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1. A computer-implemented method comprising: detecting, by a mobile computing device, a current context associated with the mobile computing device, the current context being external to the mobile computing device and indicating a current state of the mobile computing device in its surrounding environment, wherein the current context includes information that identifies a received signal strength, at the mobile computing device, of a short or medium-range wireless network that the mobile computing device is currently able to access, and wherein the current context further includes information from a scheduling application that identifies scheduled activities for a user who is associated with the mobile computing device; comparing the received signal strength to a plurality of values of received signal strengths for the short or medium-range wireless network, the plurality of values of received signal strengths being associated with a plurality of different physical locations; identifying, based on at least the comparison of the received signal strength to the plurality of values of received signal strengths, a particular physical location where the mobile computing device is currently located from among the plurality of different physical locations from which the mobile computing device is able to access the short or medium-range wireless network; determining, based on the current context, a current activity of the user; determining, based on the identified particular physical location and the determined current activity of the user, whether to switch the mobile computing device from operating using a current profile to operating using a second profile, wherein the current profile and the second profile each define one or more settings of the mobile computing device, and wherein determining whether to switch the mobile computing device to operating using the second profile is based on applying one or more learned rules to the identified particular physical location and the determined current activity of the user; and in response to determining whether to switch to the second profile, adjusting one or more setting of the mobile computing device based on the second profile, further comprising, over a period of time before determining whether to switch the mobile computing device to operating using the second profile, defining the rules based on user adjustment of the settings of the mobile computing device and a detected context or change in context of the mobile computing device at or around a time the settings were adjusted. | 1. A computer-implemented method comprising: detecting, by a mobile computing device, a current context associated with the mobile computing device, the current context being external to the mobile computing device and indicating a current state of the mobile computing device in its surrounding environment, wherein the current context includes information that identifies a received signal strength, at the mobile computing device, of a short or medium-range wireless network that the mobile computing device is currently able to access, and wherein the current context further includes information from a scheduling application that identifies scheduled activities for a user who is associated with the mobile computing device; comparing the received signal strength to a plurality of values of received signal strengths for the short or medium-range wireless network, the plurality of values of received signal strengths being associated with a plurality of different physical locations; identifying, based on at least the comparison of the received signal strength to the plurality of values of received signal strengths, a particular physical location where the mobile computing device is currently located from among the plurality of different physical locations from which the mobile computing device is able to access the short or medium-range wireless network; determining, based on the current context, a current activity of the user; determining, based on the identified particular physical location and the determined current activity of the user, whether to switch the mobile computing device from operating using a current profile to operating using a second profile, wherein the current profile and the second profile each define one or more settings of the mobile computing device, and wherein determining whether to switch the mobile computing device to operating using the second profile is based on applying one or more learned rules to the identified particular physical location and the determined current activity of the user; and in response to determining whether to switch to the second profile, adjusting one or more setting of the mobile computing device based on the second profile, further comprising, over a period of time before determining whether to switch the mobile computing device to operating using the second profile, defining the rules based on user adjustment of the settings of the mobile computing device and a detected context or change in context of the mobile computing device at or around a time the settings were adjusted. 2. The computer-implemented method of claim 1 , wherein the particular physical location is associated with a name and not with a unique geographic location identifier. | 0.545904 |
20. The system recited in claim 19, wherein one of said languages is selected at a point in the call flow, said means for designating and said means for searching and playing thereafter operating upon the base subsets and other subsets of prompt definitions of the selected language. | 20. The system recited in claim 19, wherein one of said languages is selected at a point in the call flow, said means for designating and said means for searching and playing thereafter operating upon the base subsets and other subsets of prompt definitions of the selected language. 21. The system recited in claim 20 wherein each of said languages is assigned a unique language identifier, and wherein said stored table specifies, for each subset of prompt definitions, both the language identifier of the language with which it is associated and the unique ID assigned to the subset. | 0.852337 |
13. A computer readable storage medium for storing a computer program providing a hot video prediction method, wherein the method comprises: providing a plurality of video comments submitted by a plurality of users during a period of time in the past; establishing a user social network of the users according to the video comments; upon receiving a new comment of a new video, determining a similar theme between the new video comment content and comment content of hot videos have been popular for the period of time according to the user social network, and predicting whether the new video is going to be popular accordingly; and verifying the prediction, and modifying the user social network accordingly. | 13. A computer readable storage medium for storing a computer program providing a hot video prediction method, wherein the method comprises: providing a plurality of video comments submitted by a plurality of users during a period of time in the past; establishing a user social network of the users according to the video comments; upon receiving a new comment of a new video, determining a similar theme between the new video comment content and comment content of hot videos have been popular for the period of time according to the user social network, and predicting whether the new video is going to be popular accordingly; and verifying the prediction, and modifying the user social network accordingly. 15. The computer readable storage medium of claim 13 , wherein the step of predicting hot video comprises: searching, based on the user social network, for other users having common interests related to the users providing the new comment to establish the user social network corresponding to the new comment; determining the similarity among users corresponding to the new comment according to inseparability of the user social network corresponding to the new comment, and predicting whether the new video is going to be popular accordingly. | 0.546341 |
26. A non-semantical method for numerically representing objects in a computer database and for computerized searching of the numerically represented objects in the database, wherein direct and indirect relationships exist between objects in the database, comprising: marking objects in the database so that each marked object may be individually identified by a computerized search; creating a first numerical representation for each identified object in the database based upon the object's direct relationship with other objects in the database; storing the first numerical representations for use in computerized searching; analyzing the first numerical representations for indirect relationships existing between or among objects in the database; generating a second numerical representation of each object based on the analysis of the first numerical representation; storing the second numerical representation for use in computerized searching; and searching the objects in the database using a computer and the stored second numerical representations, wherein the search identifies one or more of the objects in the database. | 26. A non-semantical method for numerically representing objects in a computer database and for computerized searching of the numerically represented objects in the database, wherein direct and indirect relationships exist between objects in the database, comprising: marking objects in the database so that each marked object may be individually identified by a computerized search; creating a first numerical representation for each identified object in the database based upon the object's direct relationship with other objects in the database; storing the first numerical representations for use in computerized searching; analyzing the first numerical representations for indirect relationships existing between or among objects in the database; generating a second numerical representation of each object based on the analysis of the first numerical representation; storing the second numerical representation for use in computerized searching; and searching the objects in the database using a computer and the stored second numerical representations, wherein the search identifies one or more of the objects in the database. 36. The non-semantical method of claim 26, wherein the step of searching the objects comprises the steps of: selecting an object; using the second numerical representation to search for objects similar to the selected object. | 0.637695 |
1. A method of processing a call received by a call center, comprising the steps of: obtaining a call at the call center; automatically identifying at least one of an accent and a language spoken by a caller making the call; directing the call to an appropriate operator at a first level of the call center based on at least one of the automatically identified accent and the automatically identified language, wherein the appropriate operator of the first level of the call center is at least familiar with at least one of the automatically identified accent and the automatically identified language spoken by the caller; estimating a plurality of costs comprising a cost associated with automatically translating original speech associated with the call and a cost associated with restating the original speech associated with the call into a form that is simpler than the original speech, wherein the speech associated with the call comprises speech attributable to the caller and speech attributable to the operator at the first level; responsive to the estimated plurality of costs, automatically translating at least one of the original speech associated with the call and the restated speech associated with the call from at least one of the automatically identified accent and the automatically identified language spoken by the caller to at least one of an accent and a language understood by an operator at a second level of the call center; and editing one or more portions of the translated speech based at least in part on a confusability of the one or more portions; wherein the step of automatically identifying at least one of an accent and a language spoken by a caller making the call comprises identifying the caller using one or more of biometrics, a profile history, and a code. | 1. A method of processing a call received by a call center, comprising the steps of: obtaining a call at the call center; automatically identifying at least one of an accent and a language spoken by a caller making the call; directing the call to an appropriate operator at a first level of the call center based on at least one of the automatically identified accent and the automatically identified language, wherein the appropriate operator of the first level of the call center is at least familiar with at least one of the automatically identified accent and the automatically identified language spoken by the caller; estimating a plurality of costs comprising a cost associated with automatically translating original speech associated with the call and a cost associated with restating the original speech associated with the call into a form that is simpler than the original speech, wherein the speech associated with the call comprises speech attributable to the caller and speech attributable to the operator at the first level; responsive to the estimated plurality of costs, automatically translating at least one of the original speech associated with the call and the restated speech associated with the call from at least one of the automatically identified accent and the automatically identified language spoken by the caller to at least one of an accent and a language understood by an operator at a second level of the call center; and editing one or more portions of the translated speech based at least in part on a confusability of the one or more portions; wherein the step of automatically identifying at least one of an accent and a language spoken by a caller making the call comprises identifying the caller using one or more of biometrics, a profile history, and a code. 9. The method of claim 1 , wherein the step of automatically identifying at least one of an accent and a language spoken by a caller making the call further comprises, during caller speech, automatically switching to an operator who speaks with one of an accent and a language that matches the caller. | 0.5 |
3. The computer-implemented method of claim 1 , wherein displayed computer-readable files of a displayed axis are disposed in a substantially equidistant and rectilinear fashion along the scrollable axis of computer-readable files. | 3. The computer-implemented method of claim 1 , wherein displayed computer-readable files of a displayed axis are disposed in a substantially equidistant and rectilinear fashion along the scrollable axis of computer-readable files. 7. The computer-implemented method of claim 3 , wherein the computer-readable files of the axis are associated with a common attribute. | 0.966772 |
1. A system of the type responsive to signals from a telephone set, said signals including a plurality of distinct interconnection supervisory signals, said system including translation means for converting said supervisory signals into a computer compatible code, and means for transmitting respective computer compatible codes to a computer device, said translation means comprising: decoder means, responsive to said signals from said telephone set, for generating a first digital code word indicative of the particular supervisory signal present in said signal from said telephone set; a plurality of translator means, each translator means responsive to said telephone signals and for generating a respective second code word indicative of different predetermined sequences of said supervisory signals; memory means, responsive to address signals applied thereto and including at least one individually addressable location corresponding to each symbol in said computer compatible code containing indicia of the corresponding code symbol, for generating an output signal indicative of the contents of an addressed location therein in accordance with said address signals; means for applying said first code word to said memory means as a portion of said address signal; selector means, responsive to control signals applied thereto, for selecting one of said plurality of translator means, and applying the second code word generated by said selected translator means to said memory means as another portion of said address signal; and means, responsive to said memory means output signals and a strobe control signal applied thereto, for selectively transmitting indicia of said symbol code to said computer. | 1. A system of the type responsive to signals from a telephone set, said signals including a plurality of distinct interconnection supervisory signals, said system including translation means for converting said supervisory signals into a computer compatible code, and means for transmitting respective computer compatible codes to a computer device, said translation means comprising: decoder means, responsive to said signals from said telephone set, for generating a first digital code word indicative of the particular supervisory signal present in said signal from said telephone set; a plurality of translator means, each translator means responsive to said telephone signals and for generating a respective second code word indicative of different predetermined sequences of said supervisory signals; memory means, responsive to address signals applied thereto and including at least one individually addressable location corresponding to each symbol in said computer compatible code containing indicia of the corresponding code symbol, for generating an output signal indicative of the contents of an addressed location therein in accordance with said address signals; means for applying said first code word to said memory means as a portion of said address signal; selector means, responsive to control signals applied thereto, for selecting one of said plurality of translator means, and applying the second code word generated by said selected translator means to said memory means as another portion of said address signal; and means, responsive to said memory means output signals and a strobe control signal applied thereto, for selectively transmitting indicia of said symbol code to said computer. 6. The system of claim 1 further comprising: means, responsive to operation control signals from said computer, for generating said control signals to said selector means. | 0.649535 |
1. A method for ranking content, comprising: receiving, using a hardware processor, a search query; generating a plurality of search results in response to the search query; determining one or more entity types associated with a content class within the plurality of search results, wherein the content class indicates a type of media content of a content item associated with at least one search result of the plurality of search results, and wherein the one or more entity types indicate information associated with the type of media content; determining whether the search query is a query for content belonging to the content class based on a plurality of criteria that includes: (i) determining whether at least one of the plurality of search results is associated with the one or more determined entity types; (ii) determining whether entities shared between the plurality of search results are associated with content corresponding to the one or more determined entity types, wherein each entity includes metadata indicating at least a topic of a corresponding search result; and (iii) determining whether the plurality of search results includes one or more authoritative result candidates having an entity with an entity type corresponding to the one or more entity types; and in response to determining that the plurality of criteria have been met, promoting at least one search result of the plurality of search results belonging to the content class. | 1. A method for ranking content, comprising: receiving, using a hardware processor, a search query; generating a plurality of search results in response to the search query; determining one or more entity types associated with a content class within the plurality of search results, wherein the content class indicates a type of media content of a content item associated with at least one search result of the plurality of search results, and wherein the one or more entity types indicate information associated with the type of media content; determining whether the search query is a query for content belonging to the content class based on a plurality of criteria that includes: (i) determining whether at least one of the plurality of search results is associated with the one or more determined entity types; (ii) determining whether entities shared between the plurality of search results are associated with content corresponding to the one or more determined entity types, wherein each entity includes metadata indicating at least a topic of a corresponding search result; and (iii) determining whether the plurality of search results includes one or more authoritative result candidates having an entity with an entity type corresponding to the one or more entity types; and in response to determining that the plurality of criteria have been met, promoting at least one search result of the plurality of search results belonging to the content class. 8. The method of claim 1 , further comprising, in response to determining that the plurality of criteria have not been met, performing at least one of: inhibiting the at least one search result of the plurality of search results belonging to the content class from being included in the list of the plurality of search results; inhibiting the at least one search result of the plurality of search results belonging to the content class from being promoted within the list of the plurality of search results; and demoting the at least one search result of the plurality of search results within a list of at least a portion of the plurality of search results. | 0.610526 |
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. | 0.92311 |
1. One or more computer-readable storage devices that store executable instructions that, when executed by a computer, cause the computer to perform acts comprising: receiving a specification of a text pattern to be matched in an input stream, said specification comprising one or more data expressions and one or more code expressions, the data expressions defining one or more phrase sets, a first one of the code expressions comprising a first container that comprises a first phrase set, a second phrase set, and a second container that comprises a third phrase set and a fourth phrase set, said specification further comprising a map set and an equivalence set that maps a plurality of phrases to a first phrase based on misspellings and equivalent phrases, said first container comprising a switch that branches between a plurality of match labels; comparing words in said input stream with said first container, said comparing act converting, based on said map set, any of said plurality of phrases to said first phrase when any of said plurality of phrases is encountered in said input stream; determining that a context in said input stream matches said first container based on a finding that said context matches either said first phrase set or said second phrase set, said first phrase set, said context being labeled as a first label if said context matches said first phrase set, or being labeled as a second label if said context matches said second phrase set; and generating a report indicating that said container matches a sequence of words in said input stream. wherein a container represents the various ways in which possible phrase sets may be combined as part of a text pattern to be matched such that the context moves forward after a match has been found between the container and a sequence of words in the input stream and a next container is processed after a location of the matching sequence of words. | 1. One or more computer-readable storage devices that store executable instructions that, when executed by a computer, cause the computer to perform acts comprising: receiving a specification of a text pattern to be matched in an input stream, said specification comprising one or more data expressions and one or more code expressions, the data expressions defining one or more phrase sets, a first one of the code expressions comprising a first container that comprises a first phrase set, a second phrase set, and a second container that comprises a third phrase set and a fourth phrase set, said specification further comprising a map set and an equivalence set that maps a plurality of phrases to a first phrase based on misspellings and equivalent phrases, said first container comprising a switch that branches between a plurality of match labels; comparing words in said input stream with said first container, said comparing act converting, based on said map set, any of said plurality of phrases to said first phrase when any of said plurality of phrases is encountered in said input stream; determining that a context in said input stream matches said first container based on a finding that said context matches either said first phrase set or said second phrase set, said first phrase set, said context being labeled as a first label if said context matches said first phrase set, or being labeled as a second label if said context matches said second phrase set; and generating a report indicating that said container matches a sequence of words in said input stream. wherein a container represents the various ways in which possible phrase sets may be combined as part of a text pattern to be matched such that the context moves forward after a match has been found between the container and a sequence of words in the input stream and a next container is processed after a location of the matching sequence of words. 20. The one or more computer-readable storage devices of claim 1 , said specification further comprising an equivalence set that comprises a plurality of phrases, said determining act finding that all of said plurality of phrases in said equivalence set are found in said context when any one of said phrases is found in said context. | 0.564276 |
2. The method of claim 1 , further comprising the steps of: selecting a target node among the nodes within the virtual n-dimensional array; comparing, using a chemical feature (“CF”) module which comprises code executing in the processor, at least one CF corresponding to the coded form contained within a first node adjacent to the target node to at least one CF corresponding to the coded form contained in at least a second node adjacent to the target node, the first and second nodes sharing a border with the target node in the virtual n-dimensional array; identifying common CFs between the target and second nodes using a commonality module which comprises code executing in the processor; generating at least one new coded form based on combinations of the identified, common CFs which, when inserted into the virtual n-dimensional array, results in a placement within the target node, using a coded form generator module which comprises code executing in the processor; and outputting a chemical identifier corresponding to the new coded form. | 2. The method of claim 1 , further comprising the steps of: selecting a target node among the nodes within the virtual n-dimensional array; comparing, using a chemical feature (“CF”) module which comprises code executing in the processor, at least one CF corresponding to the coded form contained within a first node adjacent to the target node to at least one CF corresponding to the coded form contained in at least a second node adjacent to the target node, the first and second nodes sharing a border with the target node in the virtual n-dimensional array; identifying common CFs between the target and second nodes using a commonality module which comprises code executing in the processor; generating at least one new coded form based on combinations of the identified, common CFs which, when inserted into the virtual n-dimensional array, results in a placement within the target node, using a coded form generator module which comprises code executing in the processor; and outputting a chemical identifier corresponding to the new coded form. 15. The method of claim 2 , further comprising: generating, with a synthesis design module configured as code executing on the processor to generate, based on the chemical identifier corresponding to the new coded form, a synthesis strategy for synthesizing a compound described by the chemical identifier corresponding to the new coded form. | 0.842084 |
12. The computing device of claim 11 , wherein the homography comprises the transformation matrix, and the transformation matrix is a 3×3 projective transformation matrix. | 12. The computing device of claim 11 , wherein the homography comprises the transformation matrix, and the transformation matrix is a 3×3 projective transformation matrix. 13. The computing device of claim 12 , wherein values u i refer to the coordinates of the vertices of the substantially rectangular region, values d i refer to the coordinates of the vertices of the region of interest, a value H refers to the homography, and the homography is determined by solving an equation u i =H*d i . | 0.856533 |
1. A method comprising: identifying, using natural language processing (NLP) techniques, a location discussed by users in a media collaboration, the media collaboration comprising a composite media stream generated from combining media streams transmitted from devices of the users; determining a location context of at least one user of the users, the location context comprising a geographic location of a device of the at least one user; identifying, based on the identified location and the location context, location information comprising a map corresponding to the identified location; generating, without user intervention, a preview of the location information identified using the NLP techniques, the preview comprising the map and a user interface (UI) element, the UI element to confirm sharing of the location information within the media collaboration; providing, without user intervention, the preview to the at least one user via a graphical user interface (GUI) of the media collaboration, the preview provided in a conversation portion of the GUI of the media collaboration without being visible to other users in the conversation portion of the GUI of the media collaboration; responsive to receiving an indication to share the location information via the UI element of the preview, providing, by a processing device to the other users, the location information comprising the map within the media collaboration; and responsive to receiving an indication that sharing of the location information is declined, removing the preview without sharing the location information in the media collaboration. | 1. A method comprising: identifying, using natural language processing (NLP) techniques, a location discussed by users in a media collaboration, the media collaboration comprising a composite media stream generated from combining media streams transmitted from devices of the users; determining a location context of at least one user of the users, the location context comprising a geographic location of a device of the at least one user; identifying, based on the identified location and the location context, location information comprising a map corresponding to the identified location; generating, without user intervention, a preview of the location information identified using the NLP techniques, the preview comprising the map and a user interface (UI) element, the UI element to confirm sharing of the location information within the media collaboration; providing, without user intervention, the preview to the at least one user via a graphical user interface (GUI) of the media collaboration, the preview provided in a conversation portion of the GUI of the media collaboration without being visible to other users in the conversation portion of the GUI of the media collaboration; responsive to receiving an indication to share the location information via the UI element of the preview, providing, by a processing device to the other users, the location information comprising the map within the media collaboration; and responsive to receiving an indication that sharing of the location information is declined, removing the preview without sharing the location information in the media collaboration. 9. The method of claim 1 , wherein the media collaboration comprises at least one of a live video recording, a pre-recorded video, a video chat, or a text-based chat. | 0.643657 |
1. A method for performing client-side page layout of web content using templates, the method comprising: receiving a source comprising a plurality of assets of different asset types; identifying a plurality of candidate templates from a template database, where each identified candidate template comprises a plurality of slots, and where each slot is configured to receive an asset of a particular asset type; for each particular slot of each of the identified candidate templates, calculating a slot subscore by: identifying an aspect ratio of an image asset included in the source, scaling the image asset to fill the particular slot when the particular slot is an image asset type slot, determining an area of the image asset to be cropped based on an area of the scaled image that extends beyond one or more boundaries of the particular slot, determining whether the determined area of the image asset to be cropped included a specified area of the image asset that should not be cropped, and calculating the slot subscore for the particular slot based at least in part on an amount of cropping to be performed inside the specified area and the determination of whether the determined area of the image asset to be cropped included the specified area; determining a score for each of the plurality of identified candidate templates, wherein the score for an identified candidate template is determined by: aggregating the calculated slot subscores for the plurality of slots included in the identified candidate template, and comparing a number and types of slots included in the identified candidate template with a number and types of assets included in the source; selecting a particular template from the scored plurality of identified candidate templates based on the scoring; and generating a readable article on a client device, the readable article comprising the assets of the source placed in the readable article according to the selected particular template. | 1. A method for performing client-side page layout of web content using templates, the method comprising: receiving a source comprising a plurality of assets of different asset types; identifying a plurality of candidate templates from a template database, where each identified candidate template comprises a plurality of slots, and where each slot is configured to receive an asset of a particular asset type; for each particular slot of each of the identified candidate templates, calculating a slot subscore by: identifying an aspect ratio of an image asset included in the source, scaling the image asset to fill the particular slot when the particular slot is an image asset type slot, determining an area of the image asset to be cropped based on an area of the scaled image that extends beyond one or more boundaries of the particular slot, determining whether the determined area of the image asset to be cropped included a specified area of the image asset that should not be cropped, and calculating the slot subscore for the particular slot based at least in part on an amount of cropping to be performed inside the specified area and the determination of whether the determined area of the image asset to be cropped included the specified area; determining a score for each of the plurality of identified candidate templates, wherein the score for an identified candidate template is determined by: aggregating the calculated slot subscores for the plurality of slots included in the identified candidate template, and comparing a number and types of slots included in the identified candidate template with a number and types of assets included in the source; selecting a particular template from the scored plurality of identified candidate templates based on the scoring; and generating a readable article on a client device, the readable article comprising the assets of the source placed in the readable article according to the selected particular template. 9. The method of claim 1 , wherein generating a readable article on a client device comprises: responsive to an entire text asset not fitting in a first frame of the selected particular template: incrementally filling the first frame with a text asset until the frame is full; and filling a next frame with remaining content from the text asset. | 0.560875 |
1. In a network router operating within a Local Area Network (LAN), a method comprising: accessing, via a configuration manager that operates from within the network router, a device independent data structure stored in a non-volatile memory of the network router and communicatively interfaced with the configuration manager, the device independent data structure comprising operational parameters for the network router, wherein the device independent data structure conforms to a standardized markup language and is not device-specific or vendor-specific; accessing, via the configuration manager of the network router, an internal memory structure stored in a volatile memory of the network router and communicatively interfaced with the configuration manager, wherein the common internal data structure is a proprietary device-specific data structure whose contents represent the then currently running configuration of the network router; mapping, via a Universal Management Object Layer (UMOL) of the network router that is communicatively interfaced with the configuration manager and operates in conjunction with the configuration manager, the operational parameters accessed from the device independent data structure to the currently running configuration in the internal memory structure; re-populating, via the UMOL of the network router in conjunction with the configuration manager, the operational parameters for the network router accessed from the device independent data structure into corresponding locations within the proprietary device-specific data structure based upon the mapping of the operational parameters to the currently running configuration; and wherein re-populating the operational parameters comprises restoring the currently running configuration of the network router from the device independent data structure stored in the non-volatile memory of the network router without having to re-process a plurality of commands used to originally generate the currently running configuration of the network router; modifying one or more operational parameters of the currently running configuration responsive to command input received at the network router; mapping the modified operational parameters of the currently running configuration to the device independent data structure; modifying the device independent data structure stored in the non-volatile memory of the network router by loading the modified operational parameters of the currently running configuration directly into the device independent data structure; and storing the modified device independent data structure in the non-volatile memory of the network router, wherein the modified device independent data structure reflects the modifications made to the currently running configuration responsive to the command input, and wherein the modified device independent data structure to be later restored to the network router as the currently running configuration, or sent to a second network router to be loaded as a new active currently running configuration for the second network router, or both. | 1. In a network router operating within a Local Area Network (LAN), a method comprising: accessing, via a configuration manager that operates from within the network router, a device independent data structure stored in a non-volatile memory of the network router and communicatively interfaced with the configuration manager, the device independent data structure comprising operational parameters for the network router, wherein the device independent data structure conforms to a standardized markup language and is not device-specific or vendor-specific; accessing, via the configuration manager of the network router, an internal memory structure stored in a volatile memory of the network router and communicatively interfaced with the configuration manager, wherein the common internal data structure is a proprietary device-specific data structure whose contents represent the then currently running configuration of the network router; mapping, via a Universal Management Object Layer (UMOL) of the network router that is communicatively interfaced with the configuration manager and operates in conjunction with the configuration manager, the operational parameters accessed from the device independent data structure to the currently running configuration in the internal memory structure; re-populating, via the UMOL of the network router in conjunction with the configuration manager, the operational parameters for the network router accessed from the device independent data structure into corresponding locations within the proprietary device-specific data structure based upon the mapping of the operational parameters to the currently running configuration; and wherein re-populating the operational parameters comprises restoring the currently running configuration of the network router from the device independent data structure stored in the non-volatile memory of the network router without having to re-process a plurality of commands used to originally generate the currently running configuration of the network router; modifying one or more operational parameters of the currently running configuration responsive to command input received at the network router; mapping the modified operational parameters of the currently running configuration to the device independent data structure; modifying the device independent data structure stored in the non-volatile memory of the network router by loading the modified operational parameters of the currently running configuration directly into the device independent data structure; and storing the modified device independent data structure in the non-volatile memory of the network router, wherein the modified device independent data structure reflects the modifications made to the currently running configuration responsive to the command input, and wherein the modified device independent data structure to be later restored to the network router as the currently running configuration, or sent to a second network router to be loaded as a new active currently running configuration for the second network router, or both. 6. The method of claim 1 , wherein the operational parameters of the device independent data structure comprises a plurality of parameter-value pairs, and wherein mapping the operational parameters of the device independent data structure to the currently running configuration in the internal memory structure comprises mapping data values within the parameter-value pairs to locations in the internal memory structure. | 0.543966 |
1. A method related to cluster analysis of a world wide web having identifiable web pages and hyperlink relationships made up of Universal Resource Locaters with pointers, wherein objects are related to the world wide web, direct non-semantic relationships relate to hyperlink relationships, and indirect non-semantic relationships relate to a series of hyperlink relationships between objects, comprising: crawling webpages on the world wide web for information used to define a set of objects to be indexed and to collect information about the direct non-semantic relationships, wherein Universal Resource Locators that either point to or point away from one or more of the web pages are crawled; defining the set of objects to be indexed, wherein each object in the set of objects has an identification and wherein a plurality of the objects in the set of objects have direct and indirect non-semantic relationships; generating, using a computer processor, a numerical representation for the set of objects in the form of a series of arrays representing each of said objects in the set of objects based upon each of said object's direct non-semantic relationships, if any, with other of said objects in the set of objects, wherein generating the numerical representation for the set of objects accounts for a plurality of direct non-semantic relationships and includes quantifying the accounted for direct non-semantic relationships, wherein the quantifying includes weighting some of the direct non-semantic relationships differently than others; generating, using a computer processor, a scalar value for each of said objecting the set of objects, wherein said scalar value accounts for direct and indirect non-semantic relationships that exist with other said objects in the set of objects and generating the scalar value includes: quantifying, for each of said objects in the set of objects that has one or more of the indirect non-semantic relationships, said object's indirect non-semantic relationships with other objects in the set of objects, wherein a.) some of the indirect non-semantic relationships contribute greater value to the scalar value than others, b.) a plurality of different types of indirect relationships, when present, contribute to the scalar value, and c.) quantifying said object's indirect non-semantic relationships includes accounting for at least the following three indirect non-semantic relationship patterns for a given object A when present: i) B cites f and f cites A, ii) B cites f, f cites e, and e cites A, and iii) B cites f, f cites e, e cites d, and d cites A, wherein B, d, e, and fare objects in the set of objects and said accounting for indirect non-semantic relationships uses weights that are calculated using one or more of said objects'quantity of outbound direct relationships; storing the generated scalar values in one or more computer memories as an index; receiving search commands wherein the search commands are received from an input device, wherein the received search commands include one or more search terms; identifying a resultant set of said objects that are associated with one or more search terms using at least a word index and the received search commands; determining a rank for objects in the resultant set of objects using said scalar values as a factor in determining the rank; and sending, for use by a display device, information for displaying identities of two or more objects in the resultant set of objects using the rank as a factor in determining an order of display. | 1. A method related to cluster analysis of a world wide web having identifiable web pages and hyperlink relationships made up of Universal Resource Locaters with pointers, wherein objects are related to the world wide web, direct non-semantic relationships relate to hyperlink relationships, and indirect non-semantic relationships relate to a series of hyperlink relationships between objects, comprising: crawling webpages on the world wide web for information used to define a set of objects to be indexed and to collect information about the direct non-semantic relationships, wherein Universal Resource Locators that either point to or point away from one or more of the web pages are crawled; defining the set of objects to be indexed, wherein each object in the set of objects has an identification and wherein a plurality of the objects in the set of objects have direct and indirect non-semantic relationships; generating, using a computer processor, a numerical representation for the set of objects in the form of a series of arrays representing each of said objects in the set of objects based upon each of said object's direct non-semantic relationships, if any, with other of said objects in the set of objects, wherein generating the numerical representation for the set of objects accounts for a plurality of direct non-semantic relationships and includes quantifying the accounted for direct non-semantic relationships, wherein the quantifying includes weighting some of the direct non-semantic relationships differently than others; generating, using a computer processor, a scalar value for each of said objecting the set of objects, wherein said scalar value accounts for direct and indirect non-semantic relationships that exist with other said objects in the set of objects and generating the scalar value includes: quantifying, for each of said objects in the set of objects that has one or more of the indirect non-semantic relationships, said object's indirect non-semantic relationships with other objects in the set of objects, wherein a.) some of the indirect non-semantic relationships contribute greater value to the scalar value than others, b.) a plurality of different types of indirect relationships, when present, contribute to the scalar value, and c.) quantifying said object's indirect non-semantic relationships includes accounting for at least the following three indirect non-semantic relationship patterns for a given object A when present: i) B cites f and f cites A, ii) B cites f, f cites e, and e cites A, and iii) B cites f, f cites e, e cites d, and d cites A, wherein B, d, e, and fare objects in the set of objects and said accounting for indirect non-semantic relationships uses weights that are calculated using one or more of said objects'quantity of outbound direct relationships; storing the generated scalar values in one or more computer memories as an index; receiving search commands wherein the search commands are received from an input device, wherein the received search commands include one or more search terms; identifying a resultant set of said objects that are associated with one or more search terms using at least a word index and the received search commands; determining a rank for objects in the resultant set of objects using said scalar values as a factor in determining the rank; and sending, for use by a display device, information for displaying identities of two or more objects in the resultant set of objects using the rank as a factor in determining an order of display. 3. The method of claim 1 further comprising: using one or more robots or spiders to crawl the world wide web for information, wherein the one or more robots or spiders reads HTML instructions. | 0.592894 |
10. A system comprising processing circuitry and at least one form of storage media, the processing circuitry executing instructions stored on the storage media to: parse content-rich pages to obtain a language-independent representation comprising a hierarchical structure of one or more nodes, the content-rich pages being parts of a live website, a static harvest of a website, or both provided by the storage media, communications circuitry connected with the system, or both; generate a node representation for each node, the node representation comprising an expression stored in the storage media; generate a feature vector for each node of the language independent representation; assign a label to each node representation according to a schema by executing a trained classification algorithm on the feature vectors of the content-rich pages, the schema comprising one or more schema elements, each schema element corresponding to a label; gather, for each schema element, all node representations having matching labels; elect from among the node representations having matching labels one node representation to assign to a schema element field in a template; and apply the template to extract the desired content, metadata, or both according to the schema from all of the content-rich pages. | 10. A system comprising processing circuitry and at least one form of storage media, the processing circuitry executing instructions stored on the storage media to: parse content-rich pages to obtain a language-independent representation comprising a hierarchical structure of one or more nodes, the content-rich pages being parts of a live website, a static harvest of a website, or both provided by the storage media, communications circuitry connected with the system, or both; generate a node representation for each node, the node representation comprising an expression stored in the storage media; generate a feature vector for each node of the language independent representation; assign a label to each node representation according to a schema by executing a trained classification algorithm on the feature vectors of the content-rich pages, the schema comprising one or more schema elements, each schema element corresponding to a label; gather, for each schema element, all node representations having matching labels; elect from among the node representations having matching labels one node representation to assign to a schema element field in a template; and apply the template to extract the desired content, metadata, or both according to the schema from all of the content-rich pages. 12. The system of claim 10 , wherein the node representation comprises an XPath expression. | 0.838028 |
20. The apparatus of claim 19 , wherein the means for selecting one of the quota cells from the set at random further comprises means for weighting random selection of the quota cells according to priorities assigned to the quota cells. | 20. The apparatus of claim 19 , wherein the means for selecting one of the quota cells from the set at random further comprises means for weighting random selection of the quota cells according to priorities assigned to the quota cells. 21. The apparatus of claim 20 , further comprising means for assigning the priorities based on percentage of progress of the project, elapsed effective field time for completion of the project, and scarcity of the quota cell criteria. | 0.916751 |
5. A method for a computer implemented system for identifying strong links and discovering hidden relationships among entities, the method comprising: identifying with the system strong links and discovering hidden relationships among entities, wherein the entities include places, time slots, people, groups, and organizations; and identifying the strong links and discovering the hidden relationships with the system based on low-level data streams, and incomplete and noisy evidence data streams. | 5. A method for a computer implemented system for identifying strong links and discovering hidden relationships among entities, the method comprising: identifying with the system strong links and discovering hidden relationships among entities, wherein the entities include places, time slots, people, groups, and organizations; and identifying the strong links and discovering the hidden relationships with the system based on low-level data streams, and incomplete and noisy evidence data streams. 7. The method of claim 5 , further comprising identifying with the system groups based on knowing a small number of group members by approximation and sampling based on algorithms including Hoeffding bound to reduce a potential error between sampled and non-sampled data to determine if an error is acceptable or not. | 0.702247 |
13. The system of claim 9 , wherein: the language-content correspondence data comprises user language preference data linked to content item presentation data; and the language selection statistics are further generated from the content item presentation data. | 13. The system of claim 9 , wherein: the language-content correspondence data comprises user language preference data linked to content item presentation data; and the language selection statistics are further generated from the content item presentation data. 14. The system of claim 13 , wherein the content item presentation data comprises a record of the presentation of a reference to a content item to a user having the opportunity to select the content item using the reference. | 0.94136 |
1. A system for bilingual communication between a first party and a second party through a remote live interpreter, comprising: a portable device for the first party communicating in a first language at a location-specific site comprising a microphone, an ear phone for the first party, a display screen, and a camera, wherein the portable device is configured to transmit via an internet network a request for a live interpreter of a selected different language to a network server that maintains a database of interpreters and languages able to be interpreted by interpreters, wherein the portable device for the first party receives user input indicative of identification of a communication device for a second party communicating in the selected different language at the location-specific site with the first party, wherein the portable device receives a second party communication in the selected different language from the communication device for the second party; and wherein, in response to the request for the live interpreter of the selected different language, the portable device establishes a connection via the internet network with a third party communication device for the remote live interpreter at a remote site and in communication with the network server, wherein the portable device transmits to the third party communication device a first party communication in the first language from the first party and the second party communication in the selected different language from the second party, and wherein the portable device is configured to transmit to the third party communication device an encrypted transmission of the second party communication in the selected different language and to receive from the third party communication device an encrypted transmission of the second party communication in the first language for output at the ear phone for the first party. | 1. A system for bilingual communication between a first party and a second party through a remote live interpreter, comprising: a portable device for the first party communicating in a first language at a location-specific site comprising a microphone, an ear phone for the first party, a display screen, and a camera, wherein the portable device is configured to transmit via an internet network a request for a live interpreter of a selected different language to a network server that maintains a database of interpreters and languages able to be interpreted by interpreters, wherein the portable device for the first party receives user input indicative of identification of a communication device for a second party communicating in the selected different language at the location-specific site with the first party, wherein the portable device receives a second party communication in the selected different language from the communication device for the second party; and wherein, in response to the request for the live interpreter of the selected different language, the portable device establishes a connection via the internet network with a third party communication device for the remote live interpreter at a remote site and in communication with the network server, wherein the portable device transmits to the third party communication device a first party communication in the first language from the first party and the second party communication in the selected different language from the second party, and wherein the portable device is configured to transmit to the third party communication device an encrypted transmission of the second party communication in the selected different language and to receive from the third party communication device an encrypted transmission of the second party communication in the first language for output at the ear phone for the first party. 8. The system of claim 1 , wherein the portable device establishes the connection via the internet network with the third party communication device that is configured to selectively separate received and transmitted communications and provide communication in both direction but only in one direction at a time. | 0.558798 |
15. Non-transitory computer readable storage comprising executable instructions that, when executed, cause one or more computing devices to perform a process comprising: generating a first score based at least partly on a likelihood that a frame, of a window of sequential frames of audio data, comprises audio data corresponding to a keyword, wherein the window comprises the frame and an equal quantity of (1) frames before the frame and (2) frames after the frame; generating a second score based at least partly on a likelihood that the frame comprises audio data corresponding to background audio; determining a difference between the first score and the second score; and determining that the frame corresponds to an end of the keyword based at least partly on the difference being greater than differences determined for the frames before the frame, and differences determined for the frames after the frame. | 15. Non-transitory computer readable storage comprising executable instructions that, when executed, cause one or more computing devices to perform a process comprising: generating a first score based at least partly on a likelihood that a frame, of a window of sequential frames of audio data, comprises audio data corresponding to a keyword, wherein the window comprises the frame and an equal quantity of (1) frames before the frame and (2) frames after the frame; generating a second score based at least partly on a likelihood that the frame comprises audio data corresponding to background audio; determining a difference between the first score and the second score; and determining that the frame corresponds to an end of the keyword based at least partly on the difference being greater than differences determined for the frames before the frame, and differences determined for the frames after the frame. 19. The non-transitory computer readable storage of claim 15 , wherein generating the first score comprises using a hidden Markov model of audio data that corresponds to the keyword, and wherein generating the second score comprises using a hidden Markov model of audio data that does not correspond to the keyword. | 0.652132 |
1. A digital music library builder comprising: a receiver to receive broadcast audio from a first broadcast station, and to receive a broadcast image from a second broadcast station; a song extractor, coupled to the receiver, to extract a song from the received broadcast audio, comprising an audio parser to mark the start and end of a song within the received broadcast audio; a meta-data generator, coupled to the receiver, to identify meta-data for the extracted song from the received broadcast image, comprising a luminance extractor to remove color burst noise from the received broadcast image; and a memory, coupled to the song extractor and the meta-data generator, wherein a memory manager automatically stores the extracted song in a digital music library in the memory and automatically associates the identified meta-data with the stored song, within the digital music library. | 1. A digital music library builder comprising: a receiver to receive broadcast audio from a first broadcast station, and to receive a broadcast image from a second broadcast station; a song extractor, coupled to the receiver, to extract a song from the received broadcast audio, comprising an audio parser to mark the start and end of a song within the received broadcast audio; a meta-data generator, coupled to the receiver, to identify meta-data for the extracted song from the received broadcast image, comprising a luminance extractor to remove color burst noise from the received broadcast image; and a memory, coupled to the song extractor and the meta-data generator, wherein a memory manager automatically stores the extracted song in a digital music library in the memory and automatically associates the identified meta-data with the stored song, within the digital music library. 9. The digital music library builder of claim 1 wherein the meta-data includes a song title. | 0.579403 |
12. The method of claim 1 , further comprising: accessing, by the processor, a social networking account associated with the user; obtaining, by the processor and from a fourth device associated with the social networking account, the feedback; generating, by the processor, feedback data comprising a summary of the feedback, wherein the summary indicates an aspect of the item and a summary of opinions relating to the aspect of the item; and providing, by the processor, the feedback data to the manufacturer to enable rapid prototyping by the manufacturer. | 12. The method of claim 1 , further comprising: accessing, by the processor, a social networking account associated with the user; obtaining, by the processor and from a fourth device associated with the social networking account, the feedback; generating, by the processor, feedback data comprising a summary of the feedback, wherein the summary indicates an aspect of the item and a summary of opinions relating to the aspect of the item; and providing, by the processor, the feedback data to the manufacturer to enable rapid prototyping by the manufacturer. 13. The method of claim 12 , wherein the summary comprises an indication of demographics associated with the summary of the feedback, the demographics comprising a geographic location and an age. | 0.864329 |
43. The computer implemented method of claim 33 , wherein the eliciting (c) involves an interactive session with the user. | 43. The computer implemented method of claim 33 , wherein the eliciting (c) involves an interactive session with the user. 47. The method of claim 43 , wherein the building (f) involves an active learning process. | 0.976505 |
1. A method to provide keyword-based notification and content, comprising: monitoring, by a processor, a communication interface, the communication interface configured to transport a communication between an agent and a customer; recognizing, by the processor, a candidate keyword in the communication; retrieving, by the processor from a non-volatile data storage, an information related to the keyword; detecting, by the processor, whether the keyword exists in the non-volatile data storage; if the keyword is not in the non-volatile data storage: searching, by the processor, for an alternative source of information for the keyword; and retrieving, by the processor, additional information from the alternative source; and presenting, by the processor, to one of the agent and the customer the information related to the keyword. | 1. A method to provide keyword-based notification and content, comprising: monitoring, by a processor, a communication interface, the communication interface configured to transport a communication between an agent and a customer; recognizing, by the processor, a candidate keyword in the communication; retrieving, by the processor from a non-volatile data storage, an information related to the keyword; detecting, by the processor, whether the keyword exists in the non-volatile data storage; if the keyword is not in the non-volatile data storage: searching, by the processor, for an alternative source of information for the keyword; and retrieving, by the processor, additional information from the alternative source; and presenting, by the processor, to one of the agent and the customer the information related to the keyword. 13. The method of claim 1 , wherein the non-volatile data storage comprises: a first memory area configured to store a mapping of keywords to keyword information; and a second memory area configured to store a mapping of a customer identifier to information related to how keyword information should be delivered to the customer. | 0.513145 |
20. An apparatus for enforcing a referential integrity constraint between a constraint set of objects and constrained object having values that are terms of an ontology in a relational database management system, the objects in the constraint set being possible values in the constrained object and the relational database management system being implemented in a workstation having a processor and a storage device to which the processor has access and the apparatus comprising: the storage device having an association between the constrained object and the constraint set of objects, the constraint set of objects having been returned by an ontology query that is executed in the relational database system and returns the objects in the constraint set to be used to define the referential integrity constraint, wherein different constraint sets can be derived by querying the same ontology, the constrained object comprising a constrained column that is defined in the relational database management system; the constraint set associated with the constrained object is a term object with a column, values in the column being the members of the constraint set; and a referential integrity constraint for the constrained object which references the column in the term object; and the processor for executing a constraint enforcer which, when an operation in the relational database management system adds a value to the constrained object, permits the operation only if the added value is a member of the constraint set, and when a modification is performed on the ontology in the relational database management system that results in a different set of values being returned by the ontology query, altering one or more values in the constrained object that are not contained in the different set of values. | 20. An apparatus for enforcing a referential integrity constraint between a constraint set of objects and constrained object having values that are terms of an ontology in a relational database management system, the objects in the constraint set being possible values in the constrained object and the relational database management system being implemented in a workstation having a processor and a storage device to which the processor has access and the apparatus comprising: the storage device having an association between the constrained object and the constraint set of objects, the constraint set of objects having been returned by an ontology query that is executed in the relational database system and returns the objects in the constraint set to be used to define the referential integrity constraint, wherein different constraint sets can be derived by querying the same ontology, the constrained object comprising a constrained column that is defined in the relational database management system; the constraint set associated with the constrained object is a term object with a column, values in the column being the members of the constraint set; and a referential integrity constraint for the constrained object which references the column in the term object; and the processor for executing a constraint enforcer which, when an operation in the relational database management system adds a value to the constrained object, permits the operation only if the added value is a member of the constraint set, and when a modification is performed on the ontology in the relational database management system that results in a different set of values being returned by the ontology query, altering one or more values in the constrained object that are not contained in the different set of values. 24. The apparatus set forth in claim 20 wherein: when a value in the constrained object is no longer contained in the different constraint set, the apparatus that alters one or more values sets the one or more values to a default value. | 0.527159 |
9. The discussion support apparatus according to claim 3 , further comprising: a related-screen display unit, wherein after the user interface accepts the discussion start operation, the processor controls the related-screen display output unit to display a screen related to any of the plural pieces of discussion state information that are different from the first discussion state information and the second discussion state information indicated by the topic-related information at points in time before the operation point in time when the user interface accepts the discussion start operation. | 9. The discussion support apparatus according to claim 3 , further comprising: a related-screen display unit, wherein after the user interface accepts the discussion start operation, the processor controls the related-screen display output unit to display a screen related to any of the plural pieces of discussion state information that are different from the first discussion state information and the second discussion state information indicated by the topic-related information at points in time before the operation point in time when the user interface accepts the discussion start operation. 10. The discussion support apparatus according to claim 9 , wherein the screen displayed by the related-screen display unit is related to discussion state information at a latest point in time among the plural pieces of discussion state information, which are different from the first discussion state information and the second discussion state information indicated by the topic-related information at the points in time before the operation point in time when the user interface accepts the discussion start operation. | 0.901843 |
14. A computer program product stored on a computer readable storage device having computer readable program code embodied thereon that is executable by a data processing system for determining and communicating biometrics of a recorded speaker in a voice transcription process, the computer program product comprising: computer readable program code for receiving a request from a user for a transcription of a voice file stored in a memory of the data processing system; computer readable program code for obtaining a profile associated with the requesting user, wherein the profile comprises biometric parameters and preferences defined by the user; computer readable program code for analyzing the requested voice file for biometric elements according to the parameters specified in the user's profile; computer readable program code for modifying, in response to detecting, in the voice file, biometric elements conforming to the parameters specified in the user's profile, a transcription output of the voice file according to the preferences specified in the user's profile for the detected biometric elements to form a modified transcription output file; computer readable program code for modifying, in response to determining that no preferences are specified in the user's profile, the transcription output of the voice file according to default settings for the detected biometric elements to form the modified transcription output file; and computer readable program code for providing the modified transcription output file to the requesting user. | 14. A computer program product stored on a computer readable storage device having computer readable program code embodied thereon that is executable by a data processing system for determining and communicating biometrics of a recorded speaker in a voice transcription process, the computer program product comprising: computer readable program code for receiving a request from a user for a transcription of a voice file stored in a memory of the data processing system; computer readable program code for obtaining a profile associated with the requesting user, wherein the profile comprises biometric parameters and preferences defined by the user; computer readable program code for analyzing the requested voice file for biometric elements according to the parameters specified in the user's profile; computer readable program code for modifying, in response to detecting, in the voice file, biometric elements conforming to the parameters specified in the user's profile, a transcription output of the voice file according to the preferences specified in the user's profile for the detected biometric elements to form a modified transcription output file; computer readable program code for modifying, in response to determining that no preferences are specified in the user's profile, the transcription output of the voice file according to default settings for the detected biometric elements to form the modified transcription output file; and computer readable program code for providing the modified transcription output file to the requesting user. 15. The computer program product of claim 14 , further comprising: computer readable program code for receiving user selections in a profile for biometric parameters to be used by the data processing system when transcribing a voice file for the user; computer readable program code for receiving user selections in the profile for preferences that specify how detected biometric elements are to be displayed in the modified transcription output file; and computer readable program code for storing the profile in the memory of the data processing system. | 0.5 |
2. The method of claim 1 , further comprising: identifying one or more terms in the textual content for the electronic document; and accentuating, in a display of the electronic document, the one or more terms in the textual content for the electronic document, wherein the one or more terms are terms that are either not defined in a local permanent dictionary or are defined in both the local permanent dictionary and the transient document associated electronic dictionary. | 2. The method of claim 1 , further comprising: identifying one or more terms in the textual content for the electronic document; and accentuating, in a display of the electronic document, the one or more terms in the textual content for the electronic document, wherein the one or more terms are terms that are either not defined in a local permanent dictionary or are defined in both the local permanent dictionary and the transient document associated electronic dictionary. 8. The method of claim 2 , further comprising: removing accentuation of the selected term in the display of the electronic document in response to receiving the dictionary definition for the selected term. | 0.924287 |
3. The method according to claim 2 , wherein said resolving the at least one likelihood value comprises averaging the at least one likelihood value. | 3. The method according to claim 2 , wherein said resolving the at least one likelihood value comprises averaging the at least one likelihood value. 4. The method according to claim 3 , wherein the likelihood value is determined via the following general equation: S ( U ❘ M ) = 1 T ∑ i = 1 L ∑ t = 1 T b i , j ( i , t ) P ( u t ❘ M { i , j ( i , t ) } ) ; wherein b_{i,j(i,t)} corresponds to grain-specific weights that satisfy ∑ i = 1 L ∑ j = 1 K ( i ) b ij = 1 ; and further wherein: S is the likelihood score; U is a test utterance, comprising T frames u 1 . . . , u T ; M(i,j) is a speaker model, with 1≦i≦L levels of detail and with 1≦j≦K(i) units on the i-th level; and P(u t |M(i,j)) is the probability that a frame u t corresponds to a speaker model unit j on the i-th level of phonetic detail of the speaker model. | 0.6186 |
6. A computer program product to retarget facial expressions by providing a parameter-parallel retargeting space between an input facial expression and an output facial expression and via a plurality of facial layers, the computer program product comprising: a non-transitory computer-readable medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to: receive input representing a facial expression of a first character; generate a plurality of facial layers for the first character, based on the received input and a composition function, wherein the plurality of facial layers includes a plurality of parameters extracted from the received input, the plurality of parameters including a simplex coefficient common to the plurality of facial layers and weighted by a respective measure of influence for each of the plurality of facial layers; wherein the simplex coefficient is extracted by an optimization operation that includes determining a set of inputs for which a given function attains a minimum value, wherein the optimization operation is subject to a set of constraints and is based on a sequential quadratic programming algorithm; wherein each facial layer encodes, in a simplicial basis, one or more semantically significant aspects of the facial expression of the first character; and wherein a simplex is formed from components of each simplicial basis; and generate, for a second character different from the first character in appearance, a facial expression corresponding to the facial expression of the first character, based on the plurality of facial layers. | 6. A computer program product to retarget facial expressions by providing a parameter-parallel retargeting space between an input facial expression and an output facial expression and via a plurality of facial layers, the computer program product comprising: a non-transitory computer-readable medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to: receive input representing a facial expression of a first character; generate a plurality of facial layers for the first character, based on the received input and a composition function, wherein the plurality of facial layers includes a plurality of parameters extracted from the received input, the plurality of parameters including a simplex coefficient common to the plurality of facial layers and weighted by a respective measure of influence for each of the plurality of facial layers; wherein the simplex coefficient is extracted by an optimization operation that includes determining a set of inputs for which a given function attains a minimum value, wherein the optimization operation is subject to a set of constraints and is based on a sequential quadratic programming algorithm; wherein each facial layer encodes, in a simplicial basis, one or more semantically significant aspects of the facial expression of the first character; and wherein a simplex is formed from components of each simplicial basis; and generate, for a second character different from the first character in appearance, a facial expression corresponding to the facial expression of the first character, based on the plurality of facial layers. 7. The computer program product of claim 6 , wherein the facial expression for the second character includes a plurality of facial features of the second character, wherein each of the plurality of facial features is modeled independently based on the plurality of facial layers. | 0.832216 |
1. A method for analyzing a telephonic communication between a customer and a contact center, the method comprising: receiving a single telephonic communication between a first communicant to the telephonic communication and a second communicant to the telephonic communication; separating the single telephonic communication into at least first constituent voice data and second constituent voice data, the first constituent voice data being generated by the first communicant and the second constituent voice data being generated by the second communicant; analyzing one of the separated first and second constituent voice data, the step of analyzing comprising: translating one of the first and second constituent voice data into a text format; applying a predetermined linguistic-based psychological behavioral model to the translated text of the one of the first and second constituent voice data, the applying step comprising the steps of: mining the translated text of the one of the first and second constituent voice data by automatically identifying a plurality of behavioral signifiers in the translated text of the one of the first and second constituent voice data, the behavioral signifiers being associated with the psychological behavioral model; and, automatically associating the identified behavioral signifiers in the translated text of the one of the first and second constituent voice data with at least one of a plurality of personality types associated with the psychological behavioral model; and, generating behavioral assessment data from the analyzed one of the separated first and second constituted voice data, the behavioral assessment data including a personality type based on the analyzed one of the first and second constituent voice data based on the step of analyzing one of the first and second constituent voice data. | 1. A method for analyzing a telephonic communication between a customer and a contact center, the method comprising: receiving a single telephonic communication between a first communicant to the telephonic communication and a second communicant to the telephonic communication; separating the single telephonic communication into at least first constituent voice data and second constituent voice data, the first constituent voice data being generated by the first communicant and the second constituent voice data being generated by the second communicant; analyzing one of the separated first and second constituent voice data, the step of analyzing comprising: translating one of the first and second constituent voice data into a text format; applying a predetermined linguistic-based psychological behavioral model to the translated text of the one of the first and second constituent voice data, the applying step comprising the steps of: mining the translated text of the one of the first and second constituent voice data by automatically identifying a plurality of behavioral signifiers in the translated text of the one of the first and second constituent voice data, the behavioral signifiers being associated with the psychological behavioral model; and, automatically associating the identified behavioral signifiers in the translated text of the one of the first and second constituent voice data with at least one of a plurality of personality types associated with the psychological behavioral model; and, generating behavioral assessment data from the analyzed one of the separated first and second constituted voice data, the behavioral assessment data including a personality type based on the analyzed one of the first and second constituent voice data based on the step of analyzing one of the first and second constituent voice data. 4. The method of claim 1 , further comprising the step of generating event data corresponding to at least one identifying indicia and time interval, the event data comprising at least one of behavioral assessment data and distress assessment data. | 0.955935 |
7. The method of claim 6 in which the first word has a second hyphenation point different from the first above-mentioned hyphenation point, the word data including a second hyphenation code representing the second hyphenation point in addition to the first above-mentioned hyphenation code, the accessing step comprising retrieving both the first and second hyphenation codes. | 7. The method of claim 6 in which the first word has a second hyphenation point different from the first above-mentioned hyphenation point, the word data including a second hyphenation code representing the second hyphenation point in addition to the first above-mentioned hyphenation code, the accessing step comprising retrieving both the first and second hyphenation codes. 8. The method of claim 7, further comprising determining where to hyphenate the first word based on the retrieved first and second hyphenation codes. | 0.959848 |
1. A payment document classification system, comprising: a communication unit which receives an image of a payment document captured by a mobile device; a preprocessing unit which extracts at least one feature from the image; a comparison unit which compares the at least one extracted feature with at least one known feature of a payment document type to determine a likelihood that the payment document matches the at least one payment document type, wherein the comparison unit is further configured to compare a plurality of extracted features with a plurality of known features in a series, starting with an extracted feature which requires the least computation time of the plurality of extracted features; and a classification unit which classified the payment document as at least one payment document type based on the likelihood that the payment document matches the at least one payment document type. | 1. A payment document classification system, comprising: a communication unit which receives an image of a payment document captured by a mobile device; a preprocessing unit which extracts at least one feature from the image; a comparison unit which compares the at least one extracted feature with at least one known feature of a payment document type to determine a likelihood that the payment document matches the at least one payment document type, wherein the comparison unit is further configured to compare a plurality of extracted features with a plurality of known features in a series, starting with an extracted feature which requires the least computation time of the plurality of extracted features; and a classification unit which classified the payment document as at least one payment document type based on the likelihood that the payment document matches the at least one payment document type. 6. The system of claim 1 , wherein the comparison unit compares a plurality of extracted features with a plurality of known features in a sequence until one of the plurality of extracted features matches one of the plurality of known features. | 0.510648 |
1. A method programmed in a non-transitory memory of a first device, the method comprising: automatically detecting a comment associated with an entity in a video or audio; in response to automatically detecting the comment associated with the entity, converting the comment into searchable information; comparing the searchable information with information from one or more sources to determine a factual accuracy of the comment; computing an entity validity rating associated with the entity based on the factual accuracy of the comment; causing the entity validity rating to be displayed on a display of a second device. | 1. A method programmed in a non-transitory memory of a first device, the method comprising: automatically detecting a comment associated with an entity in a video or audio; in response to automatically detecting the comment associated with the entity, converting the comment into searchable information; comparing the searchable information with information from one or more sources to determine a factual accuracy of the comment; computing an entity validity rating associated with the entity based on the factual accuracy of the comment; causing the entity validity rating to be displayed on a display of a second device. 21. The method of claim 1 , wherein converting the comment comprises parsing the searchable information into one or more fact checkable portions, one or more segments of searchable text, or one or more word phrases. | 0.673653 |
1. A system of a vehicle comprising: a sensor set including one or more sensors configured to measure sensor data describing a task and a driver of the vehicle; an onboard vehicle computer including a tangible memory, a processor, a task tracker and a familiarity application, wherein the task tracker and the familiarity application are stored in the tangible memory which is communicatively coupled to the processor; wherein the task tracker, responsive to being executed by the processor, causes the processor to determine, based on the sensor data describing the task, a vehicle operation to be proactively performed by the task tracker to autocomplete the task for the driver of the vehicle; wherein the familiarity application, responsive to being executed by the processor, causes the processor to: estimate how familiar the driver is with the vehicle operation based on the sensor data describing the driver of the vehicle; retrieve a template explanation associated with the vehicle operation; adapt the template explanation, based on the estimate of how familiar the driver is with the vehicle operation, to generate a driver familiarity adapted explanation describing the autocomplete of the vehicle operation; and provide the driver familiarity adapted explanation to the driver. | 1. A system of a vehicle comprising: a sensor set including one or more sensors configured to measure sensor data describing a task and a driver of the vehicle; an onboard vehicle computer including a tangible memory, a processor, a task tracker and a familiarity application, wherein the task tracker and the familiarity application are stored in the tangible memory which is communicatively coupled to the processor; wherein the task tracker, responsive to being executed by the processor, causes the processor to determine, based on the sensor data describing the task, a vehicle operation to be proactively performed by the task tracker to autocomplete the task for the driver of the vehicle; wherein the familiarity application, responsive to being executed by the processor, causes the processor to: estimate how familiar the driver is with the vehicle operation based on the sensor data describing the driver of the vehicle; retrieve a template explanation associated with the vehicle operation; adapt the template explanation, based on the estimate of how familiar the driver is with the vehicle operation, to generate a driver familiarity adapted explanation describing the autocomplete of the vehicle operation; and provide the driver familiarity adapted explanation to the driver. 9. The system of claim 1 , wherein the estimate of how familiar the driver is with the vehicle operation is based at least in part on a count of how many times the task tracker has proactively performed the task. | 0.629962 |
1. A method of character recognition, the character having a main stroke defining a main form of the character and optional secondary strokes external to the main form of the character, the method comprising: removing one or more duplicate successive points of a plurality of points in a handwritten character to form an enhanced handwritten character; spacing the plurality of points of the enhanced handwritten character a uniform distance apart; detecting, via circuitry, one or more primary strokes corresponding to the main form of the character and one or more ancillary strokes of the enhanced handwritten character; generating a primary merged stroke from the one or more primary strokes; extracting, via the circuitry, one or more raw point-based features from local features of the primary merged stroke, wherein the raw point-based features are geometric characteristics selected from the group consisting of an axis coordinate, a relative position, an aspect ratio, a slope and an angle; extracting, via the circuitry, one or more statistical features from statistics in the form of such a histogram, mean, mode, maximum, minimum, variance, and standard deviation from the raw point-based features computed over the one or more raw point-based features to form one or more primary merged stroke features; extracting, via the circuitry, one or more features from the ancillary strokes to form one or more ancillary stroke features; training one or more stroke models on features of the main stroke and features of the secondary strokes and classifying data from the one or more primary merged stroke features and the one or more ancillary stroke features using the trained one or more stroke models; determining, via the circuitry, a set of main stroke candidates and a set of secondary stroke candidates from the data classified by the one or more stroke models; computing, via the circuitry, likelihood values indicative of whether respective main strokes of the set of main stroke candidates combined with respective secondary strokes from the set of secondary stroke candidates form the character; and determining, via the circuitry, the character from the likelihood values. | 1. A method of character recognition, the character having a main stroke defining a main form of the character and optional secondary strokes external to the main form of the character, the method comprising: removing one or more duplicate successive points of a plurality of points in a handwritten character to form an enhanced handwritten character; spacing the plurality of points of the enhanced handwritten character a uniform distance apart; detecting, via circuitry, one or more primary strokes corresponding to the main form of the character and one or more ancillary strokes of the enhanced handwritten character; generating a primary merged stroke from the one or more primary strokes; extracting, via the circuitry, one or more raw point-based features from local features of the primary merged stroke, wherein the raw point-based features are geometric characteristics selected from the group consisting of an axis coordinate, a relative position, an aspect ratio, a slope and an angle; extracting, via the circuitry, one or more statistical features from statistics in the form of such a histogram, mean, mode, maximum, minimum, variance, and standard deviation from the raw point-based features computed over the one or more raw point-based features to form one or more primary merged stroke features; extracting, via the circuitry, one or more features from the ancillary strokes to form one or more ancillary stroke features; training one or more stroke models on features of the main stroke and features of the secondary strokes and classifying data from the one or more primary merged stroke features and the one or more ancillary stroke features using the trained one or more stroke models; determining, via the circuitry, a set of main stroke candidates and a set of secondary stroke candidates from the data classified by the one or more stroke models; computing, via the circuitry, likelihood values indicative of whether respective main strokes of the set of main stroke candidates combined with respective secondary strokes from the set of secondary stroke candidates form the character; and determining, via the circuitry, the character from the likelihood values. 7. The method of claim 1 , further comprising: utilizing one or more of a K-Nearest Neighbor classifier, a Bayes Network classifier; or a Dynamic Bayes Network classifier for secondary stroke classification. | 0.790323 |
5. The method of claim 4 , wherein said generalization also comprises a precedence inclusion pattern. | 5. The method of claim 4 , wherein said generalization also comprises a precedence inclusion pattern. 6. The method of claim 5 , wherein said precedence inclusion pattern of said generalization comprises a most specific generalization (MSG). | 0.970649 |
1. A method executed by a processor of a computing device, the method comprising: generating a graphical summary of a research document for presentment on a display of a computing device in response to receipt of user input that identifies the research document, the research document having a publication date assigned thereto that indicates a date upon which the research document was published, the graphical summary of the research document is based upon content from other research documents, the other research documents include citations to the research document, the other research documents having publication dates that are subsequent to the publication date of the research document, the graphical summary of the research document comprises: a node that is representative of the research document; and portions of sentences in the other research documents that include the citations to the research document; and presenting the graphical summary of the research document on the display of the computing device. | 1. A method executed by a processor of a computing device, the method comprising: generating a graphical summary of a research document for presentment on a display of a computing device in response to receipt of user input that identifies the research document, the research document having a publication date assigned thereto that indicates a date upon which the research document was published, the graphical summary of the research document is based upon content from other research documents, the other research documents include citations to the research document, the other research documents having publication dates that are subsequent to the publication date of the research document, the graphical summary of the research document comprises: a node that is representative of the research document; and portions of sentences in the other research documents that include the citations to the research document; and presenting the graphical summary of the research document on the display of the computing device. 8. The method of claim 1 , wherein the graphical summary further comprises: a second node that is representative of an annotation made to the research document by a non-author of the research document, at least a portion of the annotation displayed in graphical relation to the second node; and an edge that couples the node to the second node. | 0.568498 |
2. The video summarization method according to claim 1 , further comprising dividing the frames of the video into a plurality of segments before the step of computing the similarity between each frame. | 2. The video summarization method according to claim 1 , further comprising dividing the frames of the video into a plurality of segments before the step of computing the similarity between each frame. 3. The video summarization method according to claim 2 , wherein the step of dividing the frames of the video into the segments is based on shots of the video or a unit of length of the video. | 0.931783 |
2. The method of claim 1 , wherein the calculating comprises calculating information gain for an attribute A in relation to documents S and categories C by which the documents S are grouped, and the calculating the information gain comprises handling separately a subset of the documents S, for which the attribute A is absent, to improve performance with respect to populating sub-concepts in the second ontology. | 2. The method of claim 1 , wherein the calculating comprises calculating information gain for an attribute A in relation to documents S and categories C by which the documents S are grouped, and the calculating the information gain comprises handling separately a subset of the documents S, for which the attribute A is absent, to improve performance with respect to populating sub-concepts in the second ontology. 5. The method of claim 2 , wherein the determining comprises discretizing frequency values V A for the attribute A in the documents S based on a statistical variance of the frequency values V A ; and wherein input for the calculating the information gain includes the discretized frequency values. | 0.891992 |
8. A computer-readable storage medium for testing software processes in a computer system, including: computer-readable instructions, the computer-readable instructions including instructions that when executed by at least one processor cause the at least one processor to perform the following acts: interfacing with a memory component that stores at least one portion of a software process as at least one model; retrieving the at least one model from the memory component; performing at least one test function on the at least one model, wherein the at least one test function is performed on the at least one model according to at least one dynamic influence, and wherein the at least one dynamic influence dynamically alters the at least one model at run-time; and enabling a reproduction of a specified test sequence of the at least one test function during run-time. | 8. A computer-readable storage medium for testing software processes in a computer system, including: computer-readable instructions, the computer-readable instructions including instructions that when executed by at least one processor cause the at least one processor to perform the following acts: interfacing with a memory component that stores at least one portion of a software process as at least one model; retrieving the at least one model from the memory component; performing at least one test function on the at least one model, wherein the at least one test function is performed on the at least one model according to at least one dynamic influence, and wherein the at least one dynamic influence dynamically alters the at least one model at run-time; and enabling a reproduction of a specified test sequence of the at least one test function during run-time. 13. The computer-readable storage medium of claim 8 , wherein the at least one test function is performed on the at least one model for a first portion of the software process, and wherein a custom test function not based on the at least one model is performed on a second portion of the software process, different from the first portion of the software process. | 0.500442 |
9. The computer-implemented method of claim 1 , where messages of the message stream have a predefined length. | 9. The computer-implemented method of claim 1 , where messages of the message stream have a predefined length. 10. The computer-implemented method of claim 9 , wherein the predefined length comprises about two-hundred words or less. | 0.966956 |
8. A system, comprising: a processor; a memory storing instructions that, when executed by the processor, cause the system to: obtain a first stream of candidate content items based on a request from a user; generate a model based on an interest of the user and a prior interaction of the user; compute an interestingness score for each of the candidate content items based upon a comparison of each of the candidate content items to the model, a quality of each candidate content item, and information relating to relationships of users from a social graph of the user, wherein the interestingness score represents similarities between attributes of the candidate content item and the model and the quality of each candidate content item is based on at least one of a popularity, an importance and a relevance of the respective candidate content item; determine a threshold for the first stream of candidate content items based on an extent to which the popularity of the candidate content items has changed in a user's location; compare the interestingness score for each of the candidate content items with the threshold to determine which candidate content items have an interestingness score that exceeds the threshold; organize a first content item and a second content item that have the interestingness score that exceeds the threshold in a second stream of content; and provide the second stream of content along with an explanation for why the first content item and the second content item are included in the second stream of content for display on a user interface of a client device associated with the user that submitted the request, wherein the user interface includes at least one button associated with each of the first content item and the second content item, which, once selected by the user, causes the one or more processors to receive a feedback to the explanation and to automatically update the model and the second stream of content based on the feedback. | 8. A system, comprising: a processor; a memory storing instructions that, when executed by the processor, cause the system to: obtain a first stream of candidate content items based on a request from a user; generate a model based on an interest of the user and a prior interaction of the user; compute an interestingness score for each of the candidate content items based upon a comparison of each of the candidate content items to the model, a quality of each candidate content item, and information relating to relationships of users from a social graph of the user, wherein the interestingness score represents similarities between attributes of the candidate content item and the model and the quality of each candidate content item is based on at least one of a popularity, an importance and a relevance of the respective candidate content item; determine a threshold for the first stream of candidate content items based on an extent to which the popularity of the candidate content items has changed in a user's location; compare the interestingness score for each of the candidate content items with the threshold to determine which candidate content items have an interestingness score that exceeds the threshold; organize a first content item and a second content item that have the interestingness score that exceeds the threshold in a second stream of content; and provide the second stream of content along with an explanation for why the first content item and the second content item are included in the second stream of content for display on a user interface of a client device associated with the user that submitted the request, wherein the user interface includes at least one button associated with each of the first content item and the second content item, which, once selected by the user, causes the one or more processors to receive a feedback to the explanation and to automatically update the model and the second stream of content based on the feedback. 13. The system of claim 8 , wherein the system is further configured to: provide a button for requesting additional content items for a first type of interest; receive a selection of the button for requesting the additional content items; and add the first type of interest to the model. | 0.556166 |
10. The computing system of claim 9 wherein the query template engine is configured to be invoked by another computing system that is distinct and separate from the computing system, the computing system used to present supplemental information to information being presented by the another computing system. | 10. The computing system of claim 9 wherein the query template engine is configured to be invoked by another computing system that is distinct and separate from the computing system, the computing system used to present supplemental information to information being presented by the another computing system. 11. The computing system of claim 10 wherein the another computing system implements at least one of a portal or a widget that can be integrated in other applications or code modules. | 0.900561 |
11. A computerized method for generating a personalized tax advice document from a tax return preparation program executing on a computer comprising: (a) entering in said computer a plurality of tax advice statements relevant to a tax liability; (b) entering in said computer an assignment of each of said plurality of tax advice statements to at least one of a plurality of tax advice categories; (c) entering in said computer an assignment of a category relevance value to each of said tax advice categories of tax advice statements; (d) entering in said computer an assignment of a statement relevance value to each of said tax advice statements in each of said tax advice categories; (e) entering in said computer an association of each of said plurality of tax advice statements with a trigger in said tax return preparation program; (f) entering in said tax return preparation program data for a taxpayer; (g) determining at said computer which tax advice statements from said plurality of tax advice statements are triggered when conditions associated with said triggers in said tax return preparation program are met; (h) determining at said computer which of said triggered tax advice statements correspond to a client requested category based on said taxpayer's responses to interview questions in said tax return preparation program; (i) specifying in said computer a category maximum number of tax advice statements to include a tax advice document for each of said categories; (j) specifying in said computer a total number of statements to include on a tax advice document; and (k) generating for said taxpayer a tax advice document comprising: (1) triggered tax advice statements from said client-requested category up to said category maximum number of tax advice statements for said client-requested category; (2) triggered tax advice statements from other categories up to said category maximum number of tax advice statements for each of said other categories; (3) a total number of said triggered tax advice statements from said client-requested category and said triggered tax advice statements from said other categories not exceeding said total number of tax advice statements; and (4) tax advice statements from said other categories ordered on said tax advice document according to said category relevance value and within each category, said statement relevance value. | 11. A computerized method for generating a personalized tax advice document from a tax return preparation program executing on a computer comprising: (a) entering in said computer a plurality of tax advice statements relevant to a tax liability; (b) entering in said computer an assignment of each of said plurality of tax advice statements to at least one of a plurality of tax advice categories; (c) entering in said computer an assignment of a category relevance value to each of said tax advice categories of tax advice statements; (d) entering in said computer an assignment of a statement relevance value to each of said tax advice statements in each of said tax advice categories; (e) entering in said computer an association of each of said plurality of tax advice statements with a trigger in said tax return preparation program; (f) entering in said tax return preparation program data for a taxpayer; (g) determining at said computer which tax advice statements from said plurality of tax advice statements are triggered when conditions associated with said triggers in said tax return preparation program are met; (h) determining at said computer which of said triggered tax advice statements correspond to a client requested category based on said taxpayer's responses to interview questions in said tax return preparation program; (i) specifying in said computer a category maximum number of tax advice statements to include a tax advice document for each of said categories; (j) specifying in said computer a total number of statements to include on a tax advice document; and (k) generating for said taxpayer a tax advice document comprising: (1) triggered tax advice statements from said client-requested category up to said category maximum number of tax advice statements for said client-requested category; (2) triggered tax advice statements from other categories up to said category maximum number of tax advice statements for each of said other categories; (3) a total number of said triggered tax advice statements from said client-requested category and said triggered tax advice statements from said other categories not exceeding said total number of tax advice statements; and (4) tax advice statements from said other categories ordered on said tax advice document according to said category relevance value and within each category, said statement relevance value. 15. The computerized method of claim 11 wherein at least one tax advice statement in said tax advice document comprises a value calculated according to said tax return data for said taxpayer. | 0.877707 |
1. A system for replying to messages comprising: a display device that displays a graphical user interface that presents a list of messages and one or more reply options to a user, that permits the user to select a message and that permits the user to select one of the one or more reply options through the graphical user interface, wherein the graphical user interface includes one or more views; and a computer-readable medium that includes a reply module, the reply module including computer-readable instructions operable to be executed on a processor, that determines, in response to the selected one or more reply options, one or more related messages that are related to the selected massage, automatically populates one or more address fields of a reply message with a message address of a sender of the selected message and a message address of a recipient of the one or more related messages, wherein the recipient of the one or more related messages is neither a sender of the selected message nor a recipient of the selected message; and stores the message address of the sender of the selected message and the message address of the recipient of the one or more related messages, along with information regarding the field of the message from which each address originated. | 1. A system for replying to messages comprising: a display device that displays a graphical user interface that presents a list of messages and one or more reply options to a user, that permits the user to select a message and that permits the user to select one of the one or more reply options through the graphical user interface, wherein the graphical user interface includes one or more views; and a computer-readable medium that includes a reply module, the reply module including computer-readable instructions operable to be executed on a processor, that determines, in response to the selected one or more reply options, one or more related messages that are related to the selected massage, automatically populates one or more address fields of a reply message with a message address of a sender of the selected message and a message address of a recipient of the one or more related messages, wherein the recipient of the one or more related messages is neither a sender of the selected message nor a recipient of the selected message; and stores the message address of the sender of the selected message and the message address of the recipient of the one or more related messages, along with information regarding the field of the message from which each address originated. 8. The system of claim 1 , wherein automatically populating the reply message comprises automatically inserting the name of the sender of the selected message, the name of the at least one recipient of the selected message, and a name of at least one second recipient into fields of the reply message corresponding to the fields from which the names originated. | 0.5 |
10. A system for facilitating creation of at least a recipe for processing at least a substrate in at least a processing system, the system comprising: a computing device; a recipe editor implemented in said computing device, said recipe editor incorporating best-known methods (BKMs), said BKMs being practice specifications for said recipe; a plurality of BKM modules based on said BKMs for said recipe, wherein each BKM module or said plurality of BKM modules relates to a process stage for processing said substrate and includes a plurality of recipe steps of said recipe; and rules for defining parameters in said plurality of BKM modules, wherein said recipe editor is configured to translate user-propagated parameter values into updated rules, said user-propagated parameter values being propagated by a first user in at least one of said processing system and said plurality of BKM modules; said computing device is configured to generate at least an updated BKM module using said updated rules, and said computing device is further configured to provide said updated BKM module to at least a second processing system that is used by a second user. | 10. A system for facilitating creation of at least a recipe for processing at least a substrate in at least a processing system, the system comprising: a computing device; a recipe editor implemented in said computing device, said recipe editor incorporating best-known methods (BKMs), said BKMs being practice specifications for said recipe; a plurality of BKM modules based on said BKMs for said recipe, wherein each BKM module or said plurality of BKM modules relates to a process stage for processing said substrate and includes a plurality of recipe steps of said recipe; and rules for defining parameters in said plurality of BKM modules, wherein said recipe editor is configured to translate user-propagated parameter values into updated rules, said user-propagated parameter values being propagated by a first user in at least one of said processing system and said plurality of BKM modules; said computing device is configured to generate at least an updated BKM module using said updated rules, and said computing device is further configured to provide said updated BKM module to at least a second processing system that is used by a second user. 12. The system of claim 10 wherein said updated BKM module includes one or more updated permissible value ranges for one or more of said parameters. | 0.58011 |
1. A non-transitory tangible storage medium residing on a server and storing a computer program for communicating page-based content among a server and a plurality of client devices via a network, the computer program comprising executable instructions that cause a computer to: define the page-based content to the plurality of client devices, the page-based content including user interface (UI) elements in a platform and language independent format, some of the UI elements organized into logical assets including data tables; relate the logical assets to objects that act as external interface for processing and validating the page-based content using a data service layer; accept requests for the page-based content from the plurality of client devices; determine platform formats and human languages used by the plurality of client devices to display the content on each of the plurality of client devices; transform the UI elements in the platform and language independent format into multiple platform formats in multiple human languages; render the UI elements using a pipeline with multiple stages to retrieve at least one action through the data service layer and to perform the at least one action on the transformed UI elements using the data tables; and outputting the rendered UI elements to the plurality of client devices. | 1. A non-transitory tangible storage medium residing on a server and storing a computer program for communicating page-based content among a server and a plurality of client devices via a network, the computer program comprising executable instructions that cause a computer to: define the page-based content to the plurality of client devices, the page-based content including user interface (UI) elements in a platform and language independent format, some of the UI elements organized into logical assets including data tables; relate the logical assets to objects that act as external interface for processing and validating the page-based content using a data service layer; accept requests for the page-based content from the plurality of client devices; determine platform formats and human languages used by the plurality of client devices to display the content on each of the plurality of client devices; transform the UI elements in the platform and language independent format into multiple platform formats in multiple human languages; render the UI elements using a pipeline with multiple stages to retrieve at least one action through the data service layer and to perform the at least one action on the transformed UI elements using the data tables; and outputting the rendered UI elements to the plurality of client devices. 2. The storage medium recited in claim 1 , wherein the UI elements include at least one of user interface UI components, actions, copy, and rendering elements. | 0.5 |
16. A host computing device comprising: a processing system; and one or more computer-readable storage media storing instructions that when executed via the processing system cause the host computing device to implement a security module that is configured to: detect connection of a connector for an accessory device to an accessory port of the host computing device based on a logic state combination obtained via a pair of detection pins integrated with the connector, the connector and accessory port configured to support reversible connection of the connector to the accessory port; identify a device type of the accessory device according to the logic state combination, the logic state combination indicating whether the accessory device is a one wire device or a two wire device; ascertain an orientation of the connection of the connector to the accessory port; and configure a switching mechanism of the host computing device to automatically route signals according to the identified device type and the ascertained orientation. | 16. A host computing device comprising: a processing system; and one or more computer-readable storage media storing instructions that when executed via the processing system cause the host computing device to implement a security module that is configured to: detect connection of a connector for an accessory device to an accessory port of the host computing device based on a logic state combination obtained via a pair of detection pins integrated with the connector, the connector and accessory port configured to support reversible connection of the connector to the accessory port; identify a device type of the accessory device according to the logic state combination, the logic state combination indicating whether the accessory device is a one wire device or a two wire device; ascertain an orientation of the connection of the connector to the accessory port; and configure a switching mechanism of the host computing device to automatically route signals according to the identified device type and the ascertained orientation. 18. A host computing system as recited in claim 16 , wherein the switching mechanism is configured based on: positions being set for switches and multiplexers of the host computing device to produce straight or reversed signal paths according to the ascertained orientation; and commands being sent to the accessory device to cause set-up of signal routing on the accessory side via signal switching components of the accessory device under the direction of the host computing device. | 0.519371 |
1. A computer-implemented method for multicultural electronic communication management, the method comprising: initiating, by a user, an electronic communication configured to be transmitted to both a first intended recipient and a second intended recipient; identifying, based on a set of profile data, a first cultural indicator for the first intended recipient; identifying, based on the set of profile data, a second cultural indicator for the second intended recipient; detecting, using a natural language processing technique, a cultural element of the electronic communication; determining, based on both the first cultural indicator and the cultural element, a first cultural-version of the cultural element for the first intended recipient; determining, based on both the second cultural indicator and the cultural element, a second cultural-version of the cultural element for the second intended recipient; establishing, using both the first cultural-version and the second cultural-version, a cultural translation object in the electronic communication; and transmitting, in response to establishing the cultural translation object in the electronic communication, the electronic communication to both the first intended recipient and the second intended recipient. | 1. A computer-implemented method for multicultural electronic communication management, the method comprising: initiating, by a user, an electronic communication configured to be transmitted to both a first intended recipient and a second intended recipient; identifying, based on a set of profile data, a first cultural indicator for the first intended recipient; identifying, based on the set of profile data, a second cultural indicator for the second intended recipient; detecting, using a natural language processing technique, a cultural element of the electronic communication; determining, based on both the first cultural indicator and the cultural element, a first cultural-version of the cultural element for the first intended recipient; determining, based on both the second cultural indicator and the cultural element, a second cultural-version of the cultural element for the second intended recipient; establishing, using both the first cultural-version and the second cultural-version, a cultural translation object in the electronic communication; and transmitting, in response to establishing the cultural translation object in the electronic communication, the electronic communication to both the first intended recipient and the second intended recipient. 5. The method of claim 1 , wherein determining, based on both the first cultural indicator and the cultural element, the first cultural-version of the cultural element for the first intended recipient includes: identifying a cultural database which corresponds to the first cultural indicator; searching the cultural database for the cultural element; and ascertaining a set of matches for the cultural element in the cultural database. | 0.516641 |
10. One or more non-transitory computer-readable storage media storing instructions that, when executed on a processor, cause the processor to: receive a specification of a statically typed first interface to a first function, the first interface including a specification of a data type of a parameter of the first function; analyze the specification of the statically typed first interface to the first function to determine a function signature; determine, based on the analyzing, the function signature; identify a second function using the function signature, the second function corresponding to the specification of the statically typed first interface to the first function, the second function written using a dynamically typed language; and generate an implementation of the first function that invokes the second function through a dynamically typed function specification associated with the second function. | 10. One or more non-transitory computer-readable storage media storing instructions that, when executed on a processor, cause the processor to: receive a specification of a statically typed first interface to a first function, the first interface including a specification of a data type of a parameter of the first function; analyze the specification of the statically typed first interface to the first function to determine a function signature; determine, based on the analyzing, the function signature; identify a second function using the function signature, the second function corresponding to the specification of the statically typed first interface to the first function, the second function written using a dynamically typed language; and generate an implementation of the first function that invokes the second function through a dynamically typed function specification associated with the second function. 15. The media of claim 10 , wherein the implementation of the first function converts one or more parameters of an output of the second function to the data type specified by one or more parameters of an output of the first function, the output of the first function specified by the first interface. | 0.5 |
1. A bio-item searching apparatus that searches for a target bio-item with a keyword input by a user, comprising a processor, a storage device, and an output device, wherein the storage device includes: a bio-item literature set storage unit that stores one or more bio-item literature sets having a literature in which a bio-item name is described for each of a plurality of bio-items, and an all-literature set storage unit that stores an all-literature set having all literatures included in each of the bio-item literature sets, and the control processor includes: a number-of-literatures acquiring unit that searches each of the bio-item literature sets with the keyword to acquire a number-of-literatures, Nh, including the keyword for each of the bio-items, and searches the all-literature set with the keyword to acquire a number-of-literatures including the keyword, Nk; a candidate bio-item selecting unit that selects the bio-items in which the number-of-literatures Nh is 1 or larger as candidate bio-items; a table creating unit that creates, for each of the candidate bio-items, a number-of-literatures table constituted by a) the number-of-literatures Nh, b) a number-of-literatures each not including the keyword and including the bio-item name, the number-of-literatures in the bio-item literature set of the bio-item minus Nh; c) a number-of-literatures each including the keyword and not including the bio-item name, Nk−Nh, and d) a number-of-literatures each not including the keyword and not including the bio-item, the total number-of-literatures in the all-literature set minus the number-of-literatures in the bio-item literature set minus Nk+Nh; a correlation score calculating unit that calculates a correlation score between the bio-item and the keyword based on a statistical calculation by using the number-of-literatures table for each of the candidate bio-items; and an output unit that outputs the candidate bio-items to the output device based on the correlation score calculated by the correlation score calculating unit. | 1. A bio-item searching apparatus that searches for a target bio-item with a keyword input by a user, comprising a processor, a storage device, and an output device, wherein the storage device includes: a bio-item literature set storage unit that stores one or more bio-item literature sets having a literature in which a bio-item name is described for each of a plurality of bio-items, and an all-literature set storage unit that stores an all-literature set having all literatures included in each of the bio-item literature sets, and the control processor includes: a number-of-literatures acquiring unit that searches each of the bio-item literature sets with the keyword to acquire a number-of-literatures, Nh, including the keyword for each of the bio-items, and searches the all-literature set with the keyword to acquire a number-of-literatures including the keyword, Nk; a candidate bio-item selecting unit that selects the bio-items in which the number-of-literatures Nh is 1 or larger as candidate bio-items; a table creating unit that creates, for each of the candidate bio-items, a number-of-literatures table constituted by a) the number-of-literatures Nh, b) a number-of-literatures each not including the keyword and including the bio-item name, the number-of-literatures in the bio-item literature set of the bio-item minus Nh; c) a number-of-literatures each including the keyword and not including the bio-item name, Nk−Nh, and d) a number-of-literatures each not including the keyword and not including the bio-item, the total number-of-literatures in the all-literature set minus the number-of-literatures in the bio-item literature set minus Nk+Nh; a correlation score calculating unit that calculates a correlation score between the bio-item and the keyword based on a statistical calculation by using the number-of-literatures table for each of the candidate bio-items; and an output unit that outputs the candidate bio-items to the output device based on the correlation score calculated by the correlation score calculating unit. 12. The bio-item searching apparatus according to claim 1 , wherein the bio-item name includes a concept word. | 0.704037 |
17. The system of claim 7 further comprising a user modeling module coupled to the processing device and the database, wherein the user modeling module is configured to: create a user model based on information received from a user, wherein the user model including at least four elements. | 17. The system of claim 7 further comprising a user modeling module coupled to the processing device and the database, wherein the user modeling module is configured to: create a user model based on information received from a user, wherein the user model including at least four elements. 18. The system of claim 17 wherein the four elements comprise use case, scope, nuance and linguistic variability. | 0.933705 |
13. One or more non-transitory computer-readable storage media storing instructions, the instructions including instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of: collecting usage data that indicates how frequently users interact with annotations for entities that are referenced in documents presented to the users; based at least in part on the usage data, generating weights for features that are associated with the entities referenced in the documents; wherein a particular weight of a particular feature is based at least in part on how frequently users interact with annotations of entities having the particular feature; identifying a set of identified entities within a document; determining a ranking for the identified entities that belong to said set of identified identities based, at least in part, on (a) feature scores for each of the identified entities, wherein the feature scores correspond to features associated with the identified entities, wherein the particular feature is associated with at least one of the identified entities; and (b) weights, including the particular weight, for the features that are associated with the identified entities; based at least in part on the ranking, automatically selecting a subset of the identified entities for annotation, wherein the subset includes fewer than all of the identified entities; automatically generating an annotated version of the document by, for each entity in the subset, adding to the document a control for displaying additional information about the entity, wherein the additional information about the entity and the control associated with the entity were not in the document before the step of automatically generating the annotated version of the document. | 13. One or more non-transitory computer-readable storage media storing instructions, the instructions including instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of: collecting usage data that indicates how frequently users interact with annotations for entities that are referenced in documents presented to the users; based at least in part on the usage data, generating weights for features that are associated with the entities referenced in the documents; wherein a particular weight of a particular feature is based at least in part on how frequently users interact with annotations of entities having the particular feature; identifying a set of identified entities within a document; determining a ranking for the identified entities that belong to said set of identified identities based, at least in part, on (a) feature scores for each of the identified entities, wherein the feature scores correspond to features associated with the identified entities, wherein the particular feature is associated with at least one of the identified entities; and (b) weights, including the particular weight, for the features that are associated with the identified entities; based at least in part on the ranking, automatically selecting a subset of the identified entities for annotation, wherein the subset includes fewer than all of the identified entities; automatically generating an annotated version of the document by, for each entity in the subset, adding to the document a control for displaying additional information about the entity, wherein the additional information about the entity and the control associated with the entity were not in the document before the step of automatically generating the annotated version of the document. 22. The one or more non-transitory computer-readable storage media of claim 13 wherein the step of generating the weights includes using a machine learning mechanism to generate the weights based at least in part on a correlation between (a) feature scores that correspond to the features associated with the entities referenced in the documents, and (b) click-through-rates, indicated by the usage data, for the annotations of the entities referenced in the documents. | 0.591231 |
1. A computer implemented method of incorporating author feedback on placement of advertising in a webpage comprising: receiving, by at least one computer, a webpage created by an author and at least one cluster of candidate advertisements, wherein the webpage comprises content and the candidate advertisements are topically similar to the content of the webpage; ranking, by the computer, the candidate advertisements by degree of topical similarity to the webpage content; pairing, by the computer, the ranked candidate advertisements to the webpage content; receiving, by the computer, feedback provided by the author of the webpage regarding the placement of the candidate advertisements within the webpage, wherein the feedback comprises at least one of a binary opinion of the author on relevance of the candidate advertisement to the webpage and a binary opinion of the author on appropriateness of the candidate advertisement to the webpage; retrieving, by the computer, a credibility score corresponding to click-through rates of previous advertisements upon which feedback has been provided; ranking, by the computer, the candidate advertisements based on the feedback of the author and the credibility score; selecting, by the computer, at least one advertisement from among the candidate advertisements for incorporation into the webpage; transmitting, by the computer, the selected advertisement to a user; receiving, at the computer, a selection of a link corresponding to the incorporated advertisement; wherein if the author has a credibility score, incrementing, by the computer, the author credibility score based on the selection of the link; and wherein if the author does not have a credibility score, creating, by the computer, the author credibility score based on the selection of the link. | 1. A computer implemented method of incorporating author feedback on placement of advertising in a webpage comprising: receiving, by at least one computer, a webpage created by an author and at least one cluster of candidate advertisements, wherein the webpage comprises content and the candidate advertisements are topically similar to the content of the webpage; ranking, by the computer, the candidate advertisements by degree of topical similarity to the webpage content; pairing, by the computer, the ranked candidate advertisements to the webpage content; receiving, by the computer, feedback provided by the author of the webpage regarding the placement of the candidate advertisements within the webpage, wherein the feedback comprises at least one of a binary opinion of the author on relevance of the candidate advertisement to the webpage and a binary opinion of the author on appropriateness of the candidate advertisement to the webpage; retrieving, by the computer, a credibility score corresponding to click-through rates of previous advertisements upon which feedback has been provided; ranking, by the computer, the candidate advertisements based on the feedback of the author and the credibility score; selecting, by the computer, at least one advertisement from among the candidate advertisements for incorporation into the webpage; transmitting, by the computer, the selected advertisement to a user; receiving, at the computer, a selection of a link corresponding to the incorporated advertisement; wherein if the author has a credibility score, incrementing, by the computer, the author credibility score based on the selection of the link; and wherein if the author does not have a credibility score, creating, by the computer, the author credibility score based on the selection of the link. 2. The computer implemented method of claim 1 , wherein the cluster of candidate advertisements, prior to being selected for placement on the webpage, are clustered using an advertisement clustering function. | 0.54112 |
1. A computer-implemented method for generating consumer sentiment and attribute correlation visualizations from data obtained from a plurality of data sources, comprising: integrating data from a plurality of data sources; receiving a business intelligence query from a user; parsing the business intelligence query to determine polarity, emotion and topicality (PET) of the query; selecting key attributes from a plurality of available attributes in the integrated data responsive to the PET; compiling the selected key attributes into a metadata construct; generating at least one consumer segment using social network data located in the metadata construct; generating sentiment for the at least one consumer segment using the metadata construct by load balancing multiple servers operating in parallel coupled to a local database for storing transient analytics data from the plurality of data sources and analysis results thereby enhancing analysis performance, wherein the generation of the sentiment is by name-value pair modeling; generating correlations between attributes of the integrated data; and generating a visualization of the segment, sentiment and correlations as an infographic and chart; outputting the visualization in an electronic format accessible by a third party application. | 1. A computer-implemented method for generating consumer sentiment and attribute correlation visualizations from data obtained from a plurality of data sources, comprising: integrating data from a plurality of data sources; receiving a business intelligence query from a user; parsing the business intelligence query to determine polarity, emotion and topicality (PET) of the query; selecting key attributes from a plurality of available attributes in the integrated data responsive to the PET; compiling the selected key attributes into a metadata construct; generating at least one consumer segment using social network data located in the metadata construct; generating sentiment for the at least one consumer segment using the metadata construct by load balancing multiple servers operating in parallel coupled to a local database for storing transient analytics data from the plurality of data sources and analysis results thereby enhancing analysis performance, wherein the generation of the sentiment is by name-value pair modeling; generating correlations between attributes of the integrated data; and generating a visualization of the segment, sentiment and correlations as an infographic and chart; outputting the visualization in an electronic format accessible by a third party application. 5. The method of claim 1 further comprising calculating polarity, emotion and topicality for a target and an audience. | 0.622685 |
3. The method of claim 2 , wherein the computing a simplified representation of the scene further comprises: applying a chromatic transformation to accommodate for a particular illumination condition of the scene; labeling each pixel in the image with a label of a descriptive type to correspond with the descriptive type of an element of the scene to which each pixel belongs; performing adaptive smoothing of the image with an amount of smoothing around each pixel in correspondence with the descriptive type of each pixel; for each pixel, determining whether each pixel is a perceptually significant pixel or a perceptually insignificant pixel; and reapplying chromatic transformation to preserve a color appearance as perceived in the human observation. | 3. The method of claim 2 , wherein the computing a simplified representation of the scene further comprises: applying a chromatic transformation to accommodate for a particular illumination condition of the scene; labeling each pixel in the image with a label of a descriptive type to correspond with the descriptive type of an element of the scene to which each pixel belongs; performing adaptive smoothing of the image with an amount of smoothing around each pixel in correspondence with the descriptive type of each pixel; for each pixel, determining whether each pixel is a perceptually significant pixel or a perceptually insignificant pixel; and reapplying chromatic transformation to preserve a color appearance as perceived in the human observation. 4. The method of claim 3 , wherein the labeling further comprises: determining if each pixel in the image represents an edge; estimating distribution of edge density in a neighborhood of the each pixel in the image; and using the distribution to determine a descriptive type of each pixel and labeling each pixel accordingly. | 0.5 |
15. An apparatus for preparing a display document for analysis comprising a processor implementing: an extractor for extracting character data from said display document, wherein the character data comprises image data representing an image of a number of characters without including character codes; an order identifier for determining a first order associated with processing of said character data and a second order associated with a logical order of said character data, and for determining whether said first order is different from said second order, wherein determining the second order comprises identifying a punctuation character that is position dependent such that a space character will appear on only one side of the punctuation character, where the side of the punctuation character on which the space character appears depends on said second order; and a reverse component for reversing at least a portion of said character data, responsive to said order identifier determining that said first order is different from said second order. | 15. An apparatus for preparing a display document for analysis comprising a processor implementing: an extractor for extracting character data from said display document, wherein the character data comprises image data representing an image of a number of characters without including character codes; an order identifier for determining a first order associated with processing of said character data and a second order associated with a logical order of said character data, and for determining whether said first order is different from said second order, wherein determining the second order comprises identifying a punctuation character that is position dependent such that a space character will appear on only one side of the punctuation character, where the side of the punctuation character on which the space character appears depends on said second order; and a reverse component for reversing at least a portion of said character data, responsive to said order identifier determining that said first order is different from said second order. 17. The apparatus of claim 15 , wherein said order identifier is configured to: determine a commonly-occurring word in said character data; compare said commonly-occurring word against a rule; and in response to said comparison, determine said second order. | 0.584919 |
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. | 0.920094 |
7. A non-transitory computer-readable storage medium storing instructions for identifying an audio content sample, the instructions which when executed by a processor cause the processor to: monitor a plurality of broadcast stations, fingerprint and save the fingerprints of broadcast audio content in a database of unidentified broadcast content as it is received; access playlists, comprising portions of identified broadcast audio content from the plurality of monitored broadcast stations, and fingerprints corresponding to the identified broadcast audio content; receive data representing sampled audio content from a portable device and search for a match between fingerprints of the sampled audio content and the fingerprints corresponding to at least parts of multiple playlists, further including: upon finding a fingerprint match against the fingerprints corresponding to a particular playlist for a particular monitored broadcast station, report the particular monitored broadcast station as a source of the sampled audio content, and identification of the sampled audio content back to the portable device; and upon not finding a fingerprint match against the fingerprints corresponding to any of the multiple playlists, further search for a match the fingerprints of sampled audio content, against at least one of: parts of the database of unidentified broadcast content from the monitored broadcast stations, to identify a source of the sampled audio content; and a reference database of identified audio content not associated with a particular broadcast station, to identify the sampled audio content; and report back to the portable device at least one of the source of the sampled audio content and the identity of the sampled audio content. | 7. A non-transitory computer-readable storage medium storing instructions for identifying an audio content sample, the instructions which when executed by a processor cause the processor to: monitor a plurality of broadcast stations, fingerprint and save the fingerprints of broadcast audio content in a database of unidentified broadcast content as it is received; access playlists, comprising portions of identified broadcast audio content from the plurality of monitored broadcast stations, and fingerprints corresponding to the identified broadcast audio content; receive data representing sampled audio content from a portable device and search for a match between fingerprints of the sampled audio content and the fingerprints corresponding to at least parts of multiple playlists, further including: upon finding a fingerprint match against the fingerprints corresponding to a particular playlist for a particular monitored broadcast station, report the particular monitored broadcast station as a source of the sampled audio content, and identification of the sampled audio content back to the portable device; and upon not finding a fingerprint match against the fingerprints corresponding to any of the multiple playlists, further search for a match the fingerprints of sampled audio content, against at least one of: parts of the database of unidentified broadcast content from the monitored broadcast stations, to identify a source of the sampled audio content; and a reference database of identified audio content not associated with a particular broadcast station, to identify the sampled audio content; and report back to the portable device at least one of the source of the sampled audio content and the identity of the sampled audio content. 11. The non-transitory computer-readable storage medium of claim 7 , further including instructions, which when executed by the processor, cause the processor to: save playlists of the broadcast audio content from the broadcast stations monitored; and send the portable device at least a recent portion of a playlist that identifies a plurality of recent items broadcast by a monitored broadcast station source. | 0.535354 |
30. The computer readable storage medium of claim 22 wherein one of the second set of attributes for one of the controls relates to a location of data for audible output. | 30. The computer readable storage medium of claim 22 wherein one of the second set of attributes for one of the controls relates to a location of data for audible output. 32. The computer readable storage medium of claim 30 wherein the data comprises text and the attribute relates to converting the text to audible output. | 0.966137 |
8. A method implemented in instructions executed by a computer processor of executing an application's process code, the method comprising: instantiating a process object based on a process XML document, wherein the process XML document adheres to a process XML schema, and wherein the process XML document includes: a trigger element having one or more attributes for identifying process code to execute in response to a user interface component experiencing a trigger event; and a plurality of process step elements, wherein at least one process step element of the plurality of process step elements includes at least one operation element having one or more attributes identifying an operation handler to execute and an associated expression, and wherein at least one process step element of the plurality of process step elements includes an operation element for directing a flow of execution to another process step element; and in response to the user interface component experiencing a trigger event with attributes matching the trigger element, causing logic represented in at least one process step element to be executed. | 8. A method implemented in instructions executed by a computer processor of executing an application's process code, the method comprising: instantiating a process object based on a process XML document, wherein the process XML document adheres to a process XML schema, and wherein the process XML document includes: a trigger element having one or more attributes for identifying process code to execute in response to a user interface component experiencing a trigger event; and a plurality of process step elements, wherein at least one process step element of the plurality of process step elements includes at least one operation element having one or more attributes identifying an operation handler to execute and an associated expression, and wherein at least one process step element of the plurality of process step elements includes an operation element for directing a flow of execution to another process step element; and in response to the user interface component experiencing a trigger event with attributes matching the trigger element, causing logic represented in at least one process step element to be executed. 9. The method as recited in claim 8 , wherein the trigger element includes view and component attributes for identifying the application view and component associated with the trigger. | 0.656404 |
26. A method of processing a signal, the signal comprising a plurality of multi-element data encoding vectors, wherein the data encoding vectors are derived from an analogue or digital input, and where the method employs at least one Gaussian Mixture Model (GMM) or GMM based Hidden Markov Model (HMM), the GMM or GMM based HMM having at least one class mean vector having multiple elements, and the elements of the class mean vector(s) are optimised in an iterative procedure, characterised in that the elements of the class mean vectors are scaled during the optimisation procedure such that the class mean vectors have a constant modulus at each iteration, and the data encoding vectors input to the GMM or GMM based HMM are processed such that they are normalised. | 26. A method of processing a signal, the signal comprising a plurality of multi-element data encoding vectors, wherein the data encoding vectors are derived from an analogue or digital input, and where the method employs at least one Gaussian Mixture Model (GMM) or GMM based Hidden Markov Model (HMM), the GMM or GMM based HMM having at least one class mean vector having multiple elements, and the elements of the class mean vector(s) are optimised in an iterative procedure, characterised in that the elements of the class mean vectors are scaled during the optimisation procedure such that the class mean vectors have a constant modulus at each iteration, and the data encoding vectors input to the GMM or GMM based HMM are processed such that they are normalised. 29. A method as claimed in claim 26 wherein the data encoding vectors are scaled in a pre-processing stage before being input to the GMM or GMM based HMM, such that the moduli of all data encoding vectors are equal. | 0.55704 |
7. A computer-implemented method performed by at least one processor, the method comprising: receiving, by the at least one processor, at least one image of a document; determining, by the at least one processor, based on the at least one image, data associated with the document; generating, by the at least one processor, a seal that encodes the data associated with the document, the seal being applicable to a tangible version of the document; based on a scan of the seal applied to the tangible version of the document, determining, by the at least one processor, the data that is encoded in the seal; employing, by the at least one processor, the data to verify at least one characteristic of the tangible version of the document; employing, by the at least one processor, the data to determine an address on a blockchain network; and accessing, by the at least one processor, funds associated with the address. | 7. A computer-implemented method performed by at least one processor, the method comprising: receiving, by the at least one processor, at least one image of a document; determining, by the at least one processor, based on the at least one image, data associated with the document; generating, by the at least one processor, a seal that encodes the data associated with the document, the seal being applicable to a tangible version of the document; based on a scan of the seal applied to the tangible version of the document, determining, by the at least one processor, the data that is encoded in the seal; employing, by the at least one processor, the data to verify at least one characteristic of the tangible version of the document; employing, by the at least one processor, the data to determine an address on a blockchain network; and accessing, by the at least one processor, funds associated with the address. 9. The method of claim 7 , wherein: the seal includes a near field communication (NFC) tag; and the scan of the seal includes receiving a signal emitted from the NFC tag, the signal carrying the data. | 0.509193 |
1. A system for evaluating passwords for a plurality of users of a computer system, the system comprising: a computer system including: a login database for storing a user name and corresponding password information for each of a plurality of users, the password information being generated by performing a security procedure on a password entry; a first data acquisition interface for receiving the user name and the password entry from the plurality of users; and a security module coupled to the login database and the first data acquisition interface for performing the security procedure on the received password entry and providing access to the computer system if the generated password information matches the password information in the login database; and a password evaluation component including: a second data acquisition interface for obtaining the password information for each user of the computer system; a database including a dictionary having sequenced entries; an analysis module coupled to the second data acquisition interface and the database for sequentially performing, for each user, the security procedure on the sequenced entries until either a match with the user's password information is detected or the sequenced entries are exhausted; wherein the password evaluation component stores in the database, for each of the sequenced entries, data associated with the number of matches detected for the sequenced entry; and wherein the password evaluation component adjusts the sequence of entries in the dictionary based on the number of matches detected for each sequenced entry. | 1. A system for evaluating passwords for a plurality of users of a computer system, the system comprising: a computer system including: a login database for storing a user name and corresponding password information for each of a plurality of users, the password information being generated by performing a security procedure on a password entry; a first data acquisition interface for receiving the user name and the password entry from the plurality of users; and a security module coupled to the login database and the first data acquisition interface for performing the security procedure on the received password entry and providing access to the computer system if the generated password information matches the password information in the login database; and a password evaluation component including: a second data acquisition interface for obtaining the password information for each user of the computer system; a database including a dictionary having sequenced entries; an analysis module coupled to the second data acquisition interface and the database for sequentially performing, for each user, the security procedure on the sequenced entries until either a match with the user's password information is detected or the sequenced entries are exhausted; wherein the password evaluation component stores in the database, for each of the sequenced entries, data associated with the number of matches detected for the sequenced entry; and wherein the password evaluation component adjusts the sequence of entries in the dictionary based on the number of matches detected for each sequenced entry. 3. The system of claim 1 , wherein the password evaluation component further comprises an accounting unit for calculating the number of matches detected, and for generating an invoice based on the number of matches detected. | 0.534397 |
28. The memory device of claim 27 , wherein the operations further comprise: determining whether the electronic message conversation data structure is missing information pertaining to a prior electronic message that is known to have been sent in the electronic message conversation prior to the received electronic message; and in response to determining that the electronic message conversation data structure is missing information pertaining to the prior electronic message, sending a request electronic message to a human participant of the electronic message conversation to request that the prior electronic message be sent again. | 28. The memory device of claim 27 , wherein the operations further comprise: determining whether the electronic message conversation data structure is missing information pertaining to a prior electronic message that is known to have been sent in the electronic message conversation prior to the received electronic message; and in response to determining that the electronic message conversation data structure is missing information pertaining to the prior electronic message, sending a request electronic message to a human participant of the electronic message conversation to request that the prior electronic message be sent again. 35. The memory device of claim 28 , wherein the information in the electronic message conversation data structure pertaining to each of the electronic messages in the electronic message conversation comprises: (1) an indication of an electronic message type for that electronic message; (2) an indication of which participant in the electronic message conversation sent that electronic message; and (3) a sequence indicator indicating an order in which that electronic message was sent by that participant relative to other electronic messages of the same electronic message type sent by that participant in the electronic message conversation. | 0.747525 |
1. A method for dynamically generating a survey result(s) comprising: storing and managing each registered user's one or more profile(s), preferences and relational connections or dynamic relationships at a central server; allowing each user to manage a Human Operating System (HOS) including one or more profiles, activities, applications, services, actions, transactions, groups, searching, sharing, communication, contents and connections; presenting one or more domain or subject or taxonomy specific survey forms to user; receiving, via categories survey forms, a plurality of categories survey data or selections from the user, wherein survey data or selections relate or map, for each of plurality of different categories of user data for sharing with one or more other connected users who can access that category of user data and customization, personalization and configuration data utilize for customization of the user's Human Operating System (HOS) including dynamically creating one or more social networks, establishing communication and sharing selective one or more user resources or profiles with one or more other connected users, customize searching and matching, e-commerce, receiving customized advertisements, applications and services lists and contents; updating survey data and survey analysis to the related categories profile(s) of the user for applying or use the survey data for customization, personalization and configuration of each user's Human Operating System (HOS); and generating and presenting a survey results to the user, wherein survey results comprises a details of customization, personalization and configuration of each user's Human Operating System (HOS) and which other connected users can access which categories of user data based on the survey data or selections. | 1. A method for dynamically generating a survey result(s) comprising: storing and managing each registered user's one or more profile(s), preferences and relational connections or dynamic relationships at a central server; allowing each user to manage a Human Operating System (HOS) including one or more profiles, activities, applications, services, actions, transactions, groups, searching, sharing, communication, contents and connections; presenting one or more domain or subject or taxonomy specific survey forms to user; receiving, via categories survey forms, a plurality of categories survey data or selections from the user, wherein survey data or selections relate or map, for each of plurality of different categories of user data for sharing with one or more other connected users who can access that category of user data and customization, personalization and configuration data utilize for customization of the user's Human Operating System (HOS) including dynamically creating one or more social networks, establishing communication and sharing selective one or more user resources or profiles with one or more other connected users, customize searching and matching, e-commerce, receiving customized advertisements, applications and services lists and contents; updating survey data and survey analysis to the related categories profile(s) of the user for applying or use the survey data for customization, personalization and configuration of each user's Human Operating System (HOS); and generating and presenting a survey results to the user, wherein survey results comprises a details of customization, personalization and configuration of each user's Human Operating System (HOS) and which other connected users can access which categories of user data based on the survey data or selections. 2. The method as claimed in claim 1 , wherein central server dynamically presents said one or more customized domain or subject specific survey forms to user based on one or more profiles, user data and connections, user selections, actions and preferences. | 0.650727 |
33. An apparatus for automatically evaluating Bayesian network models for decision support, the apparatus comprising a computer system including a processor, a memory coupled with the processor, an input coupled with the processor for receiving user input and data input, and an output coupled with the processor for outputting display data, wherein: the input is configured for receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes, where the conclusion nodes are linked with the evidence nodes by causal dependency links, and where the evidence nodes have evidence states and the conclusion nodes have conclusion states; the processor is configured for setting the states of the conclusion nodes to desired conclusion states and determining, by propagating down the causal dependency links, a corresponding probability of occurrence of evidence states of the evidence nodes and producing, from the probability of occurrence, a plurality of samples of most likely states of the evidence nodes; and for setting the states of the evidence nodes to states corresponding to the plurality of samples of the evidence states, and propagating the evidence states back up the causal dependency links to the conclusion nodes, to obtain a plurality of probabilities of the resulting states of the conclusion nodes; and the output is configured for outputting a representation of the plurality of the probabilities of the resulting states of the conclusion nodes. | 33. An apparatus for automatically evaluating Bayesian network models for decision support, the apparatus comprising a computer system including a processor, a memory coupled with the processor, an input coupled with the processor for receiving user input and data input, and an output coupled with the processor for outputting display data, wherein: the input is configured for receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes, where the conclusion nodes are linked with the evidence nodes by causal dependency links, and where the evidence nodes have evidence states and the conclusion nodes have conclusion states; the processor is configured for setting the states of the conclusion nodes to desired conclusion states and determining, by propagating down the causal dependency links, a corresponding probability of occurrence of evidence states of the evidence nodes and producing, from the probability of occurrence, a plurality of samples of most likely states of the evidence nodes; and for setting the states of the evidence nodes to states corresponding to the plurality of samples of the evidence states, and propagating the evidence states back up the causal dependency links to the conclusion nodes, to obtain a plurality of probabilities of the resulting states of the conclusion nodes; and the output is configured for outputting a representation of the plurality of the probabilities of the resulting states of the conclusion nodes. 47. An apparatus for automatically evaluating Bayesian network models for decision support as set forth in claim 33 , wherein the conclusion nodes are weighted by weights representing their importance; whereby an accuracy of the BN model's propensity to yield proper conclusions may be weighted for particular conclusions based on their relative importance. | 0.526659 |
1. An electronic device comprising: storage configured to maintain a primary web-browser application and a secondary web-browser application; at least one processor connected to said storage and configured to execute said primary web-browser application; an interface connected to said processor, said processor configured to receive a web-page stored at a web-server via said interface, said web-page including context sensitive content related to a plurality of context sensitive items on said web-page, said context sensitive content being able to change without further input from the web-server, said context sensitive content comprising a data portion and a non-data portion, said non-data portion comprising scripts executable by said primary web-browser application and said data portion comprising tags, labels, or text; a display connected to said processor; said processor further configured to render said web-page on said display; an input device connected to said processor, said processor configured to receive focus on one of said plurality of context sensitive items via said input device placing a pointer over the one context sensitive item; said processor further configured to respond to receiving the focus by rendering only the tags, labels, or text constituting the data portion of the context sensitive content related to said one of the plurality of context sensitive items on said display via the secondary web-browser application; and said processor further configured to not execute said scripts associated with said context sensitive content using said secondary web-browser application. | 1. An electronic device comprising: storage configured to maintain a primary web-browser application and a secondary web-browser application; at least one processor connected to said storage and configured to execute said primary web-browser application; an interface connected to said processor, said processor configured to receive a web-page stored at a web-server via said interface, said web-page including context sensitive content related to a plurality of context sensitive items on said web-page, said context sensitive content being able to change without further input from the web-server, said context sensitive content comprising a data portion and a non-data portion, said non-data portion comprising scripts executable by said primary web-browser application and said data portion comprising tags, labels, or text; a display connected to said processor; said processor further configured to render said web-page on said display; an input device connected to said processor, said processor configured to receive focus on one of said plurality of context sensitive items via said input device placing a pointer over the one context sensitive item; said processor further configured to respond to receiving the focus by rendering only the tags, labels, or text constituting the data portion of the context sensitive content related to said one of the plurality of context sensitive items on said display via the secondary web-browser application; and said processor further configured to not execute said scripts associated with said context sensitive content using said secondary web-browser application. 12. The electronic device of claim 1 wherein said secondary web-browser application is optimized to different input devices of said portable computing device. | 0.526267 |
1. A method for automatically identifying voice tags on an electronic device configured to execute at least a voice interface program and a non-voice interface application program, the method comprising: receiving, through the voice interface program, a voice input command that includes a command element and a content element; identifying from the voice input command the command element; storing the command element in a buffer; ending the voice interface program without performing the voice input command; receiving, through the non-voice interface application program, a user input which identifies input command data for executing an application program command; and upon successful execution of the application program command: determining that the non-voice interface application program is associated with the command element which was buffered in the buffer, and identifying audio of at least the content element or the entire voice input command as a voice tag associated with the input command data identified by the user input. | 1. A method for automatically identifying voice tags on an electronic device configured to execute at least a voice interface program and a non-voice interface application program, the method comprising: receiving, through the voice interface program, a voice input command that includes a command element and a content element; identifying from the voice input command the command element; storing the command element in a buffer; ending the voice interface program without performing the voice input command; receiving, through the non-voice interface application program, a user input which identifies input command data for executing an application program command; and upon successful execution of the application program command: determining that the non-voice interface application program is associated with the command element which was buffered in the buffer, and identifying audio of at least the content element or the entire voice input command as a voice tag associated with the input command data identified by the user input. 6. The method as claimed in claim 1 , wherein receiving the user input is unprompted by the voice interface program. | 0.846859 |
13. An electronic device, comprising: a processor; a display coupled to the processor; wherein the processor is configured to: receive an input of a character from a virtual keyboard displayed on the display; generate two or more sets of predicted input characters based on the input character; display two or more of the generated sets of predicted input characters at two or more set positions within the virtual keyboard rather than at a key corresponding to a subsequent candidate input character, wherein the two or more set positions are defined by two or more partitions of the virtual keyboard, and a highest ranked set of predicted input characters is displayed in a partition associated with a subsequent candidate input character of the highest ranked set of predicted input characters; determine a candidate partition of a next highest rank set of predicted input characters, wherein the candidate partition of the next highest rank set of predicted input characters is to be associated with a subsequent candidate input character of the next highest ranked set of predicted input characters; and in response to determining that the candidate partition of the next highest rank set of predicted input characters is allocated, discard the next highest rank set of predicted input characters from displaying. | 13. An electronic device, comprising: a processor; a display coupled to the processor; wherein the processor is configured to: receive an input of a character from a virtual keyboard displayed on the display; generate two or more sets of predicted input characters based on the input character; display two or more of the generated sets of predicted input characters at two or more set positions within the virtual keyboard rather than at a key corresponding to a subsequent candidate input character, wherein the two or more set positions are defined by two or more partitions of the virtual keyboard, and a highest ranked set of predicted input characters is displayed in a partition associated with a subsequent candidate input character of the highest ranked set of predicted input characters; determine a candidate partition of a next highest rank set of predicted input characters, wherein the candidate partition of the next highest rank set of predicted input characters is to be associated with a subsequent candidate input character of the next highest ranked set of predicted input characters; and in response to determining that the candidate partition of the next highest rank set of predicted input characters is allocated, discard the next highest rank set of predicted input characters from displaying. 14. The electronic device of claim 13 , wherein the processor is configured to: in response to determining that the candidate partition of the next highest rank set of predicted input characters is unallocated, allocate the next highest rank set of predicted input characters to the candidate partition. | 0.5 |
1. A method comprising: determining that tokens have been incorrectly separated due to an incorrect Natural Language Processing (NLP) operation on a corpus; and recombining, as a part of correcting the incorrect NLP operation, the incorrectly separated tokens, the recombining comprising: determining whether a token from an ordered set of tokens is present in a dictionary, the dictionary being related to the corpus from which the ordered set of tokens is extracted; determining whether a compounding threshold has been reached, wherein the compounding threshold limits a number of tokens that can be agglutinated to form a compound token; agglutinating using a processor and a memory, responsive to the token not being present in the dictionary, and responsive to the compounding threshold not having been reached, the token with a next adjacent token in the ordered set of tokens to form the compound token; determining whether the compound token is present in the dictionary; assigning a weight to the compound token responsive to the compound token being present in the dictionary; computing a confidence rating of the compound token, the confidence rating being a function of the weight; and using the compound token and the confidence rating in performing an NLP operation on the corpus. | 1. A method comprising: determining that tokens have been incorrectly separated due to an incorrect Natural Language Processing (NLP) operation on a corpus; and recombining, as a part of correcting the incorrect NLP operation, the incorrectly separated tokens, the recombining comprising: determining whether a token from an ordered set of tokens is present in a dictionary, the dictionary being related to the corpus from which the ordered set of tokens is extracted; determining whether a compounding threshold has been reached, wherein the compounding threshold limits a number of tokens that can be agglutinated to form a compound token; agglutinating using a processor and a memory, responsive to the token not being present in the dictionary, and responsive to the compounding threshold not having been reached, the token with a next adjacent token in the ordered set of tokens to form the compound token; determining whether the compound token is present in the dictionary; assigning a weight to the compound token responsive to the compound token being present in the dictionary; computing a confidence rating of the compound token, the confidence rating being a function of the weight; and using the compound token and the confidence rating in performing an NLP operation on the corpus. 12. The method of claim 1 , further comprising: selecting the dictionary, wherein the dictionary is related to the corpus because the dictionary is specific to a language of the corpus. | 0.631381 |
1. A computer-implemented method executed by one or more computing devices for assessing an innovation farming level of an entity, said method comprising: gathering, by at least one of the one or more computing devices, information relating to a capability sphere of the entity, wherein the capability sphere includes one or more categories that correspond to one or more capabilities of the entity and wherein in the information is gathered through interaction with the entity; gathering, by at least one of the one or more computing devices, information relating to a behavior sphere of the entity, wherein the behavior sphere includes one or more categories that correspond to one or more behaviors of the entity and wherein the information is gathered through interaction with the entity; gathering, by at least one of the one or more computing devices, information relating to an outcome sphere of the entity, wherein the outcome sphere includes one or more outcome metrics associated with the entity and wherein the information is gathered through interaction with the entity; determining, by at least one of the one or more computing devices, one or more strengths and one or more improvement opportunities of the entity based at least in part on the gathered information; generating, by at least one of the one or more computing devices, one or more scores for the capability sphere of the entity, the behavior sphere of the entity, and the outcome sphere of the entity based at least in part on the gathered information; gathering, by at least one of the one or more computing devices, trending information reflecting a chance in the one or more scores over time; generating, by at least one of the one or more computing devices, a report based on at least one of (i)the one or more strengths, (ii) the one or more improvement opportunities, (iii) the one or more scores, or (iv) the trending information, wherein the report includes an assessed innovation farming level of the entity; and transmitting, by at least one of the one or more computing devices, the report to the entity. | 1. A computer-implemented method executed by one or more computing devices for assessing an innovation farming level of an entity, said method comprising: gathering, by at least one of the one or more computing devices, information relating to a capability sphere of the entity, wherein the capability sphere includes one or more categories that correspond to one or more capabilities of the entity and wherein in the information is gathered through interaction with the entity; gathering, by at least one of the one or more computing devices, information relating to a behavior sphere of the entity, wherein the behavior sphere includes one or more categories that correspond to one or more behaviors of the entity and wherein the information is gathered through interaction with the entity; gathering, by at least one of the one or more computing devices, information relating to an outcome sphere of the entity, wherein the outcome sphere includes one or more outcome metrics associated with the entity and wherein the information is gathered through interaction with the entity; determining, by at least one of the one or more computing devices, one or more strengths and one or more improvement opportunities of the entity based at least in part on the gathered information; generating, by at least one of the one or more computing devices, one or more scores for the capability sphere of the entity, the behavior sphere of the entity, and the outcome sphere of the entity based at least in part on the gathered information; gathering, by at least one of the one or more computing devices, trending information reflecting a chance in the one or more scores over time; generating, by at least one of the one or more computing devices, a report based on at least one of (i)the one or more strengths, (ii) the one or more improvement opportunities, (iii) the one or more scores, or (iv) the trending information, wherein the report includes an assessed innovation farming level of the entity; and transmitting, by at least one of the one or more computing devices, the report to the entity. 3. The method of claim 1 , wherein the gathered information includes information relating to one or more acts performed by the entity within each of one or more disciplines. | 0.53609 |
1. A non-transitory computer-readable medium comprising computer-readable instructions for correlating a free text expression of an identity to at least one known identity having a plurality of expressions in a database, said computer-readable instructions comprising instructions that: allow an input of a plurality of expressions associated with an input identity; electronically convert the plurality of expressions to phonetic equivalent codes; compare the phonetic equivalent codes to determine correlations between the codes; correlate at least one of the plurality of expressions with the known identity based on the phonetic equivalent code associated with the at least one expression having a threshold correlation to a phonetic equivalent code associated with the known identity; electronically provide the known identity using the at least one expression having a threshold correlation to a phonetic equivalent code associated with the known identity; allow an input of a verification that the electronically-provided known identity matches a desired identity intended at a time of the input of the plurality of expressions associated with the input identity; and eliminate from the database another of the plurality of expressions having a threshold correlation to the phonetic equivalent code for the known identity. | 1. A non-transitory computer-readable medium comprising computer-readable instructions for correlating a free text expression of an identity to at least one known identity having a plurality of expressions in a database, said computer-readable instructions comprising instructions that: allow an input of a plurality of expressions associated with an input identity; electronically convert the plurality of expressions to phonetic equivalent codes; compare the phonetic equivalent codes to determine correlations between the codes; correlate at least one of the plurality of expressions with the known identity based on the phonetic equivalent code associated with the at least one expression having a threshold correlation to a phonetic equivalent code associated with the known identity; electronically provide the known identity using the at least one expression having a threshold correlation to a phonetic equivalent code associated with the known identity; allow an input of a verification that the electronically-provided known identity matches a desired identity intended at a time of the input of the plurality of expressions associated with the input identity; and eliminate from the database another of the plurality of expressions having a threshold correlation to the phonetic equivalent code for the known identity. 6. The non-transitory computer-readable medium of claim 1 , wherein the instructions that electronically convert the plurality of expressions associated with the input identity comprises instructions that use frequency-weighted, lossy equivalent letter characterizations. | 0.583686 |
1. A method to answer a query, comprising: saving, in a first database, website classifications of websites based on contents; receiving keywords in the query; determining query specifications from the keywords, the query specifications being website classifications of websites that may contain an answer to the query; determining a group of websites based on the query specifications and the website classifications saved in the first database; selecting a website from the group based on credibility of the websites saved in a second database; searching web pages of the website for the answer; selecting the answer from the web pages; and transmitting the answer. | 1. A method to answer a query, comprising: saving, in a first database, website classifications of websites based on contents; receiving keywords in the query; determining query specifications from the keywords, the query specifications being website classifications of websites that may contain an answer to the query; determining a group of websites based on the query specifications and the website classifications saved in the first database; selecting a website from the group based on credibility of the websites saved in a second database; searching web pages of the website for the answer; selecting the answer from the web pages; and transmitting the answer. 11. The method of claim 1 , wherein the credibility of the websites is based on information closeness, information closeness being determined for a website being based on a past answer determined from the website compared to past answers provided from other websites in response to a past query. | 0.656064 |
8. A system comprising: one or more processors; one or more computer-readable storage mediums containing instructions configured to cause the one or more processors to perform operations comprising: receiving a first query; receiving a second, follow-up query; determining that an anaphora is present in the second, follow-up query by determining that the second, follow-up query includes a pronoun that refers to an entity that is not present in the second, follow-up query; in response to determining that the anaphora is present in the second, follow-up query, determining that the first query is associated with a plurality of possible entities; generating a plurality of candidate queries, wherein each candidate query of the plurality of candidate queries is generated by replacing the pronoun in the second, follow-up query with a corresponding possible entity; obtaining a ranking for the plurality of candidate queries from a ranking engine; determining that a corresponding possible entity in a highest-ranked candidate query of the plurality of candidate queries is an entity associated with the anaphora from the second, follow-up query, wherein the entity associated with the anaphora from the second, follow-up query is the entity that is not present in the second, follow-up query and is referred to by the pronoun in the second, follow-up query; and based on determining that the corresponding possible entity in the highest-ranked candidate query is the entity associated with the anaphora, providing the highest-ranked candidate query to a search engine and obtaining search results for the highest-ranked candidate query from the search engine. | 8. A system comprising: one or more processors; one or more computer-readable storage mediums containing instructions configured to cause the one or more processors to perform operations comprising: receiving a first query; receiving a second, follow-up query; determining that an anaphora is present in the second, follow-up query by determining that the second, follow-up query includes a pronoun that refers to an entity that is not present in the second, follow-up query; in response to determining that the anaphora is present in the second, follow-up query, determining that the first query is associated with a plurality of possible entities; generating a plurality of candidate queries, wherein each candidate query of the plurality of candidate queries is generated by replacing the pronoun in the second, follow-up query with a corresponding possible entity; obtaining a ranking for the plurality of candidate queries from a ranking engine; determining that a corresponding possible entity in a highest-ranked candidate query of the plurality of candidate queries is an entity associated with the anaphora from the second, follow-up query, wherein the entity associated with the anaphora from the second, follow-up query is the entity that is not present in the second, follow-up query and is referred to by the pronoun in the second, follow-up query; and based on determining that the corresponding possible entity in the highest-ranked candidate query is the entity associated with the anaphora, providing the highest-ranked candidate query to a search engine and obtaining search results for the highest-ranked candidate query from the search engine. 11. The system of claim 8 , the operations further comprising: modifying one or more of the plurality of candidate queries before sending the plurality of candidate queries to the ranking engine. | 0.549839 |
1. A method comprising: intercepting an item of glyph data directed to at least one of a printing device and a display device, wherein the item of glyph data comprises a font-specific representation of a text character associated with a device context of the at least one of the printing device and the display device; converting the item of glyph data to an item of text data based on one or more items of font-specific information; classifying one or more aspects of content in the item of text data into one of multiple classifications based on (i) the presence of one or more keywords in the item of text data, (ii) the frequency of the one or more keywords in the item of text data, and (iii) the location of each occurrence of the one or more keywords in the item of text data, wherein the one or more keywords are pre-defined as part of one or more data policies; and analyzing the classified one or more aspects of content in the item of text data against the one or more data policies to determine eligibility of the item of text data for transmission to the at least one of a printing device and a display device. | 1. A method comprising: intercepting an item of glyph data directed to at least one of a printing device and a display device, wherein the item of glyph data comprises a font-specific representation of a text character associated with a device context of the at least one of the printing device and the display device; converting the item of glyph data to an item of text data based on one or more items of font-specific information; classifying one or more aspects of content in the item of text data into one of multiple classifications based on (i) the presence of one or more keywords in the item of text data, (ii) the frequency of the one or more keywords in the item of text data, and (iii) the location of each occurrence of the one or more keywords in the item of text data, wherein the one or more keywords are pre-defined as part of one or more data policies; and analyzing the classified one or more aspects of content in the item of text data against the one or more data policies to determine eligibility of the item of text data for transmission to the at least one of a printing device and a display device. 6. The method of claim 1 , further comprising: transmitting the item of glyph data to the at least one of a printing device and a display device. | 0.696193 |
1. A computer-readable medium having stored thereon a set of instructions which when executed perform a method for recommending an item to a user, the method, comprising: defining an ontology as nodes in a graph, the nodes including a scored node and an unscored node, the nodes in the graph representing concepts; wherein, the scored node has an associated score based on a preference of the user for a concept represented by the scored node; using a propagating function and the associated score of the scored node, to determine, for the user, a personalized score of the unscored node in the ontology; wherein, the propagation function determines the personalized score of the unscored node based on a relationship of the unscored node and the scored node in the graph representing the ontology; identifying, for the user, a qualifying concept from the concepts in the ontology for which the personalized scores have been computed, wherein, the qualifying concept that is identified from the concepts, is one that is associated a qualifying score among the personalized scores that have been computed for the concepts at the nodes of the ontology; and selecting the item which is an instance of the qualifying concept to be recommended to the user. | 1. A computer-readable medium having stored thereon a set of instructions which when executed perform a method for recommending an item to a user, the method, comprising: defining an ontology as nodes in a graph, the nodes including a scored node and an unscored node, the nodes in the graph representing concepts; wherein, the scored node has an associated score based on a preference of the user for a concept represented by the scored node; using a propagating function and the associated score of the scored node, to determine, for the user, a personalized score of the unscored node in the ontology; wherein, the propagation function determines the personalized score of the unscored node based on a relationship of the unscored node and the scored node in the graph representing the ontology; identifying, for the user, a qualifying concept from the concepts in the ontology for which the personalized scores have been computed, wherein, the qualifying concept that is identified from the concepts, is one that is associated a qualifying score among the personalized scores that have been computed for the concepts at the nodes of the ontology; and selecting the item which is an instance of the qualifying concept to be recommended to the user. 3. The method of claim 1 further comprising, recommending the item to the user. | 0.607073 |
8. A method comprising: (a) recognizing utterances of a speaker through converting the utterances into a recognized string, the utterances being received by a speaker input and converted to digital signals; (b) comparing the recognized string with a reference string to determine errors; (c) calculating estimated weights for sections of the utterances; (d) marking the errors in the utterances and providing corresponding estimated weights to form adaptation enrollment data; and (e) using the adaptation enrollment data to convert a speaker independent model to a speaker dependent model; wherein calculating the estimated weights comprises computing an average likelihood difference per frame and then computing a weight value by averaging the average likelihood difference over all error words. | 8. A method comprising: (a) recognizing utterances of a speaker through converting the utterances into a recognized string, the utterances being received by a speaker input and converted to digital signals; (b) comparing the recognized string with a reference string to determine errors; (c) calculating estimated weights for sections of the utterances; (d) marking the errors in the utterances and providing corresponding estimated weights to form adaptation enrollment data; and (e) using the adaptation enrollment data to convert a speaker independent model to a speaker dependent model; wherein calculating the estimated weights comprises computing an average likelihood difference per frame and then computing a weight value by averaging the average likelihood difference over all error words. 9. The method of claim 8 , wherein the utterances are converted into the recognized string through applying the speaker independent model. | 0.889908 |
1. A method for generating for simultaneous display media guidance data in at least two languages, the method comprising: receiving, at a program guide equipment, from a user input device, a user selection of a preferred language for interacting with an interactive media guidance application; subsequent to receiving the user selection of the preferred language: receiving, at the program guide equipment, from the user input device, an indication to generate for simultaneous display a first media guidance data relating to a first program and a second media guidance data relating to a second program; determining, by a control circuitry of the program guide equipment, that the first media guidance data is available in the preferred language, and that the second media guidance data is not available in the preferred language, wherein the second media guidance data is available in an alternate language; and based on the determining that the first media guidance data is available in the preferred language, and that the second media guidance data is not available in the preferred language, generating for simultaneous display, by the control circuitry, the first media guidance data in the preferred language, and the second media guidance data in the alternate language. | 1. A method for generating for simultaneous display media guidance data in at least two languages, the method comprising: receiving, at a program guide equipment, from a user input device, a user selection of a preferred language for interacting with an interactive media guidance application; subsequent to receiving the user selection of the preferred language: receiving, at the program guide equipment, from the user input device, an indication to generate for simultaneous display a first media guidance data relating to a first program and a second media guidance data relating to a second program; determining, by a control circuitry of the program guide equipment, that the first media guidance data is available in the preferred language, and that the second media guidance data is not available in the preferred language, wherein the second media guidance data is available in an alternate language; and based on the determining that the first media guidance data is available in the preferred language, and that the second media guidance data is not available in the preferred language, generating for simultaneous display, by the control circuitry, the first media guidance data in the preferred language, and the second media guidance data in the alternate language. 5. The method of claim 1 , wherein the first media guidance data includes a first list of languages in which audio for the first program is available and wherein the second media guidance data includes a second list of languages in which audio for the second program is available. | 0.773148 |
1. A method, implemented at a computer system that includes one or more processors, for dynamically transforming user interface actions into executable script commands, the method comprising: tracking a plurality of user interface actions at a software program user interface, the user interface actions comprising selection of one or more features which consist of program elements of a software program which provide for user interaction with the software program, and wherein the tracked user interface actions include marking the beginning of a task and the end of the task in the software program; inputting said plurality of tracked user actions to a transforming module which transforms the plurality of user interface actions into one or more executable script commands corresponding to each tracked user action that is input, the executable script commands being configured to perform the corresponding tracked user interface action when executed; dynamically displaying in a first portion of a script command window executable script commands being generated as a live feed in said first portion of the script command window so that transformed user interface actions are immediately viewable as executable script commands by the user as said user interface actions are transformed into executable script commands at said transforming module, and wherein the marked beginning of the task and the marked end of the task are automatically generated by the transform module and appear in corresponding portions of the dynamically displayed script command window; the transform module automatically highlighting software program elements in the software program that correspond to the tracked user interactions dynamically displayed as script commands in the script command window; the transforming module filtering one or more executable script commands displayed in the script command window based on specified relevance criteria, and when executable script commands do not meet the specified relevance criteria the transforming module automatically filtering the executable script commands so that the filtered executable script commands do not dynamically appear in the script command window as part of the live feed from the transforming module; and simultaneously displaying in a second portion of the script command window past script commands that have been generated. | 1. A method, implemented at a computer system that includes one or more processors, for dynamically transforming user interface actions into executable script commands, the method comprising: tracking a plurality of user interface actions at a software program user interface, the user interface actions comprising selection of one or more features which consist of program elements of a software program which provide for user interaction with the software program, and wherein the tracked user interface actions include marking the beginning of a task and the end of the task in the software program; inputting said plurality of tracked user actions to a transforming module which transforms the plurality of user interface actions into one or more executable script commands corresponding to each tracked user action that is input, the executable script commands being configured to perform the corresponding tracked user interface action when executed; dynamically displaying in a first portion of a script command window executable script commands being generated as a live feed in said first portion of the script command window so that transformed user interface actions are immediately viewable as executable script commands by the user as said user interface actions are transformed into executable script commands at said transforming module, and wherein the marked beginning of the task and the marked end of the task are automatically generated by the transform module and appear in corresponding portions of the dynamically displayed script command window; the transform module automatically highlighting software program elements in the software program that correspond to the tracked user interactions dynamically displayed as script commands in the script command window; the transforming module filtering one or more executable script commands displayed in the script command window based on specified relevance criteria, and when executable script commands do not meet the specified relevance criteria the transforming module automatically filtering the executable script commands so that the filtered executable script commands do not dynamically appear in the script command window as part of the live feed from the transforming module; and simultaneously displaying in a second portion of the script command window past script commands that have been generated. 11. The method of claim 1 , further comprising grouping one or more executable script commands using one or more different identifying characteristics. | 0.574324 |
42. A communication system comprising: a hard of hearing user's captioned device, the captioned device including a display screen and a processor, the captioned device configured to: establish communication with a hard of hearing user's wireless phone device that is independent of the hard of hearing user's captioned device; receive a hearing user's voice signal originating at a hearing user's phone device from the hard of hearing user's phone device; establish a first communication link with a relay; route the hearing user's voice signal via the first communication link to the relay for transcription; receive a text communication originating at the relay on a second communication link that is separate from the first communication link, the text communication corresponding to a transcription of the hearing user's voice signal; and display a text caption corresponding to the text communication on the display. | 42. A communication system comprising: a hard of hearing user's captioned device, the captioned device including a display screen and a processor, the captioned device configured to: establish communication with a hard of hearing user's wireless phone device that is independent of the hard of hearing user's captioned device; receive a hearing user's voice signal originating at a hearing user's phone device from the hard of hearing user's phone device; establish a first communication link with a relay; route the hearing user's voice signal via the first communication link to the relay for transcription; receive a text communication originating at the relay on a second communication link that is separate from the first communication link, the text communication corresponding to a transcription of the hearing user's voice signal; and display a text caption corresponding to the text communication on the display. 47. The communication system of claim 42 wherein the captioned device comprises a communication connection configured to establish the communication with the hard of hearing user's phone device. | 0.591479 |
23. A system comprising: an electronic data store configured to store one or more algorithms that, when executed, implement an automatic speech recognition engine; and one or more computing devices in communication with the electronic data store and with a web service configured to host one or more profiles, wherein the one or more computing devices are configured to: receive a message type indicator identifying a message type from a first client device; set a message preference based at least in part on the message type indicator received from the first client device; receive audio data from the first client device; receive a designation of a second client device from the first client device; transcribe the audio data to transcribed text; generate a message of the message type using the automatic speech recognition engine and based at least in part on the message preference received from the first client device, the message comprising the transcribed text; obtain profile information from the transcribed text using the message type indicator, wherein the profile information comprises at least one of an interest or a preference of a user of the first client device, and wherein the profile information is obtained without input from the first client device; provide the profile information that is obtained without input from the first client device to the web service for updating a profile account associated with the user of the first client device and associated with the message type indicator, wherein the profile information, including the profile information that is obtained without input from the first client device and that is provided to the web service, is available for dissemination from the profile account to a computing device of a contact authorized by the user; and transmit the message to the second client device. | 23. A system comprising: an electronic data store configured to store one or more algorithms that, when executed, implement an automatic speech recognition engine; and one or more computing devices in communication with the electronic data store and with a web service configured to host one or more profiles, wherein the one or more computing devices are configured to: receive a message type indicator identifying a message type from a first client device; set a message preference based at least in part on the message type indicator received from the first client device; receive audio data from the first client device; receive a designation of a second client device from the first client device; transcribe the audio data to transcribed text; generate a message of the message type using the automatic speech recognition engine and based at least in part on the message preference received from the first client device, the message comprising the transcribed text; obtain profile information from the transcribed text using the message type indicator, wherein the profile information comprises at least one of an interest or a preference of a user of the first client device, and wherein the profile information is obtained without input from the first client device; provide the profile information that is obtained without input from the first client device to the web service for updating a profile account associated with the user of the first client device and associated with the message type indicator, wherein the profile information, including the profile information that is obtained without input from the first client device and that is provided to the web service, is available for dissemination from the profile account to a computing device of a contact authorized by the user; and transmit the message to the second client device. 30. The system of claim 23 , wherein the one or more computing devices are further configured to create an account associated with the user of the first client device, wherein the account is further associated with the web service and with the profile of the user of the first client device. | 0.521944 |
1. A method for improving search and retrieval of input items being published to a computer network, the method comprising: while publishing an input item to the computer network: generating, by a content generation module executing at a computing device and based at least in part on information about the input item, a descriptive context of the input item by at least: performing a query for the input item by searching an academic database that includes at least one of: books, research papers, or journal articles or searching the Internet using a publicly available search engine; and adding, to the descriptive context of the input item, at least one textual item from a response to the query for the input item; comparing, by a comparison module executing at the computing device, the descriptive context of the input item to respective descriptive contexts of a plurality of other items previously published to the computer network to determine, using a text similarity model, respective levels of similarity between the descriptive context of the input item and the respective descriptive context of each of the plurality of other items, wherein each of the plurality of other items is already tagged with one or more existing tags that a search engine searches when performing a query on the computer network; automatically tagging, by the content generation module, and based at least in part on the respective levels of similarity between the descriptive context of the input item and the respective descriptive context of each of the plurality of other items, the input item with at least one existing tag from the one or more existing tags that a particular content item from the plurality of content items is tagged; and after automatically tagging the input item, automatically publishing, by the computing device, the item to the computer network. | 1. A method for improving search and retrieval of input items being published to a computer network, the method comprising: while publishing an input item to the computer network: generating, by a content generation module executing at a computing device and based at least in part on information about the input item, a descriptive context of the input item by at least: performing a query for the input item by searching an academic database that includes at least one of: books, research papers, or journal articles or searching the Internet using a publicly available search engine; and adding, to the descriptive context of the input item, at least one textual item from a response to the query for the input item; comparing, by a comparison module executing at the computing device, the descriptive context of the input item to respective descriptive contexts of a plurality of other items previously published to the computer network to determine, using a text similarity model, respective levels of similarity between the descriptive context of the input item and the respective descriptive context of each of the plurality of other items, wherein each of the plurality of other items is already tagged with one or more existing tags that a search engine searches when performing a query on the computer network; automatically tagging, by the content generation module, and based at least in part on the respective levels of similarity between the descriptive context of the input item and the respective descriptive context of each of the plurality of other items, the input item with at least one existing tag from the one or more existing tags that a particular content item from the plurality of content items is tagged; and after automatically tagging the input item, automatically publishing, by the computing device, the item to the computer network. 13. The method of claim 1 , wherein the information about the input item comprises a document title. | 0.648069 |
8. The system of claim 3 wherein: each optimum set is represented by a corresponding master row having the set of columns from the corresponding initial subset of rows, each corresponding master row comprising a binary vector comprising a binary 1 in particular columns in the set of columns for the corresponding initial subset of rows that are elements of the corresponding optimum set and a binary 0 in other particular columns in the set of columns for the corresponding initial subset of rows that are not elements of the corresponding optimum set; and the division module comprises a thresholding module using a thresholding algorithm to: determine a distance threshold for each corresponding initial subset of rows for distances from each of the one or more text rows in each corresponding initial subset of rows to the corresponding master row; split the one or more text rows in each corresponding initial subset of rows into at least a first group of text rows and a second group of text rows, the first group of text rows comprising first text rows with first distances under the threshold, the second group of text rows comprising either no text rows or second text rows with second distances not under the threshold; and select the first group of text rows to be in the corresponding final subset of rows. | 8. The system of claim 3 wherein: each optimum set is represented by a corresponding master row having the set of columns from the corresponding initial subset of rows, each corresponding master row comprising a binary vector comprising a binary 1 in particular columns in the set of columns for the corresponding initial subset of rows that are elements of the corresponding optimum set and a binary 0 in other particular columns in the set of columns for the corresponding initial subset of rows that are not elements of the corresponding optimum set; and the division module comprises a thresholding module using a thresholding algorithm to: determine a distance threshold for each corresponding initial subset of rows for distances from each of the one or more text rows in each corresponding initial subset of rows to the corresponding master row; split the one or more text rows in each corresponding initial subset of rows into at least a first group of text rows and a second group of text rows, the first group of text rows comprising first text rows with first distances under the threshold, the second group of text rows comprising either no text rows or second text rows with second distances not under the threshold; and select the first group of text rows to be in the corresponding final subset of rows. 9. The system of claim 8 wherein: the thresholding module determines a final distances vector for each final subset of rows, each final distances vector comprising corresponding first distances of a corresponding first group of text rows; and the confidence factor comprises a confidence factor ratio with at least one member of a another group consisting of: a rows frequency in the numerator and a variance of the first distances of the first group of text rows in the corresponding final distances vector in a denominator, the rows frequency comprising a number of the first text rows in the corresponding final subset of rows; the rows frequency and a master row length in the numerator and the variance and an average of the first distances of the first group of text rows in the corresponding final distances vector in the denominator; and a quantity of a rows frequency cubed multiplied by the master row length in the numerator and another quantity of the variance multiplied by the average of the first distances of the first group of text rows in the corresponding final distances vector plus one in the denominator. | 0.752541 |
1. A method comprising: accessing a plurality of submitted queries aggregated from a plurality of users; identifying one or more of the plurality of submitted queries as corresponding to a theme by determining a strength score of each of one or more of the plurality of submitted queries, the strength score indicating a pertinence of a corresponding submitted query to the theme, the identifying being performed by a processor; generating a thematic query based on the one or more identified submitted queries and one or more of the determined strength scores; obtaining search results by executing the thematic query, the search results referencing a plurality of items that each correspond to the theme; and presenting at least some of the plurality of items in a single collection that corresponds to the theme. | 1. A method comprising: accessing a plurality of submitted queries aggregated from a plurality of users; identifying one or more of the plurality of submitted queries as corresponding to a theme by determining a strength score of each of one or more of the plurality of submitted queries, the strength score indicating a pertinence of a corresponding submitted query to the theme, the identifying being performed by a processor; generating a thematic query based on the one or more identified submitted queries and one or more of the determined strength scores; obtaining search results by executing the thematic query, the search results referencing a plurality of items that each correspond to the theme; and presenting at least some of the plurality of items in a single collection that corresponds to the theme. 14. The method of claim 1 further comprising: the submitted query and a further query among the plurality of submitted queries are submitted within a time span; and the determining of the strength score includes determining that a subset of the plurality of submitted queries is submitted during the time span. | 0.516734 |
1. A method for recording, categorizing, organizing, managing and retrieving speech information, said method comprising, a. obtaining a speech stream, b. storing the speech stream in at least a temporary storage, c. extracting multiple, selected features from the speech stream, wherein the multiple features include the speaker's identity or location, duration of speech phrases, and pauses in speaking, d. constructing a visual representation of the selected features of the speech stream, e. providing the visual representation to a user, f. categorizing portions of the speech stream, with or without the aid of the representation, by at least one of the following categorization techniques: user command and, automatic recognition of speech qualities, including tempo, fundamental pitch, and phonemes, and g. storing, in at least a temporary storage, data structure which represents the categorized portions of the speech stream. | 1. A method for recording, categorizing, organizing, managing and retrieving speech information, said method comprising, a. obtaining a speech stream, b. storing the speech stream in at least a temporary storage, c. extracting multiple, selected features from the speech stream, wherein the multiple features include the speaker's identity or location, duration of speech phrases, and pauses in speaking, d. constructing a visual representation of the selected features of the speech stream, e. providing the visual representation to a user, f. categorizing portions of the speech stream, with or without the aid of the representation, by at least one of the following categorization techniques: user command and, automatic recognition of speech qualities, including tempo, fundamental pitch, and phonemes, and g. storing, in at least a temporary storage, data structure which represents the categorized portions of the speech stream. 3. The invention defined in claim 1 including selectively retrieving one or more of the categorized portions of the speech stream. | 0.672715 |
1. A method of protecting electronically published documents, which comprises the step of: operating a computer system, including a copyright server and a document server connected thereto, and a network for electronic publication of documents stored in the document server, and including therein the steps of: a.) receiving requests for documents from a plurality of users having computers with display devices or printers, said computers being connected by said network to said computer system, said requests including unique user identification for each of said plurality of users; b.) authenticating said requests from said plurality of users with the copyright server; c.) using said copyright server to direct the document server to act upon proper authentication of each request; d.) in response to direction from said copyright server, using the document server to create encrypted documents from an encoded document along with a unique identification for each authenticated request and forwarding said documents to each authenticated request user through said network to corresponding agents located at each authenticated request user, each of said agents being selected from display agents and printer agents; e.) encoding a requested document as an encoded document using the document server so that each encoded document created is uniquely encoded based upon said unique identification; and, f.) decrypting said documents at each of said agents and making said documents available for use only in response to receiving correct secret keys provided by said authenticated request user to said agents. | 1. A method of protecting electronically published documents, which comprises the step of: operating a computer system, including a copyright server and a document server connected thereto, and a network for electronic publication of documents stored in the document server, and including therein the steps of: a.) receiving requests for documents from a plurality of users having computers with display devices or printers, said computers being connected by said network to said computer system, said requests including unique user identification for each of said plurality of users; b.) authenticating said requests from said plurality of users with the copyright server; c.) using said copyright server to direct the document server to act upon proper authentication of each request; d.) in response to direction from said copyright server, using the document server to create encrypted documents from an encoded document along with a unique identification for each authenticated request and forwarding said documents to each authenticated request user through said network to corresponding agents located at each authenticated request user, each of said agents being selected from display agents and printer agents; e.) encoding a requested document as an encoded document using the document server so that each encoded document created is uniquely encoded based upon said unique identification; and, f.) decrypting said documents at each of said agents and making said documents available for use only in response to receiving correct secret keys provided by said authenticated request user to said agents. 14. The method of claim 1 wherein said documents are uniquely encoded after being forwarded to each authenticated request user. | 0.544018 |
7. A three-dimensional (3D) display apparatus comprising: a signal receiving unit for receiving a broadcast signal including 3D caption data based on a code space, wherein the code space contains base code sets and extended code sets; a caption decoding unit for decoding the caption data to acquiring 3D command and caption text from the 3D caption data, wherein the 3D command is delivered in at least one extended code set, wherein the at least one extended code set is accessed by using an ‘EXT1’ code in a base code set, wherein the 3D command provides 3D information of a caption window; and a processing unit for processing the 3D information and the caption text such that the caption text is written to the caption window for 3D captioning. | 7. A three-dimensional (3D) display apparatus comprising: a signal receiving unit for receiving a broadcast signal including 3D caption data based on a code space, wherein the code space contains base code sets and extended code sets; a caption decoding unit for decoding the caption data to acquiring 3D command and caption text from the 3D caption data, wherein the 3D command is delivered in at least one extended code set, wherein the at least one extended code set is accessed by using an ‘EXT1’ code in a base code set, wherein the 3D command provides 3D information of a caption window; and a processing unit for processing the 3D information and the caption text such that the caption text is written to the caption window for 3D captioning. 10. The apparatus of claim 7 , wherein the 3D command is accessed by using the ‘EXT1’ code in the base code set. | 0.695652 |
12. A natural language understanding system comprising: a computer processing unit; a decoder; at least one grammar having grammar rules for terminals and non-terminals; and a conditional random field model accessible by the decoder, the conditional random field model being configured to model alignments between textual characters in an observed natural language input and semantic frames using the computer processing unit, wherein the semantic frames define semantic slots and the textual characters comprise at least one of words and numbers, wherein the conditional random field model models prior knowledge of relationships between elements of the semantic frames and models relationships between the textual characters and the elements of the semantic frames, the conditional random field model including features comprising: chunk coverage features indicative of a likelihood of a word or number that is covered by a grammar rule being assigned to a given semantic state; transition features indicative of a likelihood of transition between slots defined by the semantic frames independent of the observed natural language input; and a slot boundary feature indicative of a likelihood of an alignment of a set of words or numbers with different states given whether a boundary between the different states is covered by a grammar rule for a non-terminal; wherein the computer processing unit, being a functional hardware component of the system and activated by the decoder and conditional model facilitating modeling alignments. | 12. A natural language understanding system comprising: a computer processing unit; a decoder; at least one grammar having grammar rules for terminals and non-terminals; and a conditional random field model accessible by the decoder, the conditional random field model being configured to model alignments between textual characters in an observed natural language input and semantic frames using the computer processing unit, wherein the semantic frames define semantic slots and the textual characters comprise at least one of words and numbers, wherein the conditional random field model models prior knowledge of relationships between elements of the semantic frames and models relationships between the textual characters and the elements of the semantic frames, the conditional random field model including features comprising: chunk coverage features indicative of a likelihood of a word or number that is covered by a grammar rule being assigned to a given semantic state; transition features indicative of a likelihood of transition between slots defined by the semantic frames independent of the observed natural language input; and a slot boundary feature indicative of a likelihood of an alignment of a set of words or numbers with different states given whether a boundary between the different states is covered by a grammar rule for a non-terminal; wherein the computer processing unit, being a functional hardware component of the system and activated by the decoder and conditional model facilitating modeling alignments. 14. The natural language understanding system of claim 12 , wherein the conditional random field model features comprise at least one of: command prior features indicative of a prior likelihood of commands defined by the semantic frames; previous slot context features indicative of a likelihood of an alignment between a word or number and a state given a context of a slot previous to the state; and n-gram features indicative of a likelihood of an observed word or number given a state defined by the semantic frames. | 0.5 |
11. The method of claim 10 , further comprising, in response to the receiving the first message update, displaying, by the system, the third message content in the first device display area, displaying, by the system, the first message content in the second device display area, and displaying, by the system, the second message content in the third device display area. | 11. The method of claim 10 , further comprising, in response to the receiving the first message update, displaying, by the system, the third message content in the first device display area, displaying, by the system, the first message content in the second device display area, and displaying, by the system, the second message content in the third device display area. 12. The method of claim 11 , further comprising, in response to the receiving the first message update, transitioning, by the system, the first message content from the first device display area to the second device display area to facilitate the displaying of the third message content in the first device display area. | 0.931519 |
1. A computer-implemented system for implementing a marketing campaign to targeted consumers, the system comprising: a server including a computer processor, the server distributing customized packages of offers to a plurality of different consumer access devices, each of the customized packages including at least one personalized offer for targeted consumers; at least one first communications interface interfacing the server with multiple information/product/service providers, such that the server receives offer information provided by a plurality of different information/product/service providers for use in personalized offers; a personalization engine facilitating creation and distribution of the customized packages from the received offer information to the targeted consumers; and at least one second communications interface delivering customized packages of offers including offers from a plurality of different providers of information/products/services to consumers over a plurality of different types of delivery channels. | 1. A computer-implemented system for implementing a marketing campaign to targeted consumers, the system comprising: a server including a computer processor, the server distributing customized packages of offers to a plurality of different consumer access devices, each of the customized packages including at least one personalized offer for targeted consumers; at least one first communications interface interfacing the server with multiple information/product/service providers, such that the server receives offer information provided by a plurality of different information/product/service providers for use in personalized offers; a personalization engine facilitating creation and distribution of the customized packages from the received offer information to the targeted consumers; and at least one second communications interface delivering customized packages of offers including offers from a plurality of different providers of information/products/services to consumers over a plurality of different types of delivery channels. 6. The system of claim 1 , wherein the at least one first communications interface and the at least one second communications interface are the same. | 0.615717 |
2. The method of claim 1 , wherein making the supplemental media content available comprises presenting an option to select the supplemental media content available to the first audience member for the audio conference, and wherein the supplemental media content is selectively presented to the first audience member responsive to selection of the supplemental media content by the first audience member. | 2. The method of claim 1 , wherein making the supplemental media content available comprises presenting an option to select the supplemental media content available to the first audience member for the audio conference, and wherein the supplemental media content is selectively presented to the first audience member responsive to selection of the supplemental media content by the first audience member. 7. The method of claim 2 , further comprising: grouping the first audience member into a plurality of groups; and presenting different options to select supplemental media content associated with the set of words to the different groups based on the grouping. | 0.793249 |
79. The system of claim 78 , wherein the ATC automatically determines the at least one location. | 79. The system of claim 78 , wherein the ATC automatically determines the at least one location. 80. The system of claim 79 , wherein location data of the at least one location is manually entered. | 0.952243 |
13. A server device comprising: a processor; a computer memory device storing a data structure, the data structure storing data representing a plurality of keyword-condition pairs and, for each keyword-condition pair, an associated action; and a network connection operable to receive from a network sequences of characters captured by a capture device, wherein the processor is configured to determine that a sequence of characters captured by the capture device includes a keyword that matches a first keyword of a first keyword-condition pair in the data structure, the first keyword-condition pair comprising the first keyword and first condition, and wherein the processor determines that the first condition is satisfied, and wherein the processor is configured to identify a first action associated with the first keyword-condition pair in the data structure. | 13. A server device comprising: a processor; a computer memory device storing a data structure, the data structure storing data representing a plurality of keyword-condition pairs and, for each keyword-condition pair, an associated action; and a network connection operable to receive from a network sequences of characters captured by a capture device, wherein the processor is configured to determine that a sequence of characters captured by the capture device includes a keyword that matches a first keyword of a first keyword-condition pair in the data structure, the first keyword-condition pair comprising the first keyword and first condition, and wherein the processor determines that the first condition is satisfied, and wherein the processor is configured to identify a first action associated with the first keyword-condition pair in the data structure. 14. The server device of claim 13 , wherein the sequence of characters is captured from a rendered document, wherein the computer memory stores a document identification index for identifying the rendered document based on the received sequence of characters, and wherein the first condition is satisfied if a document identifier of the rendered document is specified by the first condition. | 0.5 |
1. A method of facilitating a user-initiated clinical study to determine the efficacy of an intervention, the method comprising: providing a graphical user interface on a client computer, the graphical user interface allowing one or more patients within a community of patients to input information regarding diseases of the one or more patients, interventions employed by the one or more patients to treat the diseases, and symptoms experienced by the one or more patients before and after the interventions are employed; receiving a request from a user to initiate the clinical study to determine the efficacy of the intervention; collecting information, on a server computer, regarding a disease of the one or more patients and a particular intervention employed by the one or more patients to treat the disease, the information having been input by the one or more patients via the graphical user interface on the client computer; collecting information, on the server computer, regarding symptoms experienced by the one or more patients before the particular intervention is employed by the one or more patients to treat the disease, the information having been input by the one or more patients via the graphical user interface on the client computer; collecting information, on the server computer, regarding symptoms experienced by the one or more patients after the particular intervention was employed by the one or more patients to treat the disease, the information having been input by the one or more patients via the graphical user interface on the client computer; and analyzing the information regarding the disease of the one or more patients, the particular intervention employed by the one or more patients to treat the disease, and the symptoms experienced by the one or more patients before and after the particular intervention is employed to determine the efficacy of the particular intervention in treating the disease. | 1. A method of facilitating a user-initiated clinical study to determine the efficacy of an intervention, the method comprising: providing a graphical user interface on a client computer, the graphical user interface allowing one or more patients within a community of patients to input information regarding diseases of the one or more patients, interventions employed by the one or more patients to treat the diseases, and symptoms experienced by the one or more patients before and after the interventions are employed; receiving a request from a user to initiate the clinical study to determine the efficacy of the intervention; collecting information, on a server computer, regarding a disease of the one or more patients and a particular intervention employed by the one or more patients to treat the disease, the information having been input by the one or more patients via the graphical user interface on the client computer; collecting information, on the server computer, regarding symptoms experienced by the one or more patients before the particular intervention is employed by the one or more patients to treat the disease, the information having been input by the one or more patients via the graphical user interface on the client computer; collecting information, on the server computer, regarding symptoms experienced by the one or more patients after the particular intervention was employed by the one or more patients to treat the disease, the information having been input by the one or more patients via the graphical user interface on the client computer; and analyzing the information regarding the disease of the one or more patients, the particular intervention employed by the one or more patients to treat the disease, and the symptoms experienced by the one or more patients before and after the particular intervention is employed to determine the efficacy of the particular intervention in treating the disease. 3. The method of claim 1 , further comprising: conducting a multivariate pattern matching search of data related to patients other than the one or more patients. | 0.82618 |
24. A data processing device for generating, from a preset code, filter data to be afforded to a speech synthesis filter adapted for synthesizing the speech based on linear prediction coefficients and a preset input signal, comprising: a decoder configured to decode said code produced by encoding original filter data, to output decoded filter data; an acquisition unit configured to acquire preset tap coefficients as found by carrying out learning, wherein said preset tap coefficients are used to predict the original filter data from said decoded filter data; and a predictor configured to carry out preset predictive calculations, using said tap coefficients and the decoded filter data, to find prediction values of said filter data, to send the so found prediction values to said speech synthesis filter for use as linear prediction coefficients in said speech syntheses filter. | 24. A data processing device for generating, from a preset code, filter data to be afforded to a speech synthesis filter adapted for synthesizing the speech based on linear prediction coefficients and a preset input signal, comprising: a decoder configured to decode said code produced by encoding original filter data, to output decoded filter data; an acquisition unit configured to acquire preset tap coefficients as found by carrying out learning, wherein said preset tap coefficients are used to predict the original filter data from said decoded filter data; and a predictor configured to carry out preset predictive calculations, using said tap coefficients and the decoded filter data, to find prediction values of said filter data, to send the so found prediction values to said speech synthesis filter for use as linear prediction coefficients in said speech syntheses filter. 33. The data processing device according to claim 24 further comprising: a speech synthesis filter. | 0.679256 |
34. The system of claim 20 , wherein the contextual speller model comprises a plurality of speller sub-models. | 34. The system of claim 20 , wherein the contextual speller model comprises a plurality of speller sub-models. 37. The system of claim 34 , wherein the plurality of speller sub-models are based at least on one or more levels of aggregation, and wherein each level of aggregation differentiates the first user from global users of the online social network. | 0.947669 |
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