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9,704,482 | 1 | 14 |
1. A method for spoken term detection, comprising: generating a time-marked word list of an automatic speech recognition system, wherein generating the time-marked word list comprises converting an indexing structure into an output, and wherein the time-marked word list comprises the output; generating an index from the time-marked word list, wherein generating the index comprises creating a word loop weighted finite state transducer for each utterance, i, of a plurality of utterances; receiving a plurality of keyword queries; and searching the index for a plurality of keyword hits; wherein the word loop weighted finite state transducer for each utterance, i, of the plurality of utterances, includes S i as a start node, E i as an end node, without a start node or an end node between S i and E i , and a plurality of arcs connected between an S i to E i pair for each utterance, the plurality of arcs corresponding to each word label, start and end time, and posterior probability in the time-marked word list and wherein the generating the time-marked word list, the generating the index, the receiving and the searching steps are performed via a processing device and a memory.
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1. A method for spoken term detection, comprising: generating a time-marked word list of an automatic speech recognition system, wherein generating the time-marked word list comprises converting an indexing structure into an output, and wherein the time-marked word list comprises the output; generating an index from the time-marked word list, wherein generating the index comprises creating a word loop weighted finite state transducer for each utterance, i, of a plurality of utterances; receiving a plurality of keyword queries; and searching the index for a plurality of keyword hits; wherein the word loop weighted finite state transducer for each utterance, i, of the plurality of utterances, includes S i as a start node, E i as an end node, without a start node or an end node between S i and E i , and a plurality of arcs connected between an S i to E i pair for each utterance, the plurality of arcs corresponding to each word label, start and end time, and posterior probability in the time-marked word list and wherein the generating the time-marked word list, the generating the index, the receiving and the searching steps are performed via a processing device and a memory. 14. The method according to claim 1 , wherein the index comprises a plurality of indexes that are simultaneously searched.
| 0.832418 |
8,032,820 | 15 | 16 |
15. The method of claim 10 , where the representation of the second web page also serves as a representation of a third web page, where the third web page includes a reference to the first web page.
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15. The method of claim 10 , where the representation of the second web page also serves as a representation of a third web page, where the third web page includes a reference to the first web page. 16. The method of claim 15 , where the representation of the second web page indicates how many web pages it serves as a representation for.
| 0.5 |
8,762,837 | 11 | 15 |
11. A document management system for managing a plurality of stored documents, said system comprising: a document maker, said document maker creating document records for each of a plurality of documents; a thumbnail rebuilder, said thumbnail rebuilder creating thumbnail representations of each page within a document; a central database storing said plurality of created documents therein, each document comprising a plurality of images for the respective pages therein and a corresponding plurality of image identification numbers, whereby said image identification numbers index said plurality of documents; a system journal, said system journal logging and tracking functions performed by the document image management system on said documents stored in said central database; and a user display device for displaying thereon at least one of said stored documents, said central database forwarding, pursuant to a given user query, a first page of said at least one document as a full image in a first file, and the remainder of pages for said at least one document in a second single file of thumbnail images, whereby when said given user of said user display device selects a given thumbnail image from said second single file of thumbnail images, the central database forwards the full image corresponding to said given thumbnail image.
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11. A document management system for managing a plurality of stored documents, said system comprising: a document maker, said document maker creating document records for each of a plurality of documents; a thumbnail rebuilder, said thumbnail rebuilder creating thumbnail representations of each page within a document; a central database storing said plurality of created documents therein, each document comprising a plurality of images for the respective pages therein and a corresponding plurality of image identification numbers, whereby said image identification numbers index said plurality of documents; a system journal, said system journal logging and tracking functions performed by the document image management system on said documents stored in said central database; and a user display device for displaying thereon at least one of said stored documents, said central database forwarding, pursuant to a given user query, a first page of said at least one document as a full image in a first file, and the remainder of pages for said at least one document in a second single file of thumbnail images, whereby when said given user of said user display device selects a given thumbnail image from said second single file of thumbnail images, the central database forwards the full image corresponding to said given thumbnail image. 15. The document management system according to claim 11 , wherein the stored documents include respective image identification numbers for indexing each of said plurality of images in said each document.
| 0.5 |
9,098,584 | 21 | 22 |
21. A method of alerting a person to appearance in a video, the method comprising the steps of: obtaining access to a first database having a plurality of first database records, each of the first database records comprising: an identification of a corresponding one of a plurality of humans; and features of a corresponding one of a plurality of photographs associated with the corresponding one of the plurality of humans; deconstructing a plurality of videos into individual frames; extracting features associated with images of human faces contained in the individual frames; populating a second database with a plurality of second database records, each of the second database records comprising: an identifier of a corresponding one of the videos; and corresponding ones of the features associated with the images of the human faces contained in the corresponding one of the videos; for at least one of the first database records, comparing the features of corresponding one of a plurality of photographs associated with the corresponding one of the plurality of humans to the features in the second database records to determine an identified human of the plurality of humans; obtaining, from the identified human, via a mechanism for expressing privacy preferences, at least one privacy preference comprising a visual query preference of the identified entity, wherein the mechanism provides for the identified entity of the comparing to express the privacy preferences to suppress a requester of the comparing from the receiving a visual query result that is associated with the identified entity, for the requester not being logged into an online account, or the requester not being in a social group of the identified entity, and further, the mechanism allows the individual subject of the query to express a preference for receiving an alert indicative of a use of the facial image of the identified entity in the second database records, and an online delivery address and frequency preference for the comparing and for receiving the alert; and advising the identified entity, corresponding to the at least one of the first database records, if the comparing yields a probable match.
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21. A method of alerting a person to appearance in a video, the method comprising the steps of: obtaining access to a first database having a plurality of first database records, each of the first database records comprising: an identification of a corresponding one of a plurality of humans; and features of a corresponding one of a plurality of photographs associated with the corresponding one of the plurality of humans; deconstructing a plurality of videos into individual frames; extracting features associated with images of human faces contained in the individual frames; populating a second database with a plurality of second database records, each of the second database records comprising: an identifier of a corresponding one of the videos; and corresponding ones of the features associated with the images of the human faces contained in the corresponding one of the videos; for at least one of the first database records, comparing the features of corresponding one of a plurality of photographs associated with the corresponding one of the plurality of humans to the features in the second database records to determine an identified human of the plurality of humans; obtaining, from the identified human, via a mechanism for expressing privacy preferences, at least one privacy preference comprising a visual query preference of the identified entity, wherein the mechanism provides for the identified entity of the comparing to express the privacy preferences to suppress a requester of the comparing from the receiving a visual query result that is associated with the identified entity, for the requester not being logged into an online account, or the requester not being in a social group of the identified entity, and further, the mechanism allows the individual subject of the query to express a preference for receiving an alert indicative of a use of the facial image of the identified entity in the second database records, and an online delivery address and frequency preference for the comparing and for receiving the alert; and advising the identified entity, corresponding to the at least one of the first database records, if the comparing yields a probable match. 22. The method of claim 21 , wherein: in the step of obtaining access to the first database, the features of the corresponding one of the plurality of photographs associated with the corresponding one of the plurality of humans are stored as first database feature vectors; and in the step of populating the second database, the corresponding ones of the features associated with the images of the human faces contained in the corresponding one of the videos are stored as second database feature vectors.
| 0.5 |
9,542,483 | 9 | 16 |
9. A computer-implemented method for visually suggesting classification for inclusion-based document cluster spines, comprising the steps of: designating a set of reference documents each associated with a classification code; obtaining a different set of un-coded documents; combining one or more of the coded reference documents with a plurality of un-coded documents into a combined document set; grouping the documents in the combined document set into clusters; organizing the clusters along one or more spines, each spine comprising a vector; and providing a visual suggestion for assigning one of the classification codes to one of the spines comprising visually representing each of the reference concepts in the clusters along that spine; identifying one of the documents as a center of one of the clusters; generating a score vector for the cluster center; comparing the score vector for the cluster center to score vectors associated with one or more of the reference documents; identifying a neighborhood of similar reference documents for the cluster based on the comparison; and assigning one of the classification codes to the cluster based on the neighborhood, further comprising: determining a distance between the cluster center and the reference documents in the neighborhood; and generating the classification code for assignment to the cluster, comprising at least one of: identifying the reference document with the closest distance to the cluster center and assigning the classification code of the reference document with the closest distance as the generated classification code for the cluster; calculating an average of the distances between the cluster center and the reference documents associated with each of the classification codes and assigning the classification code with the closest average distance as the generated classification code of the cluster; and counting the reference documents in the neighborhood for each of the classification codes, weighing each count based on the distance between the reference documents with the classification code and the cluster center, and assigning the classification code with the highest weighted count as the generated classification code of the cluster, wherein the steps are performed by a suitably programmed computer.
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9. A computer-implemented method for visually suggesting classification for inclusion-based document cluster spines, comprising the steps of: designating a set of reference documents each associated with a classification code; obtaining a different set of un-coded documents; combining one or more of the coded reference documents with a plurality of un-coded documents into a combined document set; grouping the documents in the combined document set into clusters; organizing the clusters along one or more spines, each spine comprising a vector; and providing a visual suggestion for assigning one of the classification codes to one of the spines comprising visually representing each of the reference concepts in the clusters along that spine; identifying one of the documents as a center of one of the clusters; generating a score vector for the cluster center; comparing the score vector for the cluster center to score vectors associated with one or more of the reference documents; identifying a neighborhood of similar reference documents for the cluster based on the comparison; and assigning one of the classification codes to the cluster based on the neighborhood, further comprising: determining a distance between the cluster center and the reference documents in the neighborhood; and generating the classification code for assignment to the cluster, comprising at least one of: identifying the reference document with the closest distance to the cluster center and assigning the classification code of the reference document with the closest distance as the generated classification code for the cluster; calculating an average of the distances between the cluster center and the reference documents associated with each of the classification codes and assigning the classification code with the closest average distance as the generated classification code of the cluster; and counting the reference documents in the neighborhood for each of the classification codes, weighing each count based on the distance between the reference documents with the classification code and the cluster center, and assigning the classification code with the highest weighted count as the generated classification code of the cluster, wherein the steps are performed by a suitably programmed computer. 16. The method according to claim 9 , wherein the visual representation of one of the reference documents associated with one of the classification codes comprises at least one of a symbol, shape, and color different from the visual representations of the reference documents with the remaining classification codes.
| 0.667368 |
5,414,644 | 6 | 8 |
6. The computer based system of claim 4 wherein the defined activities are different events related to each other, the relationship being shown on a relationship chart.
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6. The computer based system of claim 4 wherein the defined activities are different events related to each other, the relationship being shown on a relationship chart. 8. The computer based system of claim 6 wherein the relationship chart is based on genealogical information.
| 0.5 |
9,223,836 | 11 | 12 |
11. The non-transitory computer-readable medium of claim 1 wherein: the negation module is further configured to determine a number of negated occurrences for the key term in each document, the number of negated occurrences corresponding to a sum of the particular occurrences of the key term in each document where the at least one of the negation terms matches the other terms within the selected proximity of the particular occurrence of the key term; and the term weight comprises a difference between the term frequency and the number of negated occurrences.
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11. The non-transitory computer-readable medium of claim 1 wherein: the negation module is further configured to determine a number of negated occurrences for the key term in each document, the number of negated occurrences corresponding to a sum of the particular occurrences of the key term in each document where the at least one of the negation terms matches the other terms within the selected proximity of the particular occurrence of the key term; and the term weight comprises a difference between the term frequency and the number of negated occurrences. 12. The non-transitory computer-readable medium of claim 11 wherein the ranking module is configured to determine the relevancy ranking value for each document as a document relevance (DR) value based on a ratio comprising the term weight (TW) value in a numerator of the ratio and the term frequency (TF) value in a denominator of the ratio.
| 0.5 |
9,444,907 | 1 | 4 |
1. A computer-implemented method comprising: obtaining keywords from user profiles of a group of users of a social networking system who previously responded to an invitation; extracting a set of keywords from a subject user profile of a subject user of the social networking system; determining a first occurrence of keywords in the set of keywords in a first group of user profiles corresponding to a group of users of the social networking system who responded positively to the invitation; determining a second occurrence of keywords in the set of keywords in a second group of user profiles corresponding to a group of users who responded negatively to the invitation; computing a score for one or more of the keywords in the set of keywords based on a comparison of the first occurrence and the second occurrence; and predicting a response to the invitation by the subject user based on the score for one or more of the keywords.
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1. A computer-implemented method comprising: obtaining keywords from user profiles of a group of users of a social networking system who previously responded to an invitation; extracting a set of keywords from a subject user profile of a subject user of the social networking system; determining a first occurrence of keywords in the set of keywords in a first group of user profiles corresponding to a group of users of the social networking system who responded positively to the invitation; determining a second occurrence of keywords in the set of keywords in a second group of user profiles corresponding to a group of users who responded negatively to the invitation; computing a score for one or more of the keywords in the set of keywords based on a comparison of the first occurrence and the second occurrence; and predicting a response to the invitation by the subject user based on the score for one or more of the keywords. 4. The method of claim 1 , wherein the invitation comprises an advertisement.
| 0.918776 |
8,554,854 | 12 | 13 |
12. The method of claim 1 , wherein identifying additional relevant terms using messages posted to at least one social network based upon the initial list of relevant terms comprises: determining the uniqueness of all combinations of a predetermined selection of the highest scoring terms from the initial list of relevant terms; and selecting combinations of the terms based upon the uniqueness of the combination.
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12. The method of claim 1 , wherein identifying additional relevant terms using messages posted to at least one social network based upon the initial list of relevant terms comprises: determining the uniqueness of all combinations of a predetermined selection of the highest scoring terms from the initial list of relevant terms; and selecting combinations of the terms based upon the uniqueness of the combination. 13. The method of claim 12 , wherein uniqueness of a combination of terms is determined based upon the message rate of the combination of terms within at least one message stream.
| 0.799327 |
9,990,423 | 30 | 39 |
30. A computer-implemented system, comprising: one or more processors; one or more non-transitory computer readable storage media; computer readable instructions stored on the one or more non-transitory computer readable storage media which, when executed by the one or more processors, implement a first cluster configured to perform operations comprising: receiving, at a first cluster, a search query, the first cluster being a first data intake and query system; transmitting, through a firewall of either the first cluster or a cloud-based cluster, a request for information identifying a plurality of indexers of the cloud-based cluster, the cloud-based cluster being a second data intake and query system; in response to the request, obtaining, from the cloud-based cluster, the information identifying the plurality of indexers, wherein the first cluster and the cloud-based cluster each include at least one master node that is knowledgeable about active indexers within its respective cluster, and the information identifies the plurality of indexers based on the at least one master node of the cloud-based cluster identifying the active indexers; distributing the search query to the plurality of indexers of the cloud-based cluster and one or more indexers of the first cluster, said distributing using the obtained information identifying the plurality of indexers and being through the firewall; and receiving, at the first cluster, a response to the distributed search query from at least one of the plurality of indexers of the cloud-based cluster wherein each response from a respective indexer is produced by the respective indexer based on an evaluation, by the respective indexer, of the distributed search query.
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30. A computer-implemented system, comprising: one or more processors; one or more non-transitory computer readable storage media; computer readable instructions stored on the one or more non-transitory computer readable storage media which, when executed by the one or more processors, implement a first cluster configured to perform operations comprising: receiving, at a first cluster, a search query, the first cluster being a first data intake and query system; transmitting, through a firewall of either the first cluster or a cloud-based cluster, a request for information identifying a plurality of indexers of the cloud-based cluster, the cloud-based cluster being a second data intake and query system; in response to the request, obtaining, from the cloud-based cluster, the information identifying the plurality of indexers, wherein the first cluster and the cloud-based cluster each include at least one master node that is knowledgeable about active indexers within its respective cluster, and the information identifies the plurality of indexers based on the at least one master node of the cloud-based cluster identifying the active indexers; distributing the search query to the plurality of indexers of the cloud-based cluster and one or more indexers of the first cluster, said distributing using the obtained information identifying the plurality of indexers and being through the firewall; and receiving, at the first cluster, a response to the distributed search query from at least one of the plurality of indexers of the cloud-based cluster wherein each response from a respective indexer is produced by the respective indexer based on an evaluation, by the respective indexer, of the distributed search query. 39. The system as described in claim 30 , wherein said first cluster and said cloud-based cluster each include a single master node that is knowledgeable about active indexers within its respective cluster, and the information identifies the plurality of indexers based on the single master node of the cloud-based cluster identifying the active indexers.
| 0.55402 |
9,967,101 | 9 | 13 |
9. A non-transitory computer-readable medium having computer instructions stored thereon that are executable by a processing device to perform or control performance of operations comprising: determining a random enrollment polynomial; extracting a set of enrollment feature points from an enrollment biometric measurement; randomly selecting one or more enrollment code words from a linear error correction code; determining obfuscated enrollment feature point data describing an obfuscated version of the set of enrollment feature points that is obfuscated using the one or more enrollment code words so that the set of enrollment feature points cannot be determined from the obfuscated enrollment feature point data without the one or more enrollment code words; determining obfuscated enrollment code word data describing an obfuscated version of the one or more enrollment code words that is obfuscated using the random enrollment polynomial so that the one or more code words cannot be determined from the obfuscated enrollment code word data without the random enrollment polynomial; determining an enrollment biometric template including the obfuscated enrollment feature point data and the obfuscated enrollment code word data; generating a public key based on the random enrollment polynomial, wherein the public key obfuscates the random enrollment polynomial; determining enrollment data that keeps the one or more enrollment code words and the random enrollment polynomial secret, the enrollment data including the enrollment biometric template and the public key; extracting a set of verification feature points from a verification biometric measurement responsive to receiving a verification challenge including the enrollment data and a random number value, wherein the enrollment data is associated with an enrollment user and the verification biometric measurement is associated with a verification user attempting to authenticate as the enrollment user; analyzing the enrollment data to determine the obfuscated enrollment feature point data included in the enrollment biometric template of the enrollment data; determining one or more verification code words based on the set of verification feature points and the obfuscated enrollment feature point data; analyzing the enrollment data to determine the public key included in the enrollment data; and determining a verification polynomial based on the one or more verification code words.
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9. A non-transitory computer-readable medium having computer instructions stored thereon that are executable by a processing device to perform or control performance of operations comprising: determining a random enrollment polynomial; extracting a set of enrollment feature points from an enrollment biometric measurement; randomly selecting one or more enrollment code words from a linear error correction code; determining obfuscated enrollment feature point data describing an obfuscated version of the set of enrollment feature points that is obfuscated using the one or more enrollment code words so that the set of enrollment feature points cannot be determined from the obfuscated enrollment feature point data without the one or more enrollment code words; determining obfuscated enrollment code word data describing an obfuscated version of the one or more enrollment code words that is obfuscated using the random enrollment polynomial so that the one or more code words cannot be determined from the obfuscated enrollment code word data without the random enrollment polynomial; determining an enrollment biometric template including the obfuscated enrollment feature point data and the obfuscated enrollment code word data; generating a public key based on the random enrollment polynomial, wherein the public key obfuscates the random enrollment polynomial; determining enrollment data that keeps the one or more enrollment code words and the random enrollment polynomial secret, the enrollment data including the enrollment biometric template and the public key; extracting a set of verification feature points from a verification biometric measurement responsive to receiving a verification challenge including the enrollment data and a random number value, wherein the enrollment data is associated with an enrollment user and the verification biometric measurement is associated with a verification user attempting to authenticate as the enrollment user; analyzing the enrollment data to determine the obfuscated enrollment feature point data included in the enrollment biometric template of the enrollment data; determining one or more verification code words based on the set of verification feature points and the obfuscated enrollment feature point data; analyzing the enrollment data to determine the public key included in the enrollment data; and determining a verification polynomial based on the one or more verification code words. 13. The non-transitory computer-readable medium of claim 9 , wherein the operations further comprise: determining a private key based on the verification polynomial; and determining a challenge answer by signing the random number value with the private key, wherein the verification user is authenticated as the enrollment user based on whether the private key corresponds to the public key to form a key pair.
| 0.5 |
9,349,371 | 1 | 4 |
1. A speech recognition terminal device capable of communicating with a speech recognition server that carries out speech recognition, the speech recognition terminal device comprising: a speech acquisition device that acquires a speech command spoken by a user; a request device that requests the speech recognition server to carry out the speech recognition of the speech command acquired by the speech acquisition device; a prediction device that predicts a present delay time until a result of the speech recognition of the speech command requested from the request device is obtained from the speech recognition server; a determination device that determines a filler word with a time length in accordance with the present delay time predicted by the prediction device; a filler speaking device that outputs the filler word determined by the determination device as speech information during a waiting time until the result of the speech recognition requested from the request device is obtained from the speech recognition server; a response device that, when the result of the speech recognition is acquired from the speech recognition server, executes a process of responding to the user based on the acquired result of the speech recognition; and an acquiring device that acquires time information expressing past delay times when the communication has been carried out with the speech recognition server in past, wherein based on the past delay times expressed by the time information acquired by the acquisition device, the prediction device predicts the present delay time until the result of the speech recognition requested from the request device is obtained from the speech recognition server.
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1. A speech recognition terminal device capable of communicating with a speech recognition server that carries out speech recognition, the speech recognition terminal device comprising: a speech acquisition device that acquires a speech command spoken by a user; a request device that requests the speech recognition server to carry out the speech recognition of the speech command acquired by the speech acquisition device; a prediction device that predicts a present delay time until a result of the speech recognition of the speech command requested from the request device is obtained from the speech recognition server; a determination device that determines a filler word with a time length in accordance with the present delay time predicted by the prediction device; a filler speaking device that outputs the filler word determined by the determination device as speech information during a waiting time until the result of the speech recognition requested from the request device is obtained from the speech recognition server; a response device that, when the result of the speech recognition is acquired from the speech recognition server, executes a process of responding to the user based on the acquired result of the speech recognition; and an acquiring device that acquires time information expressing past delay times when the communication has been carried out with the speech recognition server in past, wherein based on the past delay times expressed by the time information acquired by the acquisition device, the prediction device predicts the present delay time until the result of the speech recognition requested from the request device is obtained from the speech recognition server. 4. The speech recognition terminal device according to claim 1 , wherein: the time information acquired by the acquiring device is association with a time of the communication, and based on the past delay times expressed by the time information associated with the communication carried out immediately prior to, or during a period near, a present time point, the prediction device predicts the present delay time until the result of the speech recognition is obtained from the speech recognition server.
| 0.52809 |
8,164,596 | 1 | 10 |
1. A method comprising: displaying a first timeline for a first animation object; displaying a second timeline for a second animation object; displaying a first identifier of the first animation object in association with the first timeline; displaying a second identifier of the second animation object in association with the second timeline; wherein the first identifier and the second identifier are displayed with respect to each other in an order that determines a z-index order of the first animation object with respect to the second animation object in a style sheet animation of at least the first animation object and the second animation object; generating style sheet language text which, when processed by a style sheet animation processor, causes the first animation object and the second animation object to be animated in the style sheet animation in accordance with the determined z-index order; wherein the method is performed by a computing device.
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1. A method comprising: displaying a first timeline for a first animation object; displaying a second timeline for a second animation object; displaying a first identifier of the first animation object in association with the first timeline; displaying a second identifier of the second animation object in association with the second timeline; wherein the first identifier and the second identifier are displayed with respect to each other in an order that determines a z-index order of the first animation object with respect to the second animation object in a style sheet animation of at least the first animation object and the second animation object; generating style sheet language text which, when processed by a style sheet animation processor, causes the first animation object and the second animation object to be animated in the style sheet animation in accordance with the determined z-index order; wherein the method is performed by a computing device. 10. The method of claim 1 , wherein displaying the first identifier in association with the first timeline includes displaying the first identifier and the first timeline in a first row; and wherein displaying the second identifier in association with the second timeline includes displaying the second identifier and the second timeline in a second row.
| 0.832861 |
9,117,006 | 1 | 5 |
1. A system for recommending keywords, comprising: one or more processors configured to: receive a set of product information including a product title; extract and parse the product title into a set of parsed elements; find a plurality of candidate keywords corresponding to at least a subset of the set of parsed elements based at least in part on stored predetermined mappings between parsed data and keywords; determine a plurality of composite correlation scores for corresponding ones of the plurality of candidate keywords, wherein to determine the plurality of composite correlation scores includes to determine a first composite correlation score associated with a first candidate keyword of the plurality of candidate keywords, wherein to determine the first composite correlation score associated with the first candidate keyword includes to determine an industry index value associated with the first candidate keyword, including to: determine a first similarity value between one or more industries associated with the first candidate keyword and one or more industries associated with sets of product information that are relevant to the first candidate keyword; determine a second similarity value between the one or more industries associated with the first candidate keyword and one or more industries of one or more seller users associated with the sets of product information that are relevant to the first candidate keyword; and determine the industry index value associated with the first candidate keyword based at least in part on a combination of the first similarity value and the second similarity value; and sort at least a subset of the plurality of candidate keywords into a ranked list based on at least a subset of the plurality of composite correlation scores; and select a set of one or more keywords to recommend from the plurality of candidate keywords based at least in part on the ranked list; and one or more memories coupled to the one or more processors and configured to provide the one or more processors with instructions.
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1. A system for recommending keywords, comprising: one or more processors configured to: receive a set of product information including a product title; extract and parse the product title into a set of parsed elements; find a plurality of candidate keywords corresponding to at least a subset of the set of parsed elements based at least in part on stored predetermined mappings between parsed data and keywords; determine a plurality of composite correlation scores for corresponding ones of the plurality of candidate keywords, wherein to determine the plurality of composite correlation scores includes to determine a first composite correlation score associated with a first candidate keyword of the plurality of candidate keywords, wherein to determine the first composite correlation score associated with the first candidate keyword includes to determine an industry index value associated with the first candidate keyword, including to: determine a first similarity value between one or more industries associated with the first candidate keyword and one or more industries associated with sets of product information that are relevant to the first candidate keyword; determine a second similarity value between the one or more industries associated with the first candidate keyword and one or more industries of one or more seller users associated with the sets of product information that are relevant to the first candidate keyword; and determine the industry index value associated with the first candidate keyword based at least in part on a combination of the first similarity value and the second similarity value; and sort at least a subset of the plurality of candidate keywords into a ranked list based on at least a subset of the plurality of composite correlation scores; and select a set of one or more keywords to recommend from the plurality of candidate keywords based at least in part on the ranked list; and one or more memories coupled to the one or more processors and configured to provide the one or more processors with instructions. 5. The system of claim 1 , wherein to determine the plurality of composite correlation scores includes to determine the first composite correlation score associated with the first candidate keyword based at least in part on a Gini index value associated with the first candidate keyword, wherein the Gini index value is used to reflect a probability that sets of product information that match the first candidate keyword are displayed among search results.
| 0.607388 |
9,165,072 | 22 | 25 |
22. A method, comprising: obtaining, by at least one computing device, a search query from a first user; determining, by the at least one computing device, a portion of a media content feature by executing a verbal media content search based at least in part on the search query, the portion of the media content feature including verbal media content that matches the search query, the verbal media content corresponding to a secondary language of the media content feature; determining, by the at least one computing device, that the first user expresses an interest in the portion of the media content feature relative to a search result listing; determining, by the at least one computing device, a second user based at least in part on an association between the secondary language and the second user; and recommending, by the at least one computing device, the media content to the second user based at least in part on the interest expressed by the first user in the portion of the media content feature.
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22. A method, comprising: obtaining, by at least one computing device, a search query from a first user; determining, by the at least one computing device, a portion of a media content feature by executing a verbal media content search based at least in part on the search query, the portion of the media content feature including verbal media content that matches the search query, the verbal media content corresponding to a secondary language of the media content feature; determining, by the at least one computing device, that the first user expresses an interest in the portion of the media content feature relative to a search result listing; determining, by the at least one computing device, a second user based at least in part on an association between the secondary language and the second user; and recommending, by the at least one computing device, the media content to the second user based at least in part on the interest expressed by the first user in the portion of the media content feature. 25. The method of claim 22 , further comprising determining, by the at least one computing device, the second user based at least in part on social network data indicating a relationship between the first user and the second user.
| 0.708122 |
8,849,811 | 11 | 14 |
11. A system comprising hardware comprising a processer and software stored on a non-transitory storage medium, wherein programmatic instructions of the software are executable on the hardware causing the system to: receive a search query comprising of at least one search criteria, wherein the at least one search criteria is a text string; perform a keyword search against an standard index, wherein the standard index is comprised of keywords obtained from a document within a standard cluster, wherein the standard cluster is a document cluster sharing a plurality of keywords: execute an enhanced search against an enhanced index, wherein the enhanced index is comprised of metadata associated with an enhanced cluster, wherein the enhanced cluster is a document cluster associated with the metadata, wherein the metadata is a user defined metadata; aggregate the enhanced cluster each into a merged document, wherein the merged document is a document comprising of the enhanced cluster contents; and execute a ranking algorithm on the merged document to obtain a final ranking of content within the single document, wherein the ranking algorithm applies a ranking formula, wherein the ranking formula comprises sum of product of a tag line presence value with a hit weight and product of a cluster rank with the hit weight subtracted from one.
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11. A system comprising hardware comprising a processer and software stored on a non-transitory storage medium, wherein programmatic instructions of the software are executable on the hardware causing the system to: receive a search query comprising of at least one search criteria, wherein the at least one search criteria is a text string; perform a keyword search against an standard index, wherein the standard index is comprised of keywords obtained from a document within a standard cluster, wherein the standard cluster is a document cluster sharing a plurality of keywords: execute an enhanced search against an enhanced index, wherein the enhanced index is comprised of metadata associated with an enhanced cluster, wherein the enhanced cluster is a document cluster associated with the metadata, wherein the metadata is a user defined metadata; aggregate the enhanced cluster each into a merged document, wherein the merged document is a document comprising of the enhanced cluster contents; and execute a ranking algorithm on the merged document to obtain a final ranking of content within the single document, wherein the ranking algorithm applies a ranking formula, wherein the ranking formula comprises sum of product of a tag line presence value with a hit weight and product of a cluster rank with the hit weight subtracted from one. 14. The system of claim 11 , wherein the computer program product is operable to populate a database with a tag, a link, and a table of contents associated with a document, wherein the document is associated with the enhanced cluster.
| 0.5 |
8,271,474 | 8 | 9 |
8. The method of claim 3 , wherein at least one user of the web site administers a portion of the content of the web site.
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8. The method of claim 3 , wherein at least one user of the web site administers a portion of the content of the web site. 9. The method of claim 8 , wherein the web site is automatically run with minimal assistance of external administrators due to content of the web site being from the search results, being entered or uploaded by the users of the web site, and being administered by the least one user of the web site.
| 0.5 |
9,717,006 | 1 | 6 |
1. A system comprising: at least one processor; and one or more computer-readable storage media including instructions stored thereon that, responsive to execution by the at least one processor, cause the system perform operations including: placing a device in a quarantine state for a wireless network in response to a request from the device to connect to the wireless network; ascertaining one or more attributes of the device while the device is in the quarantine state including a hardware attribute and at least one attribute pertaining to a software attribute, a driver attribute, or a configuration setting attribute of the device, at least one of the attributes pertaining to a prospective quality of performance capability for the device over the wireless network; determining, based on the one or more attributes, one or more connection parameters for connection of the device to the wireless network based on comparing the one or more attributes to a local specification for the wireless network to ascertain compatibility of the one or more attributes with the wireless network, at least one local specification pertaining to a baseline quality of performance capability for a device over the wireless network; and releasing the device from the quarantine state such that the device is connected to the wireless network subject to the one or more connection parameters.
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1. A system comprising: at least one processor; and one or more computer-readable storage media including instructions stored thereon that, responsive to execution by the at least one processor, cause the system perform operations including: placing a device in a quarantine state for a wireless network in response to a request from the device to connect to the wireless network; ascertaining one or more attributes of the device while the device is in the quarantine state including a hardware attribute and at least one attribute pertaining to a software attribute, a driver attribute, or a configuration setting attribute of the device, at least one of the attributes pertaining to a prospective quality of performance capability for the device over the wireless network; determining, based on the one or more attributes, one or more connection parameters for connection of the device to the wireless network based on comparing the one or more attributes to a local specification for the wireless network to ascertain compatibility of the one or more attributes with the wireless network, at least one local specification pertaining to a baseline quality of performance capability for a device over the wireless network; and releasing the device from the quarantine state such that the device is connected to the wireless network subject to the one or more connection parameters. 6. A system as recited in claim 1 , wherein said ascertaining comprises: querying the device for the one or more attributes; and receiving a response from the device identifying the one or more attributes.
| 0.719945 |
8,279,343 | 17 | 18 |
17. The summary content generation device according to claim 1 , comprising human detection unit which analyzes video data comprised by said digital broadcast signals, detects humans appearing in images, and judges whether the humans are facing forward, and facial expression detection unit which analyzes the image data and judges the facial expression of a human detected by said human detection unit, and wherein said still image extraction unit extracts images in which specific facial expressions of humans appear as said still images.
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17. The summary content generation device according to claim 1 , comprising human detection unit which analyzes video data comprised by said digital broadcast signals, detects humans appearing in images, and judges whether the humans are facing forward, and facial expression detection unit which analyzes the image data and judges the facial expression of a human detected by said human detection unit, and wherein said still image extraction unit extracts images in which specific facial expressions of humans appear as said still images. 18. The summary content generation device according to claim 17 , wherein said specific facial expressions are expressions in which the eyes are open, or are laughing expressions, or are crying expressions.
| 0.5 |
9,779,084 | 1 | 2 |
1. A system adapted to assist instructors with interactions with students, comprising: a network server comprising a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, where the plurality of instructions comprises: instructions, that when executed, receive a communication posted by a student; instructions, that when executed, receive a communication written by an instructor; instructions, that when executed, apply a psychologically-based linguistic analysis to text of the student communication and instructor communication to predict a likelihood of a student outcome with the instructor; instructions, that when executed, monitor the electronic communication between the instructor and the student to measure instructor performance; instructions, that when executed, apply a scoring algorithm to the measured instructor performance and to the analyzed text of the instructor communication to detect keywords and phrases; instructions, that when executed, generate a score for the instructor communication from the application of the scoring algorithm and from comparison of the detected keywords and phrases with a plurality of keywords and phrases stored in a library; and instructions, that when executed, create an evaluation report that provides guidance to the instructor to facilitate a responsive communication with the student based on the score for the instructor communication, wherein the responsive communication is received on a student device using a modality for the responsive communication based on the predicted likelihood of the student outcome.
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1. A system adapted to assist instructors with interactions with students, comprising: a network server comprising a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, where the plurality of instructions comprises: instructions, that when executed, receive a communication posted by a student; instructions, that when executed, receive a communication written by an instructor; instructions, that when executed, apply a psychologically-based linguistic analysis to text of the student communication and instructor communication to predict a likelihood of a student outcome with the instructor; instructions, that when executed, monitor the electronic communication between the instructor and the student to measure instructor performance; instructions, that when executed, apply a scoring algorithm to the measured instructor performance and to the analyzed text of the instructor communication to detect keywords and phrases; instructions, that when executed, generate a score for the instructor communication from the application of the scoring algorithm and from comparison of the detected keywords and phrases with a plurality of keywords and phrases stored in a library; and instructions, that when executed, create an evaluation report that provides guidance to the instructor to facilitate a responsive communication with the student based on the score for the instructor communication, wherein the responsive communication is received on a student device using a modality for the responsive communication based on the predicted likelihood of the student outcome. 2. The system of claim 1 , wherein the student communication is received during a class.
| 0.813559 |
9,843,555 | 11 | 12 |
11. The method of claim 9 , wherein the advising includes advising by the server system of the voice message from the first communication device and the common identifier associated with the first and second communication devices.
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11. The method of claim 9 , wherein the advising includes advising by the server system of the voice message from the first communication device and the common identifier associated with the first and second communication devices. 12. The method of claim 11 , further comprising receiving by the server system a call from the second communication device directed to the common identifier and playing back by the server system the voice message.
| 0.5 |
9,286,572 | 15 | 16 |
15. The method of claim 12 , further comprising altering the self-assessment identifier for said autonomous actor based in part on the assessment value.
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15. The method of claim 12 , further comprising altering the self-assessment identifier for said autonomous actor based in part on the assessment value. 16. The method of claim 15 , wherein altering said self-assessment identifier comprises at least one of changing the metadata elements for the autonomous actor, the quantity of metadata information stored by the autonomous actor, or the relationship between the metadata elements for the autonomous actor and the quantity of metadata information stored for each metadata element.
| 0.5 |
8,543,619 | 21 | 22 |
21. A method of merging data containing XML documents, said method comprising: receiving a first XML document and a second XML document, said first XML document containing a first plurality of elements and said second XML document containing a second plurality of elements, wherein each of said first plurality of elements and said second plurality of elements have a same identifier, each of said first plurality of elements and said second plurality of elements having a corresponding set of attributes, with each attribute having an associated value; receiving a merge attribute for merging said first plurality of elements with said second plurality of elements, wherein said merge attribute is an XML attribute and is present in the set of attributes corresponding to all of said first plurality of elements and said second plurality of elements; comparing a first element of said first plurality of elements with a second element of a second plurality of elements, wherein said comparing checks whether a first value of said merge attribute in said first element equals a second value of said merge attribute in said second element; forming a merged XML document of said first XML document and said second XML document, wherein if said comparing determines that said first value equals said second value, said forming includes in said merged XML document a single element as a result of processing of said first element and said second element, and otherwise, said forming to include two elements corresponding respectively to said first element and said second element.
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21. A method of merging data containing XML documents, said method comprising: receiving a first XML document and a second XML document, said first XML document containing a first plurality of elements and said second XML document containing a second plurality of elements, wherein each of said first plurality of elements and said second plurality of elements have a same identifier, each of said first plurality of elements and said second plurality of elements having a corresponding set of attributes, with each attribute having an associated value; receiving a merge attribute for merging said first plurality of elements with said second plurality of elements, wherein said merge attribute is an XML attribute and is present in the set of attributes corresponding to all of said first plurality of elements and said second plurality of elements; comparing a first element of said first plurality of elements with a second element of a second plurality of elements, wherein said comparing checks whether a first value of said merge attribute in said first element equals a second value of said merge attribute in said second element; forming a merged XML document of said first XML document and said second XML document, wherein if said comparing determines that said first value equals said second value, said forming includes in said merged XML document a single element as a result of processing of said first element and said second element, and otherwise, said forming to include two elements corresponding respectively to said first element and said second element. 22. The method of claim 21 , wherein each of said first plurality of elements and said second plurality of elements contains a corresponding set of child elements, wherein said first element contains a first set of child elements and said second element contains a second set of child elements, wherein said single element comprises a merged set of elements formed based on said first set of child elements and said second set of child elements, wherein said forming decides to include said single element or said two elements solely based on said comparing of said first value and said second value, without having to examine said first set of child elements and said second set of child elements for equality.
| 0.5 |
7,542,973 | 1 | 6 |
1. A computer program product for adaptive matching of records in a data repository comprising: a computer usable memory medium having computer readable program code embodied therein wherein said computer readable program code comprises a matching executable unit configured to: present at least one field common to a first record and a second record wherein said at least one field is used to perform a match between said first record and said second record and wherein said at least one field is presented to a user; obtain a first selected field from said first record and a second selected field from said second record wherein said first selected field and said second selected field is obtained from said user; obtain a first data entry in said first selected field for said first record, said first data entry comprising a first string; tokenize said first string to retrieve a first tokenized data entry string; obtain a second data entry in said second selected field for said second record, said second data entry comprising a second string; tokenize said second string to retrieve a second tokenized data entry string; exclude at least one character from said first tokenized data entry string for utilization in said match that involves said first selected field and said second selected field; exclude at least one different character with respect to said at least one character from said second tokenized data entry string for utilization in said match that involves said first selected field and said second selected field; remove frequently used strings from said first tokenized data entry string and from said second tokenized data entry string; normalize data from said first selected field and from said second selected field to cleanse strings; accept a first list of tokens desired for said match to occur utilizing said first selected field; accept a second list of tokens desired for said match to occur utilizing said second selected field; assign weights to each token in said first list of tokens and each token in said second list of tokens; calculate a score for said match through summation of said weights for each token occurring in said first tokenized data entry string and said first record and for each token that occurs in said second tokenized data entry string and said second record; generate a group of similar records when said score is above a threshold; display said group of similar records to said user; learn at least one token that is relevant; learn at least one weight that results in a match; and learn at least one match criteria appropriate for said match for use in future matching based on user input regarding said score.
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1. A computer program product for adaptive matching of records in a data repository comprising: a computer usable memory medium having computer readable program code embodied therein wherein said computer readable program code comprises a matching executable unit configured to: present at least one field common to a first record and a second record wherein said at least one field is used to perform a match between said first record and said second record and wherein said at least one field is presented to a user; obtain a first selected field from said first record and a second selected field from said second record wherein said first selected field and said second selected field is obtained from said user; obtain a first data entry in said first selected field for said first record, said first data entry comprising a first string; tokenize said first string to retrieve a first tokenized data entry string; obtain a second data entry in said second selected field for said second record, said second data entry comprising a second string; tokenize said second string to retrieve a second tokenized data entry string; exclude at least one character from said first tokenized data entry string for utilization in said match that involves said first selected field and said second selected field; exclude at least one different character with respect to said at least one character from said second tokenized data entry string for utilization in said match that involves said first selected field and said second selected field; remove frequently used strings from said first tokenized data entry string and from said second tokenized data entry string; normalize data from said first selected field and from said second selected field to cleanse strings; accept a first list of tokens desired for said match to occur utilizing said first selected field; accept a second list of tokens desired for said match to occur utilizing said second selected field; assign weights to each token in said first list of tokens and each token in said second list of tokens; calculate a score for said match through summation of said weights for each token occurring in said first tokenized data entry string and said first record and for each token that occurs in said second tokenized data entry string and said second record; generate a group of similar records when said score is above a threshold; display said group of similar records to said user; learn at least one token that is relevant; learn at least one weight that results in a match; and learn at least one match criteria appropriate for said match for use in future matching based on user input regarding said score. 6. The computer program product of claim 1 wherein said computer readable program code is further configured to: accept input that signifies if said first list of tokens is required to match in sequential order.
| 0.666139 |
9,348,935 | 1 | 6 |
1. A method for delivering related video content for augmented keywords on a web page, the method comprising: (a) receiving, by a server from an agent executing within a browser responsive to the agent detecting a mouse over a keyword currently displayed on a web page of a client, the keyword identified for augmentation via a user interface overlay, the server configured to dynamically select, responsive to receiving the keyword at a time of the mouse over, one or more videos for the user interface overlay for the keyword; (b) identifying, by the server responsive to receiving the keyword from the agent responsive to the agent detecting the mouse over, a plurality of videos related to the keyword; (c) determining, by a content relevancy engine, an order of relevance of the plurality of videos to the keyword; (d) selecting, by the server within a predetermined time period from receipt of the keyword from the agent, one or more videos of the plurality of videos with a higher order of relevance; (e) transmitting, by the server to the agent within the predetermined time period from receipt of the keyword from the agent, the user interface overlay, to be displayed by the agent responsive to the mouse over, to include the selected one or more videos of the plurality of videos with the higher order of relevance for at least one of user selection or display in the user interface overlay, the predetermined time period comprising a time threshold within which the server is to complete the selection of the one or more videos and to complete delivery of the selected one or more videos to the client.
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1. A method for delivering related video content for augmented keywords on a web page, the method comprising: (a) receiving, by a server from an agent executing within a browser responsive to the agent detecting a mouse over a keyword currently displayed on a web page of a client, the keyword identified for augmentation via a user interface overlay, the server configured to dynamically select, responsive to receiving the keyword at a time of the mouse over, one or more videos for the user interface overlay for the keyword; (b) identifying, by the server responsive to receiving the keyword from the agent responsive to the agent detecting the mouse over, a plurality of videos related to the keyword; (c) determining, by a content relevancy engine, an order of relevance of the plurality of videos to the keyword; (d) selecting, by the server within a predetermined time period from receipt of the keyword from the agent, one or more videos of the plurality of videos with a higher order of relevance; (e) transmitting, by the server to the agent within the predetermined time period from receipt of the keyword from the agent, the user interface overlay, to be displayed by the agent responsive to the mouse over, to include the selected one or more videos of the plurality of videos with the higher order of relevance for at least one of user selection or display in the user interface overlay, the predetermined time period comprising a time threshold within which the server is to complete the selection of the one or more videos and to complete delivery of the selected one or more videos to the client. 6. The method of claim 1 , wherein step (b) further comprises identifying, by the server, the plurality of videos from a web site hosting the web page.
| 0.803896 |
7,971,186 | 1 | 11 |
1. A computer-implemented method for use in a computer programming environment, comprising: invoking a script; and determining an execution order for the invoked script predicated on the passing of parameters between scripted actions, comprising: selecting a first scripted action from a plurality of actions, wherein the plurality of actions are contemporaneously selectable with respect to the first scripted action; executing the first selected scripted action if sufficient parameter information is available to execute the selected first scripted action; selecting a second scripted action from the plurality of actions in response to determining that there is insufficient parameter information to execute the first selected scripted action; executing the second selected action and; wherein the second scripted action converts data from an incompatible data type into a compatible data type and wherein the scripted action are selected from candidate actions chosen according to a determination of relevance relative to previously chosen action and need for conversion of data to compatible data types.
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1. A computer-implemented method for use in a computer programming environment, comprising: invoking a script; and determining an execution order for the invoked script predicated on the passing of parameters between scripted actions, comprising: selecting a first scripted action from a plurality of actions, wherein the plurality of actions are contemporaneously selectable with respect to the first scripted action; executing the first selected scripted action if sufficient parameter information is available to execute the selected first scripted action; selecting a second scripted action from the plurality of actions in response to determining that there is insufficient parameter information to execute the first selected scripted action; executing the second selected action and; wherein the second scripted action converts data from an incompatible data type into a compatible data type and wherein the scripted action are selected from candidate actions chosen according to a determination of relevance relative to previously chosen action and need for conversion of data to compatible data types. 11. The computer implemented method of claim 1 , further comprising automatically converting data types between scripted actions.
| 0.773684 |
8,024,190 | 1 | 13 |
1. A method of training an automatic speech recognition module, the method comprising: training, via a processor of a computing device, acoustic and language models using a set of transcribed data, speech recognition scores, and word confidence scores for a retrieved set of un-transcribed data, wherein the set of transcribed data, speech recognition scores, and word confidence scores is generated by steps comprising: recognizing utterances in a set of candidates for transcription using acoustic and language models trained using an initial set of transcribed data; computing confidence scores of the utterances; selecting a subset of utterances that have the smallest confidence scores from the set of candidates and transcribing them into a transcribed set; adding the transcribed set to the initial set of transcribed data to produce an updated set of transcribed data, speech recognition scores, and word confidence scores; selecting a pre-determined amount of un-transcribed data, the un-transcribed data being associated with utterances not selected in the selecting of the subset of utterances; and applying the pre-determined amount of un-transcribed data to train the acoustic and language models; and iteratively performing the training if word accuracy has not converged.
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1. A method of training an automatic speech recognition module, the method comprising: training, via a processor of a computing device, acoustic and language models using a set of transcribed data, speech recognition scores, and word confidence scores for a retrieved set of un-transcribed data, wherein the set of transcribed data, speech recognition scores, and word confidence scores is generated by steps comprising: recognizing utterances in a set of candidates for transcription using acoustic and language models trained using an initial set of transcribed data; computing confidence scores of the utterances; selecting a subset of utterances that have the smallest confidence scores from the set of candidates and transcribing them into a transcribed set; adding the transcribed set to the initial set of transcribed data to produce an updated set of transcribed data, speech recognition scores, and word confidence scores; selecting a pre-determined amount of un-transcribed data, the un-transcribed data being associated with utterances not selected in the selecting of the subset of utterances; and applying the pre-determined amount of un-transcribed data to train the acoustic and language models; and iteratively performing the training if word accuracy has not converged. 13. The method of claim 1 , wherein a word is considered to be correctly recognized if the word has a confidence score higher than a threshold value.
| 0.537267 |
8,098,409 | 1 | 20 |
1. An image distribution system via e-mail comprising: a first user terminal; a server serving to receive a message consisting of an ideogram string input from said first user terminal and to transmit the received message together with an image attached thereto to said first user terminal; and an internetwork via which said first user terminal and said server are connected to each other, wherein said server comprises: storage means adapted to store an ideogram string element or elements including characters, symbols, graphics or combination thereof respectively corresponding to an expression, attitude or posture representing an emotion or situation put into a message inputted by a user of said first user terminal, said storage means being adapted to store images corresponding to said ideogram string element or elements, recognizing means adapted to recognize the ideogram string element or elements from the message inputted by the user of said first user terminal, and image distribution means adapted to pick up the image corresponding to the ideogram string element or elements having been recognized by said recognizing means and to distribute the corresponding image to said first user terminal via the internetwork.
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1. An image distribution system via e-mail comprising: a first user terminal; a server serving to receive a message consisting of an ideogram string input from said first user terminal and to transmit the received message together with an image attached thereto to said first user terminal; and an internetwork via which said first user terminal and said server are connected to each other, wherein said server comprises: storage means adapted to store an ideogram string element or elements including characters, symbols, graphics or combination thereof respectively corresponding to an expression, attitude or posture representing an emotion or situation put into a message inputted by a user of said first user terminal, said storage means being adapted to store images corresponding to said ideogram string element or elements, recognizing means adapted to recognize the ideogram string element or elements from the message inputted by the user of said first user terminal, and image distribution means adapted to pick up the image corresponding to the ideogram string element or elements having been recognized by said recognizing means and to distribute the corresponding image to said first user terminal via the internetwork. 20. The image distribution system via e-mail according to claim 1 , wherein the internetwork is the internet.
| 0.908557 |
8,166,022 | 1 | 3 |
1. A computer program product comprising a non-transitory computer useable storage medium to store a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform operations comprising: receiving a query for which a query execution plan (QEP) is to be computed, wherein a set comprising all possible QEPs for the query describes a search space, wherein each QEP of the set references a plurality of quantifiers; dividing the search space into a plurality of subproblems for which constituent QEPs referencing a smaller number of quantifiers are to be created; generating a skip vector array (SVA) that indicates disjoint quantifier sets between two subproblems; partitioning the plurality of subproblems into a plurality of partitions, wherein each of the subproblems within a partition references the same number of quantifiers; allocating each of the plurality of partitions to a thread of a plurality of threads within a multiple thread architecture, wherein a partition containing subproblems referencing fewer quantifiers is executed before a partition containing subproblems referencing more quantifiers; receiving from each of the plurality of threads a constituent QEP for each subproblem; and combining two constituents QEPs at the QEP optimizer server to determine a QEP referencing the combined set of quantifiers for the two constituent QEPs.
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1. A computer program product comprising a non-transitory computer useable storage medium to store a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform operations comprising: receiving a query for which a query execution plan (QEP) is to be computed, wherein a set comprising all possible QEPs for the query describes a search space, wherein each QEP of the set references a plurality of quantifiers; dividing the search space into a plurality of subproblems for which constituent QEPs referencing a smaller number of quantifiers are to be created; generating a skip vector array (SVA) that indicates disjoint quantifier sets between two subproblems; partitioning the plurality of subproblems into a plurality of partitions, wherein each of the subproblems within a partition references the same number of quantifiers; allocating each of the plurality of partitions to a thread of a plurality of threads within a multiple thread architecture, wherein a partition containing subproblems referencing fewer quantifiers is executed before a partition containing subproblems referencing more quantifiers; receiving from each of the plurality of threads a constituent QEP for each subproblem; and combining two constituents QEPs at the QEP optimizer server to determine a QEP referencing the combined set of quantifiers for the two constituent QEPs. 3. The computer program product of claim 1 , further comprising determining if quantifiers of the two constituent QEPs are disjoint and filtering the combination of QEPs in response to determining that the quantifiers of the two quantifiers QEPs are not disjoint.
| 0.687648 |
8,050,503 | 15 | 19 |
15. A method for computer vision, the method including: receiving a query image; generating a query signature for the query image based at least in part on a curvelet transform, the query signature including a portion of a plurality of curvelet coefficients of the curvelet transform that is significant, wherein the query signature is a vector of real numbers with a length less than a number of corresponding image pixels; and matching at least one image from a plurality of images based at least in part on the query signature.
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15. A method for computer vision, the method including: receiving a query image; generating a query signature for the query image based at least in part on a curvelet transform, the query signature including a portion of a plurality of curvelet coefficients of the curvelet transform that is significant, wherein the query signature is a vector of real numbers with a length less than a number of corresponding image pixels; and matching at least one image from a plurality of images based at least in part on the query signature. 19. The method of claim 15 , wherein the query image is a query texture.
| 0.681416 |
8,359,201 | 7 | 8 |
7. The computer-implemented method of claim 6 , wherein when the corpus is larger than a predetermined size indicating an acceptable size for storage by the computing system, the generating of the language model based on the modified array further includes: dividing, at the computing system, the corpus into a plurality of distinct portions; and generating, at the computing system, a language model for each of the plurality of distinct portions of the corpus to obtain a plurality of language models.
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7. The computer-implemented method of claim 6 , wherein when the corpus is larger than a predetermined size indicating an acceptable size for storage by the computing system, the generating of the language model based on the modified array further includes: dividing, at the computing system, the corpus into a plurality of distinct portions; and generating, at the computing system, a language model for each of the plurality of distinct portions of the corpus to obtain a plurality of language models. 8. The computer-implemented method of claim 7 , further comprising determining a number of distinct portions for dividing the corpus and for generating the plurality of language models such that each of the plurality of language models can achieve less than or equal to an acceptable error rate.
| 0.5 |
7,747,428 | 7 | 8 |
7. A non-transitory computer-readable storage medium carrying one or more sequences of instructions which, when executed by one or more processors on a computer system, causes the one or more processors to carry out the steps of: receiving data that specifies a first form of a component word; locating, within said compound word, a second form of said component word that differs from said first form of said component word; and displaying said compound word with said second form of said component word visibly distinguished from the remainder of said compound word, wherein said compound word contains two or more component words.
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7. A non-transitory computer-readable storage medium carrying one or more sequences of instructions which, when executed by one or more processors on a computer system, causes the one or more processors to carry out the steps of: receiving data that specifies a first form of a component word; locating, within said compound word, a second form of said component word that differs from said first form of said component word; and displaying said compound word with said second form of said component word visibly distinguished from the remainder of said compound word, wherein said compound word contains two or more component words. 8. The non-transitory computer-readable storage medium of claim 7 , wherein said second form of said component word is a superlative form of said first form of said component word, and wherein said compound word is a non-English language word.
| 0.5 |
8,489,538 | 16 | 17 |
16. The method of claim 1 , further comprising applying confidence threshold validation testing on one or more of the plurality of documents.
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16. The method of claim 1 , further comprising applying confidence threshold validation testing on one or more of the plurality of documents. 17. The method of claim 16 , wherein applying confidence threshold validation testing comprises: setting a size of a quality control (QC) sample set at the size of the initial control set, creating the QC sample set by random sampling from unreviewed documents, and reviewing the QC sample set.
| 0.5 |
9,037,573 | 1 | 5 |
1. A method of personalizing a search of a document collection to a user, the method comprising: monitoring a plurality of documents accessed by the user; identifying a plurality of first phrases present in one or more of the accessed documents; receiving a query from the user, the query including one or more second phrases; selecting a plurality of documents selected from the document collection that are responsive to the query; identifying one or more of the first phrases as related phrases that are related to the one or more second phrases, wherein a particular related phrase is related to a particular second phrase when an information gain exceeds a threshold, the information gain being a ratio of an actual co-occurrence rate of the particular related phrase and the particular second phrase in documents of the document collection and an expected occurrence rate of the particular related phrase and the particular second phrase in the documents; weighting a plurality of scores, each score corresponding to a respective document of the plurality of selected documents responsive to the query, wherein the score of the respective document that includes the one or more related phrases is boosted by the weighting; ranking the plurality of selected documents for presentation to the user based on their corresponding weighted scores, to provide personalized search results; and presenting the personalized search results to the user.
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1. A method of personalizing a search of a document collection to a user, the method comprising: monitoring a plurality of documents accessed by the user; identifying a plurality of first phrases present in one or more of the accessed documents; receiving a query from the user, the query including one or more second phrases; selecting a plurality of documents selected from the document collection that are responsive to the query; identifying one or more of the first phrases as related phrases that are related to the one or more second phrases, wherein a particular related phrase is related to a particular second phrase when an information gain exceeds a threshold, the information gain being a ratio of an actual co-occurrence rate of the particular related phrase and the particular second phrase in documents of the document collection and an expected occurrence rate of the particular related phrase and the particular second phrase in the documents; weighting a plurality of scores, each score corresponding to a respective document of the plurality of selected documents responsive to the query, wherein the score of the respective document that includes the one or more related phrases is boosted by the weighting; ranking the plurality of selected documents for presentation to the user based on their corresponding weighted scores, to provide personalized search results; and presenting the personalized search results to the user. 5. The method of claim 1 , wherein a document accessed by the user comprises a document sent by email by the user.
| 0.897112 |
9,607,046 | 19 | 20 |
19. The system of claim 15 , wherein the instructions are further configured to: indicate to the user a query ambiguity of the follow-up query; and upon receiving from the user a clarifying query addressing the query ambiguity: supplement the follow-up query with the clarifying query to generate a supplemented query; and recalculate the query state modification probabilities of the supplemented query for respective query state modifications in the query state modification set.
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19. The system of claim 15 , wherein the instructions are further configured to: indicate to the user a query ambiguity of the follow-up query; and upon receiving from the user a clarifying query addressing the query ambiguity: supplement the follow-up query with the clarifying query to generate a supplemented query; and recalculate the query state modification probabilities of the supplemented query for respective query state modifications in the query state modification set. 20. The system of claim 19 , wherein: indicating the query ambiguity to the user comprises: for query state modifications identifiable for the follow-up query, presenting to the user a clarifying query option addressing the query ambiguity toward the respective query state modification; and receiving the clarifying query comprises: receiving from the user a selected clarifying query option.
| 0.5 |
9,405,965 | 11 | 13 |
11. The method of claim 1 , wherein receiving an image of a face comprises: receiving, by a crawler, input of a seed network address, accessing, by the crawler, the seed network address, and retrieving, by the crawler, an image located at the seed network address.
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11. The method of claim 1 , wherein receiving an image of a face comprises: receiving, by a crawler, input of a seed network address, accessing, by the crawler, the seed network address, and retrieving, by the crawler, an image located at the seed network address. 13. The method of claim 11 , wherein receiving an image of a face further comprises: determining, by a duplicate filter, whether the image has previously been retrieved, and if the image has previously been retrieved, preventing creation of the a new feature vector corresponding to the image.
| 0.540752 |
7,661,068 | 1 | 10 |
1. A method for using an eraser for additional operations, the method comprising: providing a pen feature that supports a plurality of eraser gestures; detecting an eraser gesture; and performing an operation associated with the eraser gestures; wherein the operation comprises providing a status of a particular application.
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1. A method for using an eraser for additional operations, the method comprising: providing a pen feature that supports a plurality of eraser gestures; detecting an eraser gesture; and performing an operation associated with the eraser gestures; wherein the operation comprises providing a status of a particular application. 10. The method of claim 1 , wherein the operation comprises taking a particular application out of a current state.
| 0.695767 |
9,569,537 | 4 | 5 |
4. The method of claim 1 , wherein the real-time interaction options include using communication logic capable of facilitating a real-time communication session.
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4. The method of claim 1 , wherein the real-time interaction options include using communication logic capable of facilitating a real-time communication session. 5. The method of claim 4 , wherein real-time communication sessions include text messaging sessions, internet telephony sessions, or real-time chat sessions, and wherein real-time chat sessions include audio, video, or textual communication.
| 0.5 |
8,332,777 | 9 | 16 |
9. An apparatus, comprising: a processor to display a user interface on a display device, wherein the user interface includes a data entry menu having one or more menu selections, wherein the processor to activate one of the one or more menu selections to display a data entry method, wherein the data entry method displayed is different depending on a specific context of the activated menu selection, wherein the activated menu selection requires the user to enter data into a plurality of simultaneously displayed data entry boxes, of which at least one data entry box requires alphabetic input and at least one data entry box requires numerical data, wherein only a virtual keyboard enabled to receive alphabetic input is displayed in the data entry method on the display device when alphabetic user input is required by the activated user selection in the data entry box requiring alphabetic input, and only a virtual keypad enabled to receive numeric input is displayed in the data entry method on the display device when numeric user input is required by the activated user selection in the data entry box requiring numeric input.
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9. An apparatus, comprising: a processor to display a user interface on a display device, wherein the user interface includes a data entry menu having one or more menu selections, wherein the processor to activate one of the one or more menu selections to display a data entry method, wherein the data entry method displayed is different depending on a specific context of the activated menu selection, wherein the activated menu selection requires the user to enter data into a plurality of simultaneously displayed data entry boxes, of which at least one data entry box requires alphabetic input and at least one data entry box requires numerical data, wherein only a virtual keyboard enabled to receive alphabetic input is displayed in the data entry method on the display device when alphabetic user input is required by the activated user selection in the data entry box requiring alphabetic input, and only a virtual keypad enabled to receive numeric input is displayed in the data entry method on the display device when numeric user input is required by the activated user selection in the data entry box requiring numeric input. 16. The apparatus of claim 9 , wherein each of the one or more menu selections and the one or more data entry method selections are adjustable and independent from any of the other menu and data entry method selections.
| 0.5 |
8,739,055 | 11 | 12 |
11. A computing device, comprising: a touch-sensitive display; a logic subsystem; and a memory including instructions executable by the logic subsystem to: identify a typographical error within text displayed on the display, the typographical error comprising an error within a word displayed on the display; upon identifying the typographical error, highlight on the display the typographical error; detect a gesture-based touch input selecting a cursor key from a virtual keyboard displayed on the display and displaying a cursor in response to the gesture-based touch input; detect a change in a location of the gesture-based touch input, and in response, move a location of an image of the cursor displayed on the display in correspondence with the gesture-based touch input; detect a release of the gesture-based touch input; if the release of the gesture-based touch input is detected within a predefined region associated with a location of the typographical error, display on the display the cursor next to the typographical error; and responsive to the cursor being displayed next to the typographical error, replace the typographical error with replacement text input from the virtual keyboard.
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11. A computing device, comprising: a touch-sensitive display; a logic subsystem; and a memory including instructions executable by the logic subsystem to: identify a typographical error within text displayed on the display, the typographical error comprising an error within a word displayed on the display; upon identifying the typographical error, highlight on the display the typographical error; detect a gesture-based touch input selecting a cursor key from a virtual keyboard displayed on the display and displaying a cursor in response to the gesture-based touch input; detect a change in a location of the gesture-based touch input, and in response, move a location of an image of the cursor displayed on the display in correspondence with the gesture-based touch input; detect a release of the gesture-based touch input; if the release of the gesture-based touch input is detected within a predefined region associated with a location of the typographical error, display on the display the cursor next to the typographical error; and responsive to the cursor being displayed next to the typographical error, replace the typographical error with replacement text input from the virtual keyboard. 12. The computing device of claim 11 , wherein the typographical error further comprises a single letter.
| 0.813167 |
7,805,306 | 8 | 11 |
8. A voice guidance device comprising: a storing unit that stores a plurality of stored voice data items for each of a plurality of voice guidance phrases, each of the plurality of voice data items for a specific voice guidance phrase including the specific voice guidance phrase at a different frequency; a voice producing unit that produces at least one produced voice data item for each of the plurality of voice guidance phrases from the plurality of stored voice data items using voice synthesis, wherein each of the plurality of stored voice data items and the at least one produced voice data item of a first voice guidance phrase of the plurality of voice guidance phrases has a different frequency; a voice mixing unit that mixes at least two voice data items from the first voice guidance phrase to thereby produce a first mixed voice data item of the first voice guidance phrase; a voice outputting unit that outputs and sounds only the first voice guidance phrase using a first mixed voice for the first voice guidance phrase based on the first mixed voice data item; a voice detecting unit that detects a response voice responding to the outputted first voice guidance phrase using the first mixed voice; and a voice measuring unit that measures a frequency with respect to the detected response voice; the voice mixing unit producing a second mixed voice data item by mixing at least two voice data items of a second voice guidance phrase of the plurality of voice guidance phrases, different than the first voice guidance phrase and different from the detected response, the second mixed voice data item having the characteristic of the frequency that is measured by the voice measuring unit with respect to the response voice detected after the first mixed voice was sounded; the voice outputting unit further outputting and sounding only the second voice guidance phrase using a second mixed voice based on the second mixed voice data item in response to the response voice, the second voice guidance phrase approximating the frequency that is measured by the voice measuring unit with respect to the response voice.
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8. A voice guidance device comprising: a storing unit that stores a plurality of stored voice data items for each of a plurality of voice guidance phrases, each of the plurality of voice data items for a specific voice guidance phrase including the specific voice guidance phrase at a different frequency; a voice producing unit that produces at least one produced voice data item for each of the plurality of voice guidance phrases from the plurality of stored voice data items using voice synthesis, wherein each of the plurality of stored voice data items and the at least one produced voice data item of a first voice guidance phrase of the plurality of voice guidance phrases has a different frequency; a voice mixing unit that mixes at least two voice data items from the first voice guidance phrase to thereby produce a first mixed voice data item of the first voice guidance phrase; a voice outputting unit that outputs and sounds only the first voice guidance phrase using a first mixed voice for the first voice guidance phrase based on the first mixed voice data item; a voice detecting unit that detects a response voice responding to the outputted first voice guidance phrase using the first mixed voice; and a voice measuring unit that measures a frequency with respect to the detected response voice; the voice mixing unit producing a second mixed voice data item by mixing at least two voice data items of a second voice guidance phrase of the plurality of voice guidance phrases, different than the first voice guidance phrase and different from the detected response, the second mixed voice data item having the characteristic of the frequency that is measured by the voice measuring unit with respect to the response voice detected after the first mixed voice was sounded; the voice outputting unit further outputting and sounding only the second voice guidance phrase using a second mixed voice based on the second mixed voice data item in response to the response voice, the second voice guidance phrase approximating the frequency that is measured by the voice measuring unit with respect to the response voice. 11. The voice guidance device of claim 8 , wherein the voice mixing unit mixes three voice data items for the first voice guidance phrase, a frequency ratio of which is 1: 1.5; 2, to thereby produce the mixed voice data.
| 0.760349 |
8,856,107 | 3 | 6 |
3. The method of claim 1 , comprising allowing a user to select a plane that cuts through the three dimensional collection of cubes, the plane being oriented relative to one common axis of the cubes, wherein selecting the plane indicates a subset of the multiple pieces of communication data.
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3. The method of claim 1 , comprising allowing a user to select a plane that cuts through the three dimensional collection of cubes, the plane being oriented relative to one common axis of the cubes, wherein selecting the plane indicates a subset of the multiple pieces of communication data. 6. The method of claim 3 , comprising updating a tag cloud associated with the selected plane, wherein the tag cloud comprises one or more of the following: frequently occurring terms in the communication data, frequently occurring words in the communication data, frequently occurring concepts in the communication data and frequently occurring objects in the communication data.
| 0.5 |
8,694,500 | 9 | 10 |
9. The method of claim 1 , comprising forming a PPAN chain from the PPAN elements.
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9. The method of claim 1 , comprising forming a PPAN chain from the PPAN elements. 10. The method of claim 9 , wherein using the PPAN elements from the structured request document to identify a subset of the stored structured service documents that contain data and structure that match the PPAN elements comprises using the PPAN chain to search for a subset of structured service documents that contains the PPAN chain.
| 0.5 |
8,583,612 | 9 | 10 |
9. A method performed by a Whois database server that is adapted to be communicatively coupled to a plurality of search engine servers, the method comprising: sampling a Whois database record read request; determining whether the read request is from a previously registered search engine server; in response to a favorable determination, allowing the search engine server to access Whois database records, wherein the Whois database server includes: a first database storing records associated exclusively with verified domain name registrations, and a second database storing records associated exclusively with unverified domain name registrations, wherein the first database excludes records associated with unverified domain name registrations; in response to an unfavorable determination, performing a human challenge response identification to authenticate the read request; and processing the Whois record for read requests that were authenticated.
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9. A method performed by a Whois database server that is adapted to be communicatively coupled to a plurality of search engine servers, the method comprising: sampling a Whois database record read request; determining whether the read request is from a previously registered search engine server; in response to a favorable determination, allowing the search engine server to access Whois database records, wherein the Whois database server includes: a first database storing records associated exclusively with verified domain name registrations, and a second database storing records associated exclusively with unverified domain name registrations, wherein the first database excludes records associated with unverified domain name registrations; in response to an unfavorable determination, performing a human challenge response identification to authenticate the read request; and processing the Whois record for read requests that were authenticated. 10. The method of claim 9 , further comprising: receiving the Whois database record read request and testing the request for existing registration as a high volume user of Whois records; and delivering the Whois records requested by the high volume read access from the search engine once registration is determined.
| 0.729452 |
8,645,825 | 16 | 28 |
16. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by the one or more computers, an n-gram of characters; identifying, in a local cache of the one or more computers, cached autocomplete suggestions for the n-gram; requesting, at a first time and over a network, additional autocomplete suggestions for the n-gram; preventing, by the one or more computers, presentation of the cached autocomplete suggestions until a presentation event occurs, wherein the presentation event comprises (i) receiving the requested additional autocomplete suggestions by the one or more computers or (ii) the end of a predetermined period after the first time; determining that the presentation event has occurred; and in response to determining that the presentation event has occurred, presenting one or more of the cached autocomplete suggestions or the additional autocomplete suggestions.
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16. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by the one or more computers, an n-gram of characters; identifying, in a local cache of the one or more computers, cached autocomplete suggestions for the n-gram; requesting, at a first time and over a network, additional autocomplete suggestions for the n-gram; preventing, by the one or more computers, presentation of the cached autocomplete suggestions until a presentation event occurs, wherein the presentation event comprises (i) receiving the requested additional autocomplete suggestions by the one or more computers or (ii) the end of a predetermined period after the first time; determining that the presentation event has occurred; and in response to determining that the presentation event has occurred, presenting one or more of the cached autocomplete suggestions or the additional autocomplete suggestions. 28. The system of claim 16 , wherein the operations further comprise: receiving the additional autocomplete suggestions; determining that the received additional autocomplete suggestions should be presented based on a number of cached additional autocomplete suggestions identified in the local cache or based on a quality score associated with one or more of the cached autocomplete suggestions; and presenting the received additional autocomplete suggestions in response to determining that the received additional autocomplete suggestions should be presented.
| 0.560938 |
10,097,631 | 1 | 2 |
1. A method comprising: by one or more computing devices of a social-networking system, receiving a reference to a first document, wherein the first document: comprises a content item and a first interactive feature for user posts, wherein the first interactive feature is displayed as a conversation thread; is associated with an entity; and is provided from a first web domain; by the one or more computing devices, selecting a second document that corresponds to the first document, wherein the second document: shares a common content item with the first document; comprises a second interactive feature for user posts, wherein the second interactive feature is displayed as a conversation thread; is provided from a second web domain; and is associated with the entity; by the one or more computing devices, receiving a user post related to the content item, the user post being submitted in connection with the first or the second document; and by the one or more computing devices, updating the first interactive feature and the second interactive feature with the user post, wherein the updating comprises: synchronizing the first interactive feature and the second interactive feature at the same time; and automating a synchronization of a moderation of the user post in connection with both the first and the second documents based on a set of banned words or character strings, wherein automating the synchronization of the moderation comprises: filtering out one or more words of the user post in connection with the first document based on a first moderation rule of the first web domain; and filtering out one or more words of the user post in connection with the second document based on a second moderation rule of the second web domain.
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1. A method comprising: by one or more computing devices of a social-networking system, receiving a reference to a first document, wherein the first document: comprises a content item and a first interactive feature for user posts, wherein the first interactive feature is displayed as a conversation thread; is associated with an entity; and is provided from a first web domain; by the one or more computing devices, selecting a second document that corresponds to the first document, wherein the second document: shares a common content item with the first document; comprises a second interactive feature for user posts, wherein the second interactive feature is displayed as a conversation thread; is provided from a second web domain; and is associated with the entity; by the one or more computing devices, receiving a user post related to the content item, the user post being submitted in connection with the first or the second document; and by the one or more computing devices, updating the first interactive feature and the second interactive feature with the user post, wherein the updating comprises: synchronizing the first interactive feature and the second interactive feature at the same time; and automating a synchronization of a moderation of the user post in connection with both the first and the second documents based on a set of banned words or character strings, wherein automating the synchronization of the moderation comprises: filtering out one or more words of the user post in connection with the first document based on a first moderation rule of the first web domain; and filtering out one or more words of the user post in connection with the second document based on a second moderation rule of the second web domain. 2. The method of claim 1 , wherein the first or second document is located in: a web page external to the social-networking system; or an application external to the social-networking system.
| 0.824449 |
9,792,282 | 1 | 3 |
1. A method comprising: analyzing, using a processor and a memory, a portion of a software product to determine a first language used in the portion and a subject-matter domain of the portion; extracting using the processor and the memory, a string from the portion, wherein the string has been translated into the first language of the string from an original string in an original language, the original string existing in a version of the software product in the original language; selecting a corpus, wherein the corpus comprises a set of stored strings, each stored string in the set being in the first language, and wherein a subset of the set of stored strings is selected from a content that is related to the subject-matter domain of the software product; selecting, responsive to the string matching a stored string in the corpus, the string into a shortlist; excluding, from the shortlist a second string extracted from the portion, the second string failing to match any stored string in the corpus; and outputting the shortlist, the outputting causing a review of an accuracy of a machine translation process to be performed.
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1. A method comprising: analyzing, using a processor and a memory, a portion of a software product to determine a first language used in the portion and a subject-matter domain of the portion; extracting using the processor and the memory, a string from the portion, wherein the string has been translated into the first language of the string from an original string in an original language, the original string existing in a version of the software product in the original language; selecting a corpus, wherein the corpus comprises a set of stored strings, each stored string in the set being in the first language, and wherein a subset of the set of stored strings is selected from a content that is related to the subject-matter domain of the software product; selecting, responsive to the string matching a stored string in the corpus, the string into a shortlist; excluding, from the shortlist a second string extracted from the portion, the second string failing to match any stored string in the corpus; and outputting the shortlist, the outputting causing a review of an accuracy of a machine translation process to be performed. 3. The method of claim 1 , wherein the content is unrelated to the software product.
| 0.863192 |
8,799,177 | 8 | 14 |
8. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for building a small business social graph, the method comprising: receiving a collection of data from a first business; and building a social graph that adheres to a Resource Description Framework Schema (RDFS) by: determining an ontology for businesses in the social graph; determining if a first node for the first business exists in the social graph, wherein the social graph is a graph-based data model that indicates relationships among various businesses; if not, adding the first node for the first business to the social graph according to the ontology; analyzing the collection of data to identify a second business; determining if a second node for the second business exists in the social graph; if not, adding the second node for the second business to the social graph according to the ontology; adding a relationship between the first node and the second node to the social graph, according to the ontology, to indicate the relationship between the first business and the second business, analyzing the collection of data to identify a first person; determining if a third node for the first person exists in the social graph; if not, adding the third node for the first person to the social graph according to the ontology; determining that the second node and the third node share a unique identifier; and relating the second business and the first person as a same entity.
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8. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for building a small business social graph, the method comprising: receiving a collection of data from a first business; and building a social graph that adheres to a Resource Description Framework Schema (RDFS) by: determining an ontology for businesses in the social graph; determining if a first node for the first business exists in the social graph, wherein the social graph is a graph-based data model that indicates relationships among various businesses; if not, adding the first node for the first business to the social graph according to the ontology; analyzing the collection of data to identify a second business; determining if a second node for the second business exists in the social graph; if not, adding the second node for the second business to the social graph according to the ontology; adding a relationship between the first node and the second node to the social graph, according to the ontology, to indicate the relationship between the first business and the second business, analyzing the collection of data to identify a first person; determining if a third node for the first person exists in the social graph; if not, adding the third node for the first person to the social graph according to the ontology; determining that the second node and the third node share a unique identifier; and relating the second business and the first person as a same entity. 14. The non-transitory computer-readable storage medium of claim 8 , wherein the collection of data can include: accounting data; financial data; social-network data; and personal information from a Personal Information Manager (PIM).
| 0.682065 |
9,129,305 | 1 | 5 |
1. A system for generating targeted advertisement recommendations, the system comprising: under control of a hardware processor: a data aggregation module configured to obtain a plurality of words from a social network; a relationship mining module in communication with the data aggregation module, the relationship mining module configured to create word relationships between selected ones of the plurality of words to produce relationship data, each of the word relationships reflecting a degree of association between two or more of the selected words, wherein the degree of association is based at least in part on an amount of social momentum between said selected words; and a recommender configured to generate targeted advertising based at least in part on said relationship data, wherein the recommender module is further configured to generate the targeted advertising by at least accessing user data to identify one or more first words in the relationship data, identifying one or more second words in the relationship data that have one or more of the word relationships with the one or more first words, and identifying one or more advertisements having at least one keyword that corresponds to the one or more second words.
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1. A system for generating targeted advertisement recommendations, the system comprising: under control of a hardware processor: a data aggregation module configured to obtain a plurality of words from a social network; a relationship mining module in communication with the data aggregation module, the relationship mining module configured to create word relationships between selected ones of the plurality of words to produce relationship data, each of the word relationships reflecting a degree of association between two or more of the selected words, wherein the degree of association is based at least in part on an amount of social momentum between said selected words; and a recommender configured to generate targeted advertising based at least in part on said relationship data, wherein the recommender module is further configured to generate the targeted advertising by at least accessing user data to identify one or more first words in the relationship data, identifying one or more second words in the relationship data that have one or more of the word relationships with the one or more first words, and identifying one or more advertisements having at least one keyword that corresponds to the one or more second words. 5. The system of claim 1 , wherein the recommender module is further configured to weight the one or more first words based at least in part on a frequency of which the one or more first words occur in the user data.
| 0.679525 |
8,743,736 | 12 | 16 |
12. A system for identifying connections among nodes of a network, the system comprising: an interface configured to receive sets of data from a plurality of sources in the network, wherein each set of data relates to at least one connectivity level within a hierarchy of connectivity levels; a first inference engine configured to determine, at a first level within the hierarchy of connectivity levels, a first connectivity graph based on a first set of data indicating connections between nodes in the network at the first level; a second inference engine configured to determine, at a second level higher than the first level within the hierarchy of connectivity levels, a second connectivity graph based on a second set of data indicating connections between nodes in the network at the second level; and a physical path creator configured to identify at least one physical path between a pair of nodes of the network based on consolidating the first and second connectivity graphs.
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12. A system for identifying connections among nodes of a network, the system comprising: an interface configured to receive sets of data from a plurality of sources in the network, wherein each set of data relates to at least one connectivity level within a hierarchy of connectivity levels; a first inference engine configured to determine, at a first level within the hierarchy of connectivity levels, a first connectivity graph based on a first set of data indicating connections between nodes in the network at the first level; a second inference engine configured to determine, at a second level higher than the first level within the hierarchy of connectivity levels, a second connectivity graph based on a second set of data indicating connections between nodes in the network at the second level; and a physical path creator configured to identify at least one physical path between a pair of nodes of the network based on consolidating the first and second connectivity graphs. 16. The system of claim 12 , wherein the first and second connectivity graphs include metadata associated with one or more inferred links that facilitates the consolidating of the first and second connectivity graphs.
| 0.5 |
8,515,160 | 14 | 15 |
14. The computer program product of claim 13 , where the input signal is a signal selected from the group consisting of a video signal, an audio signal, a radar signal, and a sonar signal.
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14. The computer program product of claim 13 , where the input signal is a signal selected from the group consisting of a video signal, an audio signal, a radar signal, and a sonar signal. 15. The computer program product of claim 14 , where the set of entities recognized are selected from the group consisting of objects, spatial patterns, events and behaviors.
| 0.5 |
5,500,881 | 13 | 14 |
13. The computer system in accordance with claim 10, wherein, if said static scope means stores a corresponding name/value pair, said processing means both retrieves a value of one of said name/value pairs corresponding to said program variable from said static scope, and binds said retrieved value as said program value to said program variable.
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13. The computer system in accordance with claim 10, wherein, if said static scope means stores a corresponding name/value pair, said processing means both retrieves a value of one of said name/value pairs corresponding to said program variable from said static scope, and binds said retrieved value as said program value to said program variable. 14. The computer system in accordance with claim 13, wherein said processing means updates said static scope means and said dynamic scope means when each of said retrieved values are bound to said program variables and when, during processing of said computer program, said program variables are assigned new values.
| 0.5 |
8,504,408 | 1 | 2 |
1. A method for building an integrated customer analytics solution for an enterprise comprising one or more data sources in a client-server architecture over a network, the client-server architecture comprising one or more user devices and one or more servers for processing requests received from the one or more user devices, the method comprising: retrieving and processing customer data from the one or more data sources, wherein retrieving and processing the customer data comprises: gathering the customer data from the one or more data sources, wherein at least two data sources are disparate data sources which store data using different formats; storing the gathered customer data in the form of data structures comprising one or more data fields in an operational data store; extracting the customer data from the operational data store if the data structure corresponds to a predetermined data structure; transforming the extracted customer data based on one or more predetermined formats; transforming the existing one or more data fields of the operational data store into a first set of new data fields based on business specifications and requirement analysis of the enterprise; loading the transformed customer data and the first set of new data fields into a data mart; and updating the data mart with a second set of new data fields and new customer data corresponding to the second set of new data fields over a period of time of predetermined customer lifecycle stages, wherein the updating of the data mart is based on the business specifications and requirement analysis of the enterprise; analyzing the processed customer data stored in the data mart using one or more codes to generate one or more statistical techniques, the statistical techniques being at least one of descriptive statistics, cluster analysis, forecasting, survival analysis and logit model, wherein the one or more codes are developed using a statistical package module, and further wherein the generated one or more statistical techniques facilitate analyzing one or more predetermined attributes related to the customer; deriving, by a processor, one or more statistical model outputs using the one or more generated statistical techniques, wherein the statistical model outputs represent one or more metrics corresponding to the analyzed attributes; generating one or more statistical models corresponding to the one or more statistical model outputs, the one or more statistical models being associated with one or more scores which are computed based on the statistical model outputs, wherein the one or more scores facilitate to predict likelihood of customer behavior towards products, services and other customer related aspects associated with the enterprise; generating one or more reports based on at least one of: the processed customer data and the one or more statistical model outputs; and building one or more analytical modules for the predetermined customer lifecycle stages comprising the one or more reports and the one or more statistical models, wherein the one or more analytical modules constitute the integrated customer analytics solution.
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1. A method for building an integrated customer analytics solution for an enterprise comprising one or more data sources in a client-server architecture over a network, the client-server architecture comprising one or more user devices and one or more servers for processing requests received from the one or more user devices, the method comprising: retrieving and processing customer data from the one or more data sources, wherein retrieving and processing the customer data comprises: gathering the customer data from the one or more data sources, wherein at least two data sources are disparate data sources which store data using different formats; storing the gathered customer data in the form of data structures comprising one or more data fields in an operational data store; extracting the customer data from the operational data store if the data structure corresponds to a predetermined data structure; transforming the extracted customer data based on one or more predetermined formats; transforming the existing one or more data fields of the operational data store into a first set of new data fields based on business specifications and requirement analysis of the enterprise; loading the transformed customer data and the first set of new data fields into a data mart; and updating the data mart with a second set of new data fields and new customer data corresponding to the second set of new data fields over a period of time of predetermined customer lifecycle stages, wherein the updating of the data mart is based on the business specifications and requirement analysis of the enterprise; analyzing the processed customer data stored in the data mart using one or more codes to generate one or more statistical techniques, the statistical techniques being at least one of descriptive statistics, cluster analysis, forecasting, survival analysis and logit model, wherein the one or more codes are developed using a statistical package module, and further wherein the generated one or more statistical techniques facilitate analyzing one or more predetermined attributes related to the customer; deriving, by a processor, one or more statistical model outputs using the one or more generated statistical techniques, wherein the statistical model outputs represent one or more metrics corresponding to the analyzed attributes; generating one or more statistical models corresponding to the one or more statistical model outputs, the one or more statistical models being associated with one or more scores which are computed based on the statistical model outputs, wherein the one or more scores facilitate to predict likelihood of customer behavior towards products, services and other customer related aspects associated with the enterprise; generating one or more reports based on at least one of: the processed customer data and the one or more statistical model outputs; and building one or more analytical modules for the predetermined customer lifecycle stages comprising the one or more reports and the one or more statistical models, wherein the one or more analytical modules constitute the integrated customer analytics solution. 2. The method of claim 1 , wherein the one or more predetermined attributes related to the customer comprises: transaction related information, campaign related information, attrition and loyalty related information, customer life time value and survivability related information, profitability related information with respect to products, and customer satisfaction related information with respect to services offered by the enterprise.
| 0.5 |
8,356,057 | 10 | 11 |
10. A computer software product for information gap filling, including a non-transitory computer-readable storage medium in which computer program instructions are stored, which instructions, when executed by a computer, cause the computer to perform the steps of: collecting respective amounts of details concerning members of a set of persons in a social network, wherein additional details are desired regarding the members of the set of persons; identifying online participants in a gap-filling game; defining for the online participants respective sets of candidates, the sets of candidates being subsets of the set of persons, wherein defining comprises computing a candidate score for each member of the set of persons, the candidate score being computed as an increasing function of a relationship strength between the member of the set of persons and the online participants, respectively and a decreasing function of the collected amount of details of the member of the set of persons; presenting the sets of candidates to other online participants, respectively; receiving information items regarding members of the presented sets of candidates from the other online participants, respectively, the information items comprising relationships between the candidates and other entities in the social network; and evaluating the online participants by identifying respective validated information items that are contributed by the online participants, the validated information items being identified with a predefined number of corresponding information items that are contributed by the other online participants; and computing a participant score, wherein the participant score increases according to a number of the validated information items, and for each of the validated information items, the participant score decreases according to a total number of occurrences of the corresponding information items.
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10. A computer software product for information gap filling, including a non-transitory computer-readable storage medium in which computer program instructions are stored, which instructions, when executed by a computer, cause the computer to perform the steps of: collecting respective amounts of details concerning members of a set of persons in a social network, wherein additional details are desired regarding the members of the set of persons; identifying online participants in a gap-filling game; defining for the online participants respective sets of candidates, the sets of candidates being subsets of the set of persons, wherein defining comprises computing a candidate score for each member of the set of persons, the candidate score being computed as an increasing function of a relationship strength between the member of the set of persons and the online participants, respectively and a decreasing function of the collected amount of details of the member of the set of persons; presenting the sets of candidates to other online participants, respectively; receiving information items regarding members of the presented sets of candidates from the other online participants, respectively, the information items comprising relationships between the candidates and other entities in the social network; and evaluating the online participants by identifying respective validated information items that are contributed by the online participants, the validated information items being identified with a predefined number of corresponding information items that are contributed by the other online participants; and computing a participant score, wherein the participant score increases according to a number of the validated information items, and for each of the validated information items, the participant score decreases according to a total number of occurrences of the corresponding information items. 11. The computer software product according to claim 10 , wherein the candidate score of one of the members of the set of persons is computed as a function F=(1−F data )+F rel , wherein F rel is a first function that retrieves the relationship strength between the one member of the set of persons and one of the online participants; and Fdata is a second function that retrieves the collected amount of data concerning the one member of the set of persons.
| 0.637876 |
6,076,061 | 31 | 33 |
31. The speech recognition method according to claim 27, wherein, in said selecting step, a maximum value is assigned to the weight for a class of the recognition information related to one of the plurality of areas in which the viewpoint stays, and a minimum value is assigned to the weight for other classes of the recognition information.
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31. The speech recognition method according to claim 27, wherein, in said selecting step, a maximum value is assigned to the weight for a class of the recognition information related to one of the plurality of areas in which the viewpoint stays, and a minimum value is assigned to the weight for other classes of the recognition information. 33. The speech recognition method according to claim 31, wherein, in a case where the viewpoint exits from the area where it has stayed, the weight for the class of the recognition information related to the area in which the viewpoint had stayed is gradually decreased, toward the minimum value as time elapses, in said selecting step.
| 0.52 |
7,571,098 | 1 | 2 |
1. A speech processing method, comprising: at an automatic speech recognition (ASR) engine: converting a word lattice that describes multiple hypotheses of a received input utterance into a modified word lattice wherein transitions without any input in the word lattice are represented as epsilon transitions in the modified word lattice; and at a spoken language understanding (SLU) engine: receiving the modified word lattice from the ASR engine; and performing an SLU determination via the SLU engine.
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1. A speech processing method, comprising: at an automatic speech recognition (ASR) engine: converting a word lattice that describes multiple hypotheses of a received input utterance into a modified word lattice wherein transitions without any input in the word lattice are represented as epsilon transitions in the modified word lattice; and at a spoken language understanding (SLU) engine: receiving the modified word lattice from the ASR engine; and performing an SLU determination via the SLU engine. 2. The speech processing method of claim 1 , wherein said converting comprises aligning words in the word lattice.
| 0.819048 |
9,336,533 | 5 | 6 |
5. The method of claim 1 , wherein querying the database system using the SIMILAR command term and passing the specified row comprises passing a complete row with the SIMILAR command term listing name=value pairs corresponding to all columns for other rows in the dataset or the indices.
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5. The method of claim 1 , wherein querying the database system using the SIMILAR command term and passing the specified row comprises passing a complete row with the SIMILAR command term listing name=value pairs corresponding to all columns for other rows in the dataset or the indices. 6. The method of claim 5 , wherein passing the complete row includes passing one or more null or blank values as the value in the name=value pairs.
| 0.5 |
10,027,578 | 1 | 8 |
1. A computer system for facilitating routable prefix queries, the system comprising: a processor; and a storage device storing instructions that when executed by the processor cause the processor to perform a method, the method comprising: generating, by a client computing device, a query for one or more indices based on a name for an interest, wherein a name is a hierarchically structured variable length identifier that includes contiguous name components ordered from a most general level to a most specific level, wherein an index indicates a number of the contiguous name components beginning from the most general level that represent a routable prefix needed to route the interest to a content producing device that can satisfy the interest; and in response to the query, receiving the one or more indices, which allows the client computing device to determine a remaining number of name components of the interest name which can be encrypted, thereby facilitating protection of private communication in a content centric network, wherein the method further comprises: generating a first interest with a name that includes a first routable prefix that corresponds to a received index, wherein the first routable prefix is in cleartext; and encrypting a remaining suffix which comprises name components of the name immediately following the first routable prefix.
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1. A computer system for facilitating routable prefix queries, the system comprising: a processor; and a storage device storing instructions that when executed by the processor cause the processor to perform a method, the method comprising: generating, by a client computing device, a query for one or more indices based on a name for an interest, wherein a name is a hierarchically structured variable length identifier that includes contiguous name components ordered from a most general level to a most specific level, wherein an index indicates a number of the contiguous name components beginning from the most general level that represent a routable prefix needed to route the interest to a content producing device that can satisfy the interest; and in response to the query, receiving the one or more indices, which allows the client computing device to determine a remaining number of name components of the interest name which can be encrypted, thereby facilitating protection of private communication in a content centric network, wherein the method further comprises: generating a first interest with a name that includes a first routable prefix that corresponds to a received index, wherein the first routable prefix is in cleartext; and encrypting a remaining suffix which comprises name components of the name immediately following the first routable prefix. 8. The computer system of claim 1 , wherein the method further comprises: in response to receiving a notification message from a first content producing device indicating that the first content producing device can serve content for a routable prefix that corresponds to a received index, configuring a forwarding information base of the client computing device based on the notification message.
| 0.571429 |
8,687,945 | 1 | 7 |
1. A method comprising: defining an abstract model to represent an interactive video, wherein the abstract model is defined independently of any export format; and interpreting the abstract model to concurrently generate a first version and a second version of the interactive video respectively encoded according to a first export format and a second export format, wherein interpreting the abstract model to generate the first version and second versions includes: for at least one feature of the interactive video described in the abstract model identifying a first player function provided by the first export format and a second player function provided by the second export format, wherein the first and second player functions implement the at least one feature under different conditions, generating first code for implementing the first player function and second code for implementing the second player function, and including the first code in the first version of the interactive video and the second code in the second version of the interactive video, wherein generating the first code and the second code comprises: identifying a condition for activating the at least one feature in the first export format, wherein the condition is absent in the second export format; and specifying that the first code is executable when the condition is satisfied, wherein the second code is executable without the condition being satisfied.
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1. A method comprising: defining an abstract model to represent an interactive video, wherein the abstract model is defined independently of any export format; and interpreting the abstract model to concurrently generate a first version and a second version of the interactive video respectively encoded according to a first export format and a second export format, wherein interpreting the abstract model to generate the first version and second versions includes: for at least one feature of the interactive video described in the abstract model identifying a first player function provided by the first export format and a second player function provided by the second export format, wherein the first and second player functions implement the at least one feature under different conditions, generating first code for implementing the first player function and second code for implementing the second player function, and including the first code in the first version of the interactive video and the second code in the second version of the interactive video, wherein generating the first code and the second code comprises: identifying a condition for activating the at least one feature in the first export format, wherein the condition is absent in the second export format; and specifying that the first code is executable when the condition is satisfied, wherein the second code is executable without the condition being satisfied. 7. The method of claim 1 , wherein generating the first code and the second code comprises: identifying a condition for activating the at least one feature in the first export format, wherein the condition is absent in the second export format; and specifying that the first code is executable when the condition is satisfied, wherein the second code is executable without the condition being satisfied.
| 0.744289 |
9,015,803 | 1 | 14 |
1. A server computer implemented method of online document collaboration by authorized users, the method comprising the following steps performed by a server computer system connected to the Internet: establishing an account for each of a plurality of users, wherein each account is associated with storage space to store one or more documents; storing a first document in the server computer system in a first account, the first document capable of being modified by a plurality of authorized users; enabling access to the first document via a browser-controlled window executing on a client computer by one or more authorized users; associating a set of restrictions with the first document, the restrictions including an ability to modify the first document in one or more permitted ways by one of a first group of users, the first group of users being users whose identities are known to the server computer system; receiving a request to access the first document from a first user, wherein the first user is a member of the first group of users, wherein the request to access accompanies the first user's identification information and authorization information; verifying the identity of the first user; if the first user is authorized to access the first document, then permitting the first user to access the first document via a first browser-controlled window executing on a client computer; and if the first user is authorized to modify the first document, then: (a) applying one or more modifications to the first document, the one or more modifications having been received from the first user; (b) electronically notifying one or more of a second group of users that the first user modified the first document, the second group of users being users whose identities are known to the server computer system; and (c) enabling a second user to further modify the first document, wherein the second user is a member of the second group of users who are notified of the one or more modifications made by the first user to the first document, and wherein the second user is not the same as the first user.
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1. A server computer implemented method of online document collaboration by authorized users, the method comprising the following steps performed by a server computer system connected to the Internet: establishing an account for each of a plurality of users, wherein each account is associated with storage space to store one or more documents; storing a first document in the server computer system in a first account, the first document capable of being modified by a plurality of authorized users; enabling access to the first document via a browser-controlled window executing on a client computer by one or more authorized users; associating a set of restrictions with the first document, the restrictions including an ability to modify the first document in one or more permitted ways by one of a first group of users, the first group of users being users whose identities are known to the server computer system; receiving a request to access the first document from a first user, wherein the first user is a member of the first group of users, wherein the request to access accompanies the first user's identification information and authorization information; verifying the identity of the first user; if the first user is authorized to access the first document, then permitting the first user to access the first document via a first browser-controlled window executing on a client computer; and if the first user is authorized to modify the first document, then: (a) applying one or more modifications to the first document, the one or more modifications having been received from the first user; (b) electronically notifying one or more of a second group of users that the first user modified the first document, the second group of users being users whose identities are known to the server computer system; and (c) enabling a second user to further modify the first document, wherein the second user is a member of the second group of users who are notified of the one or more modifications made by the first user to the first document, and wherein the second user is not the same as the first user. 14. The server computer implemented method of claim 1 , wherein if the first document is being used by a predetermined number of simultaneous users, the server computer system is capable of notifying a subsequent requester seeking access to the first document that the first document is unavailable for access at the time.
| 0.814088 |
7,783,476 | 1 | 14 |
1. A method of collecting new words for addition to a lexicon for an agglutinative language, the method comprising: using a processor to retrieve sentences in the agglutinative language from documents; using the processor to identify new word candidate character strings in the retrieved sentences having a predetermined range of number characters; using the processor to filter the identified new word candidate character strings using a combination of a plurality of statistical criteria to generate a new words list, the plurality of statistical criteria comprising: a frequency criteria comprising a frequency of occurrence of a new word candidate character string in the retrieved sentences; a variance criteria comprising a left-hand side variance criteria comprising a number of different single characters which appear adjacent a left-hand side of the new word candidate character string divided by the frequency of occurrence of the new word candidate character string in the retrieved sentences; a character association criteria comprising a frequency of occurrence of two characters appearing adjacent one another in the retrieved sentences, divided by the multiplication product of a frequency of occurrence of a first of the two characters in the retrieved sentences and a frequency of occurrence of a second of the two characters in the retrieved sentences; using the processor to filter the identified new word candidate character strings further comprising: calculating the frequency criteria for all of the new word candidate character strings; calculating the variance criteria for only new word candidate character strings that have a frequency criteria greater than a first threshold; calculating the character association criteria for only new word candidate character strings that have a variance criteria greater than a second threshold; adding a new word candidate character string to the new words list when the character association criteria is greater than a third threshold; and using the processor to add words from the new words list to the lexicon.
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1. A method of collecting new words for addition to a lexicon for an agglutinative language, the method comprising: using a processor to retrieve sentences in the agglutinative language from documents; using the processor to identify new word candidate character strings in the retrieved sentences having a predetermined range of number characters; using the processor to filter the identified new word candidate character strings using a combination of a plurality of statistical criteria to generate a new words list, the plurality of statistical criteria comprising: a frequency criteria comprising a frequency of occurrence of a new word candidate character string in the retrieved sentences; a variance criteria comprising a left-hand side variance criteria comprising a number of different single characters which appear adjacent a left-hand side of the new word candidate character string divided by the frequency of occurrence of the new word candidate character string in the retrieved sentences; a character association criteria comprising a frequency of occurrence of two characters appearing adjacent one another in the retrieved sentences, divided by the multiplication product of a frequency of occurrence of a first of the two characters in the retrieved sentences and a frequency of occurrence of a second of the two characters in the retrieved sentences; using the processor to filter the identified new word candidate character strings further comprising: calculating the frequency criteria for all of the new word candidate character strings; calculating the variance criteria for only new word candidate character strings that have a frequency criteria greater than a first threshold; calculating the character association criteria for only new word candidate character strings that have a variance criteria greater than a second threshold; adding a new word candidate character string to the new words list when the character association criteria is greater than a third threshold; and using the processor to add words from the new words list to the lexicon. 14. The method of claim 1 , wherein the agglutinative language is Chinese.
| 0.889552 |
5,452,442 | 1 | 2 |
1. A method for operating a digital data processor to obtain one or more valid signatures of an undesirable software entity, the digital data processor including a memory that is bidirectionally coupled to the digital data processor, the method comprising the steps of: storing in the memory a corpus of computer programs that are representative of computer programs that are likely to be infected by an undesirable software entity; inputting to the digital data processor at least one portion of the undesirable software entity, the at least one portion including a sequence of bytes of the undesirable software entity that are likely to remain substantially invariant from a first instance of the undesirable software entity to a second instance of the undesirable software entity; storing the at least one inputted portion in the memory; selecting at least one candidate signature of the undesirable software entity from the stored at least one portion of the undesirable software entity; constructing with the digital data processor a list of unique n-grams from the sequence of bytes, each of the unique n-grams being comprised of from one to a chosen maximal number of sequential bytes (B) of the sequence of bytes, the constructed list of unique n-grams being stored in the memory; for each of the unique n-grams of the stored list, estimating with the digital data processor a probability of an occurrence of the unique n-gram within sequences of bytes obtained from the stored corpus of computer programs; for each candidate signature that is comprised of one or more of the unique n-grams, estimating with the digital data processor a false-positive probability of an occurrence of the candidate signature within the sequences of bytes obtained from the corpus of computer programs; comparing the estimated false-positive probabilities of the candidate signatures with one another and with a set threshold probabilities, the threshold probabilities having values selected to reduce a likelihood of an occurrence of a false positive indication during the use of a signature; and outputting at least one signature for subsequent use in identifying an occurrence of the undesirable software entity or a modified version of the undesirable software entity, the outputted at least one signature being determined to exhibit a false alarm probability that is comparable to or less than a lowest false alarm probability of others of the candidate signatures.
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1. A method for operating a digital data processor to obtain one or more valid signatures of an undesirable software entity, the digital data processor including a memory that is bidirectionally coupled to the digital data processor, the method comprising the steps of: storing in the memory a corpus of computer programs that are representative of computer programs that are likely to be infected by an undesirable software entity; inputting to the digital data processor at least one portion of the undesirable software entity, the at least one portion including a sequence of bytes of the undesirable software entity that are likely to remain substantially invariant from a first instance of the undesirable software entity to a second instance of the undesirable software entity; storing the at least one inputted portion in the memory; selecting at least one candidate signature of the undesirable software entity from the stored at least one portion of the undesirable software entity; constructing with the digital data processor a list of unique n-grams from the sequence of bytes, each of the unique n-grams being comprised of from one to a chosen maximal number of sequential bytes (B) of the sequence of bytes, the constructed list of unique n-grams being stored in the memory; for each of the unique n-grams of the stored list, estimating with the digital data processor a probability of an occurrence of the unique n-gram within sequences of bytes obtained from the stored corpus of computer programs; for each candidate signature that is comprised of one or more of the unique n-grams, estimating with the digital data processor a false-positive probability of an occurrence of the candidate signature within the sequences of bytes obtained from the corpus of computer programs; comparing the estimated false-positive probabilities of the candidate signatures with one another and with a set threshold probabilities, the threshold probabilities having values selected to reduce a likelihood of an occurrence of a false positive indication during the use of a signature; and outputting at least one signature for subsequent use in identifying an occurrence of the undesirable software entity or a modified version of the undesirable software entity, the outputted at least one signature being determined to exhibit a false alarm probability that is comparable to or less than a lowest false alarm probability of others of the candidate signatures. 2. A method as set forth in claim 1 wherein the step of inputting includes a step of inputting one or more undesirable software entity signatures, each of the one or more undesirable software entity signatures including at least one portion of the undesirable software entity, the at least one portion including a sequence of bytes that is unlikely to vary from a first instance of the undesirable software entity to a second instance of the undesirable software entity.
| 0.732041 |
8,131,740 | 23 | 24 |
23. The system of claim 18 , wherein the interaction condition specifies an action a user avatar performed with a given virtual object present in the virtual environment.
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23. The system of claim 18 , wherein the interaction condition specifies an action a user avatar performed with a given virtual object present in the virtual environment. 24. The system of claim 23 , wherein the action performed with the given virtual object is selected from at least one of a user avatar touching the given virtual object, the user clicking on the virtual object with a mouse cursor, and a length of time the avatar of the user interacted with the virtual object.
| 0.5 |
10,091,003 | 7 | 8 |
7. The medium of claim 2 , wherein the URI is generated for a one-time use.
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7. The medium of claim 2 , wherein the URI is generated for a one-time use. 8. The medium of claim 7 , wherein the generated URI includes an expiration period.
| 0.5 |
7,716,040 | 2 | 5 |
2. The method of claim 1 , wherein the first feature comprises a specified relationship between the first coding and a second coding, and wherein (E) comprises modifying the relationship between the first coding and the second coding.
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2. The method of claim 1 , wherein the first feature comprises a specified relationship between the first coding and a second coding, and wherein (E) comprises modifying the relationship between the first coding and the second coding. 5. The method of claim 2 , wherein (E) comprises: severing the specified relationship.
| 0.823045 |
7,577,644 | 1 | 4 |
1. A method, for providing relevant search results to a query, comprising: receiving, at a first computer server system, a query comprising one or more search terms; obtaining a context corresponding to the query, the context being representative of a currently viewed content or a history of viewed content; executing a first algorithmic search using at least one of the one or more search terms in the query; executing a second contextual search using the context and the at least one of the one or more search terms in the query; obtaining a first search result set from the executed first search; obtaining a second search result set from the executed second search; merging the first and second search result sets into a merged result set, wherein the merging includes conditionally excluding results in the first result set from the merged result set based on a threshold similarity to the context such that results in the first result set that are not within the threshold similarity to the context are excluded from the merged result set; and transmitting the merged result set as a response to the query.
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1. A method, for providing relevant search results to a query, comprising: receiving, at a first computer server system, a query comprising one or more search terms; obtaining a context corresponding to the query, the context being representative of a currently viewed content or a history of viewed content; executing a first algorithmic search using at least one of the one or more search terms in the query; executing a second contextual search using the context and the at least one of the one or more search terms in the query; obtaining a first search result set from the executed first search; obtaining a second search result set from the executed second search; merging the first and second search result sets into a merged result set, wherein the merging includes conditionally excluding results in the first result set from the merged result set based on a threshold similarity to the context such that results in the first result set that are not within the threshold similarity to the context are excluded from the merged result set; and transmitting the merged result set as a response to the query. 4. The method according to claim 1 , wherein the step of merging the search results ends upon achieving a threshold number of merged search results.
| 0.671111 |
10,043,521 | 1 | 2 |
1. A computer-implemented method for user dependent key phrase enrollment comprising: receiving, via a microphone, an audio input representing a user defined key phrase and converting the audio input to received audio data representative of the audio input; determining a sequence of most probable audio units corresponding to the received audio data, wherein each audio unit of most probable audio units corresponds to a frame of a plurality of frames of the audio data; processing the sequence of most probable audio units to eliminate at least one audio unit from the sequence of most probable audio units to generate a final sequence of audio units by determining a first silence audio unit of the sequence and a number of silence audio units immediately temporally following the first silence audio unit, wherein the first silence audio unit and the number of silence audio units are between non-silence audio units of the sequence, and eliminating the first silence audio unit and the immediately temporally following silence audio units in response to the total number of consecutive silence audio units not exceeding a threshold; generating a key phrase recognition model representing the user defined key phrase based on the final sequence of audio units, the key phrase recognition model comprising a single start state based rejection model, a key phrase model, and a transition from the single start state based rejection model to the key phrase model, wherein the single start state based rejection model includes a single rejection state having a plurality of rejection model self loops, wherein the key phrase model comprises a plurality of states having transitions therebetween, the plurality of states including a final state of the key phrase model, and wherein the plurality of states of the key phrase model correspond to the final sequence of audio units; receiving a further audio input for evaluation by the key phrase recognition model; generating a time series of scores of audio units based on a time series of feature vectors representative of the further audio input; scoring the key phrase recognition model based on the time series of scores of audio units to generate a rejection likelihood score and a key phrase likelihood score; and recognizing that the further audio input corresponds to the user defined key phrase based on the rejection likelihood score and the key phrase likelihood score.
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1. A computer-implemented method for user dependent key phrase enrollment comprising: receiving, via a microphone, an audio input representing a user defined key phrase and converting the audio input to received audio data representative of the audio input; determining a sequence of most probable audio units corresponding to the received audio data, wherein each audio unit of most probable audio units corresponds to a frame of a plurality of frames of the audio data; processing the sequence of most probable audio units to eliminate at least one audio unit from the sequence of most probable audio units to generate a final sequence of audio units by determining a first silence audio unit of the sequence and a number of silence audio units immediately temporally following the first silence audio unit, wherein the first silence audio unit and the number of silence audio units are between non-silence audio units of the sequence, and eliminating the first silence audio unit and the immediately temporally following silence audio units in response to the total number of consecutive silence audio units not exceeding a threshold; generating a key phrase recognition model representing the user defined key phrase based on the final sequence of audio units, the key phrase recognition model comprising a single start state based rejection model, a key phrase model, and a transition from the single start state based rejection model to the key phrase model, wherein the single start state based rejection model includes a single rejection state having a plurality of rejection model self loops, wherein the key phrase model comprises a plurality of states having transitions therebetween, the plurality of states including a final state of the key phrase model, and wherein the plurality of states of the key phrase model correspond to the final sequence of audio units; receiving a further audio input for evaluation by the key phrase recognition model; generating a time series of scores of audio units based on a time series of feature vectors representative of the further audio input; scoring the key phrase recognition model based on the time series of scores of audio units to generate a rejection likelihood score and a key phrase likelihood score; and recognizing that the further audio input corresponds to the user defined key phrase based on the rejection likelihood score and the key phrase likelihood score. 2. The method of claim 1 , wherein processing the sequence of most probable audio units to eliminate at least one audio unit comprises determining a first sub-phonetic audio unit of the sequence and a second sub-phonetic audio unit of the sequence immediately temporally following the first sub-phonetic audio unit match and eliminating the first or second sub-phonetic audio unit from the sequence of most probable audio units responsive to the first and second sub-phonetic audio unit matching.
| 0.627068 |
9,444,934 | 1 | 12 |
1. A method comprising: receiving, at a processor of a computing device, an audio voice signal of a first call participant during a first call, wherein the first call is a communication across a communication network; determining, by the processor of the computing device, an identity of the first call participant; determining, by the processor of the computing device, a speech to text profile associated with the identity of the first call participant, wherein the speech to text profile comprises a plurality of rules, and further wherein each of the plurality of rules is a rule for transcribing a word in the audio voice signal into text; and wherein the speech to text profile is adequately trained when a number of the plurality of rules reaches a predetermined threshold; and generating, by the processor of the computing device, a text output, wherein the text output is a transcribed version of a plurality of words identified in the audio voice signal of the first call participant, and further wherein at least one of the plurality of words identified is identified using at least one rule of the plurality of rules; and wherein the speech to text profile is used to generate a text output after the speech to text profile has been adequately trained.
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1. A method comprising: receiving, at a processor of a computing device, an audio voice signal of a first call participant during a first call, wherein the first call is a communication across a communication network; determining, by the processor of the computing device, an identity of the first call participant; determining, by the processor of the computing device, a speech to text profile associated with the identity of the first call participant, wherein the speech to text profile comprises a plurality of rules, and further wherein each of the plurality of rules is a rule for transcribing a word in the audio voice signal into text; and wherein the speech to text profile is adequately trained when a number of the plurality of rules reaches a predetermined threshold; and generating, by the processor of the computing device, a text output, wherein the text output is a transcribed version of a plurality of words identified in the audio voice signal of the first call participant, and further wherein at least one of the plurality of words identified is identified using at least one rule of the plurality of rules; and wherein the speech to text profile is used to generate a text output after the speech to text profile has been adequately trained. 12. The method of claim 1 , further comprising sending, by the processor of the computing device, the text output to a visual display.
| 0.826873 |
8,965,126 | 1 | 6 |
1. A character recognition device comprising: circuitry configured to: receive an image containing characters to be recognized as an input; detect a character region where the characters are present in the image; separate the character region on a character-by-character basis; perform a character-by-character recognition on the characters present in separated regions and output one or more character recognition result candidates for each character; receive the candidates as an input, calculate weights for transitions to the candidates, and create first character string transition data being character string transition data based on a set of the candidates and the weights, wherein: the weights are corrected based on a character size of each of the candidates, and the created first character string transition data contains a first epsilon transition from an initial state of a character string transition to the candidate, a second epsilon transition from the candidate to a final state of the character string transition, and a third epsilon transition for skipping the candidate on a character-by-character basis; and sequentially perform state transitions based on the first character string transition data, accumulate the weights in each state transition and calculate a cumulative weight for each state transition, and output one or more state transition results based on the cumulative weight.
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1. A character recognition device comprising: circuitry configured to: receive an image containing characters to be recognized as an input; detect a character region where the characters are present in the image; separate the character region on a character-by-character basis; perform a character-by-character recognition on the characters present in separated regions and output one or more character recognition result candidates for each character; receive the candidates as an input, calculate weights for transitions to the candidates, and create first character string transition data being character string transition data based on a set of the candidates and the weights, wherein: the weights are corrected based on a character size of each of the candidates, and the created first character string transition data contains a first epsilon transition from an initial state of a character string transition to the candidate, a second epsilon transition from the candidate to a final state of the character string transition, and a third epsilon transition for skipping the candidate on a character-by-character basis; and sequentially perform state transitions based on the first character string transition data, accumulate the weights in each state transition and calculate a cumulative weight for each state transition, and output one or more state transition results based on the cumulative weight. 6. The character recognition device according to claim 1 , wherein the circuitry is further configured to: calculate the weights by taking character string transitions of words registered in a language database into account.
| 0.816993 |
9,031,384 | 3 | 11 |
3. The interesting section identifying device of claim 2 , further comprising a reference index calculating unit configured to calculate a reference vector based on a plurality of second unit section frequency vectors in a reference section composed of a plurality of continuous second unit sections that include the designated time, and to assign, to the reference value, a largest one of the variances of the second unit sections included in the reference section, wherein the interesting section candidate extracting unit initially designates the reference section as a temporary interesting section candidate and repeats a process of (i) determining whether the second unit section frequency vector of a second unit section adjacent to the temporary interesting section candidate has at least a predetermined correlation to the reference vector and (ii) including the second unit section adjacent to the temporary interesting section candidate in the temporary interesting section candidate when determining that the second unit section frequency vector and the reference vector have at least the predetermined correlation, the interesting section candidate extracting unit terminating repetition of the process and designating the temporary interesting section candidate as the interesting section candidate upon determining that the second unit section frequency vector and the reference vector do not have at least the predetermined correlation.
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3. The interesting section identifying device of claim 2 , further comprising a reference index calculating unit configured to calculate a reference vector based on a plurality of second unit section frequency vectors in a reference section composed of a plurality of continuous second unit sections that include the designated time, and to assign, to the reference value, a largest one of the variances of the second unit sections included in the reference section, wherein the interesting section candidate extracting unit initially designates the reference section as a temporary interesting section candidate and repeats a process of (i) determining whether the second unit section frequency vector of a second unit section adjacent to the temporary interesting section candidate has at least a predetermined correlation to the reference vector and (ii) including the second unit section adjacent to the temporary interesting section candidate in the temporary interesting section candidate when determining that the second unit section frequency vector and the reference vector have at least the predetermined correlation, the interesting section candidate extracting unit terminating repetition of the process and designating the temporary interesting section candidate as the interesting section candidate upon determining that the second unit section frequency vector and the reference vector do not have at least the predetermined correlation. 11. The interesting section identifying device of claim 3 , wherein the detailed structure determining unit determines whether the detailed structure is included in every second unit section throughout the interesting section candidate.
| 0.911211 |
9,715,877 | 15 | 18 |
15. Non-transitory computer-readable media bearing software instructions configured to instruct a processor to search navigation system data including phonetic data and text data stored in a storage device accessible from within a mobile platform, wherein the phonetic data includes a set of point-of-interest names in phonetic form, and the text data includes at least a portion of the same set of point-of-interest names in text form, by performing the steps of: receiving a representation of spoken utterance from a user; querying the phonetic data of the navigation system data with the spoken utterance to find a corresponding match; if a corresponding match is not found via the querying of the phonetic data, processing the representation of the spoken utterance to produce a dictation text substantially corresponding to the spoken utterance, wherein the dictation text is tuned for speech that is typical of navigation destination entry types; and querying the text data of the navigation system data with the dictation text using an approximate string matching criteria and producing a results list associated therewith.
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15. Non-transitory computer-readable media bearing software instructions configured to instruct a processor to search navigation system data including phonetic data and text data stored in a storage device accessible from within a mobile platform, wherein the phonetic data includes a set of point-of-interest names in phonetic form, and the text data includes at least a portion of the same set of point-of-interest names in text form, by performing the steps of: receiving a representation of spoken utterance from a user; querying the phonetic data of the navigation system data with the spoken utterance to find a corresponding match; if a corresponding match is not found via the querying of the phonetic data, processing the representation of the spoken utterance to produce a dictation text substantially corresponding to the spoken utterance, wherein the dictation text is tuned for speech that is typical of navigation destination entry types; and querying the text data of the navigation system data with the dictation text using an approximate string matching criteria and producing a results list associated therewith. 18. The non-transitory computer-readable media of claim 15 , further including providing the results list to the user for selection of a desired result.
| 0.530864 |
9,588,678 | 1 | 7 |
1. A method of recognizing handwriting, comprising: receiving at least two handwriting strokes from a touch screen; determining text regions corresponding to respective the at least two handwriting strokes; calculating each size of the text regions; determining whether the text regions overlap each other; selecting a specific text region among the text regions into an excluding strokes group based on at least one of the size of the specific text region, a ratio of an overlap region between the specific text region and other text regions to the specific text region, and a number of overlap regions between the specific text region and the other text regions; and recognizing characters of the text regions except for the excluding strokes group.
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1. A method of recognizing handwriting, comprising: receiving at least two handwriting strokes from a touch screen; determining text regions corresponding to respective the at least two handwriting strokes; calculating each size of the text regions; determining whether the text regions overlap each other; selecting a specific text region among the text regions into an excluding strokes group based on at least one of the size of the specific text region, a ratio of an overlap region between the specific text region and other text regions to the specific text region, and a number of overlap regions between the specific text region and the other text regions; and recognizing characters of the text regions except for the excluding strokes group. 7. The method of claim 1 , further comprising storing results of the text recognition along with handwriting information corresponding to an image input by the handwriting.
| 0.783375 |
8,954,326 | 8 | 9 |
8. A portable communication device comprising: a voice interface unit configured to receive a voice command signal corresponding to a voice command of a user, and to output a voice response; a voice command recognition unit configured to recognize a command intention of the voice command through a command intention probability distribution corresponding to the voice command signal that is input through the voice interface to recognize the command intention, to select an application corresponding to the command intention, and to generate an execution signal of the appication; and an operating unit configured to operate the application according to the application execution signal that is generated in the voice command recognition unit.
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8. A portable communication device comprising: a voice interface unit configured to receive a voice command signal corresponding to a voice command of a user, and to output a voice response; a voice command recognition unit configured to recognize a command intention of the voice command through a command intention probability distribution corresponding to the voice command signal that is input through the voice interface to recognize the command intention, to select an application corresponding to the command intention, and to generate an execution signal of the appication; and an operating unit configured to operate the application according to the application execution signal that is generated in the voice command recognition unit. 9. The portable communication device of claim 8 , further comprising a communication unit configured to perform a wired/wireless communication with a web server, wherein the portable communication device downloads related information from the web server through the communication unit according to the application execution signal that is generated in the voice command recognition unit.
| 0.5 |
8,464,227 | 11 | 12 |
11. A method for developing and executing supervisory process control and manufacturing information applications including scripts, the method comprising: specifying, by a script editor interface, scripts for objects, wherein the script editor interface supports multiple distinct user-side script languages; inserting the user-side scripts to customize an object prior to execution of the instructions within the object; inserting post-execution user-side scripts to an object; rendering, by a script translation component, execution-side script of a single execution-side scripting language from source script rendered by the script editor and written according to any of a set of user-side script languages; wherein the user-side script languages comprise a plurality of script languages and wherein the script editor produces output scripts including tags indicating a particular one of plurality of user-side script languages; and executing, by a scripting engine, the execution-side script of the single execution-side scripting language generated by the script translation component during each scan cycle.
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11. A method for developing and executing supervisory process control and manufacturing information applications including scripts, the method comprising: specifying, by a script editor interface, scripts for objects, wherein the script editor interface supports multiple distinct user-side script languages; inserting the user-side scripts to customize an object prior to execution of the instructions within the object; inserting post-execution user-side scripts to an object; rendering, by a script translation component, execution-side script of a single execution-side scripting language from source script rendered by the script editor and written according to any of a set of user-side script languages; wherein the user-side script languages comprise a plurality of script languages and wherein the script editor produces output scripts including tags indicating a particular one of plurality of user-side script languages; and executing, by a scripting engine, the execution-side script of the single execution-side scripting language generated by the script translation component during each scan cycle. 12. The method of claim 11 wherein the script translation component supports designating aliases for referenced attributes.
| 0.642442 |
8,862,989 | 10 | 11 |
10. The method of claim 8 wherein: the sequence is a first sequence; the single text character is a single text character; the reading is a first reading; the method further includes: receiving, by the processor, a second sequence of English characters from the corresponding keys on the keyboard; receiving, by the processor, an input of a second single text character of the language different than English; and defining, by the processor, a second reading for the language by mapping the received second sequence of the English characters to the received second single text character of the language different than English.
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10. The method of claim 8 wherein: the sequence is a first sequence; the single text character is a single text character; the reading is a first reading; the method further includes: receiving, by the processor, a second sequence of English characters from the corresponding keys on the keyboard; receiving, by the processor, an input of a second single text character of the language different than English; and defining, by the processor, a second reading for the language by mapping the received second sequence of the English characters to the received second single text character of the language different than English. 11. The method of claim 10 , further comprising combining the first and second readings to create a compound reading that corresponds to multiple sequences of English characters.
| 0.5 |
7,613,719 | 22 | 27 |
22. A method of displaying information retrieved from a data source stored on a computer storage medium, comprising: receiving a first natural language input from a user; analyzing the first natural language input to identify semantic information contained therein; associating portions of the first natural language input with a command object, a frame object and an entity object of a schema based on the semantic information and the first natural language input; displaying a table of columns and rows to the user illustrating data retrieved from the data source as a function of the command object, the frame object and the entity object; receiving a second natural language input from the user referring to the table of columns and rows; altering the schema based on the second natural language input; and modifying the arrangement of the previously displayed data in the table as a function of the altered schema and displaying the newly arranged data in a modified table to the user.
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22. A method of displaying information retrieved from a data source stored on a computer storage medium, comprising: receiving a first natural language input from a user; analyzing the first natural language input to identify semantic information contained therein; associating portions of the first natural language input with a command object, a frame object and an entity object of a schema based on the semantic information and the first natural language input; displaying a table of columns and rows to the user illustrating data retrieved from the data source as a function of the command object, the frame object and the entity object; receiving a second natural language input from the user referring to the table of columns and rows; altering the schema based on the second natural language input; and modifying the arrangement of the previously displayed data in the table as a function of the altered schema and displaying the newly arranged data in a modified table to the user. 27. The method of claim 22 wherein the second natural language input relates to sorting at least a portion of the table.
| 0.615385 |
9,141,695 | 1 | 2 |
1. A method for creating a playlist based on a search query, wherein the playlist comprises a plurality of audio microposts, the method being implemented in a computer that includes one or more physical processors programmed with one or more computer program instructions that, when executed by the one or more physical processors, programs the computer to perform the method, the method comprising: obtaining, by the computer, a search query comprising one or more hashtags and/or keywords; obtaining, by the computer, a plurality of audio microposts that are associated with the one or more hashtags and/or keywords, wherein each individual audio micropost of the plurality of audio microposts is associated with a run time length that is less than a predetermined time limit; ranking, by the computer, the plurality of audio microposts based on one or more ranking criteria; determining, by computer, a playback order of the plurality of audio microposts based on the ranking; creating, by the computer, a playlist comprising the plurality of audio microposts arranged based on the playback order; causing, by the computer, a playback of the playlist to occur in accordance with the playback order; receiving, by the computer, a new audio input during the playback; generating, by the computer, a new audio micropost based on the new audio input; obtaining, by the computer, an indication that the new audio input is associated with the one or more hashtags and/or keywords; associating, by the computer, the new audio micropost with the playlist based on the indication; and automatically adding, by the computer, the new audio micropost to the playlist based on the association; and receiving, by the computer, authorization information from a user, the authorization information comprising an indication of whether one or more other users are authorized to generate audio microposts to the playlist.
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1. A method for creating a playlist based on a search query, wherein the playlist comprises a plurality of audio microposts, the method being implemented in a computer that includes one or more physical processors programmed with one or more computer program instructions that, when executed by the one or more physical processors, programs the computer to perform the method, the method comprising: obtaining, by the computer, a search query comprising one or more hashtags and/or keywords; obtaining, by the computer, a plurality of audio microposts that are associated with the one or more hashtags and/or keywords, wherein each individual audio micropost of the plurality of audio microposts is associated with a run time length that is less than a predetermined time limit; ranking, by the computer, the plurality of audio microposts based on one or more ranking criteria; determining, by computer, a playback order of the plurality of audio microposts based on the ranking; creating, by the computer, a playlist comprising the plurality of audio microposts arranged based on the playback order; causing, by the computer, a playback of the playlist to occur in accordance with the playback order; receiving, by the computer, a new audio input during the playback; generating, by the computer, a new audio micropost based on the new audio input; obtaining, by the computer, an indication that the new audio input is associated with the one or more hashtags and/or keywords; associating, by the computer, the new audio micropost with the playlist based on the indication; and automatically adding, by the computer, the new audio micropost to the playlist based on the association; and receiving, by the computer, authorization information from a user, the authorization information comprising an indication of whether one or more other users are authorized to generate audio microposts to the playlist. 2. The method of claim 1 , wherein the new audio input is received from the user.
| 0.927027 |
9,031,830 | 8 | 9 |
8. The process of claim 7 , wherein the context indicator further specifies a field in the web form in which the user input is received.
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8. The process of claim 7 , wherein the context indicator further specifies a field in the web form in which the user input is received. 9. The process of claim 8 , wherein the context indicator further specifies metadata associated with the field in the web form.
| 0.5 |
8,726,256 | 34 | 41 |
34. A computer-implemented method comprising: converting a quantification into an automaton, wherein converting includes unrolling the quantification to control an out-degree of the automaton; and converting the automaton into machine code corresponding to a target device.
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34. A computer-implemented method comprising: converting a quantification into an automaton, wherein converting includes unrolling the quantification to control an out-degree of the automaton; and converting the automaton into machine code corresponding to a target device. 41. The computer-implemented method of claim 34 , wherein unrolling the quantification to control an out-degree of the automaton comprises unrolling the quantification to control an out-degree of the automaton based on a threshold.
| 0.5 |
8,346,553 | 4 | 5 |
4. A speech recognition system comprising: an input identification means for identifying each of a plurality of users of received signals of utterance; recognition result storage for storing top N recognition vocabularies having high recognition scores starting from the best solution as N best solutions, N being an integer equal to one or more, the recognition scores being calculated by comparing data corresponding to the utterance with a plurality of recognition vocabularies, a recognition word having the highest recognition score being the best solution; a recognition result extraction means for extracting N best solutions extracted as following N best solutions from the recognition result storage, the following N best solutions following chronologically the utterance corresponding to a preceding N best solutions, the following N best solutions having been made by one of the users different from the user of the utterance corresponding to the preceding N best solutions; a degree of association calculation means for calculating a degree of association representing a likelihood that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions; a response utterance determination means for determining that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions in the case of the degree of association being equal to or more than a threshold value; a repeat utterance determination means for determining whether the following N best solutions are N best solutions obtained by a repeat utterance in response to the utterance corresponding to the preceding N best solution, in the case that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions; and an agreement determination means for: determining whether a preceding best solution and a following best solution agree with each other in the case of the following N best solutions being best solutions obtained by a repeat utterance in response to the utterance corresponding to the preceding N best solutions, the preceding best solution being a best solution of the preceding N best solutions, the following best solution being a best solution of the following N best solutions is the following best solution; and determining that some or all of the preceding N best solutions can be replaced with some or all of the following N best solutions in the case that the preceding best solution and the following best solution do not agree with each other.
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4. A speech recognition system comprising: an input identification means for identifying each of a plurality of users of received signals of utterance; recognition result storage for storing top N recognition vocabularies having high recognition scores starting from the best solution as N best solutions, N being an integer equal to one or more, the recognition scores being calculated by comparing data corresponding to the utterance with a plurality of recognition vocabularies, a recognition word having the highest recognition score being the best solution; a recognition result extraction means for extracting N best solutions extracted as following N best solutions from the recognition result storage, the following N best solutions following chronologically the utterance corresponding to a preceding N best solutions, the following N best solutions having been made by one of the users different from the user of the utterance corresponding to the preceding N best solutions; a degree of association calculation means for calculating a degree of association representing a likelihood that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions; a response utterance determination means for determining that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions in the case of the degree of association being equal to or more than a threshold value; a repeat utterance determination means for determining whether the following N best solutions are N best solutions obtained by a repeat utterance in response to the utterance corresponding to the preceding N best solution, in the case that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions; and an agreement determination means for: determining whether a preceding best solution and a following best solution agree with each other in the case of the following N best solutions being best solutions obtained by a repeat utterance in response to the utterance corresponding to the preceding N best solutions, the preceding best solution being a best solution of the preceding N best solutions, the following best solution being a best solution of the following N best solutions is the following best solution; and determining that some or all of the preceding N best solutions can be replaced with some or all of the following N best solutions in the case that the preceding best solution and the following best solution do not agree with each other. 5. The speech recognition system according to claim 4 , further comprising: a recognition result correction means for updating the preceding best solution in the recognition result storage to the following best solution, the recognition storage storing the preceding N best solutions, in the case that the agreement determination means determines that the preceding best solution and the following best solution do not agree with each other; and a result output means for outputting the following best solution updated by the recognition result correction means.
| 0.80085 |
9,413,702 | 2 | 3 |
2. The method of claim 1 comprising: searching for a data element in the operational memory of the repository such that a data element is considered to match with the search term when a difference or a distance between the data element in the operational memory of the repository and the search term is smaller than a predetermined limit, and controlling sending of the subsequent message to the second broker based on a result of said searching.
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2. The method of claim 1 comprising: searching for a data element in the operational memory of the repository such that a data element is considered to match with the search term when a difference or a distance between the data element in the operational memory of the repository and the search term is smaller than a predetermined limit, and controlling sending of the subsequent message to the second broker based on a result of said searching. 3. The method of claim 2 comprising changing a merit value when a matching data element is found.
| 0.5 |
8,370,117 | 13 | 20 |
13. A proofing tool for a computer-aided design (CAD) object having at least one drawing note, the tool stored in a memory of a computer system having a processor, the memory storing the CAD object, the tool comprising: an extractor to extract the drawing note from the CAD object; a rule module to obtain a plurality of rules from the memory, the plurality of rules relating to acceptable drawing notes for the CAD object, the plurality of rules including a first rule having a first plurality of keywords and a second rule having a second plurality of keywords; a comparator to compare the extracted note with the first rule and with the second rule; and a tagging module to generate a result based on the comparisons.
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13. A proofing tool for a computer-aided design (CAD) object having at least one drawing note, the tool stored in a memory of a computer system having a processor, the memory storing the CAD object, the tool comprising: an extractor to extract the drawing note from the CAD object; a rule module to obtain a plurality of rules from the memory, the plurality of rules relating to acceptable drawing notes for the CAD object, the plurality of rules including a first rule having a first plurality of keywords and a second rule having a second plurality of keywords; a comparator to compare the extracted note with the first rule and with the second rule; and a tagging module to generate a result based on the comparisons. 20. The proofing tool of claim 13 , further comprising a criteria selector to prompt for and receive a proofing criterion.
| 0.67027 |
8,542,132 | 1 | 3 |
1. A method of enabling input on a handheld electronic device that comprises an input apparatus, an output apparatus, and a memory having stored therein a plurality of language objects, the input apparatus comprising at least one input key, the at least one input key having a plurality of characters assigned thereto, the handheld electronic device further having operable thereon a disambiguation routine that is structured to disambiguate ambiguous text input, the method comprising: outputting a number of language objects on a display, the number of language objects including a default portion and a number of variant portions, and each language object comprising a number of characters; detecting an input from the input apparatus for selecting one of the language objects on the display; detecting a selection of the at least one input key as an editing input for editing the selected language object; disambiguating a combination of the selected language object and the editing input; and outputting on the display a number of new language objects, the new language objects including a new default portion and a number of new variant portions, the new variant portions including at least a combination of an unselected language object prior to the editing input and a character corresponding to the editing input.
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1. A method of enabling input on a handheld electronic device that comprises an input apparatus, an output apparatus, and a memory having stored therein a plurality of language objects, the input apparatus comprising at least one input key, the at least one input key having a plurality of characters assigned thereto, the handheld electronic device further having operable thereon a disambiguation routine that is structured to disambiguate ambiguous text input, the method comprising: outputting a number of language objects on a display, the number of language objects including a default portion and a number of variant portions, and each language object comprising a number of characters; detecting an input from the input apparatus for selecting one of the language objects on the display; detecting a selection of the at least one input key as an editing input for editing the selected language object; disambiguating a combination of the selected language object and the editing input; and outputting on the display a number of new language objects, the new language objects including a new default portion and a number of new variant portions, the new variant portions including at least a combination of an unselected language object prior to the editing input and a character corresponding to the editing input. 3. The method of claim 1 , wherein the input apparatus comprises a thumbwheel, and the method further comprises detecting a rotation of the thumbwheel for selecting the one of the language objects.
| 0.5 |
9,037,610 | 1 | 9 |
1. A method of providing access control (AC) in respect of a database storing information in tables and columns and being accessible from a user interface, which is configured to accept a database query and return information extracted from the database, the method being implemented at least in part at a policy enforcement point (PEP), which is located between the database and the user interface, and comprising i) intercepting, at the PEP, a database query; ii) assigning attribute values by performing at least one of the following: ii-a) assigning a resource attribute value based on at least one target table or target column appearing in the query; ii-b) assigning an action attribute value based on a construct type appearing in the query; and ii-c) assigning a subject and/or environment attribute value based on an identity of the user or on environment data; iii) providing an attributed-based AC policy defined in terms of said attributes iv) deriving an access condition, for which the AC policy, when evaluated for the attribute value(s) assigned in step ii), evaluates to permit access; and v) amending the database query by imposing said access condition and vi) transmitting the amended query to the database.
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1. A method of providing access control (AC) in respect of a database storing information in tables and columns and being accessible from a user interface, which is configured to accept a database query and return information extracted from the database, the method being implemented at least in part at a policy enforcement point (PEP), which is located between the database and the user interface, and comprising i) intercepting, at the PEP, a database query; ii) assigning attribute values by performing at least one of the following: ii-a) assigning a resource attribute value based on at least one target table or target column appearing in the query; ii-b) assigning an action attribute value based on a construct type appearing in the query; and ii-c) assigning a subject and/or environment attribute value based on an identity of the user or on environment data; iii) providing an attributed-based AC policy defined in terms of said attributes iv) deriving an access condition, for which the AC policy, when evaluated for the attribute value(s) assigned in step ii), evaluates to permit access; and v) amending the database query by imposing said access condition and vi) transmitting the amended query to the database. 9. The method of claim 1 , wherein the AC policy is encoded in Extended Access Control Markup Language (XACML).
| 0.925901 |
8,155,949 | 4 | 5 |
4. The method of claim 1 , wherein the semi-structured database stores active and passive information and wherein the step of retrieving information comprises the step of retrieving passive information from the semi-structured database.
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4. The method of claim 1 , wherein the semi-structured database stores active and passive information and wherein the step of retrieving information comprises the step of retrieving passive information from the semi-structured database. 5. The method of claim 4 , wherein the step of retrieving information comprises the step of retrieving active information for computing information.
| 0.5 |
9,954,890 | 11 | 12 |
11. The system of claim 1 , wherein the one or more virtual machines are configured based on one or more specification version numbers of the PDF document.
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11. The system of claim 1 , wherein the one or more virtual machines are configured based on one or more specification version numbers of the PDF document. 12. The system of claim 11 , wherein a specification version number of the one or more specification version numbers is used to identify a reader application version to be run in a virtual machine of the one or more virtual machines.
| 0.5 |
9,390,725 | 11 | 13 |
11. A method to use a system using a user device in communication with a stored data repository, that reduces the background noise from a speech audio signal generated by a user, comprising: receiving a speech audio signal with a user device, said user device further comprises a processor and a memory; and providing a noise reduction device, in communication with a stored data repository, and in communication with said user device, is configured to: convert said received speech audio signal to text; generate synthetic speech based on a speech data corpus or speech model data of the user stored in said stored data repository and said converted text; determine the predicted subjective quality of the received speech audio signal if that signal were to be transmitted to a far end listener; determine the predicted subjective quality of said synthetic speech; and transmit, selectively, said speech audio signal or said synthetic speech, whichever has higher predicted quality based on a comparison between the value of objective quality metrics computed for the speech audio signal and the synthetic speech signal.
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11. A method to use a system using a user device in communication with a stored data repository, that reduces the background noise from a speech audio signal generated by a user, comprising: receiving a speech audio signal with a user device, said user device further comprises a processor and a memory; and providing a noise reduction device, in communication with a stored data repository, and in communication with said user device, is configured to: convert said received speech audio signal to text; generate synthetic speech based on a speech data corpus or speech model data of the user stored in said stored data repository and said converted text; determine the predicted subjective quality of the received speech audio signal if that signal were to be transmitted to a far end listener; determine the predicted subjective quality of said synthetic speech; and transmit, selectively, said speech audio signal or said synthetic speech, whichever has higher predicted quality based on a comparison between the value of objective quality metrics computed for the speech audio signal and the synthetic speech signal. 13. The claim according to claim 11 , wherein said received speech audio signal is a live speech audio signal.
| 0.736842 |
7,696,999 | 12 | 13 |
12. The Transmitter as claimed 11 , wherein the control codes comprises horizontal scrolling control codes for the control of a horizontal scrolling of characters representing the coded text lines.
|
12. The Transmitter as claimed 11 , wherein the control codes comprises horizontal scrolling control codes for the control of a horizontal scrolling of characters representing the coded text lines. 13. The transmitter as claimed in claim 12 , wherein the control codes comprise codes for controlling the scrolling speed.
| 0.5 |
6,163,852 | 17 | 19 |
17. A computer system supporting synchronous random access memory, comprising: a processor; a synchronous random access memory; a memory controller coupled between the processor and the synchronous random access memory; a data input, within the memory controller, for receiving a stream of data from the synchronous random access memory; a data clock input, within the memory controller, for receiving a data clock signal from the synchronous random access memory for clocking the stream of data; a first memory register, that is clocked by the data clock signal, for receiving data from the stream of data; a second memory register, that is clocked by the data clock signal, for receiving data from the stream of data; a first system register for receiving data from the first memory register, the first system register being clocked by a system clock signal, which is slower than the data clock signal; a second system register, that is clocked by the system clock signal, for receiving data from the second memory register; and a controller for coordinating actions of the first and second memory registers as well as the first and second system registers so that data is loaded into the second memory register by the data clock signal while data is being loaded into the first system register by the system clock signal and, during alternate cycles, so that data is loaded into the first memory register by the data clock signal while data is being loaded into the second system register by the system clock signal.
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17. A computer system supporting synchronous random access memory, comprising: a processor; a synchronous random access memory; a memory controller coupled between the processor and the synchronous random access memory; a data input, within the memory controller, for receiving a stream of data from the synchronous random access memory; a data clock input, within the memory controller, for receiving a data clock signal from the synchronous random access memory for clocking the stream of data; a first memory register, that is clocked by the data clock signal, for receiving data from the stream of data; a second memory register, that is clocked by the data clock signal, for receiving data from the stream of data; a first system register for receiving data from the first memory register, the first system register being clocked by a system clock signal, which is slower than the data clock signal; a second system register, that is clocked by the system clock signal, for receiving data from the second memory register; and a controller for coordinating actions of the first and second memory registers as well as the first and second system registers so that data is loaded into the second memory register by the data clock signal while data is being loaded into the first system register by the system clock signal and, during alternate cycles, so that data is loaded into the first memory register by the data clock signal while data is being loaded into the second system register by the system clock signal. 19. The computer system of claim 17, wherein the memory controller resides inside the processor.
| 0.622047 |
8,761,500 | 1 | 4 |
1. A method for automatically recognizing Arabic text, comprising: building an Arabic corpus comprising Arabic text files and ground truths corresponding to each of the Arabic text files, wherein the Arabic text files include Arabic texts written in different writing styles; storing writing-style indices in association with the Arabic text files by a computer, wherein each of the writing-style indices indicates that one of the Arabic text files is written in one of the writing styles; acquiring a text image containing a line of Arabic characters; digitizing the line of the Arabic characters to form a two-dimensional array of pixels each associated with a pixel value, wherein the pixel value is expressed in a binary number; dividing the line of the Arabic characters into a plurality of line images; defining a plurality of cells in one of the plurality of line images, wherein each of the plurality of cells comprises a group of adjacent pixels; serializing pixel values of pixels in each of the plurality of cells in one of the plurality of line images to form a binary cell number; forming a text feature vector according to binary cell numbers obtained from the plurality of cells in one of the plurality of line images; training a Hidden Markov Model using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices in association with the Arabic text files; and feeding the text feature vector into the Hidden Markov Model to recognize the line of Arabic characters.
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1. A method for automatically recognizing Arabic text, comprising: building an Arabic corpus comprising Arabic text files and ground truths corresponding to each of the Arabic text files, wherein the Arabic text files include Arabic texts written in different writing styles; storing writing-style indices in association with the Arabic text files by a computer, wherein each of the writing-style indices indicates that one of the Arabic text files is written in one of the writing styles; acquiring a text image containing a line of Arabic characters; digitizing the line of the Arabic characters to form a two-dimensional array of pixels each associated with a pixel value, wherein the pixel value is expressed in a binary number; dividing the line of the Arabic characters into a plurality of line images; defining a plurality of cells in one of the plurality of line images, wherein each of the plurality of cells comprises a group of adjacent pixels; serializing pixel values of pixels in each of the plurality of cells in one of the plurality of line images to form a binary cell number; forming a text feature vector according to binary cell numbers obtained from the plurality of cells in one of the plurality of line images; training a Hidden Markov Model using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices in association with the Arabic text files; and feeding the text feature vector into the Hidden Markov Model to recognize the line of Arabic characters. 4. The method of claim 1 , wherein the writing styles specify with or without vowelization in the Arabic text.
| 0.873853 |
8,375,048 | 15 | 17 |
15. A query processing method, implemented at least in part via a processing unit, for modifying search queries comprising: receiving a query from a user; and attaching one or more query augmentation terms to the query based on an intent associated with one or more terms comprised in the query, the intent determined based at least in part on: one or more past queries; one or more user experiences related to one or more associated non-temporal contractual obligations; an origin of the query; and at least one of an algorithmic intent determination or a list-match intent determination, the list-match intent determination based upon a dictionary comprising two or more entries, respective entries of the dictionary comprising: at least one of a word or a phrase; a category; and a probability of at least one of the word or the phrase corresponding to the category; parsing the query comprising the one or more attached query augmentation terms; deriving at least one keyword term from the parsed query; assigning respective weight values and confidence levels to one or more keyword terms derived from the parsed query; and availing the parsed query to a search engine such that search results are influenced by the respective weight values and confidence levels assigned to one or more keyword terms derived from the parsed query that comprised the one or more attached query augmentation terms attached based upon intent.
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15. A query processing method, implemented at least in part via a processing unit, for modifying search queries comprising: receiving a query from a user; and attaching one or more query augmentation terms to the query based on an intent associated with one or more terms comprised in the query, the intent determined based at least in part on: one or more past queries; one or more user experiences related to one or more associated non-temporal contractual obligations; an origin of the query; and at least one of an algorithmic intent determination or a list-match intent determination, the list-match intent determination based upon a dictionary comprising two or more entries, respective entries of the dictionary comprising: at least one of a word or a phrase; a category; and a probability of at least one of the word or the phrase corresponding to the category; parsing the query comprising the one or more attached query augmentation terms; deriving at least one keyword term from the parsed query; assigning respective weight values and confidence levels to one or more keyword terms derived from the parsed query; and availing the parsed query to a search engine such that search results are influenced by the respective weight values and confidence levels assigned to one or more keyword terms derived from the parsed query that comprised the one or more attached query augmentation terms attached based upon intent. 17. The method of claim 15 , the respective weight values and confidence levels used for ranking the search results and for federation, the federation comprising: evaluating, based at least in part on at least one of the algorithmic intent determination or the list-match intent determination, one or more search queries using two or more data sources in parallel; and determining whether to send at least one of the one or more evaluated search queries to at least one of an encyclopedic provider, a news provider, a travel provider, a document serving provider, or a phone directory provider.
| 0.576923 |
8,560,311 | 1 | 5 |
1. A speech recognition system comprising at least one computer including at least one processor, the at least one computer comprising a natural language processing component, a machine learning engine, and an automated speech recognition component, the natural language processing component and the automated speech recognition component being distinct from each other such that uncertainty in speech recognition is isolated from uncertainty in natural language understanding, wherein the natural language processing component and the automated speech recognition component communicate corresponding weighted meta-information representative of the uncertainty, the corresponding weighted meta-information generated at least partially by the machine learning engine, wherein the machine learning engine is configured to: receive, from the automated speech recognition component, weighted meta-information; adjust a weighting of the weighted meta-information based at least partially on a statistical learning algorithm; and communicate the adjusted weighted meta-information to the natural language processing component.
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1. A speech recognition system comprising at least one computer including at least one processor, the at least one computer comprising a natural language processing component, a machine learning engine, and an automated speech recognition component, the natural language processing component and the automated speech recognition component being distinct from each other such that uncertainty in speech recognition is isolated from uncertainty in natural language understanding, wherein the natural language processing component and the automated speech recognition component communicate corresponding weighted meta-information representative of the uncertainty, the corresponding weighted meta-information generated at least partially by the machine learning engine, wherein the machine learning engine is configured to: receive, from the automated speech recognition component, weighted meta-information; adjust a weighting of the weighted meta-information based at least partially on a statistical learning algorithm; and communicate the adjusted weighted meta-information to the natural language processing component. 5. The speech recognition system of claim 1 , wherein the machine learning engine is further configured to receive, from the automated speech recognition component, a plurality of words or word segments from the automated speech recognition component, and wherein the weighted meta-information received from the automated speech recognition component comprises match-likelihood values for at least a portion of the plurality of words or word segments.
| 0.5 |
8,572,013 | 2 | 4 |
2. A system, comprising: at least one hardware computing device; and an item classification application executable in the at least one hardware computing device, the item classification application comprising: logic that identifies one of a plurality of classifications based at least in part on data associated with an item and a data dictionary, thereby producing an identified classification that is associated with a confidence score; logic that automatically assigns the identified classification to the item when the confidence score meets a threshold; logic that obtains a manual confirmation of the identified classification when the confidence score is below the threshold, the manual confirmation indicating whether the identified classification is correct; logic that builds the data dictionary based at least in part on data describing a plurality of preclassified items, each one of the preclassified items being assigned a manually determined one of the classifications; and logic that periodically rebuilds the data dictionary based at least in part on data associated with automatically classified items haying manually confirmed classifications.
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2. A system, comprising: at least one hardware computing device; and an item classification application executable in the at least one hardware computing device, the item classification application comprising: logic that identifies one of a plurality of classifications based at least in part on data associated with an item and a data dictionary, thereby producing an identified classification that is associated with a confidence score; logic that automatically assigns the identified classification to the item when the confidence score meets a threshold; logic that obtains a manual confirmation of the identified classification when the confidence score is below the threshold, the manual confirmation indicating whether the identified classification is correct; logic that builds the data dictionary based at least in part on data describing a plurality of preclassified items, each one of the preclassified items being assigned a manually determined one of the classifications; and logic that periodically rebuilds the data dictionary based at least in part on data associated with automatically classified items haying manually confirmed classifications. 4. The system of claim 2 , wherein the identified classification is identified based at least in part on at least one preclassified item that is similar to the item.
| 0.85424 |
10,120,534 | 1 | 4 |
1. A method for automatically generating a user interface for an application program, the method comprising the computer-implemented steps of: in response to detecting an event, selecting, by the application program, a primary widget from a plurality of widgets to display on the user interface; in response to selecting the primary widget, querying a data store that is storing tags associated with widgets, using one or more particular tags that are associated with the primary widget; based on the querying, determining one or more secondary widgets from the plurality of widgets, wherein the one or more secondary widgets are associated with at least one of the one or more particular tags of the primary widget; for each particular secondary widget among the one or more secondary widgets, determining correlation data that measures correlation of the particular secondary widget to the primary widget; based on the correlation data of the particular secondary widget, determining whether to display the particular secondary widget on the user interface in a particular arrangement comprising the particular secondary widget and one or more other secondary widgets from among the one or more secondary widgets.
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1. A method for automatically generating a user interface for an application program, the method comprising the computer-implemented steps of: in response to detecting an event, selecting, by the application program, a primary widget from a plurality of widgets to display on the user interface; in response to selecting the primary widget, querying a data store that is storing tags associated with widgets, using one or more particular tags that are associated with the primary widget; based on the querying, determining one or more secondary widgets from the plurality of widgets, wherein the one or more secondary widgets are associated with at least one of the one or more particular tags of the primary widget; for each particular secondary widget among the one or more secondary widgets, determining correlation data that measures correlation of the particular secondary widget to the primary widget; based on the correlation data of the particular secondary widget, determining whether to display the particular secondary widget on the user interface in a particular arrangement comprising the particular secondary widget and one or more other secondary widgets from among the one or more secondary widgets. 4. The method of claim 1 , wherein the primary widget comprises one or more graphical user interface controls that are programmed to configure a particular feature for a managed system element.
| 0.760546 |
8,688,453 | 1 | 4 |
1. A method comprising: via computer processor hardware, performing operations of: parsing an utterance to identify syntactic relationships amongst words in the utterance; creating sets of words from the utterance based on the syntactic relationships; mapping each set of the sets of words to a respective candidate intent value to produce a list of candidate intent values for the utterance; ranking the candidate intent values in the list based on usage of the sets of words in previously received utterances; selecting, from the list, a candidate intent value as being representative of an intent of the utterance; detecting that the selected candidate intent value maps to multiple possible labels in a label listing, an entry in the label listing indicating that the selected candidate intent value maps to the multiple possible labels, the multiple possible labels including a majority label representing a first classification and a minority label representing a second classification; selecting the majority label; assigning the selected majority label to the utterance to indicate that the utterance falls into the first classification, the selected majority label representative of a dominant subject matter likely intended by the utterance.
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1. A method comprising: via computer processor hardware, performing operations of: parsing an utterance to identify syntactic relationships amongst words in the utterance; creating sets of words from the utterance based on the syntactic relationships; mapping each set of the sets of words to a respective candidate intent value to produce a list of candidate intent values for the utterance; ranking the candidate intent values in the list based on usage of the sets of words in previously received utterances; selecting, from the list, a candidate intent value as being representative of an intent of the utterance; detecting that the selected candidate intent value maps to multiple possible labels in a label listing, an entry in the label listing indicating that the selected candidate intent value maps to the multiple possible labels, the multiple possible labels including a majority label representing a first classification and a minority label representing a second classification; selecting the majority label; assigning the selected majority label to the utterance to indicate that the utterance falls into the first classification, the selected majority label representative of a dominant subject matter likely intended by the utterance. 4. The method as in claim 1 , wherein creating the sets of words in the utterance includes: utilizing the identified syntactic relationships of words to identify groupings of related words in the utterance; and applying a set of pattern rules to the identified syntactic relationships to identify locations of words in the utterance to create the sets of words.
| 0.648833 |
9,037,519 | 1 | 5 |
1. A system, comprising: a memory configured to store computer-executable components; and a processor, communicatively coupled to the memory, configured to execute or facilitate execution of the computer-executable components, the computer-executable components comprising: a parameter extraction component configured to determine simulation data that represents an environment of a set of vehicles within a specified area; and a fusion component configured to generate a cascaded two-tier classifier based on a first fusion of a first support vector machine classifier and a first multilayer perceptron classifier, wherein the fusion component is further configured to facilitate traffic state detection based on a second fusion of the cascaded two-tier classifier with a second support vector machine classifier and a second multilayer perceptron classifier, and wherein the cascaded two-tier classifier is trained based in part on the simulation data.
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1. A system, comprising: a memory configured to store computer-executable components; and a processor, communicatively coupled to the memory, configured to execute or facilitate execution of the computer-executable components, the computer-executable components comprising: a parameter extraction component configured to determine simulation data that represents an environment of a set of vehicles within a specified area; and a fusion component configured to generate a cascaded two-tier classifier based on a first fusion of a first support vector machine classifier and a first multilayer perceptron classifier, wherein the fusion component is further configured to facilitate traffic state detection based on a second fusion of the cascaded two-tier classifier with a second support vector machine classifier and a second multilayer perceptron classifier, and wherein the cascaded two-tier classifier is trained based in part on the simulation data. 5. The system of claim 1 , wherein the computer-executable components further comprise: a voting component configured to analyze respective classification outputs of the cascaded two-tier classifier, the second support vector machine classifier, and the second multilayer perceptron classifier, to facilitate a determination of the state data.
| 0.514164 |
8,600,745 | 7 | 8 |
7. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, perform operations comprising: transcribing a voicemail message for a first person from a second person via a phone call, to yield a transcribed voicemail message; receiving, via a voicemail interface enabling the first person to access the voicemail message, a selection of a first recipient; transmitting a first portion of the transcribed voicemail message to the first recipient in a first message; receiving, via the voicemail interface, a selection of a second portion of the transcribed voicemail message, the second portion being noncontiguous to the first portion; selecting a second recipient; and transmitting the second portion of the transcribed voicemail message to the second recipient in a second message.
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7. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, perform operations comprising: transcribing a voicemail message for a first person from a second person via a phone call, to yield a transcribed voicemail message; receiving, via a voicemail interface enabling the first person to access the voicemail message, a selection of a first recipient; transmitting a first portion of the transcribed voicemail message to the first recipient in a first message; receiving, via the voicemail interface, a selection of a second portion of the transcribed voicemail message, the second portion being noncontiguous to the first portion; selecting a second recipient; and transmitting the second portion of the transcribed voicemail message to the second recipient in a second message. 8. The system of claim 7 , wherein the first recipient and the second recipient are a same recipient.
| 0.784188 |
9,262,408 | 1 | 3 |
1. A machine translation apparatus comprising: an input device configured to input a first sentence in a first language; an additional information acquisition unit configured to acquire first additional information relating to a first user, wherein the first additional information comprises a location of the apparatus along with a facility name of the first user, and gender or age information for the first user; a reference data storage device configured to store plural pieces of second reference data each of which associates a second sentence in a second language with second additional information relating to a second user of the second sentence and a usage location of the second sentence; a candidate acquisition unit configured to translate the first sentence in the first language to a plurality of candidates, for a translated sentence, in a second language and acquire confidence scores of the candidates; a text data acquisition unit configured to acquire a plurality of the second sentences from the second reference data stored in the reference data storage device, the plurality of the second sentences being associated with the second additional information each of which is identical with at least a part of the first additional information, the text data acquisition unit configured to determine degrees of similarity between the candidates and the plurality of the second sentences as a function of identifying a correspondence in a number quantity of character n-grams associated with the first sentence and a number quantity of character n-grams associated with each of the plurality of candidates divided by a total number of n-grams that respectively comprise the first sentence and each of the plurality of candidates, and to output a part of the plurality of the second sentences having degrees of similarity equal to or larger than a predetermined value; and an adaptive model learning unit configured to modify an adaptive model representing a trend of machine translation by using the second sentences output from the text data acquisition unit, wherein the second additional information associated with each output second sentence is identical with at least a part of the first additional information; an adaptive translation unit configured to update the confidence scores based on an adaptive score derived from the adaptive model and the confidence scores for each candidate, wherein the adaptive translation unit selects one of the candidates based on updated confidence scores, and an output device configured to output one candidate selected based on the updated confidence scores.
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1. A machine translation apparatus comprising: an input device configured to input a first sentence in a first language; an additional information acquisition unit configured to acquire first additional information relating to a first user, wherein the first additional information comprises a location of the apparatus along with a facility name of the first user, and gender or age information for the first user; a reference data storage device configured to store plural pieces of second reference data each of which associates a second sentence in a second language with second additional information relating to a second user of the second sentence and a usage location of the second sentence; a candidate acquisition unit configured to translate the first sentence in the first language to a plurality of candidates, for a translated sentence, in a second language and acquire confidence scores of the candidates; a text data acquisition unit configured to acquire a plurality of the second sentences from the second reference data stored in the reference data storage device, the plurality of the second sentences being associated with the second additional information each of which is identical with at least a part of the first additional information, the text data acquisition unit configured to determine degrees of similarity between the candidates and the plurality of the second sentences as a function of identifying a correspondence in a number quantity of character n-grams associated with the first sentence and a number quantity of character n-grams associated with each of the plurality of candidates divided by a total number of n-grams that respectively comprise the first sentence and each of the plurality of candidates, and to output a part of the plurality of the second sentences having degrees of similarity equal to or larger than a predetermined value; and an adaptive model learning unit configured to modify an adaptive model representing a trend of machine translation by using the second sentences output from the text data acquisition unit, wherein the second additional information associated with each output second sentence is identical with at least a part of the first additional information; an adaptive translation unit configured to update the confidence scores based on an adaptive score derived from the adaptive model and the confidence scores for each candidate, wherein the adaptive translation unit selects one of the candidates based on updated confidence scores, and an output device configured to output one candidate selected based on the updated confidence scores. 3. The apparatus according to claim 1 , wherein the second reference data is acquired by another apparatus.
| 0.932873 |
4,381,551 | 1 | 2 |
1. An electronic language interpreter device wherein a first word represented in a first language is entered to obtain a translated word in a second language equivalent to the first word, comprising: input means for entering the first word; memory means for storing a plurality of second words in the first language; access means responsive to the first word entered by said input means for addressing said memory means for retrieving the second words; similarity detection means responsive to said access means for detecting similarity between letters of the first word and letters of the second words; means responsive to said similarity detection means for displaying a translated word corresponding to a determined one of the second words when the determined second word is the same as the first word; and means responsive to said similarity detection means for displaying a selected one of the second words which is most similar to the first word when none of the second words are the same as the first word.
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1. An electronic language interpreter device wherein a first word represented in a first language is entered to obtain a translated word in a second language equivalent to the first word, comprising: input means for entering the first word; memory means for storing a plurality of second words in the first language; access means responsive to the first word entered by said input means for addressing said memory means for retrieving the second words; similarity detection means responsive to said access means for detecting similarity between letters of the first word and letters of the second words; means responsive to said similarity detection means for displaying a translated word corresponding to a determined one of the second words when the determined second word is the same as the first word; and means responsive to said similarity detection means for displaying a selected one of the second words which is most similar to the first word when none of the second words are the same as the first word. 2. The device of claim 1, further comprising means for displaying a particular symbol with said selected second word which is most similar to the first word.
| 0.8744 |
8,266,078 | 15 | 16 |
15. The computing environment of claim 14 , further comprising using one or more of the computers to perform process actions to: transform each data sample of the subset of the training data set into n-dimensional feature vectors using the feature extraction algorithm; perform a feature analysis to find a feature selection matrix M based on the feature vectors; reduce the n-dimensional feature vectors into m-dimensional feature vectors using the feature selection matrix M, wherein m is less than n; and train the recognition model using the m-dimensional feature vectors based on the classifier algorithm.
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15. The computing environment of claim 14 , further comprising using one or more of the computers to perform process actions to: transform each data sample of the subset of the training data set into n-dimensional feature vectors using the feature extraction algorithm; perform a feature analysis to find a feature selection matrix M based on the feature vectors; reduce the n-dimensional feature vectors into m-dimensional feature vectors using the feature selection matrix M, wherein m is less than n; and train the recognition model using the m-dimensional feature vectors based on the classifier algorithm. 16. The computing environment of claim 15 , further comprising using one or more of the computers to perform process actions to determine an accuracy of the recognition model in identifying each data sample of the subset of the training data set.
| 0.5 |
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