member of the pre-existing classification structure to perform a prediction process on the instance, wherein the prediction process is based at least in part on a relevance factor associated with the attribute; and returning a prediction profile, comprising at least one possible value for the at least one attribute to predict and the corresponding confidence level for the at least one possible value for the at least one attribute to be predicted. | 1. A computer implemented method of predicting, comprising the steps of: receiving a pre-existing classification structure; receiving an instance to be predicted, comprising at least one attribute to be predicted; determining a best host for the instance to be predicted; optionally placing the instance to be predicted into a location relative to at least one child of the best host within the pre-existing classification structure; determining a confidence level for an occurrence of an at least one possible value for the at least one attribute to be predicted; querying each member of the pre-existing classification structure to perform a prediction process on the instance, wherein the prediction process is based at least in part on a relevance factor associated with the attribute; and returning a prediction profile, comprising at least one possible value for the at least one attribute to predict and the corresponding confidence level for the at least one possible value for the at least one attribute to be predicted. 3. The computer implemented method of claim 1 , further comprising the step of: displaying the classification structure; determining at least one distinguishing feature of the instance to be predicted; and visually contrasting the instance to be predicted within the classification structure, based upon the value of the at least one distinguishing feature. | 0.754132 |
5,384,702 | 10 | 11 | 10. The method of claim 1, wherein a grammar marker of said plurality of grammar markers includes a progressive mode marker. | 10. The method of claim 1, wherein a grammar marker of said plurality of grammar markers includes a progressive mode marker. 11. The method of claim 10, wherein said first database includes present participle conversion rules. | 0.989003 |
10,042,622 | 13 | 14 | 13. A computer program product operable on a computer system for generating ease of use interfaces for legacy system management facilities (SMF), comprising a non-transitory computer storage medium readable by the computer system having a processor and a memory configured to store computer executable instructions for execution by the processor of the computer system for performing a method comprising: retrieving an SMF record from an SMF data source through an SMF data interface from a file, over a network or via a real-time API, wherein the SMF record comprises a data control section (DSECT) with code comments for storing a mapping of the SMF record defining data structure of the SMF record with one or more fields; converting the mapping of the SMF record into an intermediate format representing the mapping of the SMF record and corresponding information extracted from the code comments among the one or more fields of the SMF record; generating at least one application programming interface (API) in a different computer language using the intermediate format; and accessing the SMF record using the API generated, wherein the converting comprises: building a set of matching keywords from the code comments including wildcard matching; weighting different matching keywords; and matching the mapping of the SMF record to the intermediate format with one or more corresponding fields by combining the matching keywords, weighting, descriptions and a location of the one or more fields. | 13. A computer program product operable on a computer system for generating ease of use interfaces for legacy system management facilities (SMF), comprising a non-transitory computer storage medium readable by the computer system having a processor and a memory configured to store computer executable instructions for execution by the processor of the computer system for performing a method comprising: retrieving an SMF record from an SMF data source through an SMF data interface from a file, over a network or via a real-time API, wherein the SMF record comprises a data control section (DSECT) with code comments for storing a mapping of the SMF record defining data structure of the SMF record with one or more fields; converting the mapping of the SMF record into an intermediate format representing the mapping of the SMF record and corresponding information extracted from the code comments among the one or more fields of the SMF record; generating at least one application programming interface (API) in a different computer language using the intermediate format; and accessing the SMF record using the API generated, wherein the converting comprises: building a set of matching keywords from the code comments including wildcard matching; weighting different matching keywords; and matching the mapping of the SMF record to the intermediate format with one or more corresponding fields by combining the matching keywords, weighting, descriptions and a location of the one or more fields. 14. The computer program product of claim 13 , wherein the SMF record comprises one or more sections, the mapping of the SMF record comprises mapping of the one or more sections, and the mapping of the one or more sections comprises: an offset where a first section is located; a size of the one or more sections; and a number of the one or more sections. | 0.501404 |
8,788,476 | 1 | 5 | 1. A method of providing access to information, comprising: receiving a request; defining, by a processor, a conditional request comprising a search query and a trigger condition when available conditional requests fail to match the request; presenting the request and the conditional request to a person; associating the conditional request with an originator of the request when the conditional request is accepted by the person; and generating a search based on the search query when the trigger condition occurs after the conditional request is accepted by the person; and performing the search via an automated resource selected based on an action obtained from the person before the trigger condition occurs when the trigger condition occurs after the conditional request is accepted by the person. | 1. A method of providing access to information, comprising: receiving a request; defining, by a processor, a conditional request comprising a search query and a trigger condition when available conditional requests fail to match the request; presenting the request and the conditional request to a person; associating the conditional request with an originator of the request when the conditional request is accepted by the person; and generating a search based on the search query when the trigger condition occurs after the conditional request is accepted by the person; and performing the search via an automated resource selected based on an action obtained from the person before the trigger condition occurs when the trigger condition occurs after the conditional request is accepted by the person. 5. The method of claim 1 , comprising: associating a search resource with the conditional request when an action of a human assistant enables the trigger condition. | 0.856643 |
7,639,257 | 65 | 67 | 65. A computer readable medium encoded with a computer program for representing a character in a text document, the computer program comprising instructions that when executed by a programmable processor of a computer cause the programmable processor to perform operations comprising: inserting into the text document a reference identifying a glyphlet, the identified glyphlet being a data structure stored in a memory of the computer including both a set of one or more character attributes defining semantic information of a character and a set of one or more glyph attributes defining appearance information from which a glyph image of a glyph representing the character can be rendered, the glyph attributes including all the information necessary to render the glyph image, the reference representing the character in the text document; and displaying the character in the text document, the displayed character being rendered from the glyph attributes. | 65. A computer readable medium encoded with a computer program for representing a character in a text document, the computer program comprising instructions that when executed by a programmable processor of a computer cause the programmable processor to perform operations comprising: inserting into the text document a reference identifying a glyphlet, the identified glyphlet being a data structure stored in a memory of the computer including both a set of one or more character attributes defining semantic information of a character and a set of one or more glyph attributes defining appearance information from which a glyph image of a glyph representing the character can be rendered, the glyph attributes including all the information necessary to render the glyph image, the reference representing the character in the text document; and displaying the character in the text document, the displayed character being rendered from the glyph attributes. 67. The computer readable medium encoded with the computer program of claim 65 , wherein: the reference includes one or more in-band values defined in an encoding standard that are interpreted to identify one or more target attributes from which the identified glyphlet can be identified. | 0.874346 |
8,412,525 | 1 | 3 | 1. A method, comprising: converting a plurality of feature vectors that represents a speech utterance into a plurality of log probability sets, the converting using a classifier ensemble including a plurality of classifiers; transforming the plurality of log probability sets into a plurality of output symbol sequences; combining the plurality of output symbol sequences, using an iterative a priori probability calculation algorithm, into a fusion output symbol sequence; and retrieving one or more speech utterances from a speech database using the plurality of output symbol sequences. | 1. A method, comprising: converting a plurality of feature vectors that represents a speech utterance into a plurality of log probability sets, the converting using a classifier ensemble including a plurality of classifiers; transforming the plurality of log probability sets into a plurality of output symbol sequences; combining the plurality of output symbol sequences, using an iterative a priori probability calculation algorithm, into a fusion output symbol sequence; and retrieving one or more speech utterances from a speech database using the plurality of output symbol sequences. 3. The method of claim 1 , wherein the classifier ensemble includes at least one supervised classifier and at least one unsupervised classifier. | 0.949474 |
7,676,400 | 1 | 3 | 1. A computer-based method of scoring recommendations for potential purchase by a customer, comprising: receiving a recommendation context from a customer; using the recommendation context to identify a plurality of candidate recommendations that match the recommendation context, where each candidate recommendation recommends at least one recommended item; with a computer system, determining a score for each candidate recommendation by subtracting a first expected margin value factor for the recommended item that is based on the candidate recommendation not being displayed from a second expected margin value factor for the recommended item that is based on the candidate recommendation being displayed; and ranking the plurality of candidate recommendations using the score for each candidate recommendation to identify at least a highest ranking candidate recommendation. | 1. A computer-based method of scoring recommendations for potential purchase by a customer, comprising: receiving a recommendation context from a customer; using the recommendation context to identify a plurality of candidate recommendations that match the recommendation context, where each candidate recommendation recommends at least one recommended item; with a computer system, determining a score for each candidate recommendation by subtracting a first expected margin value factor for the recommended item that is based on the candidate recommendation not being displayed from a second expected margin value factor for the recommended item that is based on the candidate recommendation being displayed; and ranking the plurality of candidate recommendations using the score for each candidate recommendation to identify at least a highest ranking candidate recommendation. 3. The method of claim 1 , further comprising generating a selling point message for each candidate recommendation by: identifying a plurality of selling point messages corresponding to the candidate recommendation, where each selling point messages is targeted to a predetermined user case profile; identifying a first user case profile most likely matches the customer; and selecting a first selling point message from the plurality of selling point messages that is targeted to the first user case profile for use with the candidate recommendation. | 0.714508 |
8,145,618 | 21 | 29 | 21. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: receiving a search criteria; identifying one or more documents responsive to the search criteria; determining a text match score for each document based on degree of match between the responsive document and the search criteria; determining a document-categories score for each of a plurality of categories based on a degree of match between each document and each of the categories; determining a search criteria-categories score for each of the one or more categories based on a degree of match between the search criteria and each of the one or more categories, wherein the search criteria-categories score for a particular category indicates the degree of match between the search criteria and the category; determining a category match score for each document by combining the document-categories score of each of the one or more categories and the respective search criteria-categories score; determining an overall score for each document based on the text match score of each document and the respective category match score; and determining, based on the overall score for each document, a ranked order for the one or more documents. | 21. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: receiving a search criteria; identifying one or more documents responsive to the search criteria; determining a text match score for each document based on degree of match between the responsive document and the search criteria; determining a document-categories score for each of a plurality of categories based on a degree of match between each document and each of the categories; determining a search criteria-categories score for each of the one or more categories based on a degree of match between the search criteria and each of the one or more categories, wherein the search criteria-categories score for a particular category indicates the degree of match between the search criteria and the category; determining a category match score for each document by combining the document-categories score of each of the one or more categories and the respective search criteria-categories score; determining an overall score for each document based on the text match score of each document and the respective category match score; and determining, based on the overall score for each document, a ranked order for the one or more documents. 29. The computer storage medium of claim 21 , further comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: determining an aggregate category match score using the document-categories score and the search criteria-categories score. | 0.622038 |
10,049,668 | 19 | 22 | 19. A method for performing speech-to-text conversion, the method comprising: at an electronic device having a processor and memory: receiving speech input; traversing, based on the speech input, a sequence of states and arcs of a weighted finite state transducer (WFST), wherein: the sequence of states and arcs represents one or more history candidate words and a current candidate word; and a first probability of the candidate word given the one or more history candidate words is determined by traversing the sequence of states and arcs of the WFST; traversing a negating finite state transducer (FST), wherein traversing the negating FST negates the first probability of the candidate word given the one or more history candidate words; composing a virtual FST using a neural network language model and based on the sequence of states and arcs of the WFST, wherein one or more virtual states of the virtual FST represent the current candidate word; traversing the one or more virtual states of the virtual FST, wherein a second probability of the candidate word given the one or more history candidate words is determined by traversing the one or more virtual states of the virtual FST; determining, based on the second probability of the candidate word given the one or more history candidate words, text corresponding to the speech input; based on the determined text, performing one or more tasks to obtain a result; and causing the result to be presented in spoken or visual form. | 19. A method for performing speech-to-text conversion, the method comprising: at an electronic device having a processor and memory: receiving speech input; traversing, based on the speech input, a sequence of states and arcs of a weighted finite state transducer (WFST), wherein: the sequence of states and arcs represents one or more history candidate words and a current candidate word; and a first probability of the candidate word given the one or more history candidate words is determined by traversing the sequence of states and arcs of the WFST; traversing a negating finite state transducer (FST), wherein traversing the negating FST negates the first probability of the candidate word given the one or more history candidate words; composing a virtual FST using a neural network language model and based on the sequence of states and arcs of the WFST, wherein one or more virtual states of the virtual FST represent the current candidate word; traversing the one or more virtual states of the virtual FST, wherein a second probability of the candidate word given the one or more history candidate words is determined by traversing the one or more virtual states of the virtual FST; determining, based on the second probability of the candidate word given the one or more history candidate words, text corresponding to the speech input; based on the determined text, performing one or more tasks to obtain a result; and causing the result to be presented in spoken or visual form. 22. The method of claim 19 , wherein only one arc transitions out of each virtual state of the one or more virtual states of the virtual FST. | 0.910305 |
8,543,394 | 9 | 16 | 9. A method of controlling a mobile terminal, the method comprising: converting voice input received via a voice receiving unit into text; displaying, via a display unit, the text in which a word of the text is emphatically displayed if a voice recognition rate of the word is less than a preset reference value; receiving, via a controller, a first touch selection signal indicating an emphasized word is touch selected for correction; displaying, via the display unit, a plurality of candidate words to replace the selected word; receiving, via the controller, a second touch signal indicating a candidate word is selected from the plurality of candidate words; replacing, via the controller, the selected word with the candidate word and stop displaying the plurality of candidate words when the second touch signal is received; displaying a slide bar with the selected word being positioned with respect to a first position on the slide bar; receiving a touch and slide action on the slide bar; and displaying candidate words at different positions on the slide bar based on the touch and slide action, wherein as the touch and slide action slides away from the position of the selected word, candidate words having a lower similarity to the selected word are displayed, and as the touch and slide action slides toward the position of the selected word, candidate words having a higher similarity to the selected words are displayed, and wherein the converted text is displayed in a first direction and the plurality of candidate words are simultaneously displayed in a list in a second direction perpendicular to the first direction. | 9. A method of controlling a mobile terminal, the method comprising: converting voice input received via a voice receiving unit into text; displaying, via a display unit, the text in which a word of the text is emphatically displayed if a voice recognition rate of the word is less than a preset reference value; receiving, via a controller, a first touch selection signal indicating an emphasized word is touch selected for correction; displaying, via the display unit, a plurality of candidate words to replace the selected word; receiving, via the controller, a second touch signal indicating a candidate word is selected from the plurality of candidate words; replacing, via the controller, the selected word with the candidate word and stop displaying the plurality of candidate words when the second touch signal is received; displaying a slide bar with the selected word being positioned with respect to a first position on the slide bar; receiving a touch and slide action on the slide bar; and displaying candidate words at different positions on the slide bar based on the touch and slide action, wherein as the touch and slide action slides away from the position of the selected word, candidate words having a lower similarity to the selected word are displayed, and as the touch and slide action slides toward the position of the selected word, candidate words having a higher similarity to the selected words are displayed, and wherein the converted text is displayed in a first direction and the plurality of candidate words are simultaneously displayed in a list in a second direction perpendicular to the first direction. 16. The method of claim 9 , wherein the word of the text is emphasized by underlining the word. | 0.876302 |
8,028,239 | 1 | 8 | 1. One or more tangible computer-readable media having computer-executable instructions for performing a method to determine one or more user interface elements to display for a user interface surface, the method comprising: receiving a definition of the user interface surface; receiving an indication of a current context with multiple current context factors, each current context factor having a possible set of management element definitions with an associated set of user interface element definitions for presentation on the user interface surface, wherein the possible sets of management element definitions comprise logic for retrieving data to be displayed on the user interface surface determining an intersection of the associated sets of user interface element definitions as a display set of user interface element definitions, the determining the intersection comprising, applying a first subtractive filter before querying a management element store for one or more interface elements, applying a second subtractive filter after querying the management element store, the second subtractive filter being associated with the management element store, and applying a third subtractive filter after receiving the definition of the user interface surface, the third subtractive filter being associated with the definition of the user interface surface; and displaying at least a portion of the display set of user interface element definitions on the user interface surface using the logic for retrieving data to be displayed on the user interface surface of the sets of management element definitions associated with the user interface element definitions. | 1. One or more tangible computer-readable media having computer-executable instructions for performing a method to determine one or more user interface elements to display for a user interface surface, the method comprising: receiving a definition of the user interface surface; receiving an indication of a current context with multiple current context factors, each current context factor having a possible set of management element definitions with an associated set of user interface element definitions for presentation on the user interface surface, wherein the possible sets of management element definitions comprise logic for retrieving data to be displayed on the user interface surface determining an intersection of the associated sets of user interface element definitions as a display set of user interface element definitions, the determining the intersection comprising, applying a first subtractive filter before querying a management element store for one or more interface elements, applying a second subtractive filter after querying the management element store, the second subtractive filter being associated with the management element store, and applying a third subtractive filter after receiving the definition of the user interface surface, the third subtractive filter being associated with the definition of the user interface surface; and displaying at least a portion of the display set of user interface element definitions on the user interface surface using the logic for retrieving data to be displayed on the user interface surface of the sets of management element definitions associated with the user interface element definitions. 8. The one or more tangible computer-readable media of claim 1 wherein: the indication of the current context is based at least on a context factor contributed by the definition of the user interface surface. | 0.927526 |
9,183,004 | 8 | 9 | 8. The method of claim 1 , further comprising: receiving a command to invoke performance of the at least one script; in response to receiving the command, executing the at least one script to interact with the web service; and during execution of the at least one script to interact with the web service, requesting input from the user for each free variable while the at least one script interacts with the web service. | 8. The method of claim 1 , further comprising: receiving a command to invoke performance of the at least one script; in response to receiving the command, executing the at least one script to interact with the web service; and during execution of the at least one script to interact with the web service, requesting input from the user for each free variable while the at least one script interacts with the web service. 9. The method of claim 8 , further comprising, during execution of the at least one script to interact with the web service, providing output to the user as received from the web service during interaction of the at least one script with the web service. | 0.922086 |
8,060,456 | 14 | 19 | 14. A system that is capable of training a search result ranker, the system comprising: a relevance score determiner to infer user interests from user interactions with search results for a particular query and to determine respective relevance scores associated with respective query-identifier pairs of the search results; a training sample handler to formulate query-identifier-relevance score triplets from the respective relevance scores associated with the respective query-identifier pairs and to submit the query-identifier-relevance score triplets as training samples to a learning machine; and a search result ranker to be trained as the learning machine with multiple training samples comprising the query-identifier-relevance score triplets. | 14. A system that is capable of training a search result ranker, the system comprising: a relevance score determiner to infer user interests from user interactions with search results for a particular query and to determine respective relevance scores associated with respective query-identifier pairs of the search results; a training sample handler to formulate query-identifier-relevance score triplets from the respective relevance scores associated with the respective query-identifier pairs and to submit the query-identifier-relevance score triplets as training samples to a learning machine; and a search result ranker to be trained as the learning machine with multiple training samples comprising the query-identifier-relevance score triplets. 19. The system as recited in claim 14 , wherein the relevance score determiner is further to implicitly infer the user interests from the user interactions automatically without manual label relevance input from users. | 0.821018 |
9,910,914 | 1 | 7 | 1. A computer-implemented, informational retrieval system for real-time, interactive semantic curation from a networked database, the system comprising: (a) a gloss vector engine configured with means to generate semantic gloss vectors for data mining by unsupervised machine learning; (b) a deconstruction engine configured with means to identify well-formed sentences; (c) a data discovery engine configured with means to identify indirect communicating links; (d) a collocation engine configured with means to identify compound nouns; (e) a disambiguation engine configured with means to assign nouns that fit the context of the sentence in which said nouns occur; (f) a generic noun disambiguation engine configured with means to identify context shifts produced by use of nouns selected from the group of generic nouns, common nouns; (g) a pronoun disambiguation engine configured with means to link common nouns to proper corresponding nouns; (h) a data evaluation engine configured with means to identify well-formed sentences containing information of maximal semantic value; (i) a retrieval engine configured with means to return information with maximal semantic value score for a given search term; (j) a context targeting engine configured with means to display in context the specific output sentence that is generated by the input query, in the context of the source article containing said sentence; wherein one or more users can dynamically obtain responses to queries of multiple search terms across bodies of corpus. | 1. A computer-implemented, informational retrieval system for real-time, interactive semantic curation from a networked database, the system comprising: (a) a gloss vector engine configured with means to generate semantic gloss vectors for data mining by unsupervised machine learning; (b) a deconstruction engine configured with means to identify well-formed sentences; (c) a data discovery engine configured with means to identify indirect communicating links; (d) a collocation engine configured with means to identify compound nouns; (e) a disambiguation engine configured with means to assign nouns that fit the context of the sentence in which said nouns occur; (f) a generic noun disambiguation engine configured with means to identify context shifts produced by use of nouns selected from the group of generic nouns, common nouns; (g) a pronoun disambiguation engine configured with means to link common nouns to proper corresponding nouns; (h) a data evaluation engine configured with means to identify well-formed sentences containing information of maximal semantic value; (i) a retrieval engine configured with means to return information with maximal semantic value score for a given search term; (j) a context targeting engine configured with means to display in context the specific output sentence that is generated by the input query, in the context of the source article containing said sentence; wherein one or more users can dynamically obtain responses to queries of multiple search terms across bodies of corpus. 7. The generic noun disambiguation engine of the computer-implemented, information retrieval system of claim 1 further comprising mean for identification of a context shift so that said shift in context retains factual information. | 0.752677 |
9,576,274 | 1 | 14 | 1. A method comprising: identifying a first user profile referenced by an action initiated by a first user; determining a similarity score that indicates a degree of similarity between the first user profile and a second user profile that describes a second user; determining a volatility score that indicates a likelihood that the second user is receptive to a proposal that the second user be employed by a potential employer, the volatility score being determined based on a current date on which the volatility score is determined being within a predetermined time span before an annual employment anniversary date of the second user in working for a current employer specified by the second user profile that describes a second user; determining a rank of the second user profile based on the similarity score and on the volatility score, the determining of the rank being performed by a processor of a machine and based on an elapsed time since the first user initiated the action that references the first user profile; and presenting the second user profile to the first user based on the rank. | 1. A method comprising: identifying a first user profile referenced by an action initiated by a first user; determining a similarity score that indicates a degree of similarity between the first user profile and a second user profile that describes a second user; determining a volatility score that indicates a likelihood that the second user is receptive to a proposal that the second user be employed by a potential employer, the volatility score being determined based on a current date on which the volatility score is determined being within a predetermined time span before an annual employment anniversary date of the second user in working for a current employer specified by the second user profile that describes a second user; determining a rank of the second user profile based on the similarity score and on the volatility score, the determining of the rank being performed by a processor of a machine and based on an elapsed time since the first user initiated the action that references the first user profile; and presenting the second user profile to the first user based on the rank. 14. The method of claim 1 , wherein: the presenting of the second user profile includes presenting a reason that references at least one of the action, the first user profile, or a time elapsed since the first user initiated the action. | 0.81388 |
8,782,595 | 7 | 12 | 7. A build system comprising: one or more processors; a computer-readable medium coupled to the one or more processors having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving one or more attribute rules and one or more extra action rules, wherein the one or more attribute rules correspond to one or more predefined default actions of the build system, wherein the extra action rules specify additional actions that are to be added to the build system; generating a graph that represents a relationship between files specified as attributes in the attribute rules and said one or more predefined default actions that correspond to the attribute rules; receiving a request to enable at least one of the extra action rules; receiving action listener rules, wherein the action listener rules indicate one or more default actions and corresponding one or more extra action rules; checking the graph to determine whether said graph includes a particular one of the default actions indicated in the action listener rules; and in response to a determination that the graph includes said particular one of the default actions, adding additional actions to the graph for one or more of the extra action rules corresponding to the particular one of the default actions. | 7. A build system comprising: one or more processors; a computer-readable medium coupled to the one or more processors having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving one or more attribute rules and one or more extra action rules, wherein the one or more attribute rules correspond to one or more predefined default actions of the build system, wherein the extra action rules specify additional actions that are to be added to the build system; generating a graph that represents a relationship between files specified as attributes in the attribute rules and said one or more predefined default actions that correspond to the attribute rules; receiving a request to enable at least one of the extra action rules; receiving action listener rules, wherein the action listener rules indicate one or more default actions and corresponding one or more extra action rules; checking the graph to determine whether said graph includes a particular one of the default actions indicated in the action listener rules; and in response to a determination that the graph includes said particular one of the default actions, adding additional actions to the graph for one or more of the extra action rules corresponding to the particular one of the default actions. 12. The build system of claim 7 , wherein the receiving the request to enable at least one of the extra action rules includes specifying a flag to the build system to enable an action listener rule. | 0.502513 |
9,767,096 | 1 | 6 | 1. A system comprising: a portable non-destructive testing (NDT) device comprising: a display; a user interface; a memory storing an operations object having a first text in a first language; and a processor configured to non-destructively test an item, wherein the processor is configured to present the first text on the operations object via the display during a sensing operation of the portable NDT device, and wherein the processor is configured to create a second text in a second language via the user interface of the NDT device, and to present the second text on the operations object as an alternative to the first text via the display during the sensing operation of the NDT device, wherein the processor is configured to present a first multimedia in the first language during the sensing operation of the portable NDT device, and wherein the processor is configured to create a second multimedia in the second language as an alternative to the first multimedia during an operation of the NDT device, wherein the portable NDT device comprises a communications system configured to transmit data wirelessly, to transmit data through one or more wired conduits, or a combination thereof, and wherein the processor is configured to transmit the second text to a second portable NDT device. | 1. A system comprising: a portable non-destructive testing (NDT) device comprising: a display; a user interface; a memory storing an operations object having a first text in a first language; and a processor configured to non-destructively test an item, wherein the processor is configured to present the first text on the operations object via the display during a sensing operation of the portable NDT device, and wherein the processor is configured to create a second text in a second language via the user interface of the NDT device, and to present the second text on the operations object as an alternative to the first text via the display during the sensing operation of the NDT device, wherein the processor is configured to present a first multimedia in the first language during the sensing operation of the portable NDT device, and wherein the processor is configured to create a second multimedia in the second language as an alternative to the first multimedia during an operation of the NDT device, wherein the portable NDT device comprises a communications system configured to transmit data wirelessly, to transmit data through one or more wired conduits, or a combination thereof, and wherein the processor is configured to transmit the second text to a second portable NDT device. 6. The system of claim 1 , wherein the processor is configured to create the second text in the second language by editing the first text. | 0.792793 |
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