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9,069,882 | 21 | 23 |
21. The program product of claim 17 , the searching further comprising: accessing a dataset description of a plurality of datasets, the dataset description including data descriptors that describe a value set in the plurality of datasets; and comparing the boosted terms and the remaining query items with the data descriptors; evaluating value sets associated with domain keywords associated with a particular formula designator; boosting a returned value set based on a designation type of the data descriptor corresponding to the returned value set, the boosting assigning a first boost value to any data descriptors having a preferred designation type that is greater than a second boost value assigned to data descriptors having an inferior designation type; ranking a solution set based on the boosted terms and the boosted return value set; and returning the ranked solution set.
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21. The program product of claim 17 , the searching further comprising: accessing a dataset description of a plurality of datasets, the dataset description including data descriptors that describe a value set in the plurality of datasets; and comparing the boosted terms and the remaining query items with the data descriptors; evaluating value sets associated with domain keywords associated with a particular formula designator; boosting a returned value set based on a designation type of the data descriptor corresponding to the returned value set, the boosting assigning a first boost value to any data descriptors having a preferred designation type that is greater than a second boost value assigned to data descriptors having an inferior designation type; ranking a solution set based on the boosted terms and the boosted return value set; and returning the ranked solution set. 23. The program product of claim 21 , the method further comprising: linking a first returned value set having a particular data descriptor with a second returned value set having a corresponding data descriptor; and returning the linking and the data descriptors with the ranked solution set.
| 0.652019 |
9,684,735 | 11 | 13 |
11. A system for font recommendation comprising: one or more processors configured to: obtain a product category; determine whether a font recommendation should be made with respect to the product category, comprising: obtain font information of a frequently used font within a webpage that corresponds to a product in the product category; and determine whether a predetermined correspondence exists between the product category and the frequently used font, including by looking up the product category and the frequently used font in the plurality of predetermined correspondences, wherein the font recommendation is to be made in the event that no predetermined correspondence is determined; in the event that the font recommendation should be made: determine a recommended font that corresponds to the product category, the determination being based at least in part on a plurality of predetermined correspondences, and the plurality of predetermined correspondences indicating associations between a plurality of product categories and a respective plurality of fonts; and output information pertaining to the recommended font; and a memory coupled to the one or more processors and configured to provide the processor with instructions.
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11. A system for font recommendation comprising: one or more processors configured to: obtain a product category; determine whether a font recommendation should be made with respect to the product category, comprising: obtain font information of a frequently used font within a webpage that corresponds to a product in the product category; and determine whether a predetermined correspondence exists between the product category and the frequently used font, including by looking up the product category and the frequently used font in the plurality of predetermined correspondences, wherein the font recommendation is to be made in the event that no predetermined correspondence is determined; in the event that the font recommendation should be made: determine a recommended font that corresponds to the product category, the determination being based at least in part on a plurality of predetermined correspondences, and the plurality of predetermined correspondences indicating associations between a plurality of product categories and a respective plurality of fonts; and output information pertaining to the recommended font; and a memory coupled to the one or more processors and configured to provide the processor with instructions. 13. The system of claim 11 , wherein the one or more processors are further configured to: receive, from a user, product information, wherein the product information includes a keyword and the product category is determined based at least in part on the keyword.
| 0.833967 |
8,560,466 | 12 | 14 |
12. A method for decoding a target document that represents at least a portion of a web page received through a communication network, said target document having been transmitted electronically and involving an encoding scheme, the method comprising: ascertaining, using identification rules, whether a charset employed to encode said target document belongs to an excluded charset group, said excluded charset group having at least two charsets, each charset in said excluded charset group selected based on charset inherent characteristics; if said charset employed to encode said target document does not belong to said excluded charset group, performing said decoding by performing at least steps (a), (b), and (c) below; (a) training, using a plurality of text document samples that have been encoded with different encoding schemes and selected for training purposes, said different encoding schemes pertaining to charset encoding, to obtain a set of machine learning models, said training including using a SVM (Support Vector Machine) technique to generate said set of machine learning models from feature vectors converted from said plurality of text document samples, said feature vectors are grouped by charsets, wherein said training including generating fundamental units from said plurality of text document samples and extracting a subset of said fundamental units to form a set of feature lists, said feature vectors are converted from said set of feature lists and said plurality of text document samples, said extracting said subset of said fundamental units includes filtering said fundamental units to obtain fundamental units that are more discriminatory in describing differences among said different encoding schemes; (b) applying said set of machine learning models against a set of target document feature vectors converted from said target document, said applying including analyzing said set of target document feature vectors using said set of machine learning models to compute similarity indicia between said set of target document feature vectors and said set of machine learning models associated with said different encoding schemes, wherein a first encoding scheme associated with said set of machine learning models is designated as said encoding scheme if characteristics of said first encoding scheme as represented by said set of machine learning models are computed to be most similar, relative to other encoding schemes of said different encoding schemes, to said set of target document feature vectors; and (c) decoding said target document to obtain decoded content of said target document based on at least said first encoding scheme; said applying including converting said target document to said set of target document feature vectors; wherein said converting said target document to said set of target document feature vectors employs a TF-IDF (Term Frequency-Inverse Document Frequency) technique; wherein said TF-IDF technique employs a VSM (Vector Space Model) representation approach.
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12. A method for decoding a target document that represents at least a portion of a web page received through a communication network, said target document having been transmitted electronically and involving an encoding scheme, the method comprising: ascertaining, using identification rules, whether a charset employed to encode said target document belongs to an excluded charset group, said excluded charset group having at least two charsets, each charset in said excluded charset group selected based on charset inherent characteristics; if said charset employed to encode said target document does not belong to said excluded charset group, performing said decoding by performing at least steps (a), (b), and (c) below; (a) training, using a plurality of text document samples that have been encoded with different encoding schemes and selected for training purposes, said different encoding schemes pertaining to charset encoding, to obtain a set of machine learning models, said training including using a SVM (Support Vector Machine) technique to generate said set of machine learning models from feature vectors converted from said plurality of text document samples, said feature vectors are grouped by charsets, wherein said training including generating fundamental units from said plurality of text document samples and extracting a subset of said fundamental units to form a set of feature lists, said feature vectors are converted from said set of feature lists and said plurality of text document samples, said extracting said subset of said fundamental units includes filtering said fundamental units to obtain fundamental units that are more discriminatory in describing differences among said different encoding schemes; (b) applying said set of machine learning models against a set of target document feature vectors converted from said target document, said applying including analyzing said set of target document feature vectors using said set of machine learning models to compute similarity indicia between said set of target document feature vectors and said set of machine learning models associated with said different encoding schemes, wherein a first encoding scheme associated with said set of machine learning models is designated as said encoding scheme if characteristics of said first encoding scheme as represented by said set of machine learning models are computed to be most similar, relative to other encoding schemes of said different encoding schemes, to said set of target document feature vectors; and (c) decoding said target document to obtain decoded content of said target document based on at least said first encoding scheme; said applying including converting said target document to said set of target document feature vectors; wherein said converting said target document to said set of target document feature vectors employs a TF-IDF (Term Frequency-Inverse Document Frequency) technique; wherein said TF-IDF technique employs a VSM (Vector Space Model) representation approach. 14. The method of claim 12 wherein said feature vectors are converted using a statistical representation technique.
| 0.5 |
8,341,144 | 1 | 2 |
1. A method comprising: a search engine or proxy receiving one or more distinct taxonomies; storing said one or more distinct taxonomies in association with a plurality of users; wherein each taxonomy of said one or more distinct taxonomies specifies categories and relationships between the categories; receiving a search query that includes one or more keywords to search and information about a user of the plurality of users requesting the search query; wherein the information indicates that the user belongs to a group selected from a set consisting of (a) a resident of a home, (b) an employee of a corporation, and (c) a customer of a business; based at least in part on the information about the user requesting the search query, selecting a taxonomy of the one or more distinct taxonomies stored in association with the plurality of users; wherein selecting the taxonomy comprises selecting a first taxonomy for all first users for whom the information indicates that those first users are resident of a home, selecting a second taxonomy for all second users for whom the information indicates that those second users are employees of a corporation, and selecting a third taxonomy for all third users for whom the information indicates that those third users are customers of a business; wherein each of the first taxonomy, the second taxonomy, and the third taxonomy differ from each other of the first taxonomy, the second taxonomy, and the third taxonomy; and generating a search engine results page, at least in part, by applying to a search engine result item a set of rules that are associated with the selected taxonomy; wherein generating the search engine results page includes: (a) identifying search results independent of the taxonomy; and (b) providing to the user the search engine results based on the taxonomy; and wherein the method is performed by one or more computing devices.
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1. A method comprising: a search engine or proxy receiving one or more distinct taxonomies; storing said one or more distinct taxonomies in association with a plurality of users; wherein each taxonomy of said one or more distinct taxonomies specifies categories and relationships between the categories; receiving a search query that includes one or more keywords to search and information about a user of the plurality of users requesting the search query; wherein the information indicates that the user belongs to a group selected from a set consisting of (a) a resident of a home, (b) an employee of a corporation, and (c) a customer of a business; based at least in part on the information about the user requesting the search query, selecting a taxonomy of the one or more distinct taxonomies stored in association with the plurality of users; wherein selecting the taxonomy comprises selecting a first taxonomy for all first users for whom the information indicates that those first users are resident of a home, selecting a second taxonomy for all second users for whom the information indicates that those second users are employees of a corporation, and selecting a third taxonomy for all third users for whom the information indicates that those third users are customers of a business; wherein each of the first taxonomy, the second taxonomy, and the third taxonomy differ from each other of the first taxonomy, the second taxonomy, and the third taxonomy; and generating a search engine results page, at least in part, by applying to a search engine result item a set of rules that are associated with the selected taxonomy; wherein generating the search engine results page includes: (a) identifying search results independent of the taxonomy; and (b) providing to the user the search engine results based on the taxonomy; and wherein the method is performed by one or more computing devices. 2. The method of claim 1 , wherein applying the set of rules to the search engine result item identifies a set of categories in the selected taxonomy to display on the search engine results page in association with the search engine result item; and wherein generating the search engine results page comprises including a description of at least a portion of the selected taxonomy in the search engine results page.
| 0.5 |
7,975,121 | 1 | 6 |
1. A method, comprising: receiving a low level language instruction that accesses a word in a memory, the word having a first type and the memory including one of a stack or a heap; labeling positions in each of the stack or the heap as one or more specified positions and one or more unspecified positions; assigning a second type to the memory, the second type including the first type, the word being at one of the one or more unspecified positions or the one or more specified positions; and determining whether the low level language instruction is well-typed by applying one or more rules to the instruction and to the second type.
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1. A method, comprising: receiving a low level language instruction that accesses a word in a memory, the word having a first type and the memory including one of a stack or a heap; labeling positions in each of the stack or the heap as one or more specified positions and one or more unspecified positions; assigning a second type to the memory, the second type including the first type, the word being at one of the one or more unspecified positions or the one or more specified positions; and determining whether the low level language instruction is well-typed by applying one or more rules to the instruction and to the second type. 6. The method of claim 1 , wherein the one or more rules include a first stack pointer rule r ≠ sp Γ ′ = Γ [ r ↦ τ ] ⊢ ( Γ , ς ) { r ← τ } ( Γ ′ , ς ) , where Γ represents an original register file, represents the stack, Γ′ represents a transformed Γ, r represents a register, τ represents the type of the word, and sp represents a stack pointer.
| 0.604444 |
9,230,540 | 7 | 8 |
7. The method of claim 1 , wherein: at least one member of the set comprises a null word; and the determining whether the first recognition result includes a member of the set and whether the at least one alternative recognition result includes any of the at least one other member comprises determining whether either of the first recognition result or the at least one alternative recognition result comprises the null word.
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7. The method of claim 1 , wherein: at least one member of the set comprises a null word; and the determining whether the first recognition result includes a member of the set and whether the at least one alternative recognition result includes any of the at least one other member comprises determining whether either of the first recognition result or the at least one alternative recognition result comprises the null word. 8. The method of claim 7 , wherein the determining whether either of the first recognition result or the at least one alternative recognition result comprises the null word comprises determining that the first recognition result or the at least one alternative recognition result includes the null word without evaluating words of the first recognition result or the at least one alternative recognition result.
| 0.5 |
7,689,572 | 12 | 29 |
12. A computer-implemented model repository system for managing data models, comprising: a processor; a computer-readable model repository containing software instructions executable on the processor, wherein the computer-readable model repository stores the data models, and wherein one or more attributes are associated with each data model; a data input module that processes the attribute information associated with each of the data models, wherein the data input module includes a model repository facility that exports the data models to the model repository, and wherein exporting includes creating an export object, initializing the export object, querying a first configuration file in the model repository to determine what data to export from the selected data models to the model repository, querying a second configuration file in the model repository to determine what data is used from the data models in the model repository to build a searchable index in the model repository, configuring the export object based upon the query results, invoking an export method on the export object, and building the searchable index based upon the query results; and at least one searchable index whose data structure contains storage locations for the attribute information and associated data models, and wherein a comparison algorithm is used to search the attributes and select a data model.
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12. A computer-implemented model repository system for managing data models, comprising: a processor; a computer-readable model repository containing software instructions executable on the processor, wherein the computer-readable model repository stores the data models, and wherein one or more attributes are associated with each data model; a data input module that processes the attribute information associated with each of the data models, wherein the data input module includes a model repository facility that exports the data models to the model repository, and wherein exporting includes creating an export object, initializing the export object, querying a first configuration file in the model repository to determine what data to export from the selected data models to the model repository, querying a second configuration file in the model repository to determine what data is used from the data models in the model repository to build a searchable index in the model repository, configuring the export object based upon the query results, invoking an export method on the export object, and building the searchable index based upon the query results; and at least one searchable index whose data structure contains storage locations for the attribute information and associated data models, and wherein a comparison algorithm is used to search the attributes and select a data model. 29. The computer-implemented model repository system of claim 12 , further comprising: a plurality of model repositories for storing the data models, wherein each of the plurality of model repositories includes one or more index structures containing a plurality of attributes that describe the data models stored in the respective model repository.
| 0.524523 |
8,706,644 | 21 | 22 |
21. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed on one or more processors, perform acts comprising: parsing a source to determine phrases comprising two or more contiguous words within the source; storing the determined phrases, the words of the stored phrases being different parts of speech; and associating at least one of the determined phrases comprising two or more contiguous words with a payment instrument, the at least one of the determined phrases, when received before or during a transaction, providing access to the payment instrument for use in a transaction.
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21. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed on one or more processors, perform acts comprising: parsing a source to determine phrases comprising two or more contiguous words within the source; storing the determined phrases, the words of the stored phrases being different parts of speech; and associating at least one of the determined phrases comprising two or more contiguous words with a payment instrument, the at least one of the determined phrases, when received before or during a transaction, providing access to the payment instrument for use in a transaction. 22. One or more non-transitory computer-readable media as recited in claim 21 , wherein the parsing of the source further comprises disregarding at least some phrases in the source that do not comprise two or more contiguous words.
| 0.823124 |
9,858,525 | 9 | 15 |
9. A method, comprising: receiving an input image; generating candidate segment masks based at least in part on the input image; and performing an initial training operation comprising: ranking the candidate segment masks to generate ranked candidate segment masks; selecting a set of the ranked candidate segment masks to generate a set of the ranked candidate segment masks; selecting a mask of the set of the ranked candidate segment masks as a selected mask; and training a neural network by applying the selected-mask of the set of the ranked candidate segment masks to the neural network.
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9. A method, comprising: receiving an input image; generating candidate segment masks based at least in part on the input image; and performing an initial training operation comprising: ranking the candidate segment masks to generate ranked candidate segment masks; selecting a set of the ranked candidate segment masks to generate a set of the ranked candidate segment masks; selecting a mask of the set of the ranked candidate segment masks as a selected mask; and training a neural network by applying the selected-mask of the set of the ranked candidate segment masks to the neural network. 15. A method as claim 9 recites, further comprising performing a subsequent training operation comprising: receiving the selected mask of the set of the ranked candidate segment masks to generate second candidate segment masks; ranking the second candidate segment masks to generate second ranked candidate segment masks; selecting a set of the second ranked candidate segment masks to generate a set of the second ranked candidate segment masks; selecting a second mask of the set of the second ranked candidate segment masks; and training the neural network by applying the second mask of the set of the second ranked candidate segment masks to the neural network.
| 0.5 |
9,886,866 | 15 | 16 |
15. A training program configured to systematically drive neurological changes to overcome social cognitive deficits, the training program including at least one computerized theory of mind game that challenges a game participant to infer the mental states of others, wherein the game is configured to: present a plurality of target and/or distractor stimuli; prompt the game participant to respond to the target and/or distractor stimuli; receive the game participant's input through a game piece; provide an indication to the game participant of whether the game participant's input was accurate or apt; provide a signal indicative of the game participant's performance or game difficulty; and repeat the presenting through providing a signal steps over multiple repetitions while adapting one or more difficulty parameters to target maintenance of a success rate within a predetermined range.
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15. A training program configured to systematically drive neurological changes to overcome social cognitive deficits, the training program including at least one computerized theory of mind game that challenges a game participant to infer the mental states of others, wherein the game is configured to: present a plurality of target and/or distractor stimuli; prompt the game participant to respond to the target and/or distractor stimuli; receive the game participant's input through a game piece; provide an indication to the game participant of whether the game participant's input was accurate or apt; provide a signal indicative of the game participant's performance or game difficulty; and repeat the presenting through providing a signal steps over multiple repetitions while adapting one or more difficulty parameters to target maintenance of a success rate within a predetermined range. 16. The training program of claim 15 , wherein the at least one computerized theory of mind game challenges game participants to apprehend a social situation and the meanings conveyed by voice inflection.
| 0.623616 |
9,304,826 | 15 | 18 |
15. An interactive self-help application system, comprising: an application script for implementing the interactive self-help service; a deployment platform on a network having a plurality of application servers distributed over the network and application resources able to execute the application script; a specification for preferred application resources needed for executing the application script; a centralized list of application resources, said centralized list of resources being checked and updated from a first node on the network to maintain a list of application resources prioritized by performance and including resource preference and availability; a selected application server located at a second node on the network for executing the application script, said selected application server being selected from the plurality of application servers on the network; a local list of application resources maintained at the selected application server, said local list of application resources listing application resources previously servicing the selected application server including quality of service of the application resources from the second node; a central view list of application resources obtained by querying the centralized list by resource type to conform to a predefined specification and prioritized by performance; a local view list of resources generated by querying the local list to select a list of previously contacted resources prioritized by availability and indicating a degree of success of previous browser attempts for each previously contacted resource; and wherein the application script is executed by the selected application server with application resources located dynamically from a final prioritized list created based upon the central view list filtered by using the local view list.
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15. An interactive self-help application system, comprising: an application script for implementing the interactive self-help service; a deployment platform on a network having a plurality of application servers distributed over the network and application resources able to execute the application script; a specification for preferred application resources needed for executing the application script; a centralized list of application resources, said centralized list of resources being checked and updated from a first node on the network to maintain a list of application resources prioritized by performance and including resource preference and availability; a selected application server located at a second node on the network for executing the application script, said selected application server being selected from the plurality of application servers on the network; a local list of application resources maintained at the selected application server, said local list of application resources listing application resources previously servicing the selected application server including quality of service of the application resources from the second node; a central view list of application resources obtained by querying the centralized list by resource type to conform to a predefined specification and prioritized by performance; a local view list of resources generated by querying the local list to select a list of previously contacted resources prioritized by availability and indicating a degree of success of previous browser attempts for each previously contacted resource; and wherein the application script is executed by the selected application server with application resources located dynamically from a final prioritized list created based upon the central view list filtered by using the local view list. 18. The system as in claim 15 , wherein the application resources include media conversion proxy servers.
| 0.803371 |
9,449,282 | 1 | 6 |
1. A method for ranking unlabeled matches, each of the unlabeled matches existing between a first member of a dating website and a corresponding one of a first set of members of the dating website, the method comprising: transmitting, by a first mobile device via a web server, messages and profile views directed toward one of a second set of members of the dating website, the first mobile device being associated with the first member of the dating website; transmitting, by each of a plurality of mobile devices via the web server, messages and profile views directed toward the first member of the dating website, each of the plurality of mobile devices being associated with a corresponding one of the second set of members of the dating website; receiving, by the web server, a plurality of labeled matches, wherein each of the plurality of labeled matches exists between the first member of the dating website and the corresponding one of the second set of members of the dating website, and wherein each of the plurality of labeled matches are labeled based on a dating profile of the first member of the dating website, a dating profile of the corresponding one of the second set of members of the dating website, and behavioral features, the behavioral features comprising: a first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website, the first density of profile views being measured within a time period between a first message and a last message exchanged between the first member of the dating website and the corresponding one of the second set of members of the dating website, a second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website, the second density of profile views being measured within the time period, a message disparity that identifies a difference between a number of messages sent by the first member of the dating website and a number of messages sent by the corresponding one of the second set of members of the dating website, a number of times that the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website, a number of times that the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website, and whether the first member of the dating website and the corresponding one of the second set of members of the dating website exchange phone numbers with one another; determining, by the web server, a probability of relevance of each of the plurality of labeled matches based on the behavioral features, wherein: the probability of relevance increases as a first difference decreases and the probability of relevance decreases as the first difference increases, the first difference being between the first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website within the time period and the second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website within the time period, the probability of relevance increases as the message disparity decreases and the probability of relevance decreases as the message disparity increases, the probability of relevance increases as a second difference decreases and the probability of relevance decreases as the second difference increases, the second difference being between the number of times that the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website and the number of times that the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website, and ones of the plurality of labeled matches where the first member of the dating website and the corresponding one of the second set of members of the dating website exchange phone numbers with one another are assigned a higher probability of relevance than other ones of the plurality of labeled matches where the first member of the dating website and the corresponding one of the second set of members of the dating website do not exchange phone numbers with one another; for each of the plurality of labeled matches: comparing, by a processor in the web server on an attribute-by-attribute basis, first binary data in each attribute of the dating profile of the first member of the dating website to second binary data in a corresponding attribute of the dating profile of the corresponding one of the second set of members of the dating website to produce ranking features of each of the plurality of labeled matches, and correlating, by the processor in the web server, the ranking features of each of the plurality of labeled matches to the probability of relevance of each of the plurality of labeled matches to produce a ranking function; training, by the web server, boosted regression trees based on the probability of relevance of each of the plurality of labeled matches, the behavioral features, the ranking features of each of the plurality of labeled matches, the ranking function, the dating profile of the first member of the dating website, and the dating profile of the corresponding one of the second set of members of the dating website, wherein, upon completion of the training, the boosted regression trees are configured to utilize, as input, ranking features observed from a given unlabeled match and generate, as output, a probability of relevance of the given unlabeled match; determining, by the processor in the web server utilizing the boosted regression trees, a probability of relevance of each of the unlabeled matches by: comparing, on an attribute-by-attribute basis, the first binary data in each attribute of the dating profile of the first member of the dating website to third binary data in a corresponding attribute of a dating profile of the corresponding one of the first set of members of the dating website to produce ranking features of each of the unlabeled matches, wherein each of the unlabeled matches lack the behavioral features, and calculating the probability of relevance each of the unlabeled matches by inputting, into the ranking function, the ranking features of each of the unlabeled matches and retrieving, from the ranking function, the probability of relevance each of the unlabeled matches, wherein the ranking features of each of the unlabeled matches are used as a proxy for the behavioral features; calculating, by the web server, a rank for each of the unlabeled matches based on the probability of relevance of each of the unlabeled matches to generate a set of ranked matches; and transmitting, by the web server over a network interface, at least a portion of the set of ranked matches to the first mobile device associated with the first member of the dating website.
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1. A method for ranking unlabeled matches, each of the unlabeled matches existing between a first member of a dating website and a corresponding one of a first set of members of the dating website, the method comprising: transmitting, by a first mobile device via a web server, messages and profile views directed toward one of a second set of members of the dating website, the first mobile device being associated with the first member of the dating website; transmitting, by each of a plurality of mobile devices via the web server, messages and profile views directed toward the first member of the dating website, each of the plurality of mobile devices being associated with a corresponding one of the second set of members of the dating website; receiving, by the web server, a plurality of labeled matches, wherein each of the plurality of labeled matches exists between the first member of the dating website and the corresponding one of the second set of members of the dating website, and wherein each of the plurality of labeled matches are labeled based on a dating profile of the first member of the dating website, a dating profile of the corresponding one of the second set of members of the dating website, and behavioral features, the behavioral features comprising: a first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website, the first density of profile views being measured within a time period between a first message and a last message exchanged between the first member of the dating website and the corresponding one of the second set of members of the dating website, a second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website, the second density of profile views being measured within the time period, a message disparity that identifies a difference between a number of messages sent by the first member of the dating website and a number of messages sent by the corresponding one of the second set of members of the dating website, a number of times that the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website, a number of times that the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website, and whether the first member of the dating website and the corresponding one of the second set of members of the dating website exchange phone numbers with one another; determining, by the web server, a probability of relevance of each of the plurality of labeled matches based on the behavioral features, wherein: the probability of relevance increases as a first difference decreases and the probability of relevance decreases as the first difference increases, the first difference being between the first density of profile views initiated by the first member of the dating website toward the corresponding one of the second set of members of the dating website within the time period and the second density of profile views initiated by the corresponding one of the second set of members of the dating website toward the first member of the dating website within the time period, the probability of relevance increases as the message disparity decreases and the probability of relevance decreases as the message disparity increases, the probability of relevance increases as a second difference decreases and the probability of relevance decreases as the second difference increases, the second difference being between the number of times that the first member of the dating website viewed the dating profile of the corresponding one of the second set of members of the dating website and the number of times that the corresponding one of the second set of members of the dating website viewed the dating profile of the first member of the dating website, and ones of the plurality of labeled matches where the first member of the dating website and the corresponding one of the second set of members of the dating website exchange phone numbers with one another are assigned a higher probability of relevance than other ones of the plurality of labeled matches where the first member of the dating website and the corresponding one of the second set of members of the dating website do not exchange phone numbers with one another; for each of the plurality of labeled matches: comparing, by a processor in the web server on an attribute-by-attribute basis, first binary data in each attribute of the dating profile of the first member of the dating website to second binary data in a corresponding attribute of the dating profile of the corresponding one of the second set of members of the dating website to produce ranking features of each of the plurality of labeled matches, and correlating, by the processor in the web server, the ranking features of each of the plurality of labeled matches to the probability of relevance of each of the plurality of labeled matches to produce a ranking function; training, by the web server, boosted regression trees based on the probability of relevance of each of the plurality of labeled matches, the behavioral features, the ranking features of each of the plurality of labeled matches, the ranking function, the dating profile of the first member of the dating website, and the dating profile of the corresponding one of the second set of members of the dating website, wherein, upon completion of the training, the boosted regression trees are configured to utilize, as input, ranking features observed from a given unlabeled match and generate, as output, a probability of relevance of the given unlabeled match; determining, by the processor in the web server utilizing the boosted regression trees, a probability of relevance of each of the unlabeled matches by: comparing, on an attribute-by-attribute basis, the first binary data in each attribute of the dating profile of the first member of the dating website to third binary data in a corresponding attribute of a dating profile of the corresponding one of the first set of members of the dating website to produce ranking features of each of the unlabeled matches, wherein each of the unlabeled matches lack the behavioral features, and calculating the probability of relevance each of the unlabeled matches by inputting, into the ranking function, the ranking features of each of the unlabeled matches and retrieving, from the ranking function, the probability of relevance each of the unlabeled matches, wherein the ranking features of each of the unlabeled matches are used as a proxy for the behavioral features; calculating, by the web server, a rank for each of the unlabeled matches based on the probability of relevance of each of the unlabeled matches to generate a set of ranked matches; and transmitting, by the web server over a network interface, at least a portion of the set of ranked matches to the first mobile device associated with the first member of the dating website. 6. The method as recited in claim 1 , wherein the training the boosted regression trees comprises: storing, for each of the boosted regression trees, a reduction in a loss function for each of the ranking features of each of the plurality of labeled matches; and summing the reduction in the loss function for each of the ranking features of each of the plurality of labeled matches across all of the boosted regression trees to produce a feature importance of each of the ranking features of each of the plurality of labeled matches, wherein the ranking function further applies a weigh to the probability of relevance of each of the plurality of labeled matches based upon the feature importance of each of the ranking features of each of the plurality of labeled matches.
| 0.5 |
8,041,715 | 25 | 26 |
25. The system of claim 24 , wherein the clustering score comprises Σ(FunctionScore(f,u)×WO(f)) for each operator (f), wherein WO(f) comprises a scalar that normalizes each operator (f) to each other.
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25. The system of claim 24 , wherein the clustering score comprises Σ(FunctionScore(f,u)×WO(f)) for each operator (f), wherein WO(f) comprises a scalar that normalizes each operator (f) to each other. 26. The system of claim 25 , wherein the re-ranking engine limits the number of sponsored search results used in the calculation of the FunctionScores and the clustering score to a specific number.
| 0.5 |
8,321,442 | 1 | 25 |
1. A computer-implemented method for facilitating searching and matching of data, comprising: receiving an input data string including one or more ideographic elements; converting the input data string to a Latin-based input data string; generating one or more input keys based on the Latin-based input data string, including removing any non-space silent element from the input data string to generate a phonetic key; searching, using the one or more input keys, a reference database stored in a memory device for one or more candidate records, wherein similar records in the database are indexed by a common lookup key; and if the one or more candidate records are found, determining a match score of the one or more candidate records.
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1. A computer-implemented method for facilitating searching and matching of data, comprising: receiving an input data string including one or more ideographic elements; converting the input data string to a Latin-based input data string; generating one or more input keys based on the Latin-based input data string, including removing any non-space silent element from the input data string to generate a phonetic key; searching, using the one or more input keys, a reference database stored in a memory device for one or more candidate records, wherein similar records in the database are indexed by a common lookup key; and if the one or more candidate records are found, determining a match score of the one or more candidate records. 25. The method of claim 1 further comprising generating multiple input keys based on the input data string.
| 0.815517 |
8,108,883 | 1 | 14 |
1. A method of populating a document with digital information content, said method comprising: launching an information management software application on a computer-based platform; opening a data drop window within said information management software application to display a representation of a document layout within said data drop window; selecting a first digital file having a first digital data content using said information management software application; dragging and dropping said selected first digital file into a displayed first data holder location of said displayed document layout representation within said data drop window, using said information management software application, such that said first data holder location of said displayed document layout representation is populated with said first digital data content; launching a third-party document generation application on said computer-based platform; and said information management software application communicating with said third-party document generation application via at least one application program interface (API) on said computer-based platform such that said at least one API directs said third-party document generation application to generate a third-party document based on said populated document layout representation within said data drop window.
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1. A method of populating a document with digital information content, said method comprising: launching an information management software application on a computer-based platform; opening a data drop window within said information management software application to display a representation of a document layout within said data drop window; selecting a first digital file having a first digital data content using said information management software application; dragging and dropping said selected first digital file into a displayed first data holder location of said displayed document layout representation within said data drop window, using said information management software application, such that said first data holder location of said displayed document layout representation is populated with said first digital data content; launching a third-party document generation application on said computer-based platform; and said information management software application communicating with said third-party document generation application via at least one application program interface (API) on said computer-based platform such that said at least one API directs said third-party document generation application to generate a third-party document based on said populated document layout representation within said data drop window. 14. The method of claim 1 wherein said first digital file comprises a graphic file and said first digital data content comprises a graphic.
| 0.918808 |
9,122,680 | 15 | 16 |
15. The computer-implemented information processing of claim 13 , wherein the sentence is a first sentence among a plurality of sentences, and the method further comprises the steps of: extracting the sentences from the document; generating a list of sentence scores for the sentences; identifying high sentence scores as the sentence scores in the list which exceed the threshold; identifying low sentence scores as the sentence scores in the list which lie below the threshold; extracting first sentences corresponding to the high sentence scores; extracting second sentences corresponding to the low scores; and displaying at least one of the first sentences or the second sentences on a display.
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15. The computer-implemented information processing of claim 13 , wherein the sentence is a first sentence among a plurality of sentences, and the method further comprises the steps of: extracting the sentences from the document; generating a list of sentence scores for the sentences; identifying high sentence scores as the sentence scores in the list which exceed the threshold; identifying low sentence scores as the sentence scores in the list which lie below the threshold; extracting first sentences corresponding to the high sentence scores; extracting second sentences corresponding to the low scores; and displaying at least one of the first sentences or the second sentences on a display. 16. The computer-implemented information processing of claim 15 , wherein the displaying step includes: determining a degree of commonness of the document; displaying first sentences on a display when the degree of commonness lies below a second threshold; and displaying second sentences on the display when the degree of commonness exceeds the second threshold.
| 0.5 |
8,095,912 | 13 | 17 |
13. The computer-readable device of claim 11 , further comprising instructions for performing an act of reading generation options.
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13. The computer-readable device of claim 11 , further comprising instructions for performing an act of reading generation options. 17. The computer-readable device of claim 13 , wherein the grammar description further comprises: a set of non-terminal symbols, wherein one symbol from the set of non-terminal symbols is a start symbol; a set of terminal symbols; and wherein act (a) comprises acts of: (g) generating a working string, the working string comprising the contents of the replacement string for the start symbol; (h) when the working string comprises non-terminal symbols, generating a new working string by replacing one or more instances of a non-terminal symbol in the working string with a replacement string for the non-terminal symbol; and (i) repeating act (h) using the new working string when the new working string comprises non-terminal symbols.
| 0.5 |
8,997,041 | 12 | 19 |
12. A script management server being connected to a user terminal and managing script codes, the script management server including a plurality of computing devices capable of distributed processing, the plurality of devices comprising: an active script database designating unit designating an active script database by providing the user terminal with a plurality of original script lists; a plurality of script databases being classified according to script version, each of the plurality of script databases storing original script codes and user-specific script codes; a meta database storing the plurality of original script lists associated with the plurality of script databases and user-specific script list; and a database updating unit storing identifiers of the user terminal and the designated active script database in the meta database and, if a script code in the active script database is updated through the user terminal, updating the script list and script codes of the corresponding user stored respectively in the meta database and the designated active script database, wherein the database updating unit, if the script code is updated through the user terminal, updates a new version of script for the corresponding script in the plurality of script databases.
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12. A script management server being connected to a user terminal and managing script codes, the script management server including a plurality of computing devices capable of distributed processing, the plurality of devices comprising: an active script database designating unit designating an active script database by providing the user terminal with a plurality of original script lists; a plurality of script databases being classified according to script version, each of the plurality of script databases storing original script codes and user-specific script codes; a meta database storing the plurality of original script lists associated with the plurality of script databases and user-specific script list; and a database updating unit storing identifiers of the user terminal and the designated active script database in the meta database and, if a script code in the active script database is updated through the user terminal, updating the script list and script codes of the corresponding user stored respectively in the meta database and the designated active script database, wherein the database updating unit, if the script code is updated through the user terminal, updates a new version of script for the corresponding script in the plurality of script databases. 19. The script management server of claim 12 , further comprising a bookmark managing unit with which the user terminal stores shortcuts to particular script codes in a script list of the corresponding user as a favorites list.
| 0.571698 |
4,742,481 | 4 | 5 |
4. An electronic dictionary as set forth in claim 3, wherein: the vocabulary entries of each of said groups are arranged so as to be successively retrieved in a predetermined order, and each of said groups contains a single linking vocabulary entry which is last in order of retrieval; said linking data for each of said groups comprises start-address data and end data, said start-address data signifying the address of the vocabulary entry of the group which is first in order of retrieval, and said end data signifying that the corresponding linking vocabulary entry of the group is the vocabulary entry of the group which is last in order of retrieval; said dictionary memory comprises one expansion-dictionary memory which is optionally installed in the electronic dictionary; and said group-linking means comprises: start-address memory means for storing a first set of start-address data for each of said groups of vocabulary entries in said basic-dictionary memory and a second set of start-address data for each group stored in said one expansion-dictionary memory, said first and second sets of start-address data corresponding to each other; and checking and processing means for checking whether said one expansion-dictionary memory is installed; and (i) if said one expansion-dictionary is not installed, causing, when the retrieved data is linking data, said retrieval means to retrieve the vocabulary entry stored in said basic vocabulary memory which is designated by the start-address data from said first set which comprises the retrieved linking data; and (ii) if said one expansion-dictionary memory is installed in the dictionary memory, causing, when the retrieved data is linking data, said retrieval means to retrieve the vocabulary entry stored in said one expansion-dictionary memory which is designated by the start-address data from said second set which corresponds to the start-address data comprising the retrieved linking data.
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4. An electronic dictionary as set forth in claim 3, wherein: the vocabulary entries of each of said groups are arranged so as to be successively retrieved in a predetermined order, and each of said groups contains a single linking vocabulary entry which is last in order of retrieval; said linking data for each of said groups comprises start-address data and end data, said start-address data signifying the address of the vocabulary entry of the group which is first in order of retrieval, and said end data signifying that the corresponding linking vocabulary entry of the group is the vocabulary entry of the group which is last in order of retrieval; said dictionary memory comprises one expansion-dictionary memory which is optionally installed in the electronic dictionary; and said group-linking means comprises: start-address memory means for storing a first set of start-address data for each of said groups of vocabulary entries in said basic-dictionary memory and a second set of start-address data for each group stored in said one expansion-dictionary memory, said first and second sets of start-address data corresponding to each other; and checking and processing means for checking whether said one expansion-dictionary memory is installed; and (i) if said one expansion-dictionary is not installed, causing, when the retrieved data is linking data, said retrieval means to retrieve the vocabulary entry stored in said basic vocabulary memory which is designated by the start-address data from said first set which comprises the retrieved linking data; and (ii) if said one expansion-dictionary memory is installed in the dictionary memory, causing, when the retrieved data is linking data, said retrieval means to retrieve the vocabulary entry stored in said one expansion-dictionary memory which is designated by the start-address data from said second set which corresponds to the start-address data comprising the retrieved linking data. 5. An electronic dictionary as set forth in claim 4, wherein said expansion-dictionary memory includes memory locations which constitute said start-address memory.
| 0.5 |
7,522,046 | 1 | 21 |
1. A document management system, the system comprising: a physical-document monitoring device comprising: a document coupling device, a sensor coupled to the document coupling device, the sensor operable to sense a state of a document and to generate a signal representative thereof, and a computer coupled to the sensor and the document coupling device, the computer operable to determine a document state based on the signal.
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1. A document management system, the system comprising: a physical-document monitoring device comprising: a document coupling device, a sensor coupled to the document coupling device, the sensor operable to sense a state of a document and to generate a signal representative thereof, and a computer coupled to the sensor and the document coupling device, the computer operable to determine a document state based on the signal. 21. The system of claim 1 , wherein the document coupling device is adapted to couple the monitoring device to a physical document.
| 0.669192 |
7,855,812 | 14 | 15 |
14. The cellular phone of claim 1 , wherein the array of photosensing elements comprises a linear array of photosensing elements; and the scanner data signal processing circuitry is configured with a capability to provide composite image data signals representative of a composite bit-mapped image of the scanned media from successive image data signals representative of successive strip field of views of the scanned media seen by the linear array.
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14. The cellular phone of claim 1 , wherein the array of photosensing elements comprises a linear array of photosensing elements; and the scanner data signal processing circuitry is configured with a capability to provide composite image data signals representative of a composite bit-mapped image of the scanned media from successive image data signals representative of successive strip field of views of the scanned media seen by the linear array. 15. The cellular phone of claim 14 , wherein the motion sensor includes a proximity sensor for mechanically or optically indicating the contact proximity of the array of photosensing elements to the scanned media.
| 0.5 |
9,582,610 | 1 | 9 |
1. A computer-implemented method, comprising: receiving a user search topic; concurrently presenting, on a display of a computing device, a collage template and search results for the user search topic, wherein the collage template comprises a grid of rectangles comprising a collage; automatically populating a first user-selected search result in a portion of the rectangles of the grid, a number and a relative positioning of the rectangles of the portion being based at least in part on a first aspect ratio of the first user-selected search result; subsequent to the automatically populating the first user-selected search result, automatically populating a second user-selected search result in the grid by repositioning the first user-selected search result based at least on a second aspect ratio of the second user-selected search result; and, automatically populating additional user-selected search results into unpopulated rectangles of the grid.
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1. A computer-implemented method, comprising: receiving a user search topic; concurrently presenting, on a display of a computing device, a collage template and search results for the user search topic, wherein the collage template comprises a grid of rectangles comprising a collage; automatically populating a first user-selected search result in a portion of the rectangles of the grid, a number and a relative positioning of the rectangles of the portion being based at least in part on a first aspect ratio of the first user-selected search result; subsequent to the automatically populating the first user-selected search result, automatically populating a second user-selected search result in the grid by repositioning the first user-selected search result based at least on a second aspect ratio of the second user-selected search result; and, automatically populating additional user-selected search results into unpopulated rectangles of the grid. 9. The computer-implemented method of claim 1 , further comprising offering a user a shuffle option and, responsive to a user selection of the shuffle option, changing positions of the first user-selected search result, the second user-selected search result, and the additional user-selected search results in the collage template.
| 0.5 |
9,280,597 | 1 | 4 |
1. A system, comprising: a citation search engine that includes a processor, which in operation, retrieves a plurality of citations composed by a plurality of subjects citing a plurality of objects that fit a searching query specified by a user; an influence evaluation engine that includes a processor, which in operation, creates a list of the plurality of subjects from the user's perspective and calculates the influence scores of the plurality of subjects on the user by flattening a user's influence network of subjects up to a given number of degrees, levels, or depth, wherein the list of plurality of subjects from the user's perspective are subjects that are in the user's influence network of subjects and the user's influence network includes subjects that are connected to the user either directly or indirectly through other sources; and an object/subject selection engine that includes a processor, which in operation, ranks the plurality of objects of the plurality of citations from the user's perspective using a bias filter, wherein the bias filter includes a ranking function based on the influence scores of the list of plurality of subjects and an inverse number of links of those plurality of subjects from the user's perspective, and selects objects from the plurality of objects as the search result for the user based on matching of the objects with the searching query as well as the influence scores of the plurality of subjects on the user.
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1. A system, comprising: a citation search engine that includes a processor, which in operation, retrieves a plurality of citations composed by a plurality of subjects citing a plurality of objects that fit a searching query specified by a user; an influence evaluation engine that includes a processor, which in operation, creates a list of the plurality of subjects from the user's perspective and calculates the influence scores of the plurality of subjects on the user by flattening a user's influence network of subjects up to a given number of degrees, levels, or depth, wherein the list of plurality of subjects from the user's perspective are subjects that are in the user's influence network of subjects and the user's influence network includes subjects that are connected to the user either directly or indirectly through other sources; and an object/subject selection engine that includes a processor, which in operation, ranks the plurality of objects of the plurality of citations from the user's perspective using a bias filter, wherein the bias filter includes a ranking function based on the influence scores of the list of plurality of subjects and an inverse number of links of those plurality of subjects from the user's perspective, and selects objects from the plurality of objects as the search result for the user based on matching of the objects with the searching query as well as the influence scores of the plurality of subjects on the user. 4. The system of claim 1 , wherein: each of the plurality of objects is one of: Internet web sites, blogs, videos, books, films, music, image, video, documents, data files, objects for sale, objects that are reviewed or recommended or cited, subjects/authors, natural or legal persons, citations, or any entities that are associated with a Uniform Resource Identifier (URI).
| 0.624498 |
8,650,561 | 1 | 6 |
1. A method for localizing display of applications for download, the method comprising: receiving, from a user device, a request to download a desired application, the desired application being localized in different languages; determining, by an online store, a local language associated with the user device, wherein determining the local language comprises: determining a plurality of factors associated with the user device, wherein each factor is associated with a secondary language and a weight; grouping the plurality of factors based on the secondary language to form a list of secondary languages; ranking the languages in the list of secondary languages according to the weight of each factor, and assigning a secondary language from the list of secondary languages as the local language according to the ranking; and presenting, by the online store, an interface to download a version of the desired application in the local language when the local language is one of the different languages.
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1. A method for localizing display of applications for download, the method comprising: receiving, from a user device, a request to download a desired application, the desired application being localized in different languages; determining, by an online store, a local language associated with the user device, wherein determining the local language comprises: determining a plurality of factors associated with the user device, wherein each factor is associated with a secondary language and a weight; grouping the plurality of factors based on the secondary language to form a list of secondary languages; ranking the languages in the list of secondary languages according to the weight of each factor, and assigning a secondary language from the list of secondary languages as the local language according to the ranking; and presenting, by the online store, an interface to download a version of the desired application in the local language when the local language is one of the different languages. 6. The method of claim 1 , wherein the factors include a user profile associated with the user device.
| 0.888158 |
7,835,504 | 4 | 5 |
4. The method of claim 1 , wherein the identifying the telephone number comprises: identifying the telephone number in a high tolerance mode.
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4. The method of claim 1 , wherein the identifying the telephone number comprises: identifying the telephone number in a high tolerance mode. 5. The method of claim 4 , wherein identifying the telephone number in a high tolerance mode comprises: identifying the telephone number with high tolerance telephone number rules, wherein the high tolerance telephone number rules allow more format character variations then the telephone number rules.
| 0.5 |
9,575,947 | 1 | 7 |
1. A computer implemented method of automatically mapping a given annotator to an aggregate of given annotators, comprising: providing, with one or more processors, a plurality of annotators stored on a machine-readable storage medium; providing, with the one or more processors, aggregates of the plurality of annotators; providing, with the one or more processors, an original document corpus stored on a machine-readable storage medium; applying, with the one or more processors, a subject annotator to the original document corpus to generate a corpus of subject annotated documents stored on a machine-readable storage medium; generating, with the one or more processors, a corpus of pre-annotated documents that contain annotations, the corpus of pre-annotated documents that contain annotations being stored on a machine-readable readable storage medium; sorting, with the one or more processors, the annotations contained in the corpus of pre-annotated documents, to generate good candidate annotations, the step of sorting comprising i) for each document in the corpus of pre-annotated documents, constructing within a graphical user interface a graph representing frequencies of instances of candidate annotations in each sentence, ii) for each document in the corpus of subject annotated documents, constructing within the graphical user interface an analogous graph superimposed on the graph from (i), wherein the analogous graph represents frequencies of instances of subject annotations, and iii) providing a visual comparison within the graphical user interface, on a per document basis, to the graph from (i) and the analogous graph from (ii) to identify the instances of the candidate annotations with correlation to the instances of the subject annotations; the method further comprises a decomposing step that comprises: modeling annotation expressions as a mapping from a corpus, document, sentence space to a span, tag space, wherein the modeling identifies, as equitagged spans, spans with a same value of a tag, a measure on the spans being represented as follows: Span S is represented as a union of non-intersecting intervals I k from a sentence associated with an annotation, and for each interval I k =[a k , a (k+1) ], where a i are coordinates (positions) within the document, the measure is defined as measure m(I k ) being |a (k+1) −a k |, and for the span the measure is defined as m(S)=·Σm(I k ); embedding the spans into a vector space of dimension greater or equal to a number of all possible spans for a sentence; mapping an annotation expression into a lattice of the possible expressions; extracting a basis on a set of all possible expressions (APE) by choosing as the basis on the set of all possible expressions (APE) a minimal set of elements from inclusion APE (IAPE), so that each expression E from APE may be represented as a union of elements from IAPE; and finding an expression of a subject equitagged Annotation in terms of the annotations in the corpus of pre-annotated documents that were inserted by said plurality of annotators and said aggregates of said plurality of annotators; and mapping, with the one or more processors, the subject annotator, the subject annotator being stored on a machine-readable storage medium.
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1. A computer implemented method of automatically mapping a given annotator to an aggregate of given annotators, comprising: providing, with one or more processors, a plurality of annotators stored on a machine-readable storage medium; providing, with the one or more processors, aggregates of the plurality of annotators; providing, with the one or more processors, an original document corpus stored on a machine-readable storage medium; applying, with the one or more processors, a subject annotator to the original document corpus to generate a corpus of subject annotated documents stored on a machine-readable storage medium; generating, with the one or more processors, a corpus of pre-annotated documents that contain annotations, the corpus of pre-annotated documents that contain annotations being stored on a machine-readable readable storage medium; sorting, with the one or more processors, the annotations contained in the corpus of pre-annotated documents, to generate good candidate annotations, the step of sorting comprising i) for each document in the corpus of pre-annotated documents, constructing within a graphical user interface a graph representing frequencies of instances of candidate annotations in each sentence, ii) for each document in the corpus of subject annotated documents, constructing within the graphical user interface an analogous graph superimposed on the graph from (i), wherein the analogous graph represents frequencies of instances of subject annotations, and iii) providing a visual comparison within the graphical user interface, on a per document basis, to the graph from (i) and the analogous graph from (ii) to identify the instances of the candidate annotations with correlation to the instances of the subject annotations; the method further comprises a decomposing step that comprises: modeling annotation expressions as a mapping from a corpus, document, sentence space to a span, tag space, wherein the modeling identifies, as equitagged spans, spans with a same value of a tag, a measure on the spans being represented as follows: Span S is represented as a union of non-intersecting intervals I k from a sentence associated with an annotation, and for each interval I k =[a k , a (k+1) ], where a i are coordinates (positions) within the document, the measure is defined as measure m(I k ) being |a (k+1) −a k |, and for the span the measure is defined as m(S)=·Σm(I k ); embedding the spans into a vector space of dimension greater or equal to a number of all possible spans for a sentence; mapping an annotation expression into a lattice of the possible expressions; extracting a basis on a set of all possible expressions (APE) by choosing as the basis on the set of all possible expressions (APE) a minimal set of elements from inclusion APE (IAPE), so that each expression E from APE may be represented as a union of elements from IAPE; and finding an expression of a subject equitagged Annotation in terms of the annotations in the corpus of pre-annotated documents that were inserted by said plurality of annotators and said aggregates of said plurality of annotators; and mapping, with the one or more processors, the subject annotator, the subject annotator being stored on a machine-readable storage medium. 7. The computer implemented method of claim 1 , wherein the subject annotator is one of the plurality of annotators.
| 0.865741 |
8,150,859 | 5 | 11 |
5. One or more computer-readable media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method comprising: receiving a search query; determining an ontology mapping exists for the search query; if an ontology mapping exists for the search query, retrieving ( 308 ) a first set of topics based on the ontology mapping and adding the first set of topics to a list of topics; performing a search using the search query to obtain a plurality of search results, each search result corresponding with a document snippet; receiving at least a portion of the document snippets as a document set for further analysis; comparing each document snippet in the document set to an ontology of topics; for each document snippet in which positive topic identification is determined, assigning the document snippet to a corresponding topic and removing the document snippet from the document set; adding at least one topic identified from the ontology of topics to the list of topics; comparing each document snippet remaining in the document set to an ontology of partial topics; for each document snippet in which positive partial topic identification is determined, assigning the document snippet to a corresponding partial topic and removing the document snippet from the document set; naming at least one partial topic having one or more assigned document snippets; adding at least one named partial topic to the list of topics; computing independent key-phrases from document snippets remaining in the document set, wherein computing the independent key-phrases from document snippets remaining in the document set comprises: generating candidate key-phrases from the document snippets remaining the document set, evaluating candidate key-phrases for independence, merging mutually dependent candidate key-phrases, and identifying a most frequent candidate key-phrase for each group of merged mutually dependent key-phrases; assigning documents to independent key-phrases; identifying at least one key-phrase topic; adding the at least one key-phrase topic to the list of topics; ranking topics within the list of topics; selecting topics based on ranking; generating a table of contents using the selected topics; and providing a search results page in response to the search query, the search results page including the table of contents and a search results area for presenting search results.
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5. One or more computer-readable media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method comprising: receiving a search query; determining an ontology mapping exists for the search query; if an ontology mapping exists for the search query, retrieving ( 308 ) a first set of topics based on the ontology mapping and adding the first set of topics to a list of topics; performing a search using the search query to obtain a plurality of search results, each search result corresponding with a document snippet; receiving at least a portion of the document snippets as a document set for further analysis; comparing each document snippet in the document set to an ontology of topics; for each document snippet in which positive topic identification is determined, assigning the document snippet to a corresponding topic and removing the document snippet from the document set; adding at least one topic identified from the ontology of topics to the list of topics; comparing each document snippet remaining in the document set to an ontology of partial topics; for each document snippet in which positive partial topic identification is determined, assigning the document snippet to a corresponding partial topic and removing the document snippet from the document set; naming at least one partial topic having one or more assigned document snippets; adding at least one named partial topic to the list of topics; computing independent key-phrases from document snippets remaining in the document set, wherein computing the independent key-phrases from document snippets remaining in the document set comprises: generating candidate key-phrases from the document snippets remaining the document set, evaluating candidate key-phrases for independence, merging mutually dependent candidate key-phrases, and identifying a most frequent candidate key-phrase for each group of merged mutually dependent key-phrases; assigning documents to independent key-phrases; identifying at least one key-phrase topic; adding the at least one key-phrase topic to the list of topics; ranking topics within the list of topics; selecting topics based on ranking; generating a table of contents using the selected topics; and providing a search results page in response to the search query, the search results page including the table of contents and a search results area for presenting search results. 11. The one or more computer-readable media of claim 5 , wherein naming a partial topic comprises: identifying occurrences of a partial topic identifier word for the partial topic within one or more document snippets assigned to the partial topic; extracting words and/or phrases occurring around identified occurrences of the partial topic identifier word within the one or more document snippets; counting frequency of each extracted word and/or phrase; selecting a most frequently used word or phrase; and naming the partial topic using the partial topic identifier and the most frequently used word or phrase.
| 0.5 |
5,493,507 | 13 | 14 |
13. A digital circuit design assist method using a plurality of digital circuit design assist systems for designing a desired digital circuit consisting of a plurality of hardware units and at least one firmware unit for controlling said digital circuit as a unit, said method comprising: a first step of creating a functional model functionally expressing a plurality of said divided digital circuits by a hardware description language; a second step of verifying the logic of said functional models by hardware description language simulation means and simultaneously executing substitution of said functional models to corresponding structural models without waiting for the verification of said logic, until creation of said functional models are completed; and a third step of independently executing automatic generation of layout for arranging said structural models in a physical space in said digital circuit, for example, on a printed board or on an integrated circuit by an automatic placing and routing tool on the basis of the dimensions of components expressed by said structural model and the amount of wiring, and verification of the logic of said hardware units.
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13. A digital circuit design assist method using a plurality of digital circuit design assist systems for designing a desired digital circuit consisting of a plurality of hardware units and at least one firmware unit for controlling said digital circuit as a unit, said method comprising: a first step of creating a functional model functionally expressing a plurality of said divided digital circuits by a hardware description language; a second step of verifying the logic of said functional models by hardware description language simulation means and simultaneously executing substitution of said functional models to corresponding structural models without waiting for the verification of said logic, until creation of said functional models are completed; and a third step of independently executing automatic generation of layout for arranging said structural models in a physical space in said digital circuit, for example, on a printed board or on an integrated circuit by an automatic placing and routing tool on the basis of the dimensions of components expressed by said structural model and the amount of wiring, and verification of the logic of said hardware units. 14. A digital circuit design assist method according to claim 13, wherein said third step independently executes automatic generation of a test pattern for fault analysis of said structural models, analysis of any fault of said structural models and verification of the logic of said hardware units by an automatic test pattern tool and a fault analysis tool; on the basis of said digital circuit for which verification of said logic is under way or is completed, and wherein all of said functional models have already been converted to said structural models.
| 0.572519 |
7,627,588 | 7 | 8 |
7. The computer-readable storage medium of claim 1 , wherein the first subset of objects comprises an electronic file.
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7. The computer-readable storage medium of claim 1 , wherein the first subset of objects comprises an electronic file. 8. The computer-readable storage medium of claim 7 , wherein the electronic file is selected from the group consisting of a Program File (*.exe), Text File (*.txt, *.prn, *.csv), Word Document (*.doc), Rich Text Format (*.rtf), Windows Write (*.wri), Word for Macintosh (*.mcw), MS-DOS Text with Layout (*.asc), Text with Layout (*.ans), E-mails (*.eml), Outlook Address Book (*.olk), Personal Address Book (*.pab), WordPerfect file (*.wpd), Schedule+Contact (*.scd), Powerpoint (*.ppt), Harvard Graphics Show (*.sh3), Harvard Graphics Chart (*.ch3), Freelance Windows file (*.pre), Excel File (*.xl*), Adobe Acrobat File (*.pdf), Web Page (*.htm*, *.asp, *.jsp), Query File (*.*qy), Lotus 1-2-3 File (*.wk*), Quattro Pro/Dos File (*.wq1), Microsoft Works File (*.wks), Works for Window (*.wps), Microsoft Access Files (*.mdb), Dbase Files (*.dbf), SYLK Files (*.slk), Data Interchange Format File (*.dif), Backup File (*.bak), Quattro Pro 1.0/5.0 (win) (*.wbl), Text Recovered from any File (*.*), Graphic Interchange Format (*.gif), Windows Bitmap (*.bmp), JPEG file interchange format (*.jpg), Tag image file format (*.tif), portable network graphics (*.png), Kodac Photo CD (*.pcd), PC Paintbrush (*.pcx), Raster file (*.ras), Audio File (*.wav, *.snd, *.aif, *.aifc, *.aiff, *.wma, *.mp3), CD Audio Track (*.cda), Media Playlist (*.asx, *.wax, *.m3u, *.wvx), MIDI File (*.mid, *.rmi, *.midi), Movie File (*.mpeg, *.mpg, *.m1v, *.mp2, *.mpa, *.mpe), Video File (*.avi, *.wmv), Windows Media File (*.asf, *.wm, *.wma, *.wmv), and Tactile Sensing File in ASCII, LabView, or MATLAB formats.
| 0.5 |
8,019,710 | 1 | 14 |
1. A system for guiding the progressive development and documentation of user thinking and knowledge about an inquiry based project according to exemplary approaches used by experts, comprising: An interface; A process manager system executable on at least one processor and operable to execute activities comprising: receiving user specification of an initial stage of understanding regarding an arbitrary problem or inquiry based project according to at least one of a plurality of entry or starting points; Providing at least one of an interactive workspace or a suggestion to a user, or both, to facilitate the further development of the understanding regarding an arbitrary problem or inquiry project towards a completion stage; Providing an integrated archetype based model of user understanding regarding the arbitrary problem or inquiry based project, in display or output or both.
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1. A system for guiding the progressive development and documentation of user thinking and knowledge about an inquiry based project according to exemplary approaches used by experts, comprising: An interface; A process manager system executable on at least one processor and operable to execute activities comprising: receiving user specification of an initial stage of understanding regarding an arbitrary problem or inquiry based project according to at least one of a plurality of entry or starting points; Providing at least one of an interactive workspace or a suggestion to a user, or both, to facilitate the further development of the understanding regarding an arbitrary problem or inquiry project towards a completion stage; Providing an integrated archetype based model of user understanding regarding the arbitrary problem or inquiry based project, in display or output or both. 14. The system of claim 1 further comprising at least one of ranking, rating, criteria setting, criteria use, commenting, annotating, notes addition, or a combination thereof, relative to at least one source, specification or model component, conclusion, answer, summary, or related content, or a combination thereof.
| 0.82753 |
9,135,653 | 185 | 186 |
185. The method of claim 176 wherein the receiving first activity information for a sender of a first link to at least one recipient comprises: sending of an e-mail including the first link by the sender via a mobile device.
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185. The method of claim 176 wherein the receiving first activity information for a sender of a first link to at least one recipient comprises: sending of an e-mail including the first link by the sender via a mobile device. 186. The method of claim 185 wherein the receiving second activity information when a recipient accesses the first link sent by the sender comprises: receiving of the e-mail including the first link by the recipient via a mobile device.
| 0.506276 |
7,861,154 | 8 | 9 |
8. The system of claim 1 , wherein an artificial intelligence component predicts a user intention for an annotation as a function of historical user criteria.
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8. The system of claim 1 , wherein an artificial intelligence component predicts a user intention for an annotation as a function of historical user criteria. 9. The system of claim 8 , wherein the artificial intelligence component includes an inference component that facilitates automatic association of at least one annotation to at least one file as a function of the predicted user intention.
| 0.5 |
6,038,274 | 6 | 7 |
6. The method of claim 5 further comprising the step of concealing errors in the information signal.
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6. The method of claim 5 further comprising the step of concealing errors in the information signal. 7. The method of claim 6 in which the step of concealing errors includes detecting and correcting the errors in the information words.
| 0.5 |
7,518,052 | 1 | 2 |
1. A computer-readable medium having computer-executable instructions for retrieving musical information from a database of musical themes, said computer-executable instructions comprising: inputting a musical query from a user, said query comprising a sequence of musical notes; characterizing the melody of the musical query based on a digital representation of the pitch of each note, in such a way that identical pitches within the sequence have the same digital representations, different pitches have different digital representations, and the pitch of the very first note in sequence is represented as a zero; characterizing the rhythm of the musical query based on a digital representation of the duration of each note; designating the characterized melody and rhythm of the musical query as a normalized representation of the musical query; comparing the normalized representation of the musical query to musical themes resident in the database of musical themes to identify one or more themes in the database that match the musical query, wherein each of the musical themes resident in the database comprise a sequence of musical notes characterized as a normalized representation in the same manner as the musical query; and reporting the matching musical themes found in the database to the user.
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1. A computer-readable medium having computer-executable instructions for retrieving musical information from a database of musical themes, said computer-executable instructions comprising: inputting a musical query from a user, said query comprising a sequence of musical notes; characterizing the melody of the musical query based on a digital representation of the pitch of each note, in such a way that identical pitches within the sequence have the same digital representations, different pitches have different digital representations, and the pitch of the very first note in sequence is represented as a zero; characterizing the rhythm of the musical query based on a digital representation of the duration of each note; designating the characterized melody and rhythm of the musical query as a normalized representation of the musical query; comparing the normalized representation of the musical query to musical themes resident in the database of musical themes to identify one or more themes in the database that match the musical query, wherein each of the musical themes resident in the database comprise a sequence of musical notes characterized as a normalized representation in the same manner as the musical query; and reporting the matching musical themes found in the database to the user. 2. The computer-readable medium of claim 1 , wherein the instruction for comparing the normalized representation of the musical query to musical themes resident in the database of musical themes to identify one or more themes in the database that match the musical query comprises instructions for: determining if an exact byte-by-byte match exists between the digital representation of the pitch of each note of the musical query and at least a portion of the digital representation of the pitch of each note of a musical theme resident in the database, for each musical theme resident in the database; designating each musical theme resident In the database having said exact byte-by-byte match to the musical query as a matching musical theme.
| 0.619776 |
8,635,069 | 6 | 7 |
6. The method of claim 5 , wherein the generated speech further comprises at least one of: (1) a pre-defined string of text converted into speech, and (2) speech converted from a text based on prior user input.
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6. The method of claim 5 , wherein the generated speech further comprises at least one of: (1) a pre-defined string of text converted into speech, and (2) speech converted from a text based on prior user input. 7. The method of claim 6 , wherein the content of the generated speech comprises at least one of: (1) a prompt, (2) help information, and (3) prior user input.
| 0.768895 |
8,065,360 | 29 | 30 |
29. The computationally-implemented system of claim 25 , wherein said means for including into the electronic message the source identity data providing one or more identities of the one or more sources comprises: means for including into the electronic message source identity data providing one or more identities of one or more sensors used to sense one or more physical characteristics of the authoring user, the inference data indicative of the inferred mental state of the authoring user being based, at least in part, on the one or more physical characteristics of the authoring user sensed by the one or more sensors.
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29. The computationally-implemented system of claim 25 , wherein said means for including into the electronic message the source identity data providing one or more identities of the one or more sources comprises: means for including into the electronic message source identity data providing one or more identities of one or more sensors used to sense one or more physical characteristics of the authoring user, the inference data indicative of the inferred mental state of the authoring user being based, at least in part, on the one or more physical characteristics of the authoring user sensed by the one or more sensors. 30. The computationally-implemented system of claim 29 , wherein said means for including into the electronic message source identity data providing one or more identities of one or more sensors used to sense one or more physical characteristics of the authoring user, the inference data indicative of the inferred mental state of the authoring user being based, at least in part, on the one or more physical characteristics of the authoring user sensed by the one or more sensors comprises: means for including into the electronic message an identity for at least a functional magnetic resonance image (fMRI) device that was used to sense the one or more physical characteristics of the authoring user.
| 0.558971 |
9,152,625 | 8 | 18 |
8. A device comprising: one or more processors; one or more computer-readable storage media comprising computer readable instructions which, when executed by the one or more processors, perform operations comprising: ranking individual clusters using an entropy measure on mapping words associated with the cluster, the entropy measure incorporating sentiment values associated with the mapping words, the clusters being associated with microblogs that are to be summarized, the mapping words being mapped from words appearing in a word dictionary, the word dictionary being comprised of individual words processed from multiple resources; summarizing each cluster's content; and displaying the summary on a display of the device.
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8. A device comprising: one or more processors; one or more computer-readable storage media comprising computer readable instructions which, when executed by the one or more processors, perform operations comprising: ranking individual clusters using an entropy measure on mapping words associated with the cluster, the entropy measure incorporating sentiment values associated with the mapping words, the clusters being associated with microblogs that are to be summarized, the mapping words being mapped from words appearing in a word dictionary, the word dictionary being comprised of individual words processed from multiple resources; summarizing each cluster's content; and displaying the summary on a display of the device. 18. The device of claim 8 , wherein the individual microblogs have 140 characters or less.
| 0.88806 |
7,587,417 | 1 | 7 |
1. A system for dynamically generating a query to be executed on a database, comprising: a properties object that contains settings for a query as specified by a user, wherein the properties object is generated at runtime and receives the settings from dynamic user input received at runtime; a finder method for initiating the query; a descriptor that contains an enable dynamic queries element, wherein the enable dynamic queries element has a value of either true or false for enabling the query, such that invoking queries when the enable dynamic queries element has a value of false results in a remote or local exception being thrown depending on whether the queries submitted after setting the enable dynamic queries element were invoked from a local interface or a remote interface and wherein the dynamic queries element is specifiable by an enable-dynamic-queries parsed character data (PCDATA) tag, wherein said tag signifies that the dynamic queries element contains character data parsed by an extensible markup language (XML) parser; a server computer that queries the database, the server computer receiving a call from the finder method and reading the settings from the properties object in order to generate the appropriate SQL query statements to be sent to the database, wherein the SQL query statements are generated by parsing the finder method and wherein options that are set for the SQL query statements are specified in the settings contained in the properties object; a database connected to the server computer for executing the dynamically generated SQL query statements; and a collection of results that is returned from the database in response to the finder method, said finder method being invoked on a query home interface used to execute dynamic queries, wherein the collection of results is stored on the server computer.
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1. A system for dynamically generating a query to be executed on a database, comprising: a properties object that contains settings for a query as specified by a user, wherein the properties object is generated at runtime and receives the settings from dynamic user input received at runtime; a finder method for initiating the query; a descriptor that contains an enable dynamic queries element, wherein the enable dynamic queries element has a value of either true or false for enabling the query, such that invoking queries when the enable dynamic queries element has a value of false results in a remote or local exception being thrown depending on whether the queries submitted after setting the enable dynamic queries element were invoked from a local interface or a remote interface and wherein the dynamic queries element is specifiable by an enable-dynamic-queries parsed character data (PCDATA) tag, wherein said tag signifies that the dynamic queries element contains character data parsed by an extensible markup language (XML) parser; a server computer that queries the database, the server computer receiving a call from the finder method and reading the settings from the properties object in order to generate the appropriate SQL query statements to be sent to the database, wherein the SQL query statements are generated by parsing the finder method and wherein options that are set for the SQL query statements are specified in the settings contained in the properties object; a database connected to the server computer for executing the dynamically generated SQL query statements; and a collection of results that is returned from the database in response to the finder method, said finder method being invoked on a query home interface used to execute dynamic queries, wherein the collection of results is stored on the server computer. 7. The system of claim 1 , wherein the finder method accepts an instance of a primary key and returns an instance of an entity type.
| 0.595092 |
8,161,044 | 39 | 40 |
39. A method for deploying an application for web searching, comprising: providing a computer infrastructure being operable to: creating a user profile which includes interests of a user to succinctly provide a basis that defines user preferred categories; defining the user preferred categories associated with a plurality of taxonomies prior to conducting a search of a database; storing the defined user preferred categories such that the user preferred categories are used subsequently in conducting a fast path search, wherein the user preferred categories are recorded and automatically added to a list associated with a user; selecting a fast path searching option that allows the search of the database to be conducted as the fast path search in only selected user preferred categories; conducting the fast path search within a plurality of the user preferred categories based upon search criteria by comparing the search criteria to content information within each of the user preferred categories; displaying search results associated with each user preferred category of the user preferred categories which have matching criteria based on the conducted fast path search; displaying the user preferred categories in an expanded manner to show sub categories of the user preferred categories and to show the user preferred categories in a hierarchical relationship; displaying a numerical representation of a number of search results associated with each of the displayed user preferred categories; during the displaying of the user preferred categories, displaying the plurality of taxonomies associated with the results along with another numerical representation of another number of search results associated with the plurality of taxonomies, wherein the plurality of taxonomies and the user preferred categories are displayed in different areas; defining one or more common categories found within multiple searches as the user preferred categories; and prompting a user to add one or more new preferred categories to the user preferred categories based on one or more previously conducted searches.
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39. A method for deploying an application for web searching, comprising: providing a computer infrastructure being operable to: creating a user profile which includes interests of a user to succinctly provide a basis that defines user preferred categories; defining the user preferred categories associated with a plurality of taxonomies prior to conducting a search of a database; storing the defined user preferred categories such that the user preferred categories are used subsequently in conducting a fast path search, wherein the user preferred categories are recorded and automatically added to a list associated with a user; selecting a fast path searching option that allows the search of the database to be conducted as the fast path search in only selected user preferred categories; conducting the fast path search within a plurality of the user preferred categories based upon search criteria by comparing the search criteria to content information within each of the user preferred categories; displaying search results associated with each user preferred category of the user preferred categories which have matching criteria based on the conducted fast path search; displaying the user preferred categories in an expanded manner to show sub categories of the user preferred categories and to show the user preferred categories in a hierarchical relationship; displaying a numerical representation of a number of search results associated with each of the displayed user preferred categories; during the displaying of the user preferred categories, displaying the plurality of taxonomies associated with the results along with another numerical representation of another number of search results associated with the plurality of taxonomies, wherein the plurality of taxonomies and the user preferred categories are displayed in different areas; defining one or more common categories found within multiple searches as the user preferred categories; and prompting a user to add one or more new preferred categories to the user preferred categories based on one or more previously conducted searches. 40. The method of claim 39 , further comprising refining the fast path search by selecting one of the displayed user preferred categories and conducting the fast path search again using a more general search category by deleting the selected one of the user preferred categories and any lower level user preferred categories in the hierarchical relationship.
| 0.5 |
8,543,982 | 1 | 10 |
1. A remote device for conducting a financial transaction, the remote device comprising: a mini-app dialog component selected by the customer for performing a financial transaction and providing information regarding the financial transaction to a presentation manager for presentation to the customer; a transaction executor component instantiated by the mini-app dialog component to perform the financial transaction by using at least one input, including at least an amount selected by the customer, regarding the financial transaction provided by the customer into the mini-app dialog component; and a rule broker component for receiving queries posed by the mini-app dialog component regarding the input provided by the customer to perform the financial transaction, determining a rule authority, from a plurality of rule authorities, to route the queries based on a parameter passed in the query, and routing the queries to the determined rule authority that supplies an answer to the query and defines context-specific behavior of the transaction executor component, wherein the answer is to resolve a specific inquiry regarding an unique identification contextualized for the customer using the dialog component for carrying out the financial transaction; wherein the rule authorities register themselves with the rule broker component as possible answers for particular queries, wherein the performed transaction is displayed on the remote device by the presentation manager; wherein registering to the rule broker by the rule authorities includes registering of rules provided by the respective authorities, the rules having associated parameters and constraints; wherein generating a query by the rule broker includes consulting a plurality of pre-established answers provided by a registry component internal to the rule broker; and responsive to not finding a matching answer, the rule broker applying the most suitable parameter constraints defined with one of the registered rules to formulate the query and direct it to the registered rule authority corresponding to the rule being used.
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1. A remote device for conducting a financial transaction, the remote device comprising: a mini-app dialog component selected by the customer for performing a financial transaction and providing information regarding the financial transaction to a presentation manager for presentation to the customer; a transaction executor component instantiated by the mini-app dialog component to perform the financial transaction by using at least one input, including at least an amount selected by the customer, regarding the financial transaction provided by the customer into the mini-app dialog component; and a rule broker component for receiving queries posed by the mini-app dialog component regarding the input provided by the customer to perform the financial transaction, determining a rule authority, from a plurality of rule authorities, to route the queries based on a parameter passed in the query, and routing the queries to the determined rule authority that supplies an answer to the query and defines context-specific behavior of the transaction executor component, wherein the answer is to resolve a specific inquiry regarding an unique identification contextualized for the customer using the dialog component for carrying out the financial transaction; wherein the rule authorities register themselves with the rule broker component as possible answers for particular queries, wherein the performed transaction is displayed on the remote device by the presentation manager; wherein registering to the rule broker by the rule authorities includes registering of rules provided by the respective authorities, the rules having associated parameters and constraints; wherein generating a query by the rule broker includes consulting a plurality of pre-established answers provided by a registry component internal to the rule broker; and responsive to not finding a matching answer, the rule broker applying the most suitable parameter constraints defined with one of the registered rules to formulate the query and direct it to the registered rule authority corresponding to the rule being used. 10. The system of claim 1 , further comprising a session bubble for each session instantiated by the remote device.
| 0.685792 |
8,000,964 | 1 | 2 |
1. A method of constructing a model of recognizing English pronunciation variations, applying to a computer connected to a non-transitory recording medium, for recognizing English pronunciations with intonations influenced by different non-English native languages, the method at least comprising: 1) providing a plurality of English expressions and at least one phonetic alphabet corresponding to each of the English expressions by the non-transitory recording medium, and collecting a plurality of corresponding sound information according to the phonetic alphabet of each of the English expression by the computer; 2) corresponding phonetic alphabets of the non-English native language and English to a plurality of international phonetic alphabets (IPAs) by the computer, so as to form a plurality of pronunciation models, wherein the computer forms each pronunciation models; 2-1) collecting a plurality of phonetic alphabet pronunciations directed to one of the IPAs, and converts each of the phonetic alphabet pronunciations into a corresponding characteristic value; 2-2) forming the characteristic values into a value group and calculates a grouping threshold value corresponding to the characteristic values; 2-3) calculating the computer calculates a mean value of the value group; 2-4) obtaining a first characteristic value from the value group which is away from the mean value by a maximum numerical distance; 2-5) calculating a second characteristic value in the value group which is away from the first characteristic value by a maximum numerical distance; 2-6) calculating numerical distances, wherein a first distance is calculated between each characteristic value and the first characteristic value and a second distance is calculated between each characteristic value and the second characteristic value, and forming value groups by the first distances and the second distances, one of the two value groups containing the characteristic values close to the first characteristic value and the other one of the two value groups containing the characteristic values close to the second characteristic value, respectively; 2-7) obtaining a within-group distance and a between-group distance of the two value groups, so as to calculate a grouping standard; and 2-8) determining whether the grouping standard is higher than the grouping threshold value through comparison, if yes, calculating each mean value of the two value groups and then, the step 2-4) to the step 2-8) are repeated for each one of the two value groups respectively, and if no, obtaining each value group of the pronunciation model that the computer want to form; 3) converting the sound information of each of the English expressions by using the pronunciation models, and constructing a pronunciation variation network corresponding to the English expression with reference to the phonetic alphabet of the English expression by the computer, so as to detect whether each of the English expressions has a pronunciation variation path; and 4) summarizing each of the pronunciation variation paths to form a plurality of pronunciation variation rules by the computer.
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1. A method of constructing a model of recognizing English pronunciation variations, applying to a computer connected to a non-transitory recording medium, for recognizing English pronunciations with intonations influenced by different non-English native languages, the method at least comprising: 1) providing a plurality of English expressions and at least one phonetic alphabet corresponding to each of the English expressions by the non-transitory recording medium, and collecting a plurality of corresponding sound information according to the phonetic alphabet of each of the English expression by the computer; 2) corresponding phonetic alphabets of the non-English native language and English to a plurality of international phonetic alphabets (IPAs) by the computer, so as to form a plurality of pronunciation models, wherein the computer forms each pronunciation models; 2-1) collecting a plurality of phonetic alphabet pronunciations directed to one of the IPAs, and converts each of the phonetic alphabet pronunciations into a corresponding characteristic value; 2-2) forming the characteristic values into a value group and calculates a grouping threshold value corresponding to the characteristic values; 2-3) calculating the computer calculates a mean value of the value group; 2-4) obtaining a first characteristic value from the value group which is away from the mean value by a maximum numerical distance; 2-5) calculating a second characteristic value in the value group which is away from the first characteristic value by a maximum numerical distance; 2-6) calculating numerical distances, wherein a first distance is calculated between each characteristic value and the first characteristic value and a second distance is calculated between each characteristic value and the second characteristic value, and forming value groups by the first distances and the second distances, one of the two value groups containing the characteristic values close to the first characteristic value and the other one of the two value groups containing the characteristic values close to the second characteristic value, respectively; 2-7) obtaining a within-group distance and a between-group distance of the two value groups, so as to calculate a grouping standard; and 2-8) determining whether the grouping standard is higher than the grouping threshold value through comparison, if yes, calculating each mean value of the two value groups and then, the step 2-4) to the step 2-8) are repeated for each one of the two value groups respectively, and if no, obtaining each value group of the pronunciation model that the computer want to form; 3) converting the sound information of each of the English expressions by using the pronunciation models, and constructing a pronunciation variation network corresponding to the English expression with reference to the phonetic alphabet of the English expression by the computer, so as to detect whether each of the English expressions has a pronunciation variation path; and 4) summarizing each of the pronunciation variation paths to form a plurality of pronunciation variation rules by the computer. 2. The method of constructing a model of recognizing English pronunciation variations as claimed in claim 1 , wherein the characteristic values of at least one value group of the pronunciation model correspond to the phonetic alphabets of the non-English native language.
| 0.803052 |
8,015,173 | 10 | 17 |
10. A processor-implemented method, comprising: receiving a request for an initial document in a plurality of documents from a user; identifying documents in the plurality of documents that are related to the initial document, comprising: assigning scores to other documents in the plurality of documents, including — determining, for each other document in the plurality of documents, if a the document has an associated compressed document surrogate, when the document has an associated compressed document surrogate, assigning a score to the document based on occurrences of at least one term included in the initial document in the associated compressed document surrogate, wherein a score of S D assigned to a document D in the plurality of documents is determined by crediting the document D, for each term T in the initial document which occurs in the compressed document surrogate associated with document D, with an amount proportional to Robertson's term frequency TF TD and to IDF T where
TF TD =N TD/ ( N TD +K 1 +K 2 *( L D /L ) )),and N TD is the number of times the term T occurs in compressed document surrogate D, L D is the length of compressed document surrogate D, L 0 is the average length of a document in the plurality of documents, K 1 and K 2 are constants, and
IDF T =log ((N+K 3 /log (N+K 4 ), and N is the number of documents in the plurality of documents, N T is the number of documents containing the term T in the plurality of documents, and K 3 and K 4 are constants, selecting a set of documents from the plurality of documents based on the assigned scores; and presenting information identifying the set of documents to the user.
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10. A processor-implemented method, comprising: receiving a request for an initial document in a plurality of documents from a user; identifying documents in the plurality of documents that are related to the initial document, comprising: assigning scores to other documents in the plurality of documents, including — determining, for each other document in the plurality of documents, if a the document has an associated compressed document surrogate, when the document has an associated compressed document surrogate, assigning a score to the document based on occurrences of at least one term included in the initial document in the associated compressed document surrogate, wherein a score of S D assigned to a document D in the plurality of documents is determined by crediting the document D, for each term T in the initial document which occurs in the compressed document surrogate associated with document D, with an amount proportional to Robertson's term frequency TF TD and to IDF T where
TF TD =N TD/ ( N TD +K 1 +K 2 *( L D /L ) )),and N TD is the number of times the term T occurs in compressed document surrogate D, L D is the length of compressed document surrogate D, L 0 is the average length of a document in the plurality of documents, K 1 and K 2 are constants, and
IDF T =log ((N+K 3 /log (N+K 4 ), and N is the number of documents in the plurality of documents, N T is the number of documents containing the term T in the plurality of documents, and K 3 and K 4 are constants, selecting a set of documents from the plurality of documents based on the assigned scores; and presenting information identifying the set of documents to the user. 17. The method of claim 10 , wherein the initial document includes at least one index term manually assigned to the initial document, the at least one term includes the at least one index term, and the score assigned to a particular one of the plurality of documents depends upon an occurrence of the at least one index term.
| 0.5 |
8,489,641 | 2 | 5 |
2. The one or more computer-readable storage devices of claim 1 , wherein the operations further comprise: displaying a user interface element for each of the search queries, wherein the user interface element for each of the search queries, when selected by a user, causes removal from the display of the information for the search query that corresponds to the selected user interface element.
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2. The one or more computer-readable storage devices of claim 1 , wherein the operations further comprise: displaying a user interface element for each of the search queries, wherein the user interface element for each of the search queries, when selected by a user, causes removal from the display of the information for the search query that corresponds to the selected user interface element. 5. The one or more computer-readable storage devices of claim 2 , wherein the operations further comprise: receiving user selection of a first user interface element that corresponds to the first search query; and adding to the display, as a consequence of the user selection of the first user interface element, a second portion of the set of locations or set of areas of the second search results.
| 0.78125 |
9,053,422 | 9 | 10 |
9. The method according to claim 1 , wherein the executing of the at least one selected model comprises at least one executing the model using code libraries, executing the model using external programs, and executing the model using visual machines.
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9. The method according to claim 1 , wherein the executing of the at least one selected model comprises at least one executing the model using code libraries, executing the model using external programs, and executing the model using visual machines. 10. The method according to claim 9 , wherein the code libraries are stored in the results database.
| 0.561404 |
9,864,956 | 7 | 15 |
7. A method comprising: receiving, via a network from a remote computing device, a feature vector representing a file stored in a memory of the remote computing device, the feature vector including: a zero-skip n-gram indicating occurrences of adjacent characters in printable characters representing the file, a skip n-gram indicating occurrences of non-adjacent characters in the printable characters representing the file; and an n-gram indicating occurrences of groups of entropy indicators in a set of entropy indicators derived from file entropy data for the file, each entropy indicator of the set of entropy indicators having a value representing entropy of a corresponding chunk of the file; generating, by a trained file classifier, classification data associated with the file based on the feature vector, the classification data indicating whether the file includes malware; and transmitting the classification data to the remote computing device via the network, wherein access to the file or execution of the file at the remote computing device is restricted responsive to the classification data indicating that the file includes malware.
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7. A method comprising: receiving, via a network from a remote computing device, a feature vector representing a file stored in a memory of the remote computing device, the feature vector including: a zero-skip n-gram indicating occurrences of adjacent characters in printable characters representing the file, a skip n-gram indicating occurrences of non-adjacent characters in the printable characters representing the file; and an n-gram indicating occurrences of groups of entropy indicators in a set of entropy indicators derived from file entropy data for the file, each entropy indicator of the set of entropy indicators having a value representing entropy of a corresponding chunk of the file; generating, by a trained file classifier, classification data associated with the file based on the feature vector, the classification data indicating whether the file includes malware; and transmitting the classification data to the remote computing device via the network, wherein access to the file or execution of the file at the remote computing device is restricted responsive to the classification data indicating that the file includes malware. 15. The method of claim 7 , further comprising comparing a hash value determined from the feature vector to file identifiers stored in a memory, wherein the classification data is generated by the trained file classifier based on a determination that the hash value does not match one of the file identifiers.
| 0.5 |
6,167,328 | 34 | 35 |
34. The programming pendant according to claim 21, wherein, for automatically adding an orientation changing point, said language processing means stores information related to a motion command which is already taught, in said storage means in association with a motion command of the orientation changing point to be added.
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34. The programming pendant according to claim 21, wherein, for automatically adding an orientation changing point, said language processing means stores information related to a motion command which is already taught, in said storage means in association with a motion command of the orientation changing point to be added. 35. The programming pendant according to claim 34, wherein, for deleting a motion command which is already taught, after said motion command is deleted, said language processing means searches said storage means, and deletes a motion command of an orientation changing point with which information related to the deleted motion command is associated.
| 0.601367 |
7,716,170 | 10 | 11 |
10. The method according to claim 1 , wherein the input is via a graphical user interface by an end user.
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10. The method according to claim 1 , wherein the input is via a graphical user interface by an end user. 11. The method according to claim 10 , further comprising: operating a server running software instructions implementing processes including receiving the input using the integrity engine and storing, wherein the input is via a web browser application running on a computer in communication with the server over a network.
| 0.5 |
7,930,262 | 13 | 14 |
13. A data processing system comprising: a bus; at least one processor coupled to the bus; a computer usable medium coupled to the bus, wherein the computer usable storage medium contains a set of instructions for performing analysis on a plurality of data stored in a database, wherein the at least one processor is adapted to carry out the set of instructions to: generate a first cohort from the plurality of data; generate an optimal control cohort from the plurality of data, wherein generating is performed based on the first cohort and at least one constraint, and wherein a mathematical process is used to derive the optimal control cohort; and generate a first inference based on a comparison of the first cohort to the optimal control cohort, wherein the first inference is absent from the database; receive an I th query at the database regarding an I th fact, wherein I is an integer reflecting how many times a recursion process has been conducted, wherein the I th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process; establish the I th fact as a frame of reference for the I th query based on the first cohort and based on the optimal control cohort; apply an I th set of rules to the I th query, wherein the I th set of rules is determined for the I th query according to a J th set of rules, wherein J is equal to I−1, wherein the I th set of rules determines how the plurality of data are to be compared to the I th fact, and wherein the first set of rules determines an I th search space for the I th query; execute the I th query to generate a second inference, wherein the second inference is determined from comparing the plurality of data according to the I th set of rules; store the second inference; and evaluating a hypothesis within a research study based on the first cohort, the optimal control cohort, the first inference, and the second inference.
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13. A data processing system comprising: a bus; at least one processor coupled to the bus; a computer usable medium coupled to the bus, wherein the computer usable storage medium contains a set of instructions for performing analysis on a plurality of data stored in a database, wherein the at least one processor is adapted to carry out the set of instructions to: generate a first cohort from the plurality of data; generate an optimal control cohort from the plurality of data, wherein generating is performed based on the first cohort and at least one constraint, and wherein a mathematical process is used to derive the optimal control cohort; and generate a first inference based on a comparison of the first cohort to the optimal control cohort, wherein the first inference is absent from the database; receive an I th query at the database regarding an I th fact, wherein I is an integer reflecting how many times a recursion process has been conducted, wherein the I th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process; establish the I th fact as a frame of reference for the I th query based on the first cohort and based on the optimal control cohort; apply an I th set of rules to the I th query, wherein the I th set of rules is determined for the I th query according to a J th set of rules, wherein J is equal to I−1, wherein the I th set of rules determines how the plurality of data are to be compared to the I th fact, and wherein the first set of rules determines an I th search space for the I th query; execute the I th query to generate a second inference, wherein the second inference is determined from comparing the plurality of data according to the I th set of rules; store the second inference; and evaluating a hypothesis within a research study based on the first cohort, the optimal control cohort, the first inference, and the second inference. 14. The data processing system of claim 13 wherein the second inference either validates the first inference or is based on the first inference.
| 0.5 |
10,127,022 | 54 | 56 |
54. A method of developing a dataflow program comprising: using a graphical user interface to specify a graphical representation of a dataflow program, the program comprising producer types, transducer types, and extractor types, wherein through the graphical user interface, the user can select and move the producer types, transducer types, and extractor types, represented using blocks, into various positions on a computer screen; using the graphical user interface, allowing the user to interconnect via interconnection links the blocks representing the producer types, transducer types, and extractor types; allowing the user to specify contents of each of the blocks through the graphical user interface, wherein for a transducer type block, the user can specify an operation; automatically generating computer source code that corresponds to the dataflow program the user specified using the graphical user interface; allowing the user to view and edit the computer source code automatically generated in a textual interface; and allowing the user to specify generating of a computer package of code executable on a target hardware platform that is an implementation of the dataflow program specified by the user using the graphical user interface.
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54. A method of developing a dataflow program comprising: using a graphical user interface to specify a graphical representation of a dataflow program, the program comprising producer types, transducer types, and extractor types, wherein through the graphical user interface, the user can select and move the producer types, transducer types, and extractor types, represented using blocks, into various positions on a computer screen; using the graphical user interface, allowing the user to interconnect via interconnection links the blocks representing the producer types, transducer types, and extractor types; allowing the user to specify contents of each of the blocks through the graphical user interface, wherein for a transducer type block, the user can specify an operation; automatically generating computer source code that corresponds to the dataflow program the user specified using the graphical user interface; allowing the user to view and edit the computer source code automatically generated in a textual interface; and allowing the user to specify generating of a computer package of code executable on a target hardware platform that is an implementation of the dataflow program specified by the user using the graphical user interface. 56. The method of claim 54 wherein the operation in a transducer block is a pattern matching operation, and the automatically generating computer source code implements the operation using a state machine reflecting technique that processes stream data from an input of a producer only once, and does not retain data previously read in a buffer to read again later.
| 0.5 |
9,652,445 | 13 | 17 |
13. A system for creating one or more tasks of digitizing an electronic document, the system comprising: one or more processors configured to: register the electronic document by receiving inputs to define one or more characteristics associated with each of the one or more fields, the one or more characteristics comprising at least a security type associated with the one or more fields, a data entry type associated with the one or more fields, redundancy rules for the one or more fields, and resolution rules for one or more fields, wherein the redundancy rules and the resolution rules for a given field are based on the data entry type and the security type for the given field, and fields of a predetermined security type and a free text data entry type are assigned consensus resolution based on a confidentiality indicated by the predetermined security type and an error risk indicated by the free text data entry type; categorize the one or more fields in one or more groups based on at least the security type associated with the one or more fields; generate a user interface for the one or more groups based on the one or more characteristics associated with the one or more categorized fields in the one or more groups; and create a task for the one or more categorized fields on the corresponding user interface and the one or more characteristics associated with the one or more categorized fields; send the created tasks to a set of remote workers such that a first subset of the set of remote workers that are sent tasks categorized in a first group are not sent tasks categorized in a second group based on based on the security type for the fields of at least one of the first group or the second group indicating that remote workers are not to be sent data associated with fields of the first group and also sent data associated with fields of the second group, wherein the tasks are presented through the user interface, and ones of tasks associated with a particular data filed categorized in the first group are sent to a number of remote workers from among the first subset of remote workers such that responses from the number of remote workers are assessed in accordance with the resolution rules for the particular field, the number of remote workers from among the first subset being based on the redundancy rules for the particular field.
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13. A system for creating one or more tasks of digitizing an electronic document, the system comprising: one or more processors configured to: register the electronic document by receiving inputs to define one or more characteristics associated with each of the one or more fields, the one or more characteristics comprising at least a security type associated with the one or more fields, a data entry type associated with the one or more fields, redundancy rules for the one or more fields, and resolution rules for one or more fields, wherein the redundancy rules and the resolution rules for a given field are based on the data entry type and the security type for the given field, and fields of a predetermined security type and a free text data entry type are assigned consensus resolution based on a confidentiality indicated by the predetermined security type and an error risk indicated by the free text data entry type; categorize the one or more fields in one or more groups based on at least the security type associated with the one or more fields; generate a user interface for the one or more groups based on the one or more characteristics associated with the one or more categorized fields in the one or more groups; and create a task for the one or more categorized fields on the corresponding user interface and the one or more characteristics associated with the one or more categorized fields; send the created tasks to a set of remote workers such that a first subset of the set of remote workers that are sent tasks categorized in a first group are not sent tasks categorized in a second group based on based on the security type for the fields of at least one of the first group or the second group indicating that remote workers are not to be sent data associated with fields of the first group and also sent data associated with fields of the second group, wherein the tasks are presented through the user interface, and ones of tasks associated with a particular data filed categorized in the first group are sent to a number of remote workers from among the first subset of remote workers such that responses from the number of remote workers are assessed in accordance with the resolution rules for the particular field, the number of remote workers from among the first subset being based on the redundancy rules for the particular field. 17. The system of claim 13 , wherein the one or more processors are further configured to generate one or more validation rules for created tasks based on the one or more characteristics of the fields associated with the created tasks, wherein received response for the tasks are validated based on the one or more validation rules.
| 0.576531 |
9,477,715 | 2 | 3 |
2. The method of claim 1 , where creating the new content comprises: receiving a request to access the news content, and creating the news content based on the request.
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2. The method of claim 1 , where creating the new content comprises: receiving a request to access the news content, and creating the news content based on the request. 3. The method of claim 2 , where creating the news content comprises: fetching a plurality of news items from a plurality of news sources based on the request, and creating the news content based on the plurality of news items.
| 0.5 |
9,086,735 | 1 | 9 |
1. A computer-implemented method, comprising: receiving a user input into a user interface of an input method editor (IME); determining, based on the user input, whether to process the user input with a script engine; when the user input indicates that the user input is to be processed with the script engine: providing the user input to the script engine, selecting a script from a plurality of scripts electronically stored in a script repository, processing the user input through the script using the script engine to generate one or more candidates, and providing the one or more candidates to an IME engine; when the user input indicates that the user input is not to be processed with the script engine: providing the user input to the IME engine, and processing the user input with the IME engine to generate the one or more candidates; and receiving an extension mode input indicating operation of the IME in an extension mode, operating the IME in the extension mode in response to receiving the extension mode input, and providing all user input to the script engine when operating in the extension mode.
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1. A computer-implemented method, comprising: receiving a user input into a user interface of an input method editor (IME); determining, based on the user input, whether to process the user input with a script engine; when the user input indicates that the user input is to be processed with the script engine: providing the user input to the script engine, selecting a script from a plurality of scripts electronically stored in a script repository, processing the user input through the script using the script engine to generate one or more candidates, and providing the one or more candidates to an IME engine; when the user input indicates that the user input is not to be processed with the script engine: providing the user input to the IME engine, and processing the user input with the IME engine to generate the one or more candidates; and receiving an extension mode input indicating operation of the IME in an extension mode, operating the IME in the extension mode in response to receiving the extension mode input, and providing all user input to the script engine when operating in the extension mode. 9. The computer-implemented method of claim 1 , wherein processing the user input through the script comprises: establishing a network connection with a web-based service; providing at least a portion of the user input to the web-based service; and receiving a response from the web-based service, the response having been generated based on the at least a portion of the user input, and the one or more candidates comprising the response.
| 0.5827 |
10,140,014 | 1 | 6 |
1. A method for controlling an application based on a handwriting input, the method comprising: displaying a first screen on a touch screen display of a terminal; determining an occurrence of a communication event associated with an application, the communication event occurring while displaying the first screen; displaying a notification indicating the communication event on the touch screen display in response to the determination while displaying the first screen; activating, in response to the determination, a handwriting recognition module to recognize a handwriting input to be associated with the application when displaying the notification; receiving the handwriting input on the touch screen display of the terminal; recognizing the handwriting input received on the touch screen display of the terminal; determining a symbol corresponding to the handwriting input; associating the symbol with a function of the application; and processing the associated symbol through the application.
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1. A method for controlling an application based on a handwriting input, the method comprising: displaying a first screen on a touch screen display of a terminal; determining an occurrence of a communication event associated with an application, the communication event occurring while displaying the first screen; displaying a notification indicating the communication event on the touch screen display in response to the determination while displaying the first screen; activating, in response to the determination, a handwriting recognition module to recognize a handwriting input to be associated with the application when displaying the notification; receiving the handwriting input on the touch screen display of the terminal; recognizing the handwriting input received on the touch screen display of the terminal; determining a symbol corresponding to the handwriting input; associating the symbol with a function of the application; and processing the associated symbol through the application. 6. The method of claim 1 , further comprising: displaying the handwriting input on an upper layer of the first screen displayed on the touch screen display.
| 0.769912 |
8,537,678 | 18 | 25 |
18. The method according to claim 17 , wherein in the step D, the constructing an SMIL file according to all the non-SMIL files obtained in step A is carried out in the following way: D1: determining a construction principle of an SMIL file, wherein the construction principle comprises: only one file of one file format can exist in a frame; a video file or an attachment file can only coexist with a text file in a frame; D2: constructing an SMIL file describing a playing layout of all the non-SMIL files according to the construction principle.
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18. The method according to claim 17 , wherein in the step D, the constructing an SMIL file according to all the non-SMIL files obtained in step A is carried out in the following way: D1: determining a construction principle of an SMIL file, wherein the construction principle comprises: only one file of one file format can exist in a frame; a video file or an attachment file can only coexist with a text file in a frame; D2: constructing an SMIL file describing a playing layout of all the non-SMIL files according to the construction principle. 25. The method according to claim 18 , wherein the data structure describing a playing layout of MMS is a slide sequence structure.
| 0.734818 |
7,644,391 | 2 | 4 |
2. The system of claim 1 further comprising a system object model containing procedures corresponding to the object model and information on characteristics of the object model.
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2. The system of claim 1 further comprising a system object model containing procedures corresponding to the object model and information on characteristics of the object model. 4. The system of claim 2 wherein the program parser module injects the system object model and the object model into the script engine.
| 0.858193 |
8,001,106 | 21 | 22 |
21. A method for processing Uniform Resource Locators (URLs), comprising: obtaining a plurality of tokens from a plurality of related URLs; obtaining a set of subtokens from the plurality of tokens, wherein each subtoken, in the set of subtokens, is either a key, or a value, that is encoded in a custom delimiter scheme; modeling a set of probabilities with a Viterbi algorithm, wherein for each subtoken, of the set of subtokens, the set of probabilities includes a probability that said each subtoken is either a key or a value; and rewriting the plurality of tokens using standard delimiters for keys and values based on the set of probabilities; wherein the steps of the method are performed by one or more computing devices.
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21. A method for processing Uniform Resource Locators (URLs), comprising: obtaining a plurality of tokens from a plurality of related URLs; obtaining a set of subtokens from the plurality of tokens, wherein each subtoken, in the set of subtokens, is either a key, or a value, that is encoded in a custom delimiter scheme; modeling a set of probabilities with a Viterbi algorithm, wherein for each subtoken, of the set of subtokens, the set of probabilities includes a probability that said each subtoken is either a key or a value; and rewriting the plurality of tokens using standard delimiters for keys and values based on the set of probabilities; wherein the steps of the method are performed by one or more computing devices. 22. The method of claim 21 , wherein information comprising frequencies of appearance of the set of subtokens within the plurality of tokens and relative arrangements of the set of subtokens within the plurality of tokens are used as observed events, and the set of probabilities obtained from the Viterbi algorithm are treated as hidden states.
| 0.5 |
8,984,386 | 65 | 80 |
65. A method comprising: providing a computer-based service over a network to a set of users, wherein the set of users includes a first user and a plurality of second users; receiving, by the computer-based service, from a given user of the plurality of second users, a given expression of interest in being notified about interactions of the first user; receiving, by the computer-based service, an indication of a first interaction of the first user; after receiving the given expression of interest and receiving the indication, sending, by the computer-based service, first information about the first interaction to each user of the plurality of second users, to cause display of the first information in a respective view of each of the plurality of second users; wherein the step of sending the first information includes sending the first information to the given user based, at least in part, on the given expression of interest; wherein the first information includes one or more links that provide access to one or more views that are associated with the first interaction; receiving, by the computer-based service from a particular second user, of the plurality of second users, a comment entered in a particular text entry interface, wherein the particular text entry interface is related to the first information; and in response to receiving the comment, the computer-based service sending the comment, to each user of the plurality of second users other than the particular second user, to enable display of the comment, in relation to the first information, to the each user of the plurality of second users other than the particular second user.
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65. A method comprising: providing a computer-based service over a network to a set of users, wherein the set of users includes a first user and a plurality of second users; receiving, by the computer-based service, from a given user of the plurality of second users, a given expression of interest in being notified about interactions of the first user; receiving, by the computer-based service, an indication of a first interaction of the first user; after receiving the given expression of interest and receiving the indication, sending, by the computer-based service, first information about the first interaction to each user of the plurality of second users, to cause display of the first information in a respective view of each of the plurality of second users; wherein the step of sending the first information includes sending the first information to the given user based, at least in part, on the given expression of interest; wherein the first information includes one or more links that provide access to one or more views that are associated with the first interaction; receiving, by the computer-based service from a particular second user, of the plurality of second users, a comment entered in a particular text entry interface, wherein the particular text entry interface is related to the first information; and in response to receiving the comment, the computer-based service sending the comment, to each user of the plurality of second users other than the particular second user, to enable display of the comment, in relation to the first information, to the each user of the plurality of second users other than the particular second user. 80. One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method recited in claim 65 .
| 0.844961 |
7,805,394 | 7 | 8 |
7. The apparatus of claim 4 , wherein said process monitor is configured to determine said current status from a trajectory of said input parameter values over said model.
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7. The apparatus of claim 4 , wherein said process monitor is configured to determine said current status from a trajectory of said input parameter values over said model. 8. The apparatus of claim 7 , process monitor is configured to provide an alert when said trajectory approaches a deviation from a region of normal process behavior specified by said model.
| 0.5 |
8,793,598 | 8 | 11 |
8. A server for providing a web application employing a cross-browser web dialog platform, the server comprising: a memory; a processor coupled to the memory, the processor executing web dialog platform, wherein the web dialog platform is configured to: present a parent web page to a user; in response to receiving a user selection, hide at least a portion of displayed web page elements by one of graying the elements, rendering the elements transparent, and modifying text and graphics colors; present a dialog over the hidden web page elements within the parent web page; in response to receiving another user selection through the dialog, present a new web page within the dialog, wherein a size of the dialog is adjusted automatically based on at least one of the contents of the new web page and a size of a view port displaying the web page, a portion of elements on the new web page are hidden by removing the elements selectively from the new web page, the selective removal including hiding of controls associated with saving, deleting, and checking entire content by at least one from a set of: graying the controls, rendering the controls transparent, and modifying text and graphics colors in response to a user interaction with the new web page changing to a content input mode, and the one or more controls are displayed upon completion of the content input mode; present one or more links within the new web page, wherein a behavior of the links within the new web page is specified based on interpreting the links within the new web page for desired link behavior and specifying separate behaviors for the links based on the interpretation.
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8. A server for providing a web application employing a cross-browser web dialog platform, the server comprising: a memory; a processor coupled to the memory, the processor executing web dialog platform, wherein the web dialog platform is configured to: present a parent web page to a user; in response to receiving a user selection, hide at least a portion of displayed web page elements by one of graying the elements, rendering the elements transparent, and modifying text and graphics colors; present a dialog over the hidden web page elements within the parent web page; in response to receiving another user selection through the dialog, present a new web page within the dialog, wherein a size of the dialog is adjusted automatically based on at least one of the contents of the new web page and a size of a view port displaying the web page, a portion of elements on the new web page are hidden by removing the elements selectively from the new web page, the selective removal including hiding of controls associated with saving, deleting, and checking entire content by at least one from a set of: graying the controls, rendering the controls transparent, and modifying text and graphics colors in response to a user interaction with the new web page changing to a content input mode, and the one or more controls are displayed upon completion of the content input mode; present one or more links within the new web page, wherein a behavior of the links within the new web page is specified based on interpreting the links within the new web page for desired link behavior and specifying separate behaviors for the links based on the interpretation. 11. The server of claim 8 , wherein the web dialog platform is further configured to: dynamically hide and activate portions of controls on the new web page based on user activity.
| 0.79638 |
9,922,654 | 14 | 15 |
14. The incremental speech recognition system of claim 9 , wherein the computer-executable instructions are further executable by the at least one processor for: decoding the spoken utterance with an additional speech decoding model of the plurality of speech decoding models to obtain an additional result; and combining the additional result with the merged results to produce a combined recognition result.
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14. The incremental speech recognition system of claim 9 , wherein the computer-executable instructions are further executable by the at least one processor for: decoding the spoken utterance with an additional speech decoding model of the plurality of speech decoding models to obtain an additional result; and combining the additional result with the merged results to produce a combined recognition result. 15. The incremental speech recognition system of claim 14 , wherein the computer-executable instructions are further executable by the at least one processor for: accepting the combined recognition result if accurate.
| 0.5 |
8,166,030 | 33 | 35 |
33. An information resource taxonomy system, including: a data collector having computer system hardware components including at least one processor operating according to one or more software modules, the at least one processor and one or more software modules configured for collecting information resources from a communications network; a taxonomy generator for generating clusters of said collected information resources based on a similarity threshold value for clustering and similarity values for said collected information resources and for iteratively generating sub-clusters of said generated clusters based on the similarity threshold value for clustering and similarity values for information resources within each of said generated clusters and within each of said generated sub-clusters, wherein the generated clusters and sub-clusters provide a hierarchy of resource clusters, wherein the number of resource clusters in each level of said hierarchy is determined by content of said collected information resources; a classifier configured to classify further information resources collected from the communication network to a plurality of the resource clusters; and a component configured to maintain the coherence of the plurality of resource clusters as further information resources are classified by at least one of: (a) reducing the similarity threshold value for clustering with increasing numbers of the further collected information resources; and (b) selecting a random subset of information resources from the collected information resources; generating a new similarity threshold value for the selected random subset of information resource; and re-clustering the collected information resources using the new similarity threshold value for clustering.
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33. An information resource taxonomy system, including: a data collector having computer system hardware components including at least one processor operating according to one or more software modules, the at least one processor and one or more software modules configured for collecting information resources from a communications network; a taxonomy generator for generating clusters of said collected information resources based on a similarity threshold value for clustering and similarity values for said collected information resources and for iteratively generating sub-clusters of said generated clusters based on the similarity threshold value for clustering and similarity values for information resources within each of said generated clusters and within each of said generated sub-clusters, wherein the generated clusters and sub-clusters provide a hierarchy of resource clusters, wherein the number of resource clusters in each level of said hierarchy is determined by content of said collected information resources; a classifier configured to classify further information resources collected from the communication network to a plurality of the resource clusters; and a component configured to maintain the coherence of the plurality of resource clusters as further information resources are classified by at least one of: (a) reducing the similarity threshold value for clustering with increasing numbers of the further collected information resources; and (b) selecting a random subset of information resources from the collected information resources; generating a new similarity threshold value for the selected random subset of information resource; and re-clustering the collected information resources using the new similarity threshold value for clustering. 35. The system as claimed in claim 33 , wherein said system is scalable with respect to the number of said information resources.
| 0.781356 |
8,244,752 | 1 | 7 |
1. A computer-implemented method for classifying search query traffic, said method comprising: receiving, from a search engine, labeled sample search query traffic, said labeled sample search traffic being labeled as human generated search query traffic or automatically generated search query traffic, said labeled sample search query traffic including one or more keywords for each search query submitted to said search engine and request times for a plurality of search queries submitted to said search engine within distinct user sessions; extracting features from said labeled sample search query traffic in accordance with a set of feature definitions partitioned into physical features related to physical limitations of human generated search queries and behavioral features of automatically generated search queries and keywords of the automatically generated search queries; generating a feature set comprising (i) said features extracted from said labeled sample search query traffic and (ii) a behavioral feature related to query word length entropy (WLE) that is calculated as: WL E ( l ij ) = - ∑ i ∑ j l ij log ( l ij ) , i being an index for each separate query submitted to a search engine by a single user ID and I ij being a length of an individual query term j in the ith query; generating a model using said labeled sample search query traffic and said feature set; receiving, from said search engine, search query traffic associated with a plurality of search queries submitted by a particular user identifier; classifying, using said model, said search query traffic associated with said plurality of search queries submitted by said particular user identifier as human generated search query traffic or automatically generated search query traffic; and modifying a quality of service provided by said search engine to said particular user identifier when said search query traffic associated with said plurality of search queries submitted by said particular user identifier is classified as automatically generated search query traffic.
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1. A computer-implemented method for classifying search query traffic, said method comprising: receiving, from a search engine, labeled sample search query traffic, said labeled sample search traffic being labeled as human generated search query traffic or automatically generated search query traffic, said labeled sample search query traffic including one or more keywords for each search query submitted to said search engine and request times for a plurality of search queries submitted to said search engine within distinct user sessions; extracting features from said labeled sample search query traffic in accordance with a set of feature definitions partitioned into physical features related to physical limitations of human generated search queries and behavioral features of automatically generated search queries and keywords of the automatically generated search queries; generating a feature set comprising (i) said features extracted from said labeled sample search query traffic and (ii) a behavioral feature related to query word length entropy (WLE) that is calculated as: WL E ( l ij ) = - ∑ i ∑ j l ij log ( l ij ) , i being an index for each separate query submitted to a search engine by a single user ID and I ij being a length of an individual query term j in the ith query; generating a model using said labeled sample search query traffic and said feature set; receiving, from said search engine, search query traffic associated with a plurality of search queries submitted by a particular user identifier; classifying, using said model, said search query traffic associated with said plurality of search queries submitted by said particular user identifier as human generated search query traffic or automatically generated search query traffic; and modifying a quality of service provided by said search engine to said particular user identifier when said search query traffic associated with said plurality of search queries submitted by said particular user identifier is classified as automatically generated search query traffic. 7. The computer-implemented method of claim 1 , wherein said feature set comprises a behavioral feature related to keyword content associated with adult content.
| 0.754573 |
7,870,486 | 1 | 2 |
1. A system for simultaneously commencing output of disparately encoded electronic documents comprising: a document processing device including a processor and associated data storage; means adapted for receiving, into the document processing device, selection data representative of a plurality of different user-selected electronic documents, each of the plurality of documents being encoded in a unique one of a plurality of disparate formats; association means adapted for associating each of the plurality of disparate formats with at least one software module; means adapted for retrieving the plurality of user-selected electronic documents in accordance with received selection data; means adapted for communicating each of the plurality of user-selected electronic documents to one of a plurality of corresponding software modules in accordance with an output of the association means; means adapted for acquiring common document output characteristics associated with each of the plurality of user-selected electronic documents; means adapted for communicating configuration data corresponding to acquired document output characteristics to each of a plurality of unique software modules in a format compatible thereto; and document processor means adapted for commencing a selected document processing operation on each of a series of the user-selected electronic documents by calling a sequence of the software modules, with each module corresponding to one of the series of user-selected electronic documents such that each module is operative in accordance with the common document output characteristics.
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1. A system for simultaneously commencing output of disparately encoded electronic documents comprising: a document processing device including a processor and associated data storage; means adapted for receiving, into the document processing device, selection data representative of a plurality of different user-selected electronic documents, each of the plurality of documents being encoded in a unique one of a plurality of disparate formats; association means adapted for associating each of the plurality of disparate formats with at least one software module; means adapted for retrieving the plurality of user-selected electronic documents in accordance with received selection data; means adapted for communicating each of the plurality of user-selected electronic documents to one of a plurality of corresponding software modules in accordance with an output of the association means; means adapted for acquiring common document output characteristics associated with each of the plurality of user-selected electronic documents; means adapted for communicating configuration data corresponding to acquired document output characteristics to each of a plurality of unique software modules in a format compatible thereto; and document processor means adapted for commencing a selected document processing operation on each of a series of the user-selected electronic documents by calling a sequence of the software modules, with each module corresponding to one of the series of user-selected electronic documents such that each module is operative in accordance with the common document output characteristics. 2. The system for simultaneously commencing output of disparately encoded electronic documents of claim 1 further comprising means adapted for prompting an associated user for input of common document output characteristics in accordance with common features associated with each software module.
| 0.51634 |
8,849,785 | 1 | 3 |
1. A computer-implemented method comprising: obtaining search results that are identified as responsive to an original search query; determining a frequency with which a particular term occurs in text associated with one or more of the search results; determining a frequency with which the particular term occurs in other text; determining that the frequency with which the particular term occurs in the text associated with the one or more search results differs, by a threshold extent, from the frequency with which the particular term occurs in the other text; in response to determining that the frequency with which the particular term occurs in a text associated with the one or more search results differs, by the threshold extent, from the frequency with which the term occurs in the other text, providing, for display, a representation of the particular term, a demote control to specify that the search results for the reformulated search query that include the particular term are to be demoted, and a promote control to specify that the search results for the formulated search query that include the particular term are to be promoted; receiving data indicative of a selection of the demote control or the promote control; and in response to receiving the data indicative of the selection of the demote control or the promote control, reformulating the original search query to promote or demote the particular term.
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1. A computer-implemented method comprising: obtaining search results that are identified as responsive to an original search query; determining a frequency with which a particular term occurs in text associated with one or more of the search results; determining a frequency with which the particular term occurs in other text; determining that the frequency with which the particular term occurs in the text associated with the one or more search results differs, by a threshold extent, from the frequency with which the particular term occurs in the other text; in response to determining that the frequency with which the particular term occurs in a text associated with the one or more search results differs, by the threshold extent, from the frequency with which the term occurs in the other text, providing, for display, a representation of the particular term, a demote control to specify that the search results for the reformulated search query that include the particular term are to be demoted, and a promote control to specify that the search results for the formulated search query that include the particular term are to be promoted; receiving data indicative of a selection of the demote control or the promote control; and in response to receiving the data indicative of the selection of the demote control or the promote control, reformulating the original search query to promote or demote the particular term. 3. The method of claim 1 , wherein determining a frequency with which a particular term occurs in the text associated with the one or more of the search results comprises determining the frequency of the particular term based on an occurrence count, and the particular term not being a stop word.
| 0.611549 |
8,645,358 | 10 | 14 |
10. The system of claim 8 , wherein the processing device is further configured to: identify a non-public content source to which the user has access; perform the search operation on the non-public content source; and include the non-public content source in the plurality of preferred content sources.
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10. The system of claim 8 , wherein the processing device is further configured to: identify a non-public content source to which the user has access; perform the search operation on the non-public content source; and include the non-public content source in the plurality of preferred content sources. 14. The system of claim 10 , wherein the non-public content is an online discussion group.
| 0.732143 |
8,949,371 | 8 | 9 |
8. An endpoint device, comprising: a memory; and a processing device coupled with the memory to: receive a data loss prevention (DLP) policy comprising an index file pertaining to structured data to be protected and a DLP response rule; load, from the index file, a set of Bloom filters into memory, wherein the set of Bloom filters comprises a plurality of Bloom filters; load, from the index file, a list of token type patterns into memory; identify free text data for monitoring; determine whether the free text data contains at least a portion of the structured data using the set of Bloom filters and the list of token type patterns; and perform an action designated by the DLP response rule responsive to detection of the structured data in the free text data.
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8. An endpoint device, comprising: a memory; and a processing device coupled with the memory to: receive a data loss prevention (DLP) policy comprising an index file pertaining to structured data to be protected and a DLP response rule; load, from the index file, a set of Bloom filters into memory, wherein the set of Bloom filters comprises a plurality of Bloom filters; load, from the index file, a list of token type patterns into memory; identify free text data for monitoring; determine whether the free text data contains at least a portion of the structured data using the set of Bloom filters and the list of token type patterns; and perform an action designated by the DLP response rule responsive to detection of the structured data in the free text data. 9. The endpoint device of claim 8 , wherein the processing device is further to: identify a set of discrete free text tokens for insertion into a discrete free text token list; compare the list of discrete free text tokens with a set of tokens associated with the first Bloom filter; discard, from the list of discrete free text tokens, a subset of free text tokens having no match in the set of tokens associated with the first Bloom filter; and maintain a surviving list of tokens remaining after discarding the subset of free text tokens having no match in the set of tokens associated with the first Bloom filter.
| 0.5 |
8,423,370 | 2 | 3 |
2. The method of claim 1 , wherein processing the received language-based content comprises interpreting the language-based content into the target language.
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2. The method of claim 1 , wherein processing the received language-based content comprises interpreting the language-based content into the target language. 3. The method of claim 2 , further comprising generating an output of the interpreting and translating as one or more of electronic text data and electronic speech data.
| 0.5 |
8,200,485 | 15 | 23 |
15. A method for improving voice recognition accuracy when a user submits a query by voice to search a domain of items, the method comprising: receiving a set of characters entered by a user, the set of characters representing a portion of a query; in response to receiving the set of characters, selecting a grammar which is derived at least in-part from text extracted from a subset of items that correspond to the set of characters entered by the user; and providing the grammar to a voice recognition system for use in interpreting the query as entered by the user by voice; whereby the user's entry of a subset of characters of the query, together with the user's utterance of the full query, are used in combination to capture the query.
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15. A method for improving voice recognition accuracy when a user submits a query by voice to search a domain of items, the method comprising: receiving a set of characters entered by a user, the set of characters representing a portion of a query; in response to receiving the set of characters, selecting a grammar which is derived at least in-part from text extracted from a subset of items that correspond to the set of characters entered by the user; and providing the grammar to a voice recognition system for use in interpreting the query as entered by the user by voice; whereby the user's entry of a subset of characters of the query, together with the user's utterance of the full query, are used in combination to capture the query. 23. The method as defined in claim 15 , wherein receiving a set of characters comprises determining in real time whether a number of entered characters is sufficient to produce a grammar that falls below a threshold size.
| 0.576628 |
8,850,591 | 10 | 11 |
10. An apparatus, comprising: a processor; and a memory, wherein the apparatus is configured to: capture packets as part of providing a firewall function in a network environment; identify a root term from at least one of search results, one or more incident lists, or user-provided input, and determine one or more other terms belonging to a group associated with the root term, wherein one or more of the terms from the group are selected; convert the selected terms to regular expressions that are mapped to attributes according to an attribute map, wherein the attributes are associated with a concept; index a document using tags stored in a tag database, wherein if a predetermined number of the regular expressions occur in the document, the tags are associated with corresponding attributes by setting a field or position in an index in the tags where each corresponding attribute has a separate field in the tag indicating whether the attribute is present in the document, and wherein the tags include a pointer to a storage location where the document is stored; apply a concept based on the selected terms from the group to a rule provided as part of a security policy that controls whether the document is permitted to be sent to a next destination as part of network traffic, wherein the rule is applied to the tags; and quarantine at least some of the network traffic based on the rule.
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10. An apparatus, comprising: a processor; and a memory, wherein the apparatus is configured to: capture packets as part of providing a firewall function in a network environment; identify a root term from at least one of search results, one or more incident lists, or user-provided input, and determine one or more other terms belonging to a group associated with the root term, wherein one or more of the terms from the group are selected; convert the selected terms to regular expressions that are mapped to attributes according to an attribute map, wherein the attributes are associated with a concept; index a document using tags stored in a tag database, wherein if a predetermined number of the regular expressions occur in the document, the tags are associated with corresponding attributes by setting a field or position in an index in the tags where each corresponding attribute has a separate field in the tag indicating whether the attribute is present in the document, and wherein the tags include a pointer to a storage location where the document is stored; apply a concept based on the selected terms from the group to a rule provided as part of a security policy that controls whether the document is permitted to be sent to a next destination as part of network traffic, wherein the rule is applied to the tags; and quarantine at least some of the network traffic based on the rule. 11. The apparatus of claim 10 , wherein the apparatus is a network appliance that is coupled to a network and a database.
| 0.789931 |
9,753,908 | 21 | 22 |
21. The medium of claim 20 wherein said instructions further cause the processor to perform the step of pre-designating mapping preferences of the user so as to create a custom map that associates types of expense data of the receipt to desired cells in the spreadsheet.
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21. The medium of claim 20 wherein said instructions further cause the processor to perform the step of pre-designating mapping preferences of the user so as to create a custom map that associates types of expense data of the receipt to desired cells in the spreadsheet. 22. The medium of claim 21 wherein said instructions further cause the processor to associate the types of expense data with addresses of the desired cells by dragging and dropping the types of expense data into the desired cells.
| 0.5 |
8,909,627 | 11 | 16 |
11. The device of claim 10 , wherein assigning the score to the particular synonym rule based at least in part on the particular type of skip count for the particular synonym rule comprises assigning the score to the particular synonym rule based on the particular type of skip count for the particular synonym rule, a click count for the particular synonym rule, and a different type of skip count for the particular synonym rule.
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11. The device of claim 10 , wherein assigning the score to the particular synonym rule based at least in part on the particular type of skip count for the particular synonym rule comprises assigning the score to the particular synonym rule based on the particular type of skip count for the particular synonym rule, a click count for the particular synonym rule, and a different type of skip count for the particular synonym rule. 16. The device of claim 11 , wherein the score assigned to the particular synonym rule satisfies: W 1 ( particular type of skip count ) + W 2 ( different type of skip count ) W 1 ( particular type of skip count ) + W 2 ( different type of skip count ) + W 3 ( click count ) , wherein W1 represents a weight associated with the particular type of skip count for the particular synonym rule, W2 represents a weight associated with the different type of skip count for the particular synonym rule, and W3 represents a weight associated with the click count for the particular synonym rule.
| 0.5 |
8,620,912 | 10 | 11 |
10. The method of claim 1 , comprising: ranking one or more ads based upon the one or more clusters to create a set of ranked ads.
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10. The method of claim 1 , comprising: ranking one or more ads based upon the one or more clusters to create a set of ranked ads. 11. The method of claim 10 , comprising: presenting a ranked ad from the set of ranked ads based upon a rank of the ranked ad.
| 0.5 |
9,710,547 | 14 | 15 |
14. The method of claim 13 , wherein a semantic matching degree is determined based on the query and the one or more pieces of content for each coincidence between the global weighted semantic representation of the query and the global semantic representation of the one or more pieces of content as the coincidence for the reliability of matching of the one or more pieces of content.
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14. The method of claim 13 , wherein a semantic matching degree is determined based on the query and the one or more pieces of content for each coincidence between the global weighted semantic representation of the query and the global semantic representation of the one or more pieces of content as the coincidence for the reliability of matching of the one or more pieces of content. 15. The method of claim 14 , wherein the response to the query is selected as the one or more pieces of content having a highest semantic matching degree.
| 0.5 |
6,161,091 | 19 | 20 |
19. The speech encoding/decoding system according to claim 18, wherein said recognition section includes an analysis frame generation section configured to divide said input speech signal into analysis frames, a feature extraction section configured to acquire a feature vector for each of the analysis frames, and a phonetic segment determination section configured to compute a similarity between said feature vector for each of the analysis frames and a feature template vector previously prepared for each phonetic segment to determine a phonetic segment of each of the analysis frames which is used to recognize the character information.
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19. The speech encoding/decoding system according to claim 18, wherein said recognition section includes an analysis frame generation section configured to divide said input speech signal into analysis frames, a feature extraction section configured to acquire a feature vector for each of the analysis frames, and a phonetic segment determination section configured to compute a similarity between said feature vector for each of the analysis frames and a feature template vector previously prepared for each phonetic segment to determine a phonetic segment of each of the analysis frames which is used to recognize the character information. 20. The speech encoding/decoding system according to claim 19, wherein said phonetic segment determination section computes a Euclidean distance based on said feature vector and said feature template vector and determines a phonetic segment which minimizes said Euclidean distance as a phonetic segment of said analysis frames.
| 0.612559 |
8,990,064 | 10 | 11 |
10. The method of claim 7 , further comprising determining the preferred translation engine from the plurality of available translation engines.
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10. The method of claim 7 , further comprising determining the preferred translation engine from the plurality of available translation engines. 11. The method of claim 10 , wherein the determining comprises: evaluating, using a translator evaluation module, previous translations performed by each of the plurality of translation engines; predicting a translation quality associated with each of the plurality of available translation engines based on the previous translations; and selecting the translation engine with the highest translation quality prediction as the preferred translation engine.
| 0.5 |
8,751,227 | 13 | 14 |
13. An acoustic model learning method comprising: a first acoustic model learning step that estimates a parameter defining a first variation model indicating a variation in a speech for each type of a first environment factor by using a plurality of sample speech data acquired for each combination of one of a plurality of types of the first environment factor and one of plurality of types of a second environment factor, the first environment factor being one of a plurality of environment factors that change and thereby cause a variation in a speech, and the second environment factor being another of the plurality of environment factors; a second variation model learning step that, using the plurality of sample speech data, with respect to each type of the second environment factor, estimates a parameter defining a second variation model indicating a variation in a speech; and an environment-independent acoustic model learning step that, using the plurality of sample speech data, estimates a parameter defining an environment-independent acoustic model not specified as any type of the first environment factor and the second environment factor, wherein each of the acoustic model learning steps estimates each parameter in such a way that an integrated degree of fitness obtained by integrating a degree of fitness of the first variation model to the sample speech data, a degree of fitness of the second variation model to the sample speech data, and a degree of fitness of the environment-independent acoustic model to the sample speech data becomes the maximum, wherein the first variation model and the second variation model are each defined by a two-stage affine transformation.
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13. An acoustic model learning method comprising: a first acoustic model learning step that estimates a parameter defining a first variation model indicating a variation in a speech for each type of a first environment factor by using a plurality of sample speech data acquired for each combination of one of a plurality of types of the first environment factor and one of plurality of types of a second environment factor, the first environment factor being one of a plurality of environment factors that change and thereby cause a variation in a speech, and the second environment factor being another of the plurality of environment factors; a second variation model learning step that, using the plurality of sample speech data, with respect to each type of the second environment factor, estimates a parameter defining a second variation model indicating a variation in a speech; and an environment-independent acoustic model learning step that, using the plurality of sample speech data, estimates a parameter defining an environment-independent acoustic model not specified as any type of the first environment factor and the second environment factor, wherein each of the acoustic model learning steps estimates each parameter in such a way that an integrated degree of fitness obtained by integrating a degree of fitness of the first variation model to the sample speech data, a degree of fitness of the second variation model to the sample speech data, and a degree of fitness of the environment-independent acoustic model to the sample speech data becomes the maximum, wherein the first variation model and the second variation model are each defined by a two-stage affine transformation. 14. An acoustic model learning method according to claim 13 , wherein each of the acoustic model learning steps uses a probability that the sample speech data is observed, represented by the parameters of the first variation model, the second variation model and the environment-independent acoustic model, as the integrated degree of fitness.
| 0.5 |
7,779,385 | 12 | 15 |
12. A computer implemented method for generating a component product extensible markup language (XML) file to be used in performing a component product integration synchronization, comprising: (a) identifying a component product for which a component product XML file is to be generated; (b) reading a tag in a component product XML schema; (c) identifying the tag read in operation (b) as representing an executor tag; (d) in response to operation (c), running an executor specified by the executor tag to retrieve data pertinent to installation of the component product from an install unit associated with the component product without running the install unit, wherein the install unit of the component product includes instructions and data for installing the given component product in a stand-alone manner; (e) in response to operation (d), storing the retrieved data in the component product XML file; (f) identifying the tag read in operation (b) as representing a static data tag; (g) in response to operation (f), retrieving static data associated with the static data tag from a static data source that includes data which is not subject to variation among different component products; (h) in response to operation (g), storing the retrieved static data in the component product XML file; and (i) repeating operations (b) through (h) until each tag instance in the component product XML schema has been read once and in a sequential manner beginning with a first tag present in the component product XML schema.
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12. A computer implemented method for generating a component product extensible markup language (XML) file to be used in performing a component product integration synchronization, comprising: (a) identifying a component product for which a component product XML file is to be generated; (b) reading a tag in a component product XML schema; (c) identifying the tag read in operation (b) as representing an executor tag; (d) in response to operation (c), running an executor specified by the executor tag to retrieve data pertinent to installation of the component product from an install unit associated with the component product without running the install unit, wherein the install unit of the component product includes instructions and data for installing the given component product in a stand-alone manner; (e) in response to operation (d), storing the retrieved data in the component product XML file; (f) identifying the tag read in operation (b) as representing a static data tag; (g) in response to operation (f), retrieving static data associated with the static data tag from a static data source that includes data which is not subject to variation among different component products; (h) in response to operation (g), storing the retrieved static data in the component product XML file; and (i) repeating operations (b) through (h) until each tag instance in the component product XML schema has been read once and in a sequential manner beginning with a first tag present in the component product XML schema. 15. A computer implemented method for generating a component product XML file to be used in performing a component product integration synchronization as recited in claim 12 , wherein the component product XML file includes, a section for identifying the component product and a platform required by the component product, a section for identifying sub-components of the component product, a section for identifying install units of the identified sub-components of the component product, a section for specifying dependencies between the component product and other component products, and a section for specifying resources required by the component product and the identified sub-components of the component product.
| 0.5 |
9,098,494 | 1 | 4 |
1. One or more computer-readable storage media comprising computer-executable instructions for providing linguistic services, the computer-executable instructions directed to steps comprising: receiving linguistic input in a first language from a user; generating a text-based linguistic input in the first language by recognizing the received linguistic input; utilizing machine translation to translate the text-based linguistic input from the first language into a second language differing from the first language; providing the translated text-based linguistic input, in the second language, to pre-existing components providing linguistic services that operate in the context of the second language; receiving, from the pre-existing components, output that is responsive to the linguistic input, the output being provided in the second language; utilizing the machine translation to translate the output in the second language into the first language; generating output that is receivable by the user in accordance with the translated output in the first language.
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1. One or more computer-readable storage media comprising computer-executable instructions for providing linguistic services, the computer-executable instructions directed to steps comprising: receiving linguistic input in a first language from a user; generating a text-based linguistic input in the first language by recognizing the received linguistic input; utilizing machine translation to translate the text-based linguistic input from the first language into a second language differing from the first language; providing the translated text-based linguistic input, in the second language, to pre-existing components providing linguistic services that operate in the context of the second language; receiving, from the pre-existing components, output that is responsive to the linguistic input, the output being provided in the second language; utilizing the machine translation to translate the output in the second language into the first language; generating output that is receivable by the user in accordance with the translated output in the first language. 4. The computer-readable storage media of claim 1 , comprising further computer-executable instructions directed to: identifying the first language from the received linguistic input; and selecting an input recognition component to perform the generating the text-based linguistic input and a machine translation component based on the identified first language.
| 0.5 |
7,664,734 | 20 | 21 |
20. The computer-readable storage medium of claim 18 , wherein at least a first event of the plurality of events comprises one or more words and the program code for identifying the plurality of user-context attributes further comprises program code for extracting a term from the one or more words.
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20. The computer-readable storage medium of claim 18 , wherein at least a first event of the plurality of events comprises one or more words and the program code for identifying the plurality of user-context attributes further comprises program code for extracting a term from the one or more words. 21. The computer-readable storage medium of claim 20 , wherein the program code for extracting the term from the one or more words comprises program code for identifying content unique to the one or more words and extracting the term from the unique content.
| 0.5 |
8,775,156 | 7 | 11 |
7. One or more non-transitory machine-readable media storing instructions that are executable by one or more processing devices for translating between languages, the instructions being executable to perform operations comprising: sending, to a server, data corresponding to a first language; sending, to the server, data corresponding to a second language; generating initial audio data based on input speech, the initial audio data being in an initial language; sending the initial audio data to the server; receiving translation data that corresponds to a translation of the initial audio data, the translation data corresponding to a target language; storing the translation data in memory on the mobile device; identifying movement of the mobile device between first and second users, wherein movement of the mobile device comprises at least moving the mobile device angularly relative to a predefined reference; and in response to the movement of the mobile device: (a) retrieving the translation data from the memory; and (b) generating output audio data from the translation data, the output audio data comprising a translation of the input speech into the target language; wherein, in a case that the mobile device is in a first location, the initial language is the first language and the target language is the second language and, in a case that the mobile device is in a second location, the initial language is the second language and the target language is the first language; and wherein subsequent operations of the mobile device relating to translation between the first language and the second language are triggered automatically by subsequent movement of the mobile device.
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7. One or more non-transitory machine-readable media storing instructions that are executable by one or more processing devices for translating between languages, the instructions being executable to perform operations comprising: sending, to a server, data corresponding to a first language; sending, to the server, data corresponding to a second language; generating initial audio data based on input speech, the initial audio data being in an initial language; sending the initial audio data to the server; receiving translation data that corresponds to a translation of the initial audio data, the translation data corresponding to a target language; storing the translation data in memory on the mobile device; identifying movement of the mobile device between first and second users, wherein movement of the mobile device comprises at least moving the mobile device angularly relative to a predefined reference; and in response to the movement of the mobile device: (a) retrieving the translation data from the memory; and (b) generating output audio data from the translation data, the output audio data comprising a translation of the input speech into the target language; wherein, in a case that the mobile device is in a first location, the initial language is the first language and the target language is the second language and, in a case that the mobile device is in a second location, the initial language is the second language and the target language is the first language; and wherein subsequent operations of the mobile device relating to translation between the first language and the second language are triggered automatically by subsequent movement of the mobile device. 11. The one or more non-transitory machine-readable media of claim 7 , wherein the operations comprise: receiving language options on the mobile device; and outputting one or more selections from among the language options; wherein at least one of the initial language and the target language are based on the one or more selections.
| 0.5 |
8,145,497 | 1 | 16 |
1. A method for processing a media interface by a device, the method comprising: displaying, by the device, a user interface screen including a text window on which a script to be converted into voice is written, and a conversion execution icon to be selected for converting the script written on the text window into voice, and menu icons for setting conversion conditions; and setting, by the device, a conversion condition on the script by dragging at least one of the menu icons and locating the dragged menu icon to a specific location on the script, wherein the step of locating the dragged menu icon comprises: locating at least one of a repeat start icon, a speed faster icon and a speed slower icon on a beginning of a section in the script, and locating at least one of a repeat end icon and an original speed icon on an ending of the section.
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1. A method for processing a media interface by a device, the method comprising: displaying, by the device, a user interface screen including a text window on which a script to be converted into voice is written, and a conversion execution icon to be selected for converting the script written on the text window into voice, and menu icons for setting conversion conditions; and setting, by the device, a conversion condition on the script by dragging at least one of the menu icons and locating the dragged menu icon to a specific location on the script, wherein the step of locating the dragged menu icon comprises: locating at least one of a repeat start icon, a speed faster icon and a speed slower icon on a beginning of a section in the script, and locating at least one of a repeat end icon and an original speed icon on an ending of the section. 16. The method according to claim 1 , wherein the conversion condition the script comprises at least one of a voice signal, a format, and sound quality.
| 0.776471 |
8,000,964 | 5 | 6 |
5. The method of constructing a model of recognizing English pronunciation variations as claimed in claim 1 , wherein the step of constructing a pronunciation variation network corresponding to the English expression comprises: setting the phonetic alphabet of the English expression as a reference; detecting whether an insertion pronunciation variation exists in each pronunciation of the phonetic alphabets of English; detecting whether a deletion pronunciation variation exists between each phonetic alphabet and its next phonetic alphabet; detecting a substitution pronunciation variation corresponding to each phonetic alphabet; and constructing the pronunciation variation network.
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5. The method of constructing a model of recognizing English pronunciation variations as claimed in claim 1 , wherein the step of constructing a pronunciation variation network corresponding to the English expression comprises: setting the phonetic alphabet of the English expression as a reference; detecting whether an insertion pronunciation variation exists in each pronunciation of the phonetic alphabets of English; detecting whether a deletion pronunciation variation exists between each phonetic alphabet and its next phonetic alphabet; detecting a substitution pronunciation variation corresponding to each phonetic alphabet; and constructing the pronunciation variation network. 6. The method of constructing a model of recognizing English pronunciation variations as claimed in claim 5 , wherein the step of detecting a substitution pronunciation variation corresponding to each phonetic alphabet comprises: obtaining a pronunciation type in the IPA for each phonetic alphabet; and using at least one IPA with the same pronunciation type as the substitution pronunciation variation of the phonetic alphabet.
| 0.635823 |
6,085,196 | 8 | 9 |
8. A system according to claim 6, wherein the map creator object comprises: a reference to a software object for an element for transformation of the first structured information format; a reference to a software object for an element of the second structured information format, for transformation of the element of the first structured information format; a reference to a software object for a property of the element of the second structured information format, for transformation of the element of the first structured information format; a reference to a software object for an attribute value of the element of the second structured information format, for transformation of the element of the first structured information format; an object method for obtaining the element for transformation of the first structured information format, which has been interactively selected by the user, using the software object for the element for transformation of the first structured information format; an object method for obtaining the element of the second structured information format which corresponds to the element of the first structured information format, which has been interactively selected by the user, using the software object for the element of the second structured information format; an object method for determining a property of the element of the second structured information format which has been selected by the user, using the software object for a property of the element of the second structured information format; an object method for obtaining a second structured information format attribute value which has been interactively input by a user, using the software object for the attribute value of the element of the second structured information format; and an object method for assigning the attribute value which has been interactively input by a user to the second structured information format attribute value.
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8. A system according to claim 6, wherein the map creator object comprises: a reference to a software object for an element for transformation of the first structured information format; a reference to a software object for an element of the second structured information format, for transformation of the element of the first structured information format; a reference to a software object for a property of the element of the second structured information format, for transformation of the element of the first structured information format; a reference to a software object for an attribute value of the element of the second structured information format, for transformation of the element of the first structured information format; an object method for obtaining the element for transformation of the first structured information format, which has been interactively selected by the user, using the software object for the element for transformation of the first structured information format; an object method for obtaining the element of the second structured information format which corresponds to the element of the first structured information format, which has been interactively selected by the user, using the software object for the element of the second structured information format; an object method for determining a property of the element of the second structured information format which has been selected by the user, using the software object for a property of the element of the second structured information format; an object method for obtaining a second structured information format attribute value which has been interactively input by a user, using the software object for the attribute value of the element of the second structured information format; and an object method for assigning the attribute value which has been interactively input by a user to the second structured information format attribute value. 9. A system according to claim 8, wherein the map creator object further comprises: a reference to a software object for registering an instance of an element for transformation of the first structured information format; and a reference to a software object for unregistering the instance of an element for transformation of the first structured information format when the element is no longer needed by the map creator.
| 0.5 |
9,606,973 | 5 | 6 |
5. The method of claim 1 , wherein the identifying a character location of the one or more potential errors proceeds sequentially through potential error character locations responsive to repeated user input associated with the string of characters.
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5. The method of claim 1 , wherein the identifying a character location of the one or more potential errors proceeds sequentially through potential error character locations responsive to repeated user input associated with the string of characters. 6. The method of claim 5 , wherein sequential error locations are visually indicated responsive to repeated user input associated with the string of characters.
| 0.5 |
10,032,134 | 3 | 4 |
3. The method of claim 2 wherein after responses from each of the set of approvers are received, an outcome of the requested decision is received.
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3. The method of claim 2 wherein after responses from each of the set of approvers are received, an outcome of the requested decision is received. 4. The method of claim 3 wherein the request for the decision is a first request for the decision, and wherein the decision manager system is a first decision manager system, further comprising: initiating a second request for a decision at a second decision manager system in response to receiving the outcome of the first requested decision.
| 0.5 |
7,519,908 | 18 | 19 |
18. The system of claim 17 , further comprising: means for executing the one or more logical tasks to configure the cluster of application server instances.
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18. The system of claim 17 , further comprising: means for executing the one or more logical tasks to configure the cluster of application server instances. 19. The system of claim 18 , wherein the means for executing the one or more logical tasks to configure the cluster of application server instances comprises: means for executing a change global settings task to set one or more global settings of the cluster of application server instances.
| 0.5 |
8,909,616 | 20 | 28 |
20. A server of an information retrieval system, the server comprising: at least one processor; and a memory storing instructions that, when executed by the processor, cause the at least one processor to perform operations, the at least one processor comprising: a preprocessor, wherein operations associated with the preprocessor include: receiving a query by the server from a requesting application presented by a client access device, the query including a first term, the requesting application having a plurality of subject areas, the query associated with a subject area of the plurality of subject areas; selecting a taxonomy that is associated with the subject area of the query, the plurality of subject areas being related to different taxonomies; and refining the query based on the taxonomy selected to include a second term in the query; a switchboard, wherein operations associated with the switchboard include retrieving a search result from at least one database in accordance with the query as refined, the search result including at least one document; and a postprocessor, wherein operations associated with the postprocessor include refining the retrieved search result for transmission by the server to the client access device.
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20. A server of an information retrieval system, the server comprising: at least one processor; and a memory storing instructions that, when executed by the processor, cause the at least one processor to perform operations, the at least one processor comprising: a preprocessor, wherein operations associated with the preprocessor include: receiving a query by the server from a requesting application presented by a client access device, the query including a first term, the requesting application having a plurality of subject areas, the query associated with a subject area of the plurality of subject areas; selecting a taxonomy that is associated with the subject area of the query, the plurality of subject areas being related to different taxonomies; and refining the query based on the taxonomy selected to include a second term in the query; a switchboard, wherein operations associated with the switchboard include retrieving a search result from at least one database in accordance with the query as refined, the search result including at least one document; and a postprocessor, wherein operations associated with the postprocessor include refining the retrieved search result for transmission by the server to the client access device. 28. The server of claim 20 , wherein operations associated with the postprocessor include refining the search result in accordance with a white list of documents, the white list indicating one or more documents to be added to or elevated in the search result.
| 0.523897 |
7,646,868 | 10 | 16 |
10. An article comprising: a machine accessible medium containing instructions, which when executed, result in encrypting a clear text message into an obscured, encrypted message using a key phrase by partitioning the key phrase and the clear text message into separate words; determining an index value for each word of the key phrase, concatenating the index values together to form a key string, and partitioning the key string into sections of a first predetermined length; determining an index value for each word of the clear text message, concatenating the index values together to form a message string, and partitioning the message string into sections of a second predetermined length; for each key section and message section pair, concatenating the key section to the message section to form a cipher text section, and adding the cipher text section to a cipher text string; and for each section of the cipher text string, locating a row of a word matrix indexed by the cipher text section, randomly selecting a template from a template file, the template including a plurality of tags, obtaining one or more words from the word matrix row according to columns selected by the tags, and replacing the cipher text section with the obtained words according to the randomly selected template to form the obscured, encrypted message.
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10. An article comprising: a machine accessible medium containing instructions, which when executed, result in encrypting a clear text message into an obscured, encrypted message using a key phrase by partitioning the key phrase and the clear text message into separate words; determining an index value for each word of the key phrase, concatenating the index values together to form a key string, and partitioning the key string into sections of a first predetermined length; determining an index value for each word of the clear text message, concatenating the index values together to form a message string, and partitioning the message string into sections of a second predetermined length; for each key section and message section pair, concatenating the key section to the message section to form a cipher text section, and adding the cipher text section to a cipher text string; and for each section of the cipher text string, locating a row of a word matrix indexed by the cipher text section, randomly selecting a template from a template file, the template including a plurality of tags, obtaining one or more words from the word matrix row according to columns selected by the tags, and replacing the cipher text section with the obtained words according to the randomly selected template to form the obscured, encrypted message. 16. The article of claim 10 , wherein the first predetermined length is three characters, and the second predetermined length is two characters.
| 0.696203 |
9,142,217 | 19 | 21 |
19. A system for facilitating the exchange of streamed speech recognition and transcription among users, the system comprising: (a) at least one system transaction manager using a uniform system protocol, including at least one post processing manager, wherein transaction manager is i) adapted to receive a streamed speech information request from at least one user employing a first user legacy protocol and flag the information request as requiring post processing, and, ii) configured to route a requested response to a speech information request to one or more users employing a second user legacy protocol, the speech information request comprised of spoken text and commands, including spoken commands, wherein the requested response comprises a transcription of spoken text and the post processed information requested, and wherein the post processing manager is configured to i) receive structured transcription from a speech recognition and/or transcription engine, ii) operate upon the transcribed speech, including spoken commands in accordance with the speech information request, and, iii) rout the requested response to a post processing application, if designated in the speech information request; (b) at least one application service adapter configured to provide bi-directional communication between the first user legacy protocol and the uniform system protocol, and between the second user legacy protocol and the uniform system protocol, and capable of bi-directional communication with the system transaction manager; and, (c) at least one speech recognition and/or transcription engine communicating with the system transaction manager, wherein the speech recognition and/or transcription engine is configured to receive the flagged streamed speech information request containing spoken text and commands, including spoken commands, from the system transaction manager, to generate a transcription in response to the speech information request and to route the response comprised of transcribed spoken text and transcribed spoken commands to the post processing manager.
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19. A system for facilitating the exchange of streamed speech recognition and transcription among users, the system comprising: (a) at least one system transaction manager using a uniform system protocol, including at least one post processing manager, wherein transaction manager is i) adapted to receive a streamed speech information request from at least one user employing a first user legacy protocol and flag the information request as requiring post processing, and, ii) configured to route a requested response to a speech information request to one or more users employing a second user legacy protocol, the speech information request comprised of spoken text and commands, including spoken commands, wherein the requested response comprises a transcription of spoken text and the post processed information requested, and wherein the post processing manager is configured to i) receive structured transcription from a speech recognition and/or transcription engine, ii) operate upon the transcribed speech, including spoken commands in accordance with the speech information request, and, iii) rout the requested response to a post processing application, if designated in the speech information request; (b) at least one application service adapter configured to provide bi-directional communication between the first user legacy protocol and the uniform system protocol, and between the second user legacy protocol and the uniform system protocol, and capable of bi-directional communication with the system transaction manager; and, (c) at least one speech recognition and/or transcription engine communicating with the system transaction manager, wherein the speech recognition and/or transcription engine is configured to receive the flagged streamed speech information request containing spoken text and commands, including spoken commands, from the system transaction manager, to generate a transcription in response to the speech information request and to route the response comprised of transcribed spoken text and transcribed spoken commands to the post processing manager. 21. The system of claim 19 wherein the speech information request is received by the transaction manager through a post processing applications portal, wherein the request is flagged for post processing.
| 0.844086 |
9,761,223 | 2 | 3 |
2. The system of claim 1 , wherein a source for the spoken utterance includes a database of recorded utterances.
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2. The system of claim 1 , wherein a source for the spoken utterance includes a database of recorded utterances. 3. The system of claim 2 , wherein the spoken utterance is provided as a digital sound file from the database of recorded utterances to the computing device.
| 0.5 |
8,265,924 | 7 | 12 |
7. A method residing in a nor-transitory computer-readable medium and executed by a machine, the method comprising: parsing, by the machine, a repository for strings and files a base language, the base language is a native language for the strings that are to be matched to within a master language data structure; inserting, by the machine, unique entries for each unique string and each unique file in the master language data structure and within the base language, each unique entry identified by a numeric value and an identifying string and at least one of a different language including a specific dialect for a given language and the unique entries providing version control; partially housing, by the machine, the master language data structure in memory of the machine; requesting, by the machine, translations for each unique entry for different supported languages, each translation requested is for a particular one of the different supported languages; receiving, by the machine, a translation for each requested translation; and linking, by the machine, the translations to their corresponding unique entry within the master language data structure, and particular translations are supplied to requestors when particular strings in the base language are provided by the requestors and those particular strings are used to find matches in the master language data structure to particular entries for those particular strings in the base language for purposes of returning to the requestors the particular translations, the requestors are automated software resources.
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7. A method residing in a nor-transitory computer-readable medium and executed by a machine, the method comprising: parsing, by the machine, a repository for strings and files a base language, the base language is a native language for the strings that are to be matched to within a master language data structure; inserting, by the machine, unique entries for each unique string and each unique file in the master language data structure and within the base language, each unique entry identified by a numeric value and an identifying string and at least one of a different language including a specific dialect for a given language and the unique entries providing version control; partially housing, by the machine, the master language data structure in memory of the machine; requesting, by the machine, translations for each unique entry for different supported languages, each translation requested is for a particular one of the different supported languages; receiving, by the machine, a translation for each requested translation; and linking, by the machine, the translations to their corresponding unique entry within the master language data structure, and particular translations are supplied to requestors when particular strings in the base language are provided by the requestors and those particular strings are used to find matches in the master language data structure to particular entries for those particular strings in the base language for purposes of returning to the requestors the particular translations, the requestors are automated software resources. 12. The method of claim 7 further comprising, updating, by the machine, a selective entry in the base language if one or its corresponding entries in the one or more different supported languages is detected as having been modified.
| 0.587189 |
9,477,991 | 56 | 68 |
56. The method of claim 53 , wherein generating the at least the second query that includes the at least one identification of the geographic context region and that is based on the search data and the at least one social network media data source includes transforming the first query in accordance with at least one interface of the at least one social network media data source such that the at least the second query is supported as a query of the at least one social network media data source.
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56. The method of claim 53 , wherein generating the at least the second query that includes the at least one identification of the geographic context region and that is based on the search data and the at least one social network media data source includes transforming the first query in accordance with at least one interface of the at least one social network media data source such that the at least the second query is supported as a query of the at least one social network media data source. 68. The method of claim 56 , wherein the second geographical area includes at least the first portion of the geographic context region and the second portion of the geographic context region.
| 0.5 |
5,530,645 | 8 | 9 |
8. A composite dictionary data compression process of claim 7 further comprising the step of updating the composite dictionary in response to detecting that the compressed data entry does not correspond to stored data from the composite dictionary.
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8. A composite dictionary data compression process of claim 7 further comprising the step of updating the composite dictionary in response to detecting that the compressed data entry does not correspond to stored data from the composite dictionary. 9. A composite dictionary data compression process of claim 8 wherein the step of updating the composite dictionary further comprises copying the literal data string from the compressed input data buffer to the composite dictionary.
| 0.5 |
7,930,286 | 1 | 5 |
1. A method, comprising steps of: generating a generated query based at least in part on a certain search query; submitting the generated query to a particular search engine; in response to submitting the generated query to the particular search engine, receiving from the particular search engine results indicating a first plurality of search engines; selecting a second plurality of search engines from the first plurality of search engines; to obtain search results for said certain search query, submitting one or more search queries to said second plurality of search engines; for each search engine of said second plurality of search engines, receiving certain search results for said one or more search queries submitted to said each search engine; and consolidating said certain search results, which are received from each search engine of said second plurality of search engines, to produce a consolidated set of search results; presenting said consolidated set of search results to a user, wherein particular search results of the consolidated set of search results are visually grouped into search result groups based on the search engine from which each of said particular search results was received; wherein the steps are performed by one or more computing devices.
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1. A method, comprising steps of: generating a generated query based at least in part on a certain search query; submitting the generated query to a particular search engine; in response to submitting the generated query to the particular search engine, receiving from the particular search engine results indicating a first plurality of search engines; selecting a second plurality of search engines from the first plurality of search engines; to obtain search results for said certain search query, submitting one or more search queries to said second plurality of search engines; for each search engine of said second plurality of search engines, receiving certain search results for said one or more search queries submitted to said each search engine; and consolidating said certain search results, which are received from each search engine of said second plurality of search engines, to produce a consolidated set of search results; presenting said consolidated set of search results to a user, wherein particular search results of the consolidated set of search results are visually grouped into search result groups based on the search engine from which each of said particular search results was received; wherein the steps are performed by one or more computing devices. 5. The method as recited in claim 1 wherein selecting said second plurality of search engines comprises selecting a search engine index.
| 0.863454 |
7,908,260 | 9 | 10 |
9. The improved information automation system of claim 1 , wherein the user interface is programmed to accept a deep web harvest query entry from a user, the deep web harvest query being applicable to a plurality of different sources and having an original syntax that includes a set of original Boolean operators; and wherein the information automation stem is programmed to determine whether the first source utilizes a query syntax that differs from the original syntax and, in response to a determination that the first source utilizes a query syntax that omits Boolean operators, to automatically-re-form the web harvest query directed at the first source into a new syntax that differs from the original syntax such that the new syntax reflects logic of the original syntax based on the set of original Boolean operators but without the presence of any Boolean operators.
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9. The improved information automation system of claim 1 , wherein the user interface is programmed to accept a deep web harvest query entry from a user, the deep web harvest query being applicable to a plurality of different sources and having an original syntax that includes a set of original Boolean operators; and wherein the information automation stem is programmed to determine whether the first source utilizes a query syntax that differs from the original syntax and, in response to a determination that the first source utilizes a query syntax that omits Boolean operators, to automatically-re-form the web harvest query directed at the first source into a new syntax that differs from the original syntax such that the new syntax reflects logic of the original syntax based on the set of original Boolean operators but without the presence of any Boolean operators. 10. The improved information automation system of claim 9 , wherein the information automation stem is programmed to determine whether the first source utilizes a query syntax that differs from the original syntax based on a language associated with the first source.
| 0.615274 |
9,772,990 | 13 | 15 |
13. A network server configured to provide a parsing service, the network server comprising: a processor; a memory coupled to the processor; and the parsing service stored in the memory and executable by the processor, wherein the parsing service is configured to: receive a copy of a network application programming interface (API) communication that includes one or more of a network API request made by a computing device or a network API response received by the computing device; parse the received copy of the network API communication by at least removal of repetitive data from the received copy of the network API communication, so as to generate parsed personal assistant data; maintain a network service library that includes service translation information for a plurality of network APIs; identify the service translation information in the network service library for a network API that corresponds to the received copy of the network API communication; convert the parsed personal assistant data by use of the identified service translation information, so as to produce converted data, wherein the service translation information comprises: terms used in connection with the network API, and corresponding translation outputs; include the converted data in the parsed personal assistant data; and send the parsed personal assistant data that includes the converted data to the computing device.
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13. A network server configured to provide a parsing service, the network server comprising: a processor; a memory coupled to the processor; and the parsing service stored in the memory and executable by the processor, wherein the parsing service is configured to: receive a copy of a network application programming interface (API) communication that includes one or more of a network API request made by a computing device or a network API response received by the computing device; parse the received copy of the network API communication by at least removal of repetitive data from the received copy of the network API communication, so as to generate parsed personal assistant data; maintain a network service library that includes service translation information for a plurality of network APIs; identify the service translation information in the network service library for a network API that corresponds to the received copy of the network API communication; convert the parsed personal assistant data by use of the identified service translation information, so as to produce converted data, wherein the service translation information comprises: terms used in connection with the network API, and corresponding translation outputs; include the converted data in the parsed personal assistant data; and send the parsed personal assistant data that includes the converted data to the computing device. 15. The network server of claim 13 , wherein the parsing service is configured to determine relative weights of relationships between the parsed personal assistant data, and to include the relative weights of relationships in the parsed personal assistant data.
| 0.5 |
8,936,623 | 5 | 6 |
5. A polyaxial bone screw assembly for fixation to a bone, the assembly including a shank with a threaded body and an upwardly extending head portion, a retainer structure sized and shaped to matingly engage the head portion, a receiver pivotally engaging the head portion and having two spaced apart arms each being internally threaded and defining gaps therebetween, and an upper pressure insert receivable in the receiver and pivotally engaging the head portion, the assembly comprising: a) a shank having a body for fixation to a bone, an upper head portion, a central cannulation bore extending the entire length of the body and upper head portion the upper head portion defining a first partial sphere; b) a retainer structure, having a second partial sphere the first and second partial spheres mating n the receiver to form a ball like structure that polyaxially rotates in the receiver during position.
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5. A polyaxial bone screw assembly for fixation to a bone, the assembly including a shank with a threaded body and an upwardly extending head portion, a retainer structure sized and shaped to matingly engage the head portion, a receiver pivotally engaging the head portion and having two spaced apart arms each being internally threaded and defining gaps therebetween, and an upper pressure insert receivable in the receiver and pivotally engaging the head portion, the assembly comprising: a) a shank having a body for fixation to a bone, an upper head portion, a central cannulation bore extending the entire length of the body and upper head portion the upper head portion defining a first partial sphere; b) a retainer structure, having a second partial sphere the first and second partial spheres mating n the receiver to form a ball like structure that polyaxially rotates in the receiver during position. 6. The assembly according to claim 5 wherein the first partial sphere has a concave region and the second partial sphere has a convex region that mates with the concave region.
| 0.5 |
8,850,409 | 1 | 2 |
1. A method for automatic computer translation of input code including constraints to a computer executable imperative output program representation, the method comprising: providing a computer processor with an input source program, wherein said input source program is expressed in a programming language that provides for imperative specifications and also provides for declarative specification of constraints, and wherein said input source program includes one or more declaratively specified constraints in accordance with said programming language; the computer processor translating the input source program to the computer executable imperative output program representation, including: automatically generating code comprising constraint representations based at least in part on said declaratively specified constraints, including automatically generating code comprising one or more corresponding constraint reactor code objects to register input change notification upon instantiation, said constraint reactor code objects having imperative procedures that enforce said declaratively specified constraints, wherein generating the constraint representations comprises automatically defining updating procedures for changes to inputs of each of said declaratively specified constraints; for each of some or all program data members of said input source program, automatically generating corresponding notification code to provide change notification and to accommodate registration for input change notification by constraint representations; for each program data member that is also an input to one or more constraint representation instances, constructing a notifiee list of constraint reactor object instances of said one or more constraint representation instances, wherein said each notifiee list is accessible in the same scope as said each program data member; and automatically defining updating procedures for each of said program data members, wherein said updating procedures are invoked if the corresponding program data member changes; and providing said computer executable imperative output program representation as an output program, wherein said imperative procedures to register said constraints are part of the output program, and wherein said constraints will be maintained at execution time by said imperative procedures to register said constraints from within the output program.
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1. A method for automatic computer translation of input code including constraints to a computer executable imperative output program representation, the method comprising: providing a computer processor with an input source program, wherein said input source program is expressed in a programming language that provides for imperative specifications and also provides for declarative specification of constraints, and wherein said input source program includes one or more declaratively specified constraints in accordance with said programming language; the computer processor translating the input source program to the computer executable imperative output program representation, including: automatically generating code comprising constraint representations based at least in part on said declaratively specified constraints, including automatically generating code comprising one or more corresponding constraint reactor code objects to register input change notification upon instantiation, said constraint reactor code objects having imperative procedures that enforce said declaratively specified constraints, wherein generating the constraint representations comprises automatically defining updating procedures for changes to inputs of each of said declaratively specified constraints; for each of some or all program data members of said input source program, automatically generating corresponding notification code to provide change notification and to accommodate registration for input change notification by constraint representations; for each program data member that is also an input to one or more constraint representation instances, constructing a notifiee list of constraint reactor object instances of said one or more constraint representation instances, wherein said each notifiee list is accessible in the same scope as said each program data member; and automatically defining updating procedures for each of said program data members, wherein said updating procedures are invoked if the corresponding program data member changes; and providing said computer executable imperative output program representation as an output program, wherein said imperative procedures to register said constraints are part of the output program, and wherein said constraints will be maintained at execution time by said imperative procedures to register said constraints from within the output program. 2. The method of claim 1 : wherein said constraints in said input source code program are organized in constraint sets, each constraint set including one or more constraints; wherein each of said constraint sets also includes explicit and/or implicit identification of which constraint data members of said included constraints are constraint inputs; and wherein said corresponding constraint representations comprise constraint set object representations corresponding to each of said constraint sets.
| 0.5 |
9,619,593 | 2 | 3 |
2. The method of claim 1 wherein the lookup table includes an output, the method further comprising: determining an error between the table data and the new table data; and utilizing the error during the verification of the model.
|
2. The method of claim 1 wherein the lookup table includes an output, the method further comprising: determining an error between the table data and the new table data; and utilizing the error during the verification of the model. 3. The method of claim 2 further comprising: determining a bound for the error between the table data and the new table data; and using the bound for the error as an unknown input during the verification of the model to verify an aspect of the model.
| 0.5 |
8,452,822 | 9 | 11 |
9. A device, comprising: a memory to store a plurality of instructions; and a processor to execute instructions in the memory to: receive, from a content provider device, a file with a custom name, extract a digital fingerprint from a content sample of the file; generate, based on the digital fingerprint, a universal file name for the file with the custom name, associate the universal file name with the custom name, determine that the universal file name is associated with a previously-stored file, where the previously-stored file and the file with the custom name have an identical digital fingerprint, discard the file with the custom name, and associate, for later retrieval of the previously-stored file, the custom name with the universal file name.
|
9. A device, comprising: a memory to store a plurality of instructions; and a processor to execute instructions in the memory to: receive, from a content provider device, a file with a custom name, extract a digital fingerprint from a content sample of the file; generate, based on the digital fingerprint, a universal file name for the file with the custom name, associate the universal file name with the custom name, determine that the universal file name is associated with a previously-stored file, where the previously-stored file and the file with the custom name have an identical digital fingerprint, discard the file with the custom name, and associate, for later retrieval of the previously-stored file, the custom name with the universal file name. 11. The device of claim 9 , where the universal file name includes a base-36 encoding of the digital fingerprint.
| 0.895176 |
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