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7,774,203 | 1 | 16 | 1. An audio signal segmentation algorithm comprising: providing an audio signal; applying an audio activity detection (AAD) step to divide the audio signal into at least one first audio segment and at least one second audio segment, wherein the audio activity detection step further comprises: dividing the audio signal into a plurality of frames; applying a frequency transformation step to signals in each of the frames to obtain a plurality of bands in each frame; performing a likelihood computation step to the bands and a noise parameter to obtain a likelihood ratio therebetween; performing a comparison step to the likelihood ratio and a noise threshold, if the noise threshold is greater than the likelihood ratio, the bands belonging to a first frame, and if the likelihood ratio is greater than the noise threshold, the bands belonging to a second frame wherein the first frame belongs to the first audio segment and the second frame belongs to the second audio segment; and when a distance between two adjacent second frames is smaller than a predetermined value, combining the two adjacent second frames to compose the second audio segment, performing an audio feature extraction step on the second audio segment to obtain a plurality of audio features of the second audio segment; applying a smoothing step to the second audio segment after the audio feature extraction step; and discriminating a plurality of speech frames and a plurality of music frames from the second audio segment wherein the speech frames and the music frames compose at least one speech segment and at least one music segment, respectively. | 1. An audio signal segmentation algorithm comprising: providing an audio signal; applying an audio activity detection (AAD) step to divide the audio signal into at least one first audio segment and at least one second audio segment, wherein the audio activity detection step further comprises: dividing the audio signal into a plurality of frames; applying a frequency transformation step to signals in each of the frames to obtain a plurality of bands in each frame; performing a likelihood computation step to the bands and a noise parameter to obtain a likelihood ratio therebetween; performing a comparison step to the likelihood ratio and a noise threshold, if the noise threshold is greater than the likelihood ratio, the bands belonging to a first frame, and if the likelihood ratio is greater than the noise threshold, the bands belonging to a second frame wherein the first frame belongs to the first audio segment and the second frame belongs to the second audio segment; and when a distance between two adjacent second frames is smaller than a predetermined value, combining the two adjacent second frames to compose the second audio segment, performing an audio feature extraction step on the second audio segment to obtain a plurality of audio features of the second audio segment; applying a smoothing step to the second audio segment after the audio feature extraction step; and discriminating a plurality of speech frames and a plurality of music frames from the second audio segment wherein the speech frames and the music frames compose at least one speech segment and at least one music segment, respectively. 16. The audio signal segmentation algorithm according to claim 1 , wherein the audio features are extracted by a frame with fixed length in the audio feature extraction step. | 0.90471 |
8,650,649 | 9 | 14 | 9. A system for determining whether to independently evaluate the trustworthiness of digitally signed files based on signer reputation, the system comprising: a file-analysis module programmed to: identify a file; determine that the file has been digitally signed; identify a signer responsible for digitally signing the file; a reputation module programmed to identify a reputation of the signer, the signer's reputation being based at least in part on the determined trustworthiness of at least one additional file that was previously signed by the signer; a security module programmed to: determine whether the signer's reputation satisfies a predetermined threshold; only perform an independent evaluation of the trustworthiness of the file if the signer's reputation fails to satisfy the predetermined threshold; at least one processor configured to execute the file-analysis module, the reputation module, and the security module. | 9. A system for determining whether to independently evaluate the trustworthiness of digitally signed files based on signer reputation, the system comprising: a file-analysis module programmed to: identify a file; determine that the file has been digitally signed; identify a signer responsible for digitally signing the file; a reputation module programmed to identify a reputation of the signer, the signer's reputation being based at least in part on the determined trustworthiness of at least one additional file that was previously signed by the signer; a security module programmed to: determine whether the signer's reputation satisfies a predetermined threshold; only perform an independent evaluation of the trustworthiness of the file if the signer's reputation fails to satisfy the predetermined threshold; at least one processor configured to execute the file-analysis module, the reputation module, and the security module. 14. The system of claim 9 , further comprising at least one of: a server-side computing device comprising at least one processor configured to execute at least one of the file-analysis module, the reputation module, and the security module; a client-side computing device comprising at least one processor configured to execute at least one of the file-analysis module, the reputation module, and the security module. | 0.578788 |
7,590,224 | 20 | 23 | 20. An automated routing system that automatically routes a user's request based on an automated task classification decision, through a natural language dialog with the user in which system prompts are not ordered in a menu, the system comprising: a recognizer that spots at least one of the plurality of meaningful phrases in substantially simultaneous user natural language verbal and non-verbal input, wherein the natural language verbal and non-verbal input each convey different information and are associated with a coordinated message that achieves an appropriate response, each of the plurality of meaningful phrases having an association with at least one of a predetermined set of task objectives, and the predetermined set of task objectives based, at least partly, on a salience measure of one of the plurality of meaningful phrases to a specified one of the predetermined task objectives, wherein the salience measure is represented as a conditional probability of the task objective being requested given an appearance of one of the plurality of meaningful phrases in the input communication, the conditional probability being a highest value in a distribution of conditional probabilities over the set of predetermined task objectives; a task classifier that makes a classification decision based, at least partly, on the spotted at least one of the plurality of meaningful phrases; and a task router that routes the user's request in order to perform at least one of the task objectives based on the classification decision. | 20. An automated routing system that automatically routes a user's request based on an automated task classification decision, through a natural language dialog with the user in which system prompts are not ordered in a menu, the system comprising: a recognizer that spots at least one of the plurality of meaningful phrases in substantially simultaneous user natural language verbal and non-verbal input, wherein the natural language verbal and non-verbal input each convey different information and are associated with a coordinated message that achieves an appropriate response, each of the plurality of meaningful phrases having an association with at least one of a predetermined set of task objectives, and the predetermined set of task objectives based, at least partly, on a salience measure of one of the plurality of meaningful phrases to a specified one of the predetermined task objectives, wherein the salience measure is represented as a conditional probability of the task objective being requested given an appearance of one of the plurality of meaningful phrases in the input communication, the conditional probability being a highest value in a distribution of conditional probabilities over the set of predetermined task objectives; a task classifier that makes a classification decision based, at least partly, on the spotted at least one of the plurality of meaningful phrases; and a task router that routes the user's request in order to perform at least one of the task objectives based on the classification decision. 23. The automated routing system of claim 20 , wherein the meaningful phrases in the user's input communication received by the recognizer are derived from the user's actions. | 0.809783 |
8,898,226 | 1 | 12 | 1. A method comprising: receiving, from a client device of a first user of an online social network, a search request at the online social network, the online social network comprising a social graph with a plurality of nodes and a plurality of edges connecting the nodes, each of one or more of the nodes being associated with one of a plurality of users of the online social network, each connecting between two nodes representing a relationship between the two nodes and establishing a single degree of separation between the two nodes; searching, by the one or more processors, a multimedia content database to find multimedia content matching one or more terms in the search request; determining, by the one or more processors, one or more matching users of the online social network within a threshold degree of separation of the first user, each matching user being associated with multimedia content matching one or more terms in the search request; retrieving, by the one or more processors, associated multimedia content information for one or more matching users; and sending, by the one or more processors, in response to the search request, information to the client device of the first user to display a web page, wherein the information to display the web page comprises, for each retrieved matching user, profile information and the associated multimedia content information for the retrieved matching user. | 1. A method comprising: receiving, from a client device of a first user of an online social network, a search request at the online social network, the online social network comprising a social graph with a plurality of nodes and a plurality of edges connecting the nodes, each of one or more of the nodes being associated with one of a plurality of users of the online social network, each connecting between two nodes representing a relationship between the two nodes and establishing a single degree of separation between the two nodes; searching, by the one or more processors, a multimedia content database to find multimedia content matching one or more terms in the search request; determining, by the one or more processors, one or more matching users of the online social network within a threshold degree of separation of the first user, each matching user being associated with multimedia content matching one or more terms in the search request; retrieving, by the one or more processors, associated multimedia content information for one or more matching users; and sending, by the one or more processors, in response to the search request, information to the client device of the first user to display a web page, wherein the information to display the web page comprises, for each retrieved matching user, profile information and the associated multimedia content information for the retrieved matching user. 12. The method of claim 1 , further comprising: determining, by the one or more processors, at least one related user by searching in a data store of relationships between users of the online social network, based on whether a relationship exists between the at least one related user and the least one of the one or more matching users; searching, by the one or more processors, the data store of social networking content to determine if the at least one related user's associated multimedia content matches one or more terms in the search request; determining, by the one or more processors, that the at least one related user has associated multimedia content matching one or more terms in the search request; retrieving, by the one or more processors, associated multimedia content information for the at least one related user; and wherein the information to display the web page further comprises the profile information and the associated multimedia content information for the at least one related user. | 0.5 |
8,095,565 | 2 | 10 | 2. The computer-readable storage medium of claim 1 , the metadata further supporting mechanisms to programmatically modify the controls. | 2. The computer-readable storage medium of claim 1 , the metadata further supporting mechanisms to programmatically modify the controls. 10. The computer-readable storage medium of claim 2 , wherein programmatically modifying the controls comprises providing access to a type defined by the code-behind assembly. | 0.521858 |
5,524,065 | 58 | 65 | 58. A pattern recognition method for providing an identity for an unknown pattern segment, comprising the steps of: inputting image information from an image input device connected to a computerized network bus which carries such image information; segmenting said image information into segmented patterns whose identities are unknown; determining distance values between an unknown segmented pattern and at least one known pattern based on plural respectively different distance functions; providing plural candidates for each of the unknown segmented patterns, each of the plural candidates being based on a different one of the plural respectively different distance functions; comparing said plural candidates so as to determine whether or not said plural candidates are comparable; selecting one of said plural candidates as the identity of the unknown pattern segment in a case where said comparing step determines that said plural candidates are comparable; performing detail processing to determine the identity of the unknown pattern segment, said detail processing step being selectively performed so as to determine the identity of the unknown pattern segment in a case where said comparing step determines that said plural candidates are not comparable; and transferring the identity for each unknown pattern segment onto the network bus. | 58. A pattern recognition method for providing an identity for an unknown pattern segment, comprising the steps of: inputting image information from an image input device connected to a computerized network bus which carries such image information; segmenting said image information into segmented patterns whose identities are unknown; determining distance values between an unknown segmented pattern and at least one known pattern based on plural respectively different distance functions; providing plural candidates for each of the unknown segmented patterns, each of the plural candidates being based on a different one of the plural respectively different distance functions; comparing said plural candidates so as to determine whether or not said plural candidates are comparable; selecting one of said plural candidates as the identity of the unknown pattern segment in a case where said comparing step determines that said plural candidates are comparable; performing detail processing to determine the identity of the unknown pattern segment, said detail processing step being selectively performed so as to determine the identity of the unknown pattern segment in a case where said comparing step determines that said plural candidates are not comparable; and transferring the identity for each unknown pattern segment onto the network bus. 65. A method according to claim 58, wherein said unknown pattern is a character pattern. | 0.80786 |
7,607,018 | 11 | 12 | 11. The method of collecting and authenticating electronic signatures as recited in claim 1 , further comprising the step of sending a deadline alert communication to at least one destination at a predetermined time prior to a desired date for signing said document. | 11. The method of collecting and authenticating electronic signatures as recited in claim 1 , further comprising the step of sending a deadline alert communication to at least one destination at a predetermined time prior to a desired date for signing said document. 12. The method of collecting and authenticating electronic signatures as recited in claim 11 , wherein said deadline alert communication is sent to said first signatory. | 0.5 |
10,083,696 | 1 | 3 | 1. A method for determining user liveness comprising: capturing, by a computing device, voice biometric data of a user during a verification transaction, the user and computing device being a user-computing device pair; calculating a first matrix for the captured voice biometric data; calculating an expansion coefficient matrix based on the first matrix and a record spectral shape matrix, the record spectral shape matrix being for the user-computing device pair; calculating a distortion vector as the average of a ratio between the first matrix and the product of the record spectral shape matrix and the expansion coefficient matrix; calculating a spectral property difference as an element-wise function between the square value of the distortion vector and the square value of a user record normalization vector; inputting the spectral property difference into a machine learning algorithm; calculating an output score with the machine learning algorithm, the output score representing the difference between a probability the voice biometric data was captured from a live user and a probability the voice biometric data was not captured from a live user; and determining the voice biometric data was captured from a live user when the output score satisfies a threshold score. | 1. A method for determining user liveness comprising: capturing, by a computing device, voice biometric data of a user during a verification transaction, the user and computing device being a user-computing device pair; calculating a first matrix for the captured voice biometric data; calculating an expansion coefficient matrix based on the first matrix and a record spectral shape matrix, the record spectral shape matrix being for the user-computing device pair; calculating a distortion vector as the average of a ratio between the first matrix and the product of the record spectral shape matrix and the expansion coefficient matrix; calculating a spectral property difference as an element-wise function between the square value of the distortion vector and the square value of a user record normalization vector; inputting the spectral property difference into a machine learning algorithm; calculating an output score with the machine learning algorithm, the output score representing the difference between a probability the voice biometric data was captured from a live user and a probability the voice biometric data was not captured from a live user; and determining the voice biometric data was captured from a live user when the output score satisfies a threshold score. 3. The method for determining user liveness in accordance with claim 1 further comprising calculating the expansion coefficient matrix using the equation Y≈HW, where Y is the first matrix, H is the expansion coefficient matrix, and W is the record spectral shape matrix. | 0.653846 |
7,979,369 | 1 | 19 | 1. A method for classifying digital content, wherein the digital content includes two or more items, the method comprising the following acts performed by one or more hardware processors: identifying a grouping of the two or more items in the digital content; assigning a raw score to the identified items based on predetermined criteria; deriving an aggregate score for the digital content, wherein the aggregate score is derived from the raw scores; and using the aggregate score to output a classification of the digital content, wherein the aggregate score is used to derive a category score, and wherein the category score is derived for a user account. | 1. A method for classifying digital content, wherein the digital content includes two or more items, the method comprising the following acts performed by one or more hardware processors: identifying a grouping of the two or more items in the digital content; assigning a raw score to the identified items based on predetermined criteria; deriving an aggregate score for the digital content, wherein the aggregate score is derived from the raw scores; and using the aggregate score to output a classification of the digital content, wherein the aggregate score is used to derive a category score, and wherein the category score is derived for a user account. 19. The method of claim 1 , further comprising: deriving one or more of the raw scores by using voting results from a user community. | 0.525 |
7,958,129 | 1 | 10 | 1. A method of checking input customer information against existing information, the method comprising tolerating writing variations in input data by the steps of: determining in an input customer data record a value of a data field, the data field representing an identifier, determining, by a processor, from a reference data set of predetermined identifier values in a computer readable database at least one synonym candidate for the value of the data field using a candidate selection criterion, determining if a synonym candidate of the determined at least one synonym candidate and the value of the data field fulfill a predetermined synonym acceptance criterion based on at least one quality parameter, wherein said at least one quality parameter takes into account writing variations that are evaluated based on differences in the value of the data field and the synonym candidate, and when the predetermined synonym acceptance criterion is fulfilled, associating the value of the data field and the synonym candidate as synonyms and automatically updating a synonym set representing known writing variations for the identifier in a computer readable database and referencing to respective entries in the reference data set by adding the value of the data field to the synonym set as a member referring to the accepted synonym candidate in the reference data set without intervention of a user before searching for a counterpart for the input customer data record, and checking the input customer data record by searching for the counterpart for the input customer data record in the reference data set by comparing the value of the data field to the updated synonym set in the computer readable database after the step of determining if the predetermined synonym acceptance criterion is fulfilled. | 1. A method of checking input customer information against existing information, the method comprising tolerating writing variations in input data by the steps of: determining in an input customer data record a value of a data field, the data field representing an identifier, determining, by a processor, from a reference data set of predetermined identifier values in a computer readable database at least one synonym candidate for the value of the data field using a candidate selection criterion, determining if a synonym candidate of the determined at least one synonym candidate and the value of the data field fulfill a predetermined synonym acceptance criterion based on at least one quality parameter, wherein said at least one quality parameter takes into account writing variations that are evaluated based on differences in the value of the data field and the synonym candidate, and when the predetermined synonym acceptance criterion is fulfilled, associating the value of the data field and the synonym candidate as synonyms and automatically updating a synonym set representing known writing variations for the identifier in a computer readable database and referencing to respective entries in the reference data set by adding the value of the data field to the synonym set as a member referring to the accepted synonym candidate in the reference data set without intervention of a user before searching for a counterpart for the input customer data record, and checking the input customer data record by searching for the counterpart for the input customer data record in the reference data set by comparing the value of the data field to the updated synonym set in the computer readable database after the step of determining if the predetermined synonym acceptance criterion is fulfilled. 10. A method as defined in claim 1 , wherein the synonym acceptance criterion requires that there is only one synonym candidate having the best at least one quality parameter. | 0.805122 |
9,369,566 | 5 | 6 | 5. The telephone device according to claim 4 , further comprising: providing, as the obtained identification of the graphical image responsive to failing to match the located at least one keyword to any of the plurality of index values, an identification of a default graphical image. | 5. The telephone device according to claim 4 , further comprising: providing, as the obtained identification of the graphical image responsive to failing to match the located at least one keyword to any of the plurality of index values, an identification of a default graphical image. 6. The telephone device according to claim 5 , further comprising: dynamically adding an entry to the mapping data structure responsive to failing to match the located at least one keyword to any of the plurality of index values, the index value of the added entry comprising the located at least one keyword which fails to match any of the plurality of index values and the corresponding graphical image identification of the added entry being selected by the user in view of the added index value. | 0.5 |
9,230,539 | 1 | 3 | 1. A system comprising: a computer-readable medium having stored thereon model data that includes at least one model of a characteristic of a language spoken by a patient; and a speech analyzer configured to analyze an audio sample including speech of the patient, identify phonemes from the speech of the patient, analyze the identified phonemes to identify prosodic characteristics of the speech of the patient using the model, and automatically measure fluency of the speech of the patient based on the prosodic characteristics. | 1. A system comprising: a computer-readable medium having stored thereon model data that includes at least one model of a characteristic of a language spoken by a patient; and a speech analyzer configured to analyze an audio sample including speech of the patient, identify phonemes from the speech of the patient, analyze the identified phonemes to identify prosodic characteristics of the speech of the patient using the model, and automatically measure fluency of the speech of the patient based on the prosodic characteristics. 3. The system of claim 1 , wherein the model data includes an acoustic model that models phoneme boundaries, and wherein the speech analyzer references the acoustic model to identify locations of phonemes, silences, and white noise. | 0.774757 |
9,298,679 | 7 | 9 | 7. A computer-implemented method for processing a web page received in a binary representation, the method comprising: receiving a web page in a binary representation, the binary representation defining a type of each page element, syntax information of each page element, and a document object model of the web page, the document object model defining one or more hypertext markup language (“HTML”) page elements as including at least a pair of an integer value and one or more attributes associated with the integer value; rendering the web page, using a processor, by processing the binary representation, the structure of the web page defined by the document object model; and reconciling a first binary representation dictionary version used in rendering the web page with a second binary representation dictionary version used in compiling the web page. | 7. A computer-implemented method for processing a web page received in a binary representation, the method comprising: receiving a web page in a binary representation, the binary representation defining a type of each page element, syntax information of each page element, and a document object model of the web page, the document object model defining one or more hypertext markup language (“HTML”) page elements as including at least a pair of an integer value and one or more attributes associated with the integer value; rendering the web page, using a processor, by processing the binary representation, the structure of the web page defined by the document object model; and reconciling a first binary representation dictionary version used in rendering the web page with a second binary representation dictionary version used in compiling the web page. 9. The method of claim 7 , further comprising requesting an updated binary representation dictionary when the first binary representation dictionary version is not greater than or equal to the second binary representation dictionary version. | 0.5 |
10,007,965 | 1 | 2 | 1. An apparatus to manage a graphics processing unit (GPU) pipeline comprising: a GPU interconnect to receive a compiled shader, the compiled shader to comprise instructions in an executable format that are ready for execution via the GPU pipeline; a shader patcher to: determine whether one or more portions of the compiled shader can be patched in the executable format based on global constants referenced in the compiled shader having corresponding values in a global constant buffer and modified prior to execution of the compiled shader, replace references, corresponding to global constants in the compiled shader and modified prior to execution of the compiled shader, with values of the global constants from the global constant buffer to generate one or more patched portions based on an optimization goal, and replace the one or more portions of the compiled shader with the one or more patched portions based on a determination that the one or more portions of the compiled shader can be patched; and a GPU operably coupled to the GPU interconnect, the GPU to repeatedly execute the compiled shader, the shader patcher to patch the compiled shader between a first instance the GPU executes the compiled shader and a second instance the GPU executes the compiled shader. | 1. An apparatus to manage a graphics processing unit (GPU) pipeline comprising: a GPU interconnect to receive a compiled shader, the compiled shader to comprise instructions in an executable format that are ready for execution via the GPU pipeline; a shader patcher to: determine whether one or more portions of the compiled shader can be patched in the executable format based on global constants referenced in the compiled shader having corresponding values in a global constant buffer and modified prior to execution of the compiled shader, replace references, corresponding to global constants in the compiled shader and modified prior to execution of the compiled shader, with values of the global constants from the global constant buffer to generate one or more patched portions based on an optimization goal, and replace the one or more portions of the compiled shader with the one or more patched portions based on a determination that the one or more portions of the compiled shader can be patched; and a GPU operably coupled to the GPU interconnect, the GPU to repeatedly execute the compiled shader, the shader patcher to patch the compiled shader between a first instance the GPU executes the compiled shader and a second instance the GPU executes the compiled shader. 2. The apparatus of claim 1 , wherein the optimization goal comprises increasing power efficiency, increasing computational power, or reducing memory fetches. | 0.697318 |
8,762,408 | 7 | 12 | 7. A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving, at the local computing system that comprises an enterprise computing system from a remote computing system that executes a hosted application, one or more configuration tables defining a predefined query scenario for querying and retrieving data from the hosted application for data stored on a database table; replicating a database table from a main database to a secondary database, the main database comprising updated data relative to data contained in the database table of the secondary database, the secondary database comprising an in-memory and the main database comprising a magnetic memory; receiving, at the local computing system, a query from the enterprise application for data stored on the database table, the query comprising a query return time and a context; comparing the context of the query to a context of the predefined query scenario, the context of the query comprising at least one of a database table name, a scenario application name or a scenario job name; based on the context of the query matching the context of the predefined query scenario, retrieving data stored on the secondary database replicated from data stored on the main database; and passing the retrieved data from the secondary database to the enterprise application. | 7. A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving, at the local computing system that comprises an enterprise computing system from a remote computing system that executes a hosted application, one or more configuration tables defining a predefined query scenario for querying and retrieving data from the hosted application for data stored on a database table; replicating a database table from a main database to a secondary database, the main database comprising updated data relative to data contained in the database table of the secondary database, the secondary database comprising an in-memory and the main database comprising a magnetic memory; receiving, at the local computing system, a query from the enterprise application for data stored on the database table, the query comprising a query return time and a context; comparing the context of the query to a context of the predefined query scenario, the context of the query comprising at least one of a database table name, a scenario application name or a scenario job name; based on the context of the query matching the context of the predefined query scenario, retrieving data stored on the secondary database replicated from data stored on the main database; and passing the retrieved data from the secondary database to the enterprise application. 12. The computer storage medium of any claim 7 , wherein the secondary database comprises an in-memory database that comprises volatile RAM memory, and the main database comprises magnetic memory. | 0.82807 |
9,294,818 | 2 | 5 | 2. The television receiver of claim 1 , further comprising: an audio-video decoder for decoding video and audio data in packets of the television program such that audio data decoded by the audio-video decoder is output to a speaker and video data decoded by the audio-video decoder is output to a display, wherein the content delivered from the server is decoded by the audio-video decoder. | 2. The television receiver of claim 1 , further comprising: an audio-video decoder for decoding video and audio data in packets of the television program such that audio data decoded by the audio-video decoder is output to a speaker and video data decoded by the audio-video decoder is output to a display, wherein the content delivered from the server is decoded by the audio-video decoder. 5. The television receiver of claim 2 , wherein a display language of the video data decoded by the audio-video decoder is the same language with the selected menu language. | 0.5 |
9,697,205 | 1 | 7 | 1. A method comprising: identifying, using one or more processors, information associated with content of a first web page, the first web page being accessed by a user, and the identified information including one or more terms; forming, using one or more processors, a search query based on the identified information and information associated with the user, the information associated with the user including one or more terms; identifying, using one or more processors, a second web page based on the search query that is based on the identified information and the information associated with the user; modifying, using one or more processors, the first web page by inserting a reference to the second web page, the reference to the second web page including text associated with content of the second web page; and providing, using one or more processors and for presentation to the user, the modified first web page. | 1. A method comprising: identifying, using one or more processors, information associated with content of a first web page, the first web page being accessed by a user, and the identified information including one or more terms; forming, using one or more processors, a search query based on the identified information and information associated with the user, the information associated with the user including one or more terms; identifying, using one or more processors, a second web page based on the search query that is based on the identified information and the information associated with the user; modifying, using one or more processors, the first web page by inserting a reference to the second web page, the reference to the second web page including text associated with content of the second web page; and providing, using one or more processors and for presentation to the user, the modified first web page. 7. The method of claim 1 , where the reference to the second web page is an advertisement. | 0.905858 |
8,560,559 | 11 | 12 | 11. A method performed by a server device, the method comprising: receiving a search query from a client device, the search query including a search term; performing, by a processor of the server device, a search, based on the search term, to generate a document of search results, each of the search results including a result link and a textual description associated with the search result; highlighting, by the processor, an occurrence of the search term in at least one of the result links or the textual descriptions in the document of search results and in a plurality of documents corresponding to a particular number of the result links, the particular number of the result links being fewer than all of the result links in the search results document, and the search term being highlighted in each of the plurality of documents prior to selection of any of the result links; outputting, by the processor, the document of search results with the highlighted search term; receiving, from the client device, information regarding a selection of one of the result links of the particular number of the result links; obtaining, by the processor, a document, of the plurality of documents, corresponding to the selected result link; and transmitting, by the processor, the document to the client device. | 11. A method performed by a server device, the method comprising: receiving a search query from a client device, the search query including a search term; performing, by a processor of the server device, a search, based on the search term, to generate a document of search results, each of the search results including a result link and a textual description associated with the search result; highlighting, by the processor, an occurrence of the search term in at least one of the result links or the textual descriptions in the document of search results and in a plurality of documents corresponding to a particular number of the result links, the particular number of the result links being fewer than all of the result links in the search results document, and the search term being highlighted in each of the plurality of documents prior to selection of any of the result links; outputting, by the processor, the document of search results with the highlighted search term; receiving, from the client device, information regarding a selection of one of the result links of the particular number of the result links; obtaining, by the processor, a document, of the plurality of documents, corresponding to the selected result link; and transmitting, by the processor, the document to the client device. 12. The method of claim 11 , where, when highlighting the occurrence of the search term in at least one of the result links or the textual descriptions in the document of search results, the method includes: altering at least one of a color, font, style, effect, or size of the search term in the at least one of the result links or the textual descriptions in the document of search results. | 0.5 |
9,886,546 | 1 | 10 | 1. A computer-implement method, comprising: a) obtaining user input identifying a point corresponding to a vertebra on a spinal image; b) in response to the user input, using the user input to determine a number of viewable vertebrae on the spinal image; c) identifying points on the spinal image corresponding to respective vertebrae based on the determined number of viewable vertebrae; d) generating annotations on the spinal image corresponding to each of the determined number of viewable vertebrae, the annotations anatomically labeling and distinguishing between two or more immediately adjacent vertebrae on the spinal image using contextual-information feature vectors, wherein the contextual-information feature vectors encode the identified points with information of at least one of size, shape, orientation, or relationships to neighboring structures; e) receiving a first subsequent user input on the previously generated annotations identifying a point corresponding to an error within the previously generated annotations; f) in response to receiving the first subsequent user input, updating the identified points and contextual-information feature vectors for the updated identified points, automatically generating subsequent annotations on the spinal image using the updated contextual-information feature vectors, the subsequent annotations correcting the error present within the previously generated annotations; and g) receiving a second subsequent user input, wherein when the second subsequent user input identifies an error within the previously generated annotations, repeating e)-g), and when the received second subsequent user input does not identify an error within the previously generated annotations, validating the previously generated annotations in association with the spinal image, and storing in a database the previously generated annotations in association with the spinal image. | 1. A computer-implement method, comprising: a) obtaining user input identifying a point corresponding to a vertebra on a spinal image; b) in response to the user input, using the user input to determine a number of viewable vertebrae on the spinal image; c) identifying points on the spinal image corresponding to respective vertebrae based on the determined number of viewable vertebrae; d) generating annotations on the spinal image corresponding to each of the determined number of viewable vertebrae, the annotations anatomically labeling and distinguishing between two or more immediately adjacent vertebrae on the spinal image using contextual-information feature vectors, wherein the contextual-information feature vectors encode the identified points with information of at least one of size, shape, orientation, or relationships to neighboring structures; e) receiving a first subsequent user input on the previously generated annotations identifying a point corresponding to an error within the previously generated annotations; f) in response to receiving the first subsequent user input, updating the identified points and contextual-information feature vectors for the updated identified points, automatically generating subsequent annotations on the spinal image using the updated contextual-information feature vectors, the subsequent annotations correcting the error present within the previously generated annotations; and g) receiving a second subsequent user input, wherein when the second subsequent user input identifies an error within the previously generated annotations, repeating e)-g), and when the received second subsequent user input does not identify an error within the previously generated annotations, validating the previously generated annotations in association with the spinal image, and storing in a database the previously generated annotations in association with the spinal image. 10. The computer implemented method of claim 1 , wherein, prior to the obtaining of the user input, no vertebrae are identified on the spinal image, and no annotation suggestion is provided. | 0.684385 |
6,053,951 | 27 | 51 | 27. A memory media which stores program instructions for automatically generating graphical code for a graphical program, wherein the graphical program performs a man/machine interface function for monitoring and/or controlling a process, wherein the method operates in a computer system including a display screen, wherein the program instructions are executable to implement: configuring a front panel interface on the display screen in response to user input, wherein the front panel interface is useable for monitoring and/or controlling the process, wherein said configuring includes selecting at least one front panel object which represents input to or output from the graphical program; associating the at least one front panel object with at least one data value in the process; configuring one or more parameter values in response to user input, wherein said one or more parameter values indicate a desired functionality of the graphical program; automatically selecting a graphical code portion in response to the at least one front panel object, wherein said selected graphical code portion corresponds to said at least one front panel object; automatically configuring said graphical code portion with said one or more parameter values to produce a configured graphical code portion, wherein said configured graphical code portion comprises at least a portion of the graphical program. | 27. A memory media which stores program instructions for automatically generating graphical code for a graphical program, wherein the graphical program performs a man/machine interface function for monitoring and/or controlling a process, wherein the method operates in a computer system including a display screen, wherein the program instructions are executable to implement: configuring a front panel interface on the display screen in response to user input, wherein the front panel interface is useable for monitoring and/or controlling the process, wherein said configuring includes selecting at least one front panel object which represents input to or output from the graphical program; associating the at least one front panel object with at least one data value in the process; configuring one or more parameter values in response to user input, wherein said one or more parameter values indicate a desired functionality of the graphical program; automatically selecting a graphical code portion in response to the at least one front panel object, wherein said selected graphical code portion corresponds to said at least one front panel object; automatically configuring said graphical code portion with said one or more parameter values to produce a configured graphical code portion, wherein said configured graphical code portion comprises at least a portion of the graphical program. 51. The memory media of claim 27, wherein said one or more parameter values include a time period value for polling the data value. | 0.932335 |
7,945,632 | 31 | 32 | 31. The hardware-implemented system of claim 1 , wherein said correlation module configured to correlate the acquired subjective user state data with the acquired objective occurrence data by determining at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence comprises: a sequential pattern comparison module configured to compare the one sequential pattern to a second sequential pattern to determine whether the one sequential pattern at least substantially matches the second sequential pattern. | 31. The hardware-implemented system of claim 1 , wherein said correlation module configured to correlate the acquired subjective user state data with the acquired objective occurrence data by determining at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence comprises: a sequential pattern comparison module configured to compare the one sequential pattern to a second sequential pattern to determine whether the one sequential pattern at least substantially matches the second sequential pattern. 32. The hardware-implemented system of claim 31 , wherein said sequential pattern comparison module configured to compare the one sequential pattern to a second sequential pattern to determine whether the one sequential pattern at least substantially matches the second sequential pattern comprises: a subjective user state equivalence determination module configured to determine whether the one subjective user state is equivalent to a second subjective user state indicated by the subjective user state data. | 0.613464 |
10,102,860 | 12 | 15 | 12. A system for analyzing verbal records to improve a textual transcript, the system comprising: a database configured to receive a plurality of transcribed verbal records; a processor operably connected to the database, and configured to: identify a training set and a test set of the transcribed verbal records, the training set comprising a first subset of the plurality of transcribed verbal records, and the test set comprising a different second subset of the plurality of transcribed verbal records; determine a plurality of possible common phrases for the each verbal record in the training set, the plurality of possible common phrases comprising a plurality of sequences of words appearing in the each verbal record in the training set, the each of the plurality of possible common phrases further having a minimum word length; determine a best parameter for each of the plurality of possible common phrases; determine a phrase accuracy based at least in part on a test for false positives; save the best parameter for the each of the plurality of possible common phrases; and, apply the each of the plurality of possible common phrases to the transcribed verbal records, using the phrase accuracy to create the textual transcript. | 12. A system for analyzing verbal records to improve a textual transcript, the system comprising: a database configured to receive a plurality of transcribed verbal records; a processor operably connected to the database, and configured to: identify a training set and a test set of the transcribed verbal records, the training set comprising a first subset of the plurality of transcribed verbal records, and the test set comprising a different second subset of the plurality of transcribed verbal records; determine a plurality of possible common phrases for the each verbal record in the training set, the plurality of possible common phrases comprising a plurality of sequences of words appearing in the each verbal record in the training set, the each of the plurality of possible common phrases further having a minimum word length; determine a best parameter for each of the plurality of possible common phrases; determine a phrase accuracy based at least in part on a test for false positives; save the best parameter for the each of the plurality of possible common phrases; and, apply the each of the plurality of possible common phrases to the transcribed verbal records, using the phrase accuracy to create the textual transcript. 15. The system of claim 12 , wherein the processor is further configured to find alternative phrases for each of the plurality of possible common phrases. | 0.901911 |
8,271,475 | 1 | 3 | 1. A method, performed on a computer system, for performing a search for a resource in a virtual universe using user context, the method comprising: using the computer system to perform the following: receiving a query from an avatar that is online in the virtual universe; scanning a collection of avatar data describing attributes that are relevant to behavioral, search and informational needs of the avatar, wherein the scanning of a collection of avatar data comprises scanning all of the following: inventory items belonging to the avatar, teleportation history of the avatar, motion history of the avatar and social tagging behavior exhibited by the user of the avatar in the real world; using the scanned collection of avatar data to determine a user context for the avatar from at least one of past behavior in the virtual universe or past behavior exhibited by the user of the avatar in the real world, wherein the determining of a user context for the avatar comprises using a plurality of matching techniques to assign a mutually exclusive category designation from a list of mutually exclusive category designations applicable to each scanned collection of avatar data including the inventory items belonging to the avatar, the teleportation history of the avatar, the motion history of the avatar and the social tagging behavior exhibited by the user of the avatar in the real world, to specific avatar data obtained therefrom and comparing the specific avatar data with criteria associated with the assigned category designation to identify a value with an attribute that provides user context of the avatar, wherein the determining of a user context for the avatar further comprises using machine learning techniques, wherein the machine learning techniques comprise an unsupervised machine learning technique that discovers and updates user contexts from the past behavior in the virtual universe and the real world and a supervised machine learning technique that refines user context attributes and values based on user interactions with a virtual universe search tool, wherein the unsupervised machine learning technique clusters avatar data according to a distance metric to determine user contexts, associated attributes and values for the attributes and updates the user contexts, associated attributes and values as more avatar data becomes available, wherein the unsupervised machine learning technique leverages user interaction data of all avatars in the virtual universe by enriching data associated with the avatar performing the search with data associated with similar avatars as determined by clustering avatar data; and performing a resource search for the query in accordance with one of the user contexts determined from the scanned collection of avatar data. | 1. A method, performed on a computer system, for performing a search for a resource in a virtual universe using user context, the method comprising: using the computer system to perform the following: receiving a query from an avatar that is online in the virtual universe; scanning a collection of avatar data describing attributes that are relevant to behavioral, search and informational needs of the avatar, wherein the scanning of a collection of avatar data comprises scanning all of the following: inventory items belonging to the avatar, teleportation history of the avatar, motion history of the avatar and social tagging behavior exhibited by the user of the avatar in the real world; using the scanned collection of avatar data to determine a user context for the avatar from at least one of past behavior in the virtual universe or past behavior exhibited by the user of the avatar in the real world, wherein the determining of a user context for the avatar comprises using a plurality of matching techniques to assign a mutually exclusive category designation from a list of mutually exclusive category designations applicable to each scanned collection of avatar data including the inventory items belonging to the avatar, the teleportation history of the avatar, the motion history of the avatar and the social tagging behavior exhibited by the user of the avatar in the real world, to specific avatar data obtained therefrom and comparing the specific avatar data with criteria associated with the assigned category designation to identify a value with an attribute that provides user context of the avatar, wherein the determining of a user context for the avatar further comprises using machine learning techniques, wherein the machine learning techniques comprise an unsupervised machine learning technique that discovers and updates user contexts from the past behavior in the virtual universe and the real world and a supervised machine learning technique that refines user context attributes and values based on user interactions with a virtual universe search tool, wherein the unsupervised machine learning technique clusters avatar data according to a distance metric to determine user contexts, associated attributes and values for the attributes and updates the user contexts, associated attributes and values as more avatar data becomes available, wherein the unsupervised machine learning technique leverages user interaction data of all avatars in the virtual universe by enriching data associated with the avatar performing the search with data associated with similar avatars as determined by clustering avatar data; and performing a resource search for the query in accordance with one of the user contexts determined from the scanned collection of avatar data. 3. The method according to claim 1 , further comprising modifying the determined user contexts based on additional queries submitted by the avatar. | 0.750847 |
9,686,596 | 20 | 21 | 20. The system of claim 19 : wherein the discovery algorithm utilizes a protocol comprising at least one of a Bonjour® protocol, a SSDP protocol, a LSD uTorrent® protocol, a multicast protocol, an anycast protocol, and another Local Area Network (LAN) based protocol that discovers services in a LAN based on a broadcast from any one of an operating system service, the security sandbox, the client device, the sandbox reachable service, and the networked device. | 20. The system of claim 19 : wherein the discovery algorithm utilizes a protocol comprising at least one of a Bonjour® protocol, a SSDP protocol, a LSD uTorrent® protocol, a multicast protocol, an anycast protocol, and another Local Area Network (LAN) based protocol that discovers services in a LAN based on a broadcast from any one of an operating system service, the security sandbox, the client device, the sandbox reachable service, and the networked device. 21. The system of claim 20 : wherein a cookie associated with the security sandbox is used to store a remote access token on a storage of the client device, wherein the remote access token identifies at least one of a set of communicable private Internet Protocol (IP) addresses and hardware addresses associated with sandbox reachable services that previously operated on a common shared network with the client device, and wherein the client device can communicate with the sandbox reachable services that previously operated on the common shared network through the remote access token. | 0.5 |
9,171,079 | 1 | 3 | 1. A method comprising, by one or more computer systems: building a profile for the end user based on one or more learned preferences of the end user; receiving a query, from the end user, for particular sensor data among a plurality of sensor data from a plurality of sensors, the received query comprising a unique resource locator that uniquely identifies a particular one of the plurality of sensors, the plurality of sensor data being indexed according to a multi-dimensional array, one or more first ones of the dimensions comprising time and one or more second ones of the dimensions comprising one or more pre-determined sensor-data attributes; translating the query to correspond to the indexing of the plurality of sensor data, the translated query comprising one or more values for one or more of the dimensions of the multi-dimensional array; appending, based on the unique resource locator that identifies the particular one of the plurality of sensors in the query received from the end user, the unique resource locator to the translated query, the unique resource locator specifying the particular one of the plurality of sensors; communicating the translated query to search among the plurality of sensor data according to the indexing of the plurality of sensor data to identify sensor data associated with the particular one of the plurality of sensors; receiving a list of matching sensor data; tailoring the list of matching sensor data based on the user profile of the end user to provide a representation expected by the end user; receiving query results comprising meta data associated with a subset of the plurality of sensors; selecting a particular one of the subset of sensors; and requesting the data available at the particular one of the subset of sensors. | 1. A method comprising, by one or more computer systems: building a profile for the end user based on one or more learned preferences of the end user; receiving a query, from the end user, for particular sensor data among a plurality of sensor data from a plurality of sensors, the received query comprising a unique resource locator that uniquely identifies a particular one of the plurality of sensors, the plurality of sensor data being indexed according to a multi-dimensional array, one or more first ones of the dimensions comprising time and one or more second ones of the dimensions comprising one or more pre-determined sensor-data attributes; translating the query to correspond to the indexing of the plurality of sensor data, the translated query comprising one or more values for one or more of the dimensions of the multi-dimensional array; appending, based on the unique resource locator that identifies the particular one of the plurality of sensors in the query received from the end user, the unique resource locator to the translated query, the unique resource locator specifying the particular one of the plurality of sensors; communicating the translated query to search among the plurality of sensor data according to the indexing of the plurality of sensor data to identify sensor data associated with the particular one of the plurality of sensors; receiving a list of matching sensor data; tailoring the list of matching sensor data based on the user profile of the end user to provide a representation expected by the end user; receiving query results comprising meta data associated with a subset of the plurality of sensors; selecting a particular one of the subset of sensors; and requesting the data available at the particular one of the subset of sensors. 3. The method of claim 1 , wherein the query is generated by a user. | 0.879004 |
9,646,164 | 5 | 6 | 5. The method of claim 1 , wherein step iv includes caching the simplified policy (P′) represented with a tree structure, wherein step v includes, whenever a sub-tree in the tree structure represents a Boolean expression but at least one of its children evaluates to a non-Boolean value, replacing said whole sub-tree by a variable (vi). | 5. The method of claim 1 , wherein step iv includes caching the simplified policy (P′) represented with a tree structure, wherein step v includes, whenever a sub-tree in the tree structure represents a Boolean expression but at least one of its children evaluates to a non-Boolean value, replacing said whole sub-tree by a variable (vi). 6. The method of claim 5 , wherein, in step v, the variable replacing the subtree comprises two associated Boolean variables for representing more than two possible values of the sub-tree. | 0.5 |
9,363,288 | 7 | 8 | 7. A system for preserving privacy of a domain name related request from a client computer comprising: an interface to a tokenized list holder storing a list of tokenized domain names, wherein the tokenized domain names have been tokenized by a first tokenizing authority based on a tokenizing function equivalent to a tokenizing function of the a second tokenizing authority, wherein the first tokenizing authority computer is different from the client computer and from the tokenized list holder; and a second tokenizing authority server different from a client computer and operatively coupled to the client computer through a network, wherein the tokenizing authority server is configured to— receive a string representing a domain name, perform a tokenizing function on the string, wherein the tokenizing function comprises a cryptographic function, and transmit the tokenized string to a requester, and wherein the tokenized list holder is configured to— receive a request to determine domain name registration availability comprising the tokenized string, compare the tokenized string to the list of tokenized domain names, determine if the tokenized string is contained in the store of tokenized domain names, and generate an indicator indicating whether the tokenized string is contained in the store of tokenized domain names. | 7. A system for preserving privacy of a domain name related request from a client computer comprising: an interface to a tokenized list holder storing a list of tokenized domain names, wherein the tokenized domain names have been tokenized by a first tokenizing authority based on a tokenizing function equivalent to a tokenizing function of the a second tokenizing authority, wherein the first tokenizing authority computer is different from the client computer and from the tokenized list holder; and a second tokenizing authority server different from a client computer and operatively coupled to the client computer through a network, wherein the tokenizing authority server is configured to— receive a string representing a domain name, perform a tokenizing function on the string, wherein the tokenizing function comprises a cryptographic function, and transmit the tokenized string to a requester, and wherein the tokenized list holder is configured to— receive a request to determine domain name registration availability comprising the tokenized string, compare the tokenized string to the list of tokenized domain names, determine if the tokenized string is contained in the store of tokenized domain names, and generate an indicator indicating whether the tokenized string is contained in the store of tokenized domain names. 8. The system of claim 7 , wherein the string comprises a fully qualified domain name. | 0.788177 |
9,483,768 | 10 | 16 | 10. An apparatus, comprising: at least one processor; and a memory having stored therein machine executable instructions, that when executed by the at least one processor, cause the apparatus to: receive interaction data corresponding to one or more interactions between a customer and a customer support representative and store said interaction data in said memory; extract the stored interaction data from the memory and detect the presence of at least one language associated with the interaction data by comparing whole text strings or portions of text in the interaction data with available language detection models for different languages, and predicting a best matching language corresponding to the interaction data; generate textual content in a plurality of languages corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language; determine at least one emotion score for text corresponding to each language from among the plurality of languages; determine an aggregate emotion score using the at least one emotion score for the text corresponding to the each language; model an interaction experience of the customer based at least in part on the aggregate emotion score; and provide at least one recommendation to the customer based on said modeled interaction experience. | 10. An apparatus, comprising: at least one processor; and a memory having stored therein machine executable instructions, that when executed by the at least one processor, cause the apparatus to: receive interaction data corresponding to one or more interactions between a customer and a customer support representative and store said interaction data in said memory; extract the stored interaction data from the memory and detect the presence of at least one language associated with the interaction data by comparing whole text strings or portions of text in the interaction data with available language detection models for different languages, and predicting a best matching language corresponding to the interaction data; generate textual content in a plurality of languages corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language; determine at least one emotion score for text corresponding to each language from among the plurality of languages; determine an aggregate emotion score using the at least one emotion score for the text corresponding to the each language; model an interaction experience of the customer based at least in part on the aggregate emotion score; and provide at least one recommendation to the customer based on said modeled interaction experience. 16. The apparatus of claim 10 , wherein the aggregate emotion score is determined by performing one of averaging or weighted averaging of the at least one emotion score. | 0.892219 |
7,657,640 | 8 | 9 | 8. A method for sorting and routing e-mail messages, the method comprising: sorting e-mail messages by language by: determining a language in which a web-site that receives the e-mail messages is written, appending a meta-tag to each e-mail message that identifies the web-site language, and sorting the messages through reference to the language meta-tags; and subsequently sorting the e-mail messages by topic by: determining a topic to which each e-mail message applies, appending a meta-tag to each e-mail message that identifies the topic, and sorting the messages through reference to the topic meta-tags. | 8. A method for sorting and routing e-mail messages, the method comprising: sorting e-mail messages by language by: determining a language in which a web-site that receives the e-mail messages is written, appending a meta-tag to each e-mail message that identifies the web-site language, and sorting the messages through reference to the language meta-tags; and subsequently sorting the e-mail messages by topic by: determining a topic to which each e-mail message applies, appending a meta-tag to each e-mail message that identifies the topic, and sorting the messages through reference to the topic meta-tags. 9. The method of claim 8 , further comprising sending all messages generated at the web-site to a global mail box. | 0.75431 |
9,501,457 | 13 | 15 | 13. The non-transitory computer storage medium of claim 12 , wherein the operation of inserting script within said markup language includes script for further defining each of the plurality of user specific objects and non-user specific objects to include at least one pointer for each object associated therewith whether the associated object is a user or non-user specific object. | 13. The non-transitory computer storage medium of claim 12 , wherein the operation of inserting script within said markup language includes script for further defining each of the plurality of user specific objects and non-user specific objects to include at least one pointer for each object associated therewith whether the associated object is a user or non-user specific object. 15. The non-transitory computer storage medium of claim 13 , wherein the operation of inserting script within said markup language includes script for re-establishing coordinates for each object of the plurality of user specific objects and non-user specific objects associated with an object that has been edited as the result of an interpreted user initiated event. | 0.5 |
7,941,428 | 15 | 16 | 15. The program storage device as recited in claim 14 , wherein the method further comprises the step of comparing a search term that was submitted to the search engine to the metadata, thus producing a list of related websites. | 15. The program storage device as recited in claim 14 , wherein the method further comprises the step of comparing a search term that was submitted to the search engine to the metadata, thus producing a list of related websites. 16. The program storage device as recited in claim 15 , wherein the method further comprises the step of determining an order value for each website in the list of the related websites which was found in the database. | 0.5 |
9,811,321 | 6 | 8 | 6. A computer-implemented method under control of a computing device configured with specific computer-executable instructions, the computer-implemented method comprising: processing a network resource request that includes compiling a first script, the first script comprising a first one or more instructions that are compilable into computer-executable instructions; dividing the first script into at least a first portion comprising a first subset of the first one or more instructions and a second portion comprising a second subset of the first one or more instructions; calculating a first portion hash based at least in part on the first portion and a second portion hash based at least in part on the second portion; obtaining one or more chunk hashes, each of the one or more chunk hashes corresponding to a respective chunk stored in a data store, each respective chunk comprising computer-executable instructions; matching a first chunk hash of the one or more chunk hashes, wherein the first chunk hash corresponds to the first portion hash; obtaining a first chunk corresponding to the first chunk hash; compiling the second portion into a second chunk; assembling at least the first chunk and the second chunk into a set of computer-executable instructions corresponding to the first script; and transmitting the set of computer-executable instructions. | 6. A computer-implemented method under control of a computing device configured with specific computer-executable instructions, the computer-implemented method comprising: processing a network resource request that includes compiling a first script, the first script comprising a first one or more instructions that are compilable into computer-executable instructions; dividing the first script into at least a first portion comprising a first subset of the first one or more instructions and a second portion comprising a second subset of the first one or more instructions; calculating a first portion hash based at least in part on the first portion and a second portion hash based at least in part on the second portion; obtaining one or more chunk hashes, each of the one or more chunk hashes corresponding to a respective chunk stored in a data store, each respective chunk comprising computer-executable instructions; matching a first chunk hash of the one or more chunk hashes, wherein the first chunk hash corresponds to the first portion hash; obtaining a first chunk corresponding to the first chunk hash; compiling the second portion into a second chunk; assembling at least the first chunk and the second chunk into a set of computer-executable instructions corresponding to the first script; and transmitting the set of computer-executable instructions. 8. The computer-implemented method of claim 6 further comprising: dividing the first script into at least a third portion comprising a third subset of the first one or more instructions and a fourth portion comprising a fourth subset of the first one or more instructions; calculating a third portion hash based at least in part on the third portion, a fourth portion hash based at least in part on the fourth portion, and a fifth hash based at least in part on the third portion hash and the fourth portion hash; matching a third chunk hash of the one or more chunk hashes, wherein the third chunk hash corresponds to the third portion hash; matching a fourth chunk hash of the one or more chunk hashes, wherein the fourth chunk hash corresponds to the fourth portion hash; matching a fifth chunk hash of the one or more chunk hashes, wherein the fifth chunk hash corresponds to the fifth hash; obtaining, from the data store, a fifth chunk corresponding to the fifth chunk hash; wherein assembling at least the first chunk and the second chunk into the compiled set of computer-executable instructions further includes assembling the fifth chunk. | 0.5 |
8,260,781 | 6 | 9 | 6. A system for detecting duplicated documents, comprising: one or more processors; memory storing one or more programs executable by the one or more processors, the one or more programs comprising instructions to: receive documents from one or more databases of documents, wherein each received document is associated with a respective query independent score; obtain a document content identifier for each received document, each document content identifier comprising an identifier of a respective document's content; index at least a subset of the received documents to produce an document index that maps terms to documents in the one or more databases of documents; and while performing the indexing, identify respective sets of received documents having the same content identifier, select a single document in each respective set of documents, in accordance with the query independent scores associated with the documents in the respective set of documents, as a representative document for the respective set of received documents, index the representative document, and with respect to each respective set of received documents, include only the representative document in the document index. | 6. A system for detecting duplicated documents, comprising: one or more processors; memory storing one or more programs executable by the one or more processors, the one or more programs comprising instructions to: receive documents from one or more databases of documents, wherein each received document is associated with a respective query independent score; obtain a document content identifier for each received document, each document content identifier comprising an identifier of a respective document's content; index at least a subset of the received documents to produce an document index that maps terms to documents in the one or more databases of documents; and while performing the indexing, identify respective sets of received documents having the same content identifier, select a single document in each respective set of documents, in accordance with the query independent scores associated with the documents in the respective set of documents, as a representative document for the respective set of received documents, index the representative document, and with respect to each respective set of received documents, include only the representative document in the document index. 9. The system of claim 6 , wherein the one or more programs further comprise instructions that, when executed by the one or more processors, cause the system to perform a data processing operation, with respect to each respective set of received documents, on only the representative document for the respective set of received documents. | 0.579602 |
8,805,677 | 1 | 5 | 1. A method, comprising: tagging an input text string; examining, via a processor, the input text string for at least one first set of substitutions based on content of the input text string; and if the input text string is a substring of a previously tagged input text string: determining whether the input text string and at least one additional input text string share a common pre-assigned tag identifier stored in a memory location, and if the input text string and the at least one additional input text string share the common pre-assigned tag identifier stored in the memory location: eliminating at least one of the input text string and the at least one additional input text string from a natural language grammar. | 1. A method, comprising: tagging an input text string; examining, via a processor, the input text string for at least one first set of substitutions based on content of the input text string; and if the input text string is a substring of a previously tagged input text string: determining whether the input text string and at least one additional input text string share a common pre-assigned tag identifier stored in a memory location, and if the input text string and the at least one additional input text string share the common pre-assigned tag identifier stored in the memory location: eliminating at least one of the input text string and the at least one additional input text string from a natural language grammar. 5. The method of claim 1 , wherein if the input text string and the at least one additional input text string do not share a common pre-assigned tag identifier stored in the memory location, then maintaining the input text string and the at least one additional input text string in the natural language grammar by writing both the input text string and at least one additional input text string to a source code stored in the memory location. | 0.5 |
9,262,407 | 1 | 5 | 1. A method for optimizing a multi-language user interface layout via reverse pseudo-translation, the method comprising: selecting at least one user interface page from a group of user interface pages in a language; selecting at least one target language from a group of target languages to pseudo-translate the at least one user interface page; specifying at least one layout requirement for formatting the selected at least one user interface page; performing pseudo-translation of the at least one user interface page based on the selected at least one target language; modifying, by a merge algorithm, the at least one pseudo-translated user interface page according to the at least one specified layout requirement, wherein the merge algorithm comprises: applying the at least one specified layout requirement to the at least one pseudo-translated user interface page, wherein the at least one specified layout requirement is applied to the at least one pseudo-translated user interface page in a hierarchy from a most shared selected layout property among the at least one selected target language to a least shared selected layout property among the at least one selected target language; and implementing a reverse pseudo-translation of the at least one modified pseudo-translated user interface page. | 1. A method for optimizing a multi-language user interface layout via reverse pseudo-translation, the method comprising: selecting at least one user interface page from a group of user interface pages in a language; selecting at least one target language from a group of target languages to pseudo-translate the at least one user interface page; specifying at least one layout requirement for formatting the selected at least one user interface page; performing pseudo-translation of the at least one user interface page based on the selected at least one target language; modifying, by a merge algorithm, the at least one pseudo-translated user interface page according to the at least one specified layout requirement, wherein the merge algorithm comprises: applying the at least one specified layout requirement to the at least one pseudo-translated user interface page, wherein the at least one specified layout requirement is applied to the at least one pseudo-translated user interface page in a hierarchy from a most shared selected layout property among the at least one selected target language to a least shared selected layout property among the at least one selected target language; and implementing a reverse pseudo-translation of the at least one modified pseudo-translated user interface page. 5. The method of claim 1 , further comprising: creating a single layout design for the at least one selected user interface page, wherein the single layout design will accommodate the translation of the at least one selected user interface page into each of the at least one selected target languages. | 0.601852 |
9,325,508 | 1 | 10 | 1. A method of authenticating an electronic document having a digital signing signature for a relying party that receives the digitally signed electronic document to evaluate a risk of relying on the digitally signed electronic document, comprising the steps of: (a) at a certification authority computer system, a certification authority generating a digital certificate certifying a cryptographic key pair of a private key and a public key for a signature authority; (b) at the signature authority, storing the private key and the digital certificate for use when constructing an electronic signature for indicating execution of a to be signed electronic document as directed from time-to-time to create a digitally signed electronic document; (c) providing an Internet computer browser computer program operative on an Internet-connected signer's computer used by a prospective signing party for directing execution of the to be signed electronic document, said signing party sending a signature creation request to the signature authority specifying the to be signed electronic document; (d) at the signature authority, in response to a receipt of the signature creation request, obtaining a copy of the to be signed electronic document specified in the signature creation request; (e) at the signature authority, creating as an electronic signature a signature data structure that includes an assertion that the signature authority applies its digital signature to the to be signed electronic document for the purpose of certifying that the signing party has legally signed the to be signed document as directed in the signature creation request (f) at the signature authority, retrieving the signature authority's private key and digital certificate; (g) at the signature authority, creating a signature data structure and, with the retrieved private key and digital certificate, creating a digital signing signature covering the signature data structure and the to be signed document and resulting in a digitally signed electronic document; and (h) at a relying party receiving the digitally signed electronic document, relying on the signature data structure, the digital signing signature, and the signature authority digital certificate for verifying the digital signing signature on the signature data structure using the signature authority digital certificate, to evaluate a risk of relying on the digitally signed electronic document. | 1. A method of authenticating an electronic document having a digital signing signature for a relying party that receives the digitally signed electronic document to evaluate a risk of relying on the digitally signed electronic document, comprising the steps of: (a) at a certification authority computer system, a certification authority generating a digital certificate certifying a cryptographic key pair of a private key and a public key for a signature authority; (b) at the signature authority, storing the private key and the digital certificate for use when constructing an electronic signature for indicating execution of a to be signed electronic document as directed from time-to-time to create a digitally signed electronic document; (c) providing an Internet computer browser computer program operative on an Internet-connected signer's computer used by a prospective signing party for directing execution of the to be signed electronic document, said signing party sending a signature creation request to the signature authority specifying the to be signed electronic document; (d) at the signature authority, in response to a receipt of the signature creation request, obtaining a copy of the to be signed electronic document specified in the signature creation request; (e) at the signature authority, creating as an electronic signature a signature data structure that includes an assertion that the signature authority applies its digital signature to the to be signed electronic document for the purpose of certifying that the signing party has legally signed the to be signed document as directed in the signature creation request (f) at the signature authority, retrieving the signature authority's private key and digital certificate; (g) at the signature authority, creating a signature data structure and, with the retrieved private key and digital certificate, creating a digital signing signature covering the signature data structure and the to be signed document and resulting in a digitally signed electronic document; and (h) at a relying party receiving the digitally signed electronic document, relying on the signature data structure, the digital signing signature, and the signature authority digital certificate for verifying the digital signing signature on the signature data structure using the signature authority digital certificate, to evaluate a risk of relying on the digitally signed electronic document. 10. The method of claim 1 , wherein the signature authority's private key and digital certificate are reused for multiple electronic document signings by the same signing party. | 0.789286 |
9,134,906 | 23 | 28 | 23. A device comprising: at least one processor that is operatively coupled to a presence-sensitive input device; and at least one module operable by the at least one processor to: output for display, a graphical keyboard comprising a plurality of keys; receive a plurality of indications of gestures detected at a presence-sensitive input device, a first indication of the plurality of indications of gestures traversing a first key and subsequently a second indication of the plurality of indications of gestures traversing a second key, wherein a first character is associated with the first key and a second character is associated with the second key; determine based at least in part on the first and second indications of the plurality of indications of gestures traversing the first key and subsequently the second key, a candidate phrase comprising a group of candidate words, wherein at least one of a first candidate word and a second candidate word from the group of candidate words comprises the second character subsequent to the first character; and output, for display, based at least in part on the group of candidate words, the candidate phrase comprising the first candidate word and the second candidate word. | 23. A device comprising: at least one processor that is operatively coupled to a presence-sensitive input device; and at least one module operable by the at least one processor to: output for display, a graphical keyboard comprising a plurality of keys; receive a plurality of indications of gestures detected at a presence-sensitive input device, a first indication of the plurality of indications of gestures traversing a first key and subsequently a second indication of the plurality of indications of gestures traversing a second key, wherein a first character is associated with the first key and a second character is associated with the second key; determine based at least in part on the first and second indications of the plurality of indications of gestures traversing the first key and subsequently the second key, a candidate phrase comprising a group of candidate words, wherein at least one of a first candidate word and a second candidate word from the group of candidate words comprises the second character subsequent to the first character; and output, for display, based at least in part on the group of candidate words, the candidate phrase comprising the first candidate word and the second candidate word. 28. The device of claim 23 , wherein the at least one module is operable by the at least one processor to: select, based at least in part on the first indication and the second indication, the first key and the second key of the plurality of keys; determine, based at least in part on the first key, a word-level token comprising a single string of a plurality of predicted characters; determine that the word-level token represents a candidate word included in a lexicon; and determine, in response to determining that the word-level token represents the candidate word in the lexicon, a phrase-level token based at least in part on the word-level token and the second key, wherein the phrase-level token comprises a plurality of character strings. | 0.5 |
9,251,468 | 1 | 5 | 1. A computer-implemented method comprising: selecting a user from a plurality of users of a social networking system, each user of the plurality of users being associated with a user profile comprising a set of user profile attributes, wherein the social networking system configures a user interface for a user to provide value for each user provided attribute and stores each user provided value as a user profile attribute; identifying, by a computer, one or more of a set of users who are connected to the selected user in the social networking system; inferring, by the computer, a value of a first user profile attribute of the user profile, the first user profile attribute storing a first type of information for the selected user, the first user profile attribute inferred based on values of a second user profile attribute of the user profiles of the one or more of the set of users connected to the selected user in the social networking system, the second user profile attribute storing a second type of information distinct from the first type of information; storing the inferred value of the user profile attributes in association with the user profile of the selected user; determining relevant information for the selected user based on the inferred value of the first user provided attribute of the user profile; and sending the relevant information to the selected user. | 1. A computer-implemented method comprising: selecting a user from a plurality of users of a social networking system, each user of the plurality of users being associated with a user profile comprising a set of user profile attributes, wherein the social networking system configures a user interface for a user to provide value for each user provided attribute and stores each user provided value as a user profile attribute; identifying, by a computer, one or more of a set of users who are connected to the selected user in the social networking system; inferring, by the computer, a value of a first user profile attribute of the user profile, the first user profile attribute storing a first type of information for the selected user, the first user profile attribute inferred based on values of a second user profile attribute of the user profiles of the one or more of the set of users connected to the selected user in the social networking system, the second user profile attribute storing a second type of information distinct from the first type of information; storing the inferred value of the user profile attributes in association with the user profile of the selected user; determining relevant information for the selected user based on the inferred value of the first user provided attribute of the user profile; and sending the relevant information to the selected user. 5. The computer-implemented method of claim 1 , further comprising: selecting the first user profile attribute of the user based on a determination of a tendency of users to provide incorrect value for the first user profile attribute. | 0.85723 |
8,069,213 | 10 | 11 | 10. The apparatus as recited in claim 9 , wherein the instructions that cause determining whether access to the specified hyperlink is allowed further comprise instructions which, when executed, cause determining a particular access response action from a plurality of allowed actions based at least in part on the sender reputation information. | 10. The apparatus as recited in claim 9 , wherein the instructions that cause determining whether access to the specified hyperlink is allowed further comprise instructions which, when executed, cause determining a particular access response action from a plurality of allowed actions based at least in part on the sender reputation information. 11. The apparatus as recited in claim 10 , wherein the allowed actions include one or more of allowing user access to the specified hyperlink, blocking user access to the specified hyperlink, blocking user access to the specified hyperlink and issuing a warning message, or allowing conditional user access to the specified hyperlink. | 0.5 |
8,810,520 | 10 | 12 | 10. The handheld electronic device of claim 9 , wherein the ambiguous input includes a portion of an intended word. | 10. The handheld electronic device of claim 9 , wherein the ambiguous input includes a portion of an intended word. 12. The handheld electronic device of claim 10 , wherein at least one of the additional interpretations of the updated ambiguous input corresponds to another portion of the intended word. | 0.5 |
7,536,521 | 6 | 12 | 6. A computer storage device for use with a file system managing logical data files of data, the computer storage device comprising: a storage medium having a plurality of blocks storing data and metadata, the metadata mapping data of logical data files to blocks; an interface accepting instructions from the file system to read data from blocks and write data to blocks; a controller implementing: (a) an access monitor circuit communicating with the interface to identify metadata stored on the storage medium and to determine from the metadata and from subsequent access to data blocks other than the metadata, whether blocks are live or dead; and (b) a media access circuit changing writing of data to a given block or reading of data from a given block based on whether the block has died as determined by the controller. | 6. A computer storage device for use with a file system managing logical data files of data, the computer storage device comprising: a storage medium having a plurality of blocks storing data and metadata, the metadata mapping data of logical data files to blocks; an interface accepting instructions from the file system to read data from blocks and write data to blocks; a controller implementing: (a) an access monitor circuit communicating with the interface to identify metadata stored on the storage medium and to determine from the metadata and from subsequent access to data blocks other than the metadata, whether blocks are live or dead; and (b) a media access circuit changing writing of data to a given block or reading of data from a given block based on whether the block has died as determined by the controller. 12. The computer storage device of claim 6 wherein the metadata is a file index relating blocks to logical data files and a bitmap indicating a status of each block as allocated to a logical file or not allocated to a logical file. | 0.627419 |
8,352,496 | 1 | 8 | 1. A method for matching entity names, comprising: performing a matching analysis between a first entity name and a second entity name comprising comparing a first entity category descriptor amended to the first entity name with a second entity category descriptor amended to the second entity name, the first entity category descriptor amended to the first entity name comprising at least one of, the first entity category descriptor substituted for at least a portion of the first entity name, or the first entity category descriptor appended to the first entity name, at least some of the performing a matching analysis implemented at least in part via a processing unit. | 1. A method for matching entity names, comprising: performing a matching analysis between a first entity name and a second entity name comprising comparing a first entity category descriptor amended to the first entity name with a second entity category descriptor amended to the second entity name, the first entity category descriptor amended to the first entity name comprising at least one of, the first entity category descriptor substituted for at least a portion of the first entity name, or the first entity category descriptor appended to the first entity name, at least some of the performing a matching analysis implemented at least in part via a processing unit. 8. The method of claim 1 , the comparing a first entity category descriptor amended to the first entity name with a second entity category descriptor amended to the second entity name comprising: determining whether an entity type can be comprised in both a first entity category of the first entity category descriptor and a second entity category of the second entity category descriptor. | 0.5 |
9,583,101 | 15 | 16 | 15. The non-transitory computer readable medium according to claim 14 , wherein the instructions further comprise: extracting face data from the user image; and performing face recognition on the face data to obtain a second user attribute recognition result. | 15. The non-transitory computer readable medium according to claim 14 , wherein the instructions further comprise: extracting face data from the user image; and performing face recognition on the face data to obtain a second user attribute recognition result. 16. The non-transitory computer readable medium according to claim 15 , wherein the instruction of extracting the face data further comprises: weighting the first user attribute recognition result and the second user attribute recognition result to obtain a final user attribute recognition result; and performing a corresponding operation according to the final user attribute recognition result and the content recognition result. | 0.5 |
9,213,885 | 3 | 16 | 3. The method of claim 2 , wherein said obtaining includes: computing a wavelet transform of each image, wherein said wavelet transform generates a plurality of transform coefficients, and wherein each transform coefficient represents a portion of said visual information from the image that is localized in space, frequency, and orientation. | 3. The method of claim 2 , wherein said obtaining includes: computing a wavelet transform of each image, wherein said wavelet transform generates a plurality of transform coefficients, and wherein each transform coefficient represents a portion of said visual information from the image that is localized in space, frequency, and orientation. 16. The method of claim 3 , further comprising: performing a lighting correction on one or more of said plurality of transform coefficients prior to classifying said 2D image. | 0.5 |
8,280,800 | 6 | 8 | 6. A method, comprising: receiving selection criteria for an account; searching, by a computer, an account database for one or more accounts having account data that satisfies at least a portion of said selection criteria, wherein said account database comprises account data associated with one or more accounts, each of the one or more accounts representing an account receivable; receiving an offer from a buyer for at least one of said one or more accounts having account data that satisfies at least a portion of said selection criteria; and accepting automatically the offer in response to the offer fulfilling a term of a commitment for the at least one of said one or more accounts having account data that satisfies at least a portion of said selection criteria, the term of the commitment comprising a fixed price upon which a seller automatically accepts the offer. | 6. A method, comprising: receiving selection criteria for an account; searching, by a computer, an account database for one or more accounts having account data that satisfies at least a portion of said selection criteria, wherein said account database comprises account data associated with one or more accounts, each of the one or more accounts representing an account receivable; receiving an offer from a buyer for at least one of said one or more accounts having account data that satisfies at least a portion of said selection criteria; and accepting automatically the offer in response to the offer fulfilling a term of a commitment for the at least one of said one or more accounts having account data that satisfies at least a portion of said selection criteria, the term of the commitment comprising a fixed price upon which a seller automatically accepts the offer. 8. The method of claim 6 , further comprising: receiving payment from the buyer for an accepted offer; and transferring ownership, by the computer, of said at least one of the said one or more accounts having account data that satisfies at least a portion of said selection criteria from a seller to the buyer. | 0.5 |
9,672,246 | 15 | 16 | 15. A computer program product, comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising: computer readable program code configured to store a plurality of timeslicing tables corresponding with a time-dependent attribute in a database, the plurality of timeslicing tables includes a first timeslicing table corresponding with the time-dependent attribute over a first time period and a second timeslicing table corresponding with the time-dependent attribute over a second time period, the plurality of timeslicing tables includes a third timeslicing table corresponding with the time-dependent attribute over a third time period; computer readable program code configured to detect a data update to the time-dependent attribute within the first time period and determine a historical rate of data updates associated with the time-dependent attribute over the first time period and the second time period; computer readable program code configured to detect that the historical rate of data updates associated with the time-dependent attribute over the first time period and the second time period is greater than an updating threshold; computer readable program code configured to consolidate and replace the first timeslicing table and the second timeslicing table within the database with a character large object representation for the time-dependent attribute over the first time period and the second time period in response to detecting that the historical rate of data updates associated with the time-dependent attribute over the first time period and the second time period is greater than the updating threshold; computer readable program code configured to acquire a query for one or more data values associated with the time-dependent attribute; computer readable program code configured to retrieve the third timeslicing table corresponding with the time-dependent attribute from the database in response to the query and retrieve the character large object representation for the time-dependent attribute over the first time period and the second time period from the database in response to the query; and computer readable program code configured to generate a plurality of segments using the character large object representation for the time-dependent attribute and generate the one or more data values associated with the time-dependent attribute using the plurality of segments and the third timeslicing table, the character large object representation for the time-dependent attribute over the first time period and the second time period is stored as a character large object within the database and the third timeslicing table corresponding with the time-dependent attribute over the third time period is stored as a binary large object within the database. | 15. A computer program product, comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising: computer readable program code configured to store a plurality of timeslicing tables corresponding with a time-dependent attribute in a database, the plurality of timeslicing tables includes a first timeslicing table corresponding with the time-dependent attribute over a first time period and a second timeslicing table corresponding with the time-dependent attribute over a second time period, the plurality of timeslicing tables includes a third timeslicing table corresponding with the time-dependent attribute over a third time period; computer readable program code configured to detect a data update to the time-dependent attribute within the first time period and determine a historical rate of data updates associated with the time-dependent attribute over the first time period and the second time period; computer readable program code configured to detect that the historical rate of data updates associated with the time-dependent attribute over the first time period and the second time period is greater than an updating threshold; computer readable program code configured to consolidate and replace the first timeslicing table and the second timeslicing table within the database with a character large object representation for the time-dependent attribute over the first time period and the second time period in response to detecting that the historical rate of data updates associated with the time-dependent attribute over the first time period and the second time period is greater than the updating threshold; computer readable program code configured to acquire a query for one or more data values associated with the time-dependent attribute; computer readable program code configured to retrieve the third timeslicing table corresponding with the time-dependent attribute from the database in response to the query and retrieve the character large object representation for the time-dependent attribute over the first time period and the second time period from the database in response to the query; and computer readable program code configured to generate a plurality of segments using the character large object representation for the time-dependent attribute and generate the one or more data values associated with the time-dependent attribute using the plurality of segments and the third timeslicing table, the character large object representation for the time-dependent attribute over the first time period and the second time period is stored as a character large object within the database and the third timeslicing table corresponding with the time-dependent attribute over the third time period is stored as a binary large object within the database. 16. The computer program product of claim 15 , wherein the computer readable program code further comprises: computer readable program code configured to identify a gap based on the plurality of segments; computer readable program code configured to determine a substitute value for the gap based on a weighted average of a subset of the plurality of segments adjacent to the gap; and computer readable program code configured to generate one or more data values based on the substitute value. | 0.5 |
8,478,740 | 4 | 5 | 4. The method as recited in claim 1 , wherein the act of identifying a plurality of candidate documents, from the among the plurality of documents, based on the corresponding weights of the specified number of the most significant keywords in the plurality of documents comprises an act of accessing at least one keyword/weight pair form a least recently used (“LRU”) cache. | 4. The method as recited in claim 1 , wherein the act of identifying a plurality of candidate documents, from the among the plurality of documents, based on the corresponding weights of the specified number of the most significant keywords in the plurality of documents comprises an act of accessing at least one keyword/weight pair form a least recently used (“LRU”) cache. 5. The method as recited in claim 4 , wherein accessing at least one keyword/weight pair from a least recently used (“LRU”) cache comprises an act of using an LRU approximation. | 0.5 |
7,752,204 | 15 | 19 | 15. A computer program product for summarizing a first unit of text data with relation to the contents of multiple documents in an existing document collection, the computer program product including a computer-readable medium encoded with computer program instructions, wherein the computer program instructions, when executed by a processor, cause the processor to perform predetermined operations comprising: creating a subspace for the existing document collection without first posting a query, an input involving latent semantic indexing; performing one of a domain driven text summarization, an example type query driven text summarization, and a term type query driven text summarization on a selected document; recomposing a vector using a projection in the subspace representing the contents of multiple documents in the existing document collection when performing the domain driven text summarization or the example type query driven text summarization; computing term relationships representing similarities between query terms and the contents of multiple documents in the existing document collection using a term-term matrix associated with an original term space when performing the term type query driven text summarization; computing a term weight that is representative of the relevance of a term to a second unit of text data with relation to the contents of multiple documents in the document collection, the computing of the term weight including generation of the subspace using the document collection for projection of the text data into the subspace and back into term space in order to get weights for all the terms in the document collection; comparing the computed term weight to a predetermined threshold; returning a relevant term based at least in part on a result of the comparison; summing a plurality of relevant term weights based on a number of occurrences of a plurality of corresponding relevant terms in a segment of the first unit of text data; comparing a plurality of summations based on a plurality of corresponding segments of the first unit of text data to identify a text summarization segment; and returning the text summarization segment. | 15. A computer program product for summarizing a first unit of text data with relation to the contents of multiple documents in an existing document collection, the computer program product including a computer-readable medium encoded with computer program instructions, wherein the computer program instructions, when executed by a processor, cause the processor to perform predetermined operations comprising: creating a subspace for the existing document collection without first posting a query, an input involving latent semantic indexing; performing one of a domain driven text summarization, an example type query driven text summarization, and a term type query driven text summarization on a selected document; recomposing a vector using a projection in the subspace representing the contents of multiple documents in the existing document collection when performing the domain driven text summarization or the example type query driven text summarization; computing term relationships representing similarities between query terms and the contents of multiple documents in the existing document collection using a term-term matrix associated with an original term space when performing the term type query driven text summarization; computing a term weight that is representative of the relevance of a term to a second unit of text data with relation to the contents of multiple documents in the document collection, the computing of the term weight including generation of the subspace using the document collection for projection of the text data into the subspace and back into term space in order to get weights for all the terms in the document collection; comparing the computed term weight to a predetermined threshold; returning a relevant term based at least in part on a result of the comparison; summing a plurality of relevant term weights based on a number of occurrences of a plurality of corresponding relevant terms in a segment of the first unit of text data; comparing a plurality of summations based on a plurality of corresponding segments of the first unit of text data to identify a text summarization segment; and returning the text summarization segment. 19. The computer program product of claim 15 , wherein the second unit of text data is a query. | 0.96157 |
9,031,493 | 14 | 15 | 14. A system for creating a custom narration for a book, the book comprising text, the system comprising: one or more processors; a data store, storing an electronic representation of a narration, the electronic representation including a first portion and a second portion that, during playback, immediately follows the first portion; and a correlation module, communicatively coupled to the data store and the one or more processors, configured to determine, using the one or more processors, a first correlation between the first portion and a first segment of the text, and apply, responsive to the second portion not corresponding to a second segment of the text that immediately follows the first segment, a correlation algorithm to determine a second correlation between the second portion and a component of the book, applying the correlation algorithm comprising: responsive to determining the second portion is a repeat of the first portion, identifying the first segment of the text as the component of the book; responsive to determining the second segment of text is spatially proximate to an illustration, identifying the illustration as the component of the book; responsive to determining the first segment of the text is immediately followed by a chapter break, identifying the chapter break as the component of the book; and responsive to determining the second portion corresponds to a third segment of the text that follows the second segment of the text, identifying the third segment of the text as the component of the book. | 14. A system for creating a custom narration for a book, the book comprising text, the system comprising: one or more processors; a data store, storing an electronic representation of a narration, the electronic representation including a first portion and a second portion that, during playback, immediately follows the first portion; and a correlation module, communicatively coupled to the data store and the one or more processors, configured to determine, using the one or more processors, a first correlation between the first portion and a first segment of the text, and apply, responsive to the second portion not corresponding to a second segment of the text that immediately follows the first segment, a correlation algorithm to determine a second correlation between the second portion and a component of the book, applying the correlation algorithm comprising: responsive to determining the second portion is a repeat of the first portion, identifying the first segment of the text as the component of the book; responsive to determining the second segment of text is spatially proximate to an illustration, identifying the illustration as the component of the book; responsive to determining the first segment of the text is immediately followed by a chapter break, identifying the chapter break as the component of the book; and responsive to determining the second portion corresponds to a third segment of the text that follows the second segment of the text, identifying the third segment of the text as the component of the book. 15. The system of claim 14 , wherein the correlation module is further configured to store the first and second correlations in the data store. | 0.730189 |
8,121,829 | 9 | 11 | 9. An apparatus for constructing translation knowledge, comprising a converting section that converts a source-language sentence and a target-language sentence by receiving the source-language sentence and the target-language sentence corresponding to the source-language sentence and attaching a prototype, a part-of-speech, relative position information, and syntactic information in a base phrase to each morpheme of the source-language sentence and the target-language sentence; a word-syntax aligning module that aligns words and syntaxes by applying a previously held bilingual dictionary and an algorithm for unsupervised learning; and a translation knowledge acquiring module that acquires translation knowledge on a word and syntax, translation knowledge on a subcategory of a bilingual inflected-word, and translation knowledge on a bilingual sentence pattern based on results of syntactic alignment by the word-syntax aligning module, wherein the word-syntax aligning section comprises: a word aligning section that outputs a plurality of results of the word alignment by searching words corresponding to each other in the source-language sentence and the target-language sentence with respect to the pairs of the plurality of reconstructed sentences, after reconstructing the converted source-language sentence and target-language sentence by using at least one of a surface type, a prototype, and a part-of-speech of a morpheme, and generating pairs of a plurality of reconstructed sentences; a post-processor for post-processing the word alignment, which corrects the results of the word alignment by using the previously held bilingual dictionary and that determines a proper word alignment as a word alignment that is obtained by, with respect to the plurality of results of the word alignment, excluding the corrected word alignment results and taking an intersection set; and a syntax aligning section that performs the syntactic alignment by using the result of the proper word alignment and information on dependency relationship between the syntaxes. | 9. An apparatus for constructing translation knowledge, comprising a converting section that converts a source-language sentence and a target-language sentence by receiving the source-language sentence and the target-language sentence corresponding to the source-language sentence and attaching a prototype, a part-of-speech, relative position information, and syntactic information in a base phrase to each morpheme of the source-language sentence and the target-language sentence; a word-syntax aligning module that aligns words and syntaxes by applying a previously held bilingual dictionary and an algorithm for unsupervised learning; and a translation knowledge acquiring module that acquires translation knowledge on a word and syntax, translation knowledge on a subcategory of a bilingual inflected-word, and translation knowledge on a bilingual sentence pattern based on results of syntactic alignment by the word-syntax aligning module, wherein the word-syntax aligning section comprises: a word aligning section that outputs a plurality of results of the word alignment by searching words corresponding to each other in the source-language sentence and the target-language sentence with respect to the pairs of the plurality of reconstructed sentences, after reconstructing the converted source-language sentence and target-language sentence by using at least one of a surface type, a prototype, and a part-of-speech of a morpheme, and generating pairs of a plurality of reconstructed sentences; a post-processor for post-processing the word alignment, which corrects the results of the word alignment by using the previously held bilingual dictionary and that determines a proper word alignment as a word alignment that is obtained by, with respect to the plurality of results of the word alignment, excluding the corrected word alignment results and taking an intersection set; and a syntax aligning section that performs the syntactic alignment by using the result of the proper word alignment and information on dependency relationship between the syntaxes. 11. The apparatus of claim 9 , further comprising a generating section for generating a bilingual sentence map, which substitutes a morpheme constituting the converted source-language sentence and target-language sentence by a unique identification number of the morpheme. | 0.890057 |
8,935,277 | 11 | 14 | 11. A non-transitory computer readable medium storing instructions thereon, which when executed by a processor cause a computer system to: receive a business intelligence (BI) request for information expressed in natural language, the BI request is sent by an agent; upon receiving the BI request, loading into a memory a situation graph of the agent, the situation graph representing information contextual to the agent; parse the BI request; based on the parsed BI request, generate a graph representing syntactic structure of the received BI request; enrich the graph based on the parsed BI request with: at least one semantic annotation representing at least one business object identified in the BI request, the business object included in a data model that at least in part forms a semantic layer of a plurality of data sources, the data model determines a structure of at least one data source from the plurality of data sources, and at least one semantic annotation representing contextual information derived from the situation graph of the agent; match the parsed BI request for information to a pattern from a plurality of patterns of features included in the BI request, wherein features of the pattern include reference to the at least one business object identified in the BI request; process by the computer a technical query associated with the pattern from the plurality of patterns to retrieve data relevant to the BI request at least from the at least one data source; generate the answer to the BI request based on the retrieved data relevant to the BI request and based at least in part on the situation graph of the agent, wherein the answer generated by triggering at least one operator of a situational recommender system; and recommending the generated answer. | 11. A non-transitory computer readable medium storing instructions thereon, which when executed by a processor cause a computer system to: receive a business intelligence (BI) request for information expressed in natural language, the BI request is sent by an agent; upon receiving the BI request, loading into a memory a situation graph of the agent, the situation graph representing information contextual to the agent; parse the BI request; based on the parsed BI request, generate a graph representing syntactic structure of the received BI request; enrich the graph based on the parsed BI request with: at least one semantic annotation representing at least one business object identified in the BI request, the business object included in a data model that at least in part forms a semantic layer of a plurality of data sources, the data model determines a structure of at least one data source from the plurality of data sources, and at least one semantic annotation representing contextual information derived from the situation graph of the agent; match the parsed BI request for information to a pattern from a plurality of patterns of features included in the BI request, wherein features of the pattern include reference to the at least one business object identified in the BI request; process by the computer a technical query associated with the pattern from the plurality of patterns to retrieve data relevant to the BI request at least from the at least one data source; generate the answer to the BI request based on the retrieved data relevant to the BI request and based at least in part on the situation graph of the agent, wherein the answer generated by triggering at least one operator of a situational recommender system; and recommending the generated answer. 14. The non-transitory computer readable medium of claim 11 , wherein recommending the answer further comprises: resolving ambiguities in the BI request for information based on the situation graph of the agent. | 0.619134 |
8,572,115 | 13 | 14 | 13. The computer-readable medium of claim 12 , wherein the operations further comprise: selecting the negative keywords to increase a number of search criteria identified as being off-topic to the advertisement item. | 13. The computer-readable medium of claim 12 , wherein the operations further comprise: selecting the negative keywords to increase a number of search criteria identified as being off-topic to the advertisement item. 14. The computer-readable medium of claim 13 , wherein selecting the negative keywords to increase the number of search criteria identified as being off-topic to the advertisement item maximizes the number of search criteria identified as being off-topic to the advertisement item. | 0.5 |
8,005,828 | 1 | 8 | 1. A computer implemented method comprising: partitioning a plurality of queries into a plurality of clusters, each cluster containing a plurality of queries; (a) for each query of the plurality of queries, generating a plurality of suggested rewrites, wherein each suggested rewrite of said plurality of suggested rewrites is the result of applying a query rewrite policy, the query rewrite policy being an ordered set of one or more query rewrite techniques, each query rewrite technique of said one or more query rewrite techniques being provided by a query rewrite provider of a plurality of query rewrite providers; (b) for each query, generating a set of rewrite scores, wherein each rewrite score of said set of rewrite scores reflects the quality of a particular suggested query rewrite generated for the query by a respective query rewrite policy; (c) for each cluster, generating aggregate rewrite scores by aggregating the rewrite scores of the same query rewrite policy for the same cluster; (d) based on the aggregate rewrite scores generated, query features of each query of said plurality of queries, and the rewrite scores generated for each query of the plurality of queries, generating a partitioning function by which to partition said plurality of queries into clusters; (e) based on the partitioning function, repartitioning said plurality of queries into a new set of clusters; repeating steps (a), (b), (c), (d) and (e) one or more times; and wherein the steps are performed by one or more computers. | 1. A computer implemented method comprising: partitioning a plurality of queries into a plurality of clusters, each cluster containing a plurality of queries; (a) for each query of the plurality of queries, generating a plurality of suggested rewrites, wherein each suggested rewrite of said plurality of suggested rewrites is the result of applying a query rewrite policy, the query rewrite policy being an ordered set of one or more query rewrite techniques, each query rewrite technique of said one or more query rewrite techniques being provided by a query rewrite provider of a plurality of query rewrite providers; (b) for each query, generating a set of rewrite scores, wherein each rewrite score of said set of rewrite scores reflects the quality of a particular suggested query rewrite generated for the query by a respective query rewrite policy; (c) for each cluster, generating aggregate rewrite scores by aggregating the rewrite scores of the same query rewrite policy for the same cluster; (d) based on the aggregate rewrite scores generated, query features of each query of said plurality of queries, and the rewrite scores generated for each query of the plurality of queries, generating a partitioning function by which to partition said plurality of queries into clusters; (e) based on the partitioning function, repartitioning said plurality of queries into a new set of clusters; repeating steps (a), (b), (c), (d) and (e) one or more times; and wherein the steps are performed by one or more computers. 8. The method of claim 1 , wherein the root node represents the query. | 0.937943 |
8,977,979 | 6 | 7 | 6. The method of claim 1 , further comprising the steps of: the computer assigning a ranking to each person in the list of people that corresponds with the identified term; and in dependence upon the rankings, the computer directing the display device to display to the user, a list of identifications of people that correspond with the identified term. | 6. The method of claim 1 , further comprising the steps of: the computer assigning a ranking to each person in the list of people that corresponds with the identified term; and in dependence upon the rankings, the computer directing the display device to display to the user, a list of identifications of people that correspond with the identified term. 7. The method of claim 6 , wherein the ranking for each person in the list is based on a number of times said each person has been identified as corresponding to the terms. | 0.596244 |
6,115,053 | 1 | 4 | 1. A computer animation method for creating animated characters which include body members coupled at joints and which perform movement gesture actions, the computer animation method comprising the steps of: selecting a first gesture action and a second different gesture actions from a library of stochastically defined movement gesture actions, each of the first and second gesture actions being defined by values over time for a set of parameters which govern a movement at the joints of the animated characters; rendering the animated characters as a function of the first gesture action; further rendering the animated characters by transitioning between the first gesture action and the second gesture action by diminishing during a predetermined time interval a weight of the values of the parameters of the first gesture action and simultaneously increasing during the predetermined time interval a weight of the values of the parameters of the second gesture action to provide a smooth transition between the first and second gesture actions; wherein the library of stochastically defined movement gesture actions includes at least one composite gesture action controlling a plurality of movements at a plurality of joints of the animated character. | 1. A computer animation method for creating animated characters which include body members coupled at joints and which perform movement gesture actions, the computer animation method comprising the steps of: selecting a first gesture action and a second different gesture actions from a library of stochastically defined movement gesture actions, each of the first and second gesture actions being defined by values over time for a set of parameters which govern a movement at the joints of the animated characters; rendering the animated characters as a function of the first gesture action; further rendering the animated characters by transitioning between the first gesture action and the second gesture action by diminishing during a predetermined time interval a weight of the values of the parameters of the first gesture action and simultaneously increasing during the predetermined time interval a weight of the values of the parameters of the second gesture action to provide a smooth transition between the first and second gesture actions; wherein the library of stochastically defined movement gesture actions includes at least one composite gesture action controlling a plurality of movements at a plurality of joints of the animated character. 4. A method for creating animated characters as in claim 1 wherein the weights of preselected gesture actions of two or more animated characters are determined as a function of at least one of a distance and an orientation between the characters. | 0.656425 |
9,361,386 | 1 | 2 | 1. A method, in a data processing system comprising a processor and a memory, for clarifying an input question, the method comprising: receiving, in the data processing system from a computing device, the input question for generation of an answer to the input question; generating, in the data processing system, a set of candidate answers for the input question based on an analysis of a corpus of information, wherein each candidate answer in the set of candidate answers corresponds to an evidence passage supporting the candidate answer as answering the input question; determining, in the data processing system, based on the set of candidate answers, whether clarification of the input question is required; identifying, in response to a determination that clarification of the input question is required, a differentiating factor in evidence passages of at least two candidate answers in the set of candidate answers; sending, by the data processing system, in response to a determination that clarification of the input question is required, a request for user input to clarify the input question, wherein the request for user input is generated based on the identified differentiating factor; receiving, in the data processing system, user input from the computing device in response to the request; and selecting, by the data processing system, at least one candidate answer in the set of candidate answers as an answer for the input question based on the user input, wherein identifying a differentiating factor in evidence passages of at least two candidate answers in the set of candidate answers comprises: identifying a plurality of differentiating factors between evidence passages of the at least two candidate answers; and selecting a subset of differentiating factors from the plurality of differentiating factors based on an evaluation of which differentiating factors in the plurality of differentiating factors clarify the input question. | 1. A method, in a data processing system comprising a processor and a memory, for clarifying an input question, the method comprising: receiving, in the data processing system from a computing device, the input question for generation of an answer to the input question; generating, in the data processing system, a set of candidate answers for the input question based on an analysis of a corpus of information, wherein each candidate answer in the set of candidate answers corresponds to an evidence passage supporting the candidate answer as answering the input question; determining, in the data processing system, based on the set of candidate answers, whether clarification of the input question is required; identifying, in response to a determination that clarification of the input question is required, a differentiating factor in evidence passages of at least two candidate answers in the set of candidate answers; sending, by the data processing system, in response to a determination that clarification of the input question is required, a request for user input to clarify the input question, wherein the request for user input is generated based on the identified differentiating factor; receiving, in the data processing system, user input from the computing device in response to the request; and selecting, by the data processing system, at least one candidate answer in the set of candidate answers as an answer for the input question based on the user input, wherein identifying a differentiating factor in evidence passages of at least two candidate answers in the set of candidate answers comprises: identifying a plurality of differentiating factors between evidence passages of the at least two candidate answers; and selecting a subset of differentiating factors from the plurality of differentiating factors based on an evaluation of which differentiating factors in the plurality of differentiating factors clarify the input question. 2. The method of claim 1 , wherein the request for user input comprises a clarification question directed to the differentiating factor and a plurality of user selectable potential answers to the clarification question, each answer corresponding to a portion of a corresponding one of the evidence passages, of the at least two candidate answers, directed to the differentiating factor. | 0.558352 |
5,487,000 | 1 | 2 | 1. A syntactic analysis apparatus for performing a syntactic analysis of an input sentence according to a given syntax analyzing rule for generating a syntactic tree of the input sentence, said syntactic analysis apparatus comprising: syntax analyzing means for outputting syntactic subtrees of said input sentence created by said syntax analyzing means up to a time point when said syntax analyzing means fails in said syntactic analysis of said input sentence due to a syntactic error included in said input sentence; error analyzing means for receiving said syntactic subtrees from said syntax analyzing means and for identifying said syntactic error by applying a given error analyzing rule to said received syntactic subtrees, error analyzing rule generating means for generating said error analyzing rule by logically inverting said syntax analyzing rule, said error analyzing rule defining a condition of said syntactic subtrees which represents said syntactic error, wherein said error analyzing rule further defines a correction command for correcting said syntactic error represented by said condition; and input sentence correcting means responsive to said correction command for correcting said input sentence in accordance with said correction command; whereby the apparatus identifies and corrects said syntactic error when said condition of the applied error analyzing rule matches said syntactic error. | 1. A syntactic analysis apparatus for performing a syntactic analysis of an input sentence according to a given syntax analyzing rule for generating a syntactic tree of the input sentence, said syntactic analysis apparatus comprising: syntax analyzing means for outputting syntactic subtrees of said input sentence created by said syntax analyzing means up to a time point when said syntax analyzing means fails in said syntactic analysis of said input sentence due to a syntactic error included in said input sentence; error analyzing means for receiving said syntactic subtrees from said syntax analyzing means and for identifying said syntactic error by applying a given error analyzing rule to said received syntactic subtrees, error analyzing rule generating means for generating said error analyzing rule by logically inverting said syntax analyzing rule, said error analyzing rule defining a condition of said syntactic subtrees which represents said syntactic error, wherein said error analyzing rule further defines a correction command for correcting said syntactic error represented by said condition; and input sentence correcting means responsive to said correction command for correcting said input sentence in accordance with said correction command; whereby the apparatus identifies and corrects said syntactic error when said condition of the applied error analyzing rule matches said syntactic error. 2. The syntactic analysis apparatus as set forth in claim 1, wherein said input sentence correcting means corrects said input sentence based on said identified syntactic error. | 0.765957 |
6,119,124 | 22 | 23 | 22. A computer-implemented method of determining the resemblance of a plurality of data objects, comprising the steps of: parsing each data object into a canonical sequence of tokens; grouping overlapping sequences of the tokens of each data object into shingles; assigning a unique identification element to each shingle; permuting the elements of the data objects to form image sets; selecting a predetermined number of minimum elements from each image to form a sketch; wherein a first and a second data object are designated as fungible when the first and the second data objects share at least one common feature, and collecting fungible data objects into clusters of closely resembling data objects. | 22. A computer-implemented method of determining the resemblance of a plurality of data objects, comprising the steps of: parsing each data object into a canonical sequence of tokens; grouping overlapping sequences of the tokens of each data object into shingles; assigning a unique identification element to each shingle; permuting the elements of the data objects to form image sets; selecting a predetermined number of minimum elements from each image to form a sketch; wherein a first and a second data object are designated as fungible when the first and the second data objects share at least one common feature, and collecting fungible data objects into clusters of closely resembling data objects. 23. The method of claim 22 further comprising the steps of storing a first list of pairs in a memory, each pair including a data object identification and a particular feature of the identified data object, sorting the first list to produce a second list, each entry in the second list identifying a unique feature and all data objects that include the unique feature, and processing the second list to identify data objects sharing the at least one common feature. | 0.5 |
9,569,593 | 61 | 90 | 61. At least one non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed, perform a method comprising: transcribing audio data comprising audio of one or more clinical personnel speaking while performing a surgical procedure, the audio data comprising audio of a first clinician speaking to one or more other clinical personnel while performing the surgical procedure; analyzing the transcribed audio data, including the transcribed audio of the first clinician speaking to the one or more other clinical personnel while performing the surgical procedure, at least in part by automatically extracting one or more clinical facts from the transcribed audio data, to identify relevant information for documenting the surgical procedure, wherein analyzing the transcribed audio data comprises identifying within the transcribed audio data a present-tense narration by the first clinician stating to the other clinical personnel that the first clinician is currently performing a particular step of the surgical procedure; automatically generating a text report including the relevant information documenting the surgical procedure, wherein automatically generating the text report comprises automatically transforming the present-tense narration into a non-present-tense portion in the report, stating that the particular step of the surgical procedure was performed; and outputting the automatically generated text report for review via a user interface on an audio and/or visual display device. | 61. At least one non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed, perform a method comprising: transcribing audio data comprising audio of one or more clinical personnel speaking while performing a surgical procedure, the audio data comprising audio of a first clinician speaking to one or more other clinical personnel while performing the surgical procedure; analyzing the transcribed audio data, including the transcribed audio of the first clinician speaking to the one or more other clinical personnel while performing the surgical procedure, at least in part by automatically extracting one or more clinical facts from the transcribed audio data, to identify relevant information for documenting the surgical procedure, wherein analyzing the transcribed audio data comprises identifying within the transcribed audio data a present-tense narration by the first clinician stating to the other clinical personnel that the first clinician is currently performing a particular step of the surgical procedure; automatically generating a text report including the relevant information documenting the surgical procedure, wherein automatically generating the text report comprises automatically transforming the present-tense narration into a non-present-tense portion in the report, stating that the particular step of the surgical procedure was performed; and outputting the automatically generated text report for review via a user interface on an audio and/or visual display device. 90. The at least one non-transitory computer-readable storage medium of claim 61 , wherein the surgical procedure is performed on a patient, and wherein the method further comprises including at least part of the relevant information in a discrete structured data repository for the patient. | 0.856367 |
9,478,216 | 17 | 20 | 17. A non-transitory computer-readable storage medium comprising instructions that, when executed by a computer processor of a device, cause the device to perform a method of speech recognition comprising: processing a speech input to produce a sequence of representative speech vectors; and performing a first recognition pass using a first acoustic model to produce at least one intermediate recognition hypothesis corresponding to the speech input; performing a second recognition pass using a second acoustic model to re-evaluate the at least one intermediate recognition hypothesis and produce a final recognition result corresponding to the speech input; and wherein the second recognition pass is a generic recognition pass that is based on a generic speech recognition arrangement using generic acoustic modeling of a broad general class of input speech and wherein the first recognition pass is an adapted recognition pass that is based on a speech adapted arrangement using pre-adapted acoustic modeling of a specific sub-class of the general class of input speech. | 17. A non-transitory computer-readable storage medium comprising instructions that, when executed by a computer processor of a device, cause the device to perform a method of speech recognition comprising: processing a speech input to produce a sequence of representative speech vectors; and performing a first recognition pass using a first acoustic model to produce at least one intermediate recognition hypothesis corresponding to the speech input; performing a second recognition pass using a second acoustic model to re-evaluate the at least one intermediate recognition hypothesis and produce a final recognition result corresponding to the speech input; and wherein the second recognition pass is a generic recognition pass that is based on a generic speech recognition arrangement using generic acoustic modeling of a broad general class of input speech and wherein the first recognition pass is an adapted recognition pass that is based on a speech adapted arrangement using pre-adapted acoustic modeling of a specific sub-class of the general class of input speech. 20. The computer-readable storage medium according to claim 17 wherein the specific sub-class is a specific speaking style. | 0.800971 |
10,102,269 | 13 | 15 | 13. The computer-implemented method of claim 11 , wherein the query object includes a select expression that sets forth at least one selection of the query. | 13. The computer-implemented method of claim 11 , wherein the query object includes a select expression that sets forth at least one selection of the query. 15. The computer-implemented method of claim 13 , wherein the select expression is a numeric expression to select fact data. | 0.560284 |
8,005,294 | 23 | 26 | 23. A method for determining a sequence of segments of a segmented image of a cursive written word processed in a word recognition system, comprising: finding the number of segments, wherein the finding step includes locating a first segment and a last segment in the imaged word; and determining the sequence of segments using an over-segmentation-relabeling algorithm, wherein the over-segmentation-relabeling algorithm includes: characterizing segments as either situated segments or unsituated segments, wherein situated segments include the first and last segments, segments having an X-coordinate or Y-coordinate coverage that exceed a threshold value, and small segments that are cursively connected to segments on each side, and wherein unsituated segments are segments not characterized as situated segments; and placing each unsituated segment having a situated segment above or below so as to either immediately precede or follow the situated segment in the sequence of segments. | 23. A method for determining a sequence of segments of a segmented image of a cursive written word processed in a word recognition system, comprising: finding the number of segments, wherein the finding step includes locating a first segment and a last segment in the imaged word; and determining the sequence of segments using an over-segmentation-relabeling algorithm, wherein the over-segmentation-relabeling algorithm includes: characterizing segments as either situated segments or unsituated segments, wherein situated segments include the first and last segments, segments having an X-coordinate or Y-coordinate coverage that exceed a threshold value, and small segments that are cursively connected to segments on each side, and wherein unsituated segments are segments not characterized as situated segments; and placing each unsituated segment having a situated segment above or below so as to either immediately precede or follow the situated segment in the sequence of segments. 26. The method of claim 23 , further comprising: rechecking the segment sequence to ensure continuity of segment placements and no multiple placement of a segment. | 0.75 |
7,743,327 | 12 | 13 | 12. A method for identifying a table of contents in a document, the method comprising: extracting text fragments from the document; identifying (i) a contiguous group of the text fragments as table of content entries and (ii) a different group of the text fragments as linked text fragments linked with corresponding table of content entries, wherein a small portion of the identified table of content entries comprise holes that do not have associated linked text fragments, a ratio of the number of the holes to the number of linked table of content entries being a user-selectable parameter that is less than about 0.2; and validating the identified table of contents entries and linked text fragments based on at least one validation criterion related to distribution of the linked text fragments. | 12. A method for identifying a table of contents in a document, the method comprising: extracting text fragments from the document; identifying (i) a contiguous group of the text fragments as table of content entries and (ii) a different group of the text fragments as linked text fragments linked with corresponding table of content entries, wherein a small portion of the identified table of content entries comprise holes that do not have associated linked text fragments, a ratio of the number of the holes to the number of linked table of content entries being a user-selectable parameter that is less than about 0.2; and validating the identified table of contents entries and linked text fragments based on at least one validation criterion related to distribution of the linked text fragments. 13. The method as set forth in claim 12 , wherein the at least one validation criterion comprises: validate conditional upon a span of the linked text fragments being greater than a validation fraction threshold of the total span of the extracted text fragments. | 0.566225 |
7,620,911 | 10 | 13 | 10. An article of manufacture comprising a program storage medium readable by a computer and embodying one or more instructions executable by the computer to perform a method for collapsing a dialog window of an application executing on the computer, the method comprising: displaying a complete dialog window of a currently active application on a display device; determining a location of a cursor with respect to the dialog window; displaying a collapsed version of the dialog window in response to the cursor moving from within the complete dialog window to outside of the complete dialog window without depressing a button of the dialog window, wherein the collapsed version of the dialog window consumes a smaller area of the display device than the complete dialog window and wherein the collapsed version of the dialog window comprises a title bar of the dialog window; and displaying the complete dialog window only in response to the cursor moving from outside of the collapsed version of the dialog window to within the tile bar of the collapsed version of the dialog window without depressing a button of the dialog window. | 10. An article of manufacture comprising a program storage medium readable by a computer and embodying one or more instructions executable by the computer to perform a method for collapsing a dialog window of an application executing on the computer, the method comprising: displaying a complete dialog window of a currently active application on a display device; determining a location of a cursor with respect to the dialog window; displaying a collapsed version of the dialog window in response to the cursor moving from within the complete dialog window to outside of the complete dialog window without depressing a button of the dialog window, wherein the collapsed version of the dialog window consumes a smaller area of the display device than the complete dialog window and wherein the collapsed version of the dialog window comprises a title bar of the dialog window; and displaying the complete dialog window only in response to the cursor moving from outside of the collapsed version of the dialog window to within the tile bar of the collapsed version of the dialog window without depressing a button of the dialog window. 13. The article of manufacture of claim 10 wherein the collapsed version of the dialog window is displayed such that system buttons, within the dialog window, are in a same position, on a display device, in the collapsed version of the dialog window as when the complete dialog window is displayed, wherein the system buttons do not move away from the cursor when the dialog window collapses or expands. | 0.697447 |
7,623,711 | 27 | 40 | 27. A computer-readable storage medium having instructions stored therein, which when executed by a system, cause the system to perform a method comprising: identifying spatial relationships between document objects of a document image; determining space separating pairs of neighboring document objects, wherein the space separating pairs of neighboring document objects is represented as weights in a weighted graph model; and determining a scaling factor based on the space separating the document objects in the document image and based on display device characteristics. | 27. A computer-readable storage medium having instructions stored therein, which when executed by a system, cause the system to perform a method comprising: identifying spatial relationships between document objects of a document image; determining space separating pairs of neighboring document objects, wherein the space separating pairs of neighboring document objects is represented as weights in a weighted graph model; and determining a scaling factor based on the space separating the document objects in the document image and based on display device characteristics. 40. The computer-readable storage medium defined in claim 27 wherein determining the space separating pairs of neighboring document objects comprises determining a length of a parameterized line segment between each pair of neighboring document objects directed between center points of said each pair of neighboring document objects that intersects the separating space between objects, the length representing measured separating space. | 0.5 |
8,635,101 | 10 | 12 | 10. The system of claim 8 , wherein the database collects and records information in: an Expense Record Log, a Weekly Spending Report, a Personal Vision Statement, a Debt Ledger, a Debt Tracker, a Savings Record, a Loan Transaction Ledger, a Goal Worksheet, a Priorities Worksheet, a Financial Planning Personal Goals and Priorities Worksheet, a Statement of Financial Position, a Cash Flow Statement, a Budget Ratio Analysis, and a Summary of Financial Findings. | 10. The system of claim 8 , wherein the database collects and records information in: an Expense Record Log, a Weekly Spending Report, a Personal Vision Statement, a Debt Ledger, a Debt Tracker, a Savings Record, a Loan Transaction Ledger, a Goal Worksheet, a Priorities Worksheet, a Financial Planning Personal Goals and Priorities Worksheet, a Statement of Financial Position, a Cash Flow Statement, a Budget Ratio Analysis, and a Summary of Financial Findings. 12. The system of claim 10 , further comprising: assessing and assigning quantitative values to character information of the user to determine the life purposed plan of the user. | 0.5 |
8,260,813 | 8 | 9 | 8. A method for using a graphical data archive meta-model for flexible data archival, the method comprising: a computer analyzing application content of an enterprise application; the computer creating a data archive specification model based on the application content of the enterprise application, wherein the data archive specification model is based on the graphical data archive meta-model and is created in a general-purpose visual modeling language; modeling archive data based on the data archive specification model using one or more graphical modeling tools, wherein the graphical data archive meta-model comprises an archive operation schedule including instructions for: archiving and purging data; indicating when to start execution of an archive procedure; indicating what is to be archived; and identifying what to archive and how much to archive; and the computer transforming the data archive specification model to generate a second data archive specification. | 8. A method for using a graphical data archive meta-model for flexible data archival, the method comprising: a computer analyzing application content of an enterprise application; the computer creating a data archive specification model based on the application content of the enterprise application, wherein the data archive specification model is based on the graphical data archive meta-model and is created in a general-purpose visual modeling language; modeling archive data based on the data archive specification model using one or more graphical modeling tools, wherein the graphical data archive meta-model comprises an archive operation schedule including instructions for: archiving and purging data; indicating when to start execution of an archive procedure; indicating what is to be archived; and identifying what to archive and how much to archive; and the computer transforming the data archive specification model to generate a second data archive specification. 9. The method as defined in claim 8 , wherein the archiving and purging data comprises purging archived data when a purge property is set as true, and archiving data when the purge property is not set as true. | 0.591797 |
9,653,000 | 1 | 14 | 1. A method of providing a context awareness-based foreign language acquisition and learning service using a smart mobile device, the method comprising: receiving, by a service provision server, user information from the smart mobile device; receiving, by the service provision server, real world event information from an electronic scheduler of a user of the smart mobile device in response to a request from the smart mobile device; generating, from electronic storage, foreign language learning content based on the received real world event information as context information of the user and using the received user information, the foreign language learning content being in a language foreign to the user; receiving, from the smart mobile device, setting information specifying times and frequency at which the foreign language learning content is to be transmitted to the smart mobile device, transmitting at the specified times and frequency, using a network, the extracted foreign language learning content as a push message to the smart mobile device, and presenting in real time, in a language foreign to the user, an augmented reality overlay including a 3D virtual object overlaid on a real world image, the augmented reality overlay including a plurality of characters providing foreign language learning content as an exemplary dialogue between the plurality of characters based on a current location of the user of the smart mobile device, wherein the plurality of characters and dialogue are overlaid on an image of the current location of the user, the image captured by a camera of the smart mobile device, and wherein the smart mobile device comprises a smart mobile device on which an application program for receiving and outputting the foreign language learning content has been installed, wherein the generated foreign language learning content is based on a current location of the user, and wherein the generated foreign language learning content is designed to be presented as a dialogue of a character presented at the smart mobile device. | 1. A method of providing a context awareness-based foreign language acquisition and learning service using a smart mobile device, the method comprising: receiving, by a service provision server, user information from the smart mobile device; receiving, by the service provision server, real world event information from an electronic scheduler of a user of the smart mobile device in response to a request from the smart mobile device; generating, from electronic storage, foreign language learning content based on the received real world event information as context information of the user and using the received user information, the foreign language learning content being in a language foreign to the user; receiving, from the smart mobile device, setting information specifying times and frequency at which the foreign language learning content is to be transmitted to the smart mobile device, transmitting at the specified times and frequency, using a network, the extracted foreign language learning content as a push message to the smart mobile device, and presenting in real time, in a language foreign to the user, an augmented reality overlay including a 3D virtual object overlaid on a real world image, the augmented reality overlay including a plurality of characters providing foreign language learning content as an exemplary dialogue between the plurality of characters based on a current location of the user of the smart mobile device, wherein the plurality of characters and dialogue are overlaid on an image of the current location of the user, the image captured by a camera of the smart mobile device, and wherein the smart mobile device comprises a smart mobile device on which an application program for receiving and outputting the foreign language learning content has been installed, wherein the generated foreign language learning content is based on a current location of the user, and wherein the generated foreign language learning content is designed to be presented as a dialogue of a character presented at the smart mobile device. 14. The method of claim 1 , wherein the augmented reality overlay is based on one or more of season information, traffic conditions, and emotion information. | 0.720641 |
9,229,919 | 1 | 2 | 1. A computer-implemented method comprising: via a computational device, configuring a document creation application with an add-in comprising instructions for linking said document creation application with a business management application that runs on a platform of a structured database management system and stores datum in said structured database management system; via a computational device, configuring said add-in with a plurality of tools that, when executed, accesses utilities of said business management application via said document creation application without leaving an interface of said document creation application, wherein said plurality of tools comprises: a check-in tool configured for allowing one or more end users to save one or more versions of a document on said platform; a field definition tool that flags objects within said document as having a field value associated with said object; wherein the field definition tool is configured to define field values to correlate with database definitions used in said structured database management system; and to automatically populate said objects with data from said structured database management system when said field values match said database definitions; and a reconciliation tool configured for comparing flagged objects in a document open in said document creation application against one or more versions of a document on said platform; wherein said reconciliation tool is further configured for presenting one or more differences between field values in the document open in said document creation application and field values in a version of a document on said platform to a user within the document creation application itself. | 1. A computer-implemented method comprising: via a computational device, configuring a document creation application with an add-in comprising instructions for linking said document creation application with a business management application that runs on a platform of a structured database management system and stores datum in said structured database management system; via a computational device, configuring said add-in with a plurality of tools that, when executed, accesses utilities of said business management application via said document creation application without leaving an interface of said document creation application, wherein said plurality of tools comprises: a check-in tool configured for allowing one or more end users to save one or more versions of a document on said platform; a field definition tool that flags objects within said document as having a field value associated with said object; wherein the field definition tool is configured to define field values to correlate with database definitions used in said structured database management system; and to automatically populate said objects with data from said structured database management system when said field values match said database definitions; and a reconciliation tool configured for comparing flagged objects in a document open in said document creation application against one or more versions of a document on said platform; wherein said reconciliation tool is further configured for presenting one or more differences between field values in the document open in said document creation application and field values in a version of a document on said platform to a user within the document creation application itself. 2. The computer-implemented method of claim 1 , wherein said document creation application comprises a word processing application. | 0.746124 |
8,515,934 | 1 | 3 | 1. A computer-implemented method comprising: receiving a search query in a first language; receiving a plurality of search results responsive to the search query, wherein the search results comprise one or more first search results and a plurality of second search results, wherein each first result identifies a respective document in the first language and a corresponding respective document in a different second language, and wherein the second search results each identify a respective document in the first language; ordering the search results based on, at least, a respective number of documents identified by each of the search results; and providing the ordered search results in response to the search query. | 1. A computer-implemented method comprising: receiving a search query in a first language; receiving a plurality of search results responsive to the search query, wherein the search results comprise one or more first search results and a plurality of second search results, wherein each first result identifies a respective document in the first language and a corresponding respective document in a different second language, and wherein the second search results each identify a respective document in the first language; ordering the search results based on, at least, a respective number of documents identified by each of the search results; and providing the ordered search results in response to the search query. 3. The method of claim 1 wherein a particular document in the second language is a document originating in the second language concerning a topic similar to a topic of the corresponding document in the first language. | 0.519912 |
10,147,107 | 11 | 12 | 11. A computing device configured to generate a social sketch corresponding to a time period, the computing device comprising a processor and a memory, and further comprises additional components for generating the social sketch, the additional components comprising: a social sketch generator configured to generate a social sketch from social communications obtained from one or more social networking services; a clustering module configured to cluster a corpus of social communications according to similarity; and a social sketch data store; wherein, in operation, the social sketch generator: obtains a corpus of social communications from one or more social networking services over a network via a network communication component, the corpus of social communications including social communications generated during a first time period; filters the obtained corpus of social communications according to the first time period such that the filtered social communications correspond to the social communications of the corpus of social communications generated during the first time period; clusters, by way of the clustering module, the filtered social communications according to the subject matter of the social communications to generate a plurality of clusters of filtered social communications; identifies a set of clusters of the plurality of clusters of social communications comprising identifying those clusters of the plurality of clusters of social communications that have a sufficient volume of social communications within the cluster, wherein the plurality of clusters of social communications that have a sufficient volume of social communications within the cluster comprises a predetermined number of clusters that have the greatest number of social communications of the plurality of clusters, each cluster of the set of the clusters being an identified cluster; for each identified cluster: extracts a topic from the identified cluster according to the subject matter of the social communications of the identified cluster; identifies a representative image of the identified cluster from the social communications of the identified cluster; identifies a non-expert set of high-quality communications from the identified cluster, the non-expert set of high-quality communications corresponding to social communications of non-experts on the topic of the identified cluster; identifies an expert set of high-quality communications from the identified cluster, the expert set of high-quality communications corresponding to social communications of experts on the topic of the identified cluster; re-clusters, via the clustering module, the identified cluster of social communications; identifies a set of sub-clusters of the identified cluster; and extracts a sub-topic from each of the identified sub-clusters of the set of sub-clusters of the identified cluster; wherein the topic, the non-expert set of high-quality communications, the expert set of high-quality communications, the extracted sub-topics, and the representative image comprise a cluster set of the identified cluster; and stores the cluster sets of each of the identified clusters as the social sketch corresponding to the identified time period in the social sketch data store. | 11. A computing device configured to generate a social sketch corresponding to a time period, the computing device comprising a processor and a memory, and further comprises additional components for generating the social sketch, the additional components comprising: a social sketch generator configured to generate a social sketch from social communications obtained from one or more social networking services; a clustering module configured to cluster a corpus of social communications according to similarity; and a social sketch data store; wherein, in operation, the social sketch generator: obtains a corpus of social communications from one or more social networking services over a network via a network communication component, the corpus of social communications including social communications generated during a first time period; filters the obtained corpus of social communications according to the first time period such that the filtered social communications correspond to the social communications of the corpus of social communications generated during the first time period; clusters, by way of the clustering module, the filtered social communications according to the subject matter of the social communications to generate a plurality of clusters of filtered social communications; identifies a set of clusters of the plurality of clusters of social communications comprising identifying those clusters of the plurality of clusters of social communications that have a sufficient volume of social communications within the cluster, wherein the plurality of clusters of social communications that have a sufficient volume of social communications within the cluster comprises a predetermined number of clusters that have the greatest number of social communications of the plurality of clusters, each cluster of the set of the clusters being an identified cluster; for each identified cluster: extracts a topic from the identified cluster according to the subject matter of the social communications of the identified cluster; identifies a representative image of the identified cluster from the social communications of the identified cluster; identifies a non-expert set of high-quality communications from the identified cluster, the non-expert set of high-quality communications corresponding to social communications of non-experts on the topic of the identified cluster; identifies an expert set of high-quality communications from the identified cluster, the expert set of high-quality communications corresponding to social communications of experts on the topic of the identified cluster; re-clusters, via the clustering module, the identified cluster of social communications; identifies a set of sub-clusters of the identified cluster; and extracts a sub-topic from each of the identified sub-clusters of the set of sub-clusters of the identified cluster; wherein the topic, the non-expert set of high-quality communications, the expert set of high-quality communications, the extracted sub-topics, and the representative image comprise a cluster set of the identified cluster; and stores the cluster sets of each of the identified clusters as the social sketch corresponding to the identified time period in the social sketch data store. 12. The computing device of claim 11 , wherein the plurality of clusters of social communications that have a sufficient volume of social communications within the cluster comprises those clusters whose volume of social communications within the cluster exceeds a predetermined number. | 0.697452 |
8,000,530 | 11 | 12 | 11. The process of claim 10 , further comprising the step of providing the formatted rules to the reasoning management unit. | 11. The process of claim 10 , further comprising the step of providing the formatted rules to the reasoning management unit. 12. The process of claim 11 , further comprising the step of providing the formatted rules to the expert system for execution. | 0.5 |
7,574,433 | 22 | 28 | 22. A computerized method for the retrieval of classified documents comprising: initiating a connection between a client software application in a client computer and a server computer; and causing at least one request by said client software application in said client computer, wherein said request initiates a method comprising: retrieving a document from a document collection, said document collection comprising at least one document(s), said document(s) having been classified according to a predefined classification scheme, said predefined classification scheme comprising classification codes, said classification codes comprising title(s) and definition(s), said document(s) further comprising at least one retrieval code, wherein said retrieval code corresponds with at least one of said classification code title(s) or classification code definition(s); retrieving from a database at least one keyword derived from at least one of said classification code title(s) or classification code definition(s); inserting said keyword into said document(s) to create a tagged document; and transmitting said tagged document to said search engine. | 22. A computerized method for the retrieval of classified documents comprising: initiating a connection between a client software application in a client computer and a server computer; and causing at least one request by said client software application in said client computer, wherein said request initiates a method comprising: retrieving a document from a document collection, said document collection comprising at least one document(s), said document(s) having been classified according to a predefined classification scheme, said predefined classification scheme comprising classification codes, said classification codes comprising title(s) and definition(s), said document(s) further comprising at least one retrieval code, wherein said retrieval code corresponds with at least one of said classification code title(s) or classification code definition(s); retrieving from a database at least one keyword derived from at least one of said classification code title(s) or classification code definition(s); inserting said keyword into said document(s) to create a tagged document; and transmitting said tagged document to said search engine. 28. The computerized method for the indexing and retrieval of classified documents of claim 22 , wherein the client software application is a web grabber. | 0.666667 |
10,133,814 | 21 | 23 | 21. A system, comprising: one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing an explanatory electronic document, the operations comprising: receiving, by the one or more processors, input from a user, the input comprising data that is at least partially representative of a subject; performing, by the one or more processors, semantic context association based on the user input, one or more computer-readable ontologies, and a computer-readable knowledge graph to provide a target subject profile, the target subject profile comprising two or more associations describing the subject at respective degrees of specificity, at least one association comprising concepts from the knowledge graph that are more general than respective entities provided in the input; providing, by the one or more processors, a set of peer user profiles based on a user profile and a superset of peer user profiles using semantic user profile association between the user profile and each peer user profile in the superset of peer user profiles; retrieving, by the one or more processors, one or more peer subject profiles from computer-readable memory, each peer subject profile being associated with a peer user profile in the set of peer user profiles, and comprising one or more associations, each association describing a past subject experienced by a peer user; filtering, by the one or more processors, at least one association from a peer subject profile based on a comparison with a respective association in the target subject profile and data provided in a knowledge graph; providing, by the one or more processors, at least one explanatory text string associated with the subject based on at least one remaining association in the peer subject profile; and providing, by the one or more processors, the explanatory electronic document comprising the at least one explanatory text string. | 21. A system, comprising: one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing an explanatory electronic document, the operations comprising: receiving, by the one or more processors, input from a user, the input comprising data that is at least partially representative of a subject; performing, by the one or more processors, semantic context association based on the user input, one or more computer-readable ontologies, and a computer-readable knowledge graph to provide a target subject profile, the target subject profile comprising two or more associations describing the subject at respective degrees of specificity, at least one association comprising concepts from the knowledge graph that are more general than respective entities provided in the input; providing, by the one or more processors, a set of peer user profiles based on a user profile and a superset of peer user profiles using semantic user profile association between the user profile and each peer user profile in the superset of peer user profiles; retrieving, by the one or more processors, one or more peer subject profiles from computer-readable memory, each peer subject profile being associated with a peer user profile in the set of peer user profiles, and comprising one or more associations, each association describing a past subject experienced by a peer user; filtering, by the one or more processors, at least one association from a peer subject profile based on a comparison with a respective association in the target subject profile and data provided in a knowledge graph; providing, by the one or more processors, at least one explanatory text string associated with the subject based on at least one remaining association in the peer subject profile; and providing, by the one or more processors, the explanatory electronic document comprising the at least one explanatory text string. 23. The system of claim 21 , wherein filtering at least one association from a peer subject profile comprises determining that the at least one association is more specific than a respective association of the target subject profile, and in response, removing the at least one association. | 0.616711 |
7,865,362 | 8 | 10 | 8. The method of claim 6 wherein the adjustment amount is dynamically varied based on the likelihood of the user saying the expected response. | 8. The method of claim 6 wherein the adjustment amount is dynamically varied based on the likelihood of the user saying the expected response. 10. The method of claim 8 wherein the likelihood of the user saying the expected response is dynamically determined based upon observance of the previous spoken responses of the user. | 0.5 |
8,249,867 | 7 | 9 | 7. A target speech extraction method for a microphone-array-based speech recognition system, comprising: separating mixed signals input through a plurality of microphone into sound-source signals by an ICA; extracting one target speech spoken for speech recognition from the separated sound-source signals; and recognizing a desired speech from the extracted target speech, wherein the extracting of the target speech comprises: extracting feature vector sequence X i from the separated sound-source signals; calculating an ith LLR (logarithm likelihood ratio) LLR i of the extracted feature vector sequence; calculating a maximum value using the LLR i ; comparing the maximum value with a predetermined threshold value; and determining the maximum value to be the target speech when the maximum value is larger than the threshold value. | 7. A target speech extraction method for a microphone-array-based speech recognition system, comprising: separating mixed signals input through a plurality of microphone into sound-source signals by an ICA; extracting one target speech spoken for speech recognition from the separated sound-source signals; and recognizing a desired speech from the extracted target speech, wherein the extracting of the target speech comprises: extracting feature vector sequence X i from the separated sound-source signals; calculating an ith LLR (logarithm likelihood ratio) LLR i of the extracted feature vector sequence; calculating a maximum value using the LLR i ; comparing the maximum value with a predetermined threshold value; and determining the maximum value to be the target speech when the maximum value is larger than the threshold value. 9. The target speech extraction method of claim 7 , wherein, when additional information representing that the target speech is a female speech is provided, the LLR i is calculated as expressed by L L R i = log P ( X i | λ Female ) P ( X i | λ Male ) by applying pre-generated male and female GMMs (Gaussian mixture models) λ Male and λ Female to the extracted feature vector sequence X i . | 0.727763 |
7,663,628 | 1 | 13 | 1. An apparatus comprising a computer processor for animating a moving and speaking enhanced-believability character in real time, comprising: i. a plurality of behavior generators each responsible for a respective aspect of facial behavior, at least some of said generators being configured with a respective time definer defining time intervals and generating behavior elements in accordance with said defined time intervals; ii. a unifying scripter, associated with said behavior generators, said unifying scripter operable to combine said generated elements into a unified animation script for said enhanced believability character; and iii. a renderer, associated with said unifying scripter, said renderer operable to render said enhanced believability character in accordance with said unified animation script, iv. an executor, associated with said renderer, operable to execute animating of said rendered enhanced believability character, wherein said behavior generators are configured to continue to generate behavior elements during execution of said animating of said rendered enhanced believability character in accordance with said defined time intervals and said renderer continues to render the enhanced believability character in accordance with respective ones of said generated behavior elements as said behavior elements are added to said unified animation script during said execution of the animating of the rendered enhanced believability character, said unifying scripter randomly selecting elements from different ones of said behavior generators in response to stimuli for addition to said unified animation script. | 1. An apparatus comprising a computer processor for animating a moving and speaking enhanced-believability character in real time, comprising: i. a plurality of behavior generators each responsible for a respective aspect of facial behavior, at least some of said generators being configured with a respective time definer defining time intervals and generating behavior elements in accordance with said defined time intervals; ii. a unifying scripter, associated with said behavior generators, said unifying scripter operable to combine said generated elements into a unified animation script for said enhanced believability character; and iii. a renderer, associated with said unifying scripter, said renderer operable to render said enhanced believability character in accordance with said unified animation script, iv. an executor, associated with said renderer, operable to execute animating of said rendered enhanced believability character, wherein said behavior generators are configured to continue to generate behavior elements during execution of said animating of said rendered enhanced believability character in accordance with said defined time intervals and said renderer continues to render the enhanced believability character in accordance with respective ones of said generated behavior elements as said behavior elements are added to said unified animation script during said execution of the animating of the rendered enhanced believability character, said unifying scripter randomly selecting elements from different ones of said behavior generators in response to stimuli for addition to said unified animation script. 13. An apparatus according to claim 1 , wherein said renderer is configured to render said character on a frame-by-frame basis. | 0.658602 |
8,239,820 | 9 | 10 | 9. The compliance system according to claim 6 , further comprising a computer coupled to said compliance system, wherein a user generates Web Services Description Language (WSDL), XML Schema (XSD) and Extensible Markup Language (XML) documents on said computer. | 9. The compliance system according to claim 6 , further comprising a computer coupled to said compliance system, wherein a user generates Web Services Description Language (WSDL), XML Schema (XSD) and Extensible Markup Language (XML) documents on said computer. 10. The compliance system according to claim 9 , said system providing a design-time testing of said WSDL, XSD and XML documents. | 0.5 |
9,043,319 | 1 | 10 | 1. A computer-implemented method, comprising: receiving a search query from a client; determining whether real-time search results should be included in a response to the search query; if real-time search results should be included, generating a user interface document that includes client software that, when executed on the client, causes the client to: receive real-time search results for the search query and a time token, wherein the time token is data that identities a most-recent time that any resource identified by any of the received real-time search results was updated, present the real-time search results for the query, re-submit the search query with the time token, obtain additional real-time search results that are more recent than the most-recent time identified by the time token, wherein an additional real-time search result is more recent than the most-recent time identified by the time token when a resource identified by the additional real-time search result was last updated more recently than the most-recent time identified by the time token, and present the additional real-time search results; if real-time search results should not be included, generating a user interface document that does not include the client software; and sending the user interface document to the client as a response to the search query. | 1. A computer-implemented method, comprising: receiving a search query from a client; determining whether real-time search results should be included in a response to the search query; if real-time search results should be included, generating a user interface document that includes client software that, when executed on the client, causes the client to: receive real-time search results for the search query and a time token, wherein the time token is data that identities a most-recent time that any resource identified by any of the received real-time search results was updated, present the real-time search results for the query, re-submit the search query with the time token, obtain additional real-time search results that are more recent than the most-recent time identified by the time token, wherein an additional real-time search result is more recent than the most-recent time identified by the time token when a resource identified by the additional real-time search result was last updated more recently than the most-recent time identified by the time token, and present the additional real-time search results; if real-time search results should not be included, generating a user interface document that does not include the client software; and sending the user interface document to the client as a response to the search query. 10. The method of claim 1 , wherein the user interface document generated if real-time search results should be included in the response further includes non-real-time search results. | 0.704839 |
8,756,210 | 26 | 28 | 26. The non-transitory computer-readable medium of claim 25 , the operations further comprising: obtaining multiple context files; and aggregating commands in the multiple context files into a set of aggregated commands. | 26. The non-transitory computer-readable medium of claim 25 , the operations further comprising: obtaining multiple context files; and aggregating commands in the multiple context files into a set of aggregated commands. 28. The non-transitory computer-readable medium of claim 26 , wherein providing the context processed search results comprises: providing the context processed search results in accordance with the aggregated commands. | 0.620209 |
8,848,897 | 1 | 8 | 1. A device comprising: a memory to store instructions; and a processor to execute the instructions to: receive, from a user device, a request via a multimedia session between the device and the user device that differs from the device, process the request to obtain first information associated with the request, determine that the first information is insufficient to identify a multimedia content item, request second information to narrow the request based on determining that the first information is insufficient to identify the multimedia content item, receive the second information from the user device, the second information including one or more of an image, a video, or a data file, a type of the second information being different from a type of the first information, and the second information being different from the multimedia content item, identify multiple recognition results after receiving the first information and the second information, provide, to the user device, information regarding the multiple recognition results, receive, from the user device, a selection of a particular recognition result of the multiple recognition results, identify the multimedia content item based on the selection of the particular recognition result, and provide, via the multimedia session, the multimedia content item to the user device. | 1. A device comprising: a memory to store instructions; and a processor to execute the instructions to: receive, from a user device, a request via a multimedia session between the device and the user device that differs from the device, process the request to obtain first information associated with the request, determine that the first information is insufficient to identify a multimedia content item, request second information to narrow the request based on determining that the first information is insufficient to identify the multimedia content item, receive the second information from the user device, the second information including one or more of an image, a video, or a data file, a type of the second information being different from a type of the first information, and the second information being different from the multimedia content item, identify multiple recognition results after receiving the first information and the second information, provide, to the user device, information regarding the multiple recognition results, receive, from the user device, a selection of a particular recognition result of the multiple recognition results, identify the multimedia content item based on the selection of the particular recognition result, and provide, via the multimedia session, the multimedia content item to the user device. 8. The device of claim 1 , where the second information further includes a link to a web page, and where, when identifying the multiple recognition results, the processor is to: obtain information associated with the web page, parse the information associated with the web page to obtain additional information, and identify the multiple recognition results based on the first information and the additional information. | 0.536424 |
9,548,066 | 12 | 16 | 12. A processor-implemented method comprising: receiving a first selection to install a first application on one or more server computers; receiving a second selection to install a second application on a device; receiving, from the device, an audio signal that represents user speech; determining an intent corresponding to the user speech; identifying the first application corresponding to the intent; providing a first indication of the intent to the one or more server computers for invocation of the first application to respond to the intent; providing a second indication of the intent to the device for invocation of the second application to respond to the intent; receiving, at the one or more server computers, a confirmation from the device that at least one of (i) the device will perform an action in response to the intent or (ii) the device has performed the action in response to the intent; and providing a third indication, based at least in part on receiving the confirmation, to the first application to cancel responding to the intent. | 12. A processor-implemented method comprising: receiving a first selection to install a first application on one or more server computers; receiving a second selection to install a second application on a device; receiving, from the device, an audio signal that represents user speech; determining an intent corresponding to the user speech; identifying the first application corresponding to the intent; providing a first indication of the intent to the one or more server computers for invocation of the first application to respond to the intent; providing a second indication of the intent to the device for invocation of the second application to respond to the intent; receiving, at the one or more server computers, a confirmation from the device that at least one of (i) the device will perform an action in response to the intent or (ii) the device has performed the action in response to the intent; and providing a third indication, based at least in part on receiving the confirmation, to the first application to cancel responding to the intent. 16. The processor-implemented method of claim 12 , wherein the first application is configured to respond to the intent by sending one or more instructions to the device. | 0.841121 |
8,995,795 | 1 | 5 | 1. A computer-implemented method for correcting textual errors in digital volumes in a corpus, the method comprising: receiving a plurality of candidate volumes comprising a basis volume and a plurality of comparison volumes; comparing the basis volume with the plurality of comparison volumes to identify identical sequences of text that are identical across all of the plurality of candidate volumes and mismatched sequences of text that contain different text in different candidate volumes; resolving at least some of the mismatched sequences by comparing the different text in the different candidate volumes to ascertain correct text for the mismatched sequences by: determining a type of mismatch for a given mismatched sequence that contains different versions of text in different candidate volumes; and applying a resolution technique to the given mismatched sequence selected responsive to the type of mismatch; and correcting errors in the plurality of candidate volumes using the ascertained correct text. | 1. A computer-implemented method for correcting textual errors in digital volumes in a corpus, the method comprising: receiving a plurality of candidate volumes comprising a basis volume and a plurality of comparison volumes; comparing the basis volume with the plurality of comparison volumes to identify identical sequences of text that are identical across all of the plurality of candidate volumes and mismatched sequences of text that contain different text in different candidate volumes; resolving at least some of the mismatched sequences by comparing the different text in the different candidate volumes to ascertain correct text for the mismatched sequences by: determining a type of mismatch for a given mismatched sequence that contains different versions of text in different candidate volumes; and applying a resolution technique to the given mismatched sequence selected responsive to the type of mismatch; and correcting errors in the plurality of candidate volumes using the ascertained correct text. 5. The method of claim 1 , wherein the type of mismatch is determined to be a spelling mismatch and applying the resolution technique comprises: considering each version of the different versions of text for the given mismatched sequence in different candidate volumes as a vote for that version; and selecting a given version of text for the given mismatched sequence as correct text for the mismatched sequence responsive to a number of votes the given version of text receives. | 0.5 |
8,988,375 | 3 | 10 | 3. An information processing apparatus comprising: a display configured to display data including a plurality of character strings; a touch panel disposed on or formed integrally with the display configured to detect a touch input; and circuitry configured to receive an output from the touch panel corresponding to a detected touch input; extract one or more character strings from the plurality of character strings based on the detected touch input; assign one of a plurality of classifications to the extracted one or more character strings; prioritize the extracted one or more character strings; and control the display to display the extracted one or more character strings in an order based on the prioritizing. | 3. An information processing apparatus comprising: a display configured to display data including a plurality of character strings; a touch panel disposed on or formed integrally with the display configured to detect a touch input; and circuitry configured to receive an output from the touch panel corresponding to a detected touch input; extract one or more character strings from the plurality of character strings based on the detected touch input; assign one of a plurality of classifications to the extracted one or more character strings; prioritize the extracted one or more character strings; and control the display to display the extracted one or more character strings in an order based on the prioritizing. 10. The information processing apparatus of claim 3 , further comprising: a memory that stores an association between each of the plurality of classifications and one or more processing operations. | 0.808738 |
8,041,694 | 26 | 95 | 26. A system comprising: one or more computers, the one or more computers implementing: a dataset tool to identify a comparison vector x having processed features and non-processed features, a first set of vectors, each vector in the first set of vectors having processed features and non-processed features corresponding to the processed features and non-processed features of the comparison vector x, and a candidate vector y from the first set of vectors; and a similarity tool to determine a similarity threshold, and a maximum similarity between the non-processed features of x and the non-processed features of y; wherein the dataset tool removes the vector y from the first set of vectors if the maximum similarity does not meet the similarity threshold. | 26. A system comprising: one or more computers, the one or more computers implementing: a dataset tool to identify a comparison vector x having processed features and non-processed features, a first set of vectors, each vector in the first set of vectors having processed features and non-processed features corresponding to the processed features and non-processed features of the comparison vector x, and a candidate vector y from the first set of vectors; and a similarity tool to determine a similarity threshold, and a maximum similarity between the non-processed features of x and the non-processed features of y; wherein the dataset tool removes the vector y from the first set of vectors if the maximum similarity does not meet the similarity threshold. 95. The system of claim 26 , in which the similarity tool stores the maximum similarity; and accumulates the partial similarity scores to determine an accumulated partial similarity score, each partial similarity score being a similarity between processed features of the comparison vector x and corresponding processed features of vectors in the first set of vectors; and the dataset tool identifies pairs of similar vectors based on the respective accumulated partial similarity scores. | 0.66205 |
5,406,477 | 3 | 14 | 3. A computer-based method to project the future value of a variable that relates to an enterprise, the method comprising the steps of: creating a model of the enterprise wherein the model is a frame-based model of the enterprise including a set of frames, each frame in the model representing real-world knowledge, each frame comprising a list of relationships, each relationship in the list of relationships specifying a relationship with another frame, each frame further comprising a list of attributes that store data relating to the frame; storing the model of the enterprise in a knowledge base; providing a set of reasoning methods; providing a set of reconciliation rules; accepting as input a query that requests information about the future value of the variable; applying each reasoning method in the set of reasoning methods, each reasoning method utilizing the set of frees, to generate from each reasoning method an intermediate hypothesis as to the future value of the variable; and reconciling between each intermediate hypothesis to obtain the future value of the variable by: (i) locating available reconciliation rules from the set of reconciliation rules; (ii) ordering the available reconciliation rules according to a pre-selected preference scheme; and (iii) applying the available reconciliation rules in the order determined at step (ii). | 3. A computer-based method to project the future value of a variable that relates to an enterprise, the method comprising the steps of: creating a model of the enterprise wherein the model is a frame-based model of the enterprise including a set of frames, each frame in the model representing real-world knowledge, each frame comprising a list of relationships, each relationship in the list of relationships specifying a relationship with another frame, each frame further comprising a list of attributes that store data relating to the frame; storing the model of the enterprise in a knowledge base; providing a set of reasoning methods; providing a set of reconciliation rules; accepting as input a query that requests information about the future value of the variable; applying each reasoning method in the set of reasoning methods, each reasoning method utilizing the set of frees, to generate from each reasoning method an intermediate hypothesis as to the future value of the variable; and reconciling between each intermediate hypothesis to obtain the future value of the variable by: (i) locating available reconciliation rules from the set of reconciliation rules; (ii) ordering the available reconciliation rules according to a pre-selected preference scheme; and (iii) applying the available reconciliation rules in the order determined at step (ii). 14. The method of claim 3 wherein the set of reasoning methods includes reasoning from productivity. | 0.880668 |
5,546,575 | 18 | 19 | 18. A database storage method comprising the following steps: selecting a first field of a database, the database containing a plurality of records; reading a first plurality of data values from the first field, one data value from the first plurality being read from each record; selecting a second field of the database; reading a second plurality of data values from the second field, one data value from the second plurality being read from each record; identifying a plurality of data value combinations, each of which comprises one data value from the first plurality and one data value from the second plurality occurring together in the same record; calculating first and second field characteristics, respectively for the first and second fields, which are an estimated mean of the number of records per slot for each field and an estimated standard deviation of the number of records per slot for each field, where the estimated mean is equal to a sample mean multiplied by a sample interval and the estimated standard deviation is equal to a sample standard deviation multiplied by the square root of the sample interval; calculating a threshold equal to a constant of proportionality multiplied by the number of records in the database raised to a power of less than one; comparing the field characteristics to the threshold, to determine if the field characteristics satisfy the criteria that the estimated mean is greater than one half of the threshold, the estimated mean plus the estimated standard deviation is greater than the threshold and the estimated mean is greater than the estimated standard deviation; applying a multiple-field compaction method whereby each data value combination is represented with a corresponding numeric equivalent only if the field characteristics satisfy the criteria, the compaction method creating a plurality of compacted data values, the compacted data values being reduced storage equivalents of the data values based on the numeric equivalents of the corresponding data value combinations; and storing the compacted data values in a plurality of compacted records, such that each compacted record contains one compacted data value. | 18. A database storage method comprising the following steps: selecting a first field of a database, the database containing a plurality of records; reading a first plurality of data values from the first field, one data value from the first plurality being read from each record; selecting a second field of the database; reading a second plurality of data values from the second field, one data value from the second plurality being read from each record; identifying a plurality of data value combinations, each of which comprises one data value from the first plurality and one data value from the second plurality occurring together in the same record; calculating first and second field characteristics, respectively for the first and second fields, which are an estimated mean of the number of records per slot for each field and an estimated standard deviation of the number of records per slot for each field, where the estimated mean is equal to a sample mean multiplied by a sample interval and the estimated standard deviation is equal to a sample standard deviation multiplied by the square root of the sample interval; calculating a threshold equal to a constant of proportionality multiplied by the number of records in the database raised to a power of less than one; comparing the field characteristics to the threshold, to determine if the field characteristics satisfy the criteria that the estimated mean is greater than one half of the threshold, the estimated mean plus the estimated standard deviation is greater than the threshold and the estimated mean is greater than the estimated standard deviation; applying a multiple-field compaction method whereby each data value combination is represented with a corresponding numeric equivalent only if the field characteristics satisfy the criteria, the compaction method creating a plurality of compacted data values, the compacted data values being reduced storage equivalents of the data values based on the numeric equivalents of the corresponding data value combinations; and storing the compacted data values in a plurality of compacted records, such that each compacted record contains one compacted data value. 19. A database storage method according to claim 18, wherein the constant of proportionality is in the range of 0.001 to 0.01 and the power is in the range of 0.5 to 0.99. | 0.5 |
7,770,182 | 15 | 17 | 15. A method for processing events in an extensible editor that communicates with a first extension and a second extension, the method comprising: sending an editing event requesting manipulation of a document to the first extension; receiving a signal from the first extension indicating whether to continue processing the event; and sending the editing event to the second extension when the signal indicates that processing should continue, wherein an order in which the editing event is routed to the first extension and the second extension is based on an order in which the first extension and the second extension were registered with the extensible editor. | 15. A method for processing events in an extensible editor that communicates with a first extension and a second extension, the method comprising: sending an editing event requesting manipulation of a document to the first extension; receiving a signal from the first extension indicating whether to continue processing the event; and sending the editing event to the second extension when the signal indicates that processing should continue, wherein an order in which the editing event is routed to the first extension and the second extension is based on an order in which the first extension and the second extension were registered with the extensible editor. 17. The method as recited in claim 15 , further comprising receiving an additional signal from the second extension indicating whether to continue processing the event, and processing the event if the additional signal indicates that processing should continue. | 0.5 |
8,200,651 | 15 | 16 | 15. A system for facilitating user comprehension of digitally encoded texts, the system comprising: a processor; and a computer memory operatively coupled to the processor; wherein the computer memory has disposed within it: computer program instructions for providing a user of the system with access to a plurality of databases; computer program instructions for associating at least one content category with each dictionary database; computer program instructions for associating at least one predetermined marking with each of the plurality of dictionary databases; computer program instructions for identifying at least one text portion marked by the user with a first marking; computer program instructions for determining a subset of dictionary database(s) from the one or more dictionary databases identified as being associated with the first marking, each dictionary database in the subset having a content category that corresponds to the at least one text portion; computer program instructions for identifying at least one additional text portion marked by the user with a second marking, the second marking being different from the first marking, the at least one text portion and the at least one additional text portion being part of a same digitally encoded text; computer program instructions for identifying one or more dictionary databases associated with the first marking; computer program instructions for identifying one or more dictionary databases associated with the second marking; computer program instructions for initiating a searching process comprising performing a first search of the at least one text portion within the subset of dictionary database(s) and performing a second search of the at least one additional text portion within the one or more dictionary databases associated with the second marking; and computer program instructions for displaying the results of the first and second searches to the user in a consolidated format. | 15. A system for facilitating user comprehension of digitally encoded texts, the system comprising: a processor; and a computer memory operatively coupled to the processor; wherein the computer memory has disposed within it: computer program instructions for providing a user of the system with access to a plurality of databases; computer program instructions for associating at least one content category with each dictionary database; computer program instructions for associating at least one predetermined marking with each of the plurality of dictionary databases; computer program instructions for identifying at least one text portion marked by the user with a first marking; computer program instructions for determining a subset of dictionary database(s) from the one or more dictionary databases identified as being associated with the first marking, each dictionary database in the subset having a content category that corresponds to the at least one text portion; computer program instructions for identifying at least one additional text portion marked by the user with a second marking, the second marking being different from the first marking, the at least one text portion and the at least one additional text portion being part of a same digitally encoded text; computer program instructions for identifying one or more dictionary databases associated with the first marking; computer program instructions for identifying one or more dictionary databases associated with the second marking; computer program instructions for initiating a searching process comprising performing a first search of the at least one text portion within the subset of dictionary database(s) and performing a second search of the at least one additional text portion within the one or more dictionary databases associated with the second marking; and computer program instructions for displaying the results of the first and second searches to the user in a consolidated format. 16. The system of claim 15 further comprising computer program instructions for accessing at least one of the plurality of dictionary databases through a network. | 0.713781 |
8,589,378 | 1 | 2 | 1. A method comprising: relating, using at least one computing system, each content item in a plurality of content items with a plurality of content item topics, each content item having a plurality of relevance values corresponding to the plurality of content item topics, each relevance value reflecting a strength of relationship between a topic of the plurality of content item topics and the content item; identifying, using the at least one computing system, topics of interest to a user, the topics of interest being identified for the user from a content item browsing history of the user, each identified topic of interest corresponding to a topic of the plurality of content item topics and having a level of interest that exceeds a predefined user-interest threshold; identifying, using the at least one computing system, a set of content items as recommendations for the user by iterating through the topics of interest to the user to identify the set of content items, each content item in the set having a relevance determined using the strength of relationship between the topic of the plurality of content item topics and the content item; and ranking, using the at least one computing system, the set of content items based on the determined relevance of each content item in the set. | 1. A method comprising: relating, using at least one computing system, each content item in a plurality of content items with a plurality of content item topics, each content item having a plurality of relevance values corresponding to the plurality of content item topics, each relevance value reflecting a strength of relationship between a topic of the plurality of content item topics and the content item; identifying, using the at least one computing system, topics of interest to a user, the topics of interest being identified for the user from a content item browsing history of the user, each identified topic of interest corresponding to a topic of the plurality of content item topics and having a level of interest that exceeds a predefined user-interest threshold; identifying, using the at least one computing system, a set of content items as recommendations for the user by iterating through the topics of interest to the user to identify the set of content items, each content item in the set having a relevance determined using the strength of relationship between the topic of the plurality of content item topics and the content item; and ranking, using the at least one computing system, the set of content items based on the determined relevance of each content item in the set. 2. The method of claim 1 , further comprising: identifying, for each content item in the plurality of content items, each topic's relevance to the content item using a topic model and token occurrences in the content item, the topic model identifying a probabilistic correspondence between token occurrences and the plurality of content item topics. | 0.5 |
9,311,568 | 1 | 3 | 1. A computer-implemented method for selecting a representative image for a recipe from among a plurality of recipe images, the method comprising: receiving a recipe comprising classified recipe text describing preparation of a food product and a plurality of candidate images; generating image features for the plurality of candidate images, features of a candidate image representative of at least one of: classified recipe text proximate to the candidate image and position of the candidate image within the recipe; determining, by a processor, image probabilities of the plurality of candidate images depicting the finished food product described by the recipe, the image probabilities determined using an image model, image feature weights, and the generated image features, the image feature weights computed based on training recipes comprising classified training recipe text and training recipe images, the training recipe images including representative training images corresponding to the training recipes; ranking the plurality of candidate images according to the determined image probabilities; selecting a representative image from the candidate images according to the ranking of the candidate images, the selected representative image having a highest image probability of the determined image probabilities; and storing the selected representative image in association with the retrieved recipe. | 1. A computer-implemented method for selecting a representative image for a recipe from among a plurality of recipe images, the method comprising: receiving a recipe comprising classified recipe text describing preparation of a food product and a plurality of candidate images; generating image features for the plurality of candidate images, features of a candidate image representative of at least one of: classified recipe text proximate to the candidate image and position of the candidate image within the recipe; determining, by a processor, image probabilities of the plurality of candidate images depicting the finished food product described by the recipe, the image probabilities determined using an image model, image feature weights, and the generated image features, the image feature weights computed based on training recipes comprising classified training recipe text and training recipe images, the training recipe images including representative training images corresponding to the training recipes; ranking the plurality of candidate images according to the determined image probabilities; selecting a representative image from the candidate images according to the ranking of the candidate images, the selected representative image having a highest image probability of the determined image probabilities; and storing the selected representative image in association with the retrieved recipe. 3. The method of claim 1 , wherein receiving the recipe comprising the candidate images comprises: filtering the plurality of candidate images to remove candidate images positioned in a header, footer, or sidebar of a structured document from which the recipe was obtained. | 0.873728 |
9,558,275 | 4 | 5 | 4. The computer system of claim 3 , also including computer-executable instructions that configure the computer system to generate the feature vector from the action based at least on one or more features extracted from the one or more content sources. | 4. The computer system of claim 3 , also including computer-executable instructions that configure the computer system to generate the feature vector from the action based at least on one or more features extracted from the one or more content sources. 5. The computer system of claim 4 , wherein the one or more features are selected from the group comprising a number feature, a category feature, and a binary feature. | 0.5 |
10,127,221 | 11 | 18 | 11. A computing device for detecting ruby text in a fixed format document, comprising: a processing unit; and a memory including computer-readable instructions which when executed by the processor are operable to: detect, at a parser, a fixed format document; detect, at a line detection engine, one or more lines in the fixed format document containing one or more attributes of a ruby line; retain the one or more lines in the fixed format document containing one or more attributes of a ruby line as ruby line candidates and a line successive to the one or more lines as ruby base line candidates; analyze, by a document processor, the ruby line candidate for finding one or more ruby texts contained in the ruby line candidate; match the one or more ruby texts with a corresponding ruby base text in a successive ruby base line candidate for reconstruction in a flow format document; and reconstruct, by a serializer, the fixed format document as the flow format document containing the matched one or more ruby texts and the corresponding ruby base text. | 11. A computing device for detecting ruby text in a fixed format document, comprising: a processing unit; and a memory including computer-readable instructions which when executed by the processor are operable to: detect, at a parser, a fixed format document; detect, at a line detection engine, one or more lines in the fixed format document containing one or more attributes of a ruby line; retain the one or more lines in the fixed format document containing one or more attributes of a ruby line as ruby line candidates and a line successive to the one or more lines as ruby base line candidates; analyze, by a document processor, the ruby line candidate for finding one or more ruby texts contained in the ruby line candidate; match the one or more ruby texts with a corresponding ruby base text in a successive ruby base line candidate for reconstruction in a flow format document; and reconstruct, by a serializer, the fixed format document as the flow format document containing the matched one or more ruby texts and the corresponding ruby base text. 18. The computing device of claim 11 , wherein matching the one or more ruby texts with a corresponding ruby base text in a successive ruby base line candidate comprises assigning characters below the ruby text in the ruby base line with the ruby text. | 0.785714 |
8,126,700 | 1 | 6 | 1. A method for facilitating computer-assisted comprehension of texts by a user of a data processing system, said method comprising: accessing a digital representation of a text, the text including at least one occurrence of each one of a plurality of expressions, associating each of the plurality of expressions with a difficulty index that estimates a corresponding comprehension difficulty, determining a set of the plurality of expressions that have corresponding difficulty indexes that exceed a threshold value, associating each of the set of the plurality of expressions with a corresponding explanation, and outputting at least part of the text with an indication of the corresponding explanation of each of the set of the plurality of expressions included in the at least part of the text that is output, updating the difficulty index associated with a first of the plurality of expressions in response to detecting selection of the first of the plurality of expressions in the at least part of the text, wherein said updating represents an increase in the estimated comprehension difficulty of the first of the plurality of expressions, associating the first of the plurality of expressions with a corresponding explanation, and adding an indication of the corresponding explanation of the first of the plurality of expressions to the output of the at least part of the text. | 1. A method for facilitating computer-assisted comprehension of texts by a user of a data processing system, said method comprising: accessing a digital representation of a text, the text including at least one occurrence of each one of a plurality of expressions, associating each of the plurality of expressions with a difficulty index that estimates a corresponding comprehension difficulty, determining a set of the plurality of expressions that have corresponding difficulty indexes that exceed a threshold value, associating each of the set of the plurality of expressions with a corresponding explanation, and outputting at least part of the text with an indication of the corresponding explanation of each of the set of the plurality of expressions included in the at least part of the text that is output, updating the difficulty index associated with a first of the plurality of expressions in response to detecting selection of the first of the plurality of expressions in the at least part of the text, wherein said updating represents an increase in the estimated comprehension difficulty of the first of the plurality of expressions, associating the first of the plurality of expressions with a corresponding explanation, and adding an indication of the corresponding explanation of the first of the plurality of expressions to the output of the at least part of the text. 6. The method according to claim 1 , wherein the text is in a first language, and associating each of the set of the plurality of expressions with the corresponding explanation comprises: using a translation in a second language of the expression from a dictionary. | 0.580696 |
8,818,092 | 12 | 17 | 12. A system for rendering text with an image comprising: a segmenter configured to receive a source image and a separate block of text to be associated with the source image, the segmenter being further configured to segment the block of text into a plurality of text segments, wherein each text segment comprises a sequence of alpha-numeric characters and wherein the block of text is not included in the source image when received; a plurality of threads running in parallel, each thread configured to generate a text bitmap for a respective text segment from the plurality of text segments, the text bitmap illustrating the sequence of alpha-numeric characters for the respective text segment; and a composite engine configured to composite each of the text bitmaps onto an image bitmap of the source image, wherein the composited image bitmap comprises a rendering of the text block onto the source image. | 12. A system for rendering text with an image comprising: a segmenter configured to receive a source image and a separate block of text to be associated with the source image, the segmenter being further configured to segment the block of text into a plurality of text segments, wherein each text segment comprises a sequence of alpha-numeric characters and wherein the block of text is not included in the source image when received; a plurality of threads running in parallel, each thread configured to generate a text bitmap for a respective text segment from the plurality of text segments, the text bitmap illustrating the sequence of alpha-numeric characters for the respective text segment; and a composite engine configured to composite each of the text bitmaps onto an image bitmap of the source image, wherein the composited image bitmap comprises a rendering of the text block onto the source image. 17. The system of claim 12 wherein the system is configured to: determine a number of processors available for the processing; and create a threadpool of the plurality of threads based on the number available processors. | 0.570313 |
6,122,628 | 26 | 44 | 26. A program storage device readable by a machine which includes one or more reduced dimensionality indexes to multidimensional data, the program storage device tangibly embodying a program of instructions executable by the machine to perform method steps for representing multidimensional data, said method steps comprising: a) partitioning the multidimensional data into one or more clusters; b) generating and storing clustering information for said one or more clusters; c) generating one or more reduced dimensionality clusters and dimensionality reduction information for said one or more clusters; and d) storing the dimensionality reduction information; e) creating a hierarchy of reduced dimensionality clusters by recursively applying said steps a) through d); and f) generating and storing one or more low-dimensional indexes for clusters at a lowest level of said hierarchy. | 26. A program storage device readable by a machine which includes one or more reduced dimensionality indexes to multidimensional data, the program storage device tangibly embodying a program of instructions executable by the machine to perform method steps for representing multidimensional data, said method steps comprising: a) partitioning the multidimensional data into one or more clusters; b) generating and storing clustering information for said one or more clusters; c) generating one or more reduced dimensionality clusters and dimensionality reduction information for said one or more clusters; and d) storing the dimensionality reduction information; e) creating a hierarchy of reduced dimensionality clusters by recursively applying said steps a) through d); and f) generating and storing one or more low-dimensional indexes for clusters at a lowest level of said hierarchy. 44. The program storage device of claim 26, for performing an exact search, comprising the steps of: recursively applying the steps of: finding a cluster to which specified data belongs, using stored clustering information; and reducing the dimensionality of the specified data using stored dimensionality reduction information, until a corresponding lowest level of the hierarchy of reduced dimensionality clusters has been reached; and searching, using the low dimensional indexes, for a reduced dimensionality version of the cluster matching the specified data. | 0.743636 |
4,730,259 | 35 | 36 | 35. A learning process for operating a general purpose computer having a knowledge base, including a matrix of learning coefficients which includes a matrix value for each combination of one of a set of resultant variables and one of a set of primary variables to enhance said matrix comprising the steps of: (a) providing an example including values for at least one primary variable and values for at least one resultant variable; (b) testing, for each resultant variable which is provided a value in said example, matrix values in a temporary matrix of learning coefficients associated with said resultant variable to determine whether the matrix values combine with the primary variable values of the example to determine the proper value of said resultant variable as provided by the example; (c) replacing the matrix values associated with a resultant variable in said matrix of learning coefficients with the matrix values for said resultant variable from said temporary matrix of learning coefficients when said temporary matrix values have determined the correct value for said resultant variable for a greater number of examples than had been correctly determined by the present matrix of learning coefficients; (d) modifying the temporary matrix values associated with a resultant variable in the temporary matrix of learning coefficients when said temporary matrix values determine an incorrect response for the example; and (e) repeating steps b-d at least a predetermined number of times. | 35. A learning process for operating a general purpose computer having a knowledge base, including a matrix of learning coefficients which includes a matrix value for each combination of one of a set of resultant variables and one of a set of primary variables to enhance said matrix comprising the steps of: (a) providing an example including values for at least one primary variable and values for at least one resultant variable; (b) testing, for each resultant variable which is provided a value in said example, matrix values in a temporary matrix of learning coefficients associated with said resultant variable to determine whether the matrix values combine with the primary variable values of the example to determine the proper value of said resultant variable as provided by the example; (c) replacing the matrix values associated with a resultant variable in said matrix of learning coefficients with the matrix values for said resultant variable from said temporary matrix of learning coefficients when said temporary matrix values have determined the correct value for said resultant variable for a greater number of examples than had been correctly determined by the present matrix of learning coefficients; (d) modifying the temporary matrix values associated with a resultant variable in the temporary matrix of learning coefficients when said temporary matrix values determine an incorrect response for the example; and (e) repeating steps b-d at least a predetermined number of times. 36. The learning process of claim 35 wherein step b is performed by computing the sum of the products of the primary variable values in said example and their associated temporary matrix values. | 0.5 |
8,667,004 | 1 | 2 | 1. A computer-implemented method comprising: receiving, via a search box comprising a native part of a Web browser, a text string associated with a user's search query, the search box not provided by a separately-installed mechanism; communicating the text string to a search provider; receiving information communicated from the search provider, wherein said information includes at least non-textual information; rendering said information in a search box drop down menu associated with said search box, wherein rendering includes rendering in the search box drop down menu at least some locally acquired information, wherein the locally acquired information comprises links associated with the user's search query; receiving a text string that the user has entered in a third-party search provider search box; replicating the text string entered in the third-party search provider search box in the search box comprising the native part of the Web browser; and providing, via the search box comprising the native part of the Web browser, one or more suggestions associated with said replicated text string. | 1. A computer-implemented method comprising: receiving, via a search box comprising a native part of a Web browser, a text string associated with a user's search query, the search box not provided by a separately-installed mechanism; communicating the text string to a search provider; receiving information communicated from the search provider, wherein said information includes at least non-textual information; rendering said information in a search box drop down menu associated with said search box, wherein rendering includes rendering in the search box drop down menu at least some locally acquired information, wherein the locally acquired information comprises links associated with the user's search query; receiving a text string that the user has entered in a third-party search provider search box; replicating the text string entered in the third-party search provider search box in the search box comprising the native part of the Web browser; and providing, via the search box comprising the native part of the Web browser, one or more suggestions associated with said replicated text string. 2. The method of claim 1 , wherein the text string received via the search box comprising a native part of the Web browser includes less than an entire portion of a user's search query. | 0.563679 |
8,996,621 | 13 | 14 | 13. A computer-implemented method comprising: receiving a plurality of comment objects from a plurality of client computers, each client computer accessing an electronic document, and each of the plurality of comment objects being associated with the electronic document accessed from the plurality of client computers and having a data structure that includes a reference specification field containing a numeric identification that describes at least one of a beginning or an ending of a commented portion in a numerical format and containing context information that identifies at least one of the beginning or the ending of the commented portion using text from the beginning or the ending of the commented portion; assigning unique identifiers to each one of the plurality of comment objects; placing the comment objects in a queue according to the unique identifiers; forwarding at least one of the plurality of comment objects to an authoring client computer for automatic propagation thereof when the authoring client computer is concurrently accessing the electronic document with at least one of the client computers; receiving an indication that the at least one of the plurality of forwarded comment objects has been merged into the electronic document on the authoring client computer and that the electronic document has been saved thereto; determining a highest unique identifier associated with the at least one of the plurality of forward comment objects; and removing, using a processor of a machine, comment objects from the queue that have unique identifiers with values less than or equal to the determined highest unique identifier based on the receiving of the indication. | 13. A computer-implemented method comprising: receiving a plurality of comment objects from a plurality of client computers, each client computer accessing an electronic document, and each of the plurality of comment objects being associated with the electronic document accessed from the plurality of client computers and having a data structure that includes a reference specification field containing a numeric identification that describes at least one of a beginning or an ending of a commented portion in a numerical format and containing context information that identifies at least one of the beginning or the ending of the commented portion using text from the beginning or the ending of the commented portion; assigning unique identifiers to each one of the plurality of comment objects; placing the comment objects in a queue according to the unique identifiers; forwarding at least one of the plurality of comment objects to an authoring client computer for automatic propagation thereof when the authoring client computer is concurrently accessing the electronic document with at least one of the client computers; receiving an indication that the at least one of the plurality of forwarded comment objects has been merged into the electronic document on the authoring client computer and that the electronic document has been saved thereto; determining a highest unique identifier associated with the at least one of the plurality of forward comment objects; and removing, using a processor of a machine, comment objects from the queue that have unique identifiers with values less than or equal to the determined highest unique identifier based on the receiving of the indication. 14. The computer-implemented method of claim 13 , wherein the assigning of the unique identifiers to each one of the plurality of comment objects comprises: assigning incrementing identifiers to each one of the plurality of comment objects. | 0.5 |
9,558,755 | 9 | 13 | 9. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for reducing noise in an audio signal, the method comprising: generating sub-band signals from a received primary acoustic signal and a received secondary acoustic signal; determining two or more features for a sub-band signal, the two or more features including a speech energy level for the sub-band noise level and at least one of the following: inter-microphone level differences, inter-microphone time differences, and inter-microphone phase differences between the primary acoustic signal and the secondary acoustic signal; suppressing a noise component in the primary acoustic signal based on the two or more features, the suppressing configured to clean the primary acoustic signal to create a cleaned speech signal optimized for accurate speech recognition processing by an automatic speech recognition processing module, the suppressing comprising: applying a gain to a sub-band of the primary acoustic signal to provide a noise suppressed signal, the applying comprising: determining a speech to noise ratio (SNR) for the sub-band of the primary acoustic signal; accessing the gain, based on the frequency of the sub-band and the determined SNR for the sub-band, from a datastore, the datastore including a plurality of pre-stored gains configured to create cleaned speech signals optimized for accurate speech recognition processing by the automatic speech recognition processing module, each pre-stored gain in the plurality of pre-stored gains associated with a corresponding frequency and an SNR value; and applying the accessed gain to the sub-band frequency; and providing the cleaned speech signal and corresponding noise suppression information to the automatic speech recognition processing module, the noise suppression information based on the two or more features and including a speech to noise ratio for each of the sub-band signals and a voice activity detection signal. | 9. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for reducing noise in an audio signal, the method comprising: generating sub-band signals from a received primary acoustic signal and a received secondary acoustic signal; determining two or more features for a sub-band signal, the two or more features including a speech energy level for the sub-band noise level and at least one of the following: inter-microphone level differences, inter-microphone time differences, and inter-microphone phase differences between the primary acoustic signal and the secondary acoustic signal; suppressing a noise component in the primary acoustic signal based on the two or more features, the suppressing configured to clean the primary acoustic signal to create a cleaned speech signal optimized for accurate speech recognition processing by an automatic speech recognition processing module, the suppressing comprising: applying a gain to a sub-band of the primary acoustic signal to provide a noise suppressed signal, the applying comprising: determining a speech to noise ratio (SNR) for the sub-band of the primary acoustic signal; accessing the gain, based on the frequency of the sub-band and the determined SNR for the sub-band, from a datastore, the datastore including a plurality of pre-stored gains configured to create cleaned speech signals optimized for accurate speech recognition processing by the automatic speech recognition processing module, each pre-stored gain in the plurality of pre-stored gains associated with a corresponding frequency and an SNR value; and applying the accessed gain to the sub-band frequency; and providing the cleaned speech signal and corresponding noise suppression information to the automatic speech recognition processing module, the noise suppression information based on the two or more features and including a speech to noise ratio for each of the sub-band signals and a voice activity detection signal. 13. The non-transitory computer readable storage medium of claim 9 , wherein the noise suppression information includes a speech to noise ratio, the method further comprising modulating a bit rate of a speech encoder or decoder based on the speech to noise ratio for a particular frame. | 0.550314 |
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