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7,966,348 | 6 | 9 | 6. An information system, comprising: at least one processor within the information system; at least one memory store within the information system having instructions operable with the at least one processor for utilizing an ontology to classify and enforce template requirements for new content items, the instructions being executed on hardware components within the storage management system for: categorizing content contained within a plurality of electronic content files provided to an information system with an defined an ontology; processing an electronic content file at the information system, the electronic content file containing new content; assigning an ontology classification to the electronic content file from a level of the ontology based on the new content contained in the electronic content file; selecting a template for the electronic content file from a set of templates within the information system based on the ontology classification of the electronic content file, each template classified to at least one level of the ontology, and each template specifying requirements for new content added to the information system using the template, wherein the requirements define content structure, minimum content specifications, and additional contents required for inclusion within the information system, and wherein selecting a template for the electronic content file includes: identifying a template classified at the ontology level of the electronic content file if a template is classified therein; identifying a template classified elsewhere within the ontology if a template is not classified at the ontology level of the electronic content file by traversing the ontology to locate a template at a nearest ancestor of the ontology level of the electronic content file; and applying, if a template was identified within the ontology, the selected template and the requirements of the selected template to the electronic content file; and implementing any applicable changes to the new content of the electronic content file resulting from applying the requirements of the selected template to the new content of the electronic content file, the applicable changes to the new content implemented prior to addition of the electronic content file to the information system. | 6. An information system, comprising: at least one processor within the information system; at least one memory store within the information system having instructions operable with the at least one processor for utilizing an ontology to classify and enforce template requirements for new content items, the instructions being executed on hardware components within the storage management system for: categorizing content contained within a plurality of electronic content files provided to an information system with an defined an ontology; processing an electronic content file at the information system, the electronic content file containing new content; assigning an ontology classification to the electronic content file from a level of the ontology based on the new content contained in the electronic content file; selecting a template for the electronic content file from a set of templates within the information system based on the ontology classification of the electronic content file, each template classified to at least one level of the ontology, and each template specifying requirements for new content added to the information system using the template, wherein the requirements define content structure, minimum content specifications, and additional contents required for inclusion within the information system, and wherein selecting a template for the electronic content file includes: identifying a template classified at the ontology level of the electronic content file if a template is classified therein; identifying a template classified elsewhere within the ontology if a template is not classified at the ontology level of the electronic content file by traversing the ontology to locate a template at a nearest ancestor of the ontology level of the electronic content file; and applying, if a template was identified within the ontology, the selected template and the requirements of the selected template to the electronic content file; and implementing any applicable changes to the new content of the electronic content file resulting from applying the requirements of the selected template to the new content of the electronic content file, the applicable changes to the new content implemented prior to addition of the electronic content file to the information system. 9. The information system of claim 6 , wherein inferencing techniques are used to traverse the ontology when locating the template at the nearest ancestor of the ontology level of the electronic content file. | 0.580645 |
8,451,275 | 11 | 20 | 11. A computer system for monitoring a process, the computer system comprising: a display device; a graphical user interface for displaying on the display device the status of components used in operation of said process, each component being represented on the graphical user interface by an associated graphical object that is generated or rendered by an application from a related vector graphic file; said vector graphic file including an animation instruction inserted into the vector graphic file, wherein the animation instruction: is not executable code, complies with a predefined syntax, does not violate a prescribed format for the vector graphic file, is ignored by said application; and defines one or more animation effects that are dependent upon a value of at least one variable and a set of conditions applying to the at least one variable; the value of said at least one variable being dependent upon the status of said component being represented; and an interpreter engine to: (a) recognize and parse said animation instruction, (b) retrieve a current value of said at least one variable, (c) determine a required animation effect based on checking the retrieved current value of the at least one variable against said set of conditions, and (d) instruct the application to modify said graphical object to exhibit the determined animation effect so as to show a change in status of the component being represented by said graphical object, wherein the display device is configured to display said graphical object with the determined animation effect. | 11. A computer system for monitoring a process, the computer system comprising: a display device; a graphical user interface for displaying on the display device the status of components used in operation of said process, each component being represented on the graphical user interface by an associated graphical object that is generated or rendered by an application from a related vector graphic file; said vector graphic file including an animation instruction inserted into the vector graphic file, wherein the animation instruction: is not executable code, complies with a predefined syntax, does not violate a prescribed format for the vector graphic file, is ignored by said application; and defines one or more animation effects that are dependent upon a value of at least one variable and a set of conditions applying to the at least one variable; the value of said at least one variable being dependent upon the status of said component being represented; and an interpreter engine to: (a) recognize and parse said animation instruction, (b) retrieve a current value of said at least one variable, (c) determine a required animation effect based on checking the retrieved current value of the at least one variable against said set of conditions, and (d) instruct the application to modify said graphical object to exhibit the determined animation effect so as to show a change in status of the component being represented by said graphical object, wherein the display device is configured to display said graphical object with the determined animation effect. 20. The computer system of claim 11 , wherein the interpreter engine modifies said graphical object by manipulating an object model or object properties or styles of said graphical object. | 0.5 |
9,495,425 | 16 | 18 | 16. A computer system comprising: one or more computer processors; and a non-transitory computer-readable storage medium containing computer program code which when executed by the one or more computer processors causes the one or more computer processors to perform operations comprising: identifying a plurality of comments associated with a media content item; generating, for each of the plurality of comments, a sentiment score indicating a likelihood that the comment expresses a type of sentiment; adjusting the sentiment score generated for a comment from the plurality of comments based on information associated with a user that provided the comment from the plurality of comments, the information describing sentiment expressed by the user in additional comments for additional media content items; determining an aggregate score for the media content item based on the sentiment scores for the plurality of comments; receiving, from a device, a search query searching for media content associated with the type of sentiment; responsive to receiving the search query, identifying the media content item based on the aggregate score indicating that comments associated with the media content item express the type of sentiment; and providing search results to the device including the media content item. | 16. A computer system comprising: one or more computer processors; and a non-transitory computer-readable storage medium containing computer program code which when executed by the one or more computer processors causes the one or more computer processors to perform operations comprising: identifying a plurality of comments associated with a media content item; generating, for each of the plurality of comments, a sentiment score indicating a likelihood that the comment expresses a type of sentiment; adjusting the sentiment score generated for a comment from the plurality of comments based on information associated with a user that provided the comment from the plurality of comments, the information describing sentiment expressed by the user in additional comments for additional media content items; determining an aggregate score for the media content item based on the sentiment scores for the plurality of comments; receiving, from a device, a search query searching for media content associated with the type of sentiment; responsive to receiving the search query, identifying the media content item based on the aggregate score indicating that comments associated with the media content item express the type of sentiment; and providing search results to the device including the media content item. 18. The computer system of claim 16 , wherein adjusting the sentiment score comprises: responsive to the information associated with the user indicating a low frequency of the type of sentiment in the additional comments, increasing the sentiment score generated for the comment from the plurality of comments. | 0.5 |
8,589,146 | 12 | 13 | 12. The computer-readable storage device of claim 11 , wherein the video signal includes an audio portion. | 12. The computer-readable storage device of claim 11 , wherein the video signal includes an audio portion. 13. The computer-readable storage device of claim 12 , the text information is extracted by performing speech recognition on the audio portion of the video signal. | 0.700368 |
9,665,571 | 14 | 20 | 14. A computer-implemented system comprising: one or more computers programmed to perform operations comprising: selecting a word or phrase of a message that was not correctly translated from a first language to a second language; selecting a plurality of users from whom to solicit user feedback for the translation, wherein each selected user has not submitted feedback more times than a quota for a time period; sending a query requesting user assistance in translating the selected word or phrase to one or more of the plurality of users; receiving at least one response to the query from one or more of the users to whom the query was sent; determining that the at least one response is approved; determining a credit based on a complexity of the selected word or phrase or an importance of the selected word or phrase; crediting with the determined credit a respective account of one or more of the users who provided the at least one approved response; updating at least one of a transformation module and a translation module according to the at least one approved response; and using at least one computer processor and at least one of the updated transformation module and the updated translation module to translate a second message comprising the selected word or phrase. | 14. A computer-implemented system comprising: one or more computers programmed to perform operations comprising: selecting a word or phrase of a message that was not correctly translated from a first language to a second language; selecting a plurality of users from whom to solicit user feedback for the translation, wherein each selected user has not submitted feedback more times than a quota for a time period; sending a query requesting user assistance in translating the selected word or phrase to one or more of the plurality of users; receiving at least one response to the query from one or more of the users to whom the query was sent; determining that the at least one response is approved; determining a credit based on a complexity of the selected word or phrase or an importance of the selected word or phrase; crediting with the determined credit a respective account of one or more of the users who provided the at least one approved response; updating at least one of a transformation module and a translation module according to the at least one approved response; and using at least one computer processor and at least one of the updated transformation module and the updated translation module to translate a second message comprising the selected word or phrase. 20. The system of claim 14 , wherein the at least one response comprises a definition for the word or phrase in the second language. | 0.8944 |
9,760,837 | 16 | 18 | 16. A depth detection apparatus comprising: a memory storing frames of raw time-of-flight sensor data received from a time-of-flight sensor; and a trained machine learning component having been trained using training data pairs, a training data pair comprising a simulated raw time-of-flight sensor frame and a corresponding simulated ground truth depth map; the trained machine learning component configured to compute in a single stage, for a frame of the stored raw time-of-flight sensor data, a depth map of surfaces depicted by the frame, by pushing the frame through the trained machine learning component. | 16. A depth detection apparatus comprising: a memory storing frames of raw time-of-flight sensor data received from a time-of-flight sensor; and a trained machine learning component having been trained using training data pairs, a training data pair comprising a simulated raw time-of-flight sensor frame and a corresponding simulated ground truth depth map; the trained machine learning component configured to compute in a single stage, for a frame of the stored raw time-of-flight sensor data, a depth map of surfaces depicted by the frame, by pushing the frame through the trained machine learning component. 18. The apparatus of claim 16 where the trained machine learning component comprises a convolutional neural network. | 0.748918 |
10,002,189 | 29 | 56 | 29. A non-transitory computer readable storage medium containing an executable program for constructing database queries for searching a database, wherein the program is configured to cause at least one processor to perform the steps of: receiving a user entered search string, the search string comprising one or more words; identifying a first node in an ontology based on the one or more words of the search string, the first node being related to at least one of the one or more words in the search string, wherein the ontology includes at least one node representing a concept and at least one node representing an attribute of the concept; constructing a first database query based on the identified first node in the ontology, the first database query comprising one or more attributes associated with the first node, and a respective value, from the search string, for each of the one or more attributes; after constructing the first database query, searching at least one database using the first database query; identifying, based on a frequency of occurrence of a pair of user events, a second node in the ontology, the second node associated with the first node, the second additional node representing a concept not represented by the received search string, wherein a first user event of the pair of user events corresponds to the first node and a second user event of the pair of user events corresponds to the second node, and wherein for each occurrence of the pair of user events, the first user event and the second user event occur within a predetermined time period; constructing a second database query based on the identified second; after constructing the second database query, searching at least one database using the second database query; and outputting results of the searching. | 29. A non-transitory computer readable storage medium containing an executable program for constructing database queries for searching a database, wherein the program is configured to cause at least one processor to perform the steps of: receiving a user entered search string, the search string comprising one or more words; identifying a first node in an ontology based on the one or more words of the search string, the first node being related to at least one of the one or more words in the search string, wherein the ontology includes at least one node representing a concept and at least one node representing an attribute of the concept; constructing a first database query based on the identified first node in the ontology, the first database query comprising one or more attributes associated with the first node, and a respective value, from the search string, for each of the one or more attributes; after constructing the first database query, searching at least one database using the first database query; identifying, based on a frequency of occurrence of a pair of user events, a second node in the ontology, the second node associated with the first node, the second additional node representing a concept not represented by the received search string, wherein a first user event of the pair of user events corresponds to the first node and a second user event of the pair of user events corresponds to the second node, and wherein for each occurrence of the pair of user events, the first user event and the second user event occur within a predetermined time period; constructing a second database query based on the identified second; after constructing the second database query, searching at least one database using the second database query; and outputting results of the searching. 56. The computer readable storage medium of claim 29 , wherein the first database query represents a user intent for the user entered search string. | 0.891813 |
9,167,274 | 20 | 21 | 20. The system of claim 16 , further comprising a far-end dictionary generator component configured to at least one of generate a new common dictionary or modify a common dictionary, associated with the far-end decoder component and the near-end encoder component, based at least in part on one or more reference video frames associated with the far-end decoder component and the near-end encoder component in relation to a video session, wherein the new common dictionary or the common dictionary comprise one or more new dictionary elements. | 20. The system of claim 16 , further comprising a far-end dictionary generator component configured to at least one of generate a new common dictionary or modify a common dictionary, associated with the far-end decoder component and the near-end encoder component, based at least in part on one or more reference video frames associated with the far-end decoder component and the near-end encoder component in relation to a video session, wherein the new common dictionary or the common dictionary comprise one or more new dictionary elements. 21. The system of claim 20 , wherein the far-end dictionary generator component is further configured to identify one or more video frames from the video session for use as the one or more reference video frames to facilitate generation of one or more dictionary elements, based at least in part on defined coding criterion the defined coding criterion relating to at least one of quality of a visual image of a video frame or an amount of change detected in a visual scene depicted in a subset of video frames. | 0.5 |
7,539,611 | 1 | 8 | 1. Method of identifying user-definable textual units and highlighting the same using a computer, comprising the steps of: (a) receiving text in a computer; (b) identifying each token in the text, where each token is selected from the group of tokens consisting of text, at least one number, at least one space, punctuation, at least one end-of-line indicator, and any combination thereof, where an alpha-numeric portion of a token is a word, and where the leading non-alpha-numeric portion of a token is a pre-word; (c) selecting a token, (d) determining in the selected token each user-definable textual-unit prefix, each user-definable textual-unit root, each user-definable textual-unit suffix, each user-definable disqualified textual-unit, and a length of the selected token; (e) replacing each determined textual-unit prefix, root, and suffix with a standard form of the same; (f) if there is an unselected token next in sequence then selecting the token next in sequence from the previously selected token and returning to step (d); (g) reselecting the token selected in step (c) and assigning to the reselected token, a descriptor of “currently reselected token”; (h) if the currently reselected token does not start a user-definable textual unit then printing the currently reselected token in a first font, reselecting a token next in sequence from the currently reselected token, reassigning the currently reselected token descriptor to the token reselected in this step, and returning to step (h), and if there is not a token to reselect then stopping the method of identifying user-definable textual units and highlighting the same using a computer; (i) if the currently reselected token does start a user-definable textual unit then marking the currently reselected token for highlighting, reselecting a token next in sequence from the currently reselected token, reassigning the currently reselected token descriptor to the token reselected in this step, and if there is not a token to reselect then printing in a second font the currently reselected token marked for highlighting and stopping the method of identifying user-definable textual units and highlighting the same using a computer; (j) if the currently reselected token reselected in the last executed step in the method, which is either step (i) or step (m), is an end-of-textual-unit indicator then printing in the second font the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, except any reselected token marked as a loose token, printing any reselected tokens marked as loose tokens in the first font, and returning to step (h); (k) if the currently reselected token reselected in step (i) is not an end-of-textual-unit indicator then determining if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, can include an additional token; (l) if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, can include an additional token then determining if a reselected token not marked for highlighting can be concatenated to the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it; (m) if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, can include an additional token and the reselected token not marked for highlighting determined in step (l) cannot be concatenated to the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, then marking the reselected token not marked for highlighting as a loose token, concatenating the reselected token not marked for highlighting to the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, reselecting a token next in sequence from the currently reselected token and returning to step (j), and if there is not a token to reselect then printing in the second font the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, except for any reselected token marked as a loose token, and printing in the first font any reselected token marked as a loose token and stopping the method of identifying user-definable textual units and highlighting the same using a computer; (n) if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, can include an additional token and the reselected token not marked for highlighting determined in step (l) can be concatenated to the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, then concatenating the reselected token not marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, in the reselected token marked for highlighting, removing the loose token mark from any reselected token so marked, reselecting a token next in sequence from the currently reselected token and returning to step (j), and if there is not a token to reselect then printing in the second font the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, and stopping the method of identifying user-definable textual units and highlighting the same using a computer; and (o) if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, cannot include an additional token then printing in the second font the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, except any reselected token marked as a loose token, printing in the first font any reselected token marked as a loose token, and returning to step (h). | 1. Method of identifying user-definable textual units and highlighting the same using a computer, comprising the steps of: (a) receiving text in a computer; (b) identifying each token in the text, where each token is selected from the group of tokens consisting of text, at least one number, at least one space, punctuation, at least one end-of-line indicator, and any combination thereof, where an alpha-numeric portion of a token is a word, and where the leading non-alpha-numeric portion of a token is a pre-word; (c) selecting a token, (d) determining in the selected token each user-definable textual-unit prefix, each user-definable textual-unit root, each user-definable textual-unit suffix, each user-definable disqualified textual-unit, and a length of the selected token; (e) replacing each determined textual-unit prefix, root, and suffix with a standard form of the same; (f) if there is an unselected token next in sequence then selecting the token next in sequence from the previously selected token and returning to step (d); (g) reselecting the token selected in step (c) and assigning to the reselected token, a descriptor of “currently reselected token”; (h) if the currently reselected token does not start a user-definable textual unit then printing the currently reselected token in a first font, reselecting a token next in sequence from the currently reselected token, reassigning the currently reselected token descriptor to the token reselected in this step, and returning to step (h), and if there is not a token to reselect then stopping the method of identifying user-definable textual units and highlighting the same using a computer; (i) if the currently reselected token does start a user-definable textual unit then marking the currently reselected token for highlighting, reselecting a token next in sequence from the currently reselected token, reassigning the currently reselected token descriptor to the token reselected in this step, and if there is not a token to reselect then printing in a second font the currently reselected token marked for highlighting and stopping the method of identifying user-definable textual units and highlighting the same using a computer; (j) if the currently reselected token reselected in the last executed step in the method, which is either step (i) or step (m), is an end-of-textual-unit indicator then printing in the second font the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, except any reselected token marked as a loose token, printing any reselected tokens marked as loose tokens in the first font, and returning to step (h); (k) if the currently reselected token reselected in step (i) is not an end-of-textual-unit indicator then determining if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, can include an additional token; (l) if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, can include an additional token then determining if a reselected token not marked for highlighting can be concatenated to the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it; (m) if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, can include an additional token and the reselected token not marked for highlighting determined in step (l) cannot be concatenated to the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, then marking the reselected token not marked for highlighting as a loose token, concatenating the reselected token not marked for highlighting to the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, reselecting a token next in sequence from the currently reselected token and returning to step (j), and if there is not a token to reselect then printing in the second font the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, except for any reselected token marked as a loose token, and printing in the first font any reselected token marked as a loose token and stopping the method of identifying user-definable textual units and highlighting the same using a computer; (n) if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, can include an additional token and the reselected token not marked for highlighting determined in step (l) can be concatenated to the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, then concatenating the reselected token not marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, in the reselected token marked for highlighting, removing the loose token mark from any reselected token so marked, reselecting a token next in sequence from the currently reselected token and returning to step (j), and if there is not a token to reselect then printing in the second font the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, and stopping the method of identifying user-definable textual units and highlighting the same using a computer; and (o) if the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, cannot include an additional token then printing in the second font the reselected token marked for highlighting, which just had the descriptor of “currently reselected token” deassigned from it, except any reselected token marked as a loose token, printing in the first font any reselected token marked as a loose token, and returning to step (h). 8. The method of claim 1 , wherein the step of if the currently reselected token reselected in the last executed step in the method, which is either step (i) or step (m), is an end-of-textual-unit indicator is comprised of the step of determining if the reselected token includes an end-of-textual-unit-indicator selected from the group of end-of-textual-unit-indicators consisting of “AKA”, “also known as”, “formerly”, and incorporated. | 0.894152 |
7,523,423 | 1 | 10 | 1. A method for producing a non-canonical data flow graph (DFG) structure by symbolic simulation of an input representation, comprising: determining a set of next execution paths to be processed from a set of active execution paths; determining a set of indication points, in the input representation, from the set of next execution paths; determining a next symbolic simulation operation to be performed, for producing the non-canonical DFG structure, using the indication points; and performing the next symbolic simulation operation on the input representation for producing the non-canonical DFG structure. | 1. A method for producing a non-canonical data flow graph (DFG) structure by symbolic simulation of an input representation, comprising: determining a set of next execution paths to be processed from a set of active execution paths; determining a set of indication points, in the input representation, from the set of next execution paths; determining a next symbolic simulation operation to be performed, for producing the non-canonical DFG structure, using the indication points; and performing the next symbolic simulation operation on the input representation for producing the non-canonical DFG structure. 10. The method of claim 1 , wherein the step of determining a set of indication points further comprises: determining a first execution path indicates, in the input representation, an evaluation of a conditional. | 0.742092 |
9,898,773 | 3 | 4 | 3. The method of claim 1 , comprising producing weights for members of the multiple different data sources, for different types of data, for different types of features, or for different features, and where producing the feature space from the plurality of features depends, at least in part, on the weights. | 3. The method of claim 1 , comprising producing weights for members of the multiple different data sources, for different types of data, for different types of features, or for different features, and where producing the feature space from the plurality of features depends, at least in part, on the weights. 4. The method of claim 3 , the one or more elements being single words, types of nouns, n-grams, short phrases, symbols, acronyms, or abbreviations. | 0.5 |
8,244,719 | 1 | 7 | 1. A computer method of social tagging computer resources, comprising: in a processor: receiving a tag candidate from an end-user commencing input of the tag candidate, the end-user commencing a tagging process; in response to the end-user commencing the tagging process, retrieving social tagging data about system user inquiries that have involved the tag candidate, said retrieving being before the end of the tagging process, wherein said social tagging data about system user inquiries include indications of how many and which system users have previously searched for the tag candidate and thus are interested in said tag candidate; forming a display of the retrieved social tagging data; and rendering the formed display to the end-user in a manner enabling a preview of the retrieved social tagging data during the end-user input of the tag candidate and prior to the end user committing to the tag candidate and ending the tagging process; wherein the formed display includes indications to the end-user of: number of users who have inquired about the tag candidate, and how the users have inquired about the tag candidate; and wherein the formed display indications of how the users have inquired about the tag candidate includes indicating: names or numbers of users that have subscriptions to the tag candidate, names or numbers of users that have searched using the tag candidate and names or numbers of users that have browsed using the tag candidate. | 1. A computer method of social tagging computer resources, comprising: in a processor: receiving a tag candidate from an end-user commencing input of the tag candidate, the end-user commencing a tagging process; in response to the end-user commencing the tagging process, retrieving social tagging data about system user inquiries that have involved the tag candidate, said retrieving being before the end of the tagging process, wherein said social tagging data about system user inquiries include indications of how many and which system users have previously searched for the tag candidate and thus are interested in said tag candidate; forming a display of the retrieved social tagging data; and rendering the formed display to the end-user in a manner enabling a preview of the retrieved social tagging data during the end-user input of the tag candidate and prior to the end user committing to the tag candidate and ending the tagging process; wherein the formed display includes indications to the end-user of: number of users who have inquired about the tag candidate, and how the users have inquired about the tag candidate; and wherein the formed display indications of how the users have inquired about the tag candidate includes indicating: names or numbers of users that have subscriptions to the tag candidate, names or numbers of users that have searched using the tag candidate and names or numbers of users that have browsed using the tag candidate. 7. A computer method as claimed in claim 1 wherein the retrieved social tagging data is from event logs and subscriptions. | 0.847118 |
7,596,619 | 1 | 2 | 1. A content delivery network (CDN) for use by participating content providers, comprising: a domain name service managed by a CDN service provider (CDNSP) and authoritative only for given content domains associated with the participating content providers; and a set of content servers operated by the CDNSP; wherein, following an end-user request for a web page that is directed to a participating content provider domain, the domain name service uses a CDNSP-specific domain to identify an IP address associated with a CDN content server of the set of content servers operated by the CDNSP; wherein the CDN content server includes code (i) that determines whether a default markup language file associated with the web page exists on the CDN content server, (b) that is responsive to a determination that the default markup language file exists on the CDN content server for serving the default markup language file in response to the end-user request for the web page, (c) that is responsive to a determination that the default markup language file does not exist on the CDN content server for directing a request for the default markup language file to a second server, for receiving from the second server the default markup language file, for serving the default markup language file in response to the end-user request for the web page, and for caching the default markup language file for a given time and (d) that logs data associated with the default markup language file served from the CDN content server. | 1. A content delivery network (CDN) for use by participating content providers, comprising: a domain name service managed by a CDN service provider (CDNSP) and authoritative only for given content domains associated with the participating content providers; and a set of content servers operated by the CDNSP; wherein, following an end-user request for a web page that is directed to a participating content provider domain, the domain name service uses a CDNSP-specific domain to identify an IP address associated with a CDN content server of the set of content servers operated by the CDNSP; wherein the CDN content server includes code (i) that determines whether a default markup language file associated with the web page exists on the CDN content server, (b) that is responsive to a determination that the default markup language file exists on the CDN content server for serving the default markup language file in response to the end-user request for the web page, (c) that is responsive to a determination that the default markup language file does not exist on the CDN content server for directing a request for the default markup language file to a second server, for receiving from the second server the default markup language file, for serving the default markup language file in response to the end-user request for the web page, and for caching the default markup language file for a given time and (d) that logs data associated with the default markup language file served from the CDN content server. 2. The content delivery network as described in claim 1 wherein the participating content provider domain is associated with the CDNSP-specific domain by DNS entry aliasing. | 0.751437 |
7,689,554 | 1 | 21 | 1. A method for identifying one or more queries related to a given query, the method comprising: receiving a query written according to one or more writing systems of a language with multiple writing systems; identifying a candidate set of queries written according to one or more writing systems of the language with multiple writing systems; calculating a number of common characters in a given candidate query before disagreement with the query received; calculating a number of total common characters between the given candidate query and the query received; calculating a quotient of the frequency with which a selected query from the candidate set follows the received query in one or more query logs and the frequency of the received query in the one or more query logs; and calculating a similarity score on the basis of the number of characters before disagreements, the number of total common characters and the quotient of the frequency with which a selected query from the candidate set follows the received query in one or more query logs and the frequency of the received query in the one or more query logs, wherein the similarity score indicates the similarity of the one or more queries with respect to the query received. | 1. A method for identifying one or more queries related to a given query, the method comprising: receiving a query written according to one or more writing systems of a language with multiple writing systems; identifying a candidate set of queries written according to one or more writing systems of the language with multiple writing systems; calculating a number of common characters in a given candidate query before disagreement with the query received; calculating a number of total common characters between the given candidate query and the query received; calculating a quotient of the frequency with which a selected query from the candidate set follows the received query in one or more query logs and the frequency of the received query in the one or more query logs; and calculating a similarity score on the basis of the number of characters before disagreements, the number of total common characters and the quotient of the frequency with which a selected query from the candidate set follows the received query in one or more query logs and the frequency of the received query in the one or more query logs, wherein the similarity score indicates the similarity of the one or more queries with respect to the query received. 21. The method of claim 1 comprising selecting one or more of the queries from the candidate set for distribution. | 0.919605 |
9,424,247 | 1 | 3 | 1. A method, comprising: identifying a task entry of a user utilizing one or more processors, wherein the task entry includes one or more information fields; identifying, utilizing one or more of the processors, one or more messages sent or received by the user, wherein each of the messages includes one or more terms; identifying, utilizing one or more of the processors, an association between the task entry and the one or more messages; identifying, utilizing one or more of the processors, that a new message is related to the one or more messages, wherein the new message is sent or received by the user after creation of the task entry of the user, and wherein the new message includes one or more new message terms; determining, utilizing one or more of the processors, an n-gram based on the one or more new message terms of the new message; determining, utilizing one or more of the processors, a similarity score between the n-gram and the task entry, wherein the similarity score is indicative of a likelihood that the user has interest in associating the n-gram with the task entry; and associating, utilizing one or more of the processors, the n-gram with the task entry based on the similarity score. | 1. A method, comprising: identifying a task entry of a user utilizing one or more processors, wherein the task entry includes one or more information fields; identifying, utilizing one or more of the processors, one or more messages sent or received by the user, wherein each of the messages includes one or more terms; identifying, utilizing one or more of the processors, an association between the task entry and the one or more messages; identifying, utilizing one or more of the processors, that a new message is related to the one or more messages, wherein the new message is sent or received by the user after creation of the task entry of the user, and wherein the new message includes one or more new message terms; determining, utilizing one or more of the processors, an n-gram based on the one or more new message terms of the new message; determining, utilizing one or more of the processors, a similarity score between the n-gram and the task entry, wherein the similarity score is indicative of a likelihood that the user has interest in associating the n-gram with the task entry; and associating, utilizing one or more of the processors, the n-gram with the task entry based on the similarity score. 3. The method of claim 1 , wherein the task entry includes a plurality of information fields and wherein the similarity score is between the n-gram and a given information field of the information fields of the task entry, and wherein associating the n-gram with the task entry includes associating the n-gram with the given information field based on the similarity score. | 0.510499 |
9,014,982 | 11 | 13 | 11. The method of claim 8 , wherein both the interdependency and the derived measure are information shared among attributes, called mutual information, wherein the mutual information is computed using entropy. | 11. The method of claim 8 , wherein both the interdependency and the derived measure are information shared among attributes, called mutual information, wherein the mutual information is computed using entropy. 13. The method of claim 11 , wherein the interdependency is based on mutuality of the information, and the mutual information is computed by cross-correlation of normal score-transformed random variables. | 0.806818 |
7,512,487 | 1 | 8 | 1. A machine-readable storage medium encoded with instructions, that when executed by one or more processors, cause the processor to carry out a process for generating directions for use in navigation during a current driving session, the process comprising: storing a plurality of target attributes; for at least a selection of the stored target attributes, storing a plurality of conditional variant models associated with the selected target attributes; receiving a route request from a user, including a target destination; generating a set of candidate routes; computing a score for each candidate route based on one or more attribute models learned from previous user driving sessions, wherein computing comprises: probabilistically determining for a target attribute that is one of the selected target attributes which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute; and applying the determined conditional variant model to the target attribute; and providing at least one scored route to the user. | 1. A machine-readable storage medium encoded with instructions, that when executed by one or more processors, cause the processor to carry out a process for generating directions for use in navigation during a current driving session, the process comprising: storing a plurality of target attributes; for at least a selection of the stored target attributes, storing a plurality of conditional variant models associated with the selected target attributes; receiving a route request from a user, including a target destination; generating a set of candidate routes; computing a score for each candidate route based on one or more attribute models learned from previous user driving sessions, wherein computing comprises: probabilistically determining for a target attribute that is one of the selected target attributes which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute; and applying the determined conditional variant model to the target attribute; and providing at least one scored route to the user. 8. The machine-readable medium of claim 1 wherein computing a score for each candidate route further comprises: computing a value of the target attribute based on a conditional variant model having a highest probability of being applicable to the current driving session. | 0.648964 |
9,342,628 | 7 | 10 | 7. A computer system to generate one or more attribute prioritized configuration answers to one or more attribute-based configuration queries, the system comprising: a processor; and a storage medium, coupled to the processor, having data encoded therein, the data comprising code executable by the processor to configure the computer system into a machine for: receiving one or more attribute-based configuration queries from a client system; processing the one or more attribute-based configuration queries, configuration rules, and attribute based preference algorithm using a combined configuration rules-attributes model and a configuration-rules processing engine to calculate valid configuration answers in accordance with the combined configuration rules-attributes model, wherein a plurality of the configuration rules define relationships between parts of the product and a plurality of attributes represent details about the parts; predetermining values of one or more combinations of attributes associated with respective configuration answers; storing the predetermined values; retrieving the stored predetermined values associated with a particular valid configuration answer if the particular valid configuration is an answer to one or more of the attribute-based configuration queries; receiving a selection of at least one of the one or more product attributes to be prioritized; prioritizing the valid configuration answers by one or more of the plurality of attributes in the combined configuration rules-attributes model; and providing at least a subset of the valid configuration answers over a communications network to the client system to enable an application program of the client server to present at least the subset of the valid configuration answers, wherein the provided valid configuration answers are prioritized by one or more of the plurality of attributes. | 7. A computer system to generate one or more attribute prioritized configuration answers to one or more attribute-based configuration queries, the system comprising: a processor; and a storage medium, coupled to the processor, having data encoded therein, the data comprising code executable by the processor to configure the computer system into a machine for: receiving one or more attribute-based configuration queries from a client system; processing the one or more attribute-based configuration queries, configuration rules, and attribute based preference algorithm using a combined configuration rules-attributes model and a configuration-rules processing engine to calculate valid configuration answers in accordance with the combined configuration rules-attributes model, wherein a plurality of the configuration rules define relationships between parts of the product and a plurality of attributes represent details about the parts; predetermining values of one or more combinations of attributes associated with respective configuration answers; storing the predetermined values; retrieving the stored predetermined values associated with a particular valid configuration answer if the particular valid configuration is an answer to one or more of the attribute-based configuration queries; receiving a selection of at least one of the one or more product attributes to be prioritized; prioritizing the valid configuration answers by one or more of the plurality of attributes in the combined configuration rules-attributes model; and providing at least a subset of the valid configuration answers over a communications network to the client system to enable an application program of the client server to present at least the subset of the valid configuration answers, wherein the provided valid configuration answers are prioritized by one or more of the plurality of attributes. 10. The computer system of claim 7 wherein the code for providing at least a subset of the valid configuration answers to the client system further comprises code for providing a user selected number of attribute-prioritized valid configuration answers to a user. | 0.691315 |
9,542,373 | 9 | 12 | 9. A non-transitory computer-readable storage medium having a program stored thereon, the program being executed by a processor, wherein, when executed by the processor, the program causes the processor to perform a process for compressing webpage text, the process comprising: according to a webpage-opening request of a mobile terminal browser, obtaining a current language environment of a terminal; according to the language environment, initializing a character container corresponding to the language environment, wherein: the character container is a storage space for storing related webpage data, and is implemented by applying for a “type” in software code to store the webpage data, when the language environment belongs to Latin languages, the character container is initialized to store utf-8 encoded data, and when the language environment does not belong to Latin languages, the character container is initialized to store utf-16 encoded data; and receiving webpage data that are requested, parsing the webpage data, and merging and storing the webpage data by using the corresponding character container, including: merging a plurality of text objects in source code of the webpage data that are originally stored in multiple character containers, and storing the merged plurality of text objects in the initialized character container, wherein the merged plurality of text objects are connected end to end, and adding an adapting index for the initialized character container, such that each of the plurality of text object is able to be retrieved in the initialized character container according to a corresponding index value of the adapting index wherein each of the plurality of text object is able to be retrieved in the initialized character container according to a corresponding index value of the adapting index without using the utf-8 encoded data or the utf-16 encoded data. | 9. A non-transitory computer-readable storage medium having a program stored thereon, the program being executed by a processor, wherein, when executed by the processor, the program causes the processor to perform a process for compressing webpage text, the process comprising: according to a webpage-opening request of a mobile terminal browser, obtaining a current language environment of a terminal; according to the language environment, initializing a character container corresponding to the language environment, wherein: the character container is a storage space for storing related webpage data, and is implemented by applying for a “type” in software code to store the webpage data, when the language environment belongs to Latin languages, the character container is initialized to store utf-8 encoded data, and when the language environment does not belong to Latin languages, the character container is initialized to store utf-16 encoded data; and receiving webpage data that are requested, parsing the webpage data, and merging and storing the webpage data by using the corresponding character container, including: merging a plurality of text objects in source code of the webpage data that are originally stored in multiple character containers, and storing the merged plurality of text objects in the initialized character container, wherein the merged plurality of text objects are connected end to end, and adding an adapting index for the initialized character container, such that each of the plurality of text object is able to be retrieved in the initialized character container according to a corresponding index value of the adapting index wherein each of the plurality of text object is able to be retrieved in the initialized character container according to a corresponding index value of the adapting index without using the utf-8 encoded data or the utf-16 encoded data. 12. The non-transitory computer-readable storage medium according to claim 9 , wherein the character container is implemented by applying for a String type in Java language to store the webpage text. | 0.772311 |
5,471,610 | 6 | 8 | 6. A text search system set forth in claim 1 further comprising, text search system further comprises first-in/first-out type buffering means for storing a string of character codes outputted from said filtering means and then outputting said stored string of character codes to said character matching means in dependence on the processing state of said character string matching means. | 6. A text search system set forth in claim 1 further comprising, text search system further comprises first-in/first-out type buffering means for storing a string of character codes outputted from said filtering means and then outputting said stored string of character codes to said character matching means in dependence on the processing state of said character string matching means. 8. A text search system set forth in claim 6, characterized in that said buffering means includes exchange buffering means. | 0.544444 |
9,519,636 | 28 | 29 | 28. A method comprising: receiving text in a system that includes a processor; generating, by the system, a query of a semantic layer in response to the receiving the text; and receiving, by the system, structured data in response to the query of the semantic layer; wherein the generating, by the system, a query of the semantic layer comprises: after receiving the text, extracting, by the system, a plurality of linguistic entities and associated linguistic entity categories based on the text; determining, by the system, one or more semantic objects of the semantic layer based on the linguistic entity categories; determining, by the system, an analysis context based on the text, the linguistic entities, and the associated linguistic entity categories; and after determination of the analysis context and the one or more semantic objects by the system, generating, by the system, the query of the semantic layer based on the analysis context determined by the system and the one or more semantic objects determined by the system, wherein the generating the query of the semantic layer comprises: determining, by the system, based on the linguistic entity categories that were extracted, whether the one or more semantic objects are to be filtered, and in response to determining to filter the one or more semantic objects, determining how to filter the semantic objects, wherein when a value from a first linguistic entity category matches a value for one of the one or more semantic objects, using the value from the first linguistic entity category as a query filter, and when only a single entity is mentioned for the first linguistic entity category, including the entity in the query filter and removing the first linguistic entity category from a result of the query of the semantic layer; and receiving a structured data result in response to the query of the semantic layer. | 28. A method comprising: receiving text in a system that includes a processor; generating, by the system, a query of a semantic layer in response to the receiving the text; and receiving, by the system, structured data in response to the query of the semantic layer; wherein the generating, by the system, a query of the semantic layer comprises: after receiving the text, extracting, by the system, a plurality of linguistic entities and associated linguistic entity categories based on the text; determining, by the system, one or more semantic objects of the semantic layer based on the linguistic entity categories; determining, by the system, an analysis context based on the text, the linguistic entities, and the associated linguistic entity categories; and after determination of the analysis context and the one or more semantic objects by the system, generating, by the system, the query of the semantic layer based on the analysis context determined by the system and the one or more semantic objects determined by the system, wherein the generating the query of the semantic layer comprises: determining, by the system, based on the linguistic entity categories that were extracted, whether the one or more semantic objects are to be filtered, and in response to determining to filter the one or more semantic objects, determining how to filter the semantic objects, wherein when a value from a first linguistic entity category matches a value for one of the one or more semantic objects, using the value from the first linguistic entity category as a query filter, and when only a single entity is mentioned for the first linguistic entity category, including the entity in the query filter and removing the first linguistic entity category from a result of the query of the semantic layer; and receiving a structured data result in response to the query of the semantic layer. 29. The method according to claim 28 , wherein each of the one or more semantic objects of the semantic layer associates one or more physical entities stored in a data source with user-friendly names. | 0.5 |
8,583,653 | 11 | 12 | 11. The method of claim 1 , wherein determining a data type comprises determining columns of the database used by the captured query. | 11. The method of claim 1 , wherein determining a data type comprises determining columns of the database used by the captured query. 12. The method of claim 11 , wherein determining whether there is a current filter comprises determining whether a current filter relates to the determined columns. | 0.5 |
8,458,276 | 13 | 14 | 13. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: evaluate, by one or more processors, a plurality of messages, each message associated with an author; log, by the one or more processors, for each message, information associated with the author, information associated with one or more designated recipients of the message, and time information associated with the message; determine, by the one or more processors, correlation values for one or more sets of the designated recipients based on at least a portion of the logged information; and determine, by the one or more processors, an association amongst a plurality of users over time, the determining being based on the correlation values, at least one of the plurality of users comprising at least one of the designated recipients. | 13. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: evaluate, by one or more processors, a plurality of messages, each message associated with an author; log, by the one or more processors, for each message, information associated with the author, information associated with one or more designated recipients of the message, and time information associated with the message; determine, by the one or more processors, correlation values for one or more sets of the designated recipients based on at least a portion of the logged information; and determine, by the one or more processors, an association amongst a plurality of users over time, the determining being based on the correlation values, at least one of the plurality of users comprising at least one of the designated recipients. 14. The media of claim 13 , wherein the correlation values are determined based on a desired profile or mathematical curve. | 0.5 |
9,633,275 | 1 | 6 | 1. A computer-implemented method for extracting pixel-level micro-features from image data captured by a video camera, the method comprising: receiving the image data; identifying a set of pixels in the image data associated with a foreground patch that depicts a foreground object; evaluating appearance values of the pixels included in the set of pixels to compute a plurality of micro-feature values representing the foreground object, each based on at least one pixel-level characteristic of the foreground patch, wherein the micro-feature values are computed independent of training data that defines a plurality of object types; generating a micro-feature vector that includes the plurality of micro-feature values; classifying the foreground object as depicting an object type as based on the micro-feature vector, wherein the object type is determined by mapping the micro-feature vector to a cluster in a self-organizing map (SOM) adaptive resonance theory (ART) network generated from a plurality of micro-feature vectors; and updating one or more cluster properties associated with the cluster based on the plurality of micro-feature values in the generated micro-feature vector. | 1. A computer-implemented method for extracting pixel-level micro-features from image data captured by a video camera, the method comprising: receiving the image data; identifying a set of pixels in the image data associated with a foreground patch that depicts a foreground object; evaluating appearance values of the pixels included in the set of pixels to compute a plurality of micro-feature values representing the foreground object, each based on at least one pixel-level characteristic of the foreground patch, wherein the micro-feature values are computed independent of training data that defines a plurality of object types; generating a micro-feature vector that includes the plurality of micro-feature values; classifying the foreground object as depicting an object type as based on the micro-feature vector, wherein the object type is determined by mapping the micro-feature vector to a cluster in a self-organizing map (SOM) adaptive resonance theory (ART) network generated from a plurality of micro-feature vectors; and updating one or more cluster properties associated with the cluster based on the plurality of micro-feature values in the generated micro-feature vector. 6. The computer-implemented method of claim 1 , one of the computed micro-feature values is a normalized magnitude-saturation ratio value and the pixel-level characteristic is magnitude and saturation values of the foreground patch. | 0.62215 |
7,716,039 | 17 | 18 | 17. The non-transitory computer-readable storage medium of claim 14 , wherein the received input data is an input sentence that is not in the grammar. | 17. The non-transitory computer-readable storage medium of claim 14 , wherein the received input data is an input sentence that is not in the grammar. 18. The non-transitory computer-readable storage medium of claim 17 , wherein the input sentence and the translated input sentence are in the same language. | 0.5 |
8,352,388 | 3 | 4 | 3. The system of claim 2 , further wherein learned associations resulting from said learning associations of natural language artifacts are formed using grouping of one natural language artifact in an interaction window with another at least one natural language artifact in said interaction window, based on a criteria of shared features of one or more sets from said grouping constituting measurements from said data sources. | 3. The system of claim 2 , further wherein learned associations resulting from said learning associations of natural language artifacts are formed using grouping of one natural language artifact in an interaction window with another at least one natural language artifact in said interaction window, based on a criteria of shared features of one or more sets from said grouping constituting measurements from said data sources. 4. The system of claim 3 , further wherein said criteria of shared features are dynamically determined without the use of a priori classifications and using conditional probability constraints between sets of learned associations. | 0.5 |
8,619,090 | 18 | 25 | 18. A device comprising: one or more processors to: receive text that includes data values, parse the text to identify data types associated with the data values, generate, based on the data values and the data types, a graphical representation of the text that includes data cells corresponding to the data values, provide the graphical representation for display, receive one or more selections of one or more data cells in the graphical representation, store the one or more selections as a selection history, the selection history storing a plurality of selections, and the plurality of selections including the one or more selections and one or more other selections received prior to receiving the one or more selections, provide the selection history for display, receive a selection of a particular selection, of the one or more selections, from the selection history, identify one or more data cells associated with the particular selection in the graphical representation, receive an instruction to import the particular selection to a technical computing environment, highlight, based on the instruction, only the one or more data cells, associated with the particular selection, in the graphical representation, the one or more data cells being associated with the data types, generate a data container associated with the technical computing environment, based on the particular selection and the identified data types associated with the one or more data cells of the particular selection, and provide the data container to the technical computing environment. | 18. A device comprising: one or more processors to: receive text that includes data values, parse the text to identify data types associated with the data values, generate, based on the data values and the data types, a graphical representation of the text that includes data cells corresponding to the data values, provide the graphical representation for display, receive one or more selections of one or more data cells in the graphical representation, store the one or more selections as a selection history, the selection history storing a plurality of selections, and the plurality of selections including the one or more selections and one or more other selections received prior to receiving the one or more selections, provide the selection history for display, receive a selection of a particular selection, of the one or more selections, from the selection history, identify one or more data cells associated with the particular selection in the graphical representation, receive an instruction to import the particular selection to a technical computing environment, highlight, based on the instruction, only the one or more data cells, associated with the particular selection, in the graphical representation, the one or more data cells being associated with the data types, generate a data container associated with the technical computing environment, based on the particular selection and the identified data types associated with the one or more data cells of the particular selection, and provide the data container to the technical computing environment. 25. The device of claim 18 , where the data container is selected by a user from a list of data containers associated with the technical computing environment. | 0.806569 |
9,185,125 | 64 | 65 | 64. The non-transitory computer-readable medium of claim 62 , wherein the one or more segments of SQL represent potentially one or more time-consuming SQL clauses. | 64. The non-transitory computer-readable medium of claim 62 , wherein the one or more segments of SQL represent potentially one or more time-consuming SQL clauses. 65. The non-transitory computer-readable medium of claim 64 , wherein each of the one or more segments of SQL is associated with one or more performance parameters, and wherein the at least one scoring algorithm calculates an estimated performance metric for the target operation based on the one or more performance parameters associated with any of the one or more segments of SQL identified within the target operation. | 0.5 |
9,501,455 | 1 | 10 | 1. A method for processing data, the method comprising: receiving, at a data processing tool, at least one data file including at least partially unstructured data from at least one data source, wherein the at least partially unstructured data includes actual data from a main application; processing, by a processor, the at least partially unstructured data to generate at least partially structured data that includes tagged data, wherein the tagged data includes a tag inserted to precede at least one identified term of interest, and wherein processing the at least partially unstructured data comprises at least one of: processing the at least partially unstructured data using an associative memory application that tags the at least one term of interest based on a generated identification score exceeding a predetermined threshold where the score is determined based on the number of matching terms between a segment of unstructured text and a segment of text in the associative memory application; and processing the at least partially unstructured data using a regular expression processing program; transmitting the at least one data file including the at least partially structured data to the main application; incorporating the at least partially structured data into the main application based at least in part on the tagged data, wherein incorporating the at least partially structured data comprises at least one of including and excluding data based on at least one of existence, content and type of a tag; displaying, at a user interface, the at least partially structured data, wherein at least partially structured data includes at least one segment of misidentified data that is at least one of incorrectly tagged and incorrectly not tagged; receiving, at the user interface, a user selection of at least one segment of misidentified data; updating the misidentified data to form re-identified data; updating the associative memory application to include the re-identified data that includes data that has been correctly tagged or correctly not tagged; receiving, at the data processing tool, text segments generated by parsing the at least partially unstructured data into discrete text segments; identifying one or more of the text segments as boilerplate data based on a comparison between the text segments and strings of text in a column incorporated in an associative memory application, wherein the text segments need not exactly match the strings of text in the associative memory application; and incorporating data including text segments parsed from the at least partially structured data into the main application, wherein the text identified as boilerplate data is excluded from the data incorporated into the main application. | 1. A method for processing data, the method comprising: receiving, at a data processing tool, at least one data file including at least partially unstructured data from at least one data source, wherein the at least partially unstructured data includes actual data from a main application; processing, by a processor, the at least partially unstructured data to generate at least partially structured data that includes tagged data, wherein the tagged data includes a tag inserted to precede at least one identified term of interest, and wherein processing the at least partially unstructured data comprises at least one of: processing the at least partially unstructured data using an associative memory application that tags the at least one term of interest based on a generated identification score exceeding a predetermined threshold where the score is determined based on the number of matching terms between a segment of unstructured text and a segment of text in the associative memory application; and processing the at least partially unstructured data using a regular expression processing program; transmitting the at least one data file including the at least partially structured data to the main application; incorporating the at least partially structured data into the main application based at least in part on the tagged data, wherein incorporating the at least partially structured data comprises at least one of including and excluding data based on at least one of existence, content and type of a tag; displaying, at a user interface, the at least partially structured data, wherein at least partially structured data includes at least one segment of misidentified data that is at least one of incorrectly tagged and incorrectly not tagged; receiving, at the user interface, a user selection of at least one segment of misidentified data; updating the misidentified data to form re-identified data; updating the associative memory application to include the re-identified data that includes data that has been correctly tagged or correctly not tagged; receiving, at the data processing tool, text segments generated by parsing the at least partially unstructured data into discrete text segments; identifying one or more of the text segments as boilerplate data based on a comparison between the text segments and strings of text in a column incorporated in an associative memory application, wherein the text segments need not exactly match the strings of text in the associative memory application; and incorporating data including text segments parsed from the at least partially structured data into the main application, wherein the text identified as boilerplate data is excluded from the data incorporated into the main application. 10. The method according to claim 1 , wherein updating the misidentified data comprises: placing the misidentified data back into the processing without correcting the misidentified data; and manually identifying the misidentified data to form the re-identified data. | 0.79108 |
9,898,457 | 17 | 19 | 17. The one or more computer-readable storage media of claim 15 , wherein the computer-executable instructions further cause the processor to: place the sliding window over a second sequence of terms from the plurality of terms, the second sequence of terms comprising the second term, the third term, and a fourth term, the second term and the fourth term being adjacent to the third term; based on the second term, the third term, and the fourth term, determine whether the third term represents non-natural language; and upon determining that the third term is non-natural language, label the third term as non-natural language to omit the third term from the content analysis of the phrase. | 17. The one or more computer-readable storage media of claim 15 , wherein the computer-executable instructions further cause the processor to: place the sliding window over a second sequence of terms from the plurality of terms, the second sequence of terms comprising the second term, the third term, and a fourth term, the second term and the fourth term being adjacent to the third term; based on the second term, the third term, and the fourth term, determine whether the third term represents non-natural language; and upon determining that the third term is non-natural language, label the third term as non-natural language to omit the third term from the content analysis of the phrase. 19. The one or more computer-readable storage media of claim 17 , wherein the second term is located between the first term and the third term, and wherein the third term is located between the second term and the fourth term. | 0.736597 |
9,251,717 | 3 | 4 | 3. The method of claim 1 , further comprising receiving, by the processing device, a second indication of a user selection of a second key displayed on the secondary screen. | 3. The method of claim 1 , further comprising receiving, by the processing device, a second indication of a user selection of a second key displayed on the secondary screen. 4. The method of claim 3 , further comprising: in response to receiving the second indication, outputting an audible signal representing a word that is represented by the second key to a user of language system; and displaying, by the processing device on the display, the home screen. | 0.5 |
6,012,027 | 18 | 20 | 18. The computer readable storage medium of claim 15, wherein step (e) further includes the steps of: (e1) determining if an amplifier gain is saturated; (e2) when the amplifier gain is saturated, going to step (a). | 18. The computer readable storage medium of claim 15, wherein step (e) further includes the steps of: (e1) determining if an amplifier gain is saturated; (e2) when the amplifier gain is saturated, going to step (a). 20. The computer readable storage medium of claim 18, wherein step (e2) further includes the step of decreasing a gain of an amplifier. | 0.5 |
9,213,684 | 1 | 8 | 1. A method for rendering a document, the method comprising: converting a plurality of resources in a document file into a plurality of files that are native to a browser; creating a style sheet based on the document file, wherein an aggregate of the plurality of files together with the style sheet are configured to cause the browser to render an appearance of the document file; and generating, based on the document file, an invisible layer to be laid on the appearance, wherein the invisible layer enables actions to be performed on the document file. | 1. A method for rendering a document, the method comprising: converting a plurality of resources in a document file into a plurality of files that are native to a browser; creating a style sheet based on the document file, wherein an aggregate of the plurality of files together with the style sheet are configured to cause the browser to render an appearance of the document file; and generating, based on the document file, an invisible layer to be laid on the appearance, wherein the invisible layer enables actions to be performed on the document file. 8. The method of claim 1 , wherein the browser is to render the appearance of the document file regardless of whether a plug-in software that supports the document file is installed for the browser. | 0.706231 |
7,496,593 | 1 | 2 | 1. A computer-implemented system for creating one or more multi-relational ontologies having a predetermined structure, the system comprising: at least one processor and a memory having instructions causing the processor to: (i) create an upper ontology that includes: a set of predetermined concept types, a set of predetermined relationship types, a set of concept type pairs, and for each concept type pair, a set of relationships permitted to be used to connect the concept types of the concept type pair; (ii) receive raw data and arrange the raw data into a plurality of individual assertions according to the upper ontology, each assertion comprising a first concept, a second concept, and a relationship between the first and second concept, wherein the first and second concept of each assertion have a concept type from the set of predetermined concept types, wherein the relationship of each assertion has a relationship type from the set of predetermined relationship types, wherein the first and second concept of each assertion belong to a concept type pair of the set of concept type pairs and are connected by a relationship from the set of possible relationships permitted for the concept type pair, wherein at least one concept within the plurality of assertions is part of more than one assertion, wherein one or more relationships in the plurality of individual assertions comprise relationships unconstrained by any hierarchical arrangement of concepts, wherein each concept within each assertion of the plurality of individual assertions is associated with a label, a concept type, and at least one property, and wherein the at least one property includes at least a version of a data source from which the concept was derived; and (iii) store on at least one data storage device: the plurality of individual assertions as one or more multi-relational ontologies, and one or more pieces of evidence supporting information contained in each assertion of the plurality of assertions, wherein each of the one or more pieces of evidence are linked to their corresponding assertion such that each of the one or more pieces of evidence are able to be accessed along with their corresponding assertion, and wherein the one or more pieces of evidence are each associated with at least a data source from which the evidence is derived. | 1. A computer-implemented system for creating one or more multi-relational ontologies having a predetermined structure, the system comprising: at least one processor and a memory having instructions causing the processor to: (i) create an upper ontology that includes: a set of predetermined concept types, a set of predetermined relationship types, a set of concept type pairs, and for each concept type pair, a set of relationships permitted to be used to connect the concept types of the concept type pair; (ii) receive raw data and arrange the raw data into a plurality of individual assertions according to the upper ontology, each assertion comprising a first concept, a second concept, and a relationship between the first and second concept, wherein the first and second concept of each assertion have a concept type from the set of predetermined concept types, wherein the relationship of each assertion has a relationship type from the set of predetermined relationship types, wherein the first and second concept of each assertion belong to a concept type pair of the set of concept type pairs and are connected by a relationship from the set of possible relationships permitted for the concept type pair, wherein at least one concept within the plurality of assertions is part of more than one assertion, wherein one or more relationships in the plurality of individual assertions comprise relationships unconstrained by any hierarchical arrangement of concepts, wherein each concept within each assertion of the plurality of individual assertions is associated with a label, a concept type, and at least one property, and wherein the at least one property includes at least a version of a data source from which the concept was derived; and (iii) store on at least one data storage device: the plurality of individual assertions as one or more multi-relational ontologies, and one or more pieces of evidence supporting information contained in each assertion of the plurality of assertions, wherein each of the one or more pieces of evidence are linked to their corresponding assertion such that each of the one or more pieces of evidence are able to be accessed along with their corresponding assertion, and wherein the one or more pieces of evidence are each associated with at least a data source from which the evidence is derived. 2. The system of claim 1 , wherein a piece of evidence comprises a document containing information underlying the information contained in one or more of the plurality of assertions. | 0.762402 |
9,418,565 | 1 | 3 | 1. A method for computer-based testing for at least one test having a presentation format and a data content, the at least one test being delivered by a test driver, the method comprising the steps of: authoring a test specification and content of the at least one test using a test definition language, wherein the test specification and content defines the presentation format and the data content of the at least one test; compiling via a computer-implemented compiler the test specification and content of the at least one test to create a compiled test specification and content, with the compiling including at least compiling a first set of data files and a second set of data files, with the first set of data files being globally accessible to the test driver and the second set of data files including the test definition language; validating, by at least one validation module, the test specification and content, the at least one validation module including an independent plugin module defined by, and compiled from, test definition language stored within a plugin file; storing to a memory the compiled test specification and content to a resource file; and retrieving from the memory the compiled test specification and content from the resource file during delivery of the test. | 1. A method for computer-based testing for at least one test having a presentation format and a data content, the at least one test being delivered by a test driver, the method comprising the steps of: authoring a test specification and content of the at least one test using a test definition language, wherein the test specification and content defines the presentation format and the data content of the at least one test; compiling via a computer-implemented compiler the test specification and content of the at least one test to create a compiled test specification and content, with the compiling including at least compiling a first set of data files and a second set of data files, with the first set of data files being globally accessible to the test driver and the second set of data files including the test definition language; validating, by at least one validation module, the test specification and content, the at least one validation module including an independent plugin module defined by, and compiled from, test definition language stored within a plugin file; storing to a memory the compiled test specification and content to a resource file; and retrieving from the memory the compiled test specification and content from the resource file during delivery of the test. 3. The method of claim 1 , wherein the test definition language is eXtensible eXam Language (XXL). | 0.829268 |
9,881,055 | 17 | 20 | 17. A processor-implemented method comprising: converting a SQL expression into an S-expression tabular structure, wherein said S-expression comprises a nested list; generating a function table based on said S-expression tabular structure, wherein said function table comprises a plurality of functions associated with said S expression tabular structure tabulated against at least one of a function name, a derived column and a derived table; generating an argument table based on said S-expression tabular structure, wherein said argument table comprises a plurality of arguments associated with said S-expression tabular structure tabulated against at least one of an argument type, a function identification, a computed from function, a reference to entity or a literal value; and converting at least one function associated with said S-expression tabular structure to a pre-determined language based on a language map of said pre-determined language and said function table and said argument table. | 17. A processor-implemented method comprising: converting a SQL expression into an S-expression tabular structure, wherein said S-expression comprises a nested list; generating a function table based on said S-expression tabular structure, wherein said function table comprises a plurality of functions associated with said S expression tabular structure tabulated against at least one of a function name, a derived column and a derived table; generating an argument table based on said S-expression tabular structure, wherein said argument table comprises a plurality of arguments associated with said S-expression tabular structure tabulated against at least one of an argument type, a function identification, a computed from function, a reference to entity or a literal value; and converting at least one function associated with said S-expression tabular structure to a pre-determined language based on a language map of said pre-determined language and said function table and said argument table. 20. The processor-implemented method of claim 17 , wherein said transforming comprises: changing at least one of one or more function names, one or more arguments, a syntax and one or more keywords of each list in a reconstructed S-expression string to be in compliance with said language map of said pre-determined language. | 0.79352 |
10,162,814 | 7 | 13 | 7. The method of claim 5 , wherein the generating the response text data based on the preferred action data comprises: acquiring parameter data required for response from a data source based on the updated conversation parameter and the preferred action data; and generating the response text data based on the acquired parameter data and the preferred action data. | 7. The method of claim 5 , wherein the generating the response text data based on the preferred action data comprises: acquiring parameter data required for response from a data source based on the updated conversation parameter and the preferred action data; and generating the response text data based on the acquired parameter data and the preferred action data. 13. The method of claim 7 , wherein the generating the response text data based on the acquired parameter data and the preferred action data further comprises: calculating values of the acquired parameter data; and generating the response text data by using the calculated values and the preferred action data. | 0.531722 |
7,917,899 | 1 | 11 | 1. A program development apparatus, comprising: a storage device configured to store a complex intrinsic function including both an operation definition defining a program description in a source program subjected to be optimized, and an inline clause describing statements including multiple extended instructions after the optimization, the multiple extended instructions being executed by an extended module of a target processor; an analyzer configured to perform a syntax analysis of the complex intrinsic function by reading the complex intrinsic function out of the storage device, so as to detect the operation definition and the inline clause; a code generator configured to generate an object code from the source program by optimizing a program description corresponding to the operation definition in the source program into the multiple extended instructions included in the statements in the inline clause; a very long instruction word (VLIW) instruction definer configured to define a VLIW instruction including a coprocessor instruction to be executed by a coprocessor of a VLIW type included in the extended module from instructions applicable to parallel execution; and a complex intrinsic function generator configured to generate the complex intrinsic function by describing the VLIW instruction as the statements in the inline clause, and by defining the program description in the source program subjected to be optimized to the VLIW instruction as the operation definition. | 1. A program development apparatus, comprising: a storage device configured to store a complex intrinsic function including both an operation definition defining a program description in a source program subjected to be optimized, and an inline clause describing statements including multiple extended instructions after the optimization, the multiple extended instructions being executed by an extended module of a target processor; an analyzer configured to perform a syntax analysis of the complex intrinsic function by reading the complex intrinsic function out of the storage device, so as to detect the operation definition and the inline clause; a code generator configured to generate an object code from the source program by optimizing a program description corresponding to the operation definition in the source program into the multiple extended instructions included in the statements in the inline clause; a very long instruction word (VLIW) instruction definer configured to define a VLIW instruction including a coprocessor instruction to be executed by a coprocessor of a VLIW type included in the extended module from instructions applicable to parallel execution; and a complex intrinsic function generator configured to generate the complex intrinsic function by describing the VLIW instruction as the statements in the inline clause, and by defining the program description in the source program subjected to be optimized to the VLIW instruction as the operation definition. 11. The program development apparatus of claim 1 , further comprising: a parallelism instruction detector configured to detect the instructions applicable to the parallel execution in the source program by generating a data flow graph from the source program. | 0.773997 |
6,038,668 | 34 | 36 | 34. A method for organizing information comprising the steps of storing at a remote location data including organizational terms; scanning said remote location for the existence of the organizational terms; retrieving from said remote location the data as based on said organizational terms. | 34. A method for organizing information comprising the steps of storing at a remote location data including organizational terms; scanning said remote location for the existence of the organizational terms; retrieving from said remote location the data as based on said organizational terms. 36. The method according to claim 34, wherein said retrieval step retrieves the data as based on Boolean criteria. | 0.648148 |
7,716,570 | 6 | 8 | 6. A computer storage medium containing instructions that when executed perform acts comprising: accessing a collection of resources specified for the creation of an XPS document, the XPS document based upon an extensible markup language; establishing individual pages from the collection of resources according to an XPS specification for a page; establishing at least one sub-document as a collection of the individual pages; establishing the XPS document as a collection of at least one sub-document, providing access to the document sequence via a document sequence writer interface and the method further comprising rendering the XPS document by providing access to the document sequence via a document sequence reader interface; defining global properties for the XPS document including accepting digital signatures for the XPS document; and providing an application programming interface that performs the acts of accessing the collection, establishing the individual pages, establishing the at least one sub-document, establishing the XPS document, and defining the global properties, the application programming interface comprises: providing a resource class that comprises the XPS resources; providing a page class that comprises the page reader interface and the page writer interface; providing a document class that comprises the document reader interface and the document writer interface; and providing a document sequence class that comprises the document sequence reader interface and the document sequence writer interface. | 6. A computer storage medium containing instructions that when executed perform acts comprising: accessing a collection of resources specified for the creation of an XPS document, the XPS document based upon an extensible markup language; establishing individual pages from the collection of resources according to an XPS specification for a page; establishing at least one sub-document as a collection of the individual pages; establishing the XPS document as a collection of at least one sub-document, providing access to the document sequence via a document sequence writer interface and the method further comprising rendering the XPS document by providing access to the document sequence via a document sequence reader interface; defining global properties for the XPS document including accepting digital signatures for the XPS document; and providing an application programming interface that performs the acts of accessing the collection, establishing the individual pages, establishing the at least one sub-document, establishing the XPS document, and defining the global properties, the application programming interface comprises: providing a resource class that comprises the XPS resources; providing a page class that comprises the page reader interface and the page writer interface; providing a document class that comprises the document reader interface and the document writer interface; and providing a document sequence class that comprises the document sequence reader interface and the document sequence writer interface. 8. The computer storage medium of claim 6 , wherein the document sequence class includes a print ticket specifying how documents of the document sequence are to be printed where documents do not have print tickets, the document class includes a print ticket that specifies how documents are to be printed, and the page class includes a print ticket specifying how pages are to be printed. | 0.5 |
8,856,249 | 9 | 10 | 9. The computing device of claim 7 , further comprising determining logic, executed by the processor, for determining an email template for each of the machine-generated emails. | 9. The computing device of claim 7 , further comprising determining logic, executed by the processor, for determining an email template for each of the machine-generated emails. 10. The computing device of claim 9 , further comprising determining logic executed by the processor for determining that an email in the machine-generated emails is part of an email thread based on the template causality graph for the email template. | 0.5 |
8,494,837 | 16 | 18 | 16. An active learning training system that a user can interact with, comprising: a translation module for translating a test set E from a first collection to a second collection using a unidirectional translation corpus and from the second collection to the first collection using a unidirectional translation corpus so as to create a set E′ in the first collection, wherein differences between E and E′ are determined; a comparison module for computing confidence scores for a translation of each item in the test set E based on a similarity of E and E′ and adding translations to a parallel corpus based on the confidence scores; and memory storage for storing a created or updated parallel corpus. | 16. An active learning training system that a user can interact with, comprising: a translation module for translating a test set E from a first collection to a second collection using a unidirectional translation corpus and from the second collection to the first collection using a unidirectional translation corpus so as to create a set E′ in the first collection, wherein differences between E and E′ are determined; a comparison module for computing confidence scores for a translation of each item in the test set E based on a similarity of E and E′ and adding translations to a parallel corpus based on the confidence scores; and memory storage for storing a created or updated parallel corpus. 18. The system as recited in claim 16 , further comprising: long-term memory storage for a plurality of parallel corpuses; and a translation module capable of performing translations. | 0.727679 |
8,996,629 | 13 | 14 | 13. The system of claim 11 , wherein the channel engine further comprises a channel generator for determining a channel attribute based at least in part on the prior interaction of the user with the heterogeneous data sources. | 13. The system of claim 11 , wherein the channel engine further comprises a channel generator for determining a channel attribute based at least in part on the prior interaction of the user with the heterogeneous data sources. 14. The system of claim 13 , wherein the channel generator further generates the stream of content and enables the user to share the plurality of channels with at least one of a friend of the user, a community, a group and an internet user. | 0.5 |
8,751,477 | 20 | 21 | 20. The method of claim 19 , further comprising: calculating a page relevance score for a page based on a combination of the challenge relevance scores for the first and second engines; publishing the page reference score; and combining the live results received from the first and second engines into one live results page that is transmitted to the live remote computer system. | 20. The method of claim 19 , further comprising: calculating a page relevance score for a page based on a combination of the challenge relevance scores for the first and second engines; publishing the page reference score; and combining the live results received from the first and second engines into one live results page that is transmitted to the live remote computer system. 21. The method of claim 20 , wherein the page relevance score is calculated based on a vector that includes a position of the challenge results received from the first and second engines. | 0.89 |
8,872,677 | 11 | 12 | 11. The computer-implemented method of claim 8 , further comprising: defining a block of dictionary data symbols; selecting every M th symbol in the defined block of dictionary data symbols and identifying its corresponding location in the dictionary; calculating a hash value HV M for each selected M th symbol; and storing the calculated hash value HV M and the corresponding dictionary location as an entry in the dictionary-index, wherein M #N. | 11. The computer-implemented method of claim 8 , further comprising: defining a block of dictionary data symbols; selecting every M th symbol in the defined block of dictionary data symbols and identifying its corresponding location in the dictionary; calculating a hash value HV M for each selected M th symbol; and storing the calculated hash value HV M and the corresponding dictionary location as an entry in the dictionary-index, wherein M #N. 12. The computer-implemented method of claim 11 , wherein N and M do not have a common factor value. | 0.5 |
9,697,823 | 1 | 6 | 1. A method, executed by one or more processors, the method comprising: receiving a channel recording corresponding to a conversation; receiving a transcription for the conversation; generating a conversation-specific language model for the conversation using the transcription; conducting speech recognition on the channel recording using the conversation-specific language model to provide time boundaries and written language corresponding to utterances within the channel recording; determining sentence or phrase boundaries for the transcription; aligning written language within the one or more transcriptions with the written language corresponding to the utterances with the channel recording to provide sentence or phrase boundaries for the channel recording; and training a speech recognizer according to the sentence or phrase boundaries for the transcription and the sentence or phrase boundaries for the channel recording. | 1. A method, executed by one or more processors, the method comprising: receiving a channel recording corresponding to a conversation; receiving a transcription for the conversation; generating a conversation-specific language model for the conversation using the transcription; conducting speech recognition on the channel recording using the conversation-specific language model to provide time boundaries and written language corresponding to utterances within the channel recording; determining sentence or phrase boundaries for the transcription; aligning written language within the one or more transcriptions with the written language corresponding to the utterances with the channel recording to provide sentence or phrase boundaries for the channel recording; and training a speech recognizer according to the sentence or phrase boundaries for the transcription and the sentence or phrase boundaries for the channel recording. 6. The method of claim 1 , wherein aligning written language comprises conducting a dynamic programming procedure. | 0.719212 |
9,256,968 | 1 | 10 | 1. A method for converting a sketch shape into a semantic element, the method comprising the steps of: receiving a request to convert a first sketch shape into a first semantic element, wherein the first sketch shape and a second semantic element are part of a first nested shape combination, wherein the first sketch shape includes a visual depiction corresponding to a first class of semantic descriptions, wherein the first semantic element is a visual depiction further including a first semantic description of the first class of semantic descriptions; determining that a first semantic relationship between the first semantic element and the second semantic element exists; and based on determining the first semantic relationship converting the first sketch shape to the first semantic element, such that the determined first semantic relationship is depicted between the second semantic element and first semantic element; wherein at least one of the steps is carried out using a computing device. | 1. A method for converting a sketch shape into a semantic element, the method comprising the steps of: receiving a request to convert a first sketch shape into a first semantic element, wherein the first sketch shape and a second semantic element are part of a first nested shape combination, wherein the first sketch shape includes a visual depiction corresponding to a first class of semantic descriptions, wherein the first semantic element is a visual depiction further including a first semantic description of the first class of semantic descriptions; determining that a first semantic relationship between the first semantic element and the second semantic element exists; and based on determining the first semantic relationship converting the first sketch shape to the first semantic element, such that the determined first semantic relationship is depicted between the second semantic element and first semantic element; wherein at least one of the steps is carried out using a computing device. 10. The method of claim 1 , wherein the first nested shape combination comprises the first sketch shape containing multiple semantic elements. | 0.898135 |
10,058,354 | 30 | 31 | 30. A pivotal bone anchor assembly for anchoring to patient bone and coupling with an elongate rod via a closure, the pivotal bone anchor assembly comprising: a shank having a longitudinal axis, a head with a partial spherical shape defining a hemisphere with an outer spherical surface extending above and below the hemisphere, and an anchor portion extending downwardly from the head for fixation to the patient bone; a receiver having a longitudinal axis, a base defining a bore centered around the longitudinal axis and in communication with a bottom surface of the receiver through a bottom opening, and a pair of upright arms extending upward from the base to define an open channel configured to receive the elongate rod, the open channel in communication with the bore, the bore having a concave inwardly-facing lower spherical seating surface proximate the bottom opening configured to frictionally engage and support the shank head and provide for pivotal movement between the shank and the receiver; an insert positionable within the receiver bore and having an upwardly-facing surface engageable with the elongate rod and a downwardly-facing surface engageable with the shank head above the hemisphere; and a concave inwardly-facing upper spherical seating surface configured to frictionally engage the shank head above the hemisphere when the shank head is frictionally engaged below the hemisphere by the lower spherical seating surface and the shank and receiver longitudinal axes are co-aligned, so as to provide a non-floppy pivotal frictional engagement between the shank and the receiver prior to the insert being positioned within the receiver bore. | 30. A pivotal bone anchor assembly for anchoring to patient bone and coupling with an elongate rod via a closure, the pivotal bone anchor assembly comprising: a shank having a longitudinal axis, a head with a partial spherical shape defining a hemisphere with an outer spherical surface extending above and below the hemisphere, and an anchor portion extending downwardly from the head for fixation to the patient bone; a receiver having a longitudinal axis, a base defining a bore centered around the longitudinal axis and in communication with a bottom surface of the receiver through a bottom opening, and a pair of upright arms extending upward from the base to define an open channel configured to receive the elongate rod, the open channel in communication with the bore, the bore having a concave inwardly-facing lower spherical seating surface proximate the bottom opening configured to frictionally engage and support the shank head and provide for pivotal movement between the shank and the receiver; an insert positionable within the receiver bore and having an upwardly-facing surface engageable with the elongate rod and a downwardly-facing surface engageable with the shank head above the hemisphere; and a concave inwardly-facing upper spherical seating surface configured to frictionally engage the shank head above the hemisphere when the shank head is frictionally engaged below the hemisphere by the lower spherical seating surface and the shank and receiver longitudinal axes are co-aligned, so as to provide a non-floppy pivotal frictional engagement between the shank and the receiver prior to the insert being positioned within the receiver bore. 31. The pivotal bone anchor assembly of claim 30 , wherein the upper and lower spherical seating surfaces are located in the receiver bore prior to the shank being positioned within the receiver bore. | 0.640288 |
8,234,118 | 1 | 2 | 1. A dialog prosody structure generating method comprising: generating discourse information based on a speech act of a user utterance for a semantic structure of a system utterance corresponding to the user utterance; generating prosody information including an utterance boundary level indicating a duration of a silent period between each semantic unit for the discourse information of the semantic structure, wherein the utterance boundary level is adjusted based on closeness between semantic units, which is determined by syntax and case; and generating an intonation pattern for the semantic structure of the system utterance based on the prosody information using by at least one computer system, wherein the speech act provides a speech act classification of the user utterance, so that even when speech acts are identical, the generated intonation pattern varies according to the speech act classification of the user utterance. | 1. A dialog prosody structure generating method comprising: generating discourse information based on a speech act of a user utterance for a semantic structure of a system utterance corresponding to the user utterance; generating prosody information including an utterance boundary level indicating a duration of a silent period between each semantic unit for the discourse information of the semantic structure, wherein the utterance boundary level is adjusted based on closeness between semantic units, which is determined by syntax and case; and generating an intonation pattern for the semantic structure of the system utterance based on the prosody information using by at least one computer system, wherein the speech act provides a speech act classification of the user utterance, so that even when speech acts are identical, the generated intonation pattern varies according to the speech act classification of the user utterance. 2. The method of claim 1 , further comprising adjusting an emphasis tag on repeated information when the semantic structure of a current system utterance is identical to that of a previous system utterance. | 0.825719 |
9,251,289 | 17 | 18 | 17. The computer storage device of claim 15 , the method comprising populating a string database with a plurality of known strings, including the known string, respective known strings in the string database associated with known string IDs. | 17. The computer storage device of claim 15 , the method comprising populating a string database with a plurality of known strings, including the known string, respective known strings in the string database associated with known string IDs. 18. The computer storage device of claim 17 , a second known string in the string database associated with a second known string ID different than the known string ID. | 0.713058 |
9,824,689 | 1 | 4 | 1. A system, comprising: an audio device configured to receive an audio input from a user; a speech recognition processor in communication with the audio device, the speech recognition processor configured to recognize at least one voice command from the audio input; and an electronic device separated from the speech recognition processor and in communication with the speech recognition processor, the electronic device configured to: determine whether the at least one voice command is valid and includes a safety-critical command; present a preview of the at least one voice command when the at least one voice command includes a safety-critical command; and execute the at least one voice command only after receiving an explicit confirmation from the user when the at least one voice command includes a safety-critical command. | 1. A system, comprising: an audio device configured to receive an audio input from a user; a speech recognition processor in communication with the audio device, the speech recognition processor configured to recognize at least one voice command from the audio input; and an electronic device separated from the speech recognition processor and in communication with the speech recognition processor, the electronic device configured to: determine whether the at least one voice command is valid and includes a safety-critical command; present a preview of the at least one voice command when the at least one voice command includes a safety-critical command; and execute the at least one voice command only after receiving an explicit confirmation from the user when the at least one voice command includes a safety-critical command. 4. The system of claim 1 , wherein at least one of the speech recognition processor and the electronic device is further configured to: cancel the at least one voice command when at least one of the following occurs: no explicit confirmation is received from the user within a configurable time period; a cancellation command is received; and a new audio input is received from the user. | 0.5 |
4,539,653 | 1 | 17 | 1. In an automatic typographic page formatter having means to receive coded text digital signals, including means to receive formatting commands with said text signals, and formatting means coupled to said receiving means for formatting a page of text using said received text digital signals and in accordance with said received formatting commands; the improvement including in combination: first means in said formatting means coupled to said receiving means and being capable of receiving said text digital signals and said formatting commands for assigning said received text digital signals to successive pages of text and indicating ending formatting of each page whereby a document having a plurality of pages of text can be created from said received text digital signals; said first means including format limiting means for sequentially placing text on each of said successive pages such that successively vertical portions of each page sequentially receive text; and named area control means coupled to said first means and said receiving means for sequentially receiving said text digital signals and said formatting commands and being responsive to named area ones of said formatting commands to select predetermined ones of said text digital signals for insertion onto predetermined ones of said pages of text independently of said first means and said sequence of receipt whereby text inserted by said named area means has a format and page location independent of the said first means sequentially created text format and the sequence of receipt of said text digital signals. | 1. In an automatic typographic page formatter having means to receive coded text digital signals, including means to receive formatting commands with said text signals, and formatting means coupled to said receiving means for formatting a page of text using said received text digital signals and in accordance with said received formatting commands; the improvement including in combination: first means in said formatting means coupled to said receiving means and being capable of receiving said text digital signals and said formatting commands for assigning said received text digital signals to successive pages of text and indicating ending formatting of each page whereby a document having a plurality of pages of text can be created from said received text digital signals; said first means including format limiting means for sequentially placing text on each of said successive pages such that successively vertical portions of each page sequentially receive text; and named area control means coupled to said first means and said receiving means for sequentially receiving said text digital signals and said formatting commands and being responsive to named area ones of said formatting commands to select predetermined ones of said text digital signals for insertion onto predetermined ones of said pages of text independently of said first means and said sequence of receipt whereby text inserted by said named area means has a format and page location independent of the said first means sequentially created text format and the sequence of receipt of said text digital signals. 17. The automatic typographic page formatter set forth in claim 1 wherein said named area control means includes section named area control means coupled to said first means and said receiving means for formatting received text signals to section named areas and for placing same onto a page within predetermined ones of said vertical portions; and page/body named control area means in said named area control means coupled to said first means and said receiving means for formatting text signals to an indeterminate length in page or body named areas and having first placement means for placing predetermined ones of said named areas anywhere on the page independent of said portions and having second placement means for placing text signals formatted to body ones of said named areas to body ones of said portions; and means in said first means coupled to said named area control means for indicating which of said portions are body ones of said portions. | 0.715808 |
6,061,646 | 12 | 19 | 12. A system including processor and memory for providing information in response to a question in one of a plurality of natural spoken languages, comprising: a microphone for detecting an utterance from a user; a plurality of small dictionaries each for respective one of the plurality of languages, each small dictionary including speech data for a selected few common words in the respective language; at least one speech recognition engine for recognizing the detected utterance, the at least one speech recognition engine using the plurality of small dictionaries to recognize words within the detected utterance; a language recognizer for selecting one of the plurality of languages as the language of the detected utterance based on a number of recognized words for each language from the small dictionaries; a plurality of large dictionaries for the plurality of languages usable by the at least one speech recognition engine for recognizing words within the detected utterance; retrieval means for retrieving the large dictionary for the selected language; and means for responding to the user in the selected language. | 12. A system including processor and memory for providing information in response to a question in one of a plurality of natural spoken languages, comprising: a microphone for detecting an utterance from a user; a plurality of small dictionaries each for respective one of the plurality of languages, each small dictionary including speech data for a selected few common words in the respective language; at least one speech recognition engine for recognizing the detected utterance, the at least one speech recognition engine using the plurality of small dictionaries to recognize words within the detected utterance; a language recognizer for selecting one of the plurality of languages as the language of the detected utterance based on a number of recognized words for each language from the small dictionaries; a plurality of large dictionaries for the plurality of languages usable by the at least one speech recognition engine for recognizing words within the detected utterance; retrieval means for retrieving the large dictionary for the selected language; and means for responding to the user in the selected language. 19. The system as recited in claim 12 further comprising a specialized speech recognition engine for utilizing the large dictionary of a particular language for recognizing words of the natural language of the detected utterance. | 0.5 |
8,090,620 | 3 | 4 | 3. The system of claim 2 , wherein the user profile information comprises at least one of the following: username, email address, city and state, and one or more user preferences. | 3. The system of claim 2 , wherein the user profile information comprises at least one of the following: username, email address, city and state, and one or more user preferences. 4. The system of claim 3 , wherein the user preferences comprise at least one of location preferences, item preferences, and seller preferences. | 0.5 |
9,734,146 | 12 | 17 | 12. A system for facilitating decision support by determining nomenclature linkages between variables in databases having different ontologies comprising: one or more computer processors; and one or more computer storage media storing computer-useable instructions that, when executed by the one or more processors, implement a method comprising: identifying a first set of documents from a first record system having a first ontology; identifying a second set of documents from a second record system having a second ontology that is different than the first ontology; determining a use-case present in the first and second sets of documents; determining a set of variables relevant to the use-case; receiving from the first set of documents, a first document containing at least one first-document variable from the set of variables; wherein each first-document variable has a first-document value associated with it; receiving from the second set of documents, a second document containing at least one second-document variable from the set of variables; (1) wherein the second-document variable has a second-document value associated with it, and (2) wherein the second-document variable is also contained in the first document; based on the determined use-case and set of variables, generating a classifier; for each first-document variable contained in the first document, applying classifier to transform the first-document value associated with the first-document variable to a categorical datatype; for each second-document variable contained in the second document, applying the classifier to transform the second-document value associated with the second-document variable to a categorical datatype; based on the categorical datatypes of the first document and the categorical datatypes of the second document, generating a set of textmatrices; applying latent semantic analysis to the set of textmatrices to determine a latent semantic space associated with the at least one first-document variable and the at least one second document variable; specifying a threshold of similarity; for a first comparison-variable, from the at least one first-document variables associated with the latent semantic space: determining a measure of similarity to a second-comparison variable from the at least one second-document variables associated with the latent semantic space: performing a comparison of the measure similarity to the threshold; and based on the comparison, determining that the measure similarity satisfies the threshold, associating the first comparison variable with the second comparison variable, and designating the association as a synonymy, wherein the threshold is satisfied if the measure of similarity is greater than the threshold. | 12. A system for facilitating decision support by determining nomenclature linkages between variables in databases having different ontologies comprising: one or more computer processors; and one or more computer storage media storing computer-useable instructions that, when executed by the one or more processors, implement a method comprising: identifying a first set of documents from a first record system having a first ontology; identifying a second set of documents from a second record system having a second ontology that is different than the first ontology; determining a use-case present in the first and second sets of documents; determining a set of variables relevant to the use-case; receiving from the first set of documents, a first document containing at least one first-document variable from the set of variables; wherein each first-document variable has a first-document value associated with it; receiving from the second set of documents, a second document containing at least one second-document variable from the set of variables; (1) wherein the second-document variable has a second-document value associated with it, and (2) wherein the second-document variable is also contained in the first document; based on the determined use-case and set of variables, generating a classifier; for each first-document variable contained in the first document, applying classifier to transform the first-document value associated with the first-document variable to a categorical datatype; for each second-document variable contained in the second document, applying the classifier to transform the second-document value associated with the second-document variable to a categorical datatype; based on the categorical datatypes of the first document and the categorical datatypes of the second document, generating a set of textmatrices; applying latent semantic analysis to the set of textmatrices to determine a latent semantic space associated with the at least one first-document variable and the at least one second document variable; specifying a threshold of similarity; for a first comparison-variable, from the at least one first-document variables associated with the latent semantic space: determining a measure of similarity to a second-comparison variable from the at least one second-document variables associated with the latent semantic space: performing a comparison of the measure similarity to the threshold; and based on the comparison, determining that the measure similarity satisfies the threshold, associating the first comparison variable with the second comparison variable, and designating the association as a synonymy, wherein the threshold is satisfied if the measure of similarity is greater than the threshold. 17. The system of claim 12 , wherein applying latent semantic analysis includes singular value decomposition. | 0.753394 |
8,503,624 | 10 | 11 | 10. The method of claim 9 , in which allowing the recipient user of the recipient system to define the at least one keyword and its associated predefined action comprises: presenting a keyword user interface to the recipient user; receiving a new keyword from the recipient user via the recipient user interface; receiving a new action associated with the new keyword; and storing the new keyword and the new action as the at least one keyword and the predefined action in a profile associated with the recipient user. | 10. The method of claim 9 , in which allowing the recipient user of the recipient system to define the at least one keyword and its associated predefined action comprises: presenting a keyword user interface to the recipient user; receiving a new keyword from the recipient user via the recipient user interface; receiving a new action associated with the new keyword; and storing the new keyword and the new action as the at least one keyword and the predefined action in a profile associated with the recipient user. 11. The method of claim 10 , wherein the user interface is at least one of an email interface and a voice interface. | 0.5 |
9,477,937 | 7 | 10 | 7. The computer program of claim 6 , further comprising a step of: if a deadlock is detected at the step of checking, returning to a user a characterization indicative of the detected deadlock via a graphical user interface (GUI). | 7. The computer program of claim 6 , further comprising a step of: if a deadlock is detected at the step of checking, returning to a user a characterization indicative of the detected deadlock via a graphical user interface (GUI). 10. The computer program of claim 7 , wherein the acyclic workflow graph and the labels of the edges as obtained during the labeling step are graphically represented in the GUI. | 0.805495 |
7,647,554 | 5 | 6 | 5. The method of claim 4 , wherein said history includes the user's letter swaps, and wherein the replacement list includes alternative words based on said history of letter swaps. | 5. The method of claim 4 , wherein said history includes the user's letter swaps, and wherein the replacement list includes alternative words based on said history of letter swaps. 6. The method of claim 5 , further comprising: (d) monitoring the user's mistypes that include letter swaps and incorporating said mistypes to said history, and wherein said history includes words that contain letter substitutions or letter swaps that are supplied by the user. | 0.5 |
8,781,991 | 23 | 24 | 23. The emotion recognition apparatus of claim 12 , wherein the emotion estimation unit further estimates an internal emotional state of the user based on the first and second emotion values, and estimates an external emotional state of the user based on the first, second, and third emotion values. | 23. The emotion recognition apparatus of claim 12 , wherein the emotion estimation unit further estimates an internal emotional state of the user based on the first and second emotion values, and estimates an external emotional state of the user based on the first, second, and third emotion values. 24. The emotion recognition apparatus of claim 23 , further comprising: an emotion provision unit to output data regarding the estimated internal emotional state and data regarding the estimated external emotional state of the user. | 0.5 |
7,590,645 | 6 | 7 | 6. The method of claim 5 , wherein an identifier of the data item and the first temporal indicator assigned to the data item are stored in an indexed database table. | 6. The method of claim 5 , wherein an identifier of the data item and the first temporal indicator assigned to the data item are stored in an indexed database table. 7. The method of claim 6 , wherein a view of the table in temporal indicator sequence is maintained. | 0.5 |
8,825,474 | 18 | 19 | 18. A non-transitory computer-readable storage medium encoded with instructions that, when executed, cause at least one processor of a computing device to: output, for display, a graphical user interface including a graphical keyboard and one or more text suggestion regions; receive an indication of gesture input detected a presence-sensitive input device; select, based at least in part on the indication of the gesture input, at least one key of the graphical keyboard; determine, based at least in part on at least one character associated with the at least one key, a plurality of candidate character strings and a respective probability for each of the plurality of candidate character strings; determine, based at least in part on an association between at least one candidate character string and a context of the gesture input, past interaction data that indicates whether the least one candidate character string was selected at a location of a text suggestion region of the graphical user interface while the at least one candidate character string was previously displayed in the text suggestion region, the at least one candidate string included in the plurality of candidate character strings and the text suggestion region included in one or more text suggestion regions of the graphical user interface; modify, based at least in part on the past interaction data, a probability of the at least one candidate character string; and output, based at least in part on the probability that was modified using the past interaction data, the at least one candidate character string for display in one of the one or more text suggestion regions. | 18. A non-transitory computer-readable storage medium encoded with instructions that, when executed, cause at least one processor of a computing device to: output, for display, a graphical user interface including a graphical keyboard and one or more text suggestion regions; receive an indication of gesture input detected a presence-sensitive input device; select, based at least in part on the indication of the gesture input, at least one key of the graphical keyboard; determine, based at least in part on at least one character associated with the at least one key, a plurality of candidate character strings and a respective probability for each of the plurality of candidate character strings; determine, based at least in part on an association between at least one candidate character string and a context of the gesture input, past interaction data that indicates whether the least one candidate character string was selected at a location of a text suggestion region of the graphical user interface while the at least one candidate character string was previously displayed in the text suggestion region, the at least one candidate string included in the plurality of candidate character strings and the text suggestion region included in one or more text suggestion regions of the graphical user interface; modify, based at least in part on the past interaction data, a probability of the at least one candidate character string; and output, based at least in part on the probability that was modified using the past interaction data, the at least one candidate character string for display in one of the one or more text suggestion regions. 19. The non-transitory computer-readable storage medium of claim 18 , wherein the past interaction data indicates at least one of: a past user input to select the at least one candidate character string while the at least one candidate character string was previously displayed in the at least one of the one or more text suggestion regions; a past user input to ignore the at least one candidate character string while the at least one candidate character string was previously displayed in the at least one of the one or more text suggestion regions; and a past user input to reject the at least one candidate character string while the at least one candidate character string was previously displayed in the at least one of the one or more text suggestion regions. | 0.641589 |
9,373,321 | 6 | 11 | 6. A system for generating one or more wake-up words, the system comprising: an interface device configured to receive a text representation of the one more wake-up words and an audio representation of the one or more wake-up words; a wake-up-word (WUW) processing engine configured to determine a strength of the text representation of the one or more wake-up words based on one or more static measures and to determine a strength of the audio representation of the one or more wake-up words based on one or more dynamic measures, wherein to determine the strength of the text representation, the WUW processing engine is configured to apply a Kullback-Leibler (KL) divergence calculation between the one or more wake-up words and words unrelated to the one or more wake-up words and compare a result of the KL divergence calculation to a predetermined distance score associated with a decoding accuracy of a speck recognizer; and a display device configured to provide a message on one or more improvements to a likelihood that the speech recognizer recognizes the one or more wake-up words based on the strengths of the text and audio representations. | 6. A system for generating one or more wake-up words, the system comprising: an interface device configured to receive a text representation of the one more wake-up words and an audio representation of the one or more wake-up words; a wake-up-word (WUW) processing engine configured to determine a strength of the text representation of the one or more wake-up words based on one or more static measures and to determine a strength of the audio representation of the one or more wake-up words based on one or more dynamic measures, wherein to determine the strength of the text representation, the WUW processing engine is configured to apply a Kullback-Leibler (KL) divergence calculation between the one or more wake-up words and words unrelated to the one or more wake-up words and compare a result of the KL divergence calculation to a predetermined distance score associated with a decoding accuracy of a speck recognizer; and a display device configured to provide a message on one or more improvements to a likelihood that the speech recognizer recognizes the one or more wake-up words based on the strengths of the text and audio representations. 11. The system of claim 6 , wherein the WUW processing engine is configured to perform a voice activity detection on the audio representation of the one or more wake-up words, calculate one or more confidence scores for the one or more wake-up words or a combination thereof. | 0.648338 |
9,274,646 | 1 | 8 | 1. An apparatus, comprising least one processor and at least one memory including computer program code, the memory and the computer program code configured to, working with the processor, cause the apparatus to perform at least the following: receive a multiple touch input comprising a first touch input having a first text position within a first word such that the first text position is a text position between a first character of the first word and a last letter of the first word, and a second touch input having a second text position such that the second text position is a text position between a first character of a second word and a last letter of the second word; determine a first text selection point positioned outside of the first word based at least in part on the first text position being within the first word, such that the first text selection point is at least one of a text position preceding a first character of the first word, or a text position following a last letter of the first word; determine a second text selection point positioned outside of the second word based at least in part on the second text position, such that the second text selection point is at least one of a text position preceding a first character of the second word, or a text position following a last letter of the second word; and select text information between the first text selection point and the second text selection point. | 1. An apparatus, comprising least one processor and at least one memory including computer program code, the memory and the computer program code configured to, working with the processor, cause the apparatus to perform at least the following: receive a multiple touch input comprising a first touch input having a first text position within a first word such that the first text position is a text position between a first character of the first word and a last letter of the first word, and a second touch input having a second text position such that the second text position is a text position between a first character of a second word and a last letter of the second word; determine a first text selection point positioned outside of the first word based at least in part on the first text position being within the first word, such that the first text selection point is at least one of a text position preceding a first character of the first word, or a text position following a last letter of the first word; determine a second text selection point positioned outside of the second word based at least in part on the second text position, such that the second text selection point is at least one of a text position preceding a first character of the second word, or a text position following a last letter of the second word; and select text information between the first text selection point and the second text selection point. 8. The apparatus of claim 1 , wherein the first text selection point is a text position associated with an end of the first word. | 0.918354 |
9,208,143 | 7 | 8 | 7. A dictionary data display method of an electronic dictionary device which includes (i) a display module including a first and a second display screen, (ii) a dictionary storage module that stores dictionary data that causes a plurality of entry words including compound words obtained by connecting a plurality of words to correspond to explanatory information on the entry words, and (iii) a processor, the dictionary data display method being implemented by the processor, and the method comprising: retrieving entry words for compound words from the entry words in the dictionary data stored in the dictionary storage module, listing words common to the retrieved compound words, and displaying the listed words on the first display screen; reading, from the dictionary data, entry words for compound words connecting with a word specified by a user operation in a list displayed on the first display screen and displaying the entry words for the compound words connecting with the word specified by the user operation in list form on the second display screen; and reading a corresponding piece of explanatory information from the dictionary data when a compound word has been specified according to a user operation in a compound word list displayed on the second display screen and displaying the corresponding piece of explanatory information on the second display screen. | 7. A dictionary data display method of an electronic dictionary device which includes (i) a display module including a first and a second display screen, (ii) a dictionary storage module that stores dictionary data that causes a plurality of entry words including compound words obtained by connecting a plurality of words to correspond to explanatory information on the entry words, and (iii) a processor, the dictionary data display method being implemented by the processor, and the method comprising: retrieving entry words for compound words from the entry words in the dictionary data stored in the dictionary storage module, listing words common to the retrieved compound words, and displaying the listed words on the first display screen; reading, from the dictionary data, entry words for compound words connecting with a word specified by a user operation in a list displayed on the first display screen and displaying the entry words for the compound words connecting with the word specified by the user operation in list form on the second display screen; and reading a corresponding piece of explanatory information from the dictionary data when a compound word has been specified according to a user operation in a compound word list displayed on the second display screen and displaying the corresponding piece of explanatory information on the second display screen. 8. The dictionary data display method of claim 7 , wherein the first display screen and the second display screen comprise separate display devices. | 0.5 |
7,536,324 | 1 | 11 | 1. An Internet-based system for managing and delivering consumer product brand information to consumers at points of presence along the World Wide Web (WWW), said Internet-based system comprising: a plurality of Web-based information servers, operably connected to the infrastructure of the Internet, serving a plurality of Web-sites on the WWW, wherein each said Web-site includes a plurality of HTML-encoded pages; a plurality of Internet-based consumer product information (CPI) servers, operably connected to the infrastructure of the Internet, and serving a plurality of consumer product information (CPI) resources located on the WWW, and related to a particular consumer product or group of consumer products registered with said Internet-based system and being marketed along the WWW; a first Internet-based subsystem, operably connected to the infrastructure of the Internet, and configured to allow manufacturer team members associated with said particular consumer product or group of consumer products, and/or authorized parties, to implement a plurality of consumer product information (CPI) requesting and graphical user interface (GUI) displaying subsystems (CPI-requesting and GUI-displaying subsystems) for said plurality of consumer products being marketed along the WWW, so that each said CPI-requesting and GUI-displaying subsystem is accessed by consumers at points of presence along the WWW, using a client subsystem supporting a Web browser; an object-oriented server operably connected to the infrastructure of the Internet; wherein each said CPI-requesting and GUI-displaying subsystem is implemented by (i) a consumer product information request (CPIR) enabling servlet stored on and executed within said object-oriented server independent of the operation of said CPI resource servers, and (ii) an HTML servlet tag embodied with a unique URL referencing said CPIR-enabling servlet, and embedded within at least one of said plurality of HTML-encoded pages, at a point of presence on the WWW; wherein said object-oriented server generates each said CPI-requesting and GUI-displaying subsystem, and serves a CPI graphical user interface (GUI) at the point of presence, for displaying a set of said plurality of CPI resources for selection by the consumer; a UPN/TM/URL database, operably connected to said object-oriented server, for storing and managing a UPN/TM/URL link structure for each consumer product registered with said Internet-based system, wherein each said UPN/TM/URL link structure includes a Universal Product Number (UPN) assigned to the consumer product registered within said Internet-based system; a Trademark (TM) assigned to the consumer product; and a set of URLs for said plurality of CPI resources being served from said plurality of Internet-based CPI servers; wherein said CPIR-enabling servlet installed on said object-oriented server, for each said consumer product, includes code stored on a medium operable to execute said code specifying: (i) a connection to said UPN/TM/URL database; (ii) a CPI query to be executed on said UPN/TM/URL database, dependent on the UPN assigned to said consumer product, and returning a set of URLs stored in said UPN/TM/URL database and associated with said UPN; and (iii) a CPI GUI, object-oriented controlled, displaying the results of the UPN-dependent CPI query at the point of presence where said corresponding HTML servlet tag is embedded within at least one said HTML-encoded page along the WWW; wherein said HTML servlet tag embodies the unique URL referencing said corresponding CPIR-enabling servlet; a second Internet-based subsystem configured to allow manufacturer team members associated with a particular consumer product or group of consumer products, and/or authorized parties, to program said set of CPI resources for display in the CPI GUI of each said CPI-requesting and GUI-displaying subsystem; and wherein, upon the Web-browser of the consumer encountering said HTML servlet tag installed in said HTML-encoded page, (a) the CPIR-enabling servlet corresponding to said HTML servlet tag is automatically executed, (b) the CPI GUI of said corresponding CPI-requesting and GUI-displaying subsystem is automatically generated by said object-oriented server, (c) said object-oriented controlled CPI GUI is served to the Web browser at the point of presence where said HTML servlet tag is embedded, and (d) then said object-oriented controlled CPI GUI displays information content that is (i) associated with one or more CPI resources having URLs returned by said UPN-dependent CPI query, and (ii) served from one or more of said plurality of Internet-based CPI servers, display and review by the consumer at the point of presence along the WWW where said HTML servlet tag has been encountered by the Web browser. | 1. An Internet-based system for managing and delivering consumer product brand information to consumers at points of presence along the World Wide Web (WWW), said Internet-based system comprising: a plurality of Web-based information servers, operably connected to the infrastructure of the Internet, serving a plurality of Web-sites on the WWW, wherein each said Web-site includes a plurality of HTML-encoded pages; a plurality of Internet-based consumer product information (CPI) servers, operably connected to the infrastructure of the Internet, and serving a plurality of consumer product information (CPI) resources located on the WWW, and related to a particular consumer product or group of consumer products registered with said Internet-based system and being marketed along the WWW; a first Internet-based subsystem, operably connected to the infrastructure of the Internet, and configured to allow manufacturer team members associated with said particular consumer product or group of consumer products, and/or authorized parties, to implement a plurality of consumer product information (CPI) requesting and graphical user interface (GUI) displaying subsystems (CPI-requesting and GUI-displaying subsystems) for said plurality of consumer products being marketed along the WWW, so that each said CPI-requesting and GUI-displaying subsystem is accessed by consumers at points of presence along the WWW, using a client subsystem supporting a Web browser; an object-oriented server operably connected to the infrastructure of the Internet; wherein each said CPI-requesting and GUI-displaying subsystem is implemented by (i) a consumer product information request (CPIR) enabling servlet stored on and executed within said object-oriented server independent of the operation of said CPI resource servers, and (ii) an HTML servlet tag embodied with a unique URL referencing said CPIR-enabling servlet, and embedded within at least one of said plurality of HTML-encoded pages, at a point of presence on the WWW; wherein said object-oriented server generates each said CPI-requesting and GUI-displaying subsystem, and serves a CPI graphical user interface (GUI) at the point of presence, for displaying a set of said plurality of CPI resources for selection by the consumer; a UPN/TM/URL database, operably connected to said object-oriented server, for storing and managing a UPN/TM/URL link structure for each consumer product registered with said Internet-based system, wherein each said UPN/TM/URL link structure includes a Universal Product Number (UPN) assigned to the consumer product registered within said Internet-based system; a Trademark (TM) assigned to the consumer product; and a set of URLs for said plurality of CPI resources being served from said plurality of Internet-based CPI servers; wherein said CPIR-enabling servlet installed on said object-oriented server, for each said consumer product, includes code stored on a medium operable to execute said code specifying: (i) a connection to said UPN/TM/URL database; (ii) a CPI query to be executed on said UPN/TM/URL database, dependent on the UPN assigned to said consumer product, and returning a set of URLs stored in said UPN/TM/URL database and associated with said UPN; and (iii) a CPI GUI, object-oriented controlled, displaying the results of the UPN-dependent CPI query at the point of presence where said corresponding HTML servlet tag is embedded within at least one said HTML-encoded page along the WWW; wherein said HTML servlet tag embodies the unique URL referencing said corresponding CPIR-enabling servlet; a second Internet-based subsystem configured to allow manufacturer team members associated with a particular consumer product or group of consumer products, and/or authorized parties, to program said set of CPI resources for display in the CPI GUI of each said CPI-requesting and GUI-displaying subsystem; and wherein, upon the Web-browser of the consumer encountering said HTML servlet tag installed in said HTML-encoded page, (a) the CPIR-enabling servlet corresponding to said HTML servlet tag is automatically executed, (b) the CPI GUI of said corresponding CPI-requesting and GUI-displaying subsystem is automatically generated by said object-oriented server, (c) said object-oriented controlled CPI GUI is served to the Web browser at the point of presence where said HTML servlet tag is embedded, and (d) then said object-oriented controlled CPI GUI displays information content that is (i) associated with one or more CPI resources having URLs returned by said UPN-dependent CPI query, and (ii) served from one or more of said plurality of Internet-based CPI servers, display and review by the consumer at the point of presence along the WWW where said HTML servlet tag has been encountered by the Web browser. 11. The Internet-based system of claim 1 , wherein said points of presence along the WWW include market spaces selected from the group consisting of BC-enabled WWW-sites, EC-enabled stores and EC-enabled online product catalogs. | 0.884965 |
9,582,804 | 1 | 8 | 1. A method comprising: accessing, by a computing device, a network resource model to identify a digital media object in a network resource, the network resource comprising a web page associated with a user and the digital media object previously embedded in the web page by the user, wherein the user is a person; analyzing, by the computing device, meta tags associated with the digital media object; determining, by the computing device, a topic relating to the meta tags; selecting, by the computing device, ad content based on the topic; identifying, by the computing device, data displayed on the web page and surrounding the digital media object within a context of the network resource model; extracting, by the computing device, one or more first terms from the data displayed on the web page and surrounding the digital media object; determining, by the computing device, whether to construct one or more hyperlinks based on formatting parameters associated with the digital media object; and when the formatting parameters associated with the digital media object are above a predetermined threshold: constructing, by the computing device, the one or more hyperlinks based on the one or more first terms and the ad content, the one or more hyperlinks comprising a content embedding entity identifier, the content embedding entity identifier comprising an identification of the user, the content embedding entity identifier in the one or more hyperlinks to allow for determinations of benefits to be shared by entities; and inserting, by the computing device, the one or more hyperlinks into the web page in proximity to the digital media object without affecting the digital media object so that the one or more hyperlinks can be viewed when the web page is viewed. | 1. A method comprising: accessing, by a computing device, a network resource model to identify a digital media object in a network resource, the network resource comprising a web page associated with a user and the digital media object previously embedded in the web page by the user, wherein the user is a person; analyzing, by the computing device, meta tags associated with the digital media object; determining, by the computing device, a topic relating to the meta tags; selecting, by the computing device, ad content based on the topic; identifying, by the computing device, data displayed on the web page and surrounding the digital media object within a context of the network resource model; extracting, by the computing device, one or more first terms from the data displayed on the web page and surrounding the digital media object; determining, by the computing device, whether to construct one or more hyperlinks based on formatting parameters associated with the digital media object; and when the formatting parameters associated with the digital media object are above a predetermined threshold: constructing, by the computing device, the one or more hyperlinks based on the one or more first terms and the ad content, the one or more hyperlinks comprising a content embedding entity identifier, the content embedding entity identifier comprising an identification of the user, the content embedding entity identifier in the one or more hyperlinks to allow for determinations of benefits to be shared by entities; and inserting, by the computing device, the one or more hyperlinks into the web page in proximity to the digital media object without affecting the digital media object so that the one or more hyperlinks can be viewed when the web page is viewed. 8. The method of claim 1 further comprising determining, by the computing device, a user identifier associated with the network resource model; encoding, by the computing device, the user identifier as the content embedding entity identifier into the one or more hyperlinks. | 0.59824 |
7,730,448 | 7 | 8 | 7. The method of claim 5 , wherein the visual feedback comprises an indication that a sub-element in the list is constrained to a particular data type by the base type system and is constrained to a particular format by the supplemental type system. | 7. The method of claim 5 , wherein the visual feedback comprises an indication that a sub-element in the list is constrained to a particular data type by the base type system and is constrained to a particular format by the supplemental type system. 8. The method of claim 7 , wherein the particular data type is string. | 0.5 |
7,519,397 | 11 | 12 | 11. Process according to claim 9 , wherein the text request is returned from the database ( 30 ) to the terminal ( 50 , 60 , 70 ) in the form of a text message. | 11. Process according to claim 9 , wherein the text request is returned from the database ( 30 ) to the terminal ( 50 , 60 , 70 ) in the form of a text message. 12. Process according to claim 11 , wherein a text request is selected by positioning a cursor over this request then by pressing an enable key of a keypad associated with the terminal ( 50 , 60 , 70 ). | 0.5 |
8,751,498 | 1 | 13 | 1. A method for identifying documents referring to an entity, the entity being associated with a first set of features, the method comprising: at a computer having one or more processors and memory storing programs for execution by the one or more processors: identifying a first set of documents based on a first model and the first set of features, wherein the first model includes a first set of rules specifying at least one combination of features from the first set of features that are sufficient for identifying a document referring to the entity, and each document in the first set of documents includes a sufficient number of features in common with the first set of features to identify a document referring to the entity according to the first model; determining a second model based on features included in one or more documents in the first set of documents, wherein the second model includes a second set of rules specifying at least one combination of features from the first set of documents that are sufficient for identifying a document referring to the entity; identifying a second set of documents based on the second model, wherein each document in the second set of documents includes a sufficient number of features in common with the first set of features to identify a document referring to the entity according to the second model, and wherein the second set of documents includes at least one document not included in the first set of documents; and extracting one or more facts from the second set of documents and associating the extracted facts with the entity. | 1. A method for identifying documents referring to an entity, the entity being associated with a first set of features, the method comprising: at a computer having one or more processors and memory storing programs for execution by the one or more processors: identifying a first set of documents based on a first model and the first set of features, wherein the first model includes a first set of rules specifying at least one combination of features from the first set of features that are sufficient for identifying a document referring to the entity, and each document in the first set of documents includes a sufficient number of features in common with the first set of features to identify a document referring to the entity according to the first model; determining a second model based on features included in one or more documents in the first set of documents, wherein the second model includes a second set of rules specifying at least one combination of features from the first set of documents that are sufficient for identifying a document referring to the entity; identifying a second set of documents based on the second model, wherein each document in the second set of documents includes a sufficient number of features in common with the first set of features to identify a document referring to the entity according to the second model, and wherein the second set of documents includes at least one document not included in the first set of documents; and extracting one or more facts from the second set of documents and associating the extracted facts with the entity. 13. The method of claim 1 , wherein identifying a second set of documents based on the second model and the first set of features comprises estimating a probability that a document of the second set of documents refers to the entity. | 0.801533 |
10,073,830 | 5 | 6 | 5. The system of claim 4 , wherein the parsing component is configured to, as pre-training, obtain the labelled text corpus, generate word vectors, generate phrase vectors, generate a parsing combination matrix, generate a parsing probability vector, and generate a part-of-speech matrix to output a randomized parsing SVT model. | 5. The system of claim 4 , wherein the parsing component is configured to, as pre-training, obtain the labelled text corpus, generate word vectors, generate phrase vectors, generate a parsing combination matrix, generate a parsing probability vector, and generate a part-of-speech matrix to output a randomized parsing SVT model. 6. The system of claim 5 , wherein the parsing component is configured to, as training, obtain the randomized parsing SVT model, calculate an error rate, generate a derivative vector, adjust the error rate and the derivative vector, determine that the error rate is not minimized, and generate a parsing SVT model. | 0.5 |
10,055,391 | 1 | 2 | 1. A method, comprising: receiving, by a computer, an unstructured input document; extracting, by the computer, a plurality of tokens from the input document, each token of the plurality of tokens having a corresponding visual style of a plurality of visual styles; producing, by the computer for a first token of the plurality of tokens, a first probability distribution of the first token, the first probability distribution comprising a plurality of first probabilities each indicating a probability that the first token belongs to a corresponding class of a plurality of classes that are each: related to information conveyed by the plurality of tokens; and specific to a type of unstructured data items of the input document; determining, by the computer from the plurality of tokens, a plurality of surrounding tokens that occur near the first token within the input document; determining, by the computer, a first classification probability of the plurality of surrounding tokens, the first classification probability identifying the class in which the plurality of surrounding tokens are most likely to be classified; modifying, by the computer based on the class identified by the first classification probability, each of the plurality of first probabilities to produce a corresponding second probability of a plurality of second probabilities in a second probability distribution; producing, by the computer based on the visual style of the first token and the second probability distribution, a third probability distribution comprising a plurality of third probabilities each associated with a corresponding second probability of the plurality of second probabilities; determining, by the computer based at least on the third probability distribution, a classification of the first token into one of the plurality of classes; and forming, by the computer, a structured document from the first token and the classification. | 1. A method, comprising: receiving, by a computer, an unstructured input document; extracting, by the computer, a plurality of tokens from the input document, each token of the plurality of tokens having a corresponding visual style of a plurality of visual styles; producing, by the computer for a first token of the plurality of tokens, a first probability distribution of the first token, the first probability distribution comprising a plurality of first probabilities each indicating a probability that the first token belongs to a corresponding class of a plurality of classes that are each: related to information conveyed by the plurality of tokens; and specific to a type of unstructured data items of the input document; determining, by the computer from the plurality of tokens, a plurality of surrounding tokens that occur near the first token within the input document; determining, by the computer, a first classification probability of the plurality of surrounding tokens, the first classification probability identifying the class in which the plurality of surrounding tokens are most likely to be classified; modifying, by the computer based on the class identified by the first classification probability, each of the plurality of first probabilities to produce a corresponding second probability of a plurality of second probabilities in a second probability distribution; producing, by the computer based on the visual style of the first token and the second probability distribution, a third probability distribution comprising a plurality of third probabilities each associated with a corresponding second probability of the plurality of second probabilities; determining, by the computer based at least on the third probability distribution, a classification of the first token into one of the plurality of classes; and forming, by the computer, a structured document from the first token and the classification. 2. The method of claim 1 , wherein the input document comprises an image, and extracting the plurality of tokens comprises detecting a column in the image, correcting the perspective of the image, super-sampling the image, or performing optical character recognition on the image. | 0.734848 |
10,013,414 | 21 | 33 | 21. A management entity comprising: a memory comprising instructions; and a processor in communication with the memory wherein the processor executes the instructions to: parse a request to collect data about a communications system for an entity in the communications system, the parsing to produce a parsed request and dependency information, generate sets of model elements in accordance with context tokens and content tokens derived from the parsed request, the content tokens including extrinsic metadata and intrinsic metadata of the parsed request, wherein the processor executing the instructions to generate the sets of model elements comprises the processor executing the instructions to: evaluate content of the request to collect the data to produce links between context tokens and the content tokens, and map the links into a model element graph, analyze context metadata tokens and content metadata tokens in accordance with the dependency information, analyze the model element graph in accordance with the dependency information, modify the model element graph for each dependency and dependency type in the dependency information, generate a platform-neutral description of results of the request from the model element graph derived from the sets of model elements, the platform-neutral description being independent of a protocol used by the management entity to collect the data about the communications system, and execute the request to collect the data as requested in accordance with the platform-neutral description; and store the data as collected in the memory. | 21. A management entity comprising: a memory comprising instructions; and a processor in communication with the memory wherein the processor executes the instructions to: parse a request to collect data about a communications system for an entity in the communications system, the parsing to produce a parsed request and dependency information, generate sets of model elements in accordance with context tokens and content tokens derived from the parsed request, the content tokens including extrinsic metadata and intrinsic metadata of the parsed request, wherein the processor executing the instructions to generate the sets of model elements comprises the processor executing the instructions to: evaluate content of the request to collect the data to produce links between context tokens and the content tokens, and map the links into a model element graph, analyze context metadata tokens and content metadata tokens in accordance with the dependency information, analyze the model element graph in accordance with the dependency information, modify the model element graph for each dependency and dependency type in the dependency information, generate a platform-neutral description of results of the request from the model element graph derived from the sets of model elements, the platform-neutral description being independent of a protocol used by the management entity to collect the data about the communications system, and execute the request to collect the data as requested in accordance with the platform-neutral description; and store the data as collected in the memory. 33. The management entity of claim 21 , wherein the processor executes the instructions to optimize the modified model element graph after each dependency and dependency type combination. | 0.795852 |
7,949,674 | 1 | 10 | 1. A computer-implemented method, comprising: making by a processor, on a display device, a presentation of a dataset with structured data, said structured data being selected from a first database based on a combination of metadata items that correspond to a respective dimension, measure or measure value; automatically selecting by the processor a plurality of subsets of metadata items from the metadata items used to make the presentation, wherein said plurality of subsets are comprised of different combinations of the metadata used to make the presentation; automatically converting by the processor each of the selected subsets of metadata items into a corresponding plurality of search requests; submitting by the processor the search requests to a search engine to search for documents located in a second database; and making by the processor, on the display device, a presentation of a plurality of search results corresponding to the search requests. | 1. A computer-implemented method, comprising: making by a processor, on a display device, a presentation of a dataset with structured data, said structured data being selected from a first database based on a combination of metadata items that correspond to a respective dimension, measure or measure value; automatically selecting by the processor a plurality of subsets of metadata items from the metadata items used to make the presentation, wherein said plurality of subsets are comprised of different combinations of the metadata used to make the presentation; automatically converting by the processor each of the selected subsets of metadata items into a corresponding plurality of search requests; submitting by the processor the search requests to a search engine to search for documents located in a second database; and making by the processor, on the display device, a presentation of a plurality of search results corresponding to the search requests. 10. A computer-readable storage device encoded with a program which, when executed by a processor, causes the computer to perform the method according to claim 1 . | 0.695896 |
8,002,803 | 13 | 16 | 13. The implant of claim 1 , wherein: said deflection rod system further comprises a third section and wherein the second section is located between the first section and the third section; and wherein the implant includes a third mount connected to the third section and adapted to secure the third section to a second vertical rod which supports a load from the adjacent vertebra; wherein the deflection rod system is adapted, by bending of the deflection rod system, to permit resilient deflection of the first mount and third mount relative to the horizontal rod and thereby support the load from the adjacent vertebra while accommodating relative movement between the vertebra and the adjacent vertebra; and wherein said outer shell also increases in diameter going from the third mount towards the second mount. | 13. The implant of claim 1 , wherein: said deflection rod system further comprises a third section and wherein the second section is located between the first section and the third section; and wherein the implant includes a third mount connected to the third section and adapted to secure the third section to a second vertical rod which supports a load from the adjacent vertebra; wherein the deflection rod system is adapted, by bending of the deflection rod system, to permit resilient deflection of the first mount and third mount relative to the horizontal rod and thereby support the load from the adjacent vertebra while accommodating relative movement between the vertebra and the adjacent vertebra; and wherein said outer shell also increases in diameter going from the third mount towards the second mount. 16. The implant of claim 13 , wherein: the first mount is ball-shaped and adapted to be received in a socket of the vertical rod; and the third mount is ball-shaped and adapted to be received in a second socket of the second vertical rod. | 0.5 |
10,037,390 | 13 | 20 | 13. A computer-implemented method, comprising: receiving a task hierarchy from a data store, the task hierarchy comprising a plurality of task entries, each task entry comprising a list of task entries or a task command, the list of task entries comprising probabilities associated with each task entry in the list of task entries, wherein task commands are executed in a simulated environment to cause actual performance of events to cause usage of physical computing resources, and wherein each probability provides a weight for selection of an associated task entry when traversing the task hierarchy; and executing one or more of the task commands in the simulated environment, wherein executing each of the one or more task commands comprises traversing through the task hierarchy based on the associated probabilities until a task command is reached and executing the task command to cause actual performance of an event to cause usage of at least one of the physical computing resources in the simulated environment. | 13. A computer-implemented method, comprising: receiving a task hierarchy from a data store, the task hierarchy comprising a plurality of task entries, each task entry comprising a list of task entries or a task command, the list of task entries comprising probabilities associated with each task entry in the list of task entries, wherein task commands are executed in a simulated environment to cause actual performance of events to cause usage of physical computing resources, and wherein each probability provides a weight for selection of an associated task entry when traversing the task hierarchy; and executing one or more of the task commands in the simulated environment, wherein executing each of the one or more task commands comprises traversing through the task hierarchy based on the associated probabilities until a task command is reached and executing the task command to cause actual performance of an event to cause usage of at least one of the physical computing resources in the simulated environment. 20. The method of claim 13 , wherein one or more of the commands simulates consuming or freeing processor resources in the simulated environment. | 0.771293 |
6,137,905 | 3 | 5 | 3. The apparatus according to claim 1, wherein if there is agreement among results of discriminating document orientation in a plurality of partial areas having attributes with the highest degrees of priority, said deciding means decides upon this document orientation as the orientation of the document image data. | 3. The apparatus according to claim 1, wherein if there is agreement among results of discriminating document orientation in a plurality of partial areas having attributes with the highest degrees of priority, said deciding means decides upon this document orientation as the orientation of the document image data. 5. The apparatus according to claim 3, wherein the attribute indicative of a character area in main body of text is adopted as an attribute having the highest degree of priority. | 0.5 |
9,977,798 | 1 | 6 | 1. A method for migrating data between tables, the method comprising: receiving, by one or more processors, a Structured Query Language (SQL) statement, wherein the SQL statement identifies a source table and a destination table, and wherein the source table comprises a plurality of source data divided into columns with a set of source column names; identifying at least one set of instructions associated with the SQL statement, wherein the at least one set of instructions further comprise: instructions for manipulating the plurality of source data; generating an interim SQL statement, based on the SQL statement; responsive to identifying the least one set of instructions associated with the SQL statement, manipulating the plurality of source data and the destination table by executing a plurality of clauses, wherein the plurality of clauses contain functional syntax comprising at least one of: migrating contents of the plurality of source data; executing the interim SQL statement, wherein executing the interim SQL statement comprises: copying the manipulated plurality of source data into a set of destination columns of the destination table; and migrating the contents of the plurality of source data to the set of destination columns of the destination table, wherein the migrated contents derive from the plurality of clauses. | 1. A method for migrating data between tables, the method comprising: receiving, by one or more processors, a Structured Query Language (SQL) statement, wherein the SQL statement identifies a source table and a destination table, and wherein the source table comprises a plurality of source data divided into columns with a set of source column names; identifying at least one set of instructions associated with the SQL statement, wherein the at least one set of instructions further comprise: instructions for manipulating the plurality of source data; generating an interim SQL statement, based on the SQL statement; responsive to identifying the least one set of instructions associated with the SQL statement, manipulating the plurality of source data and the destination table by executing a plurality of clauses, wherein the plurality of clauses contain functional syntax comprising at least one of: migrating contents of the plurality of source data; executing the interim SQL statement, wherein executing the interim SQL statement comprises: copying the manipulated plurality of source data into a set of destination columns of the destination table; and migrating the contents of the plurality of source data to the set of destination columns of the destination table, wherein the migrated contents derive from the plurality of clauses. 6. The method of claim 1 , wherein the at least one set of instructions associated with the SQL statement, further comprises: identifying a second clause among the plurality of clauses, wherein the second clause comprises a name element; determining whether the name element is substantially similar to at least one column name of the set of source column names; in response to determining that the name element is substantially similar to the at least one column name of the set of source column names, removing a column, associated with the substantially similar column name of the set of source column names, from the destination table. | 0.685531 |
9,792,270 | 1 | 6 | 1. A non-transitory computer readable medium storing computer-executable instructions for performing a method of processing an electronic document, the method comprising: providing a first post-design editing session of a first instance of a document, wherein the document is complete; during the post-design editing session, iteratively triggering transformation of the document, to produce a second instance of the document, via user interaction within a displayable interview pane that is superimposed over or juxtaposed with the document, the interview pane including multiple, separate user-interaction components arranged to provide variability in the strict content of the document, the user-interaction components including: a guided-fill component configured to guide users to enter text into data fields, with the entered text becoming part of the second instance of the document or triggering inclusion of additional content in the second instance of the document; a free-form text component configured to permit users to enter text in free-form in portions of the document, including permission to add free-form text as new portions of the document; and an interview component configured to prompt queries to the user, wherein answers to the queries determine at least one format parameter and at least one content parameter, and wherein the user is permitted to vary the strict content of the document via directly adding or removing text in free-form in the document while the interview pane is an active state and simultaneously displayed with the document; and controlling, as a function of user characteristics including at least one of an identity, a title, or a role, user access to predetermined content of the interactive document and user access regarding which, if any, of the respective displayable user-interaction components within the interview pane associated with the first instance of the document are accessible by a user. | 1. A non-transitory computer readable medium storing computer-executable instructions for performing a method of processing an electronic document, the method comprising: providing a first post-design editing session of a first instance of a document, wherein the document is complete; during the post-design editing session, iteratively triggering transformation of the document, to produce a second instance of the document, via user interaction within a displayable interview pane that is superimposed over or juxtaposed with the document, the interview pane including multiple, separate user-interaction components arranged to provide variability in the strict content of the document, the user-interaction components including: a guided-fill component configured to guide users to enter text into data fields, with the entered text becoming part of the second instance of the document or triggering inclusion of additional content in the second instance of the document; a free-form text component configured to permit users to enter text in free-form in portions of the document, including permission to add free-form text as new portions of the document; and an interview component configured to prompt queries to the user, wherein answers to the queries determine at least one format parameter and at least one content parameter, and wherein the user is permitted to vary the strict content of the document via directly adding or removing text in free-form in the document while the interview pane is an active state and simultaneously displayed with the document; and controlling, as a function of user characteristics including at least one of an identity, a title, or a role, user access to predetermined content of the interactive document and user access regarding which, if any, of the respective displayable user-interaction components within the interview pane associated with the first instance of the document are accessible by a user. 6. The computer readable medium of claim 1 , wherein prior to the first post-design editing session of the document, establishing during design of the document a scope of the document that is not changeable by the user during subsequent modification of the interactive document during the post-design editing session, the scope including at least one of: a type of content; which non-asynchronous external data sources are accessible during the post-design editing session; when the non-asynchronous external data sources are accessible relative to selecting the content of the document; and format of components forming a graphical user interface that includes the document and interview pane. | 0.577345 |
8,874,606 | 5 | 6 | 5. The method of claim 4 , further comprising: determining whether the third search result is responsive to an interest of the user; and modifying the third search query in accordance with the interest of the user, when the third search result fails to be responsive. | 5. The method of claim 4 , further comprising: determining whether the third search result is responsive to an interest of the user; and modifying the third search query in accordance with the interest of the user, when the third search result fails to be responsive. 6. The method of claim 5 , wherein the modifying comprises: identifying a search term of the third search query that is associated with the third search result; and deleting the identified search term from the third search query. | 0.668116 |
8,396,735 | 1 | 6 | 1. A non-transitory machine-readable medium having stored thereon a set of instructions, which when executed on a computing device causes the device to perform a method, the method comprising: providing to a user a visual display advertisement on a media channel on behalf of an advisor, wherein the visual display advertisement includes at least a user selectable reference to establish a real-time communication connection with the advisor, an indication of whether the advisor is currently available to communicate via real time communication at a time when the user is viewing the visual display advertisement, and a display of an item associated with the advisor selected from a group consisting of a compensation rate for the advisor and a quality score for the advisor, wherein the visual display advertisement advertises the advisor to the user at least partially based on an order relative to other advisors, the order at least partially based on one or both of the compensation rate and the quality score; while the advisor is currently available, receiving a user selection of the user selectable reference corresponding to the advisor, and a central controller using the user selection of the user selectable reference to establish a real-time communication connection between the advisor and the user prior to the user submitting a question for the advisor including the central controller using the user selection to establish a real-time communication connection with the advisor and with the user prior to the user submitting a question for the advisor, wherein the establishing the real-time communication connection comprises the central controller originating a first communication connection to the advisor and a second communication connection to the user; and charging an amount for the real-time communication connection established between the advisor and the user based at least in part on the compensation rate. | 1. A non-transitory machine-readable medium having stored thereon a set of instructions, which when executed on a computing device causes the device to perform a method, the method comprising: providing to a user a visual display advertisement on a media channel on behalf of an advisor, wherein the visual display advertisement includes at least a user selectable reference to establish a real-time communication connection with the advisor, an indication of whether the advisor is currently available to communicate via real time communication at a time when the user is viewing the visual display advertisement, and a display of an item associated with the advisor selected from a group consisting of a compensation rate for the advisor and a quality score for the advisor, wherein the visual display advertisement advertises the advisor to the user at least partially based on an order relative to other advisors, the order at least partially based on one or both of the compensation rate and the quality score; while the advisor is currently available, receiving a user selection of the user selectable reference corresponding to the advisor, and a central controller using the user selection of the user selectable reference to establish a real-time communication connection between the advisor and the user prior to the user submitting a question for the advisor including the central controller using the user selection to establish a real-time communication connection with the advisor and with the user prior to the user submitting a question for the advisor, wherein the establishing the real-time communication connection comprises the central controller originating a first communication connection to the advisor and a second communication connection to the user; and charging an amount for the real-time communication connection established between the advisor and the user based at least in part on the compensation rate. 6. The medium of claim 1 , wherein the visual display advertisement is provided in response to a search submitted by the user. | 0.844059 |
9,158,538 | 13 | 18 | 13. A computer program product comprising at least one non-transitory computer usable storage medium having stored therein computer usable program code for user-extensible rule-based source code modification, the computer usable program code, which when executed by a computer hardware device, causes the computer hardware device to perform loading, in a rule definition interface, multiple different end-user established source code modification rules for modifying source code to run on a source platform to be ported to a target platform so that the software application can run on the target platform, wherein the rule definition interface provides different templates to match different language constructs for different languages supported for the source code, the rules identifying a relative location of each of the different lines of source code to be modified based upon a position of each of the different lines relative to other lines of source code; loading a source code file of source code into a memory of a computer; parsing source code of the source code file into different sets of tokens; matching the rules to the different sets of tokens based upon the relative location of the rules; generating suggested modifications to the source code according to selected matched ones of the rules; and providing a user interface for end users to specify the relative location as one of a line before, a same line, and a same file and to select modifications from the suggested modifications and applying the selected modifications to the source code. | 13. A computer program product comprising at least one non-transitory computer usable storage medium having stored therein computer usable program code for user-extensible rule-based source code modification, the computer usable program code, which when executed by a computer hardware device, causes the computer hardware device to perform loading, in a rule definition interface, multiple different end-user established source code modification rules for modifying source code to run on a source platform to be ported to a target platform so that the software application can run on the target platform, wherein the rule definition interface provides different templates to match different language constructs for different languages supported for the source code, the rules identifying a relative location of each of the different lines of source code to be modified based upon a position of each of the different lines relative to other lines of source code; loading a source code file of source code into a memory of a computer; parsing source code of the source code file into different sets of tokens; matching the rules to the different sets of tokens based upon the relative location of the rules; generating suggested modifications to the source code according to selected matched ones of the rules; and providing a user interface for end users to specify the relative location as one of a line before, a same line, and a same file and to select modifications from the suggested modifications and applying the selected modifications to the source code. 18. The computer program product of claim 13 , wherein the computer usable program code further causes the computer hardware system to perform providing a compare view of the source code and a modified form of the source code. | 0.672464 |
9,239,823 | 1 | 4 | 1. A computer-implemented method comprising: obtaining, at one or more computers, a pair of terms in a first language, the pair of terms being commonly co-occurring non-synonyms in a corpus of documents, the corpus of documents being in the first language; determining a set of variations for each term in the pair of terms; generating a set of known related input pairs based on the sets of variations for each term in the pair of terms; for each input pair of terms in the set of known related input pairs, translating, by an automatic translation system, each term in the pair of terms into a second language plurality of languages to generate a set of translated terms; adding, at the one or more computers, the set of translated terms to a blacklist of known non-synonym pairs for at least one of the plurality of languages; and determining, based on the blacklist of known non-synonym pairs, whether a pair of candidate terms in at least one of the plurality of languages are synonyms. | 1. A computer-implemented method comprising: obtaining, at one or more computers, a pair of terms in a first language, the pair of terms being commonly co-occurring non-synonyms in a corpus of documents, the corpus of documents being in the first language; determining a set of variations for each term in the pair of terms; generating a set of known related input pairs based on the sets of variations for each term in the pair of terms; for each input pair of terms in the set of known related input pairs, translating, by an automatic translation system, each term in the pair of terms into a second language plurality of languages to generate a set of translated terms; adding, at the one or more computers, the set of translated terms to a blacklist of known non-synonym pairs for at least one of the plurality of languages; and determining, based on the blacklist of known non-synonym pairs, whether a pair of candidate terms in at least one of the plurality of languages are synonyms. 4. The method of claim 1 , further comprising generating one or more normalized versions of one or more terms in the set of translated terms. | 0.698718 |
9,535,883 | 6 | 9 | 6. A method comprising: receiving a request from a client device for a preview of a native document, the preview representing the native document in a file format other than the file format of the native document; inserting, by a document mapping module in the native document prior to rendering a preview, a set of unique links comprising a uniform resource locator (URL) and each unique link associated with a different word in the native document, the native object comprising a renderable portion of the native document; rendering, by a document rendering module, the native document into a preview of the native document and thereby generating a bounding area for each of the unique links in the set of unique links, the bounding area mapping a page and pixel location on the preview to the native object associated with the unique link; and providing the preview to the client device for display. | 6. A method comprising: receiving a request from a client device for a preview of a native document, the preview representing the native document in a file format other than the file format of the native document; inserting, by a document mapping module in the native document prior to rendering a preview, a set of unique links comprising a uniform resource locator (URL) and each unique link associated with a different word in the native document, the native object comprising a renderable portion of the native document; rendering, by a document rendering module, the native document into a preview of the native document and thereby generating a bounding area for each of the unique links in the set of unique links, the bounding area mapping a page and pixel location on the preview to the native object associated with the unique link; and providing the preview to the client device for display. 9. The method of claim 6 , wherein the unique links are hyperlinks. | 0.9375 |
8,521,721 | 6 | 9 | 6. A computer program product for implementing a method for generating a partitioned representation of a sequence of query operators in a parallel query engine, the computer program product comprising one or more hardware computer-readable storage devices having stored thereon computer-executable instructions that, when executed by one or more processors of a computing system, cause the computing system to perform the method, the method comprising: an act of accessing a sequence of operators configured to process a portion of partitioned input data in a parallel query system, the sequence of operators comprising one or more built-in operators that are part of a parallel query engine and at least one user-defined custom operator, the at least one user-defined custom operator being provided to the parallel query engine by a user and being configured for processing along with the one or more built-in operators by being configured to: poll a predecessor operator of the at least one user-defined custom operator in the sequence of operators to determine the predecessor operator's output information, the output information including (i) a number of partitions of the input data that are requestable by the at least one user-defined custom operator and (ii) one or more ordering guarantees that apply to output of the at least one user-defined custom operator; repartition the input data by adding or reducing the number of partitions of the input data during processing of the input data; and determine whether changes have occurred that affect the one or more ordering guarantees and, when changes have occurred that affect the one or more ordering guarantees, modify at least a portion of the one or more ordering guarantees that apply to the output of the at least one user-defined custom operator; an act of generating a list of partitions into which the input data has been partitioned; an act of determining a number of partitions that will be made during a re-partitioning operation at an indicated built-in operator in the sequence of operators; and an act of generating a partitioned representation of the sequence of operators, wherein the partitioned representation of the sequence of operators provides internal information regarding the processing of the input data by the indicated built-in operator, the internal information enabling processing of the input data by one of the at least one user-defined custom operator. | 6. A computer program product for implementing a method for generating a partitioned representation of a sequence of query operators in a parallel query engine, the computer program product comprising one or more hardware computer-readable storage devices having stored thereon computer-executable instructions that, when executed by one or more processors of a computing system, cause the computing system to perform the method, the method comprising: an act of accessing a sequence of operators configured to process a portion of partitioned input data in a parallel query system, the sequence of operators comprising one or more built-in operators that are part of a parallel query engine and at least one user-defined custom operator, the at least one user-defined custom operator being provided to the parallel query engine by a user and being configured for processing along with the one or more built-in operators by being configured to: poll a predecessor operator of the at least one user-defined custom operator in the sequence of operators to determine the predecessor operator's output information, the output information including (i) a number of partitions of the input data that are requestable by the at least one user-defined custom operator and (ii) one or more ordering guarantees that apply to output of the at least one user-defined custom operator; repartition the input data by adding or reducing the number of partitions of the input data during processing of the input data; and determine whether changes have occurred that affect the one or more ordering guarantees and, when changes have occurred that affect the one or more ordering guarantees, modify at least a portion of the one or more ordering guarantees that apply to the output of the at least one user-defined custom operator; an act of generating a list of partitions into which the input data has been partitioned; an act of determining a number of partitions that will be made during a re-partitioning operation at an indicated built-in operator in the sequence of operators; and an act of generating a partitioned representation of the sequence of operators, wherein the partitioned representation of the sequence of operators provides internal information regarding the processing of the input data by the indicated built-in operator, the internal information enabling processing of the input data by one of the at least one user-defined custom operator. 9. The computer program product of claim 6 , wherein the partitioned representation provides an indication of those characteristics that are necessary for operators to be included as part of the sequence of operators. | 0.632203 |
8,674,855 | 2 | 3 | 2. The method of claim 1 , further comprising using a single fixed string of one or more characters as the key symbol string. | 2. The method of claim 1 , further comprising using a single fixed string of one or more characters as the key symbol string. 3. The method of claim 2 , further comprising using a letter, a symbol, and/or a space as the one or more characters. | 0.5 |
7,487,095 | 1 | 21 | 1. A method comprising: receiving an arbitrary natural language communication from a user; applying a concept recognition process to automatically derive a representation of concepts embodied in the communication; using the concept representation to provide to a human agent information useful in responding to the natural language communication, wherein the information provided to the human agent includes a plurality of possible responses to the user's communication; enabling the human agent to select a response from the plurality of possible responses; and delivering the selected response to the user. | 1. A method comprising: receiving an arbitrary natural language communication from a user; applying a concept recognition process to automatically derive a representation of concepts embodied in the communication; using the concept representation to provide to a human agent information useful in responding to the natural language communication, wherein the information provided to the human agent includes a plurality of possible responses to the user's communication; enabling the human agent to select a response from the plurality of possible responses; and delivering the selected response to the user. 21. The method of claim 1 in which the first mode and second mode of expression comprise at least one of text or speech. | 0.799331 |
7,856,472 | 144 | 147 | 144. The computer program product of claim 70 , wherein first preloaded information derived from the first message is preloaded and initially hidden, and later displayed in response to a first user interaction; and second preloaded information derived from the second message is preloaded and initially hidden, and later displayed in response to a second user interaction. | 144. The computer program product of claim 70 , wherein first preloaded information derived from the first message is preloaded and initially hidden, and later displayed in response to a first user interaction; and second preloaded information derived from the second message is preloaded and initially hidden, and later displayed in response to a second user interaction. 147. The computer program product of claim 144 , wherein the computer program product is operable such that the first preloaded information and the second preloaded information each includes a beginning of message text. | 0.614437 |
9,167,091 | 20 | 22 | 20. A human-machine interface telephone gateway for providing a remote voice interface with a supervisory control and data acquisition system, comprising: a processor; an application programming interface with the supervisory control and data acquisition system; a data connection with a cloud telephone service; a non-transitory computer-usable medium having computer readable instructions stored thereon that, when executed by the processor, cause the processor to perform operations for voice communication with the supervisory control and data acquisition system, the operations comprising: receiving via the interface with the cloud telephone service a text command identifying an element of the supervisory control and data acquisition system and an action to be performed, the text command being derived from an audio voice command received by the cloud telephone service from a human operator; transmitting, via the application interface with the supervisory control and data acquisition system, a system command based on the text command, after receiving a text approval in response to an approval request via a conference call; receiving, via the application interface with the supervisory control and data acquisition system, a confirmation message in response to the system command; and transmitting, via the interface with the cloud telephone service, text for conversion to an audio message reporting the confirmation message to the human operator. | 20. A human-machine interface telephone gateway for providing a remote voice interface with a supervisory control and data acquisition system, comprising: a processor; an application programming interface with the supervisory control and data acquisition system; a data connection with a cloud telephone service; a non-transitory computer-usable medium having computer readable instructions stored thereon that, when executed by the processor, cause the processor to perform operations for voice communication with the supervisory control and data acquisition system, the operations comprising: receiving via the interface with the cloud telephone service a text command identifying an element of the supervisory control and data acquisition system and an action to be performed, the text command being derived from an audio voice command received by the cloud telephone service from a human operator; transmitting, via the application interface with the supervisory control and data acquisition system, a system command based on the text command, after receiving a text approval in response to an approval request via a conference call; receiving, via the application interface with the supervisory control and data acquisition system, a confirmation message in response to the system command; and transmitting, via the interface with the cloud telephone service, text for conversion to an audio message reporting the confirmation message to the human operator. 22. A human-machine interface telephone gateway as in claim 20 , wherein the audio voice command is an audio status request, the system command is a system status request, the confirmation message contains a system status and the audio message reports the system status. | 0.632153 |
8,626,511 | 14 | 16 | 14. A computer-implemented method comprising: obtaining information specifying two or more displayed candidate transcriptions of a single voice command and one or more displayed possible intended actions for each of the two or more displayed candidate transcriptions of the single voice command, including two or more displayed possible intended actions for a particular transcription of the two or more displayed candidate transcriptions of the single voice command; receiving data indicating a selection of a particular displayed possible intended action from among the one or more possible displayed intended actions for each of the two or more transcriptions of the single voice command and the two or more possible displayed intended actions for the particular transcription; providing data indicating the selection of the particular displayed possible intended action to a server; and invoking the selected particular displayed possible intended action. | 14. A computer-implemented method comprising: obtaining information specifying two or more displayed candidate transcriptions of a single voice command and one or more displayed possible intended actions for each of the two or more displayed candidate transcriptions of the single voice command, including two or more displayed possible intended actions for a particular transcription of the two or more displayed candidate transcriptions of the single voice command; receiving data indicating a selection of a particular displayed possible intended action from among the one or more possible displayed intended actions for each of the two or more transcriptions of the single voice command and the two or more possible displayed intended actions for the particular transcription; providing data indicating the selection of the particular displayed possible intended action to a server; and invoking the selected particular displayed possible intended action. 16. The method of claim 14 , comprising: determining an ambiguity value for each of the one or more displayed possible intended actions for each of the two or more displayed candidate transcriptions and for each of the two or more displayed possible intended actions for the particular transcription; determining that the ambiguity value for the particular displayed possible intended action satisfies a threshold; automatically invoking the particular displayed possible intended action based on determining that the ambiguity value satisfies the threshold; determining that the user has cancelled the particular displayed possible intended action; and decrementing the ambiguity value for the particular displayed possible intended action based on determining that the user has cancelled the particular displayed possible intended action. | 0.5 |
8,880,517 | 1 | 6 | 1. A method of adding terms from a related document to a document description for a target document, the method comprising: determining that a term found in the related document does not match a filter criteria, wherein terms that match the filter criteria are not added to a document description for the target document, wherein the document description for the target document comprises terms within a plurality of signal streams that are associated with the target document, wherein the related document is related because the target document comprises hyperlinks to the related document; calculating a similarity score for the term, wherein the similarity score is based on cosine similarity between terms of the target document and terms of the related document; calculating a corroboration score for the term based on similarity between terms used in hyperlinks to the related document and the term; calculating a uniqueness score for the term based on whether the term is currently associated with the document description through other signal streams of the plurality of signal streams; calculating a term score for the term based on the similarity score, the corroboration score, and the uniqueness score; and associating the term with the document description because the term score is above a threshold score. | 1. A method of adding terms from a related document to a document description for a target document, the method comprising: determining that a term found in the related document does not match a filter criteria, wherein terms that match the filter criteria are not added to a document description for the target document, wherein the document description for the target document comprises terms within a plurality of signal streams that are associated with the target document, wherein the related document is related because the target document comprises hyperlinks to the related document; calculating a similarity score for the term, wherein the similarity score is based on cosine similarity between terms of the target document and terms of the related document; calculating a corroboration score for the term based on similarity between terms used in hyperlinks to the related document and the term; calculating a uniqueness score for the term based on whether the term is currently associated with the document description through other signal streams of the plurality of signal streams; calculating a term score for the term based on the similarity score, the corroboration score, and the uniqueness score; and associating the term with the document description because the term score is above a threshold score. 6. The method of claim 1 , wherein the threshold score is determined by ranking term scores calculated for each of a plurality of terms in the linked to document. | 0.650862 |
9,268,560 | 1 | 7 | 1. A method for indicating a change to a dependent file, the method comprising: receiving a first change to a program file; performing, via a computing device, a second change to code or program data in a first dependent file on the program file; wherein the second change is related to the first change; and displaying, in a document editor via the computing device, a first identifier for the first dependent file in a first text style, if the first dependent file is changed based on the first change to the program file; displaying, in the document editor via the computing device, a second identifier, in a second text style, for a second dependent file, if the second dependent file is not changed based on the first change to the program file wherein: code of the program file calls code or program data of the first dependent file and the second dependent file, the first text style indicates the first dependent file has been changed based on the first change to the program file, and the first text style and the second text style are different styles. | 1. A method for indicating a change to a dependent file, the method comprising: receiving a first change to a program file; performing, via a computing device, a second change to code or program data in a first dependent file on the program file; wherein the second change is related to the first change; and displaying, in a document editor via the computing device, a first identifier for the first dependent file in a first text style, if the first dependent file is changed based on the first change to the program file; displaying, in the document editor via the computing device, a second identifier, in a second text style, for a second dependent file, if the second dependent file is not changed based on the first change to the program file wherein: code of the program file calls code or program data of the first dependent file and the second dependent file, the first text style indicates the first dependent file has been changed based on the first change to the program file, and the first text style and the second text style are different styles. 7. The method of claim 1 , wherein the first identifier includes a non-alphabetic identifier that indicates the second change to the first dependent file based on the first change to the program file. | 0.579832 |
8,547,589 | 18 | 24 | 18. A system for capturing data from a document image, the system comprising: an imaging component capable of capturing the document image of a document; a processor; and a memory coupled to the processor and in electronic communication with the imaging component, the memory configured with instructions for causing the processor to: process document images into one or more documents, wherein a document of the one or more documents includes multiple pages; maintain a page-based coordinate system to specify a location of structures within individual pages of the document; combine the multiple pages to form a multi-page sheet, wherein a sheet- based coordinate system specifies a location of structures within the multi-page sheet; and perform a data extraction operation to extract data from the document, said data extraction operation including: detecting the structures on the individual pages using the page- based coordinate system; defining a repeating group of fields, wherein the repeating group of fields is capable of flowing over from one page onto another page; detecting whether all fields of an instance of the repeating group of fields are found on consecutive pages of the document; and depending on whether all fields of the instance of the repeating group of fields are found on consecutive pages, detecting structures within the document using the sheet-based coordinate system. | 18. A system for capturing data from a document image, the system comprising: an imaging component capable of capturing the document image of a document; a processor; and a memory coupled to the processor and in electronic communication with the imaging component, the memory configured with instructions for causing the processor to: process document images into one or more documents, wherein a document of the one or more documents includes multiple pages; maintain a page-based coordinate system to specify a location of structures within individual pages of the document; combine the multiple pages to form a multi-page sheet, wherein a sheet- based coordinate system specifies a location of structures within the multi-page sheet; and perform a data extraction operation to extract data from the document, said data extraction operation including: detecting the structures on the individual pages using the page- based coordinate system; defining a repeating group of fields, wherein the repeating group of fields is capable of flowing over from one page onto another page; detecting whether all fields of an instance of the repeating group of fields are found on consecutive pages of the document; and depending on whether all fields of the instance of the repeating group of fields are found on consecutive pages, detecting structures within the document using the sheet-based coordinate system. 24. The system of claim 18 , wherein the sheet-based coordinate system includes a global system of coordinates for the document. | 0.724138 |
8,595,207 | 17 | 19 | 17. A machine resident host system for dynamically providing search suggestions to a user of an on-demand service in a multi-tenant database environment, the host system comprising: a processor system, a memory system including at least volatile memory, and non-volatile memory; the non-volatile memory including at least one machine readable medium carrying one or more sequences of instructions which when implemented causes the processor system to implement a method comprising: receiving, at a host system that is remote from a user system, user input for conducting a search; sending, from the host system to the user system, search suggestions based on the user input; receiving, at the host system, a search term for conducting a search; calculating, by the a processor system of the host system, a score for search suggestions related to the search term for conducting a search, based on a set of factors, for each given search suggestion, one of the factors of the score of the given search suggestion being a range of relevancy of the documents retrieved as a result of a search conducted with that given search suggestion as a search query; updating, by the host system, a ranking of search terms, based on the calculated score; conducting, a search based on the search term; and sending, search results based on the search conducted, to the user system. | 17. A machine resident host system for dynamically providing search suggestions to a user of an on-demand service in a multi-tenant database environment, the host system comprising: a processor system, a memory system including at least volatile memory, and non-volatile memory; the non-volatile memory including at least one machine readable medium carrying one or more sequences of instructions which when implemented causes the processor system to implement a method comprising: receiving, at a host system that is remote from a user system, user input for conducting a search; sending, from the host system to the user system, search suggestions based on the user input; receiving, at the host system, a search term for conducting a search; calculating, by the a processor system of the host system, a score for search suggestions related to the search term for conducting a search, based on a set of factors, for each given search suggestion, one of the factors of the score of the given search suggestion being a range of relevancy of the documents retrieved as a result of a search conducted with that given search suggestion as a search query; updating, by the host system, a ranking of search terms, based on the calculated score; conducting, a search based on the search term; and sending, search results based on the search conducted, to the user system. 19. The machine resident host system of claim 17 , the method further comprising: storing in a first object for storing data, ranked search suggestions, wherein the search suggestions are previously submitted search terms; storing in a second object for storing data, search terms for conducting a search; and calculating a score for a search suggestion includes normalizing the set of factors for calculating a score, the set of factors for calculating a score includes a number of previous search occurrences based on the search term, a top relevancy of search results based on the search term, an average relevancy of search results based on the search term, a range of relevancy of search results based on the search term, and a number of words of the search term found in the search term, weighting the set of normalized factors for calculating a score, and summing the weighted set of normalized factors to determine the score. | 0.5 |
9,280,538 | 7 | 8 | 7. The system according to claim 1 , wherein the sentence extractor and displayer comprises a hidden image receiver configured to receive the hidden image; a selected language receiver configured to receive the selected language from the sentence display interface; a parser configured to extract the at least one sentence expressed in the selected language based on the hidden image and the selected language; and a sentence displayer configured to display on the original image the at least one sentence expressed in the selected language. | 7. The system according to claim 1 , wherein the sentence extractor and displayer comprises a hidden image receiver configured to receive the hidden image; a selected language receiver configured to receive the selected language from the sentence display interface; a parser configured to extract the at least one sentence expressed in the selected language based on the hidden image and the selected language; and a sentence displayer configured to display on the original image the at least one sentence expressed in the selected language. 8. The system according to claim 7 , wherein in response to the at least one sentence expressed in the selected language that is created by automatic translating, the parser transmits the fact that the at least one sentence is translated by the automatic translating to the sentence display interface. | 0.5 |
7,546,590 | 1 | 3 | 1. A computer comprising a processor; a memory; and computer executable instructions executable on the processor from the memory wherein the computer executable instructions comprise: executing an object oriented program; creating an object representing an element in a tag-based display language, wherein the object is created by the object-oriented program and comprises a key value pair representing at least one attribute for the object and one style for the object; maintaining at least three states, each one of the states indicating if the object is available on a browser; monitoring the state of the object; and updating the state of the object upon an occurrence of an event that moves the object to a different one of the states; and generating a string of code in the tag-based display language from the object wherein the string is formatted for a particular browser. | 1. A computer comprising a processor; a memory; and computer executable instructions executable on the processor from the memory wherein the computer executable instructions comprise: executing an object oriented program; creating an object representing an element in a tag-based display language, wherein the object is created by the object-oriented program and comprises a key value pair representing at least one attribute for the object and one style for the object; maintaining at least three states, each one of the states indicating if the object is available on a browser; monitoring the state of the object; and updating the state of the object upon an occurrence of an event that moves the object to a different one of the states; and generating a string of code in the tag-based display language from the object wherein the string is formatted for a particular browser. 3. The computer of claim 1 , wherein the tag-based display language is Dynamic HTML. | 0.588235 |
8,612,367 | 1 | 2 | 1. A method comprising: determining training data including queries; determining n-grams from own content of the queries; determining an n-gram space that represents the queries as one or more vectors in the n-gram space, each dimension of the n-gram space being independent and represented by a unique one of the n-grams; identifying similar query pairs and dissimilar query pairs of the queries based at least in part on user behavior data, wherein the user behavior data includes at least one of click-through data or session data; and learning a similarity function using the identified similar query pairs and the dissimilar query pairs in the training data, the similarity function based at least in part on a transform of the n-gram space, the transform resulting in a transformed n-gram space having one or more of the unique n-grams being dependent on one or more other of the unique n-grams. | 1. A method comprising: determining training data including queries; determining n-grams from own content of the queries; determining an n-gram space that represents the queries as one or more vectors in the n-gram space, each dimension of the n-gram space being independent and represented by a unique one of the n-grams; identifying similar query pairs and dissimilar query pairs of the queries based at least in part on user behavior data, wherein the user behavior data includes at least one of click-through data or session data; and learning a similarity function using the identified similar query pairs and the dissimilar query pairs in the training data, the similarity function based at least in part on a transform of the n-gram space, the transform resulting in a transformed n-gram space having one or more of the unique n-grams being dependent on one or more other of the unique n-grams. 2. A method as recited in claim 1 , wherein each query in the training data comprises a common query. | 0.847892 |
8,209,175 | 1 | 7 | 1. A method for content sensing a user's communication, the method comprising: generating based on speech recognition a first lattice for a first component of the user's communication, the first lattice representing at least one hypothesis for the first component of the user's communication, the first lattice comprising a first certainty value for a first content word of at least one hypothesis for the first component of the user's communication; generating a first uncertainty value for the first content word; generating based on speech recognition a second lattice for a second component of the user's communication, wherein the second component is a different segment of the user's communication than the first component, the second lattice representing at least one hypothesis for the second component of the user's communication, the second lattice comprising a second certainty value for the first content word, wherein the first content word is also a content word of at least one hypothesis for the second component of the user's communication; generating a second uncertainty value for the first content word based on the first uncertainty value for the first content word and the second certainty value for the first content word; comparing the second uncertainty value for the first content word to an established confidence threshold; and using the first content word to provide user information if the second uncertainty value of the first content word has a defined relationship to the established confidence threshold. | 1. A method for content sensing a user's communication, the method comprising: generating based on speech recognition a first lattice for a first component of the user's communication, the first lattice representing at least one hypothesis for the first component of the user's communication, the first lattice comprising a first certainty value for a first content word of at least one hypothesis for the first component of the user's communication; generating a first uncertainty value for the first content word; generating based on speech recognition a second lattice for a second component of the user's communication, wherein the second component is a different segment of the user's communication than the first component, the second lattice representing at least one hypothesis for the second component of the user's communication, the second lattice comprising a second certainty value for the first content word, wherein the first content word is also a content word of at least one hypothesis for the second component of the user's communication; generating a second uncertainty value for the first content word based on the first uncertainty value for the first content word and the second certainty value for the first content word; comparing the second uncertainty value for the first content word to an established confidence threshold; and using the first content word to provide user information if the second uncertainty value of the first content word has a defined relationship to the established confidence threshold. 7. The method for content sensing a user's communication of claim 1 , wherein the information provided the user is at least one document that is generated using at least the first content word. | 0.876123 |
8,473,491 | 9 | 11 | 9. A computer-implemented method of providing search results, the method comprising: accessing, by a processor, a list of legitimate business titles, each legitimate business title including one or more words; generating a matrix of surprisingness values by: examining each legitimate business title to identify pairs of words occurring in that title, adding a count value to the matrix for each pair of words identified, such that the matrix includes a plurality of count values for different pairs of words occurring in the legitimate business titles, and normalizing the plurality of count values for the matrix to generate the matrix of surprisingness values, where each surprisingness value of the matrix of surprisingness values indicates how unlikely a pair of words are to appear in a legitimate business title, receiving a request for information from a client device; identifying a plurality of search results including a business listing having a title of two or more words; determining a surprisingness value for the business listing based on the two or more words of the title of the business listing and the matrix of surprisingness values; and transmitting to the client device the business listing based on a comparison of the surprisingness value for the business listing and a threshold value. | 9. A computer-implemented method of providing search results, the method comprising: accessing, by a processor, a list of legitimate business titles, each legitimate business title including one or more words; generating a matrix of surprisingness values by: examining each legitimate business title to identify pairs of words occurring in that title, adding a count value to the matrix for each pair of words identified, such that the matrix includes a plurality of count values for different pairs of words occurring in the legitimate business titles, and normalizing the plurality of count values for the matrix to generate the matrix of surprisingness values, where each surprisingness value of the matrix of surprisingness values indicates how unlikely a pair of words are to appear in a legitimate business title, receiving a request for information from a client device; identifying a plurality of search results including a business listing having a title of two or more words; determining a surprisingness value for the business listing based on the two or more words of the title of the business listing and the matrix of surprisingness values; and transmitting to the client device the business listing based on a comparison of the surprisingness value for the business listing and a threshold value. 11. The method of claim 9 , further comprising transmitting the search results without including the business listing to the client device if the surprising-ness value for the business listing is greater than or equal to the threshold value. | 0.799501 |
7,818,166 | 15 | 18 | 15. A mobile communication device comprising: an intention n-gram database that stores intention n-grams generated from stored intention attributes and stored audio; and an intention determination engine that receives an input from a user of the mobile communication device and converts speech portions in the user's input into linguistic representations, generates a phoneme lattice based on the linguistic representations, scores stored intention n-grams against the generated phoneme lattice, scores intentions from the intention n grams, determines the highest scoring intention, and determines whether the highest scoring intention is above a predetermined threshold, wherein if the highest scoring intention is above the predetermined threshold, then the intention determination engine executes the determined intention. | 15. A mobile communication device comprising: an intention n-gram database that stores intention n-grams generated from stored intention attributes and stored audio; and an intention determination engine that receives an input from a user of the mobile communication device and converts speech portions in the user's input into linguistic representations, generates a phoneme lattice based on the linguistic representations, scores stored intention n-grams against the generated phoneme lattice, scores intentions from the intention n grams, determines the highest scoring intention, and determines whether the highest scoring intention is above a predetermined threshold, wherein if the highest scoring intention is above the predetermined threshold, then the intention determination engine executes the determined intention. 18. The mobile communication device of claim 15 wherein the intention n-grams are at least one of: bigrams, trigrams, four-grams, and five-grams and include attributes used by the intention determination engine to determine the user's intentions. | 0.575862 |
8,515,943 | 1 | 7 | 1. A computer-implemented method comprising: retrieving, by a computing device, a first query from a first user; retrieving, by the computing device, a second query from a second user, wherein the second query is linked to the first query; generating, by the computing device, a derivative query based, at least in part, upon merging at least a portion of the second query with at least a portion of the first query, wherein generating the derivative query includes retrieving the first query and the second query prior to generation of the derivative query; determining, by the computing device, whether the derivative query contains one or more conflicts; and if it is determined that the derivative query contains one or more conflicts, resolving, by the computing device, the one or more conflicts in the derivative query based upon a priority measure, wherein the priority measure assigns priority to the one or more conflicts based upon a hierarchy of the first user and the second user. | 1. A computer-implemented method comprising: retrieving, by a computing device, a first query from a first user; retrieving, by the computing device, a second query from a second user, wherein the second query is linked to the first query; generating, by the computing device, a derivative query based, at least in part, upon merging at least a portion of the second query with at least a portion of the first query, wherein generating the derivative query includes retrieving the first query and the second query prior to generation of the derivative query; determining, by the computing device, whether the derivative query contains one or more conflicts; and if it is determined that the derivative query contains one or more conflicts, resolving, by the computing device, the one or more conflicts in the derivative query based upon a priority measure, wherein the priority measure assigns priority to the one or more conflicts based upon a hierarchy of the first user and the second user. 7. The computer-implemented method of claim 1 wherein the computing device stores one or more of the first query, the second query, and the derivative query. | 0.577957 |
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