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9,837,076 | 15 | 18 | 15. One or more non-transitory computer storage media encoded with a computer program, the computer program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: building an event model by identifying, from user data of one or more applications associated with a user, events related to the user; receiving a voice input from the user that includes a command to perform a specific action, the action having a plurality of parameters, and each parameter specifying information necessary to perform the action; determining whether the voice input includes sufficient information to perform the action; in response to determining that the voice input does not include sufficient information to perform the action, identifying one or more missing parameters from the voice input required to perform the action; identifying, using the event model, one or more current or future events related to the user that are relevant to the action, wherein each current or future event either is (i) currently occurring relative to a time when the voice input was received or (ii) will occur in the future relative to the time when the voice input was received; customizing the action based on the one or more current or future events, comprising assigning values to one or more of the identified missing parameters of the action based on data associated with the one or more current or future events; and performing the customized action in response to the voice input. | 15. One or more non-transitory computer storage media encoded with a computer program, the computer program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: building an event model by identifying, from user data of one or more applications associated with a user, events related to the user; receiving a voice input from the user that includes a command to perform a specific action, the action having a plurality of parameters, and each parameter specifying information necessary to perform the action; determining whether the voice input includes sufficient information to perform the action; in response to determining that the voice input does not include sufficient information to perform the action, identifying one or more missing parameters from the voice input required to perform the action; identifying, using the event model, one or more current or future events related to the user that are relevant to the action, wherein each current or future event either is (i) currently occurring relative to a time when the voice input was received or (ii) will occur in the future relative to the time when the voice input was received; customizing the action based on the one or more current or future events, comprising assigning values to one or more of the identified missing parameters of the action based on data associated with the one or more current or future events; and performing the customized action in response to the voice input. 18. The non-transitory computer storage media of claim 15 , wherein: identifying, using the event model, one or more current or future events related to the user that are relevant to the action comprises: identifying user data from the event model that is potentially relevant to the action; determining a level of confidence for an association of the one or more potentially relevant current or future events from the identified user data and the context of the voice input; and identifying one or more current or future events related to the user that are relevant to the action from the potentially relevant events based on the determined level of confidence. | 0.5 |
8,996,360 | 1 | 2 | 1. A method for generating a journal, wherein the method is performed by a hardware processor in a computer, the method comprising: obtaining a source data set and a journal description data set corresponding to the source data set; calculating an alignment probability between each source data sequence in the source data set and each journal description data sequence in the journal description data set to obtain an alignment probability set, wherein the source data sequence comprises at least one piece of source data, and wherein the journal description data sequence comprises at least one piece of journal description data; calculating a probability that each journal description data sequence occurs in the journal description data set to obtain an occurrence probability set; determining, according to the alignment probability set and the occurrence probability set and from each journal description data sequence, a target journal description data sequence corresponding to a source data sequence to be translated, wherein the source data sequence to be translated is any one of a plurality of source data sequences; and translating the target journal description data sequence into a journal description text. | 1. A method for generating a journal, wherein the method is performed by a hardware processor in a computer, the method comprising: obtaining a source data set and a journal description data set corresponding to the source data set; calculating an alignment probability between each source data sequence in the source data set and each journal description data sequence in the journal description data set to obtain an alignment probability set, wherein the source data sequence comprises at least one piece of source data, and wherein the journal description data sequence comprises at least one piece of journal description data; calculating a probability that each journal description data sequence occurs in the journal description data set to obtain an occurrence probability set; determining, according to the alignment probability set and the occurrence probability set and from each journal description data sequence, a target journal description data sequence corresponding to a source data sequence to be translated, wherein the source data sequence to be translated is any one of a plurality of source data sequences; and translating the target journal description data sequence into a journal description text. 2. The method for generating the journal according to claim 1 , further comprising: performing lexical processing on the source data in the source data set to obtain lexically processed source data; and performing lexical processing on the journal description data in the journal description data set to obtain lexically processed journal description data. | 0.753804 |
9,201,865 | 1 | 12 | 1. A method for providing automated assistance for a user using a computing device selected from a group consisting of: a telephone; a wireless communicator; a tablet computer; a laptop computer; a personal digital assistant; a desktop computer; a processor with memory; a kiosk; a consumer electronic device; a consumer entertainment device; a music player; a camera; a television; an electronic gaming unit; and a set-top box, the method comprising: receiving a user request for assistance spoken in a first language; translating the user request spoken in the first language to a second language; determining semantics of the user request and identifying at least one domain, at least one task, and at least one parameter for the user request; searching a semantic database on the Internet for the at least one matching domain, task, and parameter; compensating for translation errors based on user history; generating a response in the second language; and translating the response to the first language and rendering the response to the user and providing information from one of: music, audiobooks, news, weather, traffic, sports, and processing the user request by an assistant software on a computing cloud to purchase, reserve, or order products or services, wherein the assistant software automatically accesses semantic data and services having one or more triples including subject, predicate, and object available over the Internet to find one or more of: movies, events, performances, exhibits, shows, attractions, travel destinations, hotels, restaurants, bars, pubs, entertainment sites, landmarks, summer camps, resorts, places. | 1. A method for providing automated assistance for a user using a computing device selected from a group consisting of: a telephone; a wireless communicator; a tablet computer; a laptop computer; a personal digital assistant; a desktop computer; a processor with memory; a kiosk; a consumer electronic device; a consumer entertainment device; a music player; a camera; a television; an electronic gaming unit; and a set-top box, the method comprising: receiving a user request for assistance spoken in a first language; translating the user request spoken in the first language to a second language; determining semantics of the user request and identifying at least one domain, at least one task, and at least one parameter for the user request; searching a semantic database on the Internet for the at least one matching domain, task, and parameter; compensating for translation errors based on user history; generating a response in the second language; and translating the response to the first language and rendering the response to the user and providing information from one of: music, audiobooks, news, weather, traffic, sports, and processing the user request by an assistant software on a computing cloud to purchase, reserve, or order products or services, wherein the assistant software automatically accesses semantic data and services having one or more triples including subject, predicate, and object available over the Internet to find one or more of: movies, events, performances, exhibits, shows, attractions, travel destinations, hotels, restaurants, bars, pubs, entertainment sites, landmarks, summer camps, resorts, places. 12. The method of claim 1 , comprising eliciting more information on the user request and restating the user request as a confirmation to the user. | 0.843949 |
9,824,188 | 19 | 20 | 19. A system comprising: one or more processors; and memory storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: initiating a conversation with a user by causing information representing a virtual assistant to be displayed via a mobile device, the virtual assistant being configured with a persona; learning one or more characteristics about the user from the conversation with the user; receiving a medical query during the conversation with the virtual assistant, the medical query comprising at least one of verbal input, keypad input, or touch input; performing natural language processing with the medical query to identify one or more concepts of the medical query; determining a response to the medical query based at least in part on (i) a medical history for the user, (ii) an intent of the user, (iii) the one or more learned characteristics, (iv) the one or more concepts of the medical query, and (v) medical information, the medical information comprising at least one of: a treatment regimen; health insurance eligibility; a medical diagnostic algorithm; nutrition information; prescription medication for the user; a healthcare entity for the user; a reading of a medical device; or an insurance record of the user; and providing the response for output via the mobile device as a message from the virtual assistant. | 19. A system comprising: one or more processors; and memory storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: initiating a conversation with a user by causing information representing a virtual assistant to be displayed via a mobile device, the virtual assistant being configured with a persona; learning one or more characteristics about the user from the conversation with the user; receiving a medical query during the conversation with the virtual assistant, the medical query comprising at least one of verbal input, keypad input, or touch input; performing natural language processing with the medical query to identify one or more concepts of the medical query; determining a response to the medical query based at least in part on (i) a medical history for the user, (ii) an intent of the user, (iii) the one or more learned characteristics, (iv) the one or more concepts of the medical query, and (v) medical information, the medical information comprising at least one of: a treatment regimen; health insurance eligibility; a medical diagnostic algorithm; nutrition information; prescription medication for the user; a healthcare entity for the user; a reading of a medical device; or an insurance record of the user; and providing the response for output via the mobile device as a message from the virtual assistant. 20. The system as recited in claim 19 , wherein the response from the virtual assistant is provided as audio output. | 0.819876 |
9,268,822 | 1 | 5 | 1. A method for operating a database to determine an organizational hierarchy from contact data, the database being configured to store the contact data as a plurality of contact records, each contact record having a defined set of entities, including at least a name and a title, comprising: receiving contact data at the database including a phrase representing a title, the phrase having a plurality of terms; converting each term of the phrase to lower case; concatenating the plurality of converted terms using a symbol to separate the terms thereby forming a normalized phrase, then setting a test phrase equal to the normalized phrase; generating one or more sets of defined phrases; and comparing the test phrase to the sets of defined phrases, the sets of defined phrases representing known titles and stored in one or more lookup table as normalized phrases, each of the lookup table including organizational hierarchy information corresponding to each known title and a rank and a weight associated with the organizational hierarchy information; wherein, if the test phrase matches a first phrase in the set of defined phrases, a contact record for the received contact data is updated to include the organizational hierarchy information and the rank and weight corresponding with the matching first phrase in the lookup table; and wherein, if the test phrase does not match any phrase in the set of defined phrases, the test phrase is shortened by removing a term, and the comparison step is repeated with the shortened test phrase. | 1. A method for operating a database to determine an organizational hierarchy from contact data, the database being configured to store the contact data as a plurality of contact records, each contact record having a defined set of entities, including at least a name and a title, comprising: receiving contact data at the database including a phrase representing a title, the phrase having a plurality of terms; converting each term of the phrase to lower case; concatenating the plurality of converted terms using a symbol to separate the terms thereby forming a normalized phrase, then setting a test phrase equal to the normalized phrase; generating one or more sets of defined phrases; and comparing the test phrase to the sets of defined phrases, the sets of defined phrases representing known titles and stored in one or more lookup table as normalized phrases, each of the lookup table including organizational hierarchy information corresponding to each known title and a rank and a weight associated with the organizational hierarchy information; wherein, if the test phrase matches a first phrase in the set of defined phrases, a contact record for the received contact data is updated to include the organizational hierarchy information and the rank and weight corresponding with the matching first phrase in the lookup table; and wherein, if the test phrase does not match any phrase in the set of defined phrases, the test phrase is shortened by removing a term, and the comparison step is repeated with the shortened test phrase. 5. The method of claim 1 , wherein the organizational hierarchy information is department. | 0.701987 |
7,686,682 | 7 | 8 | 7. The computer game of claim 1 , further comprising a timer counting a time period T 3 , wherein each time period T 3 designates a game round, and wherein the tag generator generates the tags only at the end of each time period T 3 . | 7. The computer game of claim 1 , further comprising a timer counting a time period T 3 , wherein each time period T 3 designates a game round, and wherein the tag generator generates the tags only at the end of each time period T 3 . 8. The computer game of claim 7 , further comprising a challenge indicator, enabling challenge of words constructed in the word tray. | 0.5 |
9,208,779 | 1 | 11 | 1. A method comprising: receiving, by a training system, a set of development sentences W; receiving a set of two or more language models G M , each language model in the set of two or more language models G M for use by an automatic speech recognition system when generating text from speech; determining a set of n-gram language model weights λ M using the development sentences W and the set of two or more language models G M ; determining a set of sentence cluster weights γ C , each of the sentence cluster weights corresponding to a cluster in a set of sentence clusters, each cluster in the set of sentence clusters associated with at least one sentence from the set of development sentences W; generating a language model from the set of two or more language models G M , the set of n-gram language model weights λ M , the set of sentence clusters, and the set of sentence cluster weights γ C ; and providing, by the training system, the language model for use by a particular automatic speech recognition system to determine n-grams included in an utterance encoded in an audio signal. | 1. A method comprising: receiving, by a training system, a set of development sentences W; receiving a set of two or more language models G M , each language model in the set of two or more language models G M for use by an automatic speech recognition system when generating text from speech; determining a set of n-gram language model weights λ M using the development sentences W and the set of two or more language models G M ; determining a set of sentence cluster weights γ C , each of the sentence cluster weights corresponding to a cluster in a set of sentence clusters, each cluster in the set of sentence clusters associated with at least one sentence from the set of development sentences W; generating a language model from the set of two or more language models G M , the set of n-gram language model weights λ M , the set of sentence clusters, and the set of sentence cluster weights γ C ; and providing, by the training system, the language model for use by a particular automatic speech recognition system to determine n-grams included in an utterance encoded in an audio signal. 11. The method of claim 1 , wherein providing the language model for use by the particular automatic speech recognition system to determine n-grams included in an utterance comprises providing the language model for use transcribing the n-grams included in the utterance. | 0.777138 |
7,644,286 | 1 | 7 | 1. A computer-implemented method of restricting data access, comprising: performing the following on a computer: detecting an attempt by a first requester to access a first data resource, said first requester comprising computer executable instructions for accessing data; if the attempted access is to retrieve data: allowing the attempted access; and adding a get token corresponding to the first data resource to a first set of get tokens maintained for the first requester; and if the attempted access is to send data: if said first set of get tokens is empty, allowing the attempted access; if said first set of get tokens includes only a single get token, allowing the attempted access if the first data resource is the same data resource corresponding to said single get token; and if said first set of get tokens includes multiple get tokens, restricting the attempted access if the first requestor previously sent data to the first data resource and said first set of get tokens has changed since the first requester last sent data to the first data resource. | 1. A computer-implemented method of restricting data access, comprising: performing the following on a computer: detecting an attempt by a first requester to access a first data resource, said first requester comprising computer executable instructions for accessing data; if the attempted access is to retrieve data: allowing the attempted access; and adding a get token corresponding to the first data resource to a first set of get tokens maintained for the first requester; and if the attempted access is to send data: if said first set of get tokens is empty, allowing the attempted access; if said first set of get tokens includes only a single get token, allowing the attempted access if the first data resource is the same data resource corresponding to said single get token; and if said first set of get tokens includes multiple get tokens, restricting the attempted access if the first requestor previously sent data to the first data resource and said first set of get tokens has changed since the first requester last sent data to the first data resource. 7. The method of claim 1 , wherein said restricting comprises: issuing an alert regarding the attempted access. | 0.859848 |
4,658,370 | 66 | 69 | 66. A computer having a memory storing a predefined knowledge base including control knowledge, factual knowledge, and judgmental rules for encoding knowledge about a particular problem domain, said factual knowledge including definitions of classes of objects, attributes defining characteristics of said objects, and structures of said classes and attributes, said computer operating under control of a stored program interpreting the knowledge base to conduct a consultation with a user regarding a specified problem in said problem domain, said stored program including means for creating instances of said classes representing particular objects, a question and answer facility for recognizing questions from the user and formatting responsive answers including an explanation of the basis for the answers, and means for determining values for said attributes and said instances of said classes describing said characteristics of said particular objects, an inference engine for applying said judgmental rules to determine the responsive answers to said questions and for resolving conflicting conclusions of said judgmental rules, wherein the improvement comprises: said knowledge base has a separate portion encoding said control knowledge separate from said factual knowledge and said judgmental rules, said control knowledge being encoded in an imperative procedural language defining a sequence of steps for conducting said consultation with the user, and further comprising language interpreter means for executing said imperative procedural language, said separate portion of said knowledge base encoding said control knowledge comprises a plurality of discrete control blocks, each control block including statements in said imperative procedural language, said statements including separate statements to ask the user a question and obtain a responsive answer, apply a specific set of said judgmental rules, display to the user the conclusions of the rules applied, and to execute another specified control block, said plurality of control blocks including at least one post-instantiation control block associated with at least one of said classes, at least one determination control block defining a predetermined method for determining a value for at least one of said attributes being undetermined and being included in the premise of one of said judgmental rules, and at least one post-determination control block using a determined value for a determined attribute, and wherein said procedural language interpreter comprises means for interrupting the execution of said procedural language to execute the procedural language statements in the post-instantiation control block when an instance of the associated class is created, means for interrupting execution of said procedural language to execute the procedural language statements in the determination control block when said inference engine applies said judgmental rule including said undetermined attribute, and said language interpreter means includes means for interrupting the execution of said procedural language to execute the procedural language statements in the post-determination control block when said judgmental rule determines the value for said undetermined attribute. | 66. A computer having a memory storing a predefined knowledge base including control knowledge, factual knowledge, and judgmental rules for encoding knowledge about a particular problem domain, said factual knowledge including definitions of classes of objects, attributes defining characteristics of said objects, and structures of said classes and attributes, said computer operating under control of a stored program interpreting the knowledge base to conduct a consultation with a user regarding a specified problem in said problem domain, said stored program including means for creating instances of said classes representing particular objects, a question and answer facility for recognizing questions from the user and formatting responsive answers including an explanation of the basis for the answers, and means for determining values for said attributes and said instances of said classes describing said characteristics of said particular objects, an inference engine for applying said judgmental rules to determine the responsive answers to said questions and for resolving conflicting conclusions of said judgmental rules, wherein the improvement comprises: said knowledge base has a separate portion encoding said control knowledge separate from said factual knowledge and said judgmental rules, said control knowledge being encoded in an imperative procedural language defining a sequence of steps for conducting said consultation with the user, and further comprising language interpreter means for executing said imperative procedural language, said separate portion of said knowledge base encoding said control knowledge comprises a plurality of discrete control blocks, each control block including statements in said imperative procedural language, said statements including separate statements to ask the user a question and obtain a responsive answer, apply a specific set of said judgmental rules, display to the user the conclusions of the rules applied, and to execute another specified control block, said plurality of control blocks including at least one post-instantiation control block associated with at least one of said classes, at least one determination control block defining a predetermined method for determining a value for at least one of said attributes being undetermined and being included in the premise of one of said judgmental rules, and at least one post-determination control block using a determined value for a determined attribute, and wherein said procedural language interpreter comprises means for interrupting the execution of said procedural language to execute the procedural language statements in the post-instantiation control block when an instance of the associated class is created, means for interrupting execution of said procedural language to execute the procedural language statements in the determination control block when said inference engine applies said judgmental rule including said undetermined attribute, and said language interpreter means includes means for interrupting the execution of said procedural language to execute the procedural language statements in the post-determination control block when said judgmental rule determines the value for said undetermined attribute. 69. The computer as claimed in claim 66, wherein said factual knowledge base includes means for defining at least one of said attributes as multivalued, means for defining hierarchies of legal values for said multivalued attribute, and wherein said inference engine comprises means for determining certainty factors for concluded legal values of said attribute, and said knowledge base interpreter includes means for propagating the determined certainty factors through said hierarchy of legal values. | 0.645686 |
10,120,929 | 16 | 17 | 16. The computer-implemented method of claim 15 , wherein assigning the primary category describing the item of interest from the first search index further comprises determining relevance values associated with at least one of the first attributes and associated first attribute values of respective first categories and the received item attributes and item attribute values. | 16. The computer-implemented method of claim 15 , wherein assigning the primary category describing the item of interest from the first search index further comprises determining relevance values associated with at least one of the first attributes and associated first attribute values of respective first categories and the received item attributes and item attribute values. 17. The computer-implemented method of claim 16 , wherein assigning the primary category describing the item of interest from the first search index further comprises identifying a selected number of the first categories based on their relevance values as first category candidates. | 0.5 |
8,650,023 | 12 | 22 | 12. A method for assisting an author, comprising: receiving an author's review of an item which includes text and an associated author's rating of the item on a predefined scale; parsing the text of the author's review of the item to identify opinion expressions in the input text; with a processor, generating an analysis of the text, based on the identified opinion expressions including computing an effective opinion of the text as a function of a measure of polarity associated with each of the identified opinion expressions, the measure of polarity being is based on the polarity measure associated with respective adjectival terms from a polar vocabulary that are in the opinion expressions, the polar vocabulary associating a polarity measure with each of a set of adjectival terms, the polarity measure being based on ratings of reviews in a corpus of reviews from which the respective adjectival term was extracted; comparing the effective opinion with the author's rating to determine whether the text and the author's rating are coherent; and generating a representation of the analysis for display on a user interface, the representation of the analysis including a representation of the effective opinion. | 12. A method for assisting an author, comprising: receiving an author's review of an item which includes text and an associated author's rating of the item on a predefined scale; parsing the text of the author's review of the item to identify opinion expressions in the input text; with a processor, generating an analysis of the text, based on the identified opinion expressions including computing an effective opinion of the text as a function of a measure of polarity associated with each of the identified opinion expressions, the measure of polarity being is based on the polarity measure associated with respective adjectival terms from a polar vocabulary that are in the opinion expressions, the polar vocabulary associating a polarity measure with each of a set of adjectival terms, the polarity measure being based on ratings of reviews in a corpus of reviews from which the respective adjectival term was extracted; comparing the effective opinion with the author's rating to determine whether the text and the author's rating are coherent; and generating a representation of the analysis for display on a user interface, the representation of the analysis including a representation of the effective opinion. 22. The method of claim 12 , wherein the polarity measure associated with polar terms in the polar vocabulary is selected from a negative polarity and a positive polarity. | 0.786783 |
4,569,026 | 29 | 33 | 29. A method of simulating a personalized voice conversation between a talking video character and a human viewer of the video character, comprising the steps of: storing digital representations of the name of said human viewer; displaying a first video frame sequence including picture representations of a talking video character accompanied by a first voice sound associated with a plurality of second voice sounds; communicating to the human viewer during said first video frame sequence a first plurality of verbal expressions each verbal expression corresponding to a second voice sound in said plurality thereof; receiving from said viewer a response signal corresponding to a selected verbal expression in said plurality of verbal expressions; displaying said first video frame sequence again as a second video frame sequence, thereby repeating said talking video character; presenting the second voice sound corresponding to said selected verbal expression; and synthesizing as a third voice sound the name of said human viwer as a function of said digital representations during said first or second video frame sequence, thereby personalizing a simulated voice conversation between the human and the talking video character. | 29. A method of simulating a personalized voice conversation between a talking video character and a human viewer of the video character, comprising the steps of: storing digital representations of the name of said human viewer; displaying a first video frame sequence including picture representations of a talking video character accompanied by a first voice sound associated with a plurality of second voice sounds; communicating to the human viewer during said first video frame sequence a first plurality of verbal expressions each verbal expression corresponding to a second voice sound in said plurality thereof; receiving from said viewer a response signal corresponding to a selected verbal expression in said plurality of verbal expressions; displaying said first video frame sequence again as a second video frame sequence, thereby repeating said talking video character; presenting the second voice sound corresponding to said selected verbal expression; and synthesizing as a third voice sound the name of said human viwer as a function of said digital representations during said first or second video frame sequence, thereby personalizing a simulated voice conversation between the human and the talking video character. 33. The method of claim 29 wherein said verbal expressions are communicated to the human viewer as voice sounds. | 0.513043 |
8,705,810 | 2 | 5 | 2. The apparatus of claim 1 , wherein: the plurality of faces of the characters of the video comprises a plurality of face sets, wherein: a face set comprises a set of faces of a single character in a shot; a shot comprises a set of video frames captured by a single camera in a consecutive recording session; the feature extraction module comprises a shot detection module to detect shots of the video; and the partitioning the plurality of faces into sets of faces comprises merging face sets from separate shots of a character of the characters of the video into a single face set of the character. | 2. The apparatus of claim 1 , wherein: the plurality of faces of the characters of the video comprises a plurality of face sets, wherein: a face set comprises a set of faces of a single character in a shot; a shot comprises a set of video frames captured by a single camera in a consecutive recording session; the feature extraction module comprises a shot detection module to detect shots of the video; and the partitioning the plurality of faces into sets of faces comprises merging face sets from separate shots of a character of the characters of the video into a single face set of the character. 5. The apparatus of claim 2 , wherein the shot detection module is to detect shot boundary and scene segmentation. | 0.676136 |
9,519,718 | 3 | 4 | 3. The method according to claim 2 , wherein before extracting the main body text from the information of the source file, the method further comprises: filtering the source file information to keep a partitionable type tag and the information in the kept tag. | 3. The method according to claim 2 , wherein before extracting the main body text from the information of the source file, the method further comprises: filtering the source file information to keep a partitionable type tag and the information in the kept tag. 4. The method according to claim 3 , wherein the step (2) further comprises: (a) dividing the filtered source file information into text blocks based on the partitionable type tags, and storing contents of all the text blocks as well as distance between each block and its next adjacent text block; (b) choosing the text block with most characters as a base text block; (c) determining a upper limit block and a lower limit block via a relationship between a pre-set threshold and a distance ratio of characters number in the above and below text blocks to the base text block, wherein contents between the upper limit block and the lower limit block are taken as a main body text. | 0.5 |
7,617,195 | 2 | 4 | 2. The method according to claim 1 , wherein the second percentage at (c) is computed by giving weight only to those keywords and their set of neighboring keywords in the first list that match in the second list and a threshold percentage of the keywords in their set of neighboring keywords. | 2. The method according to claim 1 , wherein the second percentage at (c) is computed by giving weight only to those keywords and their set of neighboring keywords in the first list that match in the second list and a threshold percentage of the keywords in their set of neighboring keywords. 4. The method according to claim 2 , wherein the threshold percentage is reduced when the first list of rated keywords is identified using OCR. | 0.859804 |
8,775,175 | 3 | 4 | 3. The method of claim 2 , wherein the data received comprises first data, the data generated comprises second data, and wherein the method further comprises: receiving third data indicative of a progression of the dictation; and causing the speech recognition to be performed on the third data, during performance of the dictation correction. | 3. The method of claim 2 , wherein the data received comprises first data, the data generated comprises second data, and wherein the method further comprises: receiving third data indicative of a progression of the dictation; and causing the speech recognition to be performed on the third data, during performance of the dictation correction. 4. The method of claim 3 , further comprising: generating fourth data for replacing, in the graphical user interface, the visual indicator of the recently dictated unit with a visual representation of the third data. | 0.5 |
9,304,681 | 10 | 14 | 10. A non-transitory storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations for identifying characters in a handwritten input on a touch-sensitive device, the operations comprising: receiving a handwritten user input via the touch-sensitive device; identifying a set of candidate characters based on the handwritten user input; estimating candidate support lines for each of the candidate characters; associating reference support lines for each candidate character; for each candidate character, measuring a deviation between the estimated support lines and reference support lines to determine one or more deviations from an expectation; ranking each candidate character based on a total deviation measurement for each candidate character; and identifying a best-ranked candidate character based at least in part on a smallest total deviation measurement. | 10. A non-transitory storage medium storing instructions that, when executed by a computing system, cause the computing system to perform operations for identifying characters in a handwritten input on a touch-sensitive device, the operations comprising: receiving a handwritten user input via the touch-sensitive device; identifying a set of candidate characters based on the handwritten user input; estimating candidate support lines for each of the candidate characters; associating reference support lines for each candidate character; for each candidate character, measuring a deviation between the estimated support lines and reference support lines to determine one or more deviations from an expectation; ranking each candidate character based on a total deviation measurement for each candidate character; and identifying a best-ranked candidate character based at least in part on a smallest total deviation measurement. 14. The computer-readable storage medium of claim 10 , wherein measuring the deviation for at least one candidate character comprises determining a scale deviation based at least in part on a comparison between a difference in distances of the reference support lines and a difference in distances of the candidate support lines. | 0.742163 |
9,721,020 | 15 | 16 | 15. The computer program product of claim 14 , the operation further comprising: obfuscating the received query by executing the first subquery at a first time and executing the second subquery at a second time, different than the first time. | 15. The computer program product of claim 14 , the operation further comprising: obfuscating the received query by executing the first subquery at a first time and executing the second subquery at a second time, different than the first time. 16. The computer program product of claim 15 , wherein the filtering is performed by a respective server hosting a respective search engine executing the plurality of subqueries. | 0.5 |
8,320,531 | 12 | 13 | 12. A method in accordance with claim 10 , wherein the step of establishing the telephone connection comprises: receiving from the user a designated telephone number; and calling the user at the designated telephone number. | 12. A method in accordance with claim 10 , wherein the step of establishing the telephone connection comprises: receiving from the user a designated telephone number; and calling the user at the designated telephone number. 13. A method in accordance with claim 12 , wherein the step of calling the user is performed by the communication system immediately after receiving the designated number from the user. | 0.5 |
10,133,718 | 1 | 7 | 1. A method of matching and completing form fields in an electronic form, the method comprising the steps of: receiving at least one field name of at least one form field on the electronic form, the at least one field name comprising multiple terms, the electronic form comprising a plurality of form fields, a plurality of field names associated with the plurality of form fields, and a plurality of readable labels; decomposing the electronic form into a hierarchical structure in descending order of form category type, section headers, and subsections, wherein the section headers and subsections each comprise one or more form fields of the plurality of form fields; scanning the plurality of readable labels for keywords in the electronic form; applying a probabilistic algorithm to the keywords to identify a form category type for the electronic form; building associations between the plurality of readable labels and the plurality of form fields to determine a readable label of the plurality of readable labels for the at least one field name associated with the at least one form field; extracting contextual information from the electronic form based on at least one of a section header corresponding to the at least one form field and a subsection corresponding to the at least one form field; composing a set of terms for the at least one form field based on the identified form category type, the determined readable label, and the contextual information, wherein the set of terms is not the same as the readable label; performing a best-fit search of a database of field names using the set of terms to identify a best-fit field name of the database of field names for the at least one form field; and transmitting a field value corresponding to the best-fit field name for completing the at least one form field of the electronic form. | 1. A method of matching and completing form fields in an electronic form, the method comprising the steps of: receiving at least one field name of at least one form field on the electronic form, the at least one field name comprising multiple terms, the electronic form comprising a plurality of form fields, a plurality of field names associated with the plurality of form fields, and a plurality of readable labels; decomposing the electronic form into a hierarchical structure in descending order of form category type, section headers, and subsections, wherein the section headers and subsections each comprise one or more form fields of the plurality of form fields; scanning the plurality of readable labels for keywords in the electronic form; applying a probabilistic algorithm to the keywords to identify a form category type for the electronic form; building associations between the plurality of readable labels and the plurality of form fields to determine a readable label of the plurality of readable labels for the at least one field name associated with the at least one form field; extracting contextual information from the electronic form based on at least one of a section header corresponding to the at least one form field and a subsection corresponding to the at least one form field; composing a set of terms for the at least one form field based on the identified form category type, the determined readable label, and the contextual information, wherein the set of terms is not the same as the readable label; performing a best-fit search of a database of field names using the set of terms to identify a best-fit field name of the database of field names for the at least one form field; and transmitting a field value corresponding to the best-fit field name for completing the at least one form field of the electronic form. 7. The method of claim 1 , further comprising: identifying a subset of form fields relevant to the electronic form based on the form category type. | 0.879705 |
7,693,829 | 21 | 22 | 21. The computer program product of claim 20 , including instructions for determining match scores for one or more matches between the search pattern and documents in the set of documents; wherein instructions for responding to the query include instructions for providing a ranked set of information items containing the identified content in accordance with the match scores. | 21. The computer program product of claim 20 , including instructions for determining match scores for one or more matches between the search pattern and documents in the set of documents; wherein instructions for responding to the query include instructions for providing a ranked set of information items containing the identified content in accordance with the match scores. 22. The computer program product of claim 21 , wherein providing at least one of the one or more potential answers includes providing a ranked list of documents containing the identified content in accordance with the match scores. | 0.5 |
7,756,930 | 101 | 106 | 101. The apparatus of claim 95 , further comprising one or more stored sequences of instructions which, when executed by the one or more processors, cause the one or more processors to carry out the step of: when the reputation score is better than a second predefined threshold, performing a second specified action associated with responding to messages that are not unsolicited, wherein the first predefined threshold is different from the second predefined threshold. | 101. The apparatus of claim 95 , further comprising one or more stored sequences of instructions which, when executed by the one or more processors, cause the one or more processors to carry out the step of: when the reputation score is better than a second predefined threshold, performing a second specified action associated with responding to messages that are not unsolicited, wherein the first predefined threshold is different from the second predefined threshold. 106. The apparatus of claim 101 , further comprising one or more stored sequences of instructions which, when executed by the one or more processors, cause the one or more processors to carry out the step of: when the reputation score is better than the first predefined threshold and worse than the second predefined threshold, performing a third specified action. | 0.5 |
9,600,562 | 1 | 4 | 1. A computer-implemented method for importing data for an Entity Relationship (E-R) model, the method comprising: receiving, by using a computer system, an exported E-R model data file and a data schema of the E-R model; determining a dependency type of an entity in the exported E-R model data file based on the data schema, wherein the dependency type corresponds to at least one of no correlation, weak correlation, and strong correlation; importing the entity in the exported E-R model data file based on the determined dependency type, wherein the importing the entity in the exported E-R model data file based on the determined dependency type includes: determining whether the imported entity with no correlation or weak correlation affects an entity recorded in a strong correlation table; responsive to the determined dependency type of the entity being at least one of weak correlation and no correlation, directly importing the entity; and responsive to the determined dependency type of the entity being strong correlation, storing the entity in the strong correlation table and deferring the importing of the entity until a minimum reference number of the strong correlation of the entity is satisfied; and responsive to the determination that the imported entity with no correlation or weak correlation affects the entity recorded in the strong correlation table, importing the entity recorded in the strong correlation table and deleting the entity recorded in the strong correlation table from the strong correlation table. | 1. A computer-implemented method for importing data for an Entity Relationship (E-R) model, the method comprising: receiving, by using a computer system, an exported E-R model data file and a data schema of the E-R model; determining a dependency type of an entity in the exported E-R model data file based on the data schema, wherein the dependency type corresponds to at least one of no correlation, weak correlation, and strong correlation; importing the entity in the exported E-R model data file based on the determined dependency type, wherein the importing the entity in the exported E-R model data file based on the determined dependency type includes: determining whether the imported entity with no correlation or weak correlation affects an entity recorded in a strong correlation table; responsive to the determined dependency type of the entity being at least one of weak correlation and no correlation, directly importing the entity; and responsive to the determined dependency type of the entity being strong correlation, storing the entity in the strong correlation table and deferring the importing of the entity until a minimum reference number of the strong correlation of the entity is satisfied; and responsive to the determination that the imported entity with no correlation or weak correlation affects the entity recorded in the strong correlation table, importing the entity recorded in the strong correlation table and deleting the entity recorded in the strong correlation table from the strong correlation table. 4. The method according to claim 1 , wherein importing the entity in the E-R model data file based on the determined dependency type further includes: responsive to the determined dependency type of the entity being weak correlation, recording weak correlation reference information of the entity in a weak correlation table. | 0.774306 |
8,601,079 | 1 | 6 | 1. A network device, comprising: a storage device for storing one or more files that are associated with a user of at least one client device; and a processor for enabling actions, the actions comprising: receiving one or more message file attachments, wherein the one or more message file attachments are associated with the user; automatically determining one or more automatic tags that are associated with the one or more message file attachments, wherein each of the one or more message file attachments is associated with at least one of the one or more automatic tags; automatically generating a personalized hierarchical structure of tags (“PHST”) from the one or more automatic tags; receiving, from the user, one or more custom tags for at least one of the one or more message file attachments; associating the at least one of the one or more message file attachments with the one or more custom tags; automatically generating at least one custom generated tag using the one or more custom tags; automatically modifying the PHST to include the at least one custom generated tag within the hierarchy; and displaying the PHST which separately indicates the one or more automatic tags and the at least one custom generated tag to the user such that the user is enabled to select at least one tag which in turn selects one or more files associated with the selected tag for attachment to a message. | 1. A network device, comprising: a storage device for storing one or more files that are associated with a user of at least one client device; and a processor for enabling actions, the actions comprising: receiving one or more message file attachments, wherein the one or more message file attachments are associated with the user; automatically determining one or more automatic tags that are associated with the one or more message file attachments, wherein each of the one or more message file attachments is associated with at least one of the one or more automatic tags; automatically generating a personalized hierarchical structure of tags (“PHST”) from the one or more automatic tags; receiving, from the user, one or more custom tags for at least one of the one or more message file attachments; associating the at least one of the one or more message file attachments with the one or more custom tags; automatically generating at least one custom generated tag using the one or more custom tags; automatically modifying the PHST to include the at least one custom generated tag within the hierarchy; and displaying the PHST which separately indicates the one or more automatic tags and the at least one custom generated tag to the user such that the user is enabled to select at least one tag which in turn selects one or more files associated with the selected tag for attachment to a message. 6. The network device of claim 1 , wherein the one or more automatic tags are determined from a feature of a message associated with a respective file attachment. | 0.851648 |
9,785,658 | 1 | 4 | 1. A method comprising: accessing a first schema comprising a first plurality of business entities including a first business entity, the first business entity having a first name in the first schema; accessing a second schema comprising a second plurality of business entities including the first business entity, the first business entity having a second name in the second schema; generating, by one or more processors, from the first schema and the second schema, a merged schema comprising a third plurality of business entities, including a single instance of the first business entity; storing, by the one or more processors, the merged schema in a database; extracting a first sequence of words from the first name of the first business entity; extracting a second sequence of words from the second name of the first business entity; generating candidate phrases for the business entity from the first and second sequences of words; ranking the candidate phrases for the first business entity; analyzing, by the one or more processors, candidate sets of labels for the third plurality of business entities, each candidate set of labels including a label for each business entity of the third plurality of business entities, no two business entities having the same label in the candidate set of labels, the label for the first business entity being selected from the candidate phrases for the first business entity; assigning labels to each business entity of the third plurality of business entities based on the analysis of the candidate sets of labels; receiving data stored using the first schema; converting the received data to the merged schema; and causing presentation of the converted data using the assigned labels. | 1. A method comprising: accessing a first schema comprising a first plurality of business entities including a first business entity, the first business entity having a first name in the first schema; accessing a second schema comprising a second plurality of business entities including the first business entity, the first business entity having a second name in the second schema; generating, by one or more processors, from the first schema and the second schema, a merged schema comprising a third plurality of business entities, including a single instance of the first business entity; storing, by the one or more processors, the merged schema in a database; extracting a first sequence of words from the first name of the first business entity; extracting a second sequence of words from the second name of the first business entity; generating candidate phrases for the business entity from the first and second sequences of words; ranking the candidate phrases for the first business entity; analyzing, by the one or more processors, candidate sets of labels for the third plurality of business entities, each candidate set of labels including a label for each business entity of the third plurality of business entities, no two business entities having the same label in the candidate set of labels, the label for the first business entity being selected from the candidate phrases for the first business entity; assigning labels to each business entity of the third plurality of business entities based on the analysis of the candidate sets of labels; receiving data stored using the first schema; converting the received data to the merged schema; and causing presentation of the converted data using the assigned labels. 4. The method of claim 1 , wherein the ranking of the candidate phrases for the first business entity includes ranking the candidate phrases for the business entity based on a frequency of words in each candidate phrase in a description of the first business entity. | 0.791862 |
7,898,546 | 1 | 3 | 1. A system for designing a graphics processing system, comprising: a class processor, said class processor receiving an abstract model of a graphics system having a class of commands and a set of state variables and in response performing semantic analysis to generate validation logic to validate commands within said class of commands, said validation logic configured to utilize a reduced memory space memory having a memory size smaller than a memory size associated with storing a full representation of said set of state variables, wherein said class processor is used to generate a database for creating a hardware description language representation of said validation logic and said reduced memory space shadow memory. | 1. A system for designing a graphics processing system, comprising: a class processor, said class processor receiving an abstract model of a graphics system having a class of commands and a set of state variables and in response performing semantic analysis to generate validation logic to validate commands within said class of commands, said validation logic configured to utilize a reduced memory space memory having a memory size smaller than a memory size associated with storing a full representation of said set of state variables, wherein said class processor is used to generate a database for creating a hardware description language representation of said validation logic and said reduced memory space shadow memory. 3. The system of claim 1 , wherein said semantic analysis includes identifying redundant state variables not required to perform validity checks. | 0.777607 |
9,767,262 | 4 | 13 | 4. A system for providing a security credential, comprising: at least one remote computing device; and a security credential manager executable in the at least one remote computing device, wherein, when executed, the security credential manager causes the at least one remote computing device to at least: automatically generate at least one security credential according to a security credential specification received from a network site at a standardized location; store the at least one security credential in association with a user account for the network site; provide a plurality of dynamically generated knowledge-based questions to a user at a client computing device and a request for a master security credential in response to a request for the at least one security credential received from the client computing device; generate a score based at least in part on a plurality of answers to the plurality of dynamically generated knowledge-based questions, the plurality of answers being received from the user via the client computing device, and individual answers of the plurality of answers being weighted with a respective different weight based at least in part on a respective knowledge-based question of the plurality of dynamically generated knowledge-based questions; and provide the at least one security credential to the client computing device in response to the score meeting or exceeding a predefined threshold and a determination that the master security credential received from the client computing device is valid. | 4. A system for providing a security credential, comprising: at least one remote computing device; and a security credential manager executable in the at least one remote computing device, wherein, when executed, the security credential manager causes the at least one remote computing device to at least: automatically generate at least one security credential according to a security credential specification received from a network site at a standardized location; store the at least one security credential in association with a user account for the network site; provide a plurality of dynamically generated knowledge-based questions to a user at a client computing device and a request for a master security credential in response to a request for the at least one security credential received from the client computing device; generate a score based at least in part on a plurality of answers to the plurality of dynamically generated knowledge-based questions, the plurality of answers being received from the user via the client computing device, and individual answers of the plurality of answers being weighted with a respective different weight based at least in part on a respective knowledge-based question of the plurality of dynamically generated knowledge-based questions; and provide the at least one security credential to the client computing device in response to the score meeting or exceeding a predefined threshold and a determination that the master security credential received from the client computing device is valid. 13. The system of claim 4 , wherein, when executed, the security credential manager further causes the at least one remote computing device to at least, in response to receiving the master security credential from the client computing device, establish the master security credential in the at least one remote computing device as a valid master security credential based at least in part on the plurality of answers to the plurality of dynamically generated knowledge-based questions. | 0.5 |
8,984,476 | 1 | 5 | 1. A computer-implemented process for target application creation, the computer-implemented process comprising: receiving a representation of a logical topology diagram for an application architecture to form a source input; locating part type information in a part type dictionary using the source input; locating application parts in an application parts repository for each located part type; composing a subset of identified parts; determining whether integration dependencies of the subset of identified parts are met; responsive to a determination that the integration dependencies are met, consolidating parts into a first application structure; determining whether to exclude parts from the first application structure; responsive to a determination to exclude parts, excluding the parts from the first application structure to create a second application structure; and building a target application based on the second application structure. | 1. A computer-implemented process for target application creation, the computer-implemented process comprising: receiving a representation of a logical topology diagram for an application architecture to form a source input; locating part type information in a part type dictionary using the source input; locating application parts in an application parts repository for each located part type; composing a subset of identified parts; determining whether integration dependencies of the subset of identified parts are met; responsive to a determination that the integration dependencies are met, consolidating parts into a first application structure; determining whether to exclude parts from the first application structure; responsive to a determination to exclude parts, excluding the parts from the first application structure to create a second application structure; and building a target application based on the second application structure. 5. The computer-implemented process of claim 1 wherein determining whether to exclude parts further comprises: identifying the parts that do not map to logical components in the topology diagram; and responsive to a determination to exclude parts, excluding the parts that do not map to the logical components; wherein the building of the target application comprises building the target application from the subset of identified parts less the parts that do not map to the logical components. | 0.683974 |
6,122,361 | 26 | 27 | 26. An automated directory assistance system as defined in claim 25, comprising a computing unit for computing said probability value for a plurality of vocabulary items. | 26. An automated directory assistance system as defined in claim 25, comprising a computing unit for computing said probability value for a plurality of vocabulary items. 27. An automated directory assistance system as defined in claim 26, comprising a ranking unit for ranking vocabulary items on a basis of the computed probability values. | 0.5 |
9,734,637 | 1 | 2 | 1. A method for generating a virtual rig to animate a virtual three-dimensional representation of a human face, the method comprising: receiving a mesh that includes a plurality of vertices to provide a virtual three-dimensional representation of a human face, the plurality of vertices defining a plurality of polygons that define a virtual three-dimensional representation of a surface of the human face; associating a plurality of semantic identifiers with the plurality of respective vertices, each semantic identifier specifying a respective physical feature of the human face; generating a virtual rig that is capable of maneuvering the mesh to a plurality of configurations, using one or more processors, based at least in part on association of the plurality of semantic identifiers with the plurality of respective vertices to animate the virtual three-dimensional representation of the human face; determining that a virtual item is to be combined with the mesh to represent a corresponding item of the human face based at least in part on a designated subset of the plurality of semantic identifiers; and generating the virtual item based at least in part on a subset of the plurality of vertices that is associated with the designated subset of the plurality of semantic identifiers and further based at least in part on whether the virtual item is a first virtual eyeball that represents a first physical eyeball of the human face or a second virtual eyeball that represents a second physical eyeball of the human face; wherein at least one of a position, a size, or a shape of the first virtual eyeball and a respective at least one of a position, a size, or a shape of the second virtual eyeball are asymmetric with respect to the mesh to represent that at least one of a position, a size, or a shape of the first physical eyeball and a respective at least one of a position, a size, or a shape of the second physical eyeball are asymmetric with respect to the human face; and wherein generating the virtual rig comprises: defining a reference element based at least in part on the designated subset of the plurality of semantic identifiers and further based at least in part on a thickness of a virtual layer that is coincident with an outer surface of the mesh to serve as a reference for maneuvering the virtual item, the virtual layer being a virtual representation of skin that is coincident with the surface of the human face. | 1. A method for generating a virtual rig to animate a virtual three-dimensional representation of a human face, the method comprising: receiving a mesh that includes a plurality of vertices to provide a virtual three-dimensional representation of a human face, the plurality of vertices defining a plurality of polygons that define a virtual three-dimensional representation of a surface of the human face; associating a plurality of semantic identifiers with the plurality of respective vertices, each semantic identifier specifying a respective physical feature of the human face; generating a virtual rig that is capable of maneuvering the mesh to a plurality of configurations, using one or more processors, based at least in part on association of the plurality of semantic identifiers with the plurality of respective vertices to animate the virtual three-dimensional representation of the human face; determining that a virtual item is to be combined with the mesh to represent a corresponding item of the human face based at least in part on a designated subset of the plurality of semantic identifiers; and generating the virtual item based at least in part on a subset of the plurality of vertices that is associated with the designated subset of the plurality of semantic identifiers and further based at least in part on whether the virtual item is a first virtual eyeball that represents a first physical eyeball of the human face or a second virtual eyeball that represents a second physical eyeball of the human face; wherein at least one of a position, a size, or a shape of the first virtual eyeball and a respective at least one of a position, a size, or a shape of the second virtual eyeball are asymmetric with respect to the mesh to represent that at least one of a position, a size, or a shape of the first physical eyeball and a respective at least one of a position, a size, or a shape of the second physical eyeball are asymmetric with respect to the human face; and wherein generating the virtual rig comprises: defining a reference element based at least in part on the designated subset of the plurality of semantic identifiers and further based at least in part on a thickness of a virtual layer that is coincident with an outer surface of the mesh to serve as a reference for maneuvering the virtual item, the virtual layer being a virtual representation of skin that is coincident with the surface of the human face. 2. The method of claim 1 , wherein the plurality of vertices is associated with a plurality of respective vertex values; wherein the method further comprises: changing at least one vertex value of the plurality of vertex values to be at least one respective changed vertex value on which the virtual three-dimensional representation is based, based at least in part on an image of the human face, to increase accuracy of the virtual three-dimensional representation of the human face; and wherein generating the virtual rig comprises: generating the virtual rig based at least in part on the at least one changed vertex value. | 0.5 |
6,045,363 | 1 | 5 | 1. An educational aid for developing sight-word vocabulary in a student, comprising: a non-syllabified readable format text which is adapted to be or is already memorized verbatim by the student, said text having one or more words and each word having at least one syllable, and said text including a series of notations, each notation denoting at least one letter of said text, wherein portions of said words that require extended pronunciation are presented in a visually extended format so as to emphasize phonetic structure; and reinforcement means, separate from said text but adapted to be used together with said text, for reinforcing the student's sight recognition of words and syllables from said text; wherein said reinforcement means is operable to allow the student to note each successive notation in said text as the student recites said text from memory, and is further operable to allow the student to match specified words and syllables to corresponding words and syllables in said text by using sight recognition. | 1. An educational aid for developing sight-word vocabulary in a student, comprising: a non-syllabified readable format text which is adapted to be or is already memorized verbatim by the student, said text having one or more words and each word having at least one syllable, and said text including a series of notations, each notation denoting at least one letter of said text, wherein portions of said words that require extended pronunciation are presented in a visually extended format so as to emphasize phonetic structure; and reinforcement means, separate from said text but adapted to be used together with said text, for reinforcing the student's sight recognition of words and syllables from said text; wherein said reinforcement means is operable to allow the student to note each successive notation in said text as the student recites said text from memory, and is further operable to allow the student to match specified words and syllables to corresponding words and syllables in said text by using sight recognition. 5. The educational aid of claim 1, wherein said visual cues are colored to be less prominent than the print of said readable format text. | 0.786604 |
9,659,018 | 16 | 19 | 16. The non-transitory computer-readable recording medium according to claim 11 , wherein said the program further causes the computer to correct at least one of a first character string described in a first character type, a second character string described in a second character type different from said first character type, and a space between said first character string and said second character string when said candidate character string includes said first character string, said second character string, and the space between said first character string and said second character string. | 16. The non-transitory computer-readable recording medium according to claim 11 , wherein said the program further causes the computer to correct at least one of a first character string described in a first character type, a second character string described in a second character type different from said first character type, and a space between said first character string and said second character string when said candidate character string includes said first character string, said second character string, and the space between said first character string and said second character string. 19. The non-transitory computer-readable recording medium according to claim 16 , wherein the program further causes the computer to delete all characters preceding the space between said first character string and said second character string. | 0.508065 |
7,668,790 | 1 | 9 | 1. A computer-implemented robust method for fusing data from different information sources using a training set having a plurality of training examples, each training example having a plurality of disjoint views, the method comprising: a) initially assigning weights, by the computer, to each of the plurality of training examples; b) sampling the training examples, by the computer, based on the distribution of the weights of the training examples; c) iteratively, by the computer, for each of the views, separately training weak classifiers in parallel on the sample of training examples; d) selecting, by the computer, the weak classifier corresponding to the view with the lowest training error rate among all the views and calculating a combination weight value associated with the selected classifier as a function of the lowest training error rate at that iteration; e) updating the weights of the sampled training examples by, for each of the sampled training examples, assigning the same updated weight for sampling the training examples for all views, the updated weight distribution being a function of-the lowest training error rate among all views at that iteration; f) repeating said b) sampling, c) training, d) selecting, and e) updating for a predetermined number of iterations; and g) forming a final classifier that is a sum of the selected weak learners weighted by the corresponding combination weight value at each iteration. | 1. A computer-implemented robust method for fusing data from different information sources using a training set having a plurality of training examples, each training example having a plurality of disjoint views, the method comprising: a) initially assigning weights, by the computer, to each of the plurality of training examples; b) sampling the training examples, by the computer, based on the distribution of the weights of the training examples; c) iteratively, by the computer, for each of the views, separately training weak classifiers in parallel on the sample of training examples; d) selecting, by the computer, the weak classifier corresponding to the view with the lowest training error rate among all the views and calculating a combination weight value associated with the selected classifier as a function of the lowest training error rate at that iteration; e) updating the weights of the sampled training examples by, for each of the sampled training examples, assigning the same updated weight for sampling the training examples for all views, the updated weight distribution being a function of-the lowest training error rate among all views at that iteration; f) repeating said b) sampling, c) training, d) selecting, and e) updating for a predetermined number of iterations; and g) forming a final classifier that is a sum of the selected weak learners weighted by the corresponding combination weight value at each iteration. 9. The computer-implemented method of claim 1 , wherein the views are different color intensities extracted from an image data set. | 0.79784 |
6,088,651 | 1 | 2 | 1. A computer system, said computer comprising: a bus; a central processing unit; computer system memory, said computer system memory being connected to said central processing unit via said bus; and a name search mechanism contained in said computer system memory for execution on said central processing unit, said name search mechanism using a single indexing structure to locate at least one computer system resource, wherein said at least one computer system resource can be located by said name search mechanism via a computer system resource name or via a computer system resource unique identifier (UID). | 1. A computer system, said computer comprising: a bus; a central processing unit; computer system memory, said computer system memory being connected to said central processing unit via said bus; and a name search mechanism contained in said computer system memory for execution on said central processing unit, said name search mechanism using a single indexing structure to locate at least one computer system resource, wherein said at least one computer system resource can be located by said name search mechanism via a computer system resource name or via a computer system resource unique identifier (UID). 2. The computer system of claim 1 wherein said name search mechanism is used to locate said at least one computer system resource in a bounded period of time. | 0.5 |
9,092,478 | 20 | 24 | 20. The medium of claim 16 , wherein searching one or more business objects data sources to identify data items of the plurality of data items stored in each of the searched one or more business objects data sources for data items that satisfy the identified metadata comprises identifying a plurality of tuples, each tuple including a first metadata portion and a second metadata portion, each of which satisfies the respective query term. | 20. The medium of claim 16 , wherein searching one or more business objects data sources to identify data items of the plurality of data items stored in each of the searched one or more business objects data sources for data items that satisfy the identified metadata comprises identifying a plurality of tuples, each tuple including a first metadata portion and a second metadata portion, each of which satisfies the respective query term. 24. The medium of claim 20 , the operations further comprising ranking the plurality of tuples according to a relevance to the search query. | 0.709544 |
7,921,063 | 25 | 26 | 25. A non-transitory computer-readable tangible storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of: training a probabilistic filter using first properties of one or more first network resource identifiers obtained from a whitelist; wherein at least one of the first properties is obtained from any of: information obtained from DNS queries based, at least in part, on the one or more first work resource identifiers; server software information; or information obtained from “whois” queries based, at least in part, on information contained in the network resource identifier; training the probabilistic filter using second properties of one or more second network resource identifiers obtained from a blocklist; testing third properties of a third network resource identifier using the probabilistic filter, resulting in creating a probability output; adding the third network resource identifier to the blocklist when the probability output is greater than a specified threshold. | 25. A non-transitory computer-readable tangible storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of: training a probabilistic filter using first properties of one or more first network resource identifiers obtained from a whitelist; wherein at least one of the first properties is obtained from any of: information obtained from DNS queries based, at least in part, on the one or more first work resource identifiers; server software information; or information obtained from “whois” queries based, at least in part, on information contained in the network resource identifier; training the probabilistic filter using second properties of one or more second network resource identifiers obtained from a blocklist; testing third properties of a third network resource identifier using the probabilistic filter, resulting in creating a probability output; adding the third network resource identifier to the blocklist when the probability output is greater than a specified threshold. 26. The invention of claim 25 , wherein the whitelist comprises network resource identifiers that are not associated with spam or threats, and wherein the blocklist comprises network resource identifiers that are associated with spam or threats. | 0.790598 |
8,281,149 | 31 | 43 | 31. A tangible computer-readable medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: registering with an IdP to establish a first pseudonym; upon successful proof of possession of the first pseudonym to the IdP, receiving a first representation of an access token from the IdP for accessing the RP; transforming, by a processor, the first representation of the access token to obtain a second representation of the access token, the second representation of the access token being a valid access token and is unlinkable to the first representation of the access token by the IdP; receiving a request from the user to access the RP; determining whether the request is for accessing the RP anonymously or pseudonymously; if the request is for anonymous access, providing the second representation of the access token to the RP anonymously; and gaining access to the RP upon verification of the second representation of the access token, the anonymous access being unlinkable to any previous and any future access at the RP, and unlinkable to the IdP's interaction with any particular user; if the request is for pseudonymous access, providing to the RP the second representation of the access token and proof of possession of a second pseudonym that is previously registered with the RP; and gaining access to the RP upon successful verification of the second representation of the access token and proof of possession of the second pseudonym, wherein the pseudonymous access is linkable to the second pseudonym, unlinkable to the IdP's interaction with any particular user, and unlinkable to any past and future access to the RP that does not employ the second pseudonym. | 31. A tangible computer-readable medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: registering with an IdP to establish a first pseudonym; upon successful proof of possession of the first pseudonym to the IdP, receiving a first representation of an access token from the IdP for accessing the RP; transforming, by a processor, the first representation of the access token to obtain a second representation of the access token, the second representation of the access token being a valid access token and is unlinkable to the first representation of the access token by the IdP; receiving a request from the user to access the RP; determining whether the request is for accessing the RP anonymously or pseudonymously; if the request is for anonymous access, providing the second representation of the access token to the RP anonymously; and gaining access to the RP upon verification of the second representation of the access token, the anonymous access being unlinkable to any previous and any future access at the RP, and unlinkable to the IdP's interaction with any particular user; if the request is for pseudonymous access, providing to the RP the second representation of the access token and proof of possession of a second pseudonym that is previously registered with the RP; and gaining access to the RP upon successful verification of the second representation of the access token and proof of possession of the second pseudonym, wherein the pseudonymous access is linkable to the second pseudonym, unlinkable to the IdP's interaction with any particular user, and unlinkable to any past and future access to the RP that does not employ the second pseudonym. 43. The computer-readable medium of claim 31 , wherein the operations further comprise: generating a temporary pseudonym based on the second pseudonym; interactively proving to the RP possession of the second pseudonym using the temporary pseudonym; and expunging linkage between the temporary pseudonym and the second pseudonym. | 0.643939 |
8,775,459 | 8 | 9 | 8. The method of claim 7 , wherein the abstracting comprises: replacing one or more attribute values in the initial user request with one or more variables. | 8. The method of claim 7 , wherein the abstracting comprises: replacing one or more attribute values in the initial user request with one or more variables. 9. The method of claim 8 , wherein the adapting comprises: replacing the one or more variables with one or more attributes defined in the subsequent user request. | 0.5 |
9,779,085 | 10 | 13 | 10. The computer-implemented method of claim 8 , the multilingual embedding being generalized across the target languages by transforming multilingual dictionaries into constraints in an underlying optimization problem. | 10. The computer-implemented method of claim 8 , the multilingual embedding being generalized across the target languages by transforming multilingual dictionaries into constraints in an underlying optimization problem. 13. The computer-implemented method of claim 10 , wherein the training a multilingual embedding comprises making a first update of a first vector, and the transforming multilingual dictionaries into constraints in the underlying optimization problem comprises: after making the first update of the first vector, looking up other words in the multilingual dictionaries based on the first update and updating respective other vectors of the other words such that angles between the first vector and the other vectors are close to each other. | 0.5 |
6,167,328 | 4 | 9 | 4. A robot language processing apparatus for describing operation details of a teaching-playback robot and teaching the robot, comprising: display means for graphically displaying a picture and capable of designating a position in the displayed picture with pointing means; storage means for storing said robot program as intermediate code; and language processing means for decoding said intermediate codes and connecting orthogonal space positions of a group of motion commands stored in a time-series manner with straight lines or curved lines, converting an obtained group of lines into coordinates in the displayed picture as viewed from an arbitrary viewpoint, graphically displaying the converted group of lines on said display means, and displaying time-series numbers of points in the group of motion commands in superimposed relation to the group of lines on said display means. | 4. A robot language processing apparatus for describing operation details of a teaching-playback robot and teaching the robot, comprising: display means for graphically displaying a picture and capable of designating a position in the displayed picture with pointing means; storage means for storing said robot program as intermediate code; and language processing means for decoding said intermediate codes and connecting orthogonal space positions of a group of motion commands stored in a time-series manner with straight lines or curved lines, converting an obtained group of lines into coordinates in the displayed picture as viewed from an arbitrary viewpoint, graphically displaying the converted group of lines on said display means, and displaying time-series numbers of points in the group of motion commands in superimposed relation to the group of lines on said display means. 9. The robot language processing apparatus according to claim 4, wherein said language processing means displays icons indicative of operation details on said display means, and when one of the displayed icons is selected by said pointing means, said language processing means inserts an operating command corresponding to the selected icon into intermediate code in said storage means. | 0.72029 |
8,250,118 | 5 | 12 | 5. The method of claim 1 wherein the indicating step includes displaying the legal case history on an output device in a graphical flow-chart format, wherein the graphical flow-chart format includes a graphical representation of a parent case and a graphical representation of at least one related case, the graphical representation of the parent case being visually connected to the graphical representation of the related case by a visual indicator, the graphical representations of the parent case and at least one related case being positioned to illustrate the relationship between the cases. | 5. The method of claim 1 wherein the indicating step includes displaying the legal case history on an output device in a graphical flow-chart format, wherein the graphical flow-chart format includes a graphical representation of a parent case and a graphical representation of at least one related case, the graphical representation of the parent case being visually connected to the graphical representation of the related case by a visual indicator, the graphical representations of the parent case and at least one related case being positioned to illustrate the relationship between the cases. 12. The method of claim 5 , wherein the step of displaying the legal case history further includes automatically positioning the graphical representation of the parent case and the graphical representations of the related cases in the graphical flow-chart format according to a set of rules. | 0.519802 |
9,996,537 | 1 | 4 | 1. A system for transforming media elements into a narrative comprising: a processor; a memory in communication with the processor; a clustering module in communication with the processor and the memory, the clustering module configured to: receive a dataset comprising a plurality of media elements each comprising metadata; and organize the plurality of media elements into a plurality of clusters based on the metadata, the plurality of clusters being organized into a clustering tree; and a narrative module in communication with the processor and the memory, the narrative module configured to create a narrative comprising a plurality of the media elements arranged into a narrative sequence, the narrative sequence being structured according to the clustering tree and for a predetermined duration, the narrative sequence comprising a story introduction for introducing the narrative, a geographic introduction for contextualizing a cluster, a time introduction for identifying a cluster time period, a show of media elements for representing the media elements, and a story end, wherein the narrative module is configured to create the narrative sequence according to an algorithm comprising specific durations for at least the following entries: story introduction (SI), geographic introduction (GI), time introduction (TI), show of photos, with a minimum duration for photo representation (P min ), and story end, given (SE guess ), where T is a total duration of the narrative, N is a total number of media elements in the dataset, and the algorithm produces the following result:
T>SI+GI+TI+P min +SE guess . | 1. A system for transforming media elements into a narrative comprising: a processor; a memory in communication with the processor; a clustering module in communication with the processor and the memory, the clustering module configured to: receive a dataset comprising a plurality of media elements each comprising metadata; and organize the plurality of media elements into a plurality of clusters based on the metadata, the plurality of clusters being organized into a clustering tree; and a narrative module in communication with the processor and the memory, the narrative module configured to create a narrative comprising a plurality of the media elements arranged into a narrative sequence, the narrative sequence being structured according to the clustering tree and for a predetermined duration, the narrative sequence comprising a story introduction for introducing the narrative, a geographic introduction for contextualizing a cluster, a time introduction for identifying a cluster time period, a show of media elements for representing the media elements, and a story end, wherein the narrative module is configured to create the narrative sequence according to an algorithm comprising specific durations for at least the following entries: story introduction (SI), geographic introduction (GI), time introduction (TI), show of photos, with a minimum duration for photo representation (P min ), and story end, given (SE guess ), where T is a total duration of the narrative, N is a total number of media elements in the dataset, and the algorithm produces the following result:
T>SI+GI+TI+P min +SE guess . 4. The system of claim 1 , wherein: the narrative sequence structure includes a fixed length of time for display of the narrative sequence; and the clustering module is configured to apportion the fixed length of time among each cluster according to at least one of the following criteria: proportional to a number of media elements present in each child cluster of the cluster; proportional to a number of clusters that are direct children or children of children of the cluster; proportional to a number of media elements including a detected face; proportional to a social media engagement level associated with at least one media element; proportional to at least one external data element associated with at least one media element; and a weighted average of at least two of the criteria. | 0.5 |
9,798,767 | 21 | 23 | 21. The method of claim 1 , further comprising: filtering the initial set of patent related publications and/or the correlated set of patent related publications based upon category information. | 21. The method of claim 1 , further comprising: filtering the initial set of patent related publications and/or the correlated set of patent related publications based upon category information. 23. The method of claim 21 wherein the category information is based upon at least one of a patent office classification information or a third party taxonomy. | 0.755385 |
9,940,365 | 30 | 34 | 30. The method of claim 19 , further comprising for each candidate table: accessing a set of static features for the candidate table; and deriving a set of ranking features for the candidate table from the set of static features and the one or more dynamic features; and wherein generating a ranking score for the candidate table comprises generating a ranking score for the candidate table from the set of ranking features for the candidate table. | 30. The method of claim 19 , further comprising for each candidate table: accessing a set of static features for the candidate table; and deriving a set of ranking features for the candidate table from the set of static features and the one or more dynamic features; and wherein generating a ranking score for the candidate table comprises generating a ranking score for the candidate table from the set of ranking features for the candidate table. 34. The method of claim 30 , wherein deriving a set of ranking features for the candidate table comprises: determining how many subject column values have keyword hits; and for each subject column value that has a keyword hit, determining how much the subject column value overlaps with the keyword hit. | 0.647674 |
8,060,639 | 1 | 10 | 1. A system for enabling searches of content at a server, comprising: a server which is configured to receive queries from clients for content; a communication protocol which enables an asynchronous connection between the clients and the server, and allows each client to communicate, under control of a user and as part of a session, a plurality of consecutive query strings to query the server for content; and a query and result cache that stores one or more query strings previously communicated from the clients, or content results previously returned from the server; wherein each of the clients provides an input field, which allows the user to enter as an input a query comprised of a plurality of consecutive query strings, and wherein the client transmits to the server within the session a plurality of queries to retrieve content from the server matching or related to the plurality of consecutive query strings, wherein each of the plurality of queries form an increasingly lengthening query string for retrieving content from the server; and wherein the server receives the plurality of queries from the requesting client, and in response to receiving each of one or more additional characters in the increasingly lengthening query string as they are being entered at the input field, automatically matches the increasingly lengthening query string initially by matching the query string against the content of the query and result cache, and subsequently by matching the query string against other content available to the server, and asynchronously returns, while the increasingly lengthening query string is being entered by the user at the input field at the client, increasingly relevant content to the client. | 1. A system for enabling searches of content at a server, comprising: a server which is configured to receive queries from clients for content; a communication protocol which enables an asynchronous connection between the clients and the server, and allows each client to communicate, under control of a user and as part of a session, a plurality of consecutive query strings to query the server for content; and a query and result cache that stores one or more query strings previously communicated from the clients, or content results previously returned from the server; wherein each of the clients provides an input field, which allows the user to enter as an input a query comprised of a plurality of consecutive query strings, and wherein the client transmits to the server within the session a plurality of queries to retrieve content from the server matching or related to the plurality of consecutive query strings, wherein each of the plurality of queries form an increasingly lengthening query string for retrieving content from the server; and wherein the server receives the plurality of queries from the requesting client, and in response to receiving each of one or more additional characters in the increasingly lengthening query string as they are being entered at the input field, automatically matches the increasingly lengthening query string initially by matching the query string against the content of the query and result cache, and subsequently by matching the query string against other content available to the server, and asynchronously returns, while the increasingly lengthening query string is being entered by the user at the input field at the client, increasingly relevant content to the client. 10. The system of claim 1 , wherein said client or a client software adds an additional qualifier value to the query strings that are passed to the server, and wherein the server can use said qualifier to execute the query on said content engines, and return appropriate results based on both each query string and the qualifier value. | 0.589461 |
7,870,087 | 60 | 61 | 60. The method of claim 59 wherein the analog processor includes at least one quantum processor including a plurality of qubits and a plurality of coupling devices coupling respective pairs of qubits, the at least one quantum processor capable of evolving to the final state, and wherein setting a number of parameters of the analog processor to embed the subproblem into the analog processor includes setting parameters of at least some of the qubits and the coupling devices. | 60. The method of claim 59 wherein the analog processor includes at least one quantum processor including a plurality of qubits and a plurality of coupling devices coupling respective pairs of qubits, the at least one quantum processor capable of evolving to the final state, and wherein setting a number of parameters of the analog processor to embed the subproblem into the analog processor includes setting parameters of at least some of the qubits and the coupling devices. 61. The method of claim 60 , further comprising: evolving the quantum processor to the final state. | 0.5 |
8,918,308 | 1 | 13 | 1. A method implemented in a computer infrastructure, comprising: receiving a search query containing a transliterated word; determining a source language corresponding to the transliterated word, wherein the determining the source language comprises: determining a weighted score for each one of a plurality of candidate languages, and designating the candidate language with the highest weighted score as the source language; converting the transliterated word to a word in the source language; translating the word in the source language to a word in a target language; and performing a search using the word in the target language wherein the weighted score of each one of the plurality of candidate languages is based on: correlating user-entered words against language-specific dictionaries; environmental variables; location-based services; and user profile and/or history. | 1. A method implemented in a computer infrastructure, comprising: receiving a search query containing a transliterated word; determining a source language corresponding to the transliterated word, wherein the determining the source language comprises: determining a weighted score for each one of a plurality of candidate languages, and designating the candidate language with the highest weighted score as the source language; converting the transliterated word to a word in the source language; translating the word in the source language to a word in a target language; and performing a search using the word in the target language wherein the weighted score of each one of the plurality of candidate languages is based on: correlating user-entered words against language-specific dictionaries; environmental variables; location-based services; and user profile and/or history. 13. The method of claim 1 , wherein the transliterated word comprises a first transliterated word and the search query contains a second transliterated word, and further comprising: determining a second source language corresponding to the second transliterated word; converting the second transliterated word to a word in the second source language; translating the word in the second source language to a second word in the target language; and performing the search using the word in the target language and the second word in the target language. | 0.5 |
9,324,034 | 11 | 12 | 11. The server of claim 10 , wherein the processor is configured with processor-executable instructions to perform operations further comprising: applying the factors to the corpus of behavior vectors to generate a second family of classifier models that identify fewer data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is not benign; and generating a mobile device classifier based on the second family of classifier models. | 11. The server of claim 10 , wherein the processor is configured with processor-executable instructions to perform operations further comprising: applying the factors to the corpus of behavior vectors to generate a second family of classifier models that identify fewer data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is not benign; and generating a mobile device classifier based on the second family of classifier models. 12. The server of claim 11 , wherein the processor is configured with processor-executable instructions to perform operations further comprising: sending the generated mobile device classifier to a mobile computing device. | 0.5 |
8,073,258 | 9 | 10 | 9. The method of claim 8 , further comprising, displaying the selected candidate in a results pane of the graphical user interface. | 9. The method of claim 8 , further comprising, displaying the selected candidate in a results pane of the graphical user interface. 10. The method of claim 9 , further comprising, evaluating a mathematical expression contained in the selected candidate. | 0.5 |
9,934,785 | 10 | 12 | 10. A system for processing audio signals, comprising: at least one processor configured to: retrieve at least one content object generated by an audio signal processing system, the at least one content object including (1) one or more text objects corresponding to one or more words and (2) one or more text object data elements representing (i) a type representing the one or more text objects as at least one of a sentence, a verb, a noun phrase a determiner, and an adjective (ii) an emphasis level value representing a level of emphasis of the one or more words, correspondingly and (iii) an electronically generated confidence level value representing a confidence level that the audio signal processing system has properly identified the one or more words from an audio signal; retrieve content metadata, generated by the audio signal processing system, the content metadata corresponding to the one or more words and including (i) one or more content metadata objects and (ii) a content metadata object confidence level value representing the confidence level that the audio signal processing system has properly identified the one or more content metadata objects, wherein the one or more content metadata objects include at least one of an emotion object, a gender object, an age object and an accent object; retrieve environmental metadata, generated by the audio signal processing system, the environmental metadata corresponding to the background noise and including (i) one or more environmental metadata objects and (ii) an environmental metadata object confidence level value representing the audio signal processing system has properly identified the one or more environmental metadata objects from the audio signal; retrieve, from a profile database, a user profile containing historical listening practices of a user; determine, based on the user profile, the at least one content object, the content metadata and the environmental metadata, at least one of (i) media content and (ii) a recommended next media track; and cause at least one of the media content and the recommended next media track to be output on an electronic device, wherein the outputting comprises playing the media content or displaying the recommended next media track, correspondingly, wherein when the recommended next media track is selected, media content corresponding to the recommended next media track is played. | 10. A system for processing audio signals, comprising: at least one processor configured to: retrieve at least one content object generated by an audio signal processing system, the at least one content object including (1) one or more text objects corresponding to one or more words and (2) one or more text object data elements representing (i) a type representing the one or more text objects as at least one of a sentence, a verb, a noun phrase a determiner, and an adjective (ii) an emphasis level value representing a level of emphasis of the one or more words, correspondingly and (iii) an electronically generated confidence level value representing a confidence level that the audio signal processing system has properly identified the one or more words from an audio signal; retrieve content metadata, generated by the audio signal processing system, the content metadata corresponding to the one or more words and including (i) one or more content metadata objects and (ii) a content metadata object confidence level value representing the confidence level that the audio signal processing system has properly identified the one or more content metadata objects, wherein the one or more content metadata objects include at least one of an emotion object, a gender object, an age object and an accent object; retrieve environmental metadata, generated by the audio signal processing system, the environmental metadata corresponding to the background noise and including (i) one or more environmental metadata objects and (ii) an environmental metadata object confidence level value representing the audio signal processing system has properly identified the one or more environmental metadata objects from the audio signal; retrieve, from a profile database, a user profile containing historical listening practices of a user; determine, based on the user profile, the at least one content object, the content metadata and the environmental metadata, at least one of (i) media content and (ii) a recommended next media track; and cause at least one of the media content and the recommended next media track to be output on an electronic device, wherein the outputting comprises playing the media content or displaying the recommended next media track, correspondingly, wherein when the recommended next media track is selected, media content corresponding to the recommended next media track is played. 12. The system according to claim 10 , wherein the environmental metadata indicates aspects of a physical environment in which the audio signal is input. | 0.858333 |
9,348,811 | 1 | 4 | 1. A method performed with a computing system for obtaining information from a set of related electronic documents, the method comprising: accessing the set of related electronic documents that are each hosted on one or more respective web servers that are accessible through a network, the accessing including retrieving data associated with the set of related electronic documents through the network; analyzing markup language of an electronic document of the set of related electronic documents to identify markup language tags of the electronic document; analyzing, using a page recognition module, the markup language tags to identify the electronic document as a product page, the page recognition model generated based on a first machine learning algorithm, and the product page comprising a plurality of terms; filtering the plurality of terms into a first set of terms and a second set of terms, the first set of terms and the second set of terms including different terms of the plurality of terms, each term in the first set of terms identified as potentially being associated with a product name, and each term in the second set of terms identified as not being associated with a product name; for each term of the first set of terms, identifying a noun phrase that includes the term and determining one or more features of each of the noun phrase and the term; for each feature of the one or more features: determining, for each term of the first of terms, a first feature value of the noun phrase and a second feature value of the term, and determining, for each term of the first set of terms, an overall feature value for the term based on the first feature value and the second feature value; identifying each term in the first set of terms as being associated with a product name or not being associated with a product name with a name recognition model, the name recognition model generated based on the overall feature value for each feature of the term; and providing for display on a graphical user interface, one or more of the first set of terms that are identified as being associated with a product name. | 1. A method performed with a computing system for obtaining information from a set of related electronic documents, the method comprising: accessing the set of related electronic documents that are each hosted on one or more respective web servers that are accessible through a network, the accessing including retrieving data associated with the set of related electronic documents through the network; analyzing markup language of an electronic document of the set of related electronic documents to identify markup language tags of the electronic document; analyzing, using a page recognition module, the markup language tags to identify the electronic document as a product page, the page recognition model generated based on a first machine learning algorithm, and the product page comprising a plurality of terms; filtering the plurality of terms into a first set of terms and a second set of terms, the first set of terms and the second set of terms including different terms of the plurality of terms, each term in the first set of terms identified as potentially being associated with a product name, and each term in the second set of terms identified as not being associated with a product name; for each term of the first set of terms, identifying a noun phrase that includes the term and determining one or more features of each of the noun phrase and the term; for each feature of the one or more features: determining, for each term of the first of terms, a first feature value of the noun phrase and a second feature value of the term, and determining, for each term of the first set of terms, an overall feature value for the term based on the first feature value and the second feature value; identifying each term in the first set of terms as being associated with a product name or not being associated with a product name with a name recognition model, the name recognition model generated based on the overall feature value for each feature of the term; and providing for display on a graphical user interface, one or more of the first set of terms that are identified as being associated with a product name. 4. The method of claim 1 , wherein identifying a product page comprises: accessing an electronic document associated with the set of related electronic documents; determining features associated with the electronic document; and analyzing the features to determine whether the electronic document is a product page using the page recognition model. | 0.671698 |
9,037,452 | 1 | 6 | 1. A method comprising: automatically collecting training data from manually created semantic relations using at least one computerized device; automatically extracting rules from said training data to produce extracted rules using said computerized device, said extracting rules comprising automatically removing noisy data from said semantic relations by filtering out ones of rules that do not occur in said training data; automatically characterizing existing semantic relations in said training data based on co-occurrence of said extracted rules in said existing semantic relations using said computerized device; automatically constructing semantic relation topics based on said characterizing of said existing semantic relations using said computerized device; and grouping instances of said training data into said semantic relation topics to detect new semantic relations using said computerized device. | 1. A method comprising: automatically collecting training data from manually created semantic relations using at least one computerized device; automatically extracting rules from said training data to produce extracted rules using said computerized device, said extracting rules comprising automatically removing noisy data from said semantic relations by filtering out ones of rules that do not occur in said training data; automatically characterizing existing semantic relations in said training data based on co-occurrence of said extracted rules in said existing semantic relations using said computerized device; automatically constructing semantic relation topics based on said characterizing of said existing semantic relations using said computerized device; and grouping instances of said training data into said semantic relation topics to detect new semantic relations using said computerized device. 6. The method according to claim 1 , each of said rules comprising relationships between two logical arguments, and each of said rules having components comprising argument1 type, argument2 type, noun, preposition and verb. | 0.791589 |
8,234,315 | 7 | 8 | 7. A computer-implemented method of generating a document for transmission to a computing device to enable the computing device to mount a virtual drive representing a folder tree, the method comprising: querying, by a computing device, a folder database for information associated with a root folder of the folder tree; authenticating that a user has rights to access the root folder; adding metadata associated with the root folder to the document; and for each subfolder within the hierarchy of the root folder: in response to determining that the subfolder has the same permissions with respect to the user as the root folder: obtaining folder metadata of the subfolder; and adding metadata associated with the subfolder to the document; and in response to determining that the subfolder has different permissions with respect to the user as the root folder: adding an XLink that references the subfolder to the document. | 7. A computer-implemented method of generating a document for transmission to a computing device to enable the computing device to mount a virtual drive representing a folder tree, the method comprising: querying, by a computing device, a folder database for information associated with a root folder of the folder tree; authenticating that a user has rights to access the root folder; adding metadata associated with the root folder to the document; and for each subfolder within the hierarchy of the root folder: in response to determining that the subfolder has the same permissions with respect to the user as the root folder: obtaining folder metadata of the subfolder; and adding metadata associated with the subfolder to the document; and in response to determining that the subfolder has different permissions with respect to the user as the root folder: adding an XLink that references the subfolder to the document. 8. The method of claim 7 , wherein the document is an extensible markup language (XML) document. | 0.725714 |
7,546,334 | 58 | 59 | 58. An information processing system as claimed in claim 49 wherein said means for retrieving includes means for retrieving contextual words, characters or data objects from said compilation of additional data related to said security sensitive words, characters or data objects based upon predetermined statistical analysis of said additional data relative to said security sensitive words, characters or data objects. | 58. An information processing system as claimed in claim 49 wherein said means for retrieving includes means for retrieving contextual words, characters or data objects from said compilation of additional data related to said security sensitive words, characters or data objects based upon predetermined statistical analysis of said additional data relative to said security sensitive words, characters or data objects. 59. An information processing system as claimed in claim 58 wherein said means for retrieving includes means for retrieving semiotic words, characters or data objects from said compilation of additional data related to said security sensitive words, characters or data objects and is based upon synonyms, antonyms, and pseudonyms of said security sensitive words, characters or data objects; syntactics of said security sensitive words, characters or data objects as reflected in said compilation of additional data; and pragmatics of said security sensitive words, characters or data objects as reflected in said compilation of additional data. | 0.5 |
8,515,923 | 1 | 15 | 1. A document repository management (DRM) system for an institution having a defined organization, said DRM system comprising: an input device for receiving a document access request; an electronically readable organization chart and organizational chart information including information identifying individuals on said organization chart; an electronic document repository containing accessible documents; a controller that: controls requested access to each document of said accessible documents in said electronic document repository; and maps to said electronically readable organization chart and said organizational chart information, each said requested access to said each document of said accessible documents; and a display device that displays a document usage summary chart history based on historical tracking of actual usage and treatment of each document by individuals and groups on said organization chart. | 1. A document repository management (DRM) system for an institution having a defined organization, said DRM system comprising: an input device for receiving a document access request; an electronically readable organization chart and organizational chart information including information identifying individuals on said organization chart; an electronic document repository containing accessible documents; a controller that: controls requested access to each document of said accessible documents in said electronic document repository; and maps to said electronically readable organization chart and said organizational chart information, each said requested access to said each document of said accessible documents; and a display device that displays a document usage summary chart history based on historical tracking of actual usage and treatment of each document by individuals and groups on said organization chart. 15. The DRM System of claim 1 , wherein said controller identifies, summarizes and tracks each said requested access for a particular document as part of a count of all requested accesses for said particular document within each department within said electronically readable organization chart. | 0.539063 |
8,214,196 | 1 | 8 | 1. A method for translating natural languages using a statistical translation system, the method comprising: parsing a first string in a first language into a parse tree using a statistical parser included in the statistical machine translation system, the parse tree including a plurality of nodes, one or more of said nodes including one or more leafs, each leaf including a first word in the first language, the nodes including child nodes having labels; determining a plurality of possible reorderings of one or more of said child nodes including one or more of the leafs using the statistical translation system, the reordering performed in response to a probability corresponding to a sequence of the child node labels; determining a probability between 0.0000% and 100.0000%, non-inclusive, of the possible reorderings by the statistical translation system; determining a plurality of possible insertions of one or more words at one or more of said nodes using the statistical translation system; determining a probability between 0.0000% and 100.0000%, non-inclusive, of the possible insertions of one or more words at one or more of said nodes by the statistical translation system; translating the first word at each leaf into a second word corresponding to a possible translation in a second language using the statistical translation system; and determining a total probability between 0.0000% and 100.0000%, non-inclusive, based on the reordering, the inserting, and the translating by the statistical translation system. | 1. A method for translating natural languages using a statistical translation system, the method comprising: parsing a first string in a first language into a parse tree using a statistical parser included in the statistical machine translation system, the parse tree including a plurality of nodes, one or more of said nodes including one or more leafs, each leaf including a first word in the first language, the nodes including child nodes having labels; determining a plurality of possible reorderings of one or more of said child nodes including one or more of the leafs using the statistical translation system, the reordering performed in response to a probability corresponding to a sequence of the child node labels; determining a probability between 0.0000% and 100.0000%, non-inclusive, of the possible reorderings by the statistical translation system; determining a plurality of possible insertions of one or more words at one or more of said nodes using the statistical translation system; determining a probability between 0.0000% and 100.0000%, non-inclusive, of the possible insertions of one or more words at one or more of said nodes by the statistical translation system; translating the first word at each leaf into a second word corresponding to a possible translation in a second language using the statistical translation system; and determining a total probability between 0.0000% and 100.0000%, non-inclusive, based on the reordering, the inserting, and the translating by the statistical translation system. 8. The method of claim 1 , further comprising: generating a second string including the second word at each leaf. | 0.773092 |
9,251,255 | 1 | 5 | 1. A method of improving music search responsiveness, comprising: monitoring, at a fast search server, for a partial entry including at least a first three characters of a query of music data: receiving, at the fast search server, the partial entry of the query of music data, the partial entry including at least the first three characters of the query upon an occurrence of at least one event, wherein the at least one event is when an amount of time between keystrokes exceed a threshold amount of time between keystrokes or when the partial entry includes a threshold number of characters; determining, from a spelling index communicatively coupled to the fast search server, whether the partial entry of the query of music data is a misspelling and correcting any misspelling; receiving, at the fast search server, a data record of a music database upon the fast search server referencing at least the first three characters of the partial entry with a reverse index stored in the fast search server and determining that at least the first three characters of the partial entry matches the data record of the music database, the reverse index being created from a combination of letters appearing as a string in a data field of the music database and a plurality of data fields of the music database including an artist field, an album field, and a track; and transmitting, from the fast search server, the data record. | 1. A method of improving music search responsiveness, comprising: monitoring, at a fast search server, for a partial entry including at least a first three characters of a query of music data: receiving, at the fast search server, the partial entry of the query of music data, the partial entry including at least the first three characters of the query upon an occurrence of at least one event, wherein the at least one event is when an amount of time between keystrokes exceed a threshold amount of time between keystrokes or when the partial entry includes a threshold number of characters; determining, from a spelling index communicatively coupled to the fast search server, whether the partial entry of the query of music data is a misspelling and correcting any misspelling; receiving, at the fast search server, a data record of a music database upon the fast search server referencing at least the first three characters of the partial entry with a reverse index stored in the fast search server and determining that at least the first three characters of the partial entry matches the data record of the music database, the reverse index being created from a combination of letters appearing as a string in a data field of the music database and a plurality of data fields of the music database including an artist field, an album field, and a track; and transmitting, from the fast search server, the data record. 5. The method of claim 1 , wherein the threshold number of characters is four. | 0.952381 |
7,543,286 | 1 | 7 | 1. A method for mapping a tag in a markup language (ML) document to a class using namespaces, comprising: analyzing a tag in the ML document; referencing a definition file location attribute in the ML document, wherein the definition file location attribute is identified by the tag; retrieving a definition file from a storage location identified by the definition file location attribute, wherein the definition file includes: a schema that limits the scope of attributes in the definition file, a list of assemblies that references the definition file, a list of common language runtime namespaces associated with the list of assemblies that references the definition file, wherein each common language runtime namespace includes a list of common language classes associated with the common language runtime namespace, and an installation tag that includes a uniform resource identifier for installing assemblies of the list of assemblies; referencing a common language runtime namespace related to the tag within the definition file to determine the common language runtime class associated with the tag; and locating the common language runtime class in an assembly such that the tag is mapped to the common language runtime class. | 1. A method for mapping a tag in a markup language (ML) document to a class using namespaces, comprising: analyzing a tag in the ML document; referencing a definition file location attribute in the ML document, wherein the definition file location attribute is identified by the tag; retrieving a definition file from a storage location identified by the definition file location attribute, wherein the definition file includes: a schema that limits the scope of attributes in the definition file, a list of assemblies that references the definition file, a list of common language runtime namespaces associated with the list of assemblies that references the definition file, wherein each common language runtime namespace includes a list of common language classes associated with the common language runtime namespace, and an installation tag that includes a uniform resource identifier for installing assemblies of the list of assemblies; referencing a common language runtime namespace related to the tag within the definition file to determine the common language runtime class associated with the tag; and locating the common language runtime class in an assembly such that the tag is mapped to the common language runtime class. 7. The method of claim 1 , wherein retrieving the definition file further comprises retrieving the definition file from a network location specified by the definition file location attribute. | 0.719118 |
9,798,980 | 9 | 15 | 9. A non-transitory computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation for inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users, the operation comprising: receiving, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user, wherein each multimedia object includes one or more attributes imputed to that object based on the metadata and wherein each multimedia object is one of an image or a video; identifying one or more concepts from the one or more attributes, wherein each concept includes at least a first attribute that co-occurs with a second attribute imputed to a first multimedia object; and associating a first one of the plurality of users with at least one of the concepts based on the attributes imputed to multimedia objects associated with the first one of the plurality of users. | 9. A non-transitory computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation for inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users, the operation comprising: receiving, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user, wherein each multimedia object includes one or more attributes imputed to that object based on the metadata and wherein each multimedia object is one of an image or a video; identifying one or more concepts from the one or more attributes, wherein each concept includes at least a first attribute that co-occurs with a second attribute imputed to a first multimedia object; and associating a first one of the plurality of users with at least one of the concepts based on the attributes imputed to multimedia objects associated with the first one of the plurality of users. 15. The computer-readable storage medium of claim 9 , wherein the concepts are identified based on Latent Dirichlet Allocation (LDA). | 0.843897 |
8,538,750 | 25 | 33 | 25. The speech communication apparatus according to claim 24 , wherein the audio output unit is further configured to output auditory communications based on a conversation history with the conversation partner. | 25. The speech communication apparatus according to claim 24 , wherein the audio output unit is further configured to output auditory communications based on a conversation history with the conversation partner. 33. The speech communication apparatus according to claim 25 , wherein a conversation partner speech utterance is accepted in response to the touch sensing unit recognizing the touch input by the conversation partner. | 0.599631 |
8,788,270 | 51 | 52 | 51. The method according to claim 49 , wherein the subject utterance of speech comprises the training utterance of speech. | 51. The method according to claim 49 , wherein the subject utterance of speech comprises the training utterance of speech. 52. The method according to claim 51 , wherein determining the emotion state of the speaker based on the comparison occurs within one day of receiving the subject utterance of speech by the speaker. | 0.507463 |
8,918,320 | 11 | 12 | 11. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following: determine one or more locations of the apparatus and one or more times that the locations were determined in response to capturing voice data of speech content associated with one or more spoken reviews of one or more entities; analyze textual data and acoustic data corresponding to the voice data to detect whether the textual data or the acoustic data comprises one or more words indicating at least one sentiment of a user that spoke the speech content; and generate a review of at least one of the entities corresponding to one of the spoken reviews based in part on assigning at least one predefined sentiment to at least one of the words in response to detecting that the word indicates the sentiment of the user. | 11. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following: determine one or more locations of the apparatus and one or more times that the locations were determined in response to capturing voice data of speech content associated with one or more spoken reviews of one or more entities; analyze textual data and acoustic data corresponding to the voice data to detect whether the textual data or the acoustic data comprises one or more words indicating at least one sentiment of a user that spoke the speech content; and generate a review of at least one of the entities corresponding to one of the spoken reviews based in part on assigning at least one predefined sentiment to at least one of the words in response to detecting that the word indicates the sentiment of the user. 12. The apparatus of claim 11 , wherein prior to generate the review, the memory and computer program code are configured to, with the processor, cause the apparatus to: analyze textual words of the textual data to assign a textual sentiment to the word and analyze spoken words of the acoustic data to assign an acoustic sentiment to the word based on detecting that the word indicates the sentiment of the user; and utilize the textual sentiment and the acoustic sentiment in the assigning of the predefined sentiment to the word, the sentiment of the user denotes at least one opinion of the user regarding the entity. | 0.5 |
5,515,455 | 16 | 19 | 16. Apparatus for recognizing a static handwritten word of cursive script, comprising; means for reading said word and forming a bit map of pixels representing said word; means for skeletonizing said word within said bit map; means for segmenting said skeletonized word into one or more primitives, said skeletonized word including a plurality of feature points and said primitives each comprising a continuous segment of said skeletonized word extending between an original feature point and a terminal feature point; means for forming a sequence representing the order in which said primitives were written by ordering said primitives in succession beginning at the left side of said word; and means for classifying said word by comparing said primitives and said sequence with each of a plurality of stored primitives and their associated sequences for known words, wherein said means for forming a sequence comprises: means for locating a primitive which is left-most in said word, examining said left-most primitive for the presence of one or more of said end points and designating said left-most primitive as a first primitive if it contains one or more of said end points; means for examining a primitive connected with said left-most primitive for the presence of an end point if said left-most primitive does not contain one or more of said end points, and for designating said connected primitive as said first primitive if it contains an end point and designating said left-most primitive as said first primitive if said connected primitive does not contain an end point; and means for ordinally designating as subsequent primitives each of said primitives which are connected with said first primitive and with said subsequent primitive. | 16. Apparatus for recognizing a static handwritten word of cursive script, comprising; means for reading said word and forming a bit map of pixels representing said word; means for skeletonizing said word within said bit map; means for segmenting said skeletonized word into one or more primitives, said skeletonized word including a plurality of feature points and said primitives each comprising a continuous segment of said skeletonized word extending between an original feature point and a terminal feature point; means for forming a sequence representing the order in which said primitives were written by ordering said primitives in succession beginning at the left side of said word; and means for classifying said word by comparing said primitives and said sequence with each of a plurality of stored primitives and their associated sequences for known words, wherein said means for forming a sequence comprises: means for locating a primitive which is left-most in said word, examining said left-most primitive for the presence of one or more of said end points and designating said left-most primitive as a first primitive if it contains one or more of said end points; means for examining a primitive connected with said left-most primitive for the presence of an end point if said left-most primitive does not contain one or more of said end points, and for designating said connected primitive as said first primitive if it contains an end point and designating said left-most primitive as said first primitive if said connected primitive does not contain an end point; and means for ordinally designating as subsequent primitives each of said primitives which are connected with said first primitive and with said subsequent primitive. 19. The apparatus according to claim 16 wherein said classifying comprises: means for determining the length and direction of each of said primitives; and means for comparing said length, direction and sequence of said primitives for said word with primitives of words stored in a memory device and for generating a list of words having a high probability of matching said word. | 0.675258 |
7,574,449 | 1 | 6 | 1. A computer storage medium having computer-executable instructions when executed by a computer cause the computer to perform steps comprising: receiving a first article for which to identify matching content; analyzing a set of raw text of the first article; analyzing a set of formatted text of the first article; analyzing one or more links contained in the first article; including the results of the analyzing the raw text step, the analyzing the formatted text step, and the analyzing the links step in a vector array; and using the vector array at least in part to find one or more other articles that are related to the first article. | 1. A computer storage medium having computer-executable instructions when executed by a computer cause the computer to perform steps comprising: receiving a first article for which to identify matching content; analyzing a set of raw text of the first article; analyzing a set of formatted text of the first article; analyzing one or more links contained in the first article; including the results of the analyzing the raw text step, the analyzing the formatted text step, and the analyzing the links step in a vector array; and using the vector array at least in part to find one or more other articles that are related to the first article. 6. The computer storage medium of claim 1 , wherein the analyzing the formatted text step further comprises the step of: weighting title tags more than bolded text, and weighting bolded text more than regular text. | 0.556017 |
8,881,041 | 22 | 23 | 22. The apparatus of claim 15 , wherein, if the current UI mode is the physics animation mode and the animation type is the shape modification type, the at least one property comprises physical UI elements and the UI information extraction unit extracts the physical UI elements, the physical UI elements comprising a shape of the UI object and an intensity and a direction of force applied to the UI object, and the UI information translation unit translates the current UI information to non-physical UI elements, the non-physical UI elements comprising an initial shape and location where the UI object is motionless. | 22. The apparatus of claim 15 , wherein, if the current UI mode is the physics animation mode and the animation type is the shape modification type, the at least one property comprises physical UI elements and the UI information extraction unit extracts the physical UI elements, the physical UI elements comprising a shape of the UI object and an intensity and a direction of force applied to the UI object, and the UI information translation unit translates the current UI information to non-physical UI elements, the non-physical UI elements comprising an initial shape and location where the UI object is motionless. 23. The apparatus of claim 22 , wherein the initial shape of the UI object when the UI object is motionless comprises a modification to the UI object or pieces of the UI object when the UI object is broken. | 0.5 |
10,147,424 | 2 | 4 | 2. The method of claim 1 , wherein the uplift model is trained using a set of A/B test results corresponding to a plurality of spoken queries referencing the identified subject matter and using a set of user attributes comprising at least one user attribute corresponding to each of the plurality of spoken queries. | 2. The method of claim 1 , wherein the uplift model is trained using a set of A/B test results corresponding to a plurality of spoken queries referencing the identified subject matter and using a set of user attributes comprising at least one user attribute corresponding to each of the plurality of spoken queries. 4. The method of claim 2 , wherein the set of user attributes comprise emotional state attributes. | 0.819853 |
7,559,052 | 26 | 28 | 26. The computer readable medium of claim 25 wherein the one or more entities are messages. | 26. The computer readable medium of claim 25 wherein the one or more entities are messages. 28. The computer readable medium of claim 26 wherein said physical meta-model further comprises at least one set of derived classes for storing physical entity meta-data specific to a particular physical representation, said at least one set of derived classes being descendent from said base classes. | 0.5 |
9,704,177 | 13 | 14 | 13. The spam avatar identification system as defined in claim 12 , further comprising a spam avatar identification table storable in the memory unit and executable by the virtual universe processing unit, the spam avatar identification table configured to store a unique identifier of known spam avatars. | 13. The spam avatar identification system as defined in claim 12 , further comprising a spam avatar identification table storable in the memory unit and executable by the virtual universe processing unit, the spam avatar identification table configured to store a unique identifier of known spam avatars. 14. The spam avatar identification system as defined in claim 13 , further comprising a behavior characteristic of known spam avatars table storable in the memory unit and executable by the virtual universe processing unit, the spam avatar identification table configured to store behavior characteristics of known spam avatars. | 0.5 |
9,852,124 | 7 | 8 | 7. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: analyzing a plurality of words included in a plurality of electronic documents, wherein the analysis identifies at least one of the plurality of words as being a portmanteau; identifying a plurality of morphemes included in the identified portmanteau and one or more candidate words that correspond to each of the identified morphemes, wherein each of the candidate words has a candidate word meaning; and deriving a portmanteau meaning corresponding to the identified portmanteau from at least two of the candidate word meanings, wherein the deriving comprises: calculating a first string metric between a leading morpheme that corresponds to a first portion of the portmanteau and the candidate word corresponding to the leading morpheme; calculating a second string metric between a trailing morpheme that corresponds to a second portion of the portmanteau and the candidate word corresponding to the trailing morpheme; and analyzing a combined usage of the leading morpheme and the trailing morpheme by combining the first and second string metric, wherein the combined usage is based on the combination of the first and second string metric; and storing the identified portmanteau and the derived portmanteau meaning in a dictionary utilized by a question answering (QA) system, wherein the QA system utilizes the derived portmanteau meaning to understand and answer one or more questions corresponding to the identified portmanteau. | 7. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: analyzing a plurality of words included in a plurality of electronic documents, wherein the analysis identifies at least one of the plurality of words as being a portmanteau; identifying a plurality of morphemes included in the identified portmanteau and one or more candidate words that correspond to each of the identified morphemes, wherein each of the candidate words has a candidate word meaning; and deriving a portmanteau meaning corresponding to the identified portmanteau from at least two of the candidate word meanings, wherein the deriving comprises: calculating a first string metric between a leading morpheme that corresponds to a first portion of the portmanteau and the candidate word corresponding to the leading morpheme; calculating a second string metric between a trailing morpheme that corresponds to a second portion of the portmanteau and the candidate word corresponding to the trailing morpheme; and analyzing a combined usage of the leading morpheme and the trailing morpheme by combining the first and second string metric, wherein the combined usage is based on the combination of the first and second string metric; and storing the identified portmanteau and the derived portmanteau meaning in a dictionary utilized by a question answering (QA) system, wherein the QA system utilizes the derived portmanteau meaning to understand and answer one or more questions corresponding to the identified portmanteau. 8. The computer program product of claim 7 wherein the actions further comprise: selecting a leading candidate word from the plurality of candidate words, wherein the leading candidate word corresponds to the leading morpheme; selecting a trailing candidate word from the plurality of candidate words, wherein the trailing candidate word corresponds to the trailing morpheme; and generating a possible meaning of the portmanteau by combining a first meaning that corresponds to the leading candidate word with a second meaning that corresponds to the trailing candidate word, wherein the combined usage is based on the generated possible meaning. | 0.5 |
7,707,245 | 18 | 20 | 18. A hardware apparatus comprising a metasearch engine for metasearching on a distributed network activated by a request executed on a client device to request the metasearch engine to send a plurality of search queries comprising at least two keyword phrases to a plurality of server devices, each search query of the plurality of search queries comprising a keyword phrase of the at least two keyword phrases, each of the at least two keyword phrases comprising at least one keyword, comprising: (a) a receiver receiving, at the metasearch engine, the request from the client device for the metasearch engine to send the plurality of search queries comprising the at least two keyword phrases to the plurality of server devices; (b) a sender sending, by the metasearch engine, the plurality of search queries comprising the at least two keyword phrases to the plurality of server devices; (c) the receiver receiving, at the metasearch engine, search results from the plurality of server devices in response to the plurality of search queries comprising the at least two keyword phrases sent to the plurality of server devices; (d) an incorporator incorporating, by the metasearch engine, the received search results into at least two different order books corresponding to the at least two keyword phrases; (e) the incorporator incorporating, by the metasearch engine, the at least two different order books of received search results into a response for communicating to the client device; (f) a communicator communicating, by the metasearch engine, the response from the metasearch engine to the client device. | 18. A hardware apparatus comprising a metasearch engine for metasearching on a distributed network activated by a request executed on a client device to request the metasearch engine to send a plurality of search queries comprising at least two keyword phrases to a plurality of server devices, each search query of the plurality of search queries comprising a keyword phrase of the at least two keyword phrases, each of the at least two keyword phrases comprising at least one keyword, comprising: (a) a receiver receiving, at the metasearch engine, the request from the client device for the metasearch engine to send the plurality of search queries comprising the at least two keyword phrases to the plurality of server devices; (b) a sender sending, by the metasearch engine, the plurality of search queries comprising the at least two keyword phrases to the plurality of server devices; (c) the receiver receiving, at the metasearch engine, search results from the plurality of server devices in response to the plurality of search queries comprising the at least two keyword phrases sent to the plurality of server devices; (d) an incorporator incorporating, by the metasearch engine, the received search results into at least two different order books corresponding to the at least two keyword phrases; (e) the incorporator incorporating, by the metasearch engine, the at least two different order books of received search results into a response for communicating to the client device; (f) a communicator communicating, by the metasearch engine, the response from the metasearch engine to the client device. 20. The hardware apparatus of claim 18 , further comprising: (g) the receiver receiving, at the metasearch engine, another request from the client device executed on the client device for ordering at least one item; (h) a processor processing and/or placing, by the metasearch engine, at least one order for the at least one item. | 0.679612 |
10,122,670 | 8 | 9 | 8. The method as recited in claim 1 , further comprising: identifying one or more social network profile settings associated with the recipient; and wherein determining whether to generate the second version of the electronic message in the second language for the recipient is further based on the identified one or more social network profile settings associated with the recipient. | 8. The method as recited in claim 1 , further comprising: identifying one or more social network profile settings associated with the recipient; and wherein determining whether to generate the second version of the electronic message in the second language for the recipient is further based on the identified one or more social network profile settings associated with the recipient. 9. The method as recited in claim 8 , wherein identifying one or more social network profile settings associated with the recipient comprises identifying one or more of a preferred language of the recipient, or a location setting of the recipient. | 0.5 |
9,081,777 | 1 | 8 | 1. A computer-implemented method for displaying multi-dimensional social media content, comprising: collecting social media content from a plurality of remote social media providers and storing said social media content in a memory location; identifying, with the aid of a computer processor operatively coupled to said memory location, social entities, social contributors and social tags in said collected social media content, wherein said social contributors have posted said social media content on said plurality of remote social media providers, wherein each of said social entities is a subject of an individual social media content, and wherein said social entities are different from said social contributors for said social media content posted on said plurality of remote social media providers; establishing, with the aid of a computer processor, relationships between said identified social entities, social contributors and social tags, which relationships are established based upon social tags present in social media content of social entities and social contributors; calculating a social engagement score and a social post score, and calculating from said calculated social engagement score and social post score a social score associated with each media content of said collected and organized social media content, wherein said social engagement score is based on a social relevance of said social contributors and social entities and is determined by the number of friends, fans, followers, or other measurement that assesses a social network presence of said social contributors and social entities, wherein said social post score is based on a degree of interaction of a social contributor or a social entity among said social contributors and social entities with social posts at one or more remote social media providers, wherein said degree of interaction is determined as a net of positive and any negative interactions by said social contributor or social entity with respect to said social posts on said one or more remote social media providers; organizing, with the aid of a computer processor, said social media content in a memory location based upon said established relationships between the social entities, social contributors and social tags, to generate organized social media content in memory that permits searching of said multi-dimensional social media content, and sorting said social media content by said social score; generating a social stream from at least a portion said collected and organized social media content, wherein said social stream includes social media activities of a social entity or social contributor that can be searched, grouped and filtered by a user; and displaying, on a graphical user interface (GUI) including a first panel, a second panel and a third panel: said social stream comprising a first subset and a second subset of the organized social media content to said user in said first panel in sequence upon a search of the multi-dimensional social media content by said user; a hierarchical relationship between said first subset and said subset in said second panel, wherein the second subset is different from the first subset; and a social search path associated with said social stream in said third panel, wherein said social search path includes a search or navigation path taken by said user in arriving at said social stream comprising said first subset and said second subset of the organized social media content, thereby enabling said user to readily identify any previous social streams, thereby presenting said social media content to said user hierarchically across multiple dimensions to reflect their relationship to a search criterion. | 1. A computer-implemented method for displaying multi-dimensional social media content, comprising: collecting social media content from a plurality of remote social media providers and storing said social media content in a memory location; identifying, with the aid of a computer processor operatively coupled to said memory location, social entities, social contributors and social tags in said collected social media content, wherein said social contributors have posted said social media content on said plurality of remote social media providers, wherein each of said social entities is a subject of an individual social media content, and wherein said social entities are different from said social contributors for said social media content posted on said plurality of remote social media providers; establishing, with the aid of a computer processor, relationships between said identified social entities, social contributors and social tags, which relationships are established based upon social tags present in social media content of social entities and social contributors; calculating a social engagement score and a social post score, and calculating from said calculated social engagement score and social post score a social score associated with each media content of said collected and organized social media content, wherein said social engagement score is based on a social relevance of said social contributors and social entities and is determined by the number of friends, fans, followers, or other measurement that assesses a social network presence of said social contributors and social entities, wherein said social post score is based on a degree of interaction of a social contributor or a social entity among said social contributors and social entities with social posts at one or more remote social media providers, wherein said degree of interaction is determined as a net of positive and any negative interactions by said social contributor or social entity with respect to said social posts on said one or more remote social media providers; organizing, with the aid of a computer processor, said social media content in a memory location based upon said established relationships between the social entities, social contributors and social tags, to generate organized social media content in memory that permits searching of said multi-dimensional social media content, and sorting said social media content by said social score; generating a social stream from at least a portion said collected and organized social media content, wherein said social stream includes social media activities of a social entity or social contributor that can be searched, grouped and filtered by a user; and displaying, on a graphical user interface (GUI) including a first panel, a second panel and a third panel: said social stream comprising a first subset and a second subset of the organized social media content to said user in said first panel in sequence upon a search of the multi-dimensional social media content by said user; a hierarchical relationship between said first subset and said subset in said second panel, wherein the second subset is different from the first subset; and a social search path associated with said social stream in said third panel, wherein said social search path includes a search or navigation path taken by said user in arriving at said social stream comprising said first subset and said second subset of the organized social media content, thereby enabling said user to readily identify any previous social streams, thereby presenting said social media content to said user hierarchically across multiple dimensions to reflect their relationship to a search criterion. 8. The computer-implemented method of claim 1 , further comprising grouping at least a portion of the social stream by social entity, social contributor and social tag. | 0.665339 |
6,029,171 | 1 | 4 | 1. A method of collaborating on projects, using a first instance of a messaging system and a second instance of the messaging system, the method comprising the computer implemented steps of: permitting entry of a message in the first instance of the messaging system; parsing the message to identify a keyword; linking the message to an information object based on the keyword identified in the message; creating a header for the message based on the keywords; sending the message, using the header, to the second instance of the messaging system; receiving the message at the second instance of the messaging system; displaying a selection of reply options; generating a reply including an automatic reply content based on the selection; creating a reply header for the reply based on the message content; and sending the reply, using the reply header, to the first instance of the messaging system; and entering an entry into a first calendar and a first list based on the message in the first instance of the messaging system; wherein the selection of reply options include an affirmative, a negative, and an other. | 1. A method of collaborating on projects, using a first instance of a messaging system and a second instance of the messaging system, the method comprising the computer implemented steps of: permitting entry of a message in the first instance of the messaging system; parsing the message to identify a keyword; linking the message to an information object based on the keyword identified in the message; creating a header for the message based on the keywords; sending the message, using the header, to the second instance of the messaging system; receiving the message at the second instance of the messaging system; displaying a selection of reply options; generating a reply including an automatic reply content based on the selection; creating a reply header for the reply based on the message content; and sending the reply, using the reply header, to the first instance of the messaging system; and entering an entry into a first calendar and a first list based on the message in the first instance of the messaging system; wherein the selection of reply options include an affirmative, a negative, and an other. 4. The method of claim 1, further comprising: entering an entry into a second calendar and a second list based on the message, in the second instance of the messaging system. | 0.5 |
8,838,748 | 1 | 10 | 1. A method by which a server system enables sharing of media among client end devices, comprising: segmenting relatively long media objects into shorter, independently referenced media chunks; transcoding each of the media chunks into one or more distinct transcoding formats compatible with respective client end devices for rendering thereon; enabling user manipulation of references to the media chunks including transmission of references from one client end device to another to enable user sharing of a viewing experience; and upon activation of a reference to one of the media chunks at a client end device, supplying the media chunk to the client end device in a respective transcoding format compatible with the client end device for viewing thereon, wherein the server system automatically detects a client end device based on a combination of one or more properties of an operating environment of the client end device, the properties including identifications of a browser and an operating system executed by the client end device, by: maintaining a device database populated with information describing media-related capabilities and attributes of a plurality of client end devices; upon receiving a request message from the client end device, applying a user agent (UA) identifier from the request message to the device database to identify a best-matching user interface (UI) to be rendered on the client end device; and supplying the best-matching UI to the client end device in response to the request. | 1. A method by which a server system enables sharing of media among client end devices, comprising: segmenting relatively long media objects into shorter, independently referenced media chunks; transcoding each of the media chunks into one or more distinct transcoding formats compatible with respective client end devices for rendering thereon; enabling user manipulation of references to the media chunks including transmission of references from one client end device to another to enable user sharing of a viewing experience; and upon activation of a reference to one of the media chunks at a client end device, supplying the media chunk to the client end device in a respective transcoding format compatible with the client end device for viewing thereon, wherein the server system automatically detects a client end device based on a combination of one or more properties of an operating environment of the client end device, the properties including identifications of a browser and an operating system executed by the client end device, by: maintaining a device database populated with information describing media-related capabilities and attributes of a plurality of client end devices; upon receiving a request message from the client end device, applying a user agent (UA) identifier from the request message to the device database to identify a best-matching user interface (UI) to be rendered on the client end device; and supplying the best-matching UI to the client end device in response to the request. 10. A method according to claim 1 , wherein the server system creates a mashup of media and related data objects for delivery to a client end device as part of a user interface (UI) tailored for use by a category of client end devices, by: maintaining a plurality of source media clips previously transcoded into distinct video formats, bit rates and resolutions; providing a customized playlist to the client end device, the customized playlist including references to at least some of the source media clips; and upon receiving a request including a reference to a requested source media clip to be played on the client end device: (1) stitching the requested source media clip together with at least one other source media clip in a single video format, bit rate and resolution appropriate for rendering on the client end device; and (2) delivering the stitched-together source media clips to the client end device for playback thereon. | 0.5 |
10,102,201 | 11 | 13 | 11. The method of claim 10 , further comprising comparing the accuracy of meaning representations for the plurality of natural language modules to one another in a side-by-side comparison. | 11. The method of claim 10 , further comprising comparing the accuracy of meaning representations for the plurality of natural language modules to one another in a side-by-side comparison. 13. The method of claim 11 , wherein comparing the results meaning representations in a side-by-side comparison comprises testing the plurality of natural language modules using one or more pre-defined test use cases. | 0.606884 |
8,984,006 | 1 | 6 | 1. A computer-implemented method comprising: identifying, by one or more processors, a candidate parent entity from an entity set, wherein the entity set comprises a plurality of entities, and wherein the candidate parent entity comprises an entity identified as having one or more characteristics indicative of the entity having a parent hierarchical relationship to another entity of the entity set; identifying, by the one or more processors, a candidate child entity set from the entity set, wherein the candidate child entity set comprises entities of the entity set that are each identified as having one or more characteristics indicative of the entity having a child hierarchical relationship to the candidate parent entity; and for each entity of the candidate child entity set: comparing, by the one or more processor, characteristics of the candidate parent entity to characteristics of the entity of the candidate child entity set to determine whether a hierarchical relationship exists between the candidate parent entity and the entity of the candidate child entity set; and in response to determining that a hierarchical relationship exists between the candidate parent entity and the entity of the candidate child entity set, updating, by the one or more processors, a hierarchical index to reflect the hierarchical relationship between the candidate parent entity and the entity of the candidate child entity set. | 1. A computer-implemented method comprising: identifying, by one or more processors, a candidate parent entity from an entity set, wherein the entity set comprises a plurality of entities, and wherein the candidate parent entity comprises an entity identified as having one or more characteristics indicative of the entity having a parent hierarchical relationship to another entity of the entity set; identifying, by the one or more processors, a candidate child entity set from the entity set, wherein the candidate child entity set comprises entities of the entity set that are each identified as having one or more characteristics indicative of the entity having a child hierarchical relationship to the candidate parent entity; and for each entity of the candidate child entity set: comparing, by the one or more processor, characteristics of the candidate parent entity to characteristics of the entity of the candidate child entity set to determine whether a hierarchical relationship exists between the candidate parent entity and the entity of the candidate child entity set; and in response to determining that a hierarchical relationship exists between the candidate parent entity and the entity of the candidate child entity set, updating, by the one or more processors, a hierarchical index to reflect the hierarchical relationship between the candidate parent entity and the entity of the candidate child entity set. 6. The method of claim 1 , wherein identifying a candidate parent entity comprises identifying an entity of the entity set having a descriptor including one or more keywords corresponding to a parent entity. | 0.911006 |
7,523,137 | 24 | 25 | 24. An event analysis system comprising: a network interface; an information database; an event database; a memory comprising: an information source model; an environment model which defines a first model entity and a focus entity; an event model which defines events; an event implication model and an event detection engine; an event implication engine; and an event processing control program comprising instructions which: read the information source model to determine information sources; retrieve articles from the information sources and store the articles in the information database; initiate execution of the event detection engine on the articles to detect events represented in the articles, generate event objects according to a common event structure which is independent of the information sources, and store the event objects in the event database; initiate execution of the event implication engine on the event objects to determine an inferred event on behalf of the focus entity in view of a focus relationship, where the event implication engine recognizes that the focus relationship exists between the focus entity and the first model entity and responsively generates the inferred event due to detection of the event involving the first model entity, generate a new event object from the inferred event, and store the new event object in the event database; and initiate communication of the event objects including the inferred event to an external system subscribed to the event analysis system; re-initiate execution of the event implication engine on the new event Object, generate an additional event object, and store the additional event object in the event database; and receive a public interest level value and importance level value for each event, where the event model comprises an event type and event attributes of the event type, and where the new event object and the additional event object each comprise: an event type field; an event type probability field; an importance field; and a public interest field; a processor coupled to the memory and operable to execute the instructions of the event processing control program. | 24. An event analysis system comprising: a network interface; an information database; an event database; a memory comprising: an information source model; an environment model which defines a first model entity and a focus entity; an event model which defines events; an event implication model and an event detection engine; an event implication engine; and an event processing control program comprising instructions which: read the information source model to determine information sources; retrieve articles from the information sources and store the articles in the information database; initiate execution of the event detection engine on the articles to detect events represented in the articles, generate event objects according to a common event structure which is independent of the information sources, and store the event objects in the event database; initiate execution of the event implication engine on the event objects to determine an inferred event on behalf of the focus entity in view of a focus relationship, where the event implication engine recognizes that the focus relationship exists between the focus entity and the first model entity and responsively generates the inferred event due to detection of the event involving the first model entity, generate a new event object from the inferred event, and store the new event object in the event database; and initiate communication of the event objects including the inferred event to an external system subscribed to the event analysis system; re-initiate execution of the event implication engine on the new event Object, generate an additional event object, and store the additional event object in the event database; and receive a public interest level value and importance level value for each event, where the event model comprises an event type and event attributes of the event type, and where the new event object and the additional event object each comprise: an event type field; an event type probability field; an importance field; and a public interest field; a processor coupled to the memory and operable to execute the instructions of the event processing control program. 25. The event analysis system of claim 24 , where the instructions which initiate communication comprise: instructions which publish the event object to a message subscription/ publication service. | 0.5 |
9,087,272 | 11 | 15 | 11. A system for enhanced optical character recognition, the system comprising: a logic unit for identifying a sample character in a textual context to be optically recognized; a logic unit for comparing the sample character with a template character, wherein the sample character is scaled into a first grid and the template character is scaled into a second grid; a logic unit for identifying one or more pixels in the sample character within the first grid and one or more pixels in the template character in the second grid, wherein the one or more pixels are identified as belonging to a foreground category in the textual content, a foreground pixel having at least N gradients corresponding to edges of the foreground pixel that are juxtaposed to a neighbor pixel, wherein a contour foreground pixel has at least one gradient that is neighbored by a background pixel in the textual context; identifying one or more template contour pixels in the template character that correspond to at least one sample contour pixel in the sample character, a logic unit for mapping the at least one sample contour pixel to the corresponding template contour pixels such that one or more distances are calculated between the at least one sample contour pixel and the respective one or more template contour pixels; and a logic unit for determining that the sample contour character and the template contour character are a match based on an analysis of the one or more distances. | 11. A system for enhanced optical character recognition, the system comprising: a logic unit for identifying a sample character in a textual context to be optically recognized; a logic unit for comparing the sample character with a template character, wherein the sample character is scaled into a first grid and the template character is scaled into a second grid; a logic unit for identifying one or more pixels in the sample character within the first grid and one or more pixels in the template character in the second grid, wherein the one or more pixels are identified as belonging to a foreground category in the textual content, a foreground pixel having at least N gradients corresponding to edges of the foreground pixel that are juxtaposed to a neighbor pixel, wherein a contour foreground pixel has at least one gradient that is neighbored by a background pixel in the textual context; identifying one or more template contour pixels in the template character that correspond to at least one sample contour pixel in the sample character, a logic unit for mapping the at least one sample contour pixel to the corresponding template contour pixels such that one or more distances are calculated between the at least one sample contour pixel and the respective one or more template contour pixels; and a logic unit for determining that the sample contour character and the template contour character are a match based on an analysis of the one or more distances. 15. The system of claim 11 , wherein the correspondence between a template contour pixel in the one or more template contour pixels and the at least one sample contour pixel is based on a number of gradients the template contour pixel has in common with the sample contour pixel. | 0.5 |
8,839,197 | 1 | 3 | 1. An apparatus comprising: at least one processor; a memory coupled to the at least one processor; a composite application residing in the memory, the composite application including a plurality of components written in a plurality of different programming languages; a plurality of component interaction rules residing in the memory, each component interaction rule comprising at least one condition between components in different programming languages and at least one action to perform depending on whether the at least one condition is satisfied; and an application analysis mechanism residing in the memory and executed by the at least one processor, the application analysis mechanism generating an application model of the composite application, wherein the application model comprises: the plurality of components in the composite application separated into a plurality of categories according to programming language; and metadata that describes interaction between components in a first of the plurality of categories with components in a second of the plurality of categories; the application analysis mechanism analyzing the application model for conformance to the plurality of component interaction rules, and outputting results of analyzing the application model for conformance to the plurality of component interaction rules. | 1. An apparatus comprising: at least one processor; a memory coupled to the at least one processor; a composite application residing in the memory, the composite application including a plurality of components written in a plurality of different programming languages; a plurality of component interaction rules residing in the memory, each component interaction rule comprising at least one condition between components in different programming languages and at least one action to perform depending on whether the at least one condition is satisfied; and an application analysis mechanism residing in the memory and executed by the at least one processor, the application analysis mechanism generating an application model of the composite application, wherein the application model comprises: the plurality of components in the composite application separated into a plurality of categories according to programming language; and metadata that describes interaction between components in a first of the plurality of categories with components in a second of the plurality of categories; the application analysis mechanism analyzing the application model for conformance to the plurality of component interaction rules, and outputting results of analyzing the application model for conformance to the plurality of component interaction rules. 3. The apparatus of claim 1 wherein the results comprise statistics for the composite application and at least one warning. | 0.605769 |
9,514,408 | 27 | 29 | 27. A computer-readable medium containing instructions that cause a processor to populate a knowledge representation system (KRS), by: creating a fact template, for a predetermined type of fact, that accepts, from predetermined information sources, a structured fact having the predetermined type, wherein the fact template constrains, based on the predetermined type of fact, an organization and permitted content within the template to be consistent with an organization of an ontology organizing the KRS, wherein the fact template includes a user interface object that constrains user data entry, for completing the fact template, based upon a predetermined set of valid entries, wherein the valid entries are determined according to the ontology organization; presenting the fact template to a reviewer of information upon receiving a selection of the predetermined type of fact; accepting a structured fact from the reviewer, wherein the structured fact is entered into the fact template as selections from the at least one user interface object, wherein the fact template includes a user entry field that permits entry of new information, from the structured fact, not included in the predetermined set of valid entries, wherein the completed fact template is automatically flagged for review responsive to the entry of new information, and wherein the fact template is completed with information extracted from the predetermined information sources; and inserting the structured fact into the KRS. | 27. A computer-readable medium containing instructions that cause a processor to populate a knowledge representation system (KRS), by: creating a fact template, for a predetermined type of fact, that accepts, from predetermined information sources, a structured fact having the predetermined type, wherein the fact template constrains, based on the predetermined type of fact, an organization and permitted content within the template to be consistent with an organization of an ontology organizing the KRS, wherein the fact template includes a user interface object that constrains user data entry, for completing the fact template, based upon a predetermined set of valid entries, wherein the valid entries are determined according to the ontology organization; presenting the fact template to a reviewer of information upon receiving a selection of the predetermined type of fact; accepting a structured fact from the reviewer, wherein the structured fact is entered into the fact template as selections from the at least one user interface object, wherein the fact template includes a user entry field that permits entry of new information, from the structured fact, not included in the predetermined set of valid entries, wherein the completed fact template is automatically flagged for review responsive to the entry of new information, and wherein the fact template is completed with information extracted from the predetermined information sources; and inserting the structured fact into the KRS. 29. The computer-readable medium of claim 27 , wherein the fact template is modified upon a determination that a new fact cannot be accommodated by at least one existing fact template. | 0.508021 |
8,788,487 | 12 | 19 | 12. A computer program product having a non-transitory computer-readable storage medium storing computer-executable code, the code comprising: a feature manager module of an online system configured to: maintain a cumulative feature store storing feature values determined from user actions performed before a time point; maintain an incremental feature store storing feature values, the maintaining comprising updating feature values of the incremental feature store responsive to receiving information describing user actions performed after the time point; a request processor module configured to: receive a request for a feature value, the request identifying a user and a feature associated with user actions of a type; receive a first partial result from the cumulative feature store, the first partial result determined from user actions of the type performed by the user before the time point; receive a second partial result from the incremental feature store, the second partial result determined from user actions of the type performed by the user after the time point; determine a weighted combination comprising the first partial result and the second partial result, wherein the first partial result is weighted by a decay factor; and return the weighted combination as the requested feature value. | 12. A computer program product having a non-transitory computer-readable storage medium storing computer-executable code, the code comprising: a feature manager module of an online system configured to: maintain a cumulative feature store storing feature values determined from user actions performed before a time point; maintain an incremental feature store storing feature values, the maintaining comprising updating feature values of the incremental feature store responsive to receiving information describing user actions performed after the time point; a request processor module configured to: receive a request for a feature value, the request identifying a user and a feature associated with user actions of a type; receive a first partial result from the cumulative feature store, the first partial result determined from user actions of the type performed by the user before the time point; receive a second partial result from the incremental feature store, the second partial result determined from user actions of the type performed by the user after the time point; determine a weighted combination comprising the first partial result and the second partial result, wherein the first partial result is weighted by a decay factor; and return the weighted combination as the requested feature value. 19. The computer program product of claim 12 , wherein the feature manager is further configured to: mark the incremental feature store as inactive and stopping the updates to the feature values stored in the incremental feature store; determine a weighted combination of feature values from the incremental feature store and corresponding feature values from the cumulative feature store, wherein the feature values from the cumulative feature store are weighted by a decay factor; and update the feature values of the cumulative feature store with the weighted combinations. | 0.5 |
9,905,229 | 17 | 19 | 17. At least one non-transitory computer-readable storage medium encoded with a plurality of computer-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method comprising: receiving an original free-form text narrative regarding a patient encounter provided by a clinician; re-formatting the original free-form text narrative at least in part by adding, removing, and/or correcting sentence boundaries and/or section boundaries with respect to the original free-form text narrative to produce a formatted text including the added and/or corrected sentence boundaries and/or section boundaries, the re-formatting comprising applying at least one statistical model to the original free-form text narrative to generate, for a word or a sequence of words in the original free-form text narrative, a probability that the word or the sequence of words would be followed by a sentence boundary and/or a section boundary, wherein the at least one statistical model is trained at least in part with other free-form text narratives having correct sentence boundaries and/or section boundaries, and in response to determining that the probability satisfies one or more criteria, adding, removing, and/or correcting a sentence boundary and/or a section boundary following the word or the sequence of words, with respect to the original free-form text narrative; extracting one or more clinical facts from the formatted text, wherein a first fact of the one or more clinical facts is extracted from a first portion of the formatted text, wherein the first portion of the formatted text is a formatted version of a first portion of the original free-form text narrative, the extracting comprising analyzing the formatted text to identify a set of one or more features of at least the first portion of the formatted text, correlating the set of features to one or more abstract semantic concepts, and generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more clinical facts extracted from the formatted text; and providing to a user an indicator that distinguishes the first portion of the original free-form text narrative that resulted in extraction of the first fact, from other portions of the original free-form text narrative that did not result in the extraction of the first fact. | 17. At least one non-transitory computer-readable storage medium encoded with a plurality of computer-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method comprising: receiving an original free-form text narrative regarding a patient encounter provided by a clinician; re-formatting the original free-form text narrative at least in part by adding, removing, and/or correcting sentence boundaries and/or section boundaries with respect to the original free-form text narrative to produce a formatted text including the added and/or corrected sentence boundaries and/or section boundaries, the re-formatting comprising applying at least one statistical model to the original free-form text narrative to generate, for a word or a sequence of words in the original free-form text narrative, a probability that the word or the sequence of words would be followed by a sentence boundary and/or a section boundary, wherein the at least one statistical model is trained at least in part with other free-form text narratives having correct sentence boundaries and/or section boundaries, and in response to determining that the probability satisfies one or more criteria, adding, removing, and/or correcting a sentence boundary and/or a section boundary following the word or the sequence of words, with respect to the original free-form text narrative; extracting one or more clinical facts from the formatted text, wherein a first fact of the one or more clinical facts is extracted from a first portion of the formatted text, wherein the first portion of the formatted text is a formatted version of a first portion of the original free-form text narrative, the extracting comprising analyzing the formatted text to identify a set of one or more features of at least the first portion of the formatted text, correlating the set of features to one or more abstract semantic concepts, and generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more clinical facts extracted from the formatted text; and providing to a user an indicator that distinguishes the first portion of the original free-form text narrative that resulted in extraction of the first fact, from other portions of the original free-form text narrative that did not result in the extraction of the first fact. 19. The at least one non-transitory computer-readable storage medium of claim 17 , wherein the re-formatting comprises normalizing at least one section heading according to a standard for an institution associated with the patient encounter. | 0.534749 |
6,044,385 | 15 | 16 | 15. A program product according to claim 14, wherein: said primary portion of said document is an original primary portion; and said document presenter responds to operator input moving said elevator box from a first position to a second position by displaying a new primary portion of said document within said lens utilizing said first magnification level and displaying all of said original primary portion that is not in said new primary portion utilizing said second magnification level. | 15. A program product according to claim 14, wherein: said primary portion of said document is an original primary portion; and said document presenter responds to operator input moving said elevator box from a first position to a second position by displaying a new primary portion of said document within said lens utilizing said first magnification level and displaying all of said original primary portion that is not in said new primary portion utilizing said second magnification level. 16. A program product according to claim 15, wherein said second magnification level comprises a range of vertical magnification levels that decrease in proportion to vertical distance from said lens. | 0.5 |
7,860,316 | 5 | 10 | 5. An image forming apparatus, comprising: a text/image separation apparatus to receive data associated with a document that is received and scanned by the image forming apparatus, the document having one or more pages and to determine data for a text area in the one or more pages from the received data associated with the document; an index determination apparatus to determine data for one or more content indicating texts from among the text area in the one or more pages of the document from the received data by selecting characters of the content indicating texts based on comparing a size of characters in the text area with a predetermined size threshold that is set as an average value of preset character properties calculated on a variation of the preset character properties, and grouping selected characters together based on relative proximity to one another; and an index page creation apparatus to create index page data for an index page for the document including the one or more content indicating texts determined from the received data, wherein the index determination apparatus receives data associated with separated symbols of the text area from the text/image separation unit, compares the data associated with separated symbols to one or more predetermined size parameters to determine whether the separated symbols are content indicating symbols, and groups adjacent ones of the content indicating symbols together and determines the grouped content indicating symbols as the content indicating texts for the index page and index page data. | 5. An image forming apparatus, comprising: a text/image separation apparatus to receive data associated with a document that is received and scanned by the image forming apparatus, the document having one or more pages and to determine data for a text area in the one or more pages from the received data associated with the document; an index determination apparatus to determine data for one or more content indicating texts from among the text area in the one or more pages of the document from the received data by selecting characters of the content indicating texts based on comparing a size of characters in the text area with a predetermined size threshold that is set as an average value of preset character properties calculated on a variation of the preset character properties, and grouping selected characters together based on relative proximity to one another; and an index page creation apparatus to create index page data for an index page for the document including the one or more content indicating texts determined from the received data, wherein the index determination apparatus receives data associated with separated symbols of the text area from the text/image separation unit, compares the data associated with separated symbols to one or more predetermined size parameters to determine whether the separated symbols are content indicating symbols, and groups adjacent ones of the content indicating symbols together and determines the grouped content indicating symbols as the content indicating texts for the index page and index page data. 10. The image forming apparatus of claim 5 , wherein the index determination apparatus determines data for the one or more content indicating texts based on a text size comparison. | 0.692833 |
9,767,448 | 1 | 2 | 1. A system comprising: a non-transitory memory storing instructions; and one or more hardware processors coupled to the non-transitory memory and configured to read the instructions from the non-transitory memory to cause the system to perform operations comprising: displaying, on a graphical user interface (GUI) of the system, a list of contacts of a user; receiving a selection of a contact from the list of contacts; providing, via the GUI, available funding sources to the user; receiving a selection of a funding source from the available funding sources from the user; providing, via the GUI, a payment indicator; receiving, via the GUI, a touch input that is associated with the payment indicator; determining a transaction amount based on a time-dependent measurement of the touch input; and providing, for display via the GUI, the transaction amount along with the payment indicator. | 1. A system comprising: a non-transitory memory storing instructions; and one or more hardware processors coupled to the non-transitory memory and configured to read the instructions from the non-transitory memory to cause the system to perform operations comprising: displaying, on a graphical user interface (GUI) of the system, a list of contacts of a user; receiving a selection of a contact from the list of contacts; providing, via the GUI, available funding sources to the user; receiving a selection of a funding source from the available funding sources from the user; providing, via the GUI, a payment indicator; receiving, via the GUI, a touch input that is associated with the payment indicator; determining a transaction amount based on a time-dependent measurement of the touch input; and providing, for display via the GUI, the transaction amount along with the payment indicator. 2. The system of claim 1 , wherein the time-dependent measurement of the touch input indicates a speed of the touch input. | 0.601307 |
8,020,187 | 7 | 8 | 7. A method for associating an electronic document work with an action, the document work comprising text, the method comprising: a) electronically extracting within a portable client device features from the electronic document work; b) transmitting the extracted features from the portable client device to one or more servers; c) receiving at the portable client device from the one or more servers an identification of the electronic document work based on the extracted features, wherein the identification is based on a non-exhaustive search identifying a neighbor; d) electronically determining an action based on the identification of the electronic document work; and e) electronically performing the action on the portable client device. | 7. A method for associating an electronic document work with an action, the document work comprising text, the method comprising: a) electronically extracting within a portable client device features from the electronic document work; b) transmitting the extracted features from the portable client device to one or more servers; c) receiving at the portable client device from the one or more servers an identification of the electronic document work based on the extracted features, wherein the identification is based on a non-exhaustive search identifying a neighbor; d) electronically determining an action based on the identification of the electronic document work; and e) electronically performing the action on the portable client device. 8. The method of claim 7 , wherein the identification is based on a non-exhaustive search identifying a neighbor within a fixed radius. | 0.711538 |
8,269,773 | 7 | 9 | 7. The method of claim 1 , wherein updating the graph creation region includes highlighting modifications based upon the association. | 7. The method of claim 1 , wherein updating the graph creation region includes highlighting modifications based upon the association. 9. The method of claim 7 , wherein the highlighting is removed when the particular variable is dropped onto the hotspot. | 0.512195 |
6,026,432 | 1 | 12 | 1. A method for retrieving information in a computer network system having at least one network server device and a user device, said user device and said network server having established a successful network session so that they are in processing communication with one another; said method comprising: displaying a target page on a screen connected to said user device, said target page including a plurality of first level link references for selection by any user where each first level link reference further includes subsequent level link references; providing a string of words; searching all reference links provided by said target page and all subsequent pages to identify only those references including said provided string of words. | 1. A method for retrieving information in a computer network system having at least one network server device and a user device, said user device and said network server having established a successful network session so that they are in processing communication with one another; said method comprising: displaying a target page on a screen connected to said user device, said target page including a plurality of first level link references for selection by any user where each first level link reference further includes subsequent level link references; providing a string of words; searching all reference links provided by said target page and all subsequent pages to identify only those references including said provided string of words. 12. The method of claim 1, further comprising the step of storing said search result in a memory location. | 0.832278 |
8,189,685 | 9 | 12 | 9. A non-transitory computer-readable storage medium for ranking a plurality of video articles, the non-transitory computer-readable storage medium comprising computer-executable instructions encoded on the medium, comprising: for each of the plurality of video articles, performing the following steps at a server: determining video-oriented characteristic data of a video article, the video-oriented characteristic data describing one or more characteristics of at least one broadcast of the video article, wherein each video article has metadata describing the video article; and determining a rank score based at least in part on the video article-oriented characteristic data and based at least in part on the metadata of the video article; determining an order for transmitting the plurality of video articles to a client, the order of transmission based on the rank score for each of the plurality of video articles. | 9. A non-transitory computer-readable storage medium for ranking a plurality of video articles, the non-transitory computer-readable storage medium comprising computer-executable instructions encoded on the medium, comprising: for each of the plurality of video articles, performing the following steps at a server: determining video-oriented characteristic data of a video article, the video-oriented characteristic data describing one or more characteristics of at least one broadcast of the video article, wherein each video article has metadata describing the video article; and determining a rank score based at least in part on the video article-oriented characteristic data and based at least in part on the metadata of the video article; determining an order for transmitting the plurality of video articles to a client, the order of transmission based on the rank score for each of the plurality of video articles. 12. The computer-readable storage medium of claim 9 , wherein the video-oriented characteristic data is third-party data of the video article, wherein the third-party data of the video article describes a cost estimation of the video article. | 0.531008 |
9,710,447 | 5 | 7 | 5. The method of claim 1 , further comprising: training, by at least one computing device, the content kernel using at least the content feature information associated with each content item of the plurality of content items in the training set; training, by at least one computing device, the semantic kernel using at least the semantic feature information associated with each content item of the plurality of content items in the training set; and training, by at least one computing device, the social kernel using at least the social network feature information associated with each content item of the plurality of content items in the training set. | 5. The method of claim 1 , further comprising: training, by at least one computing device, the content kernel using at least the content feature information associated with each content item of the plurality of content items in the training set; training, by at least one computing device, the semantic kernel using at least the semantic feature information associated with each content item of the plurality of content items in the training set; and training, by at least one computing device, the social kernel using at least the social network feature information associated with each content item of the plurality of content items in the training set. 7. The method of claim 5 , the social network kernel comprising a network of nodes and edges, each node corresponding to an individual represented in at least one content item of the plurality and each edge indicating a relationship between two individuals represented together in at least one content item of the plurality of content items. | 0.5 |
8,433,576 | 1 | 7 | 1. A computer-implemented method comprising: providing a user option to tune a weighting parameter, and responding to tuning of the weighting parameter by raising or lowering criteria for a general-domain garbage language model to adjust a miscue detection rate and a rate of false alarms; displaying a text output having target words; dynamically generating a domain-specific target language model for the text output, the target language model being specific to the text output having the target words and including a language score for the target words of the text output; receiving an acoustic input; modeling, using a processor of a computer, the acoustic input with the dynamically generated domain-specific target language model, comprising calculating an acoustic score for the target words with reference to the acoustic input; further modeling the acoustic input with the general-domain garbage language model to identify an element of the acoustic input as a miscue that does not correspond properly to the target words of the text output; and providing user-perceptible feedback. | 1. A computer-implemented method comprising: providing a user option to tune a weighting parameter, and responding to tuning of the weighting parameter by raising or lowering criteria for a general-domain garbage language model to adjust a miscue detection rate and a rate of false alarms; displaying a text output having target words; dynamically generating a domain-specific target language model for the text output, the target language model being specific to the text output having the target words and including a language score for the target words of the text output; receiving an acoustic input; modeling, using a processor of a computer, the acoustic input with the dynamically generated domain-specific target language model, comprising calculating an acoustic score for the target words with reference to the acoustic input; further modeling the acoustic input with the general-domain garbage language model to identify an element of the acoustic input as a miscue that does not correspond properly to the target words of the text output; and providing user-perceptible feedback. 7. The method of claim 1 , further comprising applying a weighting parameter to the criteria for the garbage language model, and applying one minus the weighting parameter to the criteria for the target language model. | 0.772443 |
9,111,173 | 9 | 16 | 9. A computer system for learning part-based object models from a set of digital representations of images, the system comprising: a non-transitory computer-readable storage medium storing executable computer program instructions; and a computer processor executing the computer program instructions to perform steps comprising: receiving a set of digital representations of images, each image having at least one object; and for each image: extracting one or more shape features from the image; extracting one or more appearance features from the image; generating one or more shape models of the object based on the shape features, a shape model corresponding to a part of the object, generating a shape model comprising: generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells; computing an appearance model for each shape model of the object based on the appearance features; and selecting one or more shape models as reference shape models of the object; selecting one or more appearance models as reference appearance models of the object; and storing the reference shape models of the images; and storing the reference appearance models of the images. | 9. A computer system for learning part-based object models from a set of digital representations of images, the system comprising: a non-transitory computer-readable storage medium storing executable computer program instructions; and a computer processor executing the computer program instructions to perform steps comprising: receiving a set of digital representations of images, each image having at least one object; and for each image: extracting one or more shape features from the image; extracting one or more appearance features from the image; generating one or more shape models of the object based on the shape features, a shape model corresponding to a part of the object, generating a shape model comprising: generating one or more histogram of oriented gradient (HOG) cells for a number of pixel of the image; and grouping two or more HOG cells into a HOG bundle based on similarity of orientations of the HOG cells with respect to the maximum magnitude of orientations of the HOG cells; computing an appearance model for each shape model of the object based on the appearance features; and selecting one or more shape models as reference shape models of the object; selecting one or more appearance models as reference appearance models of the object; and storing the reference shape models of the images; and storing the reference appearance models of the images. 16. The system of claim 9 , wherein generating a shape model further comprises: comparing orientation of a HOG cell with a predefined orientation threshold; and excluding the HOG cell from grouping with other HOG cells responsive to the evaluation of the difference between the orientation of the HOG cell and the predefined orientation threshold. | 0.710351 |
8,027,988 | 1 | 6 | 1. A method performed by one or more server devices, comprising: receiving, at a processor of the one or more server devices, a search query from a client device; selecting, using a processor of the one or more server devices, a plurality of documents based on the search query; identifying, using a processor of the one or more server devices, one or more categories associated with the plurality of documents; generating, using a processor of the one or more server devices, a score for each of the one or more categories; selecting, using a processor of the one or more server devices, a category of the one or more categories, as a recommended category, based on the scores generated for the one or more categories; and presenting, using a processor of the one or more server devices, information regarding the plurality of documents and the recommended category to the client device. | 1. A method performed by one or more server devices, comprising: receiving, at a processor of the one or more server devices, a search query from a client device; selecting, using a processor of the one or more server devices, a plurality of documents based on the search query; identifying, using a processor of the one or more server devices, one or more categories associated with the plurality of documents; generating, using a processor of the one or more server devices, a score for each of the one or more categories; selecting, using a processor of the one or more server devices, a category of the one or more categories, as a recommended category, based on the scores generated for the one or more categories; and presenting, using a processor of the one or more server devices, information regarding the plurality of documents and the recommended category to the client device. 6. The method of claim 1 , where generating the score for each of the one or more categories includes, for a given category: obtaining scores of documents, in the plurality of documents, associated with the category, and combining the obtained scores to generate the score for the category. | 0.83237 |
9,189,361 | 16 | 17 | 16. A method for proactive management of performance of an application, the method comprising: monitoring, by a digital data processor, (i) usage of the application by a user and/or one or more components thereof by the user, and (ii) consumption of the one or more resources during usage of the application and/or one or more components thereof by the user, wherein the resources include any of processing capacity, memory capacity, and/or network bandwidth, and signaling an alert upon determining that such usage by the user and/or consumption of resources during such usage violates a model, wherein the model defines one or more quotas for the user with respect to usage of a component of the application by the user, wherein the one or more quotas defined by the model include a maximum resource consumption quota and an expected resource consumption quota, wherein the model indicates that the user is permitted to consume no more than a pre-determined amount of processor capacity and no more than a pre-determined amount of network bandwidth, and wherein the maximum resource consumption quota is between 0.1-0.6 percent peak of the processor capacity to be consumed by the user, and between 10-200 kilobytes per second peak on the network to be consumed by the user during use of the application. | 16. A method for proactive management of performance of an application, the method comprising: monitoring, by a digital data processor, (i) usage of the application by a user and/or one or more components thereof by the user, and (ii) consumption of the one or more resources during usage of the application and/or one or more components thereof by the user, wherein the resources include any of processing capacity, memory capacity, and/or network bandwidth, and signaling an alert upon determining that such usage by the user and/or consumption of resources during such usage violates a model, wherein the model defines one or more quotas for the user with respect to usage of a component of the application by the user, wherein the one or more quotas defined by the model include a maximum resource consumption quota and an expected resource consumption quota, wherein the model indicates that the user is permitted to consume no more than a pre-determined amount of processor capacity and no more than a pre-determined amount of network bandwidth, and wherein the maximum resource consumption quota is between 0.1-0.6 percent peak of the processor capacity to be consumed by the user, and between 10-200 kilobytes per second peak on the network to be consumed by the user during use of the application. 17. The method of claim 16 , further comprising limiting usage and/or consumption of resources. | 0.797009 |
8,527,313 | 14 | 15 | 14. A system comprising: a database of an enterprise system to store data; a memory coupled to the database, the memory to load and execute an office suite application from which a request is generated to open a document as an instance of data stored in the database, the document incorporating at least one data object from the enterprise system, the enterprise system including multiple backend systems accessible via at least one frontend interface on the client device, the enterprise system including workflow management configured to generate a distributed workflow specific to performing work related to a business goal of an associated business scenario; a document instantiation identifier module to identify the request to open the document and determine in response to the request to open the document, that the document has an associated business scenario from among multiple different business scenarios in the enterprise system, the document being associated with the business scenario and not the other different business scenarios, the business scenario defined in reference to resources and services to use to perform work related to a business goal, the resources including the document; and a workflow generator coupled to the document instantiation identifier module to initiate, in response to the request to open the document, the distributed workflow with the workflow management, the initiating including instantiating multiple work actions each representing an atomic business activity of the distributed workflow related to the document, at least one work action to be performed by a user other than a user that requested opening the document, and the initiating further including initiating a frontend interface on the client device for one of the backend systems and a connection by the client device to a service of the one backend system to perform work related to the document, wherein the distributed workflow uses the document as a resource to perform the work related to the business goal of the associated business scenario, and the workflow generator further to instantiate the document on the client device in response to the request to open the document including create an instance of the enterprise data object, wherein the document is opened in a context of the associated business scenario and the distributed workflow initiated in response to the request to open the document. | 14. A system comprising: a database of an enterprise system to store data; a memory coupled to the database, the memory to load and execute an office suite application from which a request is generated to open a document as an instance of data stored in the database, the document incorporating at least one data object from the enterprise system, the enterprise system including multiple backend systems accessible via at least one frontend interface on the client device, the enterprise system including workflow management configured to generate a distributed workflow specific to performing work related to a business goal of an associated business scenario; a document instantiation identifier module to identify the request to open the document and determine in response to the request to open the document, that the document has an associated business scenario from among multiple different business scenarios in the enterprise system, the document being associated with the business scenario and not the other different business scenarios, the business scenario defined in reference to resources and services to use to perform work related to a business goal, the resources including the document; and a workflow generator coupled to the document instantiation identifier module to initiate, in response to the request to open the document, the distributed workflow with the workflow management, the initiating including instantiating multiple work actions each representing an atomic business activity of the distributed workflow related to the document, at least one work action to be performed by a user other than a user that requested opening the document, and the initiating further including initiating a frontend interface on the client device for one of the backend systems and a connection by the client device to a service of the one backend system to perform work related to the document, wherein the distributed workflow uses the document as a resource to perform the work related to the business goal of the associated business scenario, and the workflow generator further to instantiate the document on the client device in response to the request to open the document including create an instance of the enterprise data object, wherein the document is opened in a context of the associated business scenario and the distributed workflow initiated in response to the request to open the document. 15. The system of claim 14 , further comprising: a business process extension to load in the memory to identify the request to open the document from the office suite and enable a connection to the enterprise system from the office suite to trigger the work action. | 0.5 |
9,208,153 | 1 | 2 | 1. A computer implemented method for filtering an event notification stream based on relevance to specific targets in a distributed file sharing and collaboration environment in which multiple users collaboratively view, modify and comment on a shared set of files, the method comprising the steps of: maintaining, by a computer, dynamic and static profile information concerning each one of the multiple users, wherein user profile information concerning a user describes the user and quantifies an interest level of the user in specific files, specific types of files and specific file content of the shared set, and quantifies a similarity level of the user to other specific users and to specific types of users; maintaining, by the computer, file profile information concerning each file of the shared set, wherein file profile information concerning a file describes the file and quantifies a similarity level of the file to other specific files, specific types of files and specific file content of the shared set; filtering the event notification stream of the file sharing and collaboration environment, by the computer, wherein the event notification stream comprises a plurality of notifications, each notification describing an event undertaken by a user and directed towards a file of the shared set; for each specific event notification in the filtered event notification stream, quantifying a relevance value, by the computer, for each specific one of the multiple users, based on user profile information concerning the specific user, file profile information concerning the file to which the event is directed, and user profile information concerning the user who undertook the event; and transmitting, by the computer, a notification describing the specific event only to those specific users for whom the relevance value exceeds a predetermined threshold value. | 1. A computer implemented method for filtering an event notification stream based on relevance to specific targets in a distributed file sharing and collaboration environment in which multiple users collaboratively view, modify and comment on a shared set of files, the method comprising the steps of: maintaining, by a computer, dynamic and static profile information concerning each one of the multiple users, wherein user profile information concerning a user describes the user and quantifies an interest level of the user in specific files, specific types of files and specific file content of the shared set, and quantifies a similarity level of the user to other specific users and to specific types of users; maintaining, by the computer, file profile information concerning each file of the shared set, wherein file profile information concerning a file describes the file and quantifies a similarity level of the file to other specific files, specific types of files and specific file content of the shared set; filtering the event notification stream of the file sharing and collaboration environment, by the computer, wherein the event notification stream comprises a plurality of notifications, each notification describing an event undertaken by a user and directed towards a file of the shared set; for each specific event notification in the filtered event notification stream, quantifying a relevance value, by the computer, for each specific one of the multiple users, based on user profile information concerning the specific user, file profile information concerning the file to which the event is directed, and user profile information concerning the user who undertook the event; and transmitting, by the computer, a notification describing the specific event only to those specific users for whom the relevance value exceeds a predetermined threshold value. 2. The method of claim 1 further comprising: monitoring actions taken by specific ones of the multiple users directed towards files of the shared set and towards received notifications of events of the file sharing and collaboration environment; and dynamically updating user profile information, based on applying machine learning techniques to the monitored actions. | 0.717358 |
9,551,590 | 11 | 12 | 11. A method of entering input into a vehicle system, said method comprising the steps of: providing a surface on a steering wheel and/or an armrest of the vehicle; sensing a plurality of locations on the surface that are touched by a user; determining an alphanumeric character best matching the plurality of touched locations on the surface; transmitting an input to an electronic system of the vehicle, the input being dependent upon the determined alphanumeric character; and confirming via feedback from the user that the determined alphanumeric character is an alphanumeric character that the user intended to convey when touching the surface, wherein the sensing and determining steps are repeated until a word formed by the determined alphanumeric characters is identified, the input being dependent upon the identified word, the word being a command or information to the electronic system. | 11. A method of entering input into a vehicle system, said method comprising the steps of: providing a surface on a steering wheel and/or an armrest of the vehicle; sensing a plurality of locations on the surface that are touched by a user; determining an alphanumeric character best matching the plurality of touched locations on the surface; transmitting an input to an electronic system of the vehicle, the input being dependent upon the determined alphanumeric character; and confirming via feedback from the user that the determined alphanumeric character is an alphanumeric character that the user intended to convey when touching the surface, wherein the sensing and determining steps are repeated until a word formed by the determined alphanumeric characters is identified, the input being dependent upon the identified word, the word being a command or information to the electronic system. 12. The method of claim 11 wherein the confirming step includes: informing the user of the determined alphanumeric character; receiving feedback from the user related to whether the determined alphanumeric character is the user's intended alphanumeric character; detecting vocalizations from the user; and ascertaining a string of alphanumeric characters best matching the vocalizations from the user as well as the plurality of touched locations on the surface, the input to the electronic system being dependent upon the ascertained string of alphanumeric characters. | 0.5 |
6,032,198 | 9 | 16 | 9. A computer system, comprising: a memory which stores an editing program; an input unit; a display unit; and a host processor which executes said editing program to support design of a plurality of application programs distributed on a network by: providing a graphical display, via said display unit, definition of application programs, definition of a logical relation between application programs, and interface definition of application program; when a user inputs, via said input device, the definition of a selected application program, the definition of the logical relation between application programs, and the interface definition of the selected application program, generating an interface definition language (IDL) file based on the input information; compiling the interface definition language (IDL) filed; and generating a makefile of the selected application program based on the input information and the compiled interface definition language (IDL) file. | 9. A computer system, comprising: a memory which stores an editing program; an input unit; a display unit; and a host processor which executes said editing program to support design of a plurality of application programs distributed on a network by: providing a graphical display, via said display unit, definition of application programs, definition of a logical relation between application programs, and interface definition of application program; when a user inputs, via said input device, the definition of a selected application program, the definition of the logical relation between application programs, and the interface definition of the selected application program, generating an interface definition language (IDL) file based on the input information; compiling the interface definition language (IDL) filed; and generating a makefile of the selected application program based on the input information and the compiled interface definition language (IDL) file. 16. A computer system according to claim 9, wherein said host processor further executes said editing program to store contents of a structure of the selected application program, the logical relation between application programs and the interface definition of the selected application program into a repository such that the contents to be edited are fetched from said repository. | 0.555814 |
7,561,158 | 1 | 4 | 1. A method of displaying feature importance in predictive modeling comprising the steps of: using a computer system having network connectivity to call a regression engine on a set of training data obtained from a storage unit connected to a computer network, said regression engine performing predictive modeling on said training data and outputting importance measures for explanatory variables for predicting a target variable; calling a graphical model structural learning module that receives the importance measures output by the regression engine, computes correlational information among the explanatory variables, and outputs a graph on the explanatory variables and representing a feature correlation structure among said explanatory variables; and displaying a feature importance measure, output by the regression engine, for each node in the graph, as an attribute of a node in the graph output by the graphical model structural learning module, to combine the predictive modeling of the regression engine with the feature correlation among the explanatory variables. | 1. A method of displaying feature importance in predictive modeling comprising the steps of: using a computer system having network connectivity to call a regression engine on a set of training data obtained from a storage unit connected to a computer network, said regression engine performing predictive modeling on said training data and outputting importance measures for explanatory variables for predicting a target variable; calling a graphical model structural learning module that receives the importance measures output by the regression engine, computes correlational information among the explanatory variables, and outputs a graph on the explanatory variables and representing a feature correlation structure among said explanatory variables; and displaying a feature importance measure, output by the regression engine, for each node in the graph, as an attribute of a node in the graph output by the graphical model structural learning module, to combine the predictive modeling of the regression engine with the feature correlation among the explanatory variables. 4. The method of displaying feature importance in predictive modeling recited in claim 1 , wherein the graphical model is obtained by calling a graphical model structure learning module using as input a correlation matrix between transforms of the input explanatory variables, in which the transform of each categorical variable is obtained by way of uni-variate regression tree with that input variable for the target variable, whereby the input explanatory variables are transformed so as to best predict the target variable. | 0.506554 |
9,626,432 | 15 | 18 | 15. A computer program product for generating plain language phrases comprising a computer readable hardware storage device having program code stored on the computer readable hardware storage device, the program code comprising: program code to receive a defect record; program code to send the defect record to a user; program code to predict a recommended first plain language phrase or word based on a user input from the user and how many times within a predetermined time period a first plain language phrase or word has been previously selected; program code to provide the recommended first plain language phrase or word to classify the defect record; program code to receive the recommended first plain language phrase or word which describes a type of testing associated with how the defect record was discovered; program code to provide connecting words; program code to receive a second plain language phrase or word which is binded to another plain language phrase or word using the connecting words to form a single plain language sentence related to how the defect associated with the defect record was resolved; program code to map the recommended first plain language phrase or word to a taxonomy; program code to map the second plain language phrase or word to the taxonomy; and program code to initiate to send the defect record with the taxonomy. | 15. A computer program product for generating plain language phrases comprising a computer readable hardware storage device having program code stored on the computer readable hardware storage device, the program code comprising: program code to receive a defect record; program code to send the defect record to a user; program code to predict a recommended first plain language phrase or word based on a user input from the user and how many times within a predetermined time period a first plain language phrase or word has been previously selected; program code to provide the recommended first plain language phrase or word to classify the defect record; program code to receive the recommended first plain language phrase or word which describes a type of testing associated with how the defect record was discovered; program code to provide connecting words; program code to receive a second plain language phrase or word which is binded to another plain language phrase or word using the connecting words to form a single plain language sentence related to how the defect associated with the defect record was resolved; program code to map the recommended first plain language phrase or word to a taxonomy; program code to map the second plain language phrase or word to the taxonomy; and program code to initiate to send the defect record with the taxonomy. 18. The computer program product of claim 15 , wherein the computer readable hardware storage device further validates classification choices of the user by sending the classification choices to another user. | 0.572016 |
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