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7,877,258 | 1 | 3 | 1. A computer-implemented method comprising: generating a compact language model, including: receiving a collection of n-grams from a corpus, each n-gram of the collection having a corresponding first probability of occurring in the corpus, and generating a trie representing the collection of n-grams including calculating a left word vector and a diversity count vector using the collection of n grams, the left word vector identifying each distinct left word for a given right context in the collection, the diversity count vector identifying a count of distinct left words for each right context in the collection, and using the language model to identify a second probability of a particular string of words occurring; and wherein generating, receiving and using are performed by one or more data processing apparatuses. | 1. A computer-implemented method comprising: generating a compact language model, including: receiving a collection of n-grams from a corpus, each n-gram of the collection having a corresponding first probability of occurring in the corpus, and generating a trie representing the collection of n-grams including calculating a left word vector and a diversity count vector using the collection of n grams, the left word vector identifying each distinct left word for a given right context in the collection, the diversity count vector identifying a count of distinct left words for each right context in the collection, and using the language model to identify a second probability of a particular string of words occurring; and wherein generating, receiving and using are performed by one or more data processing apparatuses. 3. The method of claim 1 , where generating the trie includes: calculating a left context vector identifying each distinct left word for each distinct context in the collection, a left diversity vector identifying a count of distinct left words for each context in the collection, a future word vector identifying each distinct predicted word for a given context in the collection, and a future diversity vector identifying a count of distinct predicted words for each context in the collection. | 0.5 |
8,717,299 | 1 | 6 | 1. A computer-implemented method of translating user input into at least one word having multiple characters, the method comprising: displaying a keyboard on a display, wherein the keyboard comprises multiple keys, and wherein each key in the keyboard has a relative position with respect to every other key in the keyboard, receiving a sequence of user inputs; translating the sequence of user inputs into a pattern of one or more relative directions, wherein each relative direction in the pattern corresponds to the relative position of a key in the keyboard with respect to a previous key; and using the pattern of one or more relative directions to identify the at least one word in a stored dictionary, wherein the stored dictionary associates the word with a pattern of relative directions that reflects relative positions of the keys associated with the multiple characters that form the word. | 1. A computer-implemented method of translating user input into at least one word having multiple characters, the method comprising: displaying a keyboard on a display, wherein the keyboard comprises multiple keys, and wherein each key in the keyboard has a relative position with respect to every other key in the keyboard, receiving a sequence of user inputs; translating the sequence of user inputs into a pattern of one or more relative directions, wherein each relative direction in the pattern corresponds to the relative position of a key in the keyboard with respect to a previous key; and using the pattern of one or more relative directions to identify the at least one word in a stored dictionary, wherein the stored dictionary associates the word with a pattern of relative directions that reflects relative positions of the keys associated with the multiple characters that form the word. 6. The computer-implemented method of claim 1 , wherein the sequence of user inputs is received via a push-button keyboard. | 0.810769 |
7,734,459 | 23 | 29 | 23. A computer-implemented method of associating dependency structures from two different languages stored on a tangible computer readable medium, wherein the dependency structures comprise nodes organized in a parent/child structure, the computer-implemented method comprising: aligning nodes of the dependency structures with correspondences on the tangible medium with a computer as a function of a set of rules comprising at least three different rules, wherein the dependency structures comprise a set of unaligned nodes and wherein after each of the rules are applied any aligned nodes are removed from the set of unaligned nodes before applying another rule, and wherein aligning does not require beginning with either a top or bottom node of the hierarchical parent/child structure of the dependency structures, and wherein aligning is not based on top-down processing or bottom-up processing of nodes; and providing an output from the computer indicative of the alignment of the dependency structures. | 23. A computer-implemented method of associating dependency structures from two different languages stored on a tangible computer readable medium, wherein the dependency structures comprise nodes organized in a parent/child structure, the computer-implemented method comprising: aligning nodes of the dependency structures with correspondences on the tangible medium with a computer as a function of a set of rules comprising at least three different rules, wherein the dependency structures comprise a set of unaligned nodes and wherein after each of the rules are applied any aligned nodes are removed from the set of unaligned nodes before applying another rule, and wherein aligning does not require beginning with either a top or bottom node of the hierarchical parent/child structure of the dependency structures, and wherein aligning is not based on top-down processing or bottom-up processing of nodes; and providing an output from the computer indicative of the alignment of the dependency structures. 29. The computer-implemented method of claim 23 wherein one rule of the set of rules comprises aligning a pair of child nodes, one from each dependency structure, if a tentative correspondence exists between them and if a parent node of each respective child node is already aligned to a corresponding parent node of the other child. | 0.682857 |
10,140,989 | 11 | 12 | 11. A computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for: assigning, using an instant messaging server (IMS), a unique identifier to speech information received from a sending end to serve as a speech ID; sending, using the IMS, the speech information to a receiving end; and in response to a determination that a speech recognition request issued from a user of the receiving end corresponding to the speech information is received: extracting, using the IMS, the speech ID corresponding to the speech information from the speech recognition request; looking up, using the IMS, the speech information; and delivering, using the IMS, a speech recognition command in the speech recognition request and the looked-up speech information to one of a speech recognition module, a speech recognition server, or a speech recognition server cluster; performing, using the one of the speech recognition module, the speech recognition server, or the speech recognition server cluster, speech recognition based on the speech information and the speech recognition command; and converting, using the one of the speech recognition module, the speech recognition server, or the speech recognition server cluster, the speech information to obtain text information corresponding to the speech information, wherein the IMS obtains the text information from the one of the speech recognition module, the speech recognition server, or the speech recognition server cluster; sending, using a sending module, the obtained text information back as a speech recognition result to the receiving end, wherein the speech recognition module is set up in the one of the IMS, the speech recognition server, or the speech recognition server cluster; storing, using the IMS, the obtained text information in a cache in correspondence with the speech ID; and in response to a determination that another speech recognition request for the same speech information is received: extracting, using the IMS, a speech ID from the other speech recognition request; and locating, using the IMS, the text information corresponding to the speech ID from the other speech recognition request. | 11. A computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for: assigning, using an instant messaging server (IMS), a unique identifier to speech information received from a sending end to serve as a speech ID; sending, using the IMS, the speech information to a receiving end; and in response to a determination that a speech recognition request issued from a user of the receiving end corresponding to the speech information is received: extracting, using the IMS, the speech ID corresponding to the speech information from the speech recognition request; looking up, using the IMS, the speech information; and delivering, using the IMS, a speech recognition command in the speech recognition request and the looked-up speech information to one of a speech recognition module, a speech recognition server, or a speech recognition server cluster; performing, using the one of the speech recognition module, the speech recognition server, or the speech recognition server cluster, speech recognition based on the speech information and the speech recognition command; and converting, using the one of the speech recognition module, the speech recognition server, or the speech recognition server cluster, the speech information to obtain text information corresponding to the speech information, wherein the IMS obtains the text information from the one of the speech recognition module, the speech recognition server, or the speech recognition server cluster; sending, using a sending module, the obtained text information back as a speech recognition result to the receiving end, wherein the speech recognition module is set up in the one of the IMS, the speech recognition server, or the speech recognition server cluster; storing, using the IMS, the obtained text information in a cache in correspondence with the speech ID; and in response to a determination that another speech recognition request for the same speech information is received: extracting, using the IMS, a speech ID from the other speech recognition request; and locating, using the IMS, the text information corresponding to the speech ID from the other speech recognition request. 12. The computer program product as described in claim 11 , further comprising computer instructions for: assigning, using the IMS, the speech ID to the speech information sent from the sending end; storing, using the IMS, the speech ID corresponding to the speech information; receiving, using the IMS, the speech recognition request from the receiving end; and locating, using the IMS, the speech information corresponding to the speech ID in the speech recognition request. | 0.709756 |
5,428,707 | 23 | 26 | 23. Word recognition apparatus comprising: input means for receiving successive user generated signals from a user, each of which represents a word to be recognized; means for storing a signal model for each of a plurality of vocabulary words; recognition means for scoring a match between each such user generated signal and each of a plurality of said signal models, and for selecting the word associated with the signal model which scores best against each user generated signal as the recognized word for that user generated signal; adaptive training means for altering the signal models of individual recognized words to take into account information derived from each such individual recognized word's associated user generated signal separately in response to the selection of each of said recognition words; and batch training means for altering the signal model of each of a plurality of words to take into account information derived from multiple user generated signals for which that word was selected as the recognized word in a multi-word sample of user generated signals, said batch training means introducing such information from multiple user genrated signals into that signal model in one training process. | 23. Word recognition apparatus comprising: input means for receiving successive user generated signals from a user, each of which represents a word to be recognized; means for storing a signal model for each of a plurality of vocabulary words; recognition means for scoring a match between each such user generated signal and each of a plurality of said signal models, and for selecting the word associated with the signal model which scores best against each user generated signal as the recognized word for that user generated signal; adaptive training means for altering the signal models of individual recognized words to take into account information derived from each such individual recognized word's associated user generated signal separately in response to the selection of each of said recognition words; and batch training means for altering the signal model of each of a plurality of words to take into account information derived from multiple user generated signals for which that word was selected as the recognized word in a multi-word sample of user generated signals, said batch training means introducing such information from multiple user genrated signals into that signal model in one training process. 26. A word recognition apparatus as in claim 23, wherein said batch training means includes means for comparing user generated signals which have been associated with the same recognition word in said multi-word sample, and for rejecting from use in said batch training means' altering of signal models those user generated signals which differ by more than a given amount from other user generated signals associated with that recognition word. | 0.740373 |
9,063,765 | 1 | 13 | 1. A system for generating source code, comprising: a processor configured to: receive a source binary representation encoded using a first programming language, trace the source binary representation to determine an intermediate representation of the source binary representation, optimize the intermediate representation, and use the optimized intermediate representation to generate a target source code in at least a second programming language that does not require a virtual machine to execute, wherein the target source code has not been compiled; wherein optimizing the intermediate representation includes using an intermediate programming language to functionally modify an object-oriented programming model object reference into a procedural programming model version; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions; wherein functionally modifying the object-oriented programming model object reference includes functionally flatting the object-oriented programming model object reference by resolving and relinking an object-oriented look-up into the procedural programming model version, generating a renamed procedural programming model method by prepending a fully qualified class name to a name of a corresponding oriented programming model method, and modifying the intermediate representation to use a chained tail recursion to approximate a multithreading not directly supported by the second programming language. | 1. A system for generating source code, comprising: a processor configured to: receive a source binary representation encoded using a first programming language, trace the source binary representation to determine an intermediate representation of the source binary representation, optimize the intermediate representation, and use the optimized intermediate representation to generate a target source code in at least a second programming language that does not require a virtual machine to execute, wherein the target source code has not been compiled; wherein optimizing the intermediate representation includes using an intermediate programming language to functionally modify an object-oriented programming model object reference into a procedural programming model version; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions; wherein functionally modifying the object-oriented programming model object reference includes functionally flatting the object-oriented programming model object reference by resolving and relinking an object-oriented look-up into the procedural programming model version, generating a renamed procedural programming model method by prepending a fully qualified class name to a name of a corresponding oriented programming model method, and modifying the intermediate representation to use a chained tail recursion to approximate a multithreading not directly supported by the second programming language. 13. The system of claim 1 , wherein optimizing the intermediate representation includes replacing a portion of the intermediate representation with a corresponding performance optimized version. | 0.616601 |
8,775,406 | 39 | 40 | 39. A method of predicting content of a future unpublished news story with a computing system comprising: a) identifying a first event described in content published in a knowledge domain including one or more social network domains, news content domains, blog domains and/or message board domains; b) automatically predicting future content for a plurality of different alternative future unpublished stories for said first event or updates to said first event, based on a comparison of characteristics of said first event to characteristics of prior events of a same type; wherein said future content is derived from content of prior stories describing said prior events; c) automatically searching for a numerical outcome associated with said first event by querying at least one of a social network, message board, blog and/or search engine. | 39. A method of predicting content of a future unpublished news story with a computing system comprising: a) identifying a first event described in content published in a knowledge domain including one or more social network domains, news content domains, blog domains and/or message board domains; b) automatically predicting future content for a plurality of different alternative future unpublished stories for said first event or updates to said first event, based on a comparison of characteristics of said first event to characteristics of prior events of a same type; wherein said future content is derived from content of prior stories describing said prior events; c) automatically searching for a numerical outcome associated with said first event by querying at least one of a social network, message board, blog and/or search engine. 40. The method of claim 39 further including a step: verifying new published content identifying said numerical outcome before presenting it to a user after consulting a set of content sources associated with a high reputation or trust score. | 0.516 |
8,200,597 | 8 | 9 | 8. A method for managing media contents, comprising: obtaining subtitle information according to a media ID of a media content to be marked; extracting subtitle content information in the obtained subtitle information, marking the subtitle content information chronologically to form multiple media content time segments, and classifying the multiple media content time segments according to defined subject contents to obtain multiple content clips of different subjects; marking start time and end time of each played content clip in media according to time information of the media content time segments, and obtaining multiple content clips which have start and end time information and different subjects; matching the content clips with concepts in an ontology library according to subjects of the content clips which have the start and end time information and different subjects, and marking the content clips through terms defined in the ontology library; extracting the subtitle content information in the obtained subtitle information, and recording an ID and time information of each subtitle content marked with the start time and end time; classifying contents in units of marked subtitle contents according to defined subjects, thus forming multiple content clips which have one or more subjects; and marking the start time and the end time of each played content clip in the media according to the time information. | 8. A method for managing media contents, comprising: obtaining subtitle information according to a media ID of a media content to be marked; extracting subtitle content information in the obtained subtitle information, marking the subtitle content information chronologically to form multiple media content time segments, and classifying the multiple media content time segments according to defined subject contents to obtain multiple content clips of different subjects; marking start time and end time of each played content clip in media according to time information of the media content time segments, and obtaining multiple content clips which have start and end time information and different subjects; matching the content clips with concepts in an ontology library according to subjects of the content clips which have the start and end time information and different subjects, and marking the content clips through terms defined in the ontology library; extracting the subtitle content information in the obtained subtitle information, and recording an ID and time information of each subtitle content marked with the start time and end time; classifying contents in units of marked subtitle contents according to defined subjects, thus forming multiple content clips which have one or more subjects; and marking the start time and the end time of each played content clip in the media according to the time information. 9. The method of claim 8 , wherein the subtitle information is a file which provides text description for a dialog in each medium or for other voices or explanations, comprising: subtitle content information, time code information which indicates time of presence of a subtitle, and/or media ID information corresponding to a subtitle file. | 0.5 |
8,694,537 | 15 | 16 | 15. The processor according to claim 11 , wherein the processor further determines a natural language query included in the search query, the natural language query being associated with a requested service, the search query comprising sound input. | 15. The processor according to claim 11 , wherein the processor further determines a natural language query included in the search query, the natural language query being associated with a requested service, the search query comprising sound input. 16. The processor according to claim 15 , wherein when the natural language query does not correspond to at least one natural language query included in the aggregated natural language library, the natural language query is provided to a plurality of service providers that each have an application that provides a service that is substantially similar to the requested service associated with the natural language query. | 0.5 |
9,304,993 | 1 | 5 | 1. A multiple citation corpus data structure stored in a computer memory for a user specified word or phrase comprising an entry for every occurrence of the user specified word or phrase, each entry comprising: a) prior context, b) the user specified word or phrase, c) subsequent context, and d) one or more internal citations, wherein the prior context, the user specified word or phrase, and the subsequent context forming a unique quote of each entry, wherein each internal citation identifies a document and the location inside the document where the unique quote of the entry is found in a plurality of formatted documents, and wherein the one or more internal citations of each entry is a combined citation comprising a complete list of every occurrence of the unique quote of the respective entry in the respective plurality of formatted documents. | 1. A multiple citation corpus data structure stored in a computer memory for a user specified word or phrase comprising an entry for every occurrence of the user specified word or phrase, each entry comprising: a) prior context, b) the user specified word or phrase, c) subsequent context, and d) one or more internal citations, wherein the prior context, the user specified word or phrase, and the subsequent context forming a unique quote of each entry, wherein each internal citation identifies a document and the location inside the document where the unique quote of the entry is found in a plurality of formatted documents, and wherein the one or more internal citations of each entry is a combined citation comprising a complete list of every occurrence of the unique quote of the respective entry in the respective plurality of formatted documents. 5. The multiple citation corpus data structure of claim 1 , further comprising identification of document parts, said document parts each comprising a distinct group of pages. | 0.636929 |
9,594,741 | 1 | 4 | 1. A computer-implemented method practiced on a client device, comprising: receiving a new term from an application on the client device; segmenting the new term into a set of n-grams; applying a differential privacy algorithm to a selected n-gram in the set of n-grams, generating a differentially private n-gram sketch; selecting a row of the differentially private n-gram sketch; storing the new term and selected row of the differentially private n-gram sketch to a sample buffer of candidates for transmission to a new term learning server. | 1. A computer-implemented method practiced on a client device, comprising: receiving a new term from an application on the client device; segmenting the new term into a set of n-grams; applying a differential privacy algorithm to a selected n-gram in the set of n-grams, generating a differentially private n-gram sketch; selecting a row of the differentially private n-gram sketch; storing the new term and selected row of the differentially private n-gram sketch to a sample buffer of candidates for transmission to a new term learning server. 4. The method of claim 1 , further comprising: for each n-gram in the set of n-grams, storing a position of the n-gram within the term in association with the n-gram and corresponding differentially private n-gram sketch. | 0.743619 |
4,592,086 | 2 | 17 | 2. A continuous speech recognition system for recognizing an input speech composed of a plurality of continuously spoken words comprising: a speech analyzing means for analyzing an input signal at every given frame time point m and outputting an input pattern expressed in a time series of a feature vector comprising a predetermined number of feature parameters; an input pattern memory to store said input pattern; a reference pattern memory to store a reference pattern comprising a feature vector in the same format as said input pattern for each of a plurality (V) of predetermined words to be recognized; a distance calculating means to calculate the distance between the feature vector of said input pattern at a time point m and the feature vector of the reference pattern of the v-th word at a time point n at every time point n under a predetermined distance formula by changing the time point n of the reference pattern of the v-th word in an arbitrary order from the start point to end point N.sup.v while changing the reference pattern V from the first word to end word V for each time point to end point M; an asymptotic calculating means to calculate a similarity measure D(l, v, n) given by the cumulative sum of said distances on the l-th digit at said time points n and a path information F(l, v, n) indicating a time point of the input pattern at the start point of said v-th reference pattern on a path through which said similarity measure D(l, v, n) has been obtained by a predetermined asymptotic expression according to a dynamic programming process while changing the time point n of the reference pattern of the v-th word in an arbitrary order from the start point to end point N.sup.v, while changing digit number l from one to L, and while changing the reference pattern v from the first word to the end word V for each time point m of the input pattern, said time point m changing from the start point to end point M; a digit similarity measure and digit path information calculating means to select a minimum similarity measure from among the similarity measures at the end time points of the reference patterns of all the words on the l-th digit obtained through said asymptotic calculating means, and to provide said minimum similarity measure as a digit similarity measure DB(l, m), a category to which the word corresponding to said minimum similarity measure belongs as a digit recognition category W(l, m)=v, and path information corresponding to said minimum similarity measure as a digit path information FB(l, m) on said digit l at said time point m while changing digit number l from one to L for each time point m of the input pattern, said each time point m changing from the start point to end point M; an initializing means to give said digit similarity measure DB (l-1, m-1) as an initial value of said similarity measure at a time point (m-1) and to give time point (m-1) as an initial value of said path information at a time point m while changing the line point m of the input pattern from the start point to end point M; a decision means to obtain a final digit from said similarity measure DB(l, m), to obtain a recognized result at said final digit from said digit recognition category W(l, M), at the end point M of said input pattern, to obtain an end point of said input pattern on the digit previous to the final digit by one from said digit path information at the end point M, to obtain a recognized result of the digit prior to said final digit by one from said digit recognition category at the end point, and to obtain a recognized result at each digit sequentially toward the start point of said input pattern. | 2. A continuous speech recognition system for recognizing an input speech composed of a plurality of continuously spoken words comprising: a speech analyzing means for analyzing an input signal at every given frame time point m and outputting an input pattern expressed in a time series of a feature vector comprising a predetermined number of feature parameters; an input pattern memory to store said input pattern; a reference pattern memory to store a reference pattern comprising a feature vector in the same format as said input pattern for each of a plurality (V) of predetermined words to be recognized; a distance calculating means to calculate the distance between the feature vector of said input pattern at a time point m and the feature vector of the reference pattern of the v-th word at a time point n at every time point n under a predetermined distance formula by changing the time point n of the reference pattern of the v-th word in an arbitrary order from the start point to end point N.sup.v while changing the reference pattern V from the first word to end word V for each time point to end point M; an asymptotic calculating means to calculate a similarity measure D(l, v, n) given by the cumulative sum of said distances on the l-th digit at said time points n and a path information F(l, v, n) indicating a time point of the input pattern at the start point of said v-th reference pattern on a path through which said similarity measure D(l, v, n) has been obtained by a predetermined asymptotic expression according to a dynamic programming process while changing the time point n of the reference pattern of the v-th word in an arbitrary order from the start point to end point N.sup.v, while changing digit number l from one to L, and while changing the reference pattern v from the first word to the end word V for each time point m of the input pattern, said time point m changing from the start point to end point M; a digit similarity measure and digit path information calculating means to select a minimum similarity measure from among the similarity measures at the end time points of the reference patterns of all the words on the l-th digit obtained through said asymptotic calculating means, and to provide said minimum similarity measure as a digit similarity measure DB(l, m), a category to which the word corresponding to said minimum similarity measure belongs as a digit recognition category W(l, m)=v, and path information corresponding to said minimum similarity measure as a digit path information FB(l, m) on said digit l at said time point m while changing digit number l from one to L for each time point m of the input pattern, said each time point m changing from the start point to end point M; an initializing means to give said digit similarity measure DB (l-1, m-1) as an initial value of said similarity measure at a time point (m-1) and to give time point (m-1) as an initial value of said path information at a time point m while changing the line point m of the input pattern from the start point to end point M; a decision means to obtain a final digit from said similarity measure DB(l, m), to obtain a recognized result at said final digit from said digit recognition category W(l, M), at the end point M of said input pattern, to obtain an end point of said input pattern on the digit previous to the final digit by one from said digit path information at the end point M, to obtain a recognized result of the digit prior to said final digit by one from said digit recognition category at the end point, and to obtain a recognized result at each digit sequentially toward the start point of said input pattern. 17. A continuous speech recognition system according to claim 2, wherein said asymptotic expression is ##EQU12## wherein D.sub.m-1 (v, n) denotes the similarity measure between the reference pattern of the v-th word at a time point n and the input pattern at a time point (m-1). | 0.882601 |
7,509,581 | 22 | 23 | 22. The storage medium of claim 21 , wherein said segment group information includes a level information. | 22. The storage medium of claim 21 , wherein said segment group information includes a level information. 23. The storage medium of claim 22 , wherein said level information defines multiple levels. | 0.5 |
7,827,028 | 9 | 10 | 9. The method of claim 3 , wherein the role labels define candidate roles of each labeled node, within the translingual parse, relative to its adjacent upstream node. | 9. The method of claim 3 , wherein the role labels define candidate roles of each labeled node, within the translingual parse, relative to its adjacent upstream node. 10. The method of claim 9 , wherein the role labels are selected from the group consisting of: HEAD, SBJ, OBJ, SYNHEAD, PPMOD, SMOD, PREMOD, POSTMOD, MIDMOD, and combinations thereof. | 0.5 |
8,131,538 | 1 | 3 | 1. A phoneme decoding system, comprising: a non-transitory medium; one or more sentences disposed on the medium, each sentence comprising one or more words, each of the words comprising at least one letter string representative of at least one of a single-source phoneme, a silent phoneme, and a multi-source phoneme; and a symbol key defining a unique mnemonic pictogram for every multi-source phoneme of the system; wherein at least one word comprises a multi-source phoneme letter string with a unique pictogram positioned thereabove or therebelow that represents the phoneme for the given usage of the underlying letter string in the word and in the sentence, and that is the only pictogram for all letter strings associated with said phoneme; all of the letter strings disposed on said medium form all or part of said one or more words disposed on said medium; and all of the pictograms are positioned above or below the letter strings they represent. | 1. A phoneme decoding system, comprising: a non-transitory medium; one or more sentences disposed on the medium, each sentence comprising one or more words, each of the words comprising at least one letter string representative of at least one of a single-source phoneme, a silent phoneme, and a multi-source phoneme; and a symbol key defining a unique mnemonic pictogram for every multi-source phoneme of the system; wherein at least one word comprises a multi-source phoneme letter string with a unique pictogram positioned thereabove or therebelow that represents the phoneme for the given usage of the underlying letter string in the word and in the sentence, and that is the only pictogram for all letter strings associated with said phoneme; all of the letter strings disposed on said medium form all or part of said one or more words disposed on said medium; and all of the pictograms are positioned above or below the letter strings they represent. 3. The phoneme decoding system of claim 1 , wherein the symbol key defines the exclusive representations in accordance with standard American English. | 0.7 |
8,777,626 | 1 | 7 | 1. A computer based interactive, multi-sensory method for teaching students to read words and comprehend passages, comprising the steps of: (a) presenting a menu of teaching components on a screen of a processor based client device associated with a student by a processor based server over a communications network, the server comprising at least a phonics component for teaching students to read through voice and handwriting recognition, the phonics component comprises a plurality of phonics modules for teaching the student an alphabetic code of the English language, each phonics module comprises a different letter category of the alphabetic code and a plurality of exercises for teaching the student the letter category of said each module with a series of multi-sensory interactions with the student; (b) determining and executing a current phonics module associated with the student by phonics component, the letter category of the current phonics module comprises a plurality of letter groups, each letter group comprises at least one of the following letter symbol: a letter, a consonant, a vowel or a syllable; (c) determining, by the phonics component, a current letter group of the current phonics module and a current letter symbol of the current letter group associated with the student; (d) retrieving an exercise for the current letter symbol of the current letter group associated with the student from a database by the phonics component, the exercise comprising at least a visual and auditory drill of the current letter symbol, a writing drill of the current letter symbol and a phonological processing drill; (e) presenting the exercise for the current letter symbol of the current letter group associated with the student on the student's client device by the phonics component to create multi-sensory interactions with the student for the current letter symbol; (f) receiving the student's responses to the multi-sensory interactions of the exercise from the client device by the phonics component over the communications network; (g) processing and scoring the student's responses to the multi-sensory interactions by the phonics component to determine whether the student advances to the next letter symbol of the current letter group or repeats the current letter symbol of the current letter group; (h) storing the student's responses to the multi-sensory interactions and the student's score on the current letter symbol in the database by the phonics component; (i) advancing the student to the next letter symbol of the current letter group by the phonics component if the student's score is greater than or equal to a predetermined threshold and repeating the steps (d)-(h) for the next letter symbol of the current letter group; (j) retrieving another exercise for the current letter symbol of the current letter group by the phonics component and repeating the steps (e)-(h) if the student's score is less than the predetermined threshold; (k) presenting a letter group assessment test of the current letter group on the student's client device by the phonics component upon completion of a last letter symbol of the current letter group, the letter group assessment test comprising at least four parts and further comprising the steps of: performing a first part of the letter group assessment test by the phonics component by performing the following steps: presenting a first set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the first set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that begins with the audio sound; receiving the student's selections for the first set from the student's client device over the communications network; processing the student's selections for the first set to determine a first assessment score; storing the student's selections from the first set and student's first letter group assessment score in the database; performing a second part of the letter group assessment test by the phonics component by performing the following steps: presenting a second set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the second set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that ends with the audio sound; receiving the student's selections for the second set from the student's client device over the communications network; processing the student's selections for the second set to determine a second assessment score; storing the student's selections for the second set of lists and student's second letter group assessment score in the database; performing a third part of the letter group assessment test by the phonics component by performing the following steps: presenting a third set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the third set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that contains the audio sound; receiving the student's selections for the third set from the student's client device over the communications network; processing the student's selections for the third set of lists to determine a third assessment score; storing the student's selections for the third set of lists and student's third letter group assessment score in the database; performing a word per minute timing drill as a fourth part of the letter group assessment test by the phonics component by performing the following steps: presenting a predetermined set of a predetermined number of words on the student's client device, each word being a real or nonsense word comprising at least two letter symbols of the current letter group; for each set, prompting the student to read the words displayed on the student's screen for a predetermined time, preferably one minute; receiving a recording of the words read by the student from the client device over the communications network; analyzing the recording to determine a fourth assessment score comprising three scores, a first score being a total number of words read accurately by the student, a second score being a total number of real words read accurately by the student, and a third score being the total number of nonsense words read accurately, each score of the fourth assessment is determined by comparing the student's pronunciation of the words to correct sounds of the words by a speech recognition engine of the server, the speech recognition engine comprising a library of correct sounds; storing the recording of the words read by the student and the student's fourth assessment score comprising the three scores in the database; (l) processing and scoring the student's responses to the letter group assessment test by the phonics component to determine whether the student advances to the next letter group of the current phonics module or repeats the current letter group of the current phonics module; (m) advancing the student to the next letter group of the current phonics module if the student's letter group assessment score is greater than or equal to a predetermined threshold and repeating the steps (c)-(l) for the next letter group of the current phonics module; and (n) repeating the steps (c)-(l) for the current letter group of the current phonics module if the student's letter group assessment score is less than the predetermined threshold. | 1. A computer based interactive, multi-sensory method for teaching students to read words and comprehend passages, comprising the steps of: (a) presenting a menu of teaching components on a screen of a processor based client device associated with a student by a processor based server over a communications network, the server comprising at least a phonics component for teaching students to read through voice and handwriting recognition, the phonics component comprises a plurality of phonics modules for teaching the student an alphabetic code of the English language, each phonics module comprises a different letter category of the alphabetic code and a plurality of exercises for teaching the student the letter category of said each module with a series of multi-sensory interactions with the student; (b) determining and executing a current phonics module associated with the student by phonics component, the letter category of the current phonics module comprises a plurality of letter groups, each letter group comprises at least one of the following letter symbol: a letter, a consonant, a vowel or a syllable; (c) determining, by the phonics component, a current letter group of the current phonics module and a current letter symbol of the current letter group associated with the student; (d) retrieving an exercise for the current letter symbol of the current letter group associated with the student from a database by the phonics component, the exercise comprising at least a visual and auditory drill of the current letter symbol, a writing drill of the current letter symbol and a phonological processing drill; (e) presenting the exercise for the current letter symbol of the current letter group associated with the student on the student's client device by the phonics component to create multi-sensory interactions with the student for the current letter symbol; (f) receiving the student's responses to the multi-sensory interactions of the exercise from the client device by the phonics component over the communications network; (g) processing and scoring the student's responses to the multi-sensory interactions by the phonics component to determine whether the student advances to the next letter symbol of the current letter group or repeats the current letter symbol of the current letter group; (h) storing the student's responses to the multi-sensory interactions and the student's score on the current letter symbol in the database by the phonics component; (i) advancing the student to the next letter symbol of the current letter group by the phonics component if the student's score is greater than or equal to a predetermined threshold and repeating the steps (d)-(h) for the next letter symbol of the current letter group; (j) retrieving another exercise for the current letter symbol of the current letter group by the phonics component and repeating the steps (e)-(h) if the student's score is less than the predetermined threshold; (k) presenting a letter group assessment test of the current letter group on the student's client device by the phonics component upon completion of a last letter symbol of the current letter group, the letter group assessment test comprising at least four parts and further comprising the steps of: performing a first part of the letter group assessment test by the phonics component by performing the following steps: presenting a first set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the first set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that begins with the audio sound; receiving the student's selections for the first set from the student's client device over the communications network; processing the student's selections for the first set to determine a first assessment score; storing the student's selections from the first set and student's first letter group assessment score in the database; performing a second part of the letter group assessment test by the phonics component by performing the following steps: presenting a second set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the second set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that ends with the audio sound; receiving the student's selections for the second set from the student's client device over the communications network; processing the student's selections for the second set to determine a second assessment score; storing the student's selections for the second set of lists and student's second letter group assessment score in the database; performing a third part of the letter group assessment test by the phonics component by performing the following steps: presenting a third set of lists on the student's client device, one list at a time, each list being associated with one different letter symbol of the current letter group and comprising at least two words; for each list on the third set, providing an audio sound of the letter symbol associated with the list on the student's client device and prompting the student to select a word on the list that contains the audio sound; receiving the student's selections for the third set from the student's client device over the communications network; processing the student's selections for the third set of lists to determine a third assessment score; storing the student's selections for the third set of lists and student's third letter group assessment score in the database; performing a word per minute timing drill as a fourth part of the letter group assessment test by the phonics component by performing the following steps: presenting a predetermined set of a predetermined number of words on the student's client device, each word being a real or nonsense word comprising at least two letter symbols of the current letter group; for each set, prompting the student to read the words displayed on the student's screen for a predetermined time, preferably one minute; receiving a recording of the words read by the student from the client device over the communications network; analyzing the recording to determine a fourth assessment score comprising three scores, a first score being a total number of words read accurately by the student, a second score being a total number of real words read accurately by the student, and a third score being the total number of nonsense words read accurately, each score of the fourth assessment is determined by comparing the student's pronunciation of the words to correct sounds of the words by a speech recognition engine of the server, the speech recognition engine comprising a library of correct sounds; storing the recording of the words read by the student and the student's fourth assessment score comprising the three scores in the database; (l) processing and scoring the student's responses to the letter group assessment test by the phonics component to determine whether the student advances to the next letter group of the current phonics module or repeats the current letter group of the current phonics module; (m) advancing the student to the next letter group of the current phonics module if the student's letter group assessment score is greater than or equal to a predetermined threshold and repeating the steps (c)-(l) for the next letter group of the current phonics module; and (n) repeating the steps (c)-(l) for the current letter group of the current phonics module if the student's letter group assessment score is less than the predetermined threshold. 7. The method of claim 1 , further comprising the step performing an auditory sound drill of the current letter group on the student's client device upon completion of a last letter symbol of the current letter group by the phonics component by performing the following steps: providing a predetermined number of sounds of the letter symbols of the current letter group, one at a time, on the student's client device; after providing each sound, prompting the student to enter the letter symbol associated with the sound; receiving the student's responses to the predetermined number of sounds from the student's client device over the communications network; processing the student's responses to determine an auditory drill score; and storing the student's response and the student's auditory drill score in the database. | 0.758226 |
5,438,180 | 36 | 38 | 36. A method for controlling a cooking oven having means defining an oven cavity, heating means for heating said oven cavity to defined heating temperatures, including the steps of: prompting a user input selection of a heating mode from one of a cooking mode and a cleaning mode; prompting a user input entry of a heating temperature for said oven cavity and displaying the entered heating temperature; prompting a user selection of up to two timing modes independently of the selected heating mode; prompting a user entry of time values for the selected timing modes and displaying the entered time values; wherein said prompting and said displaying steps define at least a portion of a grammatical sentence to the user; establishing a time and temperature heating profile of said oven cavity from the selected heating function and timing functions and entered heating temperature and time values; and controlling said heating means according to said heating profile. | 36. A method for controlling a cooking oven having means defining an oven cavity, heating means for heating said oven cavity to defined heating temperatures, including the steps of: prompting a user input selection of a heating mode from one of a cooking mode and a cleaning mode; prompting a user input entry of a heating temperature for said oven cavity and displaying the entered heating temperature; prompting a user selection of up to two timing modes independently of the selected heating mode; prompting a user entry of time values for the selected timing modes and displaying the entered time values; wherein said prompting and said displaying steps define at least a portion of a grammatical sentence to the user; establishing a time and temperature heating profile of said oven cavity from the selected heating function and timing functions and entered heating temperature and time values; and controlling said heating means according to said heating profile. 38. The method in claim 36 wherein said cooking mode includes one of broiling food and baking food. | 0.840836 |
10,037,456 | 1 | 3 | 1. An attribute-assigning image-processing system comprising: a distributed computing system that includes multiple computer systems, each having one or more processors, one or more memories, one or more mass-storage devices, and one or more network interconnections; an input-image memory for receiving an input image provided by one or more of the multiple computer systems; a set of first-level feature detectors, provided by one or more of the multiple computer systems, that detect face-containing subimages within the input image and, for each face-containing subimage, generate a set of normalized regions; a set of second-level feature detectors that generate a set of feature vectors from each normalized region; a set of attribute classifiers that each outputs an attribute value and an associated probability that reflects a likelihood that the attribute value returned in response to input of one or more feature vectors reflects the attribute value that would be assigned to the image of the facial feature contained in the normalized region from which the set of second-level feature detectors produced the input feature vectors; and a controller that submits the input image to the first-level feature detectors in order to generate sets of normalized regions for each face-containing subimage in the input image; submits each set of normalized regions to generate a set of feature vectors; and submits subsets of feature vectors to each attribute classifier in an ordered list of attribute classifiers to assign attributes to each face-containing subimage. | 1. An attribute-assigning image-processing system comprising: a distributed computing system that includes multiple computer systems, each having one or more processors, one or more memories, one or more mass-storage devices, and one or more network interconnections; an input-image memory for receiving an input image provided by one or more of the multiple computer systems; a set of first-level feature detectors, provided by one or more of the multiple computer systems, that detect face-containing subimages within the input image and, for each face-containing subimage, generate a set of normalized regions; a set of second-level feature detectors that generate a set of feature vectors from each normalized region; a set of attribute classifiers that each outputs an attribute value and an associated probability that reflects a likelihood that the attribute value returned in response to input of one or more feature vectors reflects the attribute value that would be assigned to the image of the facial feature contained in the normalized region from which the set of second-level feature detectors produced the input feature vectors; and a controller that submits the input image to the first-level feature detectors in order to generate sets of normalized regions for each face-containing subimage in the input image; submits each set of normalized regions to generate a set of feature vectors; and submits subsets of feature vectors to each attribute classifier in an ordered list of attribute classifiers to assign attributes to each face-containing subimage. 3. The attribute-assigning image-processing system of claim 1 wherein a set of normalized regions corresponding to a face-containing subimage is generated by one or more of: performing rotation-matrix and translation-matrix operations to reorient the face-containing subimage and the corresponding regions that each contains an image of a facial feature to produce a canonical region arrangement; and applying one or more perspective transformations to corresponding regions that each contains a perspective-distorted image of a facial feature. | 0.534247 |
10,089,388 | 11 | 13 | 11. The non-transitory computer-readable medium of claim 10 , wherein the computer instructions are further configured to cause the computing device to at least: obtain a user request to adjust an amount of the contextual content for the search term that is included in the rendering of the search results; and generate an updated rendering of the search results that comprises an adjusted amount of contextual content for the search term. | 11. The non-transitory computer-readable medium of claim 10 , wherein the computer instructions are further configured to cause the computing device to at least: obtain a user request to adjust an amount of the contextual content for the search term that is included in the rendering of the search results; and generate an updated rendering of the search results that comprises an adjusted amount of contextual content for the search term. 13. The non-transitory computer-readable medium of claim 11 , wherein the user request comprises a request for less contextual content for the search term to be rendered by the computing device. | 0.515 |
8,924,211 | 7 | 9 | 7. At least one computer-readable storage medium having encoded thereon computer-executable instructions that, when executed by at least one computer, cause the at least one computer to carry out a method of processing results of a recognition by an automatic speech recognition (ASR) system on an utterance, the results comprising two or more results identified by the ASR system as likely to be accurate recognition results for the utterance, the two or more results comprising a first result and a second result, wherein the first result is identified by the ASR system as most likely among the two or more results to be an accurate recognition result, the method comprising: evaluating the first result using a medical fact extractor to extract a first set of one or more medical facts; evaluating the second result using the medical fact extractor to extract a second set of one or more medical facts; determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from a meaning of the second set of one or more medical facts; and in response to determining that the first set of one or more medical facts has a meaning that is different in a medically significant way from the meaning of the second set of one or more medical facts, triggering an alert, wherein the determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from the second set of one or more medical facts comprises: determining whether the first set comprises a medical fact having a fact type that is marked as significant; when it is determined that the first set comprises the medical fact having the fact type that is marked as significant, determining whether the second set comprises the medical fact; and in response to determining that the second set does not comprise the medical fact, determining that the second set has a meaning that differs from a meaning of the first set in a medically significant way. | 7. At least one computer-readable storage medium having encoded thereon computer-executable instructions that, when executed by at least one computer, cause the at least one computer to carry out a method of processing results of a recognition by an automatic speech recognition (ASR) system on an utterance, the results comprising two or more results identified by the ASR system as likely to be accurate recognition results for the utterance, the two or more results comprising a first result and a second result, wherein the first result is identified by the ASR system as most likely among the two or more results to be an accurate recognition result, the method comprising: evaluating the first result using a medical fact extractor to extract a first set of one or more medical facts; evaluating the second result using the medical fact extractor to extract a second set of one or more medical facts; determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from a meaning of the second set of one or more medical facts; and in response to determining that the first set of one or more medical facts has a meaning that is different in a medically significant way from the meaning of the second set of one or more medical facts, triggering an alert, wherein the determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from the second set of one or more medical facts comprises: determining whether the first set comprises a medical fact having a fact type that is marked as significant; when it is determined that the first set comprises the medical fact having the fact type that is marked as significant, determining whether the second set comprises the medical fact; and in response to determining that the second set does not comprise the medical fact, determining that the second set has a meaning that differs from a meaning of the first set in a medically significant way. 9. The at least one computer-readable storage medium of claim 7 , wherein the determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from the second set of one or more medical facts further comprises: comparing the first set to the second set to determine whether the first set of medical facts differs from the second set of medical facts; and when it is determined that the first set differs from the second set, determining that the first set has a meaning that differs in a medically significant way from a meaning of the second set. | 0.5 |
9,756,161 | 16 | 25 | 16. A control method of a vehicle, comprising: creating a phone number candidate group corresponding to a received voice signal from a context model created by modeling each name included in a phone book; classifying the phone book according to lengths of names, and creating a context model for each of the lengths of the names; calculate a length of speech based on a Begin of Speech (BoS) and an End of Speech (EoS); determining reliability weight values according to the length of the user's speech; and applying the reliability weight values according to lengths of syllables of phone number candidates selected from the context model for each of the lengths of the names. | 16. A control method of a vehicle, comprising: creating a phone number candidate group corresponding to a received voice signal from a context model created by modeling each name included in a phone book; classifying the phone book according to lengths of names, and creating a context model for each of the lengths of the names; calculate a length of speech based on a Begin of Speech (BoS) and an End of Speech (EoS); determining reliability weight values according to the length of the user's speech; and applying the reliability weight values according to lengths of syllables of phone number candidates selected from the context model for each of the lengths of the names. 25. The control method according to claim 16 , further comprising: receiving a voice signal according to the user's speech; and pre-processing the voice signal. | 0.778393 |
9,244,665 | 1 | 10 | 1. A method executed by a processor for optimizing execution of dynamic language code, the method comprising: identifying a first dynamic language function call during runtime, the function call including argument values for one or more arguments of the function; calculating a type signature for the one or more argument values of the function; determining if a function associated with the type signature is stored in a cache; looking up the function in the cache when the function associated with the type signature is stored in the cache; and dynamically calling the function associated with the type signature when the function for the type signature is not stored in the cache. | 1. A method executed by a processor for optimizing execution of dynamic language code, the method comprising: identifying a first dynamic language function call during runtime, the function call including argument values for one or more arguments of the function; calculating a type signature for the one or more argument values of the function; determining if a function associated with the type signature is stored in a cache; looking up the function in the cache when the function associated with the type signature is stored in the cache; and dynamically calling the function associated with the type signature when the function for the type signature is not stored in the cache. 10. The method of claim 1 , wherein determining the type signature comprises: determining if the one or more arguments of the function are associated with a pre-determined type set; and determining the type signature based on the pre-determined type set for each of the one or more arguments associated with a pre-determined type set. | 0.510264 |
7,926,031 | 1 | 2 | 1. A Non-Transitory machine-readable medium storing a set of machine-readable instructions executable by the machine to implement a configuration management database (CMDB) comprising: a plurality of statements, wherein the statements comprise: a configuration item wherein the configuration item comprises a first item identifying a resource, a second item identifying an object, wherein the statements are made in a markup language; an ontology wherein the ontology comprises a set of defined constraints and relationships for the configuration item; a subset of instructions wherein the subset of instructions requests changing the CMDB by substituting configuration items; an inference service wherein the inference service uses the markup language and one or more constraints included in the ontology to infer relationships not explicitly defined between the plurality of statements; wherein the CMDB defines an actual state of an information system and a desired state of the information system, and the actual state and the desired state are comparable to reveal inconsistencies therebetween; and wherein the inference service analyzes the inconsistencies to create one or more rules for applying the requested substitution to the CMDB. | 1. A Non-Transitory machine-readable medium storing a set of machine-readable instructions executable by the machine to implement a configuration management database (CMDB) comprising: a plurality of statements, wherein the statements comprise: a configuration item wherein the configuration item comprises a first item identifying a resource, a second item identifying an object, wherein the statements are made in a markup language; an ontology wherein the ontology comprises a set of defined constraints and relationships for the configuration item; a subset of instructions wherein the subset of instructions requests changing the CMDB by substituting configuration items; an inference service wherein the inference service uses the markup language and one or more constraints included in the ontology to infer relationships not explicitly defined between the plurality of statements; wherein the CMDB defines an actual state of an information system and a desired state of the information system, and the actual state and the desired state are comparable to reveal inconsistencies therebetween; and wherein the inference service analyzes the inconsistencies to create one or more rules for applying the requested substitution to the CMDB. 2. The Non-Transitory machine-readable medium as claimed in claim 1 , wherein the plurality of statements comprise a plurality of Resource Description Framework (RDF) statements. | 0.526596 |
7,975,216 | 1 | 2 | 1. A computer-implemented method of annotating pages of an electronic document independently of the contents of the electronic document, the computer-implemented method comprising the steps of: displaying a page of the electronic document on a computer display device using a document browser that permits a user to move forward and backward among a plurality of document pages; detecting a selection of an annotation mode that permits the user to annotate the currently displayed document page, wherein at least an ink annotation mode, a highlight annotation mode, and an eraser annotation mode are provided as options from which the annotation mode is selected; receiving annotation stroke input from a user input device indicating movement associated with the user input device for a continuous distance about a stroke location on the currently displayed document page; storing annotation stroke data based on the received annotation stroke input, said annotation stroke data comprising data corresponding to the stroke location and the movement associated with the user input device, wherein the annotation stroke data is stored in an annotation file associated with the user, the annotation file stored separately from the electronic document; storing annotations made in the highlight annotation mode as a bitmap image; and blending pixels in the annotation file with pixels in the electronic document to cause the computer display device to display the electronic document with annotations. | 1. A computer-implemented method of annotating pages of an electronic document independently of the contents of the electronic document, the computer-implemented method comprising the steps of: displaying a page of the electronic document on a computer display device using a document browser that permits a user to move forward and backward among a plurality of document pages; detecting a selection of an annotation mode that permits the user to annotate the currently displayed document page, wherein at least an ink annotation mode, a highlight annotation mode, and an eraser annotation mode are provided as options from which the annotation mode is selected; receiving annotation stroke input from a user input device indicating movement associated with the user input device for a continuous distance about a stroke location on the currently displayed document page; storing annotation stroke data based on the received annotation stroke input, said annotation stroke data comprising data corresponding to the stroke location and the movement associated with the user input device, wherein the annotation stroke data is stored in an annotation file associated with the user, the annotation file stored separately from the electronic document; storing annotations made in the highlight annotation mode as a bitmap image; and blending pixels in the annotation file with pixels in the electronic document to cause the computer display device to display the electronic document with annotations. 2. The computer-implemented method of claim 1 , wherein the user input device comprises a stylus. | 0.759901 |
7,647,415 | 63 | 64 | 63. A computer-accessible storage medium comprising program instructions, wherein the program instructions are configured to implement: a Web services stack on a system configured to communicate with other systems using either a binary encoding protocol or a markup language protocol using a single API (application programming interface) negotiating with another system to determine if the other system supports the binary encoding protocol, wherein the markup language protocol is based on XML (eXtensible Markup Language), and wherein communication according to the binary encoding protocol comprises mapping from an XML schema to a binary encoding schema and generating a binary encoding from the binary encoding schema; if the other system supports the binary encoding protocol, the Web services stack communicating with the other system according to the binary encoding protocol; and if the other system does not support the binary encoding protocol, the Web services stack communicating with the other system according to the markup language protocol. | 63. A computer-accessible storage medium comprising program instructions, wherein the program instructions are configured to implement: a Web services stack on a system configured to communicate with other systems using either a binary encoding protocol or a markup language protocol using a single API (application programming interface) negotiating with another system to determine if the other system supports the binary encoding protocol, wherein the markup language protocol is based on XML (eXtensible Markup Language), and wherein communication according to the binary encoding protocol comprises mapping from an XML schema to a binary encoding schema and generating a binary encoding from the binary encoding schema; if the other system supports the binary encoding protocol, the Web services stack communicating with the other system according to the binary encoding protocol; and if the other system does not support the binary encoding protocol, the Web services stack communicating with the other system according to the markup language protocol. 64. The computer-accessible storage medium as recited in claim 63 , wherein the system is a JAX-RPC (Java API for XML (eXtensible Markup Language)-based RPC (Remote Procedure Call)) server. | 0.800633 |
8,646,097 | 1 | 6 | 1. A security module for an audio/video digital data processing unit for conditional access, said audio/video digital data being encrypted within an encoded data stream by control words, said security module comprising a processor programmed to: process a security message included within a control message stream and containing at least one cryptogram relative to a control word and one instruction relative to the control word; download at least two micro programs to the security module within said security messages; receive the security message and extract the cryptogram and the instruction; select one micro program among the micro programs according to a value of the instruction; execute the selected micro program with at least the extracted cryptogram as an entry variable of the selected micro program; in response to executing the selected micro program, calculate the control word; and transmit the calculated control word to a decoding device to decrypt the encrypted audio/video digital data; and a memory programmed to store said at least two micro programs after being downloaded to the security module. | 1. A security module for an audio/video digital data processing unit for conditional access, said audio/video digital data being encrypted within an encoded data stream by control words, said security module comprising a processor programmed to: process a security message included within a control message stream and containing at least one cryptogram relative to a control word and one instruction relative to the control word; download at least two micro programs to the security module within said security messages; receive the security message and extract the cryptogram and the instruction; select one micro program among the micro programs according to a value of the instruction; execute the selected micro program with at least the extracted cryptogram as an entry variable of the selected micro program; in response to executing the selected micro program, calculate the control word; and transmit the calculated control word to a decoding device to decrypt the encrypted audio/video digital data; and a memory programmed to store said at least two micro programs after being downloaded to the security module. 6. The security module according to claim 1 , wherein the security module includes a programmable logic module, said micro program being a configuration program of programmable logic. | 0.742254 |
8,654,942 | 19 | 20 | 19. The multi-device video communication system in claim 14 wherein a first rating is received from a first participant of the plurality of participants and the first rating represents an assessment of the importance of a second participant of the plurality of participants. | 19. The multi-device video communication system in claim 14 wherein a first rating is received from a first participant of the plurality of participants and the first rating represents an assessment of the importance of a second participant of the plurality of participants. 20. The multi-device video communication system in claim 19 wherein a second rating is received from the second participant of the plurality of participants and represents an assessment of the importance of the first participant of the plurality of participants. | 0.5 |
9,628,506 | 15 | 18 | 15. The system of claim 11 , wherein the generation module generates, for each group of facets, the weak classifier by tuning parameters of the weak classifier. | 15. The system of claim 11 , wherein the generation module generates, for each group of facets, the weak classifier by tuning parameters of the weak classifier. 18. The system of claim 15 , wherein the generation module performs, after the generation module performing an initial round of tuning and then the performance module applying the weak classifiers, a subsequent round of tuning that begins with security scores resulting from applying the weak classifiers. | 0.5 |
10,102,269 | 2 | 3 | 2. The computing device of claim 1 , wherein the target query language comprises a first target query language, and the instructions cause the computing device to receive the intermediate description and generate a query string in a second target query language that is different than the first target query language and corresponds, to a data model implemented by a second data source. | 2. The computing device of claim 1 , wherein the target query language comprises a first target query language, and the instructions cause the computing device to receive the intermediate description and generate a query string in a second target query language that is different than the first target query language and corresponds, to a data model implemented by a second data source. 3. The computing device of claim 2 , wherein one of the first or second target query languages is SQL. | 0.5 |
9,418,059 | 9 | 11 | 9. A method in accordance with claim 8 , wherein the spatial concept is further defined as one of a geographical region and a building, said method further comprising deriving a property of a spatial region from a plurality of properties of the spatial region. | 9. A method in accordance with claim 8 , wherein the spatial concept is further defined as one of a geographical region and a building, said method further comprising deriving a property of a spatial region from a plurality of properties of the spatial region. 11. A method in accordance with claim 9 , further comprising defining a default property for a specified class of objects. | 0.5 |
8,831,403 | 11 | 13 | 11. Logic encoded in non-transitory media that includes code for execution and when executed by a processor is operable to perform operations, comprising: receiving a search query that includes one or more attributes; evaluating a plurality of video files, wherein when a specific search attribute is recognized in a particular video file, the particular video file may be divided into a plurality of video clips with at least one video clip having the recognized specific search attribute; identifying video clips within the video files that have one or more of the recognized specific search attributes; and creating a video report comprising a contiguous sequence of the identified video clips, wherein the video clips are stitched together according to a stitch criterion, wherein each video clip in the video report comprises an embedded links, which can be selected to access a corresponding video file that includes the particular video clip. | 11. Logic encoded in non-transitory media that includes code for execution and when executed by a processor is operable to perform operations, comprising: receiving a search query that includes one or more attributes; evaluating a plurality of video files, wherein when a specific search attribute is recognized in a particular video file, the particular video file may be divided into a plurality of video clips with at least one video clip having the recognized specific search attribute; identifying video clips within the video files that have one or more of the recognized specific search attributes; and creating a video report comprising a contiguous sequence of the identified video clips, wherein the video clips are stitched together according to a stitch criterion, wherein each video clip in the video report comprises an embedded links, which can be selected to access a corresponding video file that includes the particular video clip. 13. The logic of claim 11 , the operations further comprising: tagging the video files with tags corresponding to predefined attributes; and identifying the predefined attributes in response to the search query. | 0.5 |
9,594,828 | 1 | 29 | 1. A computer-implemented method performed by one or more computing devices, comprising: transmitting a pilot query to an unstructured data store that stores records in JSON-based format; responsive to transmitting the pilot query, receiving a plurality of records generated by execution of the pilot query against the unstructured data store, each record including one or more pairs of field names and values in JSON-based format; automatically identifying field names and data types from the pairs in the plurality of records generated by execution of the pilot query against the unstructured data store that stores records in JSON-based format and defining a first set of fields that corresponds to the identified field names and data types; receiving, at a query converter, a structured query whose portions all correspond to a structured query language; converting, by the query converter, the structured query into an unstructured query in an unstructured query language associated with searching the records in the unstructured data store, the unstructured query referencing at least one field in the first set of fields; and transmitting an instruction to the unstructured data store requesting that the unstructured data store execute the unstructured query against the records. | 1. A computer-implemented method performed by one or more computing devices, comprising: transmitting a pilot query to an unstructured data store that stores records in JSON-based format; responsive to transmitting the pilot query, receiving a plurality of records generated by execution of the pilot query against the unstructured data store, each record including one or more pairs of field names and values in JSON-based format; automatically identifying field names and data types from the pairs in the plurality of records generated by execution of the pilot query against the unstructured data store that stores records in JSON-based format and defining a first set of fields that corresponds to the identified field names and data types; receiving, at a query converter, a structured query whose portions all correspond to a structured query language; converting, by the query converter, the structured query into an unstructured query in an unstructured query language associated with searching the records in the unstructured data store, the unstructured query referencing at least one field in the first set of fields; and transmitting an instruction to the unstructured data store requesting that the unstructured data store execute the unstructured query against the records. 29. The computer-implemented method of claim 1 , wherein defining the first set of fields comprises identifying fields in the unstructured data store as a function of a formatting of the records. | 0.78139 |
8,165,880 | 1 | 7 | 1. An end-pointer that determines a beginning and an end of a speech segment comprising: a voice triggering module that identifies a portion of an audio stream comprising an audio speech segment; a rule module in communication with the voice triggering module, the rule module comprising a plurality of rules used by a processor to analyze a part of the audio stream to detect a beginning and an end of the audio speech segment; and a consonant detector that calculates a difference between a signal-to-noise ratio in a high frequency band and a signal-to-noise ratio in a low frequency band, where the consonant detector converts the difference between the signal-to-noise ratio in the high frequency band and the signal-to-noise ratio in the low frequency band into a probability value that predicts a likelihood of a high frequency consonant in the portion of the audio stream; where the beginning of the audio speech segment and the end of the audio speech segment represent boundaries between speech and non-speech portions of the audio stream, and where the rule module identifies the beginning of the audio speech segment or the end of the audio speech segment based on an output of the consonant detector. | 1. An end-pointer that determines a beginning and an end of a speech segment comprising: a voice triggering module that identifies a portion of an audio stream comprising an audio speech segment; a rule module in communication with the voice triggering module, the rule module comprising a plurality of rules used by a processor to analyze a part of the audio stream to detect a beginning and an end of the audio speech segment; and a consonant detector that calculates a difference between a signal-to-noise ratio in a high frequency band and a signal-to-noise ratio in a low frequency band, where the consonant detector converts the difference between the signal-to-noise ratio in the high frequency band and the signal-to-noise ratio in the low frequency band into a probability value that predicts a likelihood of a high frequency consonant in the portion of the audio stream; where the beginning of the audio speech segment and the end of the audio speech segment represent boundaries between speech and non-speech portions of the audio stream, and where the rule module identifies the beginning of the audio speech segment or the end of the audio speech segment based on an output of the consonant detector. 7. The end-pointer of claim 1 , where the rule module analyzes a predetermined number of plosives in the portion of the audio stream. | 0.799699 |
8,744,852 | 13 | 14 | 13. A non-transitory computer readable medium having stored therein instructions, which when executed by a device with a touch screen display, cause the device to: provide a plurality of user interface objects on a desktop, the plurality of user interface objects including an active application window and a first user interface object that is completely visually obscured by the active application window; provide a pointer cursor and an accessibility cursor that is distinct from the pointer cursor; while the first user interface object is completely visually obscured by the active application window, detect a user input accessing the first user interface object with the accessibility cursor, independently of the pointer cursor; and while the accessibility cursor is accessing the first user interface object, visually reveal the first user interface element from behind the active application window. | 13. A non-transitory computer readable medium having stored therein instructions, which when executed by a device with a touch screen display, cause the device to: provide a plurality of user interface objects on a desktop, the plurality of user interface objects including an active application window and a first user interface object that is completely visually obscured by the active application window; provide a pointer cursor and an accessibility cursor that is distinct from the pointer cursor; while the first user interface object is completely visually obscured by the active application window, detect a user input accessing the first user interface object with the accessibility cursor, independently of the pointer cursor; and while the accessibility cursor is accessing the first user interface object, visually reveal the first user interface element from behind the active application window. 14. The non-transitory computer readable medium of claim 13 , wherein visually revealing the first user interface element from behind the active application window is temporary. | 0.659615 |
9,436,682 | 1 | 9 | 1. A computer-implemented method, comprising: receiving, at a server from a mobile computing device, the server having one or more processors, an image including a text; obtaining, at the server, optical character recognition (OCR) text corresponding to the text, the OCR text having been obtained by performing OCR on the image; identifying, at the server, non-textual context information from the image, the non-textual context information (i) representing context information other than the text itself, (ii) being indicative of a context of the image, and (iii) including at least a color of an object in the image; based on the color of the object, determining, at the server, whether the image was captured indoors or outdoors; based on (i) the non-textual context information and (ii) whether the image was captured indoors or outdoors, obtaining, at the server, a translation of the OCR text to a target language to obtain a translated OCR text; and outputting, from the server to the mobile computing device, the translated OCR text. | 1. A computer-implemented method, comprising: receiving, at a server from a mobile computing device, the server having one or more processors, an image including a text; obtaining, at the server, optical character recognition (OCR) text corresponding to the text, the OCR text having been obtained by performing OCR on the image; identifying, at the server, non-textual context information from the image, the non-textual context information (i) representing context information other than the text itself, (ii) being indicative of a context of the image, and (iii) including at least a color of an object in the image; based on the color of the object, determining, at the server, whether the image was captured indoors or outdoors; based on (i) the non-textual context information and (ii) whether the image was captured indoors or outdoors, obtaining, at the server, a translation of the OCR text to a target language to obtain a translated OCR text; and outputting, from the server to the mobile computing device, the translated OCR text. 9. The computer-implemented method of claim 1 , wherein the non-textual context information includes a shape of the object. | 0.814199 |
7,565,281 | 1 | 3 | 1. A computer natural language translation system, comprising: means for inputting source language text; means for outputting target language text; and transfer means for generating said target language text from said source language text using stored translation data generated from examples of source and corresponding target language texts, wherein said stored translation data comprises a plurality of translation units, each unit comprising: respective surface data representative of the order of occurrence of language units of said source and target languages; and respective dependency data related to the semantic relationship between said language units of said source and target languages; the dependency data of language units of said source language being aligned with corresponding dependency data of language units of said target language, and wherein said transfer means comprises: (i) analyzing means for analyzing said source language text using said surface data of said source language; (ii) generating means for generating said target language text using said surface data of said target language; and (iii) transforming means for transforming the analysis of said source text into an analysis for said target language using said dependency data. | 1. A computer natural language translation system, comprising: means for inputting source language text; means for outputting target language text; and transfer means for generating said target language text from said source language text using stored translation data generated from examples of source and corresponding target language texts, wherein said stored translation data comprises a plurality of translation units, each unit comprising: respective surface data representative of the order of occurrence of language units of said source and target languages; and respective dependency data related to the semantic relationship between said language units of said source and target languages; the dependency data of language units of said source language being aligned with corresponding dependency data of language units of said target language, and wherein said transfer means comprises: (i) analyzing means for analyzing said source language text using said surface data of said source language; (ii) generating means for generating said target language text using said surface data of said target language; and (iii) transforming means for transforming the analysis of said source text into an analysis for said target language using said dependency data. 3. A system according to claim 1 , in which for each said translation unit said surface data and said dependency data each comprise, for each language, a head language unit, and a data structure for storing daughter language units, each linked to said head language unit. | 0.548333 |
8,364,134 | 11 | 12 | 11. Electronic equipment for wireless communication with a language-sensitive keypad input mode control for a text message entry, the electronic equipment for wireless communication comprising: a keypad having at least one key to which more than one character is assigned for alphabetic input, a language setting determination means for determining a language setting, a keypad mode control means adapted for controlling the keypad input mode in correspondence to a dictionary-based disambiguation mode relating to a language setting determined by the language setting determination means, whereby the language setting determination means is adapted to determine the language setting used at the beginning of the text message corresponding to the language setting used for the last text message entered on the electronic equipment for wireless communication or corresponding to the language determined for a received text message to be replied by the text message, and the language setting determination means is further adapted to change the language setting if a match for a predetermined number of text elements entered for the text message is present in a further dictionary-based disambiguation mode relating to a different language setting, and comprising a storage means which is adapted to store a file comprising information concerning the language setting used for the last text message entered on the electronic equipment for wireless communication. | 11. Electronic equipment for wireless communication with a language-sensitive keypad input mode control for a text message entry, the electronic equipment for wireless communication comprising: a keypad having at least one key to which more than one character is assigned for alphabetic input, a language setting determination means for determining a language setting, a keypad mode control means adapted for controlling the keypad input mode in correspondence to a dictionary-based disambiguation mode relating to a language setting determined by the language setting determination means, whereby the language setting determination means is adapted to determine the language setting used at the beginning of the text message corresponding to the language setting used for the last text message entered on the electronic equipment for wireless communication or corresponding to the language determined for a received text message to be replied by the text message, and the language setting determination means is further adapted to change the language setting if a match for a predetermined number of text elements entered for the text message is present in a further dictionary-based disambiguation mode relating to a different language setting, and comprising a storage means which is adapted to store a file comprising information concerning the language setting used for the last text message entered on the electronic equipment for wireless communication. 12. Electronic equipment for wireless communication according to claim 11 , wherein the language setting determination means is adapted to determine the language of the received text message to be replied by examining the text elements of the received text message with each of the dictionary-based disambiguation modes available on the electronic equipment for wireless communication and by defining the language of the received text message as the language of the dictionary-based disambiguation mode yielding the highest percentage of matches with a predefined number of text elements at the beginning of the received text message or with all text elements of the message. | 0.5 |
8,543,649 | 1 | 4 | 1. A computer-implemented method, comprising: intercepting, by the computer, emails sent by a first entity while the emails are being transmitted on a network; automatically constructing, by the computer, a knowledge profile of the first entity based on a plurality of sets of text from the intercepted emails; storing, by the computer, the knowledge profile as part of a knowledge base stored in a machine-accessible storage facility; in response to preparation of a particular email to be sent by a second entity to at least one other entity of the network, identifying, by the computer, prior to sending the particular email by the second entity, one or more suggested potential recipients as meeting one or more criteria based at least in part on a comparison of the knowledge profile of the one or more suggested potential recipients and text contained in the particular email to be sent by the second entity; outputting, by the computer, a recommendation to include at least a first entity as a recipient of the particular email to be sent by the second entity, the first entity meeting the one or more criteria and being one of the one or more suggested potential recipients; and providing, by the computer, a matching metric indicative of a relative strength of the recommendation for the one or more suggested potential recipients, the matching metric comprises a sum of confidence level values associated with the one or more suggested potential recipients. | 1. A computer-implemented method, comprising: intercepting, by the computer, emails sent by a first entity while the emails are being transmitted on a network; automatically constructing, by the computer, a knowledge profile of the first entity based on a plurality of sets of text from the intercepted emails; storing, by the computer, the knowledge profile as part of a knowledge base stored in a machine-accessible storage facility; in response to preparation of a particular email to be sent by a second entity to at least one other entity of the network, identifying, by the computer, prior to sending the particular email by the second entity, one or more suggested potential recipients as meeting one or more criteria based at least in part on a comparison of the knowledge profile of the one or more suggested potential recipients and text contained in the particular email to be sent by the second entity; outputting, by the computer, a recommendation to include at least a first entity as a recipient of the particular email to be sent by the second entity, the first entity meeting the one or more criteria and being one of the one or more suggested potential recipients; and providing, by the computer, a matching metric indicative of a relative strength of the recommendation for the one or more suggested potential recipients, the matching metric comprises a sum of confidence level values associated with the one or more suggested potential recipients. 4. The method as recited in claim 1 , wherein operating processing system to identify the first entity as meeting one or more criteria is done without reliance upon any predefined association between the first entity and the second entity. | 0.5 |
7,680,812 | 86 | 88 | 86. A tangible computer readable storage medium having stored thereon a program that when executed performs a method of searching electronic material stored in a computing environment, said method comprising: providing a plurality of documents; determining an undirected, weighted link between at least two of said plurality of documents, based on similarity; determining a directed, weighted link between at least two of said plurality of documents; adding said determined undirected, weighted links to said determined directed, weighted links to create a hybrid web having links; and performing a link analysis algorithm taking the links of said hybrid web as its input, said algorithm including at least one of forward link analysis and backward link analysis, wherein the output of said algorithm is a set of link analysis scores. | 86. A tangible computer readable storage medium having stored thereon a program that when executed performs a method of searching electronic material stored in a computing environment, said method comprising: providing a plurality of documents; determining an undirected, weighted link between at least two of said plurality of documents, based on similarity; determining a directed, weighted link between at least two of said plurality of documents; adding said determined undirected, weighted links to said determined directed, weighted links to create a hybrid web having links; and performing a link analysis algorithm taking the links of said hybrid web as its input, said algorithm including at least one of forward link analysis and backward link analysis, wherein the output of said algorithm is a set of link analysis scores. 88. The tangible computer readable storage medium according to claim 86 , wherein said at least one of forward and backward link analysis comprises using at least one of a non-compound Forward operator and a non-compound Backward operator. | 0.831215 |
7,493,252 | 1 | 18 | 1. A method of mining a collection of data, comprising: receiving the collection of data, the collection of data comprising key words, wherein a key word comprises a coherent character string; converting the collection of data into labeled data by grouping various types of data into a same format and assigning a label indicating a category of item contents, such that the labeled data is in analyzable condition for concept extraction, and wherein the labeled data comprises the label and a clause comprising the item contents; assigning a category to the key words, wherein the category references a concept so that the key words can be handled as concepts with a meaning; separating the clauses into pairs comprising an independent word and an attached word; assigning categories to the separated clauses using syntactic patterns and a category dictionary; generating, by syntactic analysis, a syntactic tree of a sentence comprising the separated clauses; receiving a syntactically analyzed sentence as input, identifying mutually dependent relationships between or among the categorized key words, according to at least one rule defining mutually dependent relationships between or among categorized key words; grouping the identified mutually dependent relationships into groups of related mutually dependent relationships; and extracting the key words with mutually dependent relationships in the same sentence as labeled data with concepts, wherein the step of extracting key words comprises using a mutually dependent relationship extraction rule comprising a string of categories of arbitrary length to be extracted; searching for unique concepts, a unique concept being a concept whose statistical characteristic is distinguished beyond a threshold with the set to which it belongs; creating and keeping statistical information; visually displaying the statistical information; and presenting a distribution of differences of the unique concepts. | 1. A method of mining a collection of data, comprising: receiving the collection of data, the collection of data comprising key words, wherein a key word comprises a coherent character string; converting the collection of data into labeled data by grouping various types of data into a same format and assigning a label indicating a category of item contents, such that the labeled data is in analyzable condition for concept extraction, and wherein the labeled data comprises the label and a clause comprising the item contents; assigning a category to the key words, wherein the category references a concept so that the key words can be handled as concepts with a meaning; separating the clauses into pairs comprising an independent word and an attached word; assigning categories to the separated clauses using syntactic patterns and a category dictionary; generating, by syntactic analysis, a syntactic tree of a sentence comprising the separated clauses; receiving a syntactically analyzed sentence as input, identifying mutually dependent relationships between or among the categorized key words, according to at least one rule defining mutually dependent relationships between or among categorized key words; grouping the identified mutually dependent relationships into groups of related mutually dependent relationships; and extracting the key words with mutually dependent relationships in the same sentence as labeled data with concepts, wherein the step of extracting key words comprises using a mutually dependent relationship extraction rule comprising a string of categories of arbitrary length to be extracted; searching for unique concepts, a unique concept being a concept whose statistical characteristic is distinguished beyond a threshold with the set to which it belongs; creating and keeping statistical information; visually displaying the statistical information; and presenting a distribution of differences of the unique concepts. 18. The method of claim 1 wherein the data is inquiry data provided by customers. | 0.879822 |
9,092,490 | 4 | 5 | 4. The method of claim 1 , further comprising obtaining price information for the book from a products corpus, wherein the rich result comprises the price information for the book. | 4. The method of claim 1 , further comprising obtaining price information for the book from a products corpus, wherein the rich result comprises the price information for the book. 5. The method of claim 4 , wherein obtaining price information for the book from a products corpus comprises: obtaining products results from the products corpus using an ISBN corresponding to the book; and determining a price for the book using the products results. | 0.5 |
9,639,782 | 8 | 14 | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, from a client device and by an image search engine, an image search query including (i) a query image and (ii) an indication of a particular image acquisition template that was overlaid on a viewfinder of the client device when the query image was captured and that depicts a canonical pose or view of a particular type of object; obtaining, by the image search engine, search results based at least on (i) the query image and (ii) the indication of the particular image acquisition template that was overlaid on the view finder of the client device when the query image was captured and that depicts the canonical pose or view of the particular type of object; and providing, by the image search engine to the client device, the search results for display on the client device. | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, from a client device and by an image search engine, an image search query including (i) a query image and (ii) an indication of a particular image acquisition template that was overlaid on a viewfinder of the client device when the query image was captured and that depicts a canonical pose or view of a particular type of object; obtaining, by the image search engine, search results based at least on (i) the query image and (ii) the indication of the particular image acquisition template that was overlaid on the view finder of the client device when the query image was captured and that depicts the canonical pose or view of the particular type of object; and providing, by the image search engine to the client device, the search results for display on the client device. 14. The system of claim 8 , wherein receiving an image search query including (i) a query image and (ii) an indication of a particular image acquisition template that was overlaid on a viewfinder of the client device when the query image was captured and that depicts the canonical pose or view of the particular type of object comprises: receiving an image file and a separate indication of the particular image acquisition template used to capture the query image. | 0.670438 |
8,396,901 | 3 | 4 | 3. The method according to claim 1 , wherein said record of said node table contains said node identifier. | 3. The method according to claim 1 , wherein said record of said node table contains said node identifier. 4. The method according to claim 3 , wherein said record of said node table further comprises a field for each simple property of the corresponding node, a simple property being capable of taking at most one value per node. | 0.5 |
9,032,343 | 9 | 10 | 9. The system of claim 1 , wherein the circuit design is for implementation on a field-programmable gate array (FPGA) type of PLD device. | 9. The system of claim 1 , wherein the circuit design is for implementation on a field-programmable gate array (FPGA) type of PLD device. 10. The system of claim 9 , wherein the circuit design is for implementation on a partially-reconfigurable FPGA. | 0.5 |
8,515,828 | 11 | 12 | 11. A computer program product, comprising: a non-transitory computer-readable storage device having computer-readable program code embodied therein that when executed by a computer cause the computer to perform a method for providing product information based on user-generated product reviews, the computer-readable program code comprising: computer-readable program code for receiving a request for product information defining at least a product or product category; computer-readable program code for identifying product reviews matching the review search criteria to generate a review analysis set; computer-readable program code for identifying negative sentiment key phrases in each review of the review analysis set; computer-readable program code for calculating a weight for each identified negative sentiment key phrase by determining a frequency of the negative sentiment key phrase in the review analysis set and calculating an inverse of the frequency; computer-readable program code for correlating the negative sentiment key phrases with product characteristics using a classification rubric, wherein the classification rubric defines which negative sentiment key phrases are associated with a product characteristic of a product class; computer-readable program code for calculating for each product characteristic a score based on the weights of the negative sentiment key phrase or phrases correlated to the product characteristic; computer-readable program code for generating an identity of each product reviewed in the review analysis set and, for each product, a summary of scored product characteristics; and computer-readable program code for communicating at least a portion of the identities of each product reviewed in the review analysis set and the corresponding summary of scored product characteristics. | 11. A computer program product, comprising: a non-transitory computer-readable storage device having computer-readable program code embodied therein that when executed by a computer cause the computer to perform a method for providing product information based on user-generated product reviews, the computer-readable program code comprising: computer-readable program code for receiving a request for product information defining at least a product or product category; computer-readable program code for identifying product reviews matching the review search criteria to generate a review analysis set; computer-readable program code for identifying negative sentiment key phrases in each review of the review analysis set; computer-readable program code for calculating a weight for each identified negative sentiment key phrase by determining a frequency of the negative sentiment key phrase in the review analysis set and calculating an inverse of the frequency; computer-readable program code for correlating the negative sentiment key phrases with product characteristics using a classification rubric, wherein the classification rubric defines which negative sentiment key phrases are associated with a product characteristic of a product class; computer-readable program code for calculating for each product characteristic a score based on the weights of the negative sentiment key phrase or phrases correlated to the product characteristic; computer-readable program code for generating an identity of each product reviewed in the review analysis set and, for each product, a summary of scored product characteristics; and computer-readable program code for communicating at least a portion of the identities of each product reviewed in the review analysis set and the corresponding summary of scored product characteristics. 12. The computer program product of claim 11 , wherein the classification rubric is derived by conducting a semantic analysis of a product review sample set to identify sentiment key phrases and assigning each sentiment key phrase to a product characteristic. | 0.507605 |
7,552,381 | 1 | 31 | 1. A computer-implemented method comprising: scanning a coversheet having an overview of a collection; receiving an image of the overview of the collection that comprises a first plurality of indication areas associated with a plurality of documents and a second plurality of indication areas associated with a plurality of actions, wherein the plurality of actions includes printing, faxing, sending by electronic mail, and grouping; identifying at least one action from the plurality of actions set forth in the image; identifying at least one document from the plurality of documents for the at least one action identified from the plurality of actions, wherein the identifying the at least one action from the plurality of actions set forth in the image is performed based on the second plurality of the indication areas in the image, the identifying the at least one document from the plurality of documents is performed based on the first plurality of the indication areas in the image, wherein the at least one action from the plurality of actions and the at least one document from the plurality of documents are identified by scanning the image; and performing the at least one action on the at least one document in response to the identifying the at least one action from the fourth plurality of actions set forth in the image and the identifying the at least one document from the third plurality of documents from the image. | 1. A computer-implemented method comprising: scanning a coversheet having an overview of a collection; receiving an image of the overview of the collection that comprises a first plurality of indication areas associated with a plurality of documents and a second plurality of indication areas associated with a plurality of actions, wherein the plurality of actions includes printing, faxing, sending by electronic mail, and grouping; identifying at least one action from the plurality of actions set forth in the image; identifying at least one document from the plurality of documents for the at least one action identified from the plurality of actions, wherein the identifying the at least one action from the plurality of actions set forth in the image is performed based on the second plurality of the indication areas in the image, the identifying the at least one document from the plurality of documents is performed based on the first plurality of the indication areas in the image, wherein the at least one action from the plurality of actions and the at least one document from the plurality of documents are identified by scanning the image; and performing the at least one action on the at least one document in response to the identifying the at least one action from the fourth plurality of actions set forth in the image and the identifying the at least one document from the third plurality of documents from the image. 31. The method of claim 1 , further comprising determining the at least one action by performing optical character recognition on an action indication area. | 0.79845 |
9,471,874 | 4 | 5 | 4. The method of claim 1 further comprising: identifying a plurality of follow-up postings corresponding to the identified question; analyzing each of the follow-up postings to identify a conversational move corresponding to each of the follow up postings, wherein at least one of the conversational postings is selected from the group consisting of an answer, a clarification, a rejection, or a different conversational move; and generating a contribution tree based on the follow-up postings and their identified conversational moves. | 4. The method of claim 1 further comprising: identifying a plurality of follow-up postings corresponding to the identified question; analyzing each of the follow-up postings to identify a conversational move corresponding to each of the follow up postings, wherein at least one of the conversational postings is selected from the group consisting of an answer, a clarification, a rejection, or a different conversational move; and generating a contribution tree based on the follow-up postings and their identified conversational moves. 5. The method of claim 4 further comprising: pruning one or more of the follow-up postings from the contribution tree based on a contribution analysis, wherein the pruned follow-up postings have a contribution analysis result selected from the group consisting of an answer leading to a new question, an overly deep follow-up posting, and another pruning criteria. | 0.5 |
8,229,903 | 26 | 28 | 26. The computer program product of claim 25 , wherein said program code for said converting feature comprises a software parser function, which includes program code for: dividing each line of the database into tokens, each token being a word that is separated by a space, wherein each line within said database is split into words separated by a blank space; generating a dictionary of tokens comprising a single occurrence of each token, wherein duplication of tokens within the dictionary of tokens is substantially avoided; and converting each line of said database into token vector with one entry for each token within the line. | 26. The computer program product of claim 25 , wherein said program code for said converting feature comprises a software parser function, which includes program code for: dividing each line of the database into tokens, each token being a word that is separated by a space, wherein each line within said database is split into words separated by a blank space; generating a dictionary of tokens comprising a single occurrence of each token, wherein duplication of tokens within the dictionary of tokens is substantially avoided; and converting each line of said database into token vector with one entry for each token within the line. 28. The computer program product of claim 26 , further comprising program code for: combining similar event examples into clusters; outputting said clusters to a user; and enabling user manipulation of parameters of said clustering algorithm to produce different clusters. | 0.665025 |
9,141,656 | 4 | 5 | 4. The method of claim 3 , further comprising: updating the search index to associate a searcher restrict comprising data identifying the second entity of the two entities with content authored by the first entity of the two entities. | 4. The method of claim 3 , further comprising: updating the search index to associate a searcher restrict comprising data identifying the second entity of the two entities with content authored by the first entity of the two entities. 5. The method of claim 4 , wherein the author restrict comprising data that identifies the first entity of the two entities is used in searching for a predefined period of time. | 0.5 |
7,761,397 | 18 | 19 | 18. The computer-implemented method of claim 17 , further comprising reordering nodes of the autoselect Include ZDD and nodes of the autoselect Exclude ZDD to reduce the number of nodes and complexity thereof. | 18. The computer-implemented method of claim 17 , further comprising reordering nodes of the autoselect Include ZDD and nodes of the autoselect Exclude ZDD to reduce the number of nodes and complexity thereof. 19. The computer-implemented method of claim 18 , wherein the nodes of the autoselect Include ZDD or the nodes of the autoselect Exclude ZDD have respective index numbers associated with the attributes and enumeration values thereof and wherein the separately building includes aligning the indices of the respective nodes of the autoselect Include ZDD and the autoselect Exclude ZDD to facilitate manipulation of the autoselect ZDD. | 0.5 |
7,711,573 | 239 | 240 | 239. The system of claim 238 , wherein each said at least one matching resume satisfies the job description when the parsed resume includes an expected salary that falls within the required salary range. | 239. The system of claim 238 , wherein each said at least one matching resume satisfies the job description when the parsed resume includes an expected salary that falls within the required salary range. 240. The system of claim 239 , wherein the expected salary falls within the required salary range when: the expected salary is greater than or equal to the minimum required salary, and the expected salary is less than or equal to the maximum required salary. | 0.5 |
6,134,235 | 53 | 54 | 53. The method according to claim 36, further comprising the step of universal messaging, said step of universal messaging including the integration of e-mail messages, facsimile messages, and voice messages into a common mailbox. | 53. The method according to claim 36, further comprising the step of universal messaging, said step of universal messaging including the integration of e-mail messages, facsimile messages, and voice messages into a common mailbox. 54. The method according to claim 53, wherein the step of universal messaging further includes converting the content of messages from one format to another. | 0.849328 |
9,463,383 | 1 | 6 | 1. A method performed by at least one computer processing executing computer program instructions stored on at least one non-transitory computer-readable medium, the method comprising: (A) receiving data representing a first plurality of social currency event data structures, wherein the first plurality of social currency event data structures includes: data representing first values of the first plurality of social currency event data structures to a first plurality of users; data representing second values of the first plurality of social currency event data structures to a second plurality of users; and data representing a first plurality of existing values of at least one first field shared by the first plurality of social currency event data structures, wherein the receiving comprises: (A) (1) receiving, over a network from a first plurality of computing devices, the data representing the first values of the first plurality of social currency event data structures to the first plurality of users; and (A) (2) receiving, over the network from a second plurality of computing devices, the data representing second values of the first plurality of social currency event data structures to a second plurality of users; (B) identifying a first subset of the first plurality of social currency event data structures based on the first plurality of existing values of the at least one first field shared by the plurality of social currency event data structures; (C) calculating a first value of a statistic based on the first subset of the first plurality of social currency event data structures; (D) identifying a first plurality of existing values of a second field shared by the first plurality of social currency event data structures; (E) calculating a first plurality of normalized values of the second field based on the first value of the statistic and the first plurality of existing values of the second field; and (F) providing, over the network, at least one of the first plurality of normalized values of the second field to a third computing device. | 1. A method performed by at least one computer processing executing computer program instructions stored on at least one non-transitory computer-readable medium, the method comprising: (A) receiving data representing a first plurality of social currency event data structures, wherein the first plurality of social currency event data structures includes: data representing first values of the first plurality of social currency event data structures to a first plurality of users; data representing second values of the first plurality of social currency event data structures to a second plurality of users; and data representing a first plurality of existing values of at least one first field shared by the first plurality of social currency event data structures, wherein the receiving comprises: (A) (1) receiving, over a network from a first plurality of computing devices, the data representing the first values of the first plurality of social currency event data structures to the first plurality of users; and (A) (2) receiving, over the network from a second plurality of computing devices, the data representing second values of the first plurality of social currency event data structures to a second plurality of users; (B) identifying a first subset of the first plurality of social currency event data structures based on the first plurality of existing values of the at least one first field shared by the plurality of social currency event data structures; (C) calculating a first value of a statistic based on the first subset of the first plurality of social currency event data structures; (D) identifying a first plurality of existing values of a second field shared by the first plurality of social currency event data structures; (E) calculating a first plurality of normalized values of the second field based on the first value of the statistic and the first plurality of existing values of the second field; and (F) providing, over the network, at least one of the first plurality of normalized values of the second field to a third computing device. 6. The method of claim 1 , further comprising: (G) receiving data representing a second plurality of social currency event data structures, wherein the second plurality of social currency event data structures includes: data representing third values of the second plurality of social currency event data structures to a third plurality of users; data representing fourth values of the second plurality of social currency event data structures to a fourth plurality of users; and data representing a second plurality of existing values of the at least one first field shared by the second plurality of social currency event data structures; (H) identifying a second subset of the first plurality of social currency event data structures based on the second plurality of existing values of the at least one first field shared by the second plurality of social currency event data structures; (I) calculating a second value of the statistic based on the second subset of the first plurality of social currency event data structures; (J) identifying a second plurality of existing values of the second field shared by the second plurality of social currency event data structures; and (K) calculating a second plurality of normalized values of the second field based on the second value of the statistic and the second plurality of existing values of the second field. | 0.5 |
8,037,407 | 23 | 25 | 23. A computer-implemented method for creating and processing a human interface description for an underlying application, the method comprising: receiving an application-specific human interface description associated with an information-gathering application, the application-specific human interface description comprising: application-specific layout elements defined according to terminology consistent with the application, the application-specific layout elements being arranged in an expandable and collapsible hierarchy having a corresponding hierarchical state; and data elements defined according to the terminology and having values specific to the application; transforming the application-specific human interface description into a standardized human interface description, the standardized human interface description comprising standardized layout information and the data elements specific to the application, the transforming comprising: translating the application-specific layout elements into the standardized layout information, the standardized layout information comprising basic layout elements in a format independent of the application and independent of a browser, wherein the standardized layout information maintains the hierarchical state of the application-specific layout elements; converting the standardized human interface description into a browser-compliant human interface description; receiving, from a user, information specific to the application; refining a data element of the browser-compliant human interface description according to the received information, wherein the refined browser-compliant human interface description comprises the data elements specific to the application and the standardized layout information independent of the underlying application; and decomposing the refined browser-compliant human interface description into a human interface layout template and a data description, the data description comprising the data elements specific to the application, and the human interface layout template comprising the standardized layout information, the decomposing comprising: extracting the standardized layout information from the browser compliant human interface description using a first transformation; and extracting the data elements specific to the application from the browser-compliant human interface description using a second transformation, wherein the second transformation scans the browser compliant human interface to identify name attributes, and at least one of the data elements specific to the application and corresponding to the identified name attributes. | 23. A computer-implemented method for creating and processing a human interface description for an underlying application, the method comprising: receiving an application-specific human interface description associated with an information-gathering application, the application-specific human interface description comprising: application-specific layout elements defined according to terminology consistent with the application, the application-specific layout elements being arranged in an expandable and collapsible hierarchy having a corresponding hierarchical state; and data elements defined according to the terminology and having values specific to the application; transforming the application-specific human interface description into a standardized human interface description, the standardized human interface description comprising standardized layout information and the data elements specific to the application, the transforming comprising: translating the application-specific layout elements into the standardized layout information, the standardized layout information comprising basic layout elements in a format independent of the application and independent of a browser, wherein the standardized layout information maintains the hierarchical state of the application-specific layout elements; converting the standardized human interface description into a browser-compliant human interface description; receiving, from a user, information specific to the application; refining a data element of the browser-compliant human interface description according to the received information, wherein the refined browser-compliant human interface description comprises the data elements specific to the application and the standardized layout information independent of the underlying application; and decomposing the refined browser-compliant human interface description into a human interface layout template and a data description, the data description comprising the data elements specific to the application, and the human interface layout template comprising the standardized layout information, the decomposing comprising: extracting the standardized layout information from the browser compliant human interface description using a first transformation; and extracting the data elements specific to the application from the browser-compliant human interface description using a second transformation, wherein the second transformation scans the browser compliant human interface to identify name attributes, and at least one of the data elements specific to the application and corresponding to the identified name attributes. 25. The method of claim 23 , further comprising the steps of: instantiating a data instance from the data description; receiving data from a user; and storing the data in the data instance. | 0.5 |
9,519,642 | 9 | 16 | 9. A computer program product comprising at least one non-transitory computer readable medium storing instructions translatable by at least one processor, the instructions when translated by the at least one processor cause a system to provide content management and locale-specific delivery of managed linguistic translations of web site content by: responsive to a request for content from a client device communicatively connected to a web server in an enterprise computing environment, dynamically resolving the request for content by: determining an exemplar linguistic translation reference, the exemplar linguistic translation reference identifying a managed web site content object associated with the content requested by the client device, the web site content object having a multilingual attribute indicating that the managed web site content object is multilingual, the managed web site content object being stored in a repository residing in the enterprise computing environment; determining a linguistic translation group utilizing the exemplar linguistic translation reference, the linguistic translation group including translation group content items representing same content translated into different languages; determining, from the determined linguistic translation group, one or more human languages associated with the managed web site content object; determining an effective locale for the request for content; determining, from the one or more human languages in the linguistic translation group and based on the effective locale, a language that is appropriate for the effective locale; retrieving a managed content item from the repository, wherein the managed content item is part of the linguistic translation group and represents a linguistic translation for the managed web site content object in the language that is appropriate for the effective locale; and responding to the request for content by dynamically rendering and delivering the managed content item that is in the language appropriate for the effective locale. | 9. A computer program product comprising at least one non-transitory computer readable medium storing instructions translatable by at least one processor, the instructions when translated by the at least one processor cause a system to provide content management and locale-specific delivery of managed linguistic translations of web site content by: responsive to a request for content from a client device communicatively connected to a web server in an enterprise computing environment, dynamically resolving the request for content by: determining an exemplar linguistic translation reference, the exemplar linguistic translation reference identifying a managed web site content object associated with the content requested by the client device, the web site content object having a multilingual attribute indicating that the managed web site content object is multilingual, the managed web site content object being stored in a repository residing in the enterprise computing environment; determining a linguistic translation group utilizing the exemplar linguistic translation reference, the linguistic translation group including translation group content items representing same content translated into different languages; determining, from the determined linguistic translation group, one or more human languages associated with the managed web site content object; determining an effective locale for the request for content; determining, from the one or more human languages in the linguistic translation group and based on the effective locale, a language that is appropriate for the effective locale; retrieving a managed content item from the repository, wherein the managed content item is part of the linguistic translation group and represents a linguistic translation for the managed web site content object in the language that is appropriate for the effective locale; and responding to the request for content by dynamically rendering and delivering the managed content item that is in the language appropriate for the effective locale. 16. The computer program product of claim 9 , wherein the effective locale is declared as required for a site through which the request for content is received and wherein the instructions further comprise automatically including members of the translation group in a publication operation when one of the members is published to the site. | 0.5 |
8,312,370 | 8 | 10 | 8. A method of generating structured documents, the method comprising: identifying a template for generating a document, wherein the template defines at least one field having a specified position and length within the document; storing a copy of the template to a memory so as to initialize the document; storing, by at least one processor, characters based on data associated with the at least one field to a location of the memory associated with the at least one field of the document, based at least in part on the text and the length of the field, storing, by the at least one processor, to portions of the memory associated with the document, at least one indicator of at least one portion of the at least one field to be removed from the document; wherein the at least one indicator of the at least one portion of the at least one field to be removed comprises at least one specified character value; wherein the at least one specified character value comprises at least two byte values, the byte values comprising a first of the byte values indicative of a start-of-heading of a character encoding of the document and at least a second of the byte values indicative of a number of characters to be removed from the document; and accessing the document from the memory by at least one circuit; removing, by the at least one circuit, the at least one portion of the at least one field based on the at least one indicator so as to generate a portion of an assembled document; and outputting the portion of the assembled document by the at least one circuit. | 8. A method of generating structured documents, the method comprising: identifying a template for generating a document, wherein the template defines at least one field having a specified position and length within the document; storing a copy of the template to a memory so as to initialize the document; storing, by at least one processor, characters based on data associated with the at least one field to a location of the memory associated with the at least one field of the document, based at least in part on the text and the length of the field, storing, by the at least one processor, to portions of the memory associated with the document, at least one indicator of at least one portion of the at least one field to be removed from the document; wherein the at least one indicator of the at least one portion of the at least one field to be removed comprises at least one specified character value; wherein the at least one specified character value comprises at least two byte values, the byte values comprising a first of the byte values indicative of a start-of-heading of a character encoding of the document and at least a second of the byte values indicative of a number of characters to be removed from the document; and accessing the document from the memory by at least one circuit; removing, by the at least one circuit, the at least one portion of the at least one field based on the at least one indicator so as to generate a portion of an assembled document; and outputting the portion of the assembled document by the at least one circuit. 10. The method of claim 8 , wherein the assembled document is reduced in size relative to the document as copied from the template. | 0.817039 |
8,024,372 | 1 | 5 | 1. A method for constructing a model that generates text, the method comprising: in a computer system, performing the operations of: representing a concept as a cluster node; representing a word as a terminal node; assigning a weight to a link between two nodes; and training the model based on a set of documents, comprising: for each cluster node, computing a probabilistic cost of a corresponding concept existing in a document but not triggering any words. | 1. A method for constructing a model that generates text, the method comprising: in a computer system, performing the operations of: representing a concept as a cluster node; representing a word as a terminal node; assigning a weight to a link between two nodes; and training the model based on a set of documents, comprising: for each cluster node, computing a probabilistic cost of a corresponding concept existing in a document but not triggering any words. 5. The method of claim 1 , further comprising iteratively training the model over progressively more documents. | 0.819218 |
7,574,453 | 4 | 6 | 4. The method of claim 1 , wherein enabling search of the data items in the collection includes identifying the selection criteria from a user-input. | 4. The method of claim 1 , wherein enabling search of the data items in the collection includes identifying the selection criteria from a user-input. 6. The method of claim 4 , wherein identifying the selection criteria from a user-input includes receiving two or more search terms with a BOOLEAN connector relating the two or more search terms. | 0.5 |
9,792,917 | 1 | 5 | 1. An audio processing device, comprising: a first receiving module, configured to receive a first audio signal; a second receiving module, configured to receive a second audio signal; an audio synthesizing module, configured to synthesize the first audio signal and the second audio signal to obtain a third audio signal; and an audio outputting module, configured to output the third audio signal; wherein the first receiving module receives the first audio signal by using a wireless receiving technology; and/or the second receiving module receives the second audio signal by using a wireless receiving technology; wherein a delay of the wireless receiving technology employed by the first receiving module is less than a first predetermined threshold; wherein the audio outputting module outputs the third audio signal by using a wireless communication technology; wherein a delay of the wireless communication technology employed by the audio outputting module is less than a second predetermined threshold; wherein a sum of the first predetermined threshold and the second predetermined threshold is less than or equal to 50 ms; and wherein the wireless receiving technology employed by the first receiving module is an analog communication receiving technology, and the wireless receiving technology employed by the second receiving module is a digital communication receiving technology. | 1. An audio processing device, comprising: a first receiving module, configured to receive a first audio signal; a second receiving module, configured to receive a second audio signal; an audio synthesizing module, configured to synthesize the first audio signal and the second audio signal to obtain a third audio signal; and an audio outputting module, configured to output the third audio signal; wherein the first receiving module receives the first audio signal by using a wireless receiving technology; and/or the second receiving module receives the second audio signal by using a wireless receiving technology; wherein a delay of the wireless receiving technology employed by the first receiving module is less than a first predetermined threshold; wherein the audio outputting module outputs the third audio signal by using a wireless communication technology; wherein a delay of the wireless communication technology employed by the audio outputting module is less than a second predetermined threshold; wherein a sum of the first predetermined threshold and the second predetermined threshold is less than or equal to 50 ms; and wherein the wireless receiving technology employed by the first receiving module is an analog communication receiving technology, and the wireless receiving technology employed by the second receiving module is a digital communication receiving technology. 5. The device according to claim 1 , wherein the first receiving module receives the first audio signal by using FM communication technology, the second receiving module receives the second audio signal by using BLUETOOTH® receiving technology, and the audio outputting module outputs the third audio signal by using an audio interface. | 0.5 |
7,831,604 | 1 | 2 | 1. A digital data processing method for enterprise application integration comprising: A. electronically downloading to one or more digital data processors functionality that effects information transfers between a first database and a second database and between the first database and a third database, B. executing the functionality on the one or more digital data processors to effect transferring information between the first database and the second database, the transferring step including at least: (i) receiving information from the second database using an application program interface (“API”) associated therewith, (ii) transforming at least some of the information received from the second database into resource definition format (“RDF”) triplets, and (iii) transmitting those RDF triplets to the first database; C. executing the functionality on the one or more digital data processors to effect transferring information between the first database and the third database, the transferring step including at least: (i) receiving the information from the third database using an application program interface (“API”) different than the API associated with the second database, (ii) transforming at least some of the information received from the third database into resource definition format (“RDF”) triplets, and (iii) transmitting those RDF triplets to the first database; D. wherein the first database stores the RDF triplets from the second and third databases for query, for coalescence, or for use in generating directed graphs that can be analyzed to discern answers to queries for information reflected by the RDF triplets and originating from any of the second and third databases. | 1. A digital data processing method for enterprise application integration comprising: A. electronically downloading to one or more digital data processors functionality that effects information transfers between a first database and a second database and between the first database and a third database, B. executing the functionality on the one or more digital data processors to effect transferring information between the first database and the second database, the transferring step including at least: (i) receiving information from the second database using an application program interface (“API”) associated therewith, (ii) transforming at least some of the information received from the second database into resource definition format (“RDF”) triplets, and (iii) transmitting those RDF triplets to the first database; C. executing the functionality on the one or more digital data processors to effect transferring information between the first database and the third database, the transferring step including at least: (i) receiving the information from the third database using an application program interface (“API”) different than the API associated with the second database, (ii) transforming at least some of the information received from the third database into resource definition format (“RDF”) triplets, and (iii) transmitting those RDF triplets to the first database; D. wherein the first database stores the RDF triplets from the second and third databases for query, for coalescence, or for use in generating directed graphs that can be analyzed to discern answers to queries for information reflected by the RDF triplets and originating from any of the second and third databases. 2. A method according to claim 1 , wherein the information transferred between the first database and the second database comprises requests for data and responses thereto. | 0.5 |
8,234,560 | 1 | 10 | 1. A method for creating documents in a hierarchy using an electronic device, which performs operations in the method, the method comprising: generating a root number which corresponds to a base level in the hierarchy; assigning document numbers to the documents, wherein the document numbers are generated based at least in part on the root number; assigning directory numbers to directories in the hierarchy, wherein a given directory number is generated based at least in part on a given document number, and wherein a given directory is in a branch that is coupled to the root level; determining paths in the hierarchy corresponding to the document numbers and the directory numbers, wherein a given path includes the base level and zero or more dependent branches; generating content numbers for the documents based at least in part on the corresponding paths through the hierarchy; creating the documents, wherein creating the documents comprises translating a content number for each document to determine content to be placed within the document; and storing, in a memory, the created documents in the directories in the hierarchy. | 1. A method for creating documents in a hierarchy using an electronic device, which performs operations in the method, the method comprising: generating a root number which corresponds to a base level in the hierarchy; assigning document numbers to the documents, wherein the document numbers are generated based at least in part on the root number; assigning directory numbers to directories in the hierarchy, wherein a given directory number is generated based at least in part on a given document number, and wherein a given directory is in a branch that is coupled to the root level; determining paths in the hierarchy corresponding to the document numbers and the directory numbers, wherein a given path includes the base level and zero or more dependent branches; generating content numbers for the documents based at least in part on the corresponding paths through the hierarchy; creating the documents, wherein creating the documents comprises translating a content number for each document to determine content to be placed within the document; and storing, in a memory, the created documents in the directories in the hierarchy. 10. The method of claim 1 , wherein a given content number is generated using a pseudorandom number generator using a path number corresponding to a given path through the hierarchy as a seed. | 0.670103 |
10,032,191 | 45 | 46 | 45. The system of claim 36 , wherein: the client device accesses the pairing server when processing identification data associated with the sandbox reachable service sharing a public address with the client device, the pairing server performs a discovery lookup of any device that has announced sharing of the public address associated with the client device, and the sandbox reachable service announces itself to the pairing server prior to the establishment of the communication session between the sandboxed application and the sandbox reachable service. | 45. The system of claim 36 , wherein: the client device accesses the pairing server when processing identification data associated with the sandbox reachable service sharing a public address with the client device, the pairing server performs a discovery lookup of any device that has announced sharing of the public address associated with the client device, and the sandbox reachable service announces itself to the pairing server prior to the establishment of the communication session between the sandboxed application and the sandbox reachable service. 46. The system of claim 45 , wherein the networked device is configured to at least one of: announce the sandbox reachable service to a discovery module using a processor and a memory, announce an availability of the sandbox reachable service across a range of public addresses such that the sandboxed application communicates with the sandbox reachable service in any one of the range of the public addresses, communicate at least one of a global unique identifier and an alphanumeric name to the pairing server along with at least one of a hardware address associated with the networked device, a public address pair associated with the sandbox reachable service of the networked device, and a private address pair associated with the sandbox reachable service of the networked device, wherein the private address pair includes a private IP address and a port number associated with the sandbox reachable service. | 0.5 |
8,363,949 | 1 | 2 | 1. A method for recognizing characters, comprising: receiving touch-based input relating to a sequence of strokes at a touch-based interface of a computing device, wherein a first subset of the sequence of strokes corresponds to a first area of the touch-based interface and a second subset of the sequence of strokes corresponds to a second area of the touch-based interface that at least partially overlaps the first area; displaying a graphical representation of the first subset of the sequence of strokes on an output device coupled to the computing device; determining a confidence level that a first character approximately matches the first subset of the sequence of strokes, wherein the confidence level is of at least a first confidence threshold, further comprising: checking a combination of the first character and a determined character against a language reference; ranking the first character based at least in part on approximate matches of the combination of the first character and one or more previously determined characters found in the language reference, wherein the ranking is related to the confidence level; and selecting the first character when the ranking is above a selected threshold level; altering the display of the graphical representation of the first subset of the sequence of strokes based on the confidence level; and providing the first character for processing by an application executing on the computing device when the confidence level is of at least a second confidence threshold, wherein the application is designed to process characters from touch-based input. | 1. A method for recognizing characters, comprising: receiving touch-based input relating to a sequence of strokes at a touch-based interface of a computing device, wherein a first subset of the sequence of strokes corresponds to a first area of the touch-based interface and a second subset of the sequence of strokes corresponds to a second area of the touch-based interface that at least partially overlaps the first area; displaying a graphical representation of the first subset of the sequence of strokes on an output device coupled to the computing device; determining a confidence level that a first character approximately matches the first subset of the sequence of strokes, wherein the confidence level is of at least a first confidence threshold, further comprising: checking a combination of the first character and a determined character against a language reference; ranking the first character based at least in part on approximate matches of the combination of the first character and one or more previously determined characters found in the language reference, wherein the ranking is related to the confidence level; and selecting the first character when the ranking is above a selected threshold level; altering the display of the graphical representation of the first subset of the sequence of strokes based on the confidence level; and providing the first character for processing by an application executing on the computing device when the confidence level is of at least a second confidence threshold, wherein the application is designed to process characters from touch-based input. 2. The method of claim 1 , wherein altering the display of the graphical representation of the first subset of the sequence of strokes further comprises: ceasing to display the graphical representation of the first subset of the sequence of strokes when the confidence level is of at least the second confidence threshold. | 0.729412 |
7,548,910 | 15 | 16 | 15. The system of claim 14 , wherein each of the semantic grouping of the concepts is identified by a semantic type. | 15. The system of claim 14 , wherein each of the semantic grouping of the concepts is identified by a semantic type. 16. The system of claim 15 , wherein the semantic type is a category name. | 0.5 |
7,814,080 | 13 | 18 | 13. A system comprising: an interface to receive first queries from a client system; one or more processors; and a software utility executable on the one or more processors to: establish plural sessions with a database system, each session associated with at least one transaction; identify transactions that operate on the same set of one or more tuples; re-allocate transactions between or among the sessions such that the identified transactions that operate on the same set of one or more tuples is allocated to one of the sessions; identify first queries of a particular one of the transactions that specify commutative and associative operations, and group the identified first queries into a second query. | 13. A system comprising: an interface to receive first queries from a client system; one or more processors; and a software utility executable on the one or more processors to: establish plural sessions with a database system, each session associated with at least one transaction; identify transactions that operate on the same set of one or more tuples; re-allocate transactions between or among the sessions such that the identified transactions that operate on the same set of one or more tuples is allocated to one of the sessions; identify first queries of a particular one of the transactions that specify commutative and associative operations, and group the identified first queries into a second query. 18. The system of claim 13 , wherein the identified first queries comprise statements <t, b 1 > through <t, b m >, m being greater than 1, where t represents a set of one or more tuples, and b 1 through bm represent respective modification operations on the set of one or more tuples, and wherein the second query comprises statement <t, c>, where c represents an aggregation of b 1 through b m . | 0.5 |
8,572,157 | 1 | 10 | 1. A method, implemented at a computer system that includes one or more processors, for brokering requests and data between a user and a content provider, the method comprising: receiving, at a middleware system, a request for data from a user, the request formatted in a first request format and requesting data from a content provider; translating the request to a second request format that is compatible with the content provider using configuration data, the configuration data comprising information used to transform data from the first request format to the second request format and information used to map custom messages in non-tabular formats from the content provider to uniform tabular format data or messaging formats; sending the translated request to the content provider; as a result of sending the translated request to the content provider, receiving a response from the content provider that comprises non-tabular, hierarchically-structured content; converting the hierarchically-structured content into tabular content, wherein converting comprises executing a series of xPath queries defined in the configuration data, including: performing one or more first xPath queries on the non-tabular hierarchically-structured content to identify repeating nodes previously defined in the configuration data in the hierarchically-structured content, and mapping the repeating nodes to rows in the tabular format; and performing one or more second xPath queries on the hierarchically-structured content to identify values for one or more elements or attributes previously defined in the configuration data and mapping the values to columns in the tabular format; and returning at least a portion of the tabular content to the user in response to the request for data from the user. | 1. A method, implemented at a computer system that includes one or more processors, for brokering requests and data between a user and a content provider, the method comprising: receiving, at a middleware system, a request for data from a user, the request formatted in a first request format and requesting data from a content provider; translating the request to a second request format that is compatible with the content provider using configuration data, the configuration data comprising information used to transform data from the first request format to the second request format and information used to map custom messages in non-tabular formats from the content provider to uniform tabular format data or messaging formats; sending the translated request to the content provider; as a result of sending the translated request to the content provider, receiving a response from the content provider that comprises non-tabular, hierarchically-structured content; converting the hierarchically-structured content into tabular content, wherein converting comprises executing a series of xPath queries defined in the configuration data, including: performing one or more first xPath queries on the non-tabular hierarchically-structured content to identify repeating nodes previously defined in the configuration data in the hierarchically-structured content, and mapping the repeating nodes to rows in the tabular format; and performing one or more second xPath queries on the hierarchically-structured content to identify values for one or more elements or attributes previously defined in the configuration data and mapping the values to columns in the tabular format; and returning at least a portion of the tabular content to the user in response to the request for data from the user. 10. The method of claim 1 , wherein the one or more second queries comprise one or more relative queries to identify one or more elements or attributes that are relative to one of the repeating nodes. | 0.72752 |
10,055,457 | 1 | 2 | 1. A computer-implemented method, comprising: obtaining one or more query terms in a first query; and for each of the one or more query terms: searching a standardized entity taxonomy to locate a standardized entity that most closely matches the query term, the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities; calculating a confidence score for a query term-standardized entity pair for the standardized entity that most closely matches the query term; in response to a determination that the confidence score transgresses a threshold, associating the query term with the entity identification corresponding to the standardized entity that most closely matches the query term; retrieving one or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification; and executing the one or more query rewriting rules to rewrite the first query such that the rewritten query is more restrictive than the first query. | 1. A computer-implemented method, comprising: obtaining one or more query terms in a first query; and for each of the one or more query terms: searching a standardized entity taxonomy to locate a standardized entity that most closely matches the query term, the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities; calculating a confidence score for a query term-standardized entity pair for the standardized entity that most closely matches the query term; in response to a determination that the confidence score transgresses a threshold, associating the query term with the entity identification corresponding to the standardized entity that most closely matches the query term; retrieving one or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification; and executing the one or more query rewriting rules to rewrite the first query such that the rewritten query is more restrictive than the first query. 2. The method of claim 1 , wherein the one or more query rewriting rules include adding the standardized entity having the entity identification to the first query with an AND connector. | 0.773723 |
9,177,063 | 11 | 16 | 11. The method of claim 1 , comprising: determining that a first member of the member network other than the first user has endorsed a particular article in the second plurality of articles, wherein the plurality of article identifiers includes an identifier for the particular article; determining, from among a plurality of different types or degrees of associations between the members in the member network, a particular type or degree of an association in the member network between the first user and the first member; and ranking articles that correspond with the plurality of article identifiers using the particular type or degree of the association in the member network between the first member and the first user, wherein responding to the first search query with the plurality of article identifiers comprises formatting a response in an arrangement that corresponds to the ranking of the articles using the particular type or degree of the association in the member network between the first member and the first user. | 11. The method of claim 1 , comprising: determining that a first member of the member network other than the first user has endorsed a particular article in the second plurality of articles, wherein the plurality of article identifiers includes an identifier for the particular article; determining, from among a plurality of different types or degrees of associations between the members in the member network, a particular type or degree of an association in the member network between the first user and the first member; and ranking articles that correspond with the plurality of article identifiers using the particular type or degree of the association in the member network between the first member and the first user, wherein responding to the first search query with the plurality of article identifiers comprises formatting a response in an arrangement that corresponds to the ranking of the articles using the particular type or degree of the association in the member network between the first member and the first user. 16. The method of claim 11 , wherein determining that the first member has endorsed the particular article in the second plurality of articles includes selecting the first member from among a plurality of members in the member network based on the first member being associated with the first user in the member network or based on the first member being associated with the first search query. | 0.575431 |
6,134,529 | 1 | 12 | 1. A method for using a computer to teach language reading skills comprising: selecting a subset of stored internal speech patterns containing at least one correct internal speech pattern which corresponds to a correct response and at least one internal speech pattern corresponding to an incorrect response, from a set of stored internal speech patterns, using a computer program, wherein said correct internal speech pattern is readily differentiable from each of said internal speech patterns corresponding to an incorrect response by a comparison subprogram; presenting a visual image to a user that corresponds to said correct internal speech pattern, obtaining a speech response segment from the user; comparing, using the comparison subprogram, said speech response segment to the subset of stored internal speech patterns and determining the degree of matching to each internal speech pattern in the subset of internal speech patterns; and presenting to the user a response to the speech segment based on the results of the step of comparing. | 1. A method for using a computer to teach language reading skills comprising: selecting a subset of stored internal speech patterns containing at least one correct internal speech pattern which corresponds to a correct response and at least one internal speech pattern corresponding to an incorrect response, from a set of stored internal speech patterns, using a computer program, wherein said correct internal speech pattern is readily differentiable from each of said internal speech patterns corresponding to an incorrect response by a comparison subprogram; presenting a visual image to a user that corresponds to said correct internal speech pattern, obtaining a speech response segment from the user; comparing, using the comparison subprogram, said speech response segment to the subset of stored internal speech patterns and determining the degree of matching to each internal speech pattern in the subset of internal speech patterns; and presenting to the user a response to the speech segment based on the results of the step of comparing. 12. The method of claim 1 wherein the step of obtaining a speech response further comprises obtaining a measurement of the airflow from a mouth of the user during the release phase of a stop or plosive consonant in the speech response segment. | 0.5 |
7,801,836 | 2 | 4 | 2. The computer-implemented method of claim 1 wherein: building a predictive data mining model for a respective template comprises building a predictive data mining model having characteristics indicated by the stored digital chromosome for the respective template; and the transforming comprises applying the genetic algorithm. | 2. The computer-implemented method of claim 1 wherein: building a predictive data mining model for a respective template comprises building a predictive data mining model having characteristics indicated by the stored digital chromosome for the respective template; and the transforming comprises applying the genetic algorithm. 4. The computer-implemented method of claim 2 wherein: at least one stored digital chromosome comprises an indication of one of a plurality of predictive data mining modeling learning schemes; and building a predictive data mining model for a respective template comprises building a predictive data mining model via a learning scheme indicated by the stored digital chromosome for the respective template. | 0.5 |
9,195,937 | 9 | 10 | 9. The apparatus of claim 3 , wherein a tree is constructed that includes the state, and wherein the tree is used to generate a concept based on the tree, and wherein the concept is applied to a rule that affects data management for one or more documents that satisfy the rule. | 9. The apparatus of claim 3 , wherein a tree is constructed that includes the state, and wherein the tree is used to generate a concept based on the tree, and wherein the concept is applied to a rule that affects data management for one or more documents that satisfy the rule. 10. The apparatus of claim 9 , wherein the tree is used to identify locations within a document set where one or more words are present, and wherein the branch point is a word or a combination of words. | 0.5 |
7,567,915 | 12 | 13 | 12. An information system as recited in claim 1 , wherein the knowledge manager includes: an interaction flow model; a rule base model; a constraint model; an optimization model; a conceptual model; a predictive model; and the ontology. | 12. An information system as recited in claim 1 , wherein the knowledge manager includes: an interaction flow model; a rule base model; a constraint model; an optimization model; a conceptual model; a predictive model; and the ontology. 13. An information system as recited in claim 12 , wherein each of the interactive flow model and the ontology is in communication with the rule base model, the constraint model, and the optimization model, wherein the interactive flow model is configured to manage interaction flows with each of the rule base model, the constraint model, and the optimization model, and wherein interaction flows include a number of situations and each situation has a context description that contains event concepts that a situation of the number of situations requires to occur. | 0.5 |
8,676,732 | 1 | 10 | 1. A computer-implemented method of searching for content in a target set of content based on a reference set of content, a reference semantic network representing knowledge associated with the reference set of content, and a target semantic network representing knowledge associated with the target set of content, the method comprising: receiving a user-specified search query; obtaining, by using at least one processor executing stored program instructions, at least one concept semantically relevant to the user-specified search query by using the target semantic network and the reference semantic network; constructing a second search query by augmenting the first search query with one or more terms associated with the at least one obtained concept; providing, to the at least one user, content associated with search results obtained based at least in part on searching the target set of content by using the second search query, wherein any concept in the semantic network is represented by a data structure storing data associated with a node in the semantic network. | 1. A computer-implemented method of searching for content in a target set of content based on a reference set of content, a reference semantic network representing knowledge associated with the reference set of content, and a target semantic network representing knowledge associated with the target set of content, the method comprising: receiving a user-specified search query; obtaining, by using at least one processor executing stored program instructions, at least one concept semantically relevant to the user-specified search query by using the target semantic network and the reference semantic network; constructing a second search query by augmenting the first search query with one or more terms associated with the at least one obtained concept; providing, to the at least one user, content associated with search results obtained based at least in part on searching the target set of content by using the second search query, wherein any concept in the semantic network is represented by a data structure storing data associated with a node in the semantic network. 10. The computer-implemented method of claim 1 , further comprising receiving user context information associated with the user, wherein the user context information comprises at least one of demographic information associated with the user, information from the user's browsing history, information typed in by the user, and/or information highlighted by the user. | 0.718364 |
8,527,538 | 13 | 14 | 13. The system of claim 10 , wherein the operations comprise: determining a relationship between the first geographic entity name and the second geographic entity name in a geographic data set that includes a plurality of names of geographic entities and respective relationships between pairs of geographic entities, wherein determining that the second geographic entity name is identified as not a substitute term for the first geographic entity name by the second substitution framework is based at least in part on the determined relationship. | 13. The system of claim 10 , wherein the operations comprise: determining a relationship between the first geographic entity name and the second geographic entity name in a geographic data set that includes a plurality of names of geographic entities and respective relationships between pairs of geographic entities, wherein determining that the second geographic entity name is identified as not a substitute term for the first geographic entity name by the second substitution framework is based at least in part on the determined relationship. 14. The system of claim 13 , wherein the geographic data set is a tree structure wherein different positions in the tree structure that share a same level correspond to different geographic locations that share a same geographic region type. | 0.5 |
7,809,575 | 13 | 15 | 13. A computer program product for enabling global grammars for a particular multimodal application, the computer program product including a multimodal browser operating on a multimodal device supporting multiple modes of user interaction with the multimodal application, the modes of user interaction including a voice mode and one or more non-voice modes, the computer program product disposed upon at least one recordable computer-readable medium, the computer program product comprising computer program instructions capable of: loading a multimodal web page; determining whether the loaded multimodal web page is one of a plurality of multimodal web pages of the particular multimodal application; if the loaded multimodal web page is one of the plurality of multimodal web pages of the particular multimodal application, loading any currently unloaded global grammars in the loaded multimodal web page and maintaining any previously loaded global grammars; and if the loaded multimodal web page is not one of the plurality of multimodal web pages of the particular multimodal application, unloading any currently loaded global grammars. | 13. A computer program product for enabling global grammars for a particular multimodal application, the computer program product including a multimodal browser operating on a multimodal device supporting multiple modes of user interaction with the multimodal application, the modes of user interaction including a voice mode and one or more non-voice modes, the computer program product disposed upon at least one recordable computer-readable medium, the computer program product comprising computer program instructions capable of: loading a multimodal web page; determining whether the loaded multimodal web page is one of a plurality of multimodal web pages of the particular multimodal application; if the loaded multimodal web page is one of the plurality of multimodal web pages of the particular multimodal application, loading any currently unloaded global grammars in the loaded multimodal web page and maintaining any previously loaded global grammars; and if the loaded multimodal web page is not one of the plurality of multimodal web pages of the particular multimodal application, unloading any currently loaded global grammars. 15. The computer program product of claim 13 , wherein the computer program instructions capable of loading any currently unloaded global grammars in the loaded multimodal web page further comprise computer program instructions capable of: identifying in dependence upon markup in the loaded multimodal web page a global grammar; determining that the identified global grammar is not currently loaded; and loading the identified global grammar. | 0.5 |
9,330,381 | 1 | 2 | 1. A method, comprising: at a portable multifunction device with a touch screen display: receiving a calendar invitation, from a party to a user of the device, while the device is locked; displaying at least a portion of the calendar invitation on the touch screen display while the device remains locked; and in response to detecting a user request to view the calendar invitation, immediately displaying the calendar invitation in a calendar application. | 1. A method, comprising: at a portable multifunction device with a touch screen display: receiving a calendar invitation, from a party to a user of the device, while the device is locked; displaying at least a portion of the calendar invitation on the touch screen display while the device remains locked; and in response to detecting a user request to view the calendar invitation, immediately displaying the calendar invitation in a calendar application. 2. The method of claim 1 , including: while the device is locked, changing an unlock icon to a view-invitation icon when displaying the portion of the calendar invitation. | 0.663386 |
9,336,140 | 5 | 6 | 5. The method of claim 1 wherein the determining comprises determining by applying a predefined optimization value to historical values of the attribute types of the leaf storage spaces. | 5. The method of claim 1 wherein the determining comprises determining by applying a predefined optimization value to historical values of the attribute types of the leaf storage spaces. 6. The method of claim 5 wherein the applying comprises determining the values such that the values are minimally deviant from the historical values. | 0.5 |
9,240,969 | 15 | 19 | 15. A system comprising: a processor; and a memory storing instructions that, when executed, cause the system to: determine one or more topics associated with a message based at least in part on message data included in the message; determine knowledge data describing the one or more topics associated with the message; determine social activity data describing one or more user activities associated with a group of one or more social users based at least in part on the knowledge data, the one or more user activities describing the one or more topics; generate a selectable tag based at least in part on the social activity data describing the one or more user activities, a selection of the selectable tag causing a display of the social activity data associated with the message; and generate graphical user interface data for displaying the social activity data associated with the message in response to the selectable tag being selected. | 15. A system comprising: a processor; and a memory storing instructions that, when executed, cause the system to: determine one or more topics associated with a message based at least in part on message data included in the message; determine knowledge data describing the one or more topics associated with the message; determine social activity data describing one or more user activities associated with a group of one or more social users based at least in part on the knowledge data, the one or more user activities describing the one or more topics; generate a selectable tag based at least in part on the social activity data describing the one or more user activities, a selection of the selectable tag causing a display of the social activity data associated with the message; and generate graphical user interface data for displaying the social activity data associated with the message in response to the selectable tag being selected. 19. The system of claim 15 , wherein the instructions, when executed, cause the system to also: identify a first user associated with the message and a group of one or more second users associated with the first user; and generate the graphical user interface data for providing an access to the group of one or more second users. | 0.563492 |
10,078,763 | 5 | 6 | 5. The method of claim 3 , wherein the metadata tags are unbounded to enforce any number of policies at the same time. | 5. The method of claim 3 , wherein the metadata tags are unbounded to enforce any number of policies at the same time. 6. The method of claim 5 , further comprising the step of: determining, on every instruction, if an operation is allowed based, at least in part on, the metadata tags and if the operation is allowed, then calculating the metadata tags for a set of results. | 0.5 |
7,861,253 | 18 | 26 | 18. A multi-level business intelligence system interface client for use with at least one productivity client comprising: a GUI layer; an API layer; a productivity client adapter layer; and a kernel layer, wherein each time a report is executed by the business intelligence system for the at least one productivity client, persistence information is stored by the multi-level interface client in a file of the productivity client containing the report. | 18. A multi-level business intelligence system interface client for use with at least one productivity client comprising: a GUI layer; an API layer; a productivity client adapter layer; and a kernel layer, wherein each time a report is executed by the business intelligence system for the at least one productivity client, persistence information is stored by the multi-level interface client in a file of the productivity client containing the report. 26. The multi-level interface client according to claim 18 , the productivity client adapter layer comprising a set of business intelligence functional operations available to the at least one productivity client, wherein the operations comprise a set of common operations and a set of operations specific to a particular productivity client. | 0.5 |
9,582,496 | 2 | 5 | 2. The computer program product of claim 1 , wherein a first combined cognitive state is associated with a first subgroup of participants, and wherein the method further comprises: identifying a transition from the first combined cognitive state to a second combined cognitive state for the first subgroup of participants based on a divergence of the cognitive states of the participants of the first subgroup during the meeting; automatically generating an advice based on the transition; and providing the advice to at least one of the participants in order to influence a course of the meeting. | 2. The computer program product of claim 1 , wherein a first combined cognitive state is associated with a first subgroup of participants, and wherein the method further comprises: identifying a transition from the first combined cognitive state to a second combined cognitive state for the first subgroup of participants based on a divergence of the cognitive states of the participants of the first subgroup during the meeting; automatically generating an advice based on the transition; and providing the advice to at least one of the participants in order to influence a course of the meeting. 5. The computer program product of claim 2 , wherein the at least one participant includes a teacher and the first subgroup includes students learning from the teacher in the meeting, wherein the advice includes a suggestion for a change in lesson plans of the teacher based on the transitions in the combined cognitive state of the students. | 0.5 |
7,739,596 | 17 | 22 | 17. A method comprising: requesting, in response to a user input received via a graphical user interface on, an audio stream, the graphical user interface displayed on a display of the media player; receiving the audio stream, the audio stream including a first portion associated with a first keyword followed by a second portion associated with a second keyword; rendering the audio stream via the media player; while rendering the first portion, displaying, on the graphical user interface, a first advertisement associated with the first keyword; and while rendering the second portion, replacing, on the graphical user interface, the first advertisement with a second advertisement associated with the second keyword. | 17. A method comprising: requesting, in response to a user input received via a graphical user interface on, an audio stream, the graphical user interface displayed on a display of the media player; receiving the audio stream, the audio stream including a first portion associated with a first keyword followed by a second portion associated with a second keyword; rendering the audio stream via the media player; while rendering the first portion, displaying, on the graphical user interface, a first advertisement associated with the first keyword; and while rendering the second portion, replacing, on the graphical user interface, the first advertisement with a second advertisement associated with the second keyword. 22. The method of claim 17 further comprising: in response to receiving the first advertisement, replacing, on the graphical user interface, a first display with the first advertisement associated with the first keyword, wherein the first display includes information generated by the media player prior to requesting the audio stream. | 0.5 |
8,825,614 | 6 | 20 | 6. A method of performing XBRL taxonomy migration comprising: receiving an XBRL document having XBRL tags of a first version of an XBRL taxonomy; migrating, by a processor, the received XBRL document to a second version of the XBRL taxonomy by gathering metadata that corresponds to the first version of the XBRL taxonomy and replacing XBRL concepts of the first version of the XBRL taxonomy in the received XBRL document with XBRL concepts of the second version of the XBRL taxonomy; detecting dependencies in calculations in the received XBRL document using the XBRL concepts in the received XBRL document; when dependencies are detected, determining whether a balance type of the first version XBRL taxonomy concept matches a balance type of a related second version XBRL taxonomy concept; when the balance type of the first version XBRL taxonomy concept matches the balance type of the related second version XBRL taxonomy concept, replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept of the matched balance type; and when the balance type of the first version XBRL taxonomy concept does not match the balance type of the related second version XBRL taxonomy concept, adjusting a weight of an arc using the related second version XBRL taxonomy concept in a calculation assertion when replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept, wherein after completion of the method of performing XBRL taxonomy migration, the migrated XBRL document no longer uses the first version of the XBRL taxonomy. | 6. A method of performing XBRL taxonomy migration comprising: receiving an XBRL document having XBRL tags of a first version of an XBRL taxonomy; migrating, by a processor, the received XBRL document to a second version of the XBRL taxonomy by gathering metadata that corresponds to the first version of the XBRL taxonomy and replacing XBRL concepts of the first version of the XBRL taxonomy in the received XBRL document with XBRL concepts of the second version of the XBRL taxonomy; detecting dependencies in calculations in the received XBRL document using the XBRL concepts in the received XBRL document; when dependencies are detected, determining whether a balance type of the first version XBRL taxonomy concept matches a balance type of a related second version XBRL taxonomy concept; when the balance type of the first version XBRL taxonomy concept matches the balance type of the related second version XBRL taxonomy concept, replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept of the matched balance type; and when the balance type of the first version XBRL taxonomy concept does not match the balance type of the related second version XBRL taxonomy concept, adjusting a weight of an arc using the related second version XBRL taxonomy concept in a calculation assertion when replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept, wherein after completion of the method of performing XBRL taxonomy migration, the migrated XBRL document no longer uses the first version of the XBRL taxonomy. 20. The method of claim 6 , further comprising automatically migrating at least one XBRL concept of the XBRL document having XBRL from the first version of the XBRL taxonomy to the second version of the XBRL taxonomy without a user manually selecting an XBRL concept of the second version of the XBRL taxonomy. | 0.660088 |
8,756,057 | 13 | 16 | 13. A computer program product stored on a non-transitory tangible computer usable medium for analyzing speech, comprising: program code that when executed by a processor: converts inputted speech received from a speaker to text; displays the text in a textual interface; automatically generates feedback information, that comprises results of an analysis of the inputted speech and/or text, by automatically analyzing the inputted speech and/or text; and automatically outputs the feedback information as annotations in the textual interface, wherein the annotations are distinct from the inputted speech and text. | 13. A computer program product stored on a non-transitory tangible computer usable medium for analyzing speech, comprising: program code that when executed by a processor: converts inputted speech received from a speaker to text; displays the text in a textual interface; automatically generates feedback information, that comprises results of an analysis of the inputted speech and/or text, by automatically analyzing the inputted speech and/or text; and automatically outputs the feedback information as annotations in the textual interface, wherein the annotations are distinct from the inputted speech and text. 16. The computer program product of claim 13 , wherein the annotations are displayed in a text window and a second window adjacent the text window. | 0.738434 |
9,455,864 | 1 | 9 | 1. A system for creating, distributing, and managing of shared compression dictionaries, comprising: a compressor configured to generate at least one shared compression dictionary based on a context of data streams flow between a client web browser and an origin server, wherein the context being derived from data streams is of at least one request and a corresponding response between the client web browser and the origin server; an origin accelerator communicatively connected to the origin server and configured to encode an encountered data stream to a compressed form based on the at least one shared compression dictionary; an edge accelerator communicatively connected to the client web browser and configured to decode the compressed form of the data stream to an uncompressed form using the at least one shared compression dictionary; and a dictionary database accessible to each of the edge accelerator, the origin accelerator and the compressor, wherein the compressor is configured to generate and save the at least one shared compression dictionary in the dictionary database developed as part of an offline process. | 1. A system for creating, distributing, and managing of shared compression dictionaries, comprising: a compressor configured to generate at least one shared compression dictionary based on a context of data streams flow between a client web browser and an origin server, wherein the context being derived from data streams is of at least one request and a corresponding response between the client web browser and the origin server; an origin accelerator communicatively connected to the origin server and configured to encode an encountered data stream to a compressed form based on the at least one shared compression dictionary; an edge accelerator communicatively connected to the client web browser and configured to decode the compressed form of the data stream to an uncompressed form using the at least one shared compression dictionary; and a dictionary database accessible to each of the edge accelerator, the origin accelerator and the compressor, wherein the compressor is configured to generate and save the at least one shared compression dictionary in the dictionary database developed as part of an offline process. 9. The system of claim 1 , wherein the communication between the client web browser and the origin server is performed using a hypertext transfer protocol (HTTP). | 0.936321 |
8,687,916 | 9 | 14 | 9. A computer program product stored on a non-transitory tangible computer readable storage medium for correcting distortion in an image of a page with a content, the computer program product including code for: identifying a set of high quality words including at least one high quality word in an undistorted region of one or more images of one or more pages having content related to the content of the page; identifying at least one distorted word in the image of the page, each distorted word of said at least one distorted word corresponding to a high quality word from the set of high quality words; generating a global transformation function for application to the image of the page so as to transform a distorted word of said at least one distorted word to its corresponding high quality word; and applying the global transformation function to pixels of the image of the page. | 9. A computer program product stored on a non-transitory tangible computer readable storage medium for correcting distortion in an image of a page with a content, the computer program product including code for: identifying a set of high quality words including at least one high quality word in an undistorted region of one or more images of one or more pages having content related to the content of the page; identifying at least one distorted word in the image of the page, each distorted word of said at least one distorted word corresponding to a high quality word from the set of high quality words; generating a global transformation function for application to the image of the page so as to transform a distorted word of said at least one distorted word to its corresponding high quality word; and applying the global transformation function to pixels of the image of the page. 14. A computer program product as claimed in claim 9 , comprising code for segmenting the image of the page or said one or more images of one or more pages into words. | 0.874436 |
8,285,580 | 8 | 9 | 8. The method of claim 5 , wherein the identifier represents a business area. | 8. The method of claim 5 , wherein the identifier represents a business area. 9. The method of claim 8 , wherein the business area includes at least one of: forecasting, replenishment, consumption, and order quantity optimization. | 0.5 |
10,102,848 | 15 | 18 | 15. A computer-implemented method comprising: storing information that indicates, for each hotword of a plurality of hotwords, information that maps a unique identifier of the hotword to a visual representation of the hotword, wherein the visual representation of a first hotword of the plurality of hotwords is visually different from the visual representation of a second hotword of the plurality of hotwords; receiving, from an application on a computing device and based on the application having identified in code for an electronic document a hotword instruction that is dedicated to activating a hotword capability for a particular hotword of the plurality of hotwords identified by the hotword instruction, a request for the visual representation of the particular hotword, the request identifying the particular hotword by its unique identifier; retrieving, using the unique identifier of the particular hotword as specified in the request, the visual representation of the particular hotword from among the visual representations of the plurality of hotwords; and returning, to the application and in response to the request, the visual representation of the particular hotword, wherein the application is configured to render a presentation of the electronic document in which the hotword capability for the particular hotword is activated, including showing the visual representation of the particular hotword in the presentation of the electronic document and enabling the application to respond to instances of spoken input that correspond to the particular hotword. | 15. A computer-implemented method comprising: storing information that indicates, for each hotword of a plurality of hotwords, information that maps a unique identifier of the hotword to a visual representation of the hotword, wherein the visual representation of a first hotword of the plurality of hotwords is visually different from the visual representation of a second hotword of the plurality of hotwords; receiving, from an application on a computing device and based on the application having identified in code for an electronic document a hotword instruction that is dedicated to activating a hotword capability for a particular hotword of the plurality of hotwords identified by the hotword instruction, a request for the visual representation of the particular hotword, the request identifying the particular hotword by its unique identifier; retrieving, using the unique identifier of the particular hotword as specified in the request, the visual representation of the particular hotword from among the visual representations of the plurality of hotwords; and returning, to the application and in response to the request, the visual representation of the particular hotword, wherein the application is configured to render a presentation of the electronic document in which the hotword capability for the particular hotword is activated, including showing the visual representation of the particular hotword in the presentation of the electronic document and enabling the application to respond to instances of spoken input that correspond to the particular hotword. 18. The computer-implemented method of claim 15 , wherein the visual representations for the hotwords comprise text of one or more terms that indicate respective actions associated with the hotwords. | 0.831926 |
10,013,499 | 1 | 2 | 1. A method by a computer comprising: generating a list of unique terms, each comprising prefix data and associated property data, contained in a defined web ontology language; receiving a resource descriptive framework (RDF) statement about a web resource; generating a list of unique terms, each comprising prefix data and associated property data, contained in the RDF statement; identifying a first problem term within the list of unique terms contained in the RDF statement that is not present among the list of unique terms contained in the defined web ontology language, the first problem term comprising an error in the RDF statement; generating a list of candidate terms contained in the defined web ontology that satisfy a threshold similarity to the first problem term; selecting a candidate term from among the list of candidate terms having a data type for the property data that matches a data type for the property data of the first problem term; and substituting the candidate term for each occurrence of the first problem term contained in the RDF statement. | 1. A method by a computer comprising: generating a list of unique terms, each comprising prefix data and associated property data, contained in a defined web ontology language; receiving a resource descriptive framework (RDF) statement about a web resource; generating a list of unique terms, each comprising prefix data and associated property data, contained in the RDF statement; identifying a first problem term within the list of unique terms contained in the RDF statement that is not present among the list of unique terms contained in the defined web ontology language, the first problem term comprising an error in the RDF statement; generating a list of candidate terms contained in the defined web ontology that satisfy a threshold similarity to the first problem term; selecting a candidate term from among the list of candidate terms having a data type for the property data that matches a data type for the property data of the first problem term; and substituting the candidate term for each occurrence of the first problem term contained in the RDF statement. 2. The method of claim 1 , further comprising: writing the RDF statement following the substituting to a web document in a memory that is readable by a networked computer. | 0.884771 |
5,442,745 | 1 | 5 | 1. A metaphor selecting/switching system in an information processor which incorporates metaphor and function groups independently, selects a matched metaphor/function combination based on similarities between metaphors and functions and lets input and output of information be performed via an input/output means, said system comprising: attribute lists having information on attributes and similarities regarding each metaphor and each function; a similarity calculator means for calculating similarities from the information on the similarities regarding each metaphor and each function; and a control means for controlling switching of one metaphor to another and matching of a particular metaphor with a particular function, said switching of one metaphor to another being performed by calculating said similarities whenever operation is performed via said input/output means. | 1. A metaphor selecting/switching system in an information processor which incorporates metaphor and function groups independently, selects a matched metaphor/function combination based on similarities between metaphors and functions and lets input and output of information be performed via an input/output means, said system comprising: attribute lists having information on attributes and similarities regarding each metaphor and each function; a similarity calculator means for calculating similarities from the information on the similarities regarding each metaphor and each function; and a control means for controlling switching of one metaphor to another and matching of a particular metaphor with a particular function, said switching of one metaphor to another being performed by calculating said similarities whenever operation is performed via said input/output means. 5. A metaphor selecting/switching system in an information processor as claimed in claim 1, characterized in that said attribute list has information which is set and updated by a particular operation performed via said input/output means. | 0.675272 |
8,700,691 | 1 | 10 | 1. A computer system comprising: at least one server computer configured to process electronic page requests and determine whether to implement normal page navigation operations or minimal download operations, wherein the minimal download operations operate to provide difference packages associated with previously-rendered electronic pages and target electronic pages, each difference package configured to include information associated with differences between a previously-rendered electronic page and a target electronic page as part of a navigation operation, the information to include a page script set, a page style sheet set, and markup differences associated with the previously-rendered electronic page and the target electronic page; and at least one client configured to request electronic pages, browse electronic pages, and use difference packages as part of processing page navigation operations, each electronic page configured to be rendered according to a number of page portions that include one or more script portions, style portions, and markup portions, the at least one client configured to use a difference package provided by the at least one server computer to perform a difference application at the at least one client to update one or multiple areas of the target electronic page using differences included with the difference package as part of simulating a page navigation mechanism that includes an unload operation associated with the previously-rendered electronic page and a render operation for the target electronic page followed by running target global script and firing target load events; and the at least one client is further configured to use the difference package to display the target electronic page including using new script, styles, and encoded markup included with the difference package and add an inline style to a global style array for each inline style of the target electronic page. | 1. A computer system comprising: at least one server computer configured to process electronic page requests and determine whether to implement normal page navigation operations or minimal download operations, wherein the minimal download operations operate to provide difference packages associated with previously-rendered electronic pages and target electronic pages, each difference package configured to include information associated with differences between a previously-rendered electronic page and a target electronic page as part of a navigation operation, the information to include a page script set, a page style sheet set, and markup differences associated with the previously-rendered electronic page and the target electronic page; and at least one client configured to request electronic pages, browse electronic pages, and use difference packages as part of processing page navigation operations, each electronic page configured to be rendered according to a number of page portions that include one or more script portions, style portions, and markup portions, the at least one client configured to use a difference package provided by the at least one server computer to perform a difference application at the at least one client to update one or multiple areas of the target electronic page using differences included with the difference package as part of simulating a page navigation mechanism that includes an unload operation associated with the previously-rendered electronic page and a render operation for the target electronic page followed by running target global script and firing target load events; and the at least one client is further configured to use the difference package to display the target electronic page including using new script, styles, and encoded markup included with the difference package and add an inline style to a global style array for each inline style of the target electronic page. 10. The computer system of claim 1 , the at least one client further configured to perform a normal navigation operation by downloading page components to provide the target electronic page locally at the client absent using minimal download features. | 0.716704 |
7,805,492 | 1 | 2 | 1. A computer implemented method, comprising: scanning, by a computer, a set of messages of a user to identify a plurality of addresses; identifying, by the computer, names of persons at the addresses to generate profiles for the persons; computing, by the computer, scores of the addresses to determine relevancy of the addresses to the user; in response to an incomplete input in an address field, identifying a set of persons in the profiles that match the incomplete input; sorting the set of persons based at least in part on the scores; presenting a first set of one or more suggestions to complete the incomplete input based on the set of persons; in response to the user selecting a suggestion from the first set of suggestions, replacing, by the computer, the incomplete input with an address corresponding to the suggestion selected by the user; obtaining a second set of suggestions from a message compose window; presenting the first and second sets of suggestions in a window; and preventing the message compose window from presenting the second set of suggestions in a separate window, the preventing comprising generating a keyboard hook to prevent keyboard input directed to the address input field from being passed onto a separate suggestion window. | 1. A computer implemented method, comprising: scanning, by a computer, a set of messages of a user to identify a plurality of addresses; identifying, by the computer, names of persons at the addresses to generate profiles for the persons; computing, by the computer, scores of the addresses to determine relevancy of the addresses to the user; in response to an incomplete input in an address field, identifying a set of persons in the profiles that match the incomplete input; sorting the set of persons based at least in part on the scores; presenting a first set of one or more suggestions to complete the incomplete input based on the set of persons; in response to the user selecting a suggestion from the first set of suggestions, replacing, by the computer, the incomplete input with an address corresponding to the suggestion selected by the user; obtaining a second set of suggestions from a message compose window; presenting the first and second sets of suggestions in a window; and preventing the message compose window from presenting the second set of suggestions in a separate window, the preventing comprising generating a keyboard hook to prevent keyboard input directed to the address input field from being passed onto a separate suggestion window. 2. The method of claim 1 , wherein the computer comprises a user terminal storing the set of messages. | 0.825939 |
9,374,087 | 3 | 16 | 3. The virtual world processing apparatus of claim 1 , further comprising: an adaptation virtual world to real world (VR) unit to encode information relating to a virtual world into second metadata, wherein the information relating to the virtual world comprises a virtual world object characteristic; and an adaptation real world to virtual world (RV) unit to generate information that is applied to the virtual world, based on the first metadata and the second metadata, and to encode the generated information into third metadata. | 3. The virtual world processing apparatus of claim 1 , further comprising: an adaptation virtual world to real world (VR) unit to encode information relating to a virtual world into second metadata, wherein the information relating to the virtual world comprises a virtual world object characteristic; and an adaptation real world to virtual world (RV) unit to generate information that is applied to the virtual world, based on the first metadata and the second metadata, and to encode the generated information into third metadata. 16. The virtual world processing apparatus of claim 3 , further comprising: an actuator to reflect information on the virtual world to the real world by decoding encoded data of a binary format received from the adaptation VR unit, such that the actuator operates in response to the decoded data. | 0.611549 |
9,597,119 | 9 | 10 | 9. A bone anchor comprising: a) a receiver having a channel adapted to receive one of an elongate member and a tensioned cord; b) a pressure insert positioned in the receiver and having upward extending arms and each of the arms having upper surfaces; c) a sleeve positioned in the channel above the pressure insert, and in combination with the one of an elongated member and a tensioned cord; and d) a closure that is positioned in the receiver above the sleeve, wherein the sleeve includes a sleeve body constructed of a non-rigid deformable polymer and a transfer structure constructed of a rigid non-deformable metal; the transfer structure having depending legs with lower surfaces that align and mate with the upper surfaces on the pressure insert arms in an overlapping relationship for transferring force from the closure through the transfer structure to the pressure insert without transferring pressure directly through the deformable body of the sleeve to the insert, the transfer structure having projections extending therefrom and into the sleeve body to resist torque of the transfer structure relative to the sleeve body. | 9. A bone anchor comprising: a) a receiver having a channel adapted to receive one of an elongate member and a tensioned cord; b) a pressure insert positioned in the receiver and having upward extending arms and each of the arms having upper surfaces; c) a sleeve positioned in the channel above the pressure insert, and in combination with the one of an elongated member and a tensioned cord; and d) a closure that is positioned in the receiver above the sleeve, wherein the sleeve includes a sleeve body constructed of a non-rigid deformable polymer and a transfer structure constructed of a rigid non-deformable metal; the transfer structure having depending legs with lower surfaces that align and mate with the upper surfaces on the pressure insert arms in an overlapping relationship for transferring force from the closure through the transfer structure to the pressure insert without transferring pressure directly through the deformable body of the sleeve to the insert, the transfer structure having projections extending therefrom and into the sleeve body to resist torque of the transfer structure relative to the sleeve body. 10. The anchor according to claim 9 , further comprising an implantable shank received in the receiver and receiving downward force from the insert. | 0.784884 |
7,509,330 | 24 | 26 | 24. A method for application-layer monitoring of communication between one or more database clients and one or more database servers, the method comprising: at a decoding layer above a network layer on a database device at a first network location between one or more database clients residing at one or more second network locations distinct from the first network location and one or more database servers residing at one or more third network locations distinct from both the first network location and the one or more second network locations: using one or more decoders on the database device for receiving database messages communicated over the network from any of the database clients residing at the one or more second network locations and intended for the database servers at the one or more third network locations and database messages communicated from the database servers and intended for the database clients; decoding the database messages, wherein decoding the database messages comprises decoding a protocol generated as an output of a database connectivity driver in response to an input by a database application residing at an application layer, wherein decoding the database messages comprises decoding database messages of database implementations different from each other and, wherein the database connectivity driver is utilized by the one or more database clients to communicate with the database serve; and extracting query-language statements from the database messages, wherein the query-language statements are created by the database application at one or more of the database clients and provided as input to the database connectivity driver and the database connectivity driver generate the database message as an output based on the query-language statement; and at an application layer above the decoding layer at the first network location, using a monitoring application on the database device for receiving query-language statements extracted at the decoders and recording observations on the database messages based at least in part on the query-language statements extracted at the decoding layer. | 24. A method for application-layer monitoring of communication between one or more database clients and one or more database servers, the method comprising: at a decoding layer above a network layer on a database device at a first network location between one or more database clients residing at one or more second network locations distinct from the first network location and one or more database servers residing at one or more third network locations distinct from both the first network location and the one or more second network locations: using one or more decoders on the database device for receiving database messages communicated over the network from any of the database clients residing at the one or more second network locations and intended for the database servers at the one or more third network locations and database messages communicated from the database servers and intended for the database clients; decoding the database messages, wherein decoding the database messages comprises decoding a protocol generated as an output of a database connectivity driver in response to an input by a database application residing at an application layer, wherein decoding the database messages comprises decoding database messages of database implementations different from each other and, wherein the database connectivity driver is utilized by the one or more database clients to communicate with the database serve; and extracting query-language statements from the database messages, wherein the query-language statements are created by the database application at one or more of the database clients and provided as input to the database connectivity driver and the database connectivity driver generate the database message as an output based on the query-language statement; and at an application layer above the decoding layer at the first network location, using a monitoring application on the database device for receiving query-language statements extracted at the decoders and recording observations on the database messages based at least in part on the query-language statements extracted at the decoding layer. 26. The method of claim 24 , further comprising, at the network layer at the first network location, in a proxy mode, receiving communication between the database clients and the database servers for communication to the decoding layer. | 0.921333 |
7,478,121 | 10 | 13 | 10. A method for receiving page-specific user feedback concerning a particular web page of a website, comprising: using a first icon viewable on the particular web page to solicit one or more page-specific subjective ratings concerning the particular web page as a whole from a user that has accessed the particular web page; using a second icon viewable on the particular web page to solicit one or more page-specific open-ended comments concerning the particular web page from the user; and using software associated with the first and second icons to receive one or more page-specific subjective ratings concerning the particular web page as a whole and one or more page-specific open-ended comments concerning the particular web page from the user for reporting to a website owner, the software operable to require the user to provide one or more page-specific subjective ratings concerning the particular web page as a whole in order to provide one or more page-specific open-ended comments concerning the particular web page, association of the one or more required page-specific subjective ratings concerning the particular web page as a whole with the one or more page-specific open-ended comments concerning the particular web page making the one or more page-specific open-ended comments concerning the particular web page more meaningful to and useable by the website owner. | 10. A method for receiving page-specific user feedback concerning a particular web page of a website, comprising: using a first icon viewable on the particular web page to solicit one or more page-specific subjective ratings concerning the particular web page as a whole from a user that has accessed the particular web page; using a second icon viewable on the particular web page to solicit one or more page-specific open-ended comments concerning the particular web page from the user; and using software associated with the first and second icons to receive one or more page-specific subjective ratings concerning the particular web page as a whole and one or more page-specific open-ended comments concerning the particular web page from the user for reporting to a website owner, the software operable to require the user to provide one or more page-specific subjective ratings concerning the particular web page as a whole in order to provide one or more page-specific open-ended comments concerning the particular web page, association of the one or more required page-specific subjective ratings concerning the particular web page as a whole with the one or more page-specific open-ended comments concerning the particular web page making the one or more page-specific open-ended comments concerning the particular web page more meaningful to and useable by the website owner. 13. The method of claim 10 , further comprising using a third icon viewable on the particular web page independent of input from the user subsequent to the user accessing the particular web page to solicit page-specific feedback concerning the particular web page from the user independent of input from the user subsequent to the user accessing the particular web page, the third icon operable to receive user input indicating a desire to provide page-specific feedback concerning the particular web page, the user input causing the first and second icons to become viewable on the particular web page. | 0.5 |
8,099,407 | 1 | 17 | 1. A computer-implemented method for processing media files using a computer, comprising: monitoring at least one application for the occurrences of events wherein at least one event is associated with a media file; capturing the at least one event upon the occurrence of the event by queuing event data associated with the event at a position in a queue; indexing and storing at least some of the events and the media file associated with the event at a time after the occurrence of the event, wherein the time is based on performance data indicating a readiness to process the event and a position in the queue; receiving a search query; locating at least one relevant media file from the indexed and stored events relevant to the search query; and outputting a result set comprising the at least one relevant media file. | 1. A computer-implemented method for processing media files using a computer, comprising: monitoring at least one application for the occurrences of events wherein at least one event is associated with a media file; capturing the at least one event upon the occurrence of the event by queuing event data associated with the event at a position in a queue; indexing and storing at least some of the events and the media file associated with the event at a time after the occurrence of the event, wherein the time is based on performance data indicating a readiness to process the event and a position in the queue; receiving a search query; locating at least one relevant media file from the indexed and stored events relevant to the search query; and outputting a result set comprising the at least one relevant media file. 17. The method of claim 1 , wherein capturing the event associated with the media file comprises identifying the event based at least in part on calls to input or output devices and identifying at least some of the event data by analyzing the calls. | 0.530189 |
9,501,551 | 6 | 14 | 6. A system for categorizing items of interest, the system comprising: a data store adapted to maintain one or more descriptions for one or more item categories, wherein each item of a plurality of items offered for sale using a network-based service is associated with at least one of the one or more item categories; and a computing device having one or more processors, wherein the computing device is adapted to operate a categorization service and is in communication with the data store, and wherein the categorization service is operative to: generate item information associated with a first item of the plurality of items; compare the item information to a first description in the one or more descriptions maintained in the data store; and determine at least one category recommendation to be a first item category based on a similarity of the item information and the first description, wherein the first description is a textual description of the first item category; and assign the first item category to the first item, wherein the item information is represented as an item vector according to a vector space model and a category vector according to the vector space model comprises a representation of at least a portion of the first description, and wherein the at least one category recommendation is automatically determined to be the first item category if a deviation of an angle between the item vector and the category vector is less than a threshold value. | 6. A system for categorizing items of interest, the system comprising: a data store adapted to maintain one or more descriptions for one or more item categories, wherein each item of a plurality of items offered for sale using a network-based service is associated with at least one of the one or more item categories; and a computing device having one or more processors, wherein the computing device is adapted to operate a categorization service and is in communication with the data store, and wherein the categorization service is operative to: generate item information associated with a first item of the plurality of items; compare the item information to a first description in the one or more descriptions maintained in the data store; and determine at least one category recommendation to be a first item category based on a similarity of the item information and the first description, wherein the first description is a textual description of the first item category; and assign the first item category to the first item, wherein the item information is represented as an item vector according to a vector space model and a category vector according to the vector space model comprises a representation of at least a portion of the first description, and wherein the at least one category recommendation is automatically determined to be the first item category if a deviation of an angle between the item vector and the category vector is less than a threshold value. 14. The system of claim 6 , wherein the item information comprises at least one of a flat file, an XML file, or information directly input from a user. | 0.796496 |
8,010,343 | 5 | 6 | 5. The method of claim 1 , further comprising calculating grammar generation statistics in order to optimize future grammar file generation. | 5. The method of claim 1 , further comprising calculating grammar generation statistics in order to optimize future grammar file generation. 6. The method of claim 5 , wherein upon determining that a the number of grammar files generated exceeds a threshold value, further comprising suggesting the use of additional disambiguation fields. | 0.5 |
9,692,894 | 13 | 21 | 13. A method of generating a customer satisfaction score based on behavioral assessment data across one or more recorded communications, which comprises: analyzing one or more communications between a customer and an agent, wherein the analyzing comprises applying a linguistic-based psychological behavioral model to each communication to determine a personality type of the customer by analyzing behavioral characteristics of the customer based on the one or more communications; selecting at least one filter criterion which comprises a customer, an agent, a team, or a call type; calculating a customer satisfaction score using the at least one selected filter criterion across a selected time interval and based on the one or more communications; and displaying a report including the calculated customer satisfaction score to a user that matches the at least one selected filter criterion for the selected time interval. | 13. A method of generating a customer satisfaction score based on behavioral assessment data across one or more recorded communications, which comprises: analyzing one or more communications between a customer and an agent, wherein the analyzing comprises applying a linguistic-based psychological behavioral model to each communication to determine a personality type of the customer by analyzing behavioral characteristics of the customer based on the one or more communications; selecting at least one filter criterion which comprises a customer, an agent, a team, or a call type; calculating a customer satisfaction score using the at least one selected filter criterion across a selected time interval and based on the one or more communications; and displaying a report including the calculated customer satisfaction score to a user that matches the at least one selected filter criterion for the selected time interval. 21. The method of claim 13 , wherein the customer satisfaction score is selected to comprise a composite value based on a ranking of relative client satisfaction scores for each call type across selected filter criteria. | 0.65625 |
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