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9,715,833 | 29 | 31 | 29. The method of claim 18 , further comprising: sending a data message from the instructor wireless communication device to each of the plurality of student wireless communication devices via the respective short-range communication links, the data message requiring a response; and receiving a response message, containing the response, from at least a portion of the plurality of student wireless communication devices via the respective short-range communication links of the portion of the plurality of student wireless communication devices. | 29. The method of claim 18 , further comprising: sending a data message from the instructor wireless communication device to each of the plurality of student wireless communication devices via the respective short-range communication links, the data message requiring a response; and receiving a response message, containing the response, from at least a portion of the plurality of student wireless communication devices via the respective short-range communication links of the portion of the plurality of student wireless communication devices. 31. The method of claim 29 wherein the response message includes data identifying a student associated with the respective ones of the portion of the plurality of student wireless communication devices from whom the response message was received. | 0.950263 |
8,239,332 | 1 | 6 | 1. One or more computer-readable storage media comprising computer-executable instructions that, when executed by one or more processors, configure the one or more processors to perform acts comprising: imposing a constraint for discriminative training, the constraint limiting a difference between an initial continuous density hidden Markov model (CDHMM) parameter value and an updated CDHMM parameter value; approximating an objective function as a smooth function of CDHMM parameters; and performing a constrained line search on the smoothed function to optimize values of the CDHMM parameters. | 1. One or more computer-readable storage media comprising computer-executable instructions that, when executed by one or more processors, configure the one or more processors to perform acts comprising: imposing a constraint for discriminative training, the constraint limiting a difference between an initial continuous density hidden Markov model (CDHMM) parameter value and an updated CDHMM parameter value; approximating an objective function as a smooth function of CDHMM parameters; and performing a constrained line search on the smoothed function to optimize values of the CDHMM parameters. 6. The one or more computer-readable storage media of claim 1 , wherein the performing a constrained line search comprises deciding if a critical point exists. | 0.567935 |
9,697,221 | 8 | 15 | 8. A method comprising: receiving a static dictionary comprising a plurality of entries up to a maximum number of dictionary entries, each of the plurality of entries mapping a token to a definition having a length up to a maximum byte size that is bounded by a hardware specification; receiving a packed sequential plurality of tokens, each of the packed sequential plurality of tokens having a fixed token size that is configured to address the maximum number of dictionary entries; processing the packed sequential plurality of tokens using the static dictionary to write into an output buffer in response to a request; wherein the method is performed by one or more computing devices. | 8. A method comprising: receiving a static dictionary comprising a plurality of entries up to a maximum number of dictionary entries, each of the plurality of entries mapping a token to a definition having a length up to a maximum byte size that is bounded by a hardware specification; receiving a packed sequential plurality of tokens, each of the packed sequential plurality of tokens having a fixed token size that is configured to address the maximum number of dictionary entries; processing the packed sequential plurality of tokens using the static dictionary to write into an output buffer in response to a request; wherein the method is performed by one or more computing devices. 15. The method of claim 8 , wherein said processing writes, from the definition in the static dictionary mapped to each token in a set of tokens of the packed sequential plurality of tokens to a writing pointer referencing the output buffer, a number of bytes equivalent to the maximum byte size, wherein the writing pointer is advanced by the length of the definition in the static dictionary mapped to each token in the set of tokens. | 0.728856 |
9,454,455 | 5 | 11 | 5. A method for deriving intelligence from multiple activity logs of at least one multitasking user to provide information to the at least one user, comprising the steps of: obtaining information about at least one past action from multiple action logs for a user, configuration information, domain knowledge, at least one task history and open task repository information; correlating the information about the at least one past action, configuration information, domain knowledge, at least one task history and open task repository information to determine a task associated with each action where there is no explicit indication of the task associated with each action performed, wherein said correlating comprises: associating each action from the multiple action logs with a task in the open task repository; computing a confidence score for each task in the open task repository associated with each action, said computing comprising: computing a first probability distribution for a set of tasks associated with the user from the open task repository as a normalized weighted average of distributions over a pre-determined number of previous actions restricted to the set of tasks performed by the user; computing a distribution of recency of tasks opened in the set of tasks; and combining the probability distribution and the distribution of recency of tasks to determine a second probability distribution for the user; and identifying the task with the highest confidence score, wherein the highest confidence score corresponds to the task having the maximal probability in the second probability distribution; automatically segmenting the multiple activity logs of the at least one multitasking user on a per-task basis based on the identified task; and using each segmented activity log, current configuration of an application, domain knowledge, configuration information and one or more action histories to provide a sequence of one or more additional actions based on the identified task to the user. | 5. A method for deriving intelligence from multiple activity logs of at least one multitasking user to provide information to the at least one user, comprising the steps of: obtaining information about at least one past action from multiple action logs for a user, configuration information, domain knowledge, at least one task history and open task repository information; correlating the information about the at least one past action, configuration information, domain knowledge, at least one task history and open task repository information to determine a task associated with each action where there is no explicit indication of the task associated with each action performed, wherein said correlating comprises: associating each action from the multiple action logs with a task in the open task repository; computing a confidence score for each task in the open task repository associated with each action, said computing comprising: computing a first probability distribution for a set of tasks associated with the user from the open task repository as a normalized weighted average of distributions over a pre-determined number of previous actions restricted to the set of tasks performed by the user; computing a distribution of recency of tasks opened in the set of tasks; and combining the probability distribution and the distribution of recency of tasks to determine a second probability distribution for the user; and identifying the task with the highest confidence score, wherein the highest confidence score corresponds to the task having the maximal probability in the second probability distribution; automatically segmenting the multiple activity logs of the at least one multitasking user on a per-task basis based on the identified task; and using each segmented activity log, current configuration of an application, domain knowledge, configuration information and one or more action histories to provide a sequence of one or more additional actions based on the identified task to the user. 11. The method of claim 5 , further comprising reconfiguring a user interface (UI) using at least one contiguous action pattern. | 0.838791 |
9,741,348 | 8 | 14 | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining training acoustic data corresponding to a user's utterance of one or more words that include one or more particular subwords; dynamically generating a verification phrase based at least on one or more of the particular subwords included in the words uttered by the user in the training acoustic data; prompting, by a mobile device that is in a locked mode, the user to speak the dynamically generated verification phrase; and in response to determining that the user has likely spoken the dynamically generated verification phrase, determining, by the mobile device, whether to exit the locked mode. | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining training acoustic data corresponding to a user's utterance of one or more words that include one or more particular subwords; dynamically generating a verification phrase based at least on one or more of the particular subwords included in the words uttered by the user in the training acoustic data; prompting, by a mobile device that is in a locked mode, the user to speak the dynamically generated verification phrase; and in response to determining that the user has likely spoken the dynamically generated verification phrase, determining, by the mobile device, whether to exit the locked mode. 14. The system of claim 8 , the operations comprising: receiving an utterance of the dynamically generated verification phrase; and in response to receiving the utterance of the dynamically generated verification phrase, verifying an identity of the user. | 0.686732 |
8,458,198 | 1 | 3 | 1. A computer-readable storage medium having instructions stored thereon for processing data information, such that the instructions, when carried out by a processing device, enable a processing device to perform the operations comprising: receiving an ordered collection of text-based terms; analyzing groupings of consecutive text-based terms in the ordered collection to identify occurrences of different combinations of consecutive text-based terms in the ordered collection; and maintaining frequency information representing the occurrences of the different combinations of consecutive text-based terms in the collection. | 1. A computer-readable storage medium having instructions stored thereon for processing data information, such that the instructions, when carried out by a processing device, enable a processing device to perform the operations comprising: receiving an ordered collection of text-based terms; analyzing groupings of consecutive text-based terms in the ordered collection to identify occurrences of different combinations of consecutive text-based terms in the ordered collection; and maintaining frequency information representing the occurrences of the different combinations of consecutive text-based terms in the collection. 3. The computer-readable storage medium of claim 1 wherein the operations further comprise, based on the analyzing, creating a tree in which a first term in a given grouping of the groupings is defined as a parent node in the tree and a second term in the given grouping is defined as a child node of the parent node in the tree, both the first term and the second term in the given grouping being consecutive text-based terms present in the order collection. | 0.739205 |
8,151,358 | 1 | 10 | 1. A method, performed at least in part by a computer, for sharing digital items with communication identities, the method comprising: receiving, from a user interface of a first communication identity, a selection of a first group of multiple communication identities, wherein the first group of multiple communication identities comprises a subset of a group of linked communication identities, wherein the first communication identity is able to send instant messages to and receive instant messages from each of the linked communication identities; receiving, from the user interface of the first communication identity, instructions to grant each of the multiple communication identities in the first group access to a digital item via the Internet; enabling the multiple communication identities in the first group to access the digital item via the Internet based on inclusion of the multiple communication identities in the first group within the instructions to grant access to the digital item via the Internet; enabling one or more of the multiple communication identities in the first group to annotate the digital item via the Internet; in response to receiving an indication that the first communication identity has selected the digital item from a list of digital items displayed in a first window in a graphical user interface, displaying the group of linked communication identities in a second window in the graphical user interface; in response to receiving an indication that the first communication identity has selected a communication identity from the second window, displaying a list of permissions associated with the selected communication identity in a third window in the graphical user interface, wherein the third window provides functionality for allowing the first communication identity to specify permissions governing the selected communication identity's access to the selected digital item, wherein the selected communication identity is one of the multiple communication identities, and wherein the list of permissions includes an option to deny the selected communication identity access to view annotations associated with the digital item; and in response to receiving instructions to deny access to view annotations associated with the digital item, denying the selected communication identity access to view annotations associated with the digital item, while still enabling the selected communication identity to access the digital item. | 1. A method, performed at least in part by a computer, for sharing digital items with communication identities, the method comprising: receiving, from a user interface of a first communication identity, a selection of a first group of multiple communication identities, wherein the first group of multiple communication identities comprises a subset of a group of linked communication identities, wherein the first communication identity is able to send instant messages to and receive instant messages from each of the linked communication identities; receiving, from the user interface of the first communication identity, instructions to grant each of the multiple communication identities in the first group access to a digital item via the Internet; enabling the multiple communication identities in the first group to access the digital item via the Internet based on inclusion of the multiple communication identities in the first group within the instructions to grant access to the digital item via the Internet; enabling one or more of the multiple communication identities in the first group to annotate the digital item via the Internet; in response to receiving an indication that the first communication identity has selected the digital item from a list of digital items displayed in a first window in a graphical user interface, displaying the group of linked communication identities in a second window in the graphical user interface; in response to receiving an indication that the first communication identity has selected a communication identity from the second window, displaying a list of permissions associated with the selected communication identity in a third window in the graphical user interface, wherein the third window provides functionality for allowing the first communication identity to specify permissions governing the selected communication identity's access to the selected digital item, wherein the selected communication identity is one of the multiple communication identities, and wherein the list of permissions includes an option to deny the selected communication identity access to view annotations associated with the digital item; and in response to receiving instructions to deny access to view annotations associated with the digital item, denying the selected communication identity access to view annotations associated with the digital item, while still enabling the selected communication identity to access the digital item. 10. The method of claim 1 wherein a digital item comprises at least one of a graphical image, a digitized photograph, an audio segment, a digital video segment, or text. | 0.799287 |
9,323,724 | 13 | 16 | 13. A mobile terminal, comprising a processor and a memory unit, and having computer programs thereon, wherein the computer programs cause the mobile terminal to perform a skin replacement method for skin replacement process of a webpage content area displayed in a browser of the mobile terminal, the method comprising: defining, by a browser client, a self-defined Cascading Style Sheet (CSS) property or a self-defined Hypertext Markup Language (HTML) element property via extending CSS properties or HTML element properties; storing a skin style library on a browser client, the skin style library containing skin style information that corresponds to one or more skin effects, respectively, wherein the skin style information that corresponds to each skin effect of the one or more skin effects is associated with one or more indexes, respectively; during creating of webpage content of a webpage, adding, by a webpage content writer, the self-defined CSS property or the self-defined HTML element property to an element in the webpage content, wherein a value of the self-defined CSS property or of the self-defined HTML element property represents an index for the skin style information of the element in the skin style library; after a skin effect of the one or more skin effects is selected by a user of the browser client, when the user of the browser client requests the webpage, parsing the webpage content received from a web server, to find the self-defined CSS property or the self-defined HTML element property in the element in the webpage content; according to the value of the CSS property or of the self-defined HTML element property, searching in the skin style library on the browser client, to find the skin style information corresponding to the index and the selected skin effect; and rendering the element in the webpage content using the found skin styles, wherein the skin style library is edited by at least one of the browser client and the webpage content writer, and the user of the browser client does not directly edit the skin style library, wherein: a skin effect of the one or more skin effects includes specified skin style associated with the one or more indexes and specified style of a browser skin, wherein the browser skin includes skins of one or more user interface widgets in a shell part of the browser client, and the method further comprises switching to another skin effect of the one or more skin effects, including: in the skin style library on the browser client, finding a second skin style information corresponding to the another skin effect and based on the index for the element; rendering the element in the webpage content using the found second skin style information; and changing the browser skin to a second specified skin style corresponding to the another skin effect, such that the another skin effect in the shell part of the browser client is directly and seamlessly integrated with the element in the webpage content displayed in the browser. | 13. A mobile terminal, comprising a processor and a memory unit, and having computer programs thereon, wherein the computer programs cause the mobile terminal to perform a skin replacement method for skin replacement process of a webpage content area displayed in a browser of the mobile terminal, the method comprising: defining, by a browser client, a self-defined Cascading Style Sheet (CSS) property or a self-defined Hypertext Markup Language (HTML) element property via extending CSS properties or HTML element properties; storing a skin style library on a browser client, the skin style library containing skin style information that corresponds to one or more skin effects, respectively, wherein the skin style information that corresponds to each skin effect of the one or more skin effects is associated with one or more indexes, respectively; during creating of webpage content of a webpage, adding, by a webpage content writer, the self-defined CSS property or the self-defined HTML element property to an element in the webpage content, wherein a value of the self-defined CSS property or of the self-defined HTML element property represents an index for the skin style information of the element in the skin style library; after a skin effect of the one or more skin effects is selected by a user of the browser client, when the user of the browser client requests the webpage, parsing the webpage content received from a web server, to find the self-defined CSS property or the self-defined HTML element property in the element in the webpage content; according to the value of the CSS property or of the self-defined HTML element property, searching in the skin style library on the browser client, to find the skin style information corresponding to the index and the selected skin effect; and rendering the element in the webpage content using the found skin styles, wherein the skin style library is edited by at least one of the browser client and the webpage content writer, and the user of the browser client does not directly edit the skin style library, wherein: a skin effect of the one or more skin effects includes specified skin style associated with the one or more indexes and specified style of a browser skin, wherein the browser skin includes skins of one or more user interface widgets in a shell part of the browser client, and the method further comprises switching to another skin effect of the one or more skin effects, including: in the skin style library on the browser client, finding a second skin style information corresponding to the another skin effect and based on the index for the element; rendering the element in the webpage content using the found second skin style information; and changing the browser skin to a second specified skin style corresponding to the another skin effect, such that the another skin effect in the shell part of the browser client is directly and seamlessly integrated with the element in the webpage content displayed in the browser. 16. The mobile terminal according to claim 13 , wherein the webpage content includes a plurality of elements, the self-defined CSS property or the self-defined HTML element property exists in source code of each element of the plurality of elements, and has a value respectively corresponding to the each element, such that the browser client displays the plurality of elements with the selected skin effect. | 0.604651 |
8,478,732 | 16 | 17 | 16. A method of information processing comprising: generating, by a computing device, a database file; extracting, by the computing device, names from the database file; normalizing, by the computing device, the extracted names; applying a language lexicon to the normalized extracted names, the language lexicon specifying a list of words along with their normalized forms; storing, by the computing device, multiple normalized extracted names that map to a single concept in an aliasing file, the single concept representing a general idea inferred or derived from specific instances; and applying, by the computing device, the aliasing file to a query directed to the database file for resolving variations in the query during the normalizing step. | 16. A method of information processing comprising: generating, by a computing device, a database file; extracting, by the computing device, names from the database file; normalizing, by the computing device, the extracted names; applying a language lexicon to the normalized extracted names, the language lexicon specifying a list of words along with their normalized forms; storing, by the computing device, multiple normalized extracted names that map to a single concept in an aliasing file, the single concept representing a general idea inferred or derived from specific instances; and applying, by the computing device, the aliasing file to a query directed to the database file for resolving variations in the query during the normalizing step. 17. The method of claim 16 further comprising: extracting numbers from the database file; normalizing the extracted numbers; applying a language lexicon to the normalized extracted numbers; and storing multiple normalized extracted numbers that map to a single concept in the aliasing file, the single concept representing a general idea inferred or derived from specific instances. | 0.540865 |
10,135,903 | 13 | 16 | 13. A method, comprising the steps of: transmitting over a computer network, by a Distributor comprising a hardware server, a webpage file configured to display a webpage rendering on a client device operated by a customer of the Distributor; receiving over the computer network, by the Distributor, a selected category from the client device, wherein the webpage rendering displayed on the client device is configured to receive the selected category from the customer; accessing by the Distributor an electronic database to identify a plurality of registered names that are related to the selected category; generating by the Distributor a plurality of available names that are related to the selected category; transmitting over the computer network, by the Distributor, to the client device the plurality of registered names related to the selected category and the plurality of available names related to the selected category, wherein the webpage file is configured to allow the customer to guess on the webpage rendering displayed on the client device whether each name in the plurality of registered names and each name in the plurality of available names is registered or available for name registration after being displayed and wherein the webpage file is configured to allow the customer to select on the webpage rendering displayed on the client device one or more of the plurality of available names for name registration after being displayed; receiving over the computer network, by the Distributor, the selected one or more of the plurality of available names from the webpage file running on the client device; and registering by the Distributor the selected one or more of the plurality of available names to the customer, wherein the selected category by the customer is current events and one or more trending hashtags from one or more social media platforms are tokenized and used in accessing by the Distributor the electronic database to identify the plurality of registered names that are related to the selected category and used in generating by the Distributor the plurality of available names that are related to the selected category. | 13. A method, comprising the steps of: transmitting over a computer network, by a Distributor comprising a hardware server, a webpage file configured to display a webpage rendering on a client device operated by a customer of the Distributor; receiving over the computer network, by the Distributor, a selected category from the client device, wherein the webpage rendering displayed on the client device is configured to receive the selected category from the customer; accessing by the Distributor an electronic database to identify a plurality of registered names that are related to the selected category; generating by the Distributor a plurality of available names that are related to the selected category; transmitting over the computer network, by the Distributor, to the client device the plurality of registered names related to the selected category and the plurality of available names related to the selected category, wherein the webpage file is configured to allow the customer to guess on the webpage rendering displayed on the client device whether each name in the plurality of registered names and each name in the plurality of available names is registered or available for name registration after being displayed and wherein the webpage file is configured to allow the customer to select on the webpage rendering displayed on the client device one or more of the plurality of available names for name registration after being displayed; receiving over the computer network, by the Distributor, the selected one or more of the plurality of available names from the webpage file running on the client device; and registering by the Distributor the selected one or more of the plurality of available names to the customer, wherein the selected category by the customer is current events and one or more trending hashtags from one or more social media platforms are tokenized and used in accessing by the Distributor the electronic database to identify the plurality of registered names that are related to the selected category and used in generating by the Distributor the plurality of available names that are related to the selected category. 16. The method of claim 13 , wherein the webpage file running on the client device is configured to, immediately after the customer has guessed on all of the names in the plurality of registered names and the customer has guessed on all of the names in the plurality of available names, display on the webpage rendering on the client device a result table comprising: the plurality of registered names, the plurality of available names, an indication whether the customer correctly or incorrectly guessed whether each name in the plurality of registered names and each name in the plurality of available names was registered or available for name registration, a displayed icon option for each name in the plurality of available names to register the each name in the plurality of available names and no displayed icon option to register each name in the plurality of registered names. | 0.519544 |
9,110,922 | 1 | 2 | 1. A method for associating semantically-related items of a plurality of item types, comprising: (a) embedding, by one or more computers, training items of the plurality of item types in a joint embedding space having more than two dimensions configured in a memory coupled to at least one processor, wherein each of the dimensions is defined by a real-valued axis, wherein each embedded training item corresponds to a respective location in the joint embedding space, wherein said each embedded training item is represented by a respective vector of real numbers corresponding to the respective location, and wherein each of the real numbers of the vector corresponds to a mapping of the respective location to one of the dimensions; (b) learning, by the one or more computers, one or more mappings into the joint embedding space for each of the plurality of item types to create a trained joint embedding space and one or more learned mappings, the learning including: selecting, based on known relationships between the training items, a related pair of the training items that includes a first item and a second item, wherein the first item and the second item are separated by a first distance in the joint embedding space; selecting a third item that is less related to the first item than the second item, but is closer to the first item than the second item in the joint embedding space; and adjusting a mapping function to increase a distance between the first item and the third item relative to a distance between the first item and the second item; and (c) associating, by the one or more computers, one or more of the embedded training items with a first item based upon a distance in the trained joint embedding space from the first item to each said associated embedded training items, wherein each said distance is determined based upon a first vector of real numbers corresponding to the first item and a second vector of real numbers corresponding to a respective one of the associated embedded training items. | 1. A method for associating semantically-related items of a plurality of item types, comprising: (a) embedding, by one or more computers, training items of the plurality of item types in a joint embedding space having more than two dimensions configured in a memory coupled to at least one processor, wherein each of the dimensions is defined by a real-valued axis, wherein each embedded training item corresponds to a respective location in the joint embedding space, wherein said each embedded training item is represented by a respective vector of real numbers corresponding to the respective location, and wherein each of the real numbers of the vector corresponds to a mapping of the respective location to one of the dimensions; (b) learning, by the one or more computers, one or more mappings into the joint embedding space for each of the plurality of item types to create a trained joint embedding space and one or more learned mappings, the learning including: selecting, based on known relationships between the training items, a related pair of the training items that includes a first item and a second item, wherein the first item and the second item are separated by a first distance in the joint embedding space; selecting a third item that is less related to the first item than the second item, but is closer to the first item than the second item in the joint embedding space; and adjusting a mapping function to increase a distance between the first item and the third item relative to a distance between the first item and the second item; and (c) associating, by the one or more computers, one or more of the embedded training items with a first item based upon a distance in the trained joint embedding space from the first item to each said associated embedded training items, wherein each said distance is determined based upon a first vector of real numbers corresponding to the first item and a second vector of real numbers corresponding to a respective one of the associated embedded training items. 2. The method of claim 1 , further comprising: (d) embedding the first item at a first location, determined by applying the one or more learned mappings for a first item type of the plurality of item types, in the trained joint embedding space. | 0.723982 |
9,251,182 | 1 | 5 | 1. A computer-implemented method of supplementing structured information within a data system for entities based on unstructured data comprising: analyzing documents with unstructured data specifying two or more entities of the structured information and interactions between those two or more entities; identifying from the interactions within the unstructured data of the documents one or more relationships between entities of the structured information; extracting attribute values from the unstructured data for one or more entities of the structured information base on a comparison of the unstructured data with one or more dictionaries each including values for a corresponding attribute of an entity within the data system, wherein extracting attribute values from the unstructured data includes: generating tokens from the unstructured data and comparing the tokens to the values within the one or more dictionaries, wherein at least one value within a dictionary includes a plurality of tokens; retrieving entity records with structured information form the data system based on the extracted attribute values; constructing entity references for corresponding one or more entities of the data system based on a comparison of the retrieved entity records and the extracted attribute values; linking the entity references to the corresponding one or more entities within the data system to supplement the structured information for the corresponding one or more entities with information extracted from the unstructured data, wherein the entity references include extracted attributes from the unstructured data for corresponding linked entities; and linking entities of the structured information to each other within the structured information to indicate related entities based on the one or more relationships between those entities identified form the interactions specified within the unstructured data of the documents. | 1. A computer-implemented method of supplementing structured information within a data system for entities based on unstructured data comprising: analyzing documents with unstructured data specifying two or more entities of the structured information and interactions between those two or more entities; identifying from the interactions within the unstructured data of the documents one or more relationships between entities of the structured information; extracting attribute values from the unstructured data for one or more entities of the structured information base on a comparison of the unstructured data with one or more dictionaries each including values for a corresponding attribute of an entity within the data system, wherein extracting attribute values from the unstructured data includes: generating tokens from the unstructured data and comparing the tokens to the values within the one or more dictionaries, wherein at least one value within a dictionary includes a plurality of tokens; retrieving entity records with structured information form the data system based on the extracted attribute values; constructing entity references for corresponding one or more entities of the data system based on a comparison of the retrieved entity records and the extracted attribute values; linking the entity references to the corresponding one or more entities within the data system to supplement the structured information for the corresponding one or more entities with information extracted from the unstructured data, wherein the entity references include extracted attributes from the unstructured data for corresponding linked entities; and linking entities of the structured information to each other within the structured information to indicate related entities based on the one or more relationships between those entities identified form the interactions specified within the unstructured data of the documents. 5. The computer-implemented method of claim 1 , wherein constructing entity references includes: constructing entity references for corresponding one or more entities of the data system based on a fuzzy match of the retrieved entity records and the extracted attribute values. | 0.610169 |
9,406,048 | 1 | 8 | 1. A method, comprising: allowing a user to specify trigger words; allowing the user to prepare an email message including adding a trigger word to indicate confidential previously presented nature of information being sent through a message or an attachment; detecting initiation of sending of the email message by detecting activation of a send function; scanning, by an email server, the email message including at least one of a message body and an attachment of the email message to determine whether the email message includes one or more of the trigger words which are indicated in a trigger word table, the trigger word table indicating that a message may be sensitive where the message comprises a message body and a message attachment and being created by the user previous to the email message preparation; alerting the user with a sound and a pop-up display message when the scanning determines that the email message includes one or more of the trigger words, the pop-up display message includes: a border surrounding that blinks, a message that the user is about to send the one or more of the trigger words determined as comprising confidential, proprietary or sensitive material in the trigger word table and, an email address of a recipient of the email message and corresponding locations of the one or more of the trigger words in the email message; wherein the pop-up display message further contains execution icons which allow the user to approve sending of the email message or to disapprove sending of the email message, wherein the trigger words are selected to stop sending of the email message determined as comprising confidential, proprietary or sensitive material, wherein the user is alerted directly when one or more of the trigger words are first found, wherein the alerting is triggered by an exact match with one or more of the trigger words, wherein the corresponding locations of the one or more of the trigger words determined to be included in the at least one of the message body and the attachment of the email message respectively identify the one or more of the trigger words as being located in one or more of a subject line, the message body and the attachment. | 1. A method, comprising: allowing a user to specify trigger words; allowing the user to prepare an email message including adding a trigger word to indicate confidential previously presented nature of information being sent through a message or an attachment; detecting initiation of sending of the email message by detecting activation of a send function; scanning, by an email server, the email message including at least one of a message body and an attachment of the email message to determine whether the email message includes one or more of the trigger words which are indicated in a trigger word table, the trigger word table indicating that a message may be sensitive where the message comprises a message body and a message attachment and being created by the user previous to the email message preparation; alerting the user with a sound and a pop-up display message when the scanning determines that the email message includes one or more of the trigger words, the pop-up display message includes: a border surrounding that blinks, a message that the user is about to send the one or more of the trigger words determined as comprising confidential, proprietary or sensitive material in the trigger word table and, an email address of a recipient of the email message and corresponding locations of the one or more of the trigger words in the email message; wherein the pop-up display message further contains execution icons which allow the user to approve sending of the email message or to disapprove sending of the email message, wherein the trigger words are selected to stop sending of the email message determined as comprising confidential, proprietary or sensitive material, wherein the user is alerted directly when one or more of the trigger words are first found, wherein the alerting is triggered by an exact match with one or more of the trigger words, wherein the corresponding locations of the one or more of the trigger words determined to be included in the at least one of the message body and the attachment of the email message respectively identify the one or more of the trigger words as being located in one or more of a subject line, the message body and the attachment. 8. The method as recited in claim 1 , wherein the scanning determines a type of the attachment and the attachment is converted into a text file for text scanning. | 0.517857 |
9,438,730 | 7 | 10 | 7. A non-transitory, computer-readable medium comprising computer-executable instructions when executed by at least one computer processor cause the at least one computer processor to: listen in on audio of a telephone call involving a party once the telephone call has been placed on hold to detect an event indicating to offer a callback to the party, the event comprising at least one of a word spoken by the party, a sound made by the party, and an emotion displayed by the party; and in response to detecting the event, interact with the party to obtain information from the party with respect to placing the callback to the party; and record the information so that the callback can be placed to the party at a later time. | 7. A non-transitory, computer-readable medium comprising computer-executable instructions when executed by at least one computer processor cause the at least one computer processor to: listen in on audio of a telephone call involving a party once the telephone call has been placed on hold to detect an event indicating to offer a callback to the party, the event comprising at least one of a word spoken by the party, a sound made by the party, and an emotion displayed by the party; and in response to detecting the event, interact with the party to obtain information from the party with respect to placing the callback to the party; and record the information so that the callback can be placed to the party at a later time. 10. The non-transitory, computer-readable medium of claim 7 , wherein the computer-readable instructions when executed by the at least one computer processor cause the at least one computer processor to also provide the party with an approximate time the party is expected to remain on hold. | 0.648551 |
7,945,662 | 3 | 4 | 3. The method of claim 2 , wherein the displaying of the updated domain name page includes displaying the keywords in the optimized keyword set as part of the updated content. | 3. The method of claim 2 , wherein the displaying of the updated domain name page includes displaying the keywords in the optimized keyword set as part of the updated content. 4. The method of claim 3 , wherein the displaying of the updated domain name page includes displaying default keywords included in a generic keyword set and related keywords in a related keyword set, wherein the related keywords are selected based on semantic analysis of a domain name used for retrieving the domain name page. | 0.935016 |
8,504,580 | 1 | 2 | 1. A computer system implemented method of creating and using multiple artificial intelligence (AI) clones of respective multiple entities comprising the following operations of a computer system: for each of the multiple AI clones, receiving respective text into the computer system from one or more sources; for each of the multiple AI clones, obtaining respective paragraphs from the text received for the AI clones; at least some of the paragraphs comprising multiple sentences, and at least some of the sentences comprising multiple clauses identified based upon figures of speech and punctuation; obtaining a first set of respective context phrases from the received paragraphs, which context phrases are obtained from the respective clauses and are indicative of the context of the respective paragraphs; obtaining respective weights of the context phrases using parameters related to frequency of occurrence of a context phrase relative to other context phrases or to absolute number of occurrences of a context phrase therein; storing the context phrases and the paragraphs as structured data in one or more tables to thereby create initial respective multiple AI clones; for multiple initial AI clones, improving the AI clones by adding paragraphs and a second set of context phrases from text subsequently supplied to the computer system by the source of the text that was used to create the initial AI clones and from one or more other sources, including one or more instructors, and by selectively deleting data from the one or more tables, to thereby create respective improved AI clones; and using the improved AI clones and any remaining initial AI clones that have not been improved, to answer questions posed by users through a process comprising using a compatibility test matching context phrases related to the respective questions to context phrases related to AI clones through a compatibility algorithm relating weights of context phrases related to a question and weights of context phrases related to AI clones, and to direct advertisements to AI clones, wherein a single advertisement is directed essentially concurrently to multiple AI clones, using for the purpose a matching algorithm that uses selected matching criteria in comparing context phrases obtained from the questions or advertisements with said structured data in said one or more tables, which matching algorithm relates context phrases related to advertisements to context phrases related to AI clones and takes into account respective weights of the context phrases that the matching algorithm relates; wherein the AI clones are configured to replace human sources of information in answering a user's question and assist advertisers in selecting plural AI clones that are likely to be receptive to a single advertisement to thereby direct the advertisement only to some of the AI clones, based on the content of the question and the advertisement. | 1. A computer system implemented method of creating and using multiple artificial intelligence (AI) clones of respective multiple entities comprising the following operations of a computer system: for each of the multiple AI clones, receiving respective text into the computer system from one or more sources; for each of the multiple AI clones, obtaining respective paragraphs from the text received for the AI clones; at least some of the paragraphs comprising multiple sentences, and at least some of the sentences comprising multiple clauses identified based upon figures of speech and punctuation; obtaining a first set of respective context phrases from the received paragraphs, which context phrases are obtained from the respective clauses and are indicative of the context of the respective paragraphs; obtaining respective weights of the context phrases using parameters related to frequency of occurrence of a context phrase relative to other context phrases or to absolute number of occurrences of a context phrase therein; storing the context phrases and the paragraphs as structured data in one or more tables to thereby create initial respective multiple AI clones; for multiple initial AI clones, improving the AI clones by adding paragraphs and a second set of context phrases from text subsequently supplied to the computer system by the source of the text that was used to create the initial AI clones and from one or more other sources, including one or more instructors, and by selectively deleting data from the one or more tables, to thereby create respective improved AI clones; and using the improved AI clones and any remaining initial AI clones that have not been improved, to answer questions posed by users through a process comprising using a compatibility test matching context phrases related to the respective questions to context phrases related to AI clones through a compatibility algorithm relating weights of context phrases related to a question and weights of context phrases related to AI clones, and to direct advertisements to AI clones, wherein a single advertisement is directed essentially concurrently to multiple AI clones, using for the purpose a matching algorithm that uses selected matching criteria in comparing context phrases obtained from the questions or advertisements with said structured data in said one or more tables, which matching algorithm relates context phrases related to advertisements to context phrases related to AI clones and takes into account respective weights of the context phrases that the matching algorithm relates; wherein the AI clones are configured to replace human sources of information in answering a user's question and assist advertisers in selecting plural AI clones that are likely to be receptive to a single advertisement to thereby direct the advertisement only to some of the AI clones, based on the content of the question and the advertisement. 2. The method of claim 1 further including adding, to an AI clone to which an advertisement has been directed in said using step, one or more paragraphs of that advertisement and one or more context phrases obtained from the last-recited paragraphs, to thereby further improve the AI clone. | 0.694737 |
7,636,732 | 5 | 6 | 5. The method of claim 4 in which processing each term of said portion comprises presenting the term to a user together with at least identifiers of a number of documents or files stored in said system containing said term. | 5. The method of claim 4 in which processing each term of said portion comprises presenting the term to a user together with at least identifiers of a number of documents or files stored in said system containing said term. 6. The method of claim 5 in which said processing includes presenting the term to a user together with at least portions of a document identified by one of said identifiers. | 0.870315 |
9,612,830 | 15 | 16 | 15. A system for discovering work-item relations, comprising: a processor; and a link discovery module operable to: execute on the processor; automatically identify mappings of work-item elements to standardized specification elements, by automatically analyzing a plurality of work-item elements and their relationships generated from a description of a collection of work-items, each of the work-items indicating a single unit of work needed to complete a project, and a plurality of standardized specification elements and their relationships represented as nodes and edges generated from a description of practice guidelines for completing the project, the practice guidelines specified in a text form and comprising a title of a task involved in the one or more projects, a relationship the task in the one or more projects has with another task in the one or more projects, description of the task, the one or more standardized specification further comprising a work-breakdown tree with different tasks comprising at least the task, wherein the mappings are identified by determining a threshold similarity between a single work-item element node and a standardized specification element, and between a pair of work-item element nodes with associated link relationship and a pair of standardized specification elements with associated link relationship, wherein the single work-item element node is mapped at most to one standardized specification element, the identifying the threshold similarity comprising one or more of string similarity, Boolean similarity and relational similarity; automatically discover one or more missing relations among said plurality of work-item elements based on said mappings, wherein the relationships comprise at least one or more of changed by, has same owner as, input from and output to; receive, based on at least on one of a determination that the mapping creates a conflict and the mapping has a low confidence, input from a user to resolve the at least one of the conflict in the mapping and the low confidence in the mapping; and automatically add the discovered one or more missing relations as edges to the mapping. | 15. A system for discovering work-item relations, comprising: a processor; and a link discovery module operable to: execute on the processor; automatically identify mappings of work-item elements to standardized specification elements, by automatically analyzing a plurality of work-item elements and their relationships generated from a description of a collection of work-items, each of the work-items indicating a single unit of work needed to complete a project, and a plurality of standardized specification elements and their relationships represented as nodes and edges generated from a description of practice guidelines for completing the project, the practice guidelines specified in a text form and comprising a title of a task involved in the one or more projects, a relationship the task in the one or more projects has with another task in the one or more projects, description of the task, the one or more standardized specification further comprising a work-breakdown tree with different tasks comprising at least the task, wherein the mappings are identified by determining a threshold similarity between a single work-item element node and a standardized specification element, and between a pair of work-item element nodes with associated link relationship and a pair of standardized specification elements with associated link relationship, wherein the single work-item element node is mapped at most to one standardized specification element, the identifying the threshold similarity comprising one or more of string similarity, Boolean similarity and relational similarity; automatically discover one or more missing relations among said plurality of work-item elements based on said mappings, wherein the relationships comprise at least one or more of changed by, has same owner as, input from and output to; receive, based on at least on one of a determination that the mapping creates a conflict and the mapping has a low confidence, input from a user to resolve the at least one of the conflict in the mapping and the low confidence in the mapping; and automatically add the discovered one or more missing relations as edges to the mapping. 16. The system of claim 15 , further including: a method parser operable to parse said description of practice guidelines and generate said plurality of standardized specification elements and their relationships; and a work-item parser operable to parse said description of a collection of work-items and generate said plurality of work-item elements and their relationships. | 0.631373 |
6,015,947 | 1 | 4 | 1. A method of teaching music comprising: a) teaching rote understanding of musical skills to the student; b) teaching basic structural elements of music to the student by using a two-line staff; c) teaching a five-line staff to the student; d) teaching rhythm to the student; and e) teaching the integration of steps c) and d) to the student. | 1. A method of teaching music comprising: a) teaching rote understanding of musical skills to the student; b) teaching basic structural elements of music to the student by using a two-line staff; c) teaching a five-line staff to the student; d) teaching rhythm to the student; and e) teaching the integration of steps c) and d) to the student. 4. The method of claim 1, wherein step c) includes the teaching of musical syllables. | 0.921004 |
8,875,016 | 1 | 6 | 1. An image analysis and conversion method comprising: receiving a digital ink image having defined perceptually salient structures by an electronic device configured to perform the receiving; converting the digital ink image into multiple structured object representations of the digital ink image by the electronic device the multiple structured object representations correlating to ones of the defined perceptually salient structures of the digital ink image; and altering at least one of the structured object representations into multiple simultaneously existing structured alternative interpretations of the at least one structured object representations of the digital ink image by the electronic device, each of the alternative interpretations being viewable by a user and being plausible intended outputs of the user. | 1. An image analysis and conversion method comprising: receiving a digital ink image having defined perceptually salient structures by an electronic device configured to perform the receiving; converting the digital ink image into multiple structured object representations of the digital ink image by the electronic device the multiple structured object representations correlating to ones of the defined perceptually salient structures of the digital ink image; and altering at least one of the structured object representations into multiple simultaneously existing structured alternative interpretations of the at least one structured object representations of the digital ink image by the electronic device, each of the alternative interpretations being viewable by a user and being plausible intended outputs of the user. 6. The method according to claim 1 , wherein the converting step includes forming an Alternative Graph, wherein the Alternative Graph is configured for the generation of the multiple simultaneously existing structured alternative interpretations of the digital ink image, wherein the Alternative Graph is configured to permit movement of each of the multiple structured object representations as at least one of an individual structured object representation, a member of a sub-group of all the multiple structured object representations, and a member of an overall group of the multiple structured object representations. | 0.829496 |
7,933,915 | 1 | 5 | 1. A computer-implemented method for conducting a database graph query, comprising: (a) obtaining a first database graph and a second database graph, wherein the first database graph and second database graph each have two or more vertices and one or more edges; (b) mapping the first database graph to the second database graph, wherein: (i) each vertex in the first database graph has a corresponding vertex in the second database graph; and (ii) each edge in the first database graph has a corresponding edge in the second database graph; wherein the mapping step (b) comprises: (1) computing an initial similarity matrix for the first database graph and the second database graph, wherein each entry of the similarity matrix represents a weight similarity of each vertex of the first database graph to each vertex of the second database graph; (2) creating a priority queue comprised of vertex pairs based on the weight similarity, wherein each vertex pair comprises a vertex from the first database graph and a most similar vertex from the second database graph based on the weight similarity; (3) processing the priority queue by: (i) marking a first vertex pair in the priority queue as matched; (ii) assigning a higher similarity weight to unmatched vertex pairs that are neighbors to the first vertex pair; (iii) repeating steps (3)(i) and (3)(ii) for each subsequent vertex pair in the priority queue until all vertices in the first database graph have been marked as matched; (c) creating a graph closure tree comprised of a union of the first database graph and the second database graph based on the mapping, wherein each node of the graph closure tree comprises a graph closure of the node's children and each child of a leaf node comprises a database graph; and (d) conducting a graph query based on the graph closure tree. | 1. A computer-implemented method for conducting a database graph query, comprising: (a) obtaining a first database graph and a second database graph, wherein the first database graph and second database graph each have two or more vertices and one or more edges; (b) mapping the first database graph to the second database graph, wherein: (i) each vertex in the first database graph has a corresponding vertex in the second database graph; and (ii) each edge in the first database graph has a corresponding edge in the second database graph; wherein the mapping step (b) comprises: (1) computing an initial similarity matrix for the first database graph and the second database graph, wherein each entry of the similarity matrix represents a weight similarity of each vertex of the first database graph to each vertex of the second database graph; (2) creating a priority queue comprised of vertex pairs based on the weight similarity, wherein each vertex pair comprises a vertex from the first database graph and a most similar vertex from the second database graph based on the weight similarity; (3) processing the priority queue by: (i) marking a first vertex pair in the priority queue as matched; (ii) assigning a higher similarity weight to unmatched vertex pairs that are neighbors to the first vertex pair; (iii) repeating steps (3)(i) and (3)(ii) for each subsequent vertex pair in the priority queue until all vertices in the first database graph have been marked as matched; (c) creating a graph closure tree comprised of a union of the first database graph and the second database graph based on the mapping, wherein each node of the graph closure tree comprises a graph closure of the node's children and each child of a leaf node comprises a database graph; and (d) conducting a graph query based on the graph closure tree. 5. The method of claim 1 , wherein the database graph query comprises a subgraph query, wherein the subgraph query comprising determining if a subgraph is sub-isomorphic by: (a) for each vertex u of the first database graph G1, defining a level-n adjacent subgraph, wherein the level-n adjacent subgraph contains all vertices reachable from the vertex u within a distance of n; (b) constructing a bipartite graph B for G1 and the second database graph G2, wherein: (i) vertex sets of the bipartite graph are vertex sets of G1 and G2; (ii) for any two vertices u εG1, v εG2, if u is level-n pseudo compatible to v, then (u,v) comprises an edge of B, wherein vertex u is called level-n pseudo compatible to v if a level-n adjacent subgraph of u is level-n sub-isomorphic to that of v, wherein G1 is called level-n sub-isomorphic if every vertex in G1 is matched to a vertex in G2. | 0.528464 |
9,380,009 | 1 | 6 | 1. A method for providing response message completion, comprising: determining a response completion model (RCM) using a linear combination of a generic response language model (LM) and a stimulus model with a mixing parameter for a mixing model, the mixing parameter being based, at least in part, on a topic probability, the topic probability to be assigned a value based, at least in part, on a likelihood that a candidate topic is associated with a received stimulus message; receiving a stimulus message (SM); if an incomplete response message includes at least one preceding word, determining at least one candidate next word for the incomplete response message based, at least in part, on the RCM, the SM, and the at least one preceding word and based, at least in part, on estimated word frequencies in stimulus messages from a plurality of stimulus-response message pairs; selecting at least one word from the at least one determined candidate next word; including the selected at least one word within the incomplete response message; and generating a complete response message that is based, at least in part, on the incomplete response message that includes the selected at least one word. | 1. A method for providing response message completion, comprising: determining a response completion model (RCM) using a linear combination of a generic response language model (LM) and a stimulus model with a mixing parameter for a mixing model, the mixing parameter being based, at least in part, on a topic probability, the topic probability to be assigned a value based, at least in part, on a likelihood that a candidate topic is associated with a received stimulus message; receiving a stimulus message (SM); if an incomplete response message includes at least one preceding word, determining at least one candidate next word for the incomplete response message based, at least in part, on the RCM, the SM, and the at least one preceding word and based, at least in part, on estimated word frequencies in stimulus messages from a plurality of stimulus-response message pairs; selecting at least one word from the at least one determined candidate next word; including the selected at least one word within the incomplete response message; and generating a complete response message that is based, at least in part, on the incomplete response message that includes the selected at least one word. 6. The method of claim 1 , wherein determining at least one candidate next word, further comprises: determining at least one LM probability for a plurality of words in a dictionary based, at least in part, on at least the at least one determined preceding word within the incomplete response message; selecting at least one word in the stimulus message; determining at least one stimulus probability for the plurality of dictionary words based, at least in part, on the selected at least one word in the stimulus message; ranking the plurality of dictionary words based, at least in part, at least on the at least one LM probability, the at least one determined stimulus probability, and the mixing parameter; and determining at least one candidate word in the incomplete response message based, at least in part, on the ranked plurality of dictionary words. | 0.662736 |
7,856,357 | 3 | 5 | 3. A method according to claim 1 , wherein selecting the M speech units for each of the segments includes: setting each segment of the segments as a target segment; calculating a first cost for each speech unit of the group in the memory, the first cost representing difference between the target segment in the target speech and the speech unit of the group; calculating a second cost for each speech unit of the group in the memory, the second cost representing a degree of distortion produced when the speech unit of the group is concatenated with speech units around the target segment in the optimal speech unit sequence; and selecting the M speech units for the target segment based on the first cost and the second cost of the each speech unit of the group. | 3. A method according to claim 1 , wherein selecting the M speech units for each of the segments includes: setting each segment of the segments as a target segment; calculating a first cost for each speech unit of the group in the memory, the first cost representing difference between the target segment in the target speech and the speech unit of the group; calculating a second cost for each speech unit of the group in the memory, the second cost representing a degree of distortion produced when the speech unit of the group is concatenated with speech units around the target segment in the optimal speech unit sequence; and selecting the M speech units for the target segment based on the first cost and the second cost of the each speech unit of the group. 5. A method according to claim 3 , wherein the second cost is calculated using at least one of a spectrum, fundamental frequency, and power of the each one of the group and another of the group. | 0.888761 |
9,251,250 | 1 | 6 | 1. A computer-implemented method for processing text to construct a model of the text, comprising executing on a processor the steps of: acquiring an electronic communication containing the text, wherein the text has a shared vocabulary, wherein the text includes words, wherein the text is partitioned into sets of texts and at least one set of text is partitioned into subsets of texts, wherein a usage of the shared vocabulary in two or more sets is different, and the topics of two or more subsets are different; defining a probabilistic model for the text, wherein the probabilistic model is stored in a memory operatively connected to the processor, and wherein the probabilistic model considers each word in the text to be a token having a position and a word value, and the usage of the shared vocabulary, topics, subtopics, and word values for each token in the text are represented using distributions of random variables in the probabilistic model, wherein the random variables are discrete, wherein each set of text has a vocabulary usage random variable, wherein each token is associated with the random variables corresponding to the topics, the subtopics, and the word values, wherein the distribution of the random variable associated with the topic for the token is dependent on the subset of text including the token, the distribution of the random variable associated with the subtopic for the token is dependent on the topic of the token, and the distribution of the random variable for the word value of the token is dependent on the associated subtopic and the vocabulary usage of the set of texts including the token; estimating parameters of the probabilistic model, based on the vocabulary usages, the word values, the topics, and the subtopics associated with the words; and classifying a text input using the probabilistic model, wherein the classifying includes one or combination of a dialect estimation, a topic estimation and a document retrieval, wherein for the dialect estimation, the text input is used in conjunction with the estimated parameters of the probabilistic model to compute dialect scores for estimating a dialect class, wherein for the topic estimation, the text input is used in conjunction with the estimated parameters of the probabilistic model to compute topic scores for estimating a topic class, and wherein for the document retrieval, the text input is used in conjunction with the estimated parameters of the probabilistic model to compute document scores for estimating a matching document. | 1. A computer-implemented method for processing text to construct a model of the text, comprising executing on a processor the steps of: acquiring an electronic communication containing the text, wherein the text has a shared vocabulary, wherein the text includes words, wherein the text is partitioned into sets of texts and at least one set of text is partitioned into subsets of texts, wherein a usage of the shared vocabulary in two or more sets is different, and the topics of two or more subsets are different; defining a probabilistic model for the text, wherein the probabilistic model is stored in a memory operatively connected to the processor, and wherein the probabilistic model considers each word in the text to be a token having a position and a word value, and the usage of the shared vocabulary, topics, subtopics, and word values for each token in the text are represented using distributions of random variables in the probabilistic model, wherein the random variables are discrete, wherein each set of text has a vocabulary usage random variable, wherein each token is associated with the random variables corresponding to the topics, the subtopics, and the word values, wherein the distribution of the random variable associated with the topic for the token is dependent on the subset of text including the token, the distribution of the random variable associated with the subtopic for the token is dependent on the topic of the token, and the distribution of the random variable for the word value of the token is dependent on the associated subtopic and the vocabulary usage of the set of texts including the token; estimating parameters of the probabilistic model, based on the vocabulary usages, the word values, the topics, and the subtopics associated with the words; and classifying a text input using the probabilistic model, wherein the classifying includes one or combination of a dialect estimation, a topic estimation and a document retrieval, wherein for the dialect estimation, the text input is used in conjunction with the estimated parameters of the probabilistic model to compute dialect scores for estimating a dialect class, wherein for the topic estimation, the text input is used in conjunction with the estimated parameters of the probabilistic model to compute topic scores for estimating a topic class, and wherein for the document retrieval, the text input is used in conjunction with the estimated parameters of the probabilistic model to compute document scores for estimating a matching document. 6. The method of claim 1 , further comprising: using the model to perform retrieval of topically related subsets, invariant to vocabulary usage. | 0.516779 |
8,635,095 | 15 | 16 | 15. The method of claim 14 , wherein each entry in the table corresponds to an asset associated with at least one of the set of intents. | 15. The method of claim 14 , wherein each entry in the table corresponds to an asset associated with at least one of the set of intents. 16. The method of claim 15 , wherein the asset comprises a telephone pole. | 0.972118 |
8,429,225 | 40 | 43 | 40. The system of claim 1 , further comprising: one or more sensors. | 40. The system of claim 1 , further comprising: one or more sensors. 43. The system of claim 40 , wherein said one or more sensors comprises: a magnetoencephalography (MEG) device. | 0.975278 |
9,747,891 | 11 | 12 | 11. A system for recommending the pronunciation of a name, the system comprising: a memory; and a processor system communicatively coupled to the memory; wherein the memory is configured to store a plurality of audio records, wherein the plurality of audio records includes a set of audio records identifying the pronunciation of a common name; wherein the processor is configured to analyze the set of audio records identifying the pronunciation of a common name to determine variations in the pronunciation; wherein the processor is further configured to determine the most frequent common pronunciation of the common name in the set of audio records; wherein the processor is further configured to deliver the most frequent common pronunciation of the common name to an end user over a communications network. | 11. A system for recommending the pronunciation of a name, the system comprising: a memory; and a processor system communicatively coupled to the memory; wherein the memory is configured to store a plurality of audio records, wherein the plurality of audio records includes a set of audio records identifying the pronunciation of a common name; wherein the processor is configured to analyze the set of audio records identifying the pronunciation of a common name to determine variations in the pronunciation; wherein the processor is further configured to determine the most frequent common pronunciation of the common name in the set of audio records; wherein the processor is further configured to deliver the most frequent common pronunciation of the common name to an end user over a communications network. 12. The system of claim 11 , wherein the memory is configured to store a name repository containing the plurality of audio records. | 0.889545 |
7,873,622 | 19 | 26 | 19. A computer-implemented method of providing a graphical user interface (GUI) for electronically displaying search related information, the method comprising: under the control of one or more computer systems configured with executable instructions, providing for electronic display a search element that enables a user to submit a search query and at least two panes arranged to collectively, responsive to submission of the search query, each display search related information or results from a different respective category, the different respective search category selected from the group consisting of: general web search results, image search results, book search results, movie search results, map search results, local area search results, reference search results, dictionary search results, address based search results, news search results, diary search results, bookmark search results, search history search results and tracking number search results, and each pane being user configurable to be in either an open state suitable for displaying search results information of a particular search category or a closed state which is narrowed to a display identifying the particular search category, wherein panes in the open state are generally arranged as columns; providing for electronic display at least one adjustable column delimiter that visibly marks a boundary between adjacent panes, wherein moving by a user of a column delimiter adjusts the relative sizes of the adjacent panes; and configuring a pane in the open state to automatically be in the closed state upon detecting a movement of a column delimiter adjacent to the pane in the open state to a position where the column width of the pane in the open state is positive and below a designated threshold for closing the pane in the open state. | 19. A computer-implemented method of providing a graphical user interface (GUI) for electronically displaying search related information, the method comprising: under the control of one or more computer systems configured with executable instructions, providing for electronic display a search element that enables a user to submit a search query and at least two panes arranged to collectively, responsive to submission of the search query, each display search related information or results from a different respective category, the different respective search category selected from the group consisting of: general web search results, image search results, book search results, movie search results, map search results, local area search results, reference search results, dictionary search results, address based search results, news search results, diary search results, bookmark search results, search history search results and tracking number search results, and each pane being user configurable to be in either an open state suitable for displaying search results information of a particular search category or a closed state which is narrowed to a display identifying the particular search category, wherein panes in the open state are generally arranged as columns; providing for electronic display at least one adjustable column delimiter that visibly marks a boundary between adjacent panes, wherein moving by a user of a column delimiter adjusts the relative sizes of the adjacent panes; and configuring a pane in the open state to automatically be in the closed state upon detecting a movement of a column delimiter adjacent to the pane in the open state to a position where the column width of the pane in the open state is positive and below a designated threshold for closing the pane in the open state. 26. The computer-implemented method of claim 19 , further comprising providing for electronic display a navigation widget that facilitates group scrolling of the contents of all of the open panes. | 0.845182 |
9,979,724 | 15 | 16 | 15. The system of claim 1 the first and second devices for independently assigning a risk score for use in authenticating the source. | 15. The system of claim 1 the first and second devices for independently assigning a risk score for use in authenticating the source. 16. The system of claim 15 wherein one or both of the recognition score and the risk score are used to assign the source to an authentication class indicative of the risk level of transactions to be executed with the source. | 0.925877 |
9,361,086 | 12 | 13 | 12. The computer system of claim 11 , wherein program instructions to extract installation procedure descriptions, parameters, and prerequisites associated with the identified annotations further comprises: program instructions to analyze the identified annotations in the one or more product installation documents to extract one or more tags and name/value pairs that describe each procedure in the one or more product installation documents, and the parameters and prerequisites of each procedure. | 12. The computer system of claim 11 , wherein program instructions to extract installation procedure descriptions, parameters, and prerequisites associated with the identified annotations further comprises: program instructions to analyze the identified annotations in the one or more product installation documents to extract one or more tags and name/value pairs that describe each procedure in the one or more product installation documents, and the parameters and prerequisites of each procedure. 13. The computer system of claim 12 , wherein analyzing the identified annotations in the one or more product installation documents further comprises: program instructions to determine a set of related tasks in the one or more product installation documents; program instructions to arrange the set of related tasks into a set of grouped tasks; and program instructions to tag the set of grouped tasks, wherein the tags define the name/value pairs associated with the identified annotations. | 0.830579 |
9,581,726 | 1 | 2 | 1. A method for determination of importance of attributes identified in a plurality of attribute importance models selected from a plurality of available attribute importance models, comprising: determining an ordered set of attributes group by applying the plurality of attribute importance models to the plurality of attributes, the ordered set of attributes group having a plurality of attributes with a ranked attribute sequence; determining a subset evaluator important attribute group by applying at least one subset evaluator attribute importance model, and at least one classifier to the plurality of attributes; creating a combined ranked attribute sequence by combining, in descending rank order, the ordered set of attributes group and the subset evaluator important attribute group according to one of a constrained model mode and a relaxed model mode; creating a normalized attribute importance array for each attribute in the combined ranked attribute sequence, the normalized importance array having a normalized weight; creating a tagged attributes set by tagging each attribute in the combined ranked attribute sequence with a partition number based on recursive partitioning of the plurality of attributes through a weight range of the normalized attribute importance array; determining a conditional maximum number of attributes from the tagged attributes set and the subset evaluator important attribute group; designating, as attributes for further consideration, the plurality of attributes in a highest two partitions of the tagged attributes set having a non-zero rank in the normalized attribute importance array and a next highest attribute in the tagged attributes set until the conditional maximum number of attributes is met; designating, as additional attributes for further consideration, each of the plurality of attributes in the subset evaluator important attribute group having a rank greater than zero and which are not designated as attributes for further consideration; creating boosted attributes by increasing a normalized weight of each attribute in the subset evaluator important attribute group which is also designated as an attribute for further consideration; creating an order of attributes according to the normalized weight for each respective attribute, from the boosted attributes, the attributes designated for further consideration that are not in the boosted attributes, and the additional attributes designated for further consideration that are not in the boosted attributes; generating an ordered attribute set for each attribute in one of the order of attributes and a modified order of attributes by ordering each occurrence of each attribute in each of the plurality of attribute importance models in a descending order according to a number of occurrences; producing a normalized global rank for each attribute in one of the order of attributes and the modified order of attributes by multiplying the number of occurrences of each attribute in one of the order of attributes and the modified order of attributes by a number of occurrences of each attribute in each ordered attribute set, an average of the normalized weight of each respective attribute from the normalized attribute importance array and the normalized weight of each attribute from the boosted attributes; generating one of a normalized ranking list and a modified normalized ranking list for each attribute in the normalized global rank using a computer processor; and adjusting drilling operations based on one of the normalized ranking list and the modified normalized ranking list. | 1. A method for determination of importance of attributes identified in a plurality of attribute importance models selected from a plurality of available attribute importance models, comprising: determining an ordered set of attributes group by applying the plurality of attribute importance models to the plurality of attributes, the ordered set of attributes group having a plurality of attributes with a ranked attribute sequence; determining a subset evaluator important attribute group by applying at least one subset evaluator attribute importance model, and at least one classifier to the plurality of attributes; creating a combined ranked attribute sequence by combining, in descending rank order, the ordered set of attributes group and the subset evaluator important attribute group according to one of a constrained model mode and a relaxed model mode; creating a normalized attribute importance array for each attribute in the combined ranked attribute sequence, the normalized importance array having a normalized weight; creating a tagged attributes set by tagging each attribute in the combined ranked attribute sequence with a partition number based on recursive partitioning of the plurality of attributes through a weight range of the normalized attribute importance array; determining a conditional maximum number of attributes from the tagged attributes set and the subset evaluator important attribute group; designating, as attributes for further consideration, the plurality of attributes in a highest two partitions of the tagged attributes set having a non-zero rank in the normalized attribute importance array and a next highest attribute in the tagged attributes set until the conditional maximum number of attributes is met; designating, as additional attributes for further consideration, each of the plurality of attributes in the subset evaluator important attribute group having a rank greater than zero and which are not designated as attributes for further consideration; creating boosted attributes by increasing a normalized weight of each attribute in the subset evaluator important attribute group which is also designated as an attribute for further consideration; creating an order of attributes according to the normalized weight for each respective attribute, from the boosted attributes, the attributes designated for further consideration that are not in the boosted attributes, and the additional attributes designated for further consideration that are not in the boosted attributes; generating an ordered attribute set for each attribute in one of the order of attributes and a modified order of attributes by ordering each occurrence of each attribute in each of the plurality of attribute importance models in a descending order according to a number of occurrences; producing a normalized global rank for each attribute in one of the order of attributes and the modified order of attributes by multiplying the number of occurrences of each attribute in one of the order of attributes and the modified order of attributes by a number of occurrences of each attribute in each ordered attribute set, an average of the normalized weight of each respective attribute from the normalized attribute importance array and the normalized weight of each attribute from the boosted attributes; generating one of a normalized ranking list and a modified normalized ranking list for each attribute in the normalized global rank using a computer processor; and adjusting drilling operations based on one of the normalized ranking list and the modified normalized ranking list. 2. The method of claim 1 , further comprising: computing a classification accuracy measure for a target attribute using a classification algorithm on the plurality of attributes, each attribute in the combined ranked attribute sequence and each attribute of the ordered attribute set according to the number of occurrences. | 0.677645 |
8,645,914 | 9 | 15 | 9. A non-transitory computer-readable storage medium tangibly embodying a set of instructions, which when executed by one or more processors of one or more computer systems, cause the one or more processors to perform a method comprising: allowing software code associated with one or more of a plurality of a plurality of programming building blocks to be concurrently edited and executed within a programming environment, including: receiving (i) information regarding the plurality of programming building blocks and (ii) information indicative of a current situation relating to the plurality of programming building blocks; and evaluating the plurality of programming building blocks based on the current situation; facilitating detection of one or more logical errors in one or more of the plurality of programming building blocks by proactively providing semantic feedback regarding those of the plurality of programming building blocks to which the current situation is relevant to an end user based on the evaluating; and wherein the semantic feedback includes a plurality of colored annotations of different colors to one or more visual representations of one or more of the plurality of programming building blocks. | 9. A non-transitory computer-readable storage medium tangibly embodying a set of instructions, which when executed by one or more processors of one or more computer systems, cause the one or more processors to perform a method comprising: allowing software code associated with one or more of a plurality of a plurality of programming building blocks to be concurrently edited and executed within a programming environment, including: receiving (i) information regarding the plurality of programming building blocks and (ii) information indicative of a current situation relating to the plurality of programming building blocks; and evaluating the plurality of programming building blocks based on the current situation; facilitating detection of one or more logical errors in one or more of the plurality of programming building blocks by proactively providing semantic feedback regarding those of the plurality of programming building blocks to which the current situation is relevant to an end user based on the evaluating; and wherein the semantic feedback includes a plurality of colored annotations of different colors to one or more visual representations of one or more of the plurality of programming building blocks. 15. The non-transitory computer-readable storage medium of claim 9 , wherein the method further comprises concurrently displaying (i) a visual representation of a programming building block of the plurality of programming building blocks within a behavior editor and (ii) a visual representation of the current situation, wherein the current situation includes (a) a selection identifying a selected agent of a plurality of agents represented by the plurality of programming building blocks and (b) data describing at least the selected agent and wherein the programming building block corresponds to the selected agent and represents one or more behavioral rules of the selected agent. | 0.61067 |
8,392,445 | 19 | 27 | 19. A computer readable storage device having instructions stored thereon that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising: associating each of a plurality of parent queries with a respective group of one or more child queries for the parent query, wherein each child query was submitted during a respective session following submission of its associated parent query during the session; identifying one or more candidate sibling queries for a particular child query, wherein the particular child query is a child query for one or more first parent queries in the plurality of parent queries and each candidate sibling query for the particular child query is a child query for one or more second parent queries in the plurality of queries, each candidate sibling query having a fan-in measure that satisfies a fan-in threshold, wherein the fan-in measure is a number of parent queries associated with a particular sibling query, wherein for each candidate sibling query, the one or more second parent queries for the candidate sibling query and the one or more first parent queries have a group of shared parent queries in common and the group of shared parent queries has a size that satisfies a common-query threshold; and selecting one or more final sibling queries for the particular child query from the one or more candidate sibling queries, and associating the final sibling queries with the particular child query as query refinements for the particular child query. | 19. A computer readable storage device having instructions stored thereon that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising: associating each of a plurality of parent queries with a respective group of one or more child queries for the parent query, wherein each child query was submitted during a respective session following submission of its associated parent query during the session; identifying one or more candidate sibling queries for a particular child query, wherein the particular child query is a child query for one or more first parent queries in the plurality of parent queries and each candidate sibling query for the particular child query is a child query for one or more second parent queries in the plurality of queries, each candidate sibling query having a fan-in measure that satisfies a fan-in threshold, wherein the fan-in measure is a number of parent queries associated with a particular sibling query, wherein for each candidate sibling query, the one or more second parent queries for the candidate sibling query and the one or more first parent queries have a group of shared parent queries in common and the group of shared parent queries has a size that satisfies a common-query threshold; and selecting one or more final sibling queries for the particular child query from the one or more candidate sibling queries, and associating the final sibling queries with the particular child query as query refinements for the particular child query. 27. The computer readable storage device of claim 19 , wherein each first parent query is a high quality parent query for the particular child query, and each second parent query for a particular candidate sibling query is a high quality parent query for the particular sibling query. | 0.843094 |
8,813,027 | 5 | 9 | 5. A computer-implemented method comprising: providing static type checking against an external data source in an interactive editing environment on a software development computer by: providing an extension point for the external data source, the extension point accessed by a public application programming interface, the public application programming interface providing an interface to an extension customized for the external data source, the extension comprising logic specific to the external data source and specific to how information from the external data source is to appear within a programming language type system within the interactive editing environment, wherein data of the external data source is dynamically accessible by calling a method on the external data source to receive the data; implementing the interface by creating a class that inherits from a system synthetic type so that a synthetic class is created for the external data source, the synthetic class representing a hosted model; importing types marked with an attribute denoting the extension and transforming the imported types into internal representations based on methods inherited from the system synthetic type; generating code when a call is made on the system synthetic type or when a method is invoked on the system synthetic type that calls a method invocation on the synthetic class for the hosted model; and replacing the synthetic types with dynamic calls to the external data source during compilation, wherein the external data source comprises at least one of an instance of a database, an Extensible Markup Language (XML) file containing data specific to a domain, a spreadsheet containing data pertaining to a specific domain or a web service. | 5. A computer-implemented method comprising: providing static type checking against an external data source in an interactive editing environment on a software development computer by: providing an extension point for the external data source, the extension point accessed by a public application programming interface, the public application programming interface providing an interface to an extension customized for the external data source, the extension comprising logic specific to the external data source and specific to how information from the external data source is to appear within a programming language type system within the interactive editing environment, wherein data of the external data source is dynamically accessible by calling a method on the external data source to receive the data; implementing the interface by creating a class that inherits from a system synthetic type so that a synthetic class is created for the external data source, the synthetic class representing a hosted model; importing types marked with an attribute denoting the extension and transforming the imported types into internal representations based on methods inherited from the system synthetic type; generating code when a call is made on the system synthetic type or when a method is invoked on the system synthetic type that calls a method invocation on the synthetic class for the hosted model; and replacing the synthetic types with dynamic calls to the external data source during compilation, wherein the external data source comprises at least one of an instance of a database, an Extensible Markup Language (XML) file containing data specific to a domain, a spreadsheet containing data pertaining to a specific domain or a web service. 9. The method of claim 5 , wherein the public application programming interface provides an extension point to a language service for the external data source. | 0.769565 |
8,200,793 | 11 | 16 | 11. A memory device having instructions stored thereon that, in response to execution by a processing device, cause the processing device to perform operations comprising: embedding a control mark within an electronic document created by a document word processor, wherein the control mark remains embedded in the electronic document after changing a body of the electronic document with the document word processor; wherein the control mark cannot be changed by or removed with the document word processor; and wherein the control mark includes an encrypted check sum configured to self-authenticate or self-validate the electronic document; and detecting transmitted network packets containing the electronic document, based on the control mark; making a determination that the electronic document contained in at least one of the transmitted packets has been changed; and blocking access to the electronic document contained in the at least one of the transmitted packets in response to the determination. | 11. A memory device having instructions stored thereon that, in response to execution by a processing device, cause the processing device to perform operations comprising: embedding a control mark within an electronic document created by a document word processor, wherein the control mark remains embedded in the electronic document after changing a body of the electronic document with the document word processor; wherein the control mark cannot be changed by or removed with the document word processor; and wherein the control mark includes an encrypted check sum configured to self-authenticate or self-validate the electronic document; and detecting transmitted network packets containing the electronic document, based on the control mark; making a determination that the electronic document contained in at least one of the transmitted packets has been changed; and blocking access to the electronic document contained in the at least one of the transmitted packets in response to the determination. 16. The memory device of claim 11 wherein said detecting further includes monitoring network packets transmitted from an internal organization network to an external organization network. | 0.697411 |
8,583,284 | 1 | 12 | 1. A decision making mechanism of an active perception module of a robot, the active perception module including at least one processing unit executing one or more instructions from a non-transitory machine-readable medium, said decision making mechanism being operable to decide on at least one prospective action of a robot from a set of actions of said robot by: the active perception module computing a prior probabilistic representation of a prior environment state; the active perception module updating of said prior probabilistic representation with targets of a new observation following the at least one prospective action after an action period, the action period comprising a period for incorporating an action observation pair into an update of an environment state, thereby reducing at least one uncertainty in a posterior probabilistic representation of a posterior environment state to be reached after an appliance of said at least one prospective action, wherein said posterior probabilistic representation is a probabilistic representation resulting from said updating; the active perception module determining an information gain between said prior probabilistic representation and said posterior probabilistic representation by use of at least one information theoretic measure; and the active perception module evaluating said at least one prospective action by estimating the costs of executing said at least one prospective action during the action period and estimating said information gain at the end of the action period. | 1. A decision making mechanism of an active perception module of a robot, the active perception module including at least one processing unit executing one or more instructions from a non-transitory machine-readable medium, said decision making mechanism being operable to decide on at least one prospective action of a robot from a set of actions of said robot by: the active perception module computing a prior probabilistic representation of a prior environment state; the active perception module updating of said prior probabilistic representation with targets of a new observation following the at least one prospective action after an action period, the action period comprising a period for incorporating an action observation pair into an update of an environment state, thereby reducing at least one uncertainty in a posterior probabilistic representation of a posterior environment state to be reached after an appliance of said at least one prospective action, wherein said posterior probabilistic representation is a probabilistic representation resulting from said updating; the active perception module determining an information gain between said prior probabilistic representation and said posterior probabilistic representation by use of at least one information theoretic measure; and the active perception module evaluating said at least one prospective action by estimating the costs of executing said at least one prospective action during the action period and estimating said information gain at the end of the action period. 12. The decision making mechanism according to claim 1 , wherein said at least one uncertainty is a state uncertainty determined by approximating differential entropy of said posterior probabilistic representation by an upper-bound estimate. | 0.694937 |
8,543,939 | 16 | 23 | 16. A method comprising: receiving data in a first format, the data including first information having a first type and second information having a second type; providing the data for display via a graphical interface that includes a vertical scroll bar, a length of the vertical scroll bar corresponding to a length of the data, and the graphical interface providing for display only a part of data at a time; receiving a selection of one or more translation rules, from a plurality of translation rules, for converting at least one of the first information or the second information to a second format that differs from the first format; determining an effect of applying the one or more translation rules to the data; providing information associated with the effect of applying the one or more translation rules to the data for display via the graphical interface; determining a portion of the data that is incompatible with the second format; and providing an indication of the portion for display via the graphical interface, providing the indication of the portion including: providing a first graphical symbol for display at a location of the vertical scroll bar, the location of the first graphical symbol in the vertical scroll bar corresponding to a position of the portion in the length of the data, and a selection of the first graphical symbol causing the graphical interface to include the portion in the part of the data provided for display in the graphical interface, receiving the data, providing the data for display, receiving the selection of the one or more translation rules, determining the effect of applying the one or more translation rules, information associated with the effect; determining the portion of the data; and providing the indication of the portion being performed by one or more processors. | 16. A method comprising: receiving data in a first format, the data including first information having a first type and second information having a second type; providing the data for display via a graphical interface that includes a vertical scroll bar, a length of the vertical scroll bar corresponding to a length of the data, and the graphical interface providing for display only a part of data at a time; receiving a selection of one or more translation rules, from a plurality of translation rules, for converting at least one of the first information or the second information to a second format that differs from the first format; determining an effect of applying the one or more translation rules to the data; providing information associated with the effect of applying the one or more translation rules to the data for display via the graphical interface; determining a portion of the data that is incompatible with the second format; and providing an indication of the portion for display via the graphical interface, providing the indication of the portion including: providing a first graphical symbol for display at a location of the vertical scroll bar, the location of the first graphical symbol in the vertical scroll bar corresponding to a position of the portion in the length of the data, and a selection of the first graphical symbol causing the graphical interface to include the portion in the part of the data provided for display in the graphical interface, receiving the data, providing the data for display, receiving the selection of the one or more translation rules, determining the effect of applying the one or more translation rules, information associated with the effect; determining the portion of the data; and providing the indication of the portion being performed by one or more processors. 23. The method of claim 16 , where the method further comprises: providing, based on receiving the selection of the first graphical symbol, information regarding the portion for display via the graphical interface. | 0.854817 |
8,566,078 | 1 | 8 | 1. A method of generating a statistical machine translation database through a game, the method comprising: retrieving from memory and providing to a plurality of players a monolingual structure; receiving a first translation attempt from each of the plurality of players; comparing, using a processor, the first translation attempts from each of the plurality of players; providing feedback to each of the plurality of players; and receiving and comparing attempts and providing feedback to iteratively converge subsequent translations from each of the plurality of players into a final translated structure. | 1. A method of generating a statistical machine translation database through a game, the method comprising: retrieving from memory and providing to a plurality of players a monolingual structure; receiving a first translation attempt from each of the plurality of players; comparing, using a processor, the first translation attempts from each of the plurality of players; providing feedback to each of the plurality of players; and receiving and comparing attempts and providing feedback to iteratively converge subsequent translations from each of the plurality of players into a final translated structure. 8. A method as recited in claim 1 , wherein the providing feedback includes presenting selected words based on a preceding translation attempt of one player of the plurality of players and a preceding translation attempt of an other player of the plurality of players to a respective one of the plurality of players with respect to portions of the respective translation attempts that do not converge to a common translation answer for the monolingual structure. | 0.50108 |
10,108,984 | 1 | 2 | 1. A method comprising: generating, by a device comprising a processor, a signal; sending, by the device, the signal to a sensor network associated with a user, wherein the sensor network comprises a plurality of vibration sensors positioned at various locations on a body of the user; receiving, at the device, a modified signal from the sensor network, wherein the modified signal comprises the signal received, from the device, by at least one vibration sensor of the plurality of vibration sensors and propagated by the at least one vibration sensor through at least one bone of the body of the user, thereby modifying the signal to create the modified signal; selecting, by the device, a body language reference model to compare to the modified signal received from the sensor network, wherein the body language reference model identifies a plurality of body language features and associated signal features; comparing, by the device, the modified signal to the body language reference model; determining, by the device, based upon comparing the modified signal to the body language reference model, a signal feature of the modified signal that maps to a body language feature of the plurality of body language features identified in the body language reference model; and outputting, by the device, the body language feature of the plurality of body language features identified in the body language reference model to an application. | 1. A method comprising: generating, by a device comprising a processor, a signal; sending, by the device, the signal to a sensor network associated with a user, wherein the sensor network comprises a plurality of vibration sensors positioned at various locations on a body of the user; receiving, at the device, a modified signal from the sensor network, wherein the modified signal comprises the signal received, from the device, by at least one vibration sensor of the plurality of vibration sensors and propagated by the at least one vibration sensor through at least one bone of the body of the user, thereby modifying the signal to create the modified signal; selecting, by the device, a body language reference model to compare to the modified signal received from the sensor network, wherein the body language reference model identifies a plurality of body language features and associated signal features; comparing, by the device, the modified signal to the body language reference model; determining, by the device, based upon comparing the modified signal to the body language reference model, a signal feature of the modified signal that maps to a body language feature of the plurality of body language features identified in the body language reference model; and outputting, by the device, the body language feature of the plurality of body language features identified in the body language reference model to an application. 2. The method of claim 1 , wherein outputting, by the device, the body language feature to the application comprises outputting, by the device, the body language feature to a context-aware application. | 0.848645 |
10,048,661 | 1 | 12 | 1. A visualization method comprising: receiving, by a user device, process data associated with a three dimensional manufacturing process of an object, wherein the process data is obtained from at least one sensor monitoring features related to at least one of the object, the three dimensional manufacturing process, and the three dimensional manufacturing apparatus; transforming, by the user device, the process data into visualization data compatible with a computer-aided design specification; receiving, by the user device, a Boolean query associated with at least one aspect of at least one of the three dimensional manufacturing process, a portion of the object, and a three dimensional manufacturing apparatus; and rendering, by the user device in response to the Boolean query, a visual depiction of the at least one aspect on a display screen, further comprising: formulating the Boolean query, with the user device, by selecting one or more sensor values and one or more acceptable ranges. | 1. A visualization method comprising: receiving, by a user device, process data associated with a three dimensional manufacturing process of an object, wherein the process data is obtained from at least one sensor monitoring features related to at least one of the object, the three dimensional manufacturing process, and the three dimensional manufacturing apparatus; transforming, by the user device, the process data into visualization data compatible with a computer-aided design specification; receiving, by the user device, a Boolean query associated with at least one aspect of at least one of the three dimensional manufacturing process, a portion of the object, and a three dimensional manufacturing apparatus; and rendering, by the user device in response to the Boolean query, a visual depiction of the at least one aspect on a display screen, further comprising: formulating the Boolean query, with the user device, by selecting one or more sensor values and one or more acceptable ranges. 12. The method of claim 1 , wherein transforming comprises normalizing three-dimensional coordinate data for compatibility with a computer-aided design (CAD) software program. | 0.704392 |
8,250,048 | 10 | 13 | 10. The computer-implemented method of claim 1 comprising determining contextual information for the query and selectively disabling security policy components based on the contextual information. | 10. The computer-implemented method of claim 1 comprising determining contextual information for the query and selectively disabling security policy components based on the contextual information. 13. The computing system of claim 10 comprising a metadata logic that stores one or more of: domain and range information for properties in the data, sub-class relationships within the data, or sub-property relationships within the data and where the access control enforcement logic retrieves information from the metadata logic to identify match patterns that are related to a received query. | 0.858781 |
7,680,860 | 1 | 11 | 1. A method for creating a vertical search engine, comprising: receiving a list of a plurality of keywords to be used for the vertical search engine on a network device with one or more processors, wherein the list of keywords includes general and specific keywords for a selected subject; processing the list of plurality of keywords to create a refined list of keywords, wherein the processing includes adding, subtracting or modifying automatically the list of plurality of keywords; creating a plurality of first index files associated with a plurality of first data files by checking a plurality of domain names from a plurality of domain name files associated with a domain name system for a computer network, wherein the plurality of first index files include a plurality of pointers to the associated data files, and wherein the plurality of first data files include a plurality of entries including electronic information extracted from a plurality of web-sites associated with a plurality of active domain names from the plurality of domain name files, wherein creating the plurality of first index files includes opening a plurality of top-level domain name files associated with the domain name system for the computer network, checking a plurality of domain names from the plurality of open top-level domain name files to determine whether any of the plurality of domain names are associated with an active web-site on the computer network, extracting domain names in the plurality of open top-level domain name files associated with active web-sites on the computer network, storing the extracted domains names in a plurality of entries in a plurality of separate files, thereby creating a plurality of separate files including the plurality of entries, and sorting each of the plurality of separate files based on a pre-determined sorting scheme to create a plurality of sorted separate files; creating a plurality of second index files with associated plurality of second data files by searching the plurality of first index files for keywords from the refined list of keywords, wherein the plurality of second index files include a plurality of pointers to the associated plurality of second data files, and wherein the plurality of second data files include a plurality of entries including electronic information extracted from a plurality of web-sites associated with the plurality of active domain names for keywords from the refined list of keywords; verifying automatically that entries in the plurality of second index files are appropriate for the selected subject; creating a final index from the plurality of entries first index; and making a vortal accessible on another network device with one or more processors via the computer network for the selected subject using the final index, wherein the final index for the vortal provides greater depth-than-breath searches for the selected subject for the vertical search engine and using the vortal from a plurality of client network devices each with one or more processors via the computer network as a vertical search engine that provides greater depth-than-breath searches for search subject selected via a client network device. | 1. A method for creating a vertical search engine, comprising: receiving a list of a plurality of keywords to be used for the vertical search engine on a network device with one or more processors, wherein the list of keywords includes general and specific keywords for a selected subject; processing the list of plurality of keywords to create a refined list of keywords, wherein the processing includes adding, subtracting or modifying automatically the list of plurality of keywords; creating a plurality of first index files associated with a plurality of first data files by checking a plurality of domain names from a plurality of domain name files associated with a domain name system for a computer network, wherein the plurality of first index files include a plurality of pointers to the associated data files, and wherein the plurality of first data files include a plurality of entries including electronic information extracted from a plurality of web-sites associated with a plurality of active domain names from the plurality of domain name files, wherein creating the plurality of first index files includes opening a plurality of top-level domain name files associated with the domain name system for the computer network, checking a plurality of domain names from the plurality of open top-level domain name files to determine whether any of the plurality of domain names are associated with an active web-site on the computer network, extracting domain names in the plurality of open top-level domain name files associated with active web-sites on the computer network, storing the extracted domains names in a plurality of entries in a plurality of separate files, thereby creating a plurality of separate files including the plurality of entries, and sorting each of the plurality of separate files based on a pre-determined sorting scheme to create a plurality of sorted separate files; creating a plurality of second index files with associated plurality of second data files by searching the plurality of first index files for keywords from the refined list of keywords, wherein the plurality of second index files include a plurality of pointers to the associated plurality of second data files, and wherein the plurality of second data files include a plurality of entries including electronic information extracted from a plurality of web-sites associated with the plurality of active domain names for keywords from the refined list of keywords; verifying automatically that entries in the plurality of second index files are appropriate for the selected subject; creating a final index from the plurality of entries first index; and making a vortal accessible on another network device with one or more processors via the computer network for the selected subject using the final index, wherein the final index for the vortal provides greater depth-than-breath searches for the selected subject for the vertical search engine and using the vortal from a plurality of client network devices each with one or more processors via the computer network as a vertical search engine that provides greater depth-than-breath searches for search subject selected via a client network device. 11. The method of claim 1 wherein the sorting step includes sorting each of the plurality of separate files based on an ASCII value of characters stored in the plurality of separate files. | 0.93704 |
10,062,041 | 2 | 7 | 2. A non-transitory computer-readable storage medium embodied with software associated with an enterprise, the software when executed using one or more computer systems is configured to: receive a negotiated meta-model over a computer network from a meta-model negotiation service, the negotiated meta-model describing collaborations between trading partners and incorporating more than two meta-model elements selected from a stored first and second set of meta-model elements, the first set of one or more meta-model elements comprising one or more supply chain elements, the one or more supply chain elements comprising one or more site supply chain elements representing one or more sites of a supply chain network that produces one or more items, one or more resource supply chain elements representing one or more resources of the supply chain network, and one or more buffer supply chain elements representing one or more buffers of the supply chain network, the second set of one or more meta-model elements representing the semantics of a machine-actionable collaboration standard, the semantics comprising a nature of a demand signal representing a demand and a software protocol used to communicate the demand signal, the more than two of meta-model elements describing a private collaboration standard unique to the trading partners for collaboration between the trading partners, the negotiated meta-model having been negotiated by the associated trading partner and the one or more other trading partners using the meta-model negotiation service; determine the semantics of the negotiated meta-model subsequent to negotiation of the negotiated meta-model, the semantics capable of being understood by collaboration software associated with each of the trading partners independent of any modification of the collaboration software; automatically collaborate with the one or more other trading partners based on the standard for collaborations reflected in the negotiated meta-model; and in response to receiving over the computer network the demand signal for the one or more items based on the negotiated meta-model and in accordance with the semantics of the negotiated meta-model, ship the one or more items by at least one of the one or more trading partners in accordance with the demand and the negotiated meta-model. | 2. A non-transitory computer-readable storage medium embodied with software associated with an enterprise, the software when executed using one or more computer systems is configured to: receive a negotiated meta-model over a computer network from a meta-model negotiation service, the negotiated meta-model describing collaborations between trading partners and incorporating more than two meta-model elements selected from a stored first and second set of meta-model elements, the first set of one or more meta-model elements comprising one or more supply chain elements, the one or more supply chain elements comprising one or more site supply chain elements representing one or more sites of a supply chain network that produces one or more items, one or more resource supply chain elements representing one or more resources of the supply chain network, and one or more buffer supply chain elements representing one or more buffers of the supply chain network, the second set of one or more meta-model elements representing the semantics of a machine-actionable collaboration standard, the semantics comprising a nature of a demand signal representing a demand and a software protocol used to communicate the demand signal, the more than two of meta-model elements describing a private collaboration standard unique to the trading partners for collaboration between the trading partners, the negotiated meta-model having been negotiated by the associated trading partner and the one or more other trading partners using the meta-model negotiation service; determine the semantics of the negotiated meta-model subsequent to negotiation of the negotiated meta-model, the semantics capable of being understood by collaboration software associated with each of the trading partners independent of any modification of the collaboration software; automatically collaborate with the one or more other trading partners based on the standard for collaborations reflected in the negotiated meta-model; and in response to receiving over the computer network the demand signal for the one or more items based on the negotiated meta-model and in accordance with the semantics of the negotiated meta-model, ship the one or more items by at least one of the one or more trading partners in accordance with the demand and the negotiated meta-model. 7. The non-transitory computer-readable storage medium of claim 2 , wherein a collaboration based on the standard for collaborations reflected in the negotiated meta-model comprises executing a business process or business transaction based on the negotiated meta-model. | 0.839286 |
6,088,699 | 26 | 27 | 26. A data receiving device, comprising: a digital data store containing a dictionary containing multiple data objects and cross-referencing each of the data objects with a different dictionary index code, where the data objects include data objects with one of the following types: a binary graphics file, or a binary executable program; and a processor, coupled to the store, and programmed to perform a method for receiving data from a sending device, the method comprising: the receiving device receiving a message including multiple subparts each subpart corresponding to a different data object and comprising: if the corresponding data object is not present in the dictionary, the content of the data object; and if the corresponding data object is present in the dictionary, the index code cross-referenced to the data object; the message further including one or more flags distinguishing between dictionary index codes and non-dictionary data objects; and in response to receiving the message, the receiving device performing operations to process the message comprising: reviewing the flags in the received message to distinguish between dictionary index codes and non-dictionary data objects; for each subpart comprising an index code, cross-referencing the index code in the dictionary contained in the receiving device's store to obtain the cross-referenced data object, and for each obtained data object that is a binary graphics file, operating the receiving device's processor to display an image represented by said binary graphics file; for each obtained data object that is a binary executable program, operating the receiving device's processor to execute said program; and for each subpart comprising a non-dictionary data object, providing an output of the non-dictionary data object. | 26. A data receiving device, comprising: a digital data store containing a dictionary containing multiple data objects and cross-referencing each of the data objects with a different dictionary index code, where the data objects include data objects with one of the following types: a binary graphics file, or a binary executable program; and a processor, coupled to the store, and programmed to perform a method for receiving data from a sending device, the method comprising: the receiving device receiving a message including multiple subparts each subpart corresponding to a different data object and comprising: if the corresponding data object is not present in the dictionary, the content of the data object; and if the corresponding data object is present in the dictionary, the index code cross-referenced to the data object; the message further including one or more flags distinguishing between dictionary index codes and non-dictionary data objects; and in response to receiving the message, the receiving device performing operations to process the message comprising: reviewing the flags in the received message to distinguish between dictionary index codes and non-dictionary data objects; for each subpart comprising an index code, cross-referencing the index code in the dictionary contained in the receiving device's store to obtain the cross-referenced data object, and for each obtained data object that is a binary graphics file, operating the receiving device's processor to display an image represented by said binary graphics file; for each obtained data object that is a binary executable program, operating the receiving device's processor to execute said program; and for each subpart comprising a non-dictionary data object, providing an output of the non-dictionary data object. 27. The device of claim 26, where the data object types further include textual data objects. | 0.955882 |
8,676,722 | 26 | 38 | 26. A computer implemented method for synthesizing media utilizing a semantic network, the method comprising: (a) generating, or facilitating the generation of, by one or more computer processors, a thought network including: an active concept translated from a text query received from a human user, selected data entities from an information domain, and relationships derived between the active concept and the selected data entities, the generating comprising: including the active concept as a node in the thought network, and populating the thought network at least in part with the selected data entities from the information domain and the derived relationships between the active concept and the selected data entities; and (b) transforming the thought network to generate and provide one or more forms of synthesized media to the human user. | 26. A computer implemented method for synthesizing media utilizing a semantic network, the method comprising: (a) generating, or facilitating the generation of, by one or more computer processors, a thought network including: an active concept translated from a text query received from a human user, selected data entities from an information domain, and relationships derived between the active concept and the selected data entities, the generating comprising: including the active concept as a node in the thought network, and populating the thought network at least in part with the selected data entities from the information domain and the derived relationships between the active concept and the selected data entities; and (b) transforming the thought network to generate and provide one or more forms of synthesized media to the human user. 38. The computer implemented method of claim 26 , further comprising integrating the generated media with mass market networks. | 0.869877 |
8,571,187 | 16 | 18 | 16. A system, comprising: a processor; and a memory configured to store a plurality of software modules executable by the processor, the software modules including, a message detection module configured to detect an electronic message, a term detection module configured to detect a term within the electronic message, a search term processor module configured to match the term with a stored term in a database, wherein the database stores a plurality of stored terms and corresponding definitions, and an interface module configured to display a definition corresponding to the stored term, in response to a match being found between the term and the stored term in the database, the interface module further configured to determine a new definition of the term based on analysis of a context of the message term within the electronic message and of a context of the message term when used within one or more electronic messages other than the message and add the new definition to the database, in response to a match not being found between the term within the electronic message and the stored term and the term appearing a threshold number of times in one or more electronic mail messages. | 16. A system, comprising: a processor; and a memory configured to store a plurality of software modules executable by the processor, the software modules including, a message detection module configured to detect an electronic message, a term detection module configured to detect a term within the electronic message, a search term processor module configured to match the term with a stored term in a database, wherein the database stores a plurality of stored terms and corresponding definitions, and an interface module configured to display a definition corresponding to the stored term, in response to a match being found between the term and the stored term in the database, the interface module further configured to determine a new definition of the term based on analysis of a context of the message term within the electronic message and of a context of the message term when used within one or more electronic messages other than the message and add the new definition to the database, in response to a match not being found between the term within the electronic message and the stored term and the term appearing a threshold number of times in one or more electronic mail messages. 18. The system according to claim 16 further comprising a profile module configured to store the electronic message, the term, and the definition. | 0.706827 |
9,245,205 | 14 | 22 | 14. A computer-implemented method for generating a global image representation of a text image, the method comprising: a) extracting a plurality of image patch descriptors representative of a plurality of respective image patches representative of the text image, the plurality of image patches including a background area and a foreground area associated with the text image; b) computing a plurality of aggregated representations of the image patch descriptors, each aggregated representation associated with an image block including two or more image patches; c) determining character annotations associated with each image block by projecting each image block's aggregated representation computer in step b) into an intermediate subspace associated with training text images including one or more annotated character bounding boxes, the intermediate subspace mapping visual features to a semantic space; and d) associating the determined character annotation with each respective image block associated with the text image to generate the global image representation of the text image. | 14. A computer-implemented method for generating a global image representation of a text image, the method comprising: a) extracting a plurality of image patch descriptors representative of a plurality of respective image patches representative of the text image, the plurality of image patches including a background area and a foreground area associated with the text image; b) computing a plurality of aggregated representations of the image patch descriptors, each aggregated representation associated with an image block including two or more image patches; c) determining character annotations associated with each image block by projecting each image block's aggregated representation computer in step b) into an intermediate subspace associated with training text images including one or more annotated character bounding boxes, the intermediate subspace mapping visual features to a semantic space; and d) associating the determined character annotation with each respective image block associated with the text image to generate the global image representation of the text image. 22. The computer-implemented method for generating a global image representation of a text image according to claim 14 , wherein the proximate relationship is associated with a maximum percentage of a character bounding box overlapping an image block, y d = max ( char_bb i , char_y i ) ∈ C l δ i , d Intersection ( block_bb , char_bb i ) char_bb i , where y d represents d dimension of label y, |•| represents an area of an overlapping region, and δ i,d equals 1 if char_y i =Σ d and 0 otherwise. | 0.779844 |
8,363,792 | 1 | 9 | 1. A method of determining the status of an answered telephone during the course of an outbound telephone call comprising: A. placing, with an automated calling system, a telephone call to a location having a telephone number at which a target person is listed; B. after said telephone call being answered, as determined by an initial spoken response, or other audio or telecommunication signals, initiating a prerecorded greeting which asks for the target person; C. receiving a spoken response from an answering person or other audio signal and determining if said spoken response or other audio signal is being provided by an answering machine; D. upon determining that the spoken response or other audio signal is not provide by an answering machine, performing a speaker-independent speech recognition analysis on said spoken response, wherein performing said speaker-independent speech recognition analysis includes initiating a speaker-independent speech recognition application with said target person, wherein the speaker-independent speech recognition application is an interactive speech application configured and arranged to provide a series of acoustic prompts to the answering person by telephonic interaction, to determine the meaning of said spoken response; and E. providing at least one of the following conditional responses based on the meaning of the subsequent spoken response as determined by the speaker-independent speech recognition analysis in accordance with a set of speaker-independent speech recognition enabled states of conversation including (1) the answering person indicates that he or she is the target person, (2) the answering person indicates that he or she is not the target person, (3) the answering person indicates that the target person is not present at the location, (4) the answering person indicates a hold request, (5) the answering person requests the identity of the caller, (6) the answering person indicates that the telephone number is not the correct number for the target person, and (7) the speaker-independent speech recognition analysis cannot determine the meaning of the spoken response from the answering person: a. if said speech recognition analysis determines that said spoken response is a request for the identity of the entity responsible for the calling system, initiating a prerecorded response indicating the identity of the calling party, repeating said prerecorded greeting which asks for the target person, and repeating step C through step E; and b. if said speech recognition analysis cannot determine a status of said spoken response, repeating said prerecorded greeting which asks for the target person, and repeating step C through step E; and F. responding appropriately to the results of the speech recognition analysis. | 1. A method of determining the status of an answered telephone during the course of an outbound telephone call comprising: A. placing, with an automated calling system, a telephone call to a location having a telephone number at which a target person is listed; B. after said telephone call being answered, as determined by an initial spoken response, or other audio or telecommunication signals, initiating a prerecorded greeting which asks for the target person; C. receiving a spoken response from an answering person or other audio signal and determining if said spoken response or other audio signal is being provided by an answering machine; D. upon determining that the spoken response or other audio signal is not provide by an answering machine, performing a speaker-independent speech recognition analysis on said spoken response, wherein performing said speaker-independent speech recognition analysis includes initiating a speaker-independent speech recognition application with said target person, wherein the speaker-independent speech recognition application is an interactive speech application configured and arranged to provide a series of acoustic prompts to the answering person by telephonic interaction, to determine the meaning of said spoken response; and E. providing at least one of the following conditional responses based on the meaning of the subsequent spoken response as determined by the speaker-independent speech recognition analysis in accordance with a set of speaker-independent speech recognition enabled states of conversation including (1) the answering person indicates that he or she is the target person, (2) the answering person indicates that he or she is not the target person, (3) the answering person indicates that the target person is not present at the location, (4) the answering person indicates a hold request, (5) the answering person requests the identity of the caller, (6) the answering person indicates that the telephone number is not the correct number for the target person, and (7) the speaker-independent speech recognition analysis cannot determine the meaning of the spoken response from the answering person: a. if said speech recognition analysis determines that said spoken response is a request for the identity of the entity responsible for the calling system, initiating a prerecorded response indicating the identity of the calling party, repeating said prerecorded greeting which asks for the target person, and repeating step C through step E; and b. if said speech recognition analysis cannot determine a status of said spoken response, repeating said prerecorded greeting which asks for the target person, and repeating step C through step E; and F. responding appropriately to the results of the speech recognition analysis. 9. The method of claim 1 wherein, in step F, if said speech recognition analysis determines that said spoken response indicates that said telephone number is not the correct number for the target person, the method further comprises initiating a prerecorded apology message and terminating said telephone call. | 0.642032 |
8,947,220 | 1 | 9 | 1. A system, comprising: a computer processor embedded in a vehicle; and logic executable by the computer processor, the logic configured to implement a method, the method comprising: detecting a presence of a mobile communications device in the vehicle via a communication component of the vehicle, the mobile communications device configured with a speech recognition component; encoding data lists of content stored in a memory device of the vehicle; transmitting the data lists of content and a unique identifier of the vehicle over a data connection to the mobile communications device, the data lists of content linked to the unique identifier; in response to receiving a request to initiate a voice recognition session via an input component of the vehicle, transmitting the request and the unique identifier over the data connection to the speech recognition component of the mobile communications device; activating the speech recognition component responsive to the request; retrieving the data lists of content from the mobile communications device via the unique identifier; and in response to a user voice command received by the speech recognition component, the speech recognition component interprets the user voice command, determines an action by evaluating the user voice command in view of the data lists of content, and transmits an instruction to the computer processor, the instruction directing the vehicle to implement the action. | 1. A system, comprising: a computer processor embedded in a vehicle; and logic executable by the computer processor, the logic configured to implement a method, the method comprising: detecting a presence of a mobile communications device in the vehicle via a communication component of the vehicle, the mobile communications device configured with a speech recognition component; encoding data lists of content stored in a memory device of the vehicle; transmitting the data lists of content and a unique identifier of the vehicle over a data connection to the mobile communications device, the data lists of content linked to the unique identifier; in response to receiving a request to initiate a voice recognition session via an input component of the vehicle, transmitting the request and the unique identifier over the data connection to the speech recognition component of the mobile communications device; activating the speech recognition component responsive to the request; retrieving the data lists of content from the mobile communications device via the unique identifier; and in response to a user voice command received by the speech recognition component, the speech recognition component interprets the user voice command, determines an action by evaluating the user voice command in view of the data lists of content, and transmits an instruction to the computer processor, the instruction directing the vehicle to implement the action. 9. The system of claim 1 , wherein the data lists of content include metadata associated with a heating, ventilation, and air-conditioning (HVAC) system of the vehicle, the metadata including an HVAC setting associated with an HVAC-controlled zone in the vehicle. | 0.75738 |
7,926,022 | 10 | 11 | 10. The computer-implemented method of claim 9 , wherein the score is based on the arrangement of extends and surrogate relationships within the candidate path. | 10. The computer-implemented method of claim 9 , wherein the score is based on the arrangement of extends and surrogate relationships within the candidate path. 11. The computer-implemented method of claim 10 , wherein the extends and surrogate relationships are weighted differently in determining the scores, wherein compiling includes selecting a candidate path, from the number of candidate paths, with the lowest score, and wherein business class of the plurality of business classes with the lowest candidate path score replaces the first business class in the compiled code. | 0.898551 |
8,504,537 | 12 | 15 | 12. An article of manufacture including program code which, when executed by a machine, causes the machine to perform a method, the method comprising: intercepting packets being transmitted over a network at a distributed match agent of a document registration system; reassembling the packets into an intercepted document; generating a set of signatures associated with the intercepted document; comparing the set of signatures associated with the intercepted document with signatures associated with registered documents, wherein the signatures associated with the registered documents are stored in a local signature database of the distributed match agent; and determining whether to notify a manager agent of the registration system based on the result of the comparison. | 12. An article of manufacture including program code which, when executed by a machine, causes the machine to perform a method, the method comprising: intercepting packets being transmitted over a network at a distributed match agent of a document registration system; reassembling the packets into an intercepted document; generating a set of signatures associated with the intercepted document; comparing the set of signatures associated with the intercepted document with signatures associated with registered documents, wherein the signatures associated with the registered documents are stored in a local signature database of the distributed match agent; and determining whether to notify a manager agent of the registration system based on the result of the comparison. 15. The article of manufacture of claim 12 , wherein comparing the set of signatures comprises: determining if at least one signature is common to both a registered document and the intercepted document. | 0.625461 |
7,668,860 | 2 | 4 | 2. The computer readable storage medium of claim 1 further comprising executable instructions to associate the entity relationship model with a set of specific data elements within the hierarchical data by storing a path reference to the data elements within the set of specific data elements. | 2. The computer readable storage medium of claim 1 further comprising executable instructions to associate the entity relationship model with a set of specific data elements within the hierarchical data by storing a path reference to the data elements within the set of specific data elements. 4. The computer readable storage medium of claim 2 further comprising executable instructions to associate the path reference with the business element term within the semantic abstraction. | 0.916 |
8,943,063 | 1 | 2 | 1. A computer-implemented method for generating a tunable finite automaton (“TFA”) from a nondeterministic finite automaton (“NFA”) having a finite set of states, a finite set of input symbols and a transition function covering each state and input symbol, the TFA having, at most, a number b of concurrent active states, the computer-implemented method comprising: a) receiving, as input by a computer system including a least one processor, a deterministic finite automaton (“DFA”) representation of the NFA; b) regrouping, with the computer system, the NFA active state combination associated with each state of the DFA into up to b subsets, with the objective of minimizing the number of total distinct subsets; c) generating, with the computer system, one TFA state for each of the distinct subsets; d) for each of the DFA states, storing, with the computer system, pointers to the up to b TFA states in a table entry associated with the NFA active state combination of the DFA state; e) associating, with the computer system, each of the TFA states with appropriate transition representations using the transition functions of the NFA states corresponding to the TFA state; f) storing, with the computer system, each of the TFA states; and g) storing, with the computer system, for each of the TFA states, each of the appropriate transition representations in association with the TFA state and a corresponding input symbol, wherein the number b is a specified parameter and is at least 2. | 1. A computer-implemented method for generating a tunable finite automaton (“TFA”) from a nondeterministic finite automaton (“NFA”) having a finite set of states, a finite set of input symbols and a transition function covering each state and input symbol, the TFA having, at most, a number b of concurrent active states, the computer-implemented method comprising: a) receiving, as input by a computer system including a least one processor, a deterministic finite automaton (“DFA”) representation of the NFA; b) regrouping, with the computer system, the NFA active state combination associated with each state of the DFA into up to b subsets, with the objective of minimizing the number of total distinct subsets; c) generating, with the computer system, one TFA state for each of the distinct subsets; d) for each of the DFA states, storing, with the computer system, pointers to the up to b TFA states in a table entry associated with the NFA active state combination of the DFA state; e) associating, with the computer system, each of the TFA states with appropriate transition representations using the transition functions of the NFA states corresponding to the TFA state; f) storing, with the computer system, each of the TFA states; and g) storing, with the computer system, for each of the TFA states, each of the appropriate transition representations in association with the TFA state and a corresponding input symbol, wherein the number b is a specified parameter and is at least 2. 2. The computer-implemented method of claim 1 , further comprising: receiving, as input, with the computer system, the NFA; and generating, with the computer system, the received DFA representation of the NFA using the subset construction scheme, such that states of the generated DFA provide all valid active state combinations of the NFA. | 0.930442 |
8,332,864 | 1 | 3 | 1. A method, comprising: receiving, by a computing device, first business logic expressed in one or more declarative languages, the first business logic including a first process description, the first process description describing a first process of a business process instance in terms of one or more flows; receiving, by the computing device, second business logic expressed in the one or more declarative languages, the second business logic including a second process description, the second process description describing a second process of the business process instance in terms of one or more rules, the second process being different from the first process; and executing, by the computing device, the first business logic and the second business logic, wherein the business process instance is associated with one or more states, wherein each of the one or more flows represents a control flow between business functions, wherein each of the one or more states represents a legal state transition for at least one business entity, and wherein each of the one or more rules represents a business rule or policy enforced on the at least one business entity in an externalized form. | 1. A method, comprising: receiving, by a computing device, first business logic expressed in one or more declarative languages, the first business logic including a first process description, the first process description describing a first process of a business process instance in terms of one or more flows; receiving, by the computing device, second business logic expressed in the one or more declarative languages, the second business logic including a second process description, the second process description describing a second process of the business process instance in terms of one or more rules, the second process being different from the first process; and executing, by the computing device, the first business logic and the second business logic, wherein the business process instance is associated with one or more states, wherein each of the one or more flows represents a control flow between business functions, wherein each of the one or more states represents a legal state transition for at least one business entity, and wherein each of the one or more rules represents a business rule or policy enforced on the at least one business entity in an externalized form. 3. The method of claim 1 , wherein the one or more flows, the one or more states, and the one or more rules are coordinated by a controller software module. | 0.861947 |
8,112,667 | 15 | 19 | 15. A non-transitory computer-useable storage medium for automatically diagnosing a system problem, said medium having a computer-readable program, wherein a program upon being processed on a computer causes the computer to implement the steps of: creating a problem description index, wherein the problem description index is created from a group consisting of: a line-wise index comprising a document entry in the problem description index for each line in a problem description information of a plurality of previously diagnosed problems, a description-wise index comprising a document entry in the problem description index for the problem description information of each of a plurality of previously diagnosed problems, and a set-wise index comprising a document entry in the problem description index for each set of the problem description information of a plurality of previously diagnosed problems grouped together based on a problem cause; receiving the problem description index and problem description information of a new problem, the problem description index comprising problem description information of previously diagnosed problems, wherein said problem description information of previously diagnosed problems and of said new problem comprises text content describing system events that have occurred; comparing problem description information of the new problem with problem description information in the problem description index, wherein comparing the problem description information of the new problem with problem description information in the problem description index for a line-wise index comprises a line-wise search of the line-wise index, wherein said line-wise search comprises searching each line of text content in the problem description index for each line of text content from the problem description information of the new problem; computing a search score for each document in the problem description index, wherein the search score is a measure of similarity between each document in the problem description index and the problem description information of the new problem; assigning a matching score to each of the previously diagnosed problems based on the search score, wherein the matching score is a measure of similarity between the new problem and each of the previously diagnosed problems; and determining a diagnosis of the new problem, wherein the diagnosis of the new problem is a diagnosis of at least one of the previously diagnosed problems. | 15. A non-transitory computer-useable storage medium for automatically diagnosing a system problem, said medium having a computer-readable program, wherein a program upon being processed on a computer causes the computer to implement the steps of: creating a problem description index, wherein the problem description index is created from a group consisting of: a line-wise index comprising a document entry in the problem description index for each line in a problem description information of a plurality of previously diagnosed problems, a description-wise index comprising a document entry in the problem description index for the problem description information of each of a plurality of previously diagnosed problems, and a set-wise index comprising a document entry in the problem description index for each set of the problem description information of a plurality of previously diagnosed problems grouped together based on a problem cause; receiving the problem description index and problem description information of a new problem, the problem description index comprising problem description information of previously diagnosed problems, wherein said problem description information of previously diagnosed problems and of said new problem comprises text content describing system events that have occurred; comparing problem description information of the new problem with problem description information in the problem description index, wherein comparing the problem description information of the new problem with problem description information in the problem description index for a line-wise index comprises a line-wise search of the line-wise index, wherein said line-wise search comprises searching each line of text content in the problem description index for each line of text content from the problem description information of the new problem; computing a search score for each document in the problem description index, wherein the search score is a measure of similarity between each document in the problem description index and the problem description information of the new problem; assigning a matching score to each of the previously diagnosed problems based on the search score, wherein the matching score is a measure of similarity between the new problem and each of the previously diagnosed problems; and determining a diagnosis of the new problem, wherein the diagnosis of the new problem is a diagnosis of at least one of the previously diagnosed problems. 19. The computer-useable storage medium of claim 15 , wherein comparing the problem description information of the new problem with problem description information in the problem description index for a set-wise index comprises a description-wise search of the set-wise index, wherein said set-wise search comprises searching each set of problem description information in the problem description index for each term of text content from the problem description information of the new problem. | 0.501012 |
7,533,933 | 9 | 15 | 9. A child seat headrest comprising: a one-piece head support including a generally vertical head-support holder, a generally planar support wall integral with and extending obliquely upwardly and rearwardly from an upper end of the generally vertical head-support holder, and right and left spaced-apart wings integral with and extending laterally from right and left sides of the generally planar support wall; a head rest member configured between the right and left spaced-apart wings, the head rest member including a pair of pins extending outwardly from a lower portion of the head rest member for pivotally coupling with the right and left spaced-apart wings, the head rest member being pivotally movable about a horizontal pivot axis defined by the pair of pins between a rearwardly-inclined sleeping position wherein the head rest member is proximate to and substantially coplanar with the generally planar support wall, and an upright waking position wherein the head rest member is at an oblique angle relative to the generally planar support wall; and a biasing mechanism that urges the head rest member into the upright waking position during a dynamic event to reduce a whiplash trauma experienced by an occupant of the child seat; the biasing mechanism comprising a spring including first and second ends, a first recess defined in an upper central portion of the head-support holder, the first recess including a first anchor pin, one of the first and second ends of the spring being connected to the first anchor pin, and a second recess defined in a bottom side of the head rest member, the second recess including a second anchor pin, the other one of the first and second ends of the spring being connected to the second anchor pin. | 9. A child seat headrest comprising: a one-piece head support including a generally vertical head-support holder, a generally planar support wall integral with and extending obliquely upwardly and rearwardly from an upper end of the generally vertical head-support holder, and right and left spaced-apart wings integral with and extending laterally from right and left sides of the generally planar support wall; a head rest member configured between the right and left spaced-apart wings, the head rest member including a pair of pins extending outwardly from a lower portion of the head rest member for pivotally coupling with the right and left spaced-apart wings, the head rest member being pivotally movable about a horizontal pivot axis defined by the pair of pins between a rearwardly-inclined sleeping position wherein the head rest member is proximate to and substantially coplanar with the generally planar support wall, and an upright waking position wherein the head rest member is at an oblique angle relative to the generally planar support wall; and a biasing mechanism that urges the head rest member into the upright waking position during a dynamic event to reduce a whiplash trauma experienced by an occupant of the child seat; the biasing mechanism comprising a spring including first and second ends, a first recess defined in an upper central portion of the head-support holder, the first recess including a first anchor pin, one of the first and second ends of the spring being connected to the first anchor pin, and a second recess defined in a bottom side of the head rest member, the second recess including a second anchor pin, the other one of the first and second ends of the spring being connected to the second anchor pin. 15. The child seat headrest of claim 9 wherein the head rest member is configured to pivot relative to the generally planar support wall through an angle in the range of about 10 degrees to about 20 degrees. | 0.749395 |
8,612,232 | 1 | 5 | 1. A method comprising: partitioning, via a processor, a speech recognizer output into independent clauses; identifying, independent of domain, a dialog act for each of the independent clauses; identifying, dependent on domain, an object within each of the independent clauses; and recursively generating, for each independent clause in the independent clauses, a semantic representation using the dialog act and the object of each independent clause. | 1. A method comprising: partitioning, via a processor, a speech recognizer output into independent clauses; identifying, independent of domain, a dialog act for each of the independent clauses; identifying, dependent on domain, an object within each of the independent clauses; and recursively generating, for each independent clause in the independent clauses, a semantic representation using the dialog act and the object of each independent clause. 5. The method of claim 1 , wherein identifying the object comprises using a domain specific classifier. | 0.899217 |
9,857,950 | 1 | 3 | 1. A system comprising a processor for processing a set of social media (SM) streams, comprising: a user interface for selecting a plurality of different SM streams, the selected plurality of different SM streams forming the set of SM streams, and for entering a username and password for each different SM stream; a stream unification system that creates a single unified stream by interleaving the set of SM streams; a content filtering system that selects relevant content items from the single unified stream based on a plurality of filtering definitions inputted via the user interface and generates a unified filtered SM stream for each filtering definition in the plurality of filtering definitions using selected content items, wherein each filtering definition includes a time period during which the unified filtered SM stream is to be displayed; and an output system that outputs each unified filtered SM stream to a user display, the output system: generating a portal page; displaying a separate window on the portal page for each unified filtered SM stream, each window displaying a respective unified filtered SM stream on the portal page only for the time period included in the filtering definition used to generate the unified filtered SM stream, the output system adding the window to the portal page at a start of the time period and removing the window from the portal page at an end of the time period; and displaying an advertising banner on the portal page, the advertising banner displaying advertising content based on the plurality of filtering definitions inputted via the user interface. | 1. A system comprising a processor for processing a set of social media (SM) streams, comprising: a user interface for selecting a plurality of different SM streams, the selected plurality of different SM streams forming the set of SM streams, and for entering a username and password for each different SM stream; a stream unification system that creates a single unified stream by interleaving the set of SM streams; a content filtering system that selects relevant content items from the single unified stream based on a plurality of filtering definitions inputted via the user interface and generates a unified filtered SM stream for each filtering definition in the plurality of filtering definitions using selected content items, wherein each filtering definition includes a time period during which the unified filtered SM stream is to be displayed; and an output system that outputs each unified filtered SM stream to a user display, the output system: generating a portal page; displaying a separate window on the portal page for each unified filtered SM stream, each window displaying a respective unified filtered SM stream on the portal page only for the time period included in the filtering definition used to generate the unified filtered SM stream, the output system adding the window to the portal page at a start of the time period and removing the window from the portal page at an end of the time period; and displaying an advertising banner on the portal page, the advertising banner displaying advertising content based on the plurality of filtering definitions inputted via the user interface. 3. The system of claim 1 , wherein the content filtering system selects relevant content items based on keywords provided by the filtering definitions. | 0.764063 |
9,201,590 | 1 | 2 | 1. An electronic device for gesture-based key input, comprising: a camera; a display configured to display a virtual keyboard; and a controller configured to: set a three-dimensional (3-D) input region, corresponding to the displayed virtual keyboard, according to a typing mode selected from among a single-hand typing mode and a double-hand typing mode, recognize a gesture for the 3-D input region, detected through the camera, and obtain a key input according to the recognized gesture, wherein the controller controls an attribute of the 3-D input region according to the selected typing mode, wherein the attribute of the 3-D input region comprises a size of the 3-D input region, a position of the 3-D input region, and a moving distance on the virtual keyboard according to a movement of the hand in the 3-D input region, and wherein the controller sets the moving distance on the virtual keyboard according to the movement of the hand in the 3-D input region so that the moving distance is smaller when the double-hand typing mode is selected than when the single-hand typing mode is selected. | 1. An electronic device for gesture-based key input, comprising: a camera; a display configured to display a virtual keyboard; and a controller configured to: set a three-dimensional (3-D) input region, corresponding to the displayed virtual keyboard, according to a typing mode selected from among a single-hand typing mode and a double-hand typing mode, recognize a gesture for the 3-D input region, detected through the camera, and obtain a key input according to the recognized gesture, wherein the controller controls an attribute of the 3-D input region according to the selected typing mode, wherein the attribute of the 3-D input region comprises a size of the 3-D input region, a position of the 3-D input region, and a moving distance on the virtual keyboard according to a movement of the hand in the 3-D input region, and wherein the controller sets the moving distance on the virtual keyboard according to the movement of the hand in the 3-D input region so that the moving distance is smaller when the double-hand typing mode is selected than when the single-hand typing mode is selected. 2. The electronic device of claim 1 , wherein the controller obtains information about a state of a hand by analyzing an image obtained through the camera and selects the typing mode based on the information about the state of the hand. | 0.848718 |
9,905,224 | 1 | 2 | 1. A computer-implemented method of automatically producing an improved transcription, the method comprising: obtaining, by a processor, an audio input, the audio input comprising a recording of audio signal; producing, by the processor, a first transcription of the audio input using a current language model; associating, by the processor, words included in the first transcription with probabilities each probability indicating for an associated word the likelihood that the word is a legitimate word; selecting, by the processor, a set of words from the first transcription having associated probabilities greater than a threshold; using, by the processor, the selected set of words to search, in at least one of: the internet and a database, for a set of additional textual objects that include at least one of the selected set of words, wherein the additional textual content objects are selected from the list consisting of: webpages, text posted in a social network, articles published on the internet and textual documents; using, by the processor, the additional textual objects to train a new language model; adapting, by the processor, the current language model based on the new language model to produce an improved adapted language model; and producing, by the processor, a second transcription of the audio input using the adapted language model. | 1. A computer-implemented method of automatically producing an improved transcription, the method comprising: obtaining, by a processor, an audio input, the audio input comprising a recording of audio signal; producing, by the processor, a first transcription of the audio input using a current language model; associating, by the processor, words included in the first transcription with probabilities each probability indicating for an associated word the likelihood that the word is a legitimate word; selecting, by the processor, a set of words from the first transcription having associated probabilities greater than a threshold; using, by the processor, the selected set of words to search, in at least one of: the internet and a database, for a set of additional textual objects that include at least one of the selected set of words, wherein the additional textual content objects are selected from the list consisting of: webpages, text posted in a social network, articles published on the internet and textual documents; using, by the processor, the additional textual objects to train a new language model; adapting, by the processor, the current language model based on the new language model to produce an improved adapted language model; and producing, by the processor, a second transcription of the audio input using the adapted language model. 2. The method of claim 1 , comprising: comparing performance of the current language model with performance of the adapted language model; and using the adapted language model for decoding audio content if performance of the adapted language model is better than a performance of the current language model. | 0.501623 |
9,065,914 | 1 | 7 | 1. A method comprising: selecting a spoken dialog application from a plurality of spoken dialog applications; transmitting, over a network, an identification of the selected spoken dialog application, the spoken dialog application having a grammar identifier; selecting a grammar from a plurality of grammars based on the grammar identifier, wherein the grammar is provided by the selected spoken dialog application and chosen from a predetermined group of grammars based upon information provided by the selected spoken dialog application; transmitting digitized user speech over the network while receiving user speech which is digitized into the digitized user speech; receiving partially synthesized speech in response to the digitized user speech, wherein the selected spoken dialog application recognizes the digitized user speech using the grammar; and receiving final synthesized speech in response to the digitized user speech, wherein the receiving of the final synthesized speech occurs after receiving the partially synthesized speech. | 1. A method comprising: selecting a spoken dialog application from a plurality of spoken dialog applications; transmitting, over a network, an identification of the selected spoken dialog application, the spoken dialog application having a grammar identifier; selecting a grammar from a plurality of grammars based on the grammar identifier, wherein the grammar is provided by the selected spoken dialog application and chosen from a predetermined group of grammars based upon information provided by the selected spoken dialog application; transmitting digitized user speech over the network while receiving user speech which is digitized into the digitized user speech; receiving partially synthesized speech in response to the digitized user speech, wherein the selected spoken dialog application recognizes the digitized user speech using the grammar; and receiving final synthesized speech in response to the digitized user speech, wherein the receiving of the final synthesized speech occurs after receiving the partially synthesized speech. 7. The method of claim 1 , further comprising: modifying the grammar based on the digitized user speech. | 0.88521 |
8,364,685 | 1 | 2 | 1. A method for annotating and ranking a user review personalized to prior user experience, the method comprising: generating a collection of content items for which a user has previously expressed interest, the collection of content items including objective attributes of interest indicated by the user for each of the content items, subjective attributes of interest indicated by the user for each of the content items by assigning a user-defined subjective value and a reference to the collection of content items stored in a user profile; identifying a new content item that is not contained in the collection of content items, the new content item comprising one or more objective attributes and one or more subjective attributes, wherein each of the one or more subjective attributes includes a subjective value; and providing a reference framework to interpret the new content item in view of one or more common objective attributes of interest by the user for a given one of the one or more content items in the collection of content items, wherein the number of common objective attributes satisfy a threshold, and in view of the one or more subjective attributes for a given one of the one or more content items in the collection of content items, wherein a difference between the user-defined subjective value and the subjective value of each of the one or more subjective attributes for a given one of the one or more content items in the collection of content items is below a threshold. | 1. A method for annotating and ranking a user review personalized to prior user experience, the method comprising: generating a collection of content items for which a user has previously expressed interest, the collection of content items including objective attributes of interest indicated by the user for each of the content items, subjective attributes of interest indicated by the user for each of the content items by assigning a user-defined subjective value and a reference to the collection of content items stored in a user profile; identifying a new content item that is not contained in the collection of content items, the new content item comprising one or more objective attributes and one or more subjective attributes, wherein each of the one or more subjective attributes includes a subjective value; and providing a reference framework to interpret the new content item in view of one or more common objective attributes of interest by the user for a given one of the one or more content items in the collection of content items, wherein the number of common objective attributes satisfy a threshold, and in view of the one or more subjective attributes for a given one of the one or more content items in the collection of content items, wherein a difference between the user-defined subjective value and the subjective value of each of the one or more subjective attributes for a given one of the one or more content items in the collection of content items is below a threshold. 2. The method of claim 1 , comprising creating the user profile by explicitly collecting user data. | 0.911922 |
10,162,605 | 11 | 14 | 11. A system to complete a code snippet to define an object literal, the system comprising: memory; and one or more processors coupled to the memory, the one or more processors configured to: determine information regarding one or more properties of the object literal from one or more comments that are included in code; and generate a recommendation that recommends content for completion of the code snippet to define the object literal based at least in part on the information, the content identifying the one or more properties of the object literal. | 11. A system to complete a code snippet to define an object literal, the system comprising: memory; and one or more processors coupled to the memory, the one or more processors configured to: determine information regarding one or more properties of the object literal from one or more comments that are included in code; and generate a recommendation that recommends content for completion of the code snippet to define the object literal based at least in part on the information, the content identifying the one or more properties of the object literal. 14. The system of claim 11 , wherein the one or more processors are configured to: determine that the object literal is to be a designated type selected from a plurality of types based at least in part on the information; and indicate that the object literal is to be the designated type. | 0.773585 |
8,281,149 | 16 | 22 | 16. A method for providing anonymous and pseudonymous access for a user to one or more relying parties (RPs), mediated by an identity provider (IdP), comprising: the user registering with the IdP to establish a first pseudonym in a previous session; the user generating an original token for accessing an RP; the user modifying the original token to obtain a modified token; the user providing the modified token to the IdP to obtain confirmation of access authorization; the user proving possession of the first pseudonym previously registered with the IdP to the IdP; upon verification of the user's possession of the first pseudonym, the IdP generating a first representation of an access token by signing the modified token, the first representation of the access token containing the confirmation of access authorization at the RP; the IdP providing the first representation of the access token to the user; the user transforming the signed modified token to obtain a second representation of the access token, wherein the second representation of the access token is unlinkable to the first representation of the access token by the RP and the IdP individually, and is unlinkable by the RP and IdP in collusion; the user determining whether to access the RP anonymously or pseudonymously; if accessing the RP anonymously, the user presenting the second representation of the access token to the RP; if accessing the RP pseudonymously, the user presenting the second representation and proof of possession of a second pseudonym, the second pseudonym being a pseudonym previously registered with the RP; upon receiving the second representation of the access token, the RP verifying the second representation of the access token; and if access is anonymous, the RP providing access to the user upon verification of the second representation of the access token; if access is pseudonymous, the RP providing access to the user upon successful verification of the second representation of the access token and successful verification of the proof of possession of the second pseudonym. | 16. A method for providing anonymous and pseudonymous access for a user to one or more relying parties (RPs), mediated by an identity provider (IdP), comprising: the user registering with the IdP to establish a first pseudonym in a previous session; the user generating an original token for accessing an RP; the user modifying the original token to obtain a modified token; the user providing the modified token to the IdP to obtain confirmation of access authorization; the user proving possession of the first pseudonym previously registered with the IdP to the IdP; upon verification of the user's possession of the first pseudonym, the IdP generating a first representation of an access token by signing the modified token, the first representation of the access token containing the confirmation of access authorization at the RP; the IdP providing the first representation of the access token to the user; the user transforming the signed modified token to obtain a second representation of the access token, wherein the second representation of the access token is unlinkable to the first representation of the access token by the RP and the IdP individually, and is unlinkable by the RP and IdP in collusion; the user determining whether to access the RP anonymously or pseudonymously; if accessing the RP anonymously, the user presenting the second representation of the access token to the RP; if accessing the RP pseudonymously, the user presenting the second representation and proof of possession of a second pseudonym, the second pseudonym being a pseudonym previously registered with the RP; upon receiving the second representation of the access token, the RP verifying the second representation of the access token; and if access is anonymous, the RP providing access to the user upon verification of the second representation of the access token; if access is pseudonymous, the RP providing access to the user upon successful verification of the second representation of the access token and successful verification of the proof of possession of the second pseudonym. 22. The method of claim 16 , wherein proof of possession of the second pseudonym is made by a zero-knowledge proof, by signing, or by decrypting a challenge. | 0.93725 |
10,037,390 | 1 | 3 | 1. At least one non-transitory computer-readable storage medium comprising instructions that, when executed, cause a system to: receive a task hierarchy from a data store, the task hierarchy comprising a plurality of task entries, each task entry comprising a list of task entries or a task command, the list of task entries comprising probabilities associated with each task entry in the list of task entries, wherein task commands are executed in a simulated environment to cause actual performance of events to cause usage of physical computing resources, and wherein each probability provides a weight for selection of an associated task entry when traversing the task hierarchy; and execute one or more of the task commands in the simulated environment, wherein executing each of the one or more task commands comprises traversing through the task hierarchy based on the associated probabilities until a task command is reached and executing the task command to cause actual performance of an event to cause usage of at least one of the physical computing resources in the simulated environment. | 1. At least one non-transitory computer-readable storage medium comprising instructions that, when executed, cause a system to: receive a task hierarchy from a data store, the task hierarchy comprising a plurality of task entries, each task entry comprising a list of task entries or a task command, the list of task entries comprising probabilities associated with each task entry in the list of task entries, wherein task commands are executed in a simulated environment to cause actual performance of events to cause usage of physical computing resources, and wherein each probability provides a weight for selection of an associated task entry when traversing the task hierarchy; and execute one or more of the task commands in the simulated environment, wherein executing each of the one or more task commands comprises traversing through the task hierarchy based on the associated probabilities until a task command is reached and executing the task command to cause actual performance of an event to cause usage of at least one of the physical computing resources in the simulated environment. 3. The computer-readable storage medium of claim 1 , the task hierarchy being a weighted task hierarchy, wherein weighting of the task entries within the weighted task hierarchy represents a scenario in a represented production environment. | 0.704433 |
7,552,055 | 9 | 10 | 9. The computer readable storage medium of claim 1 wherein the set of controls includes a reference to specify re-use of a grammar. | 9. The computer readable storage medium of claim 1 wherein the set of controls includes a reference to specify re-use of a grammar. 10. The computer readable storage medium of claim 9 wherein the set of controls includes a reference to specify re-use of only a portion of a grammar. | 0.958746 |
8,527,509 | 1 | 7 | 1. A search method, comprising: receiving a search request from a search client; extracting a user interest model from user personalized data according to the search request; obtaining a meta index of each member engine; selecting a member engine according to the meta index of each member engine, the search request, and the user interest model; and sending the search request to the selected member engine, so as to enable the selected member engine to complete searching; wherein the user interest model is a vector formed with scores given to each of several interest dimensions denoting user interests; the user interest model comprises a static interest model and a dynamic interest model: the static interest model is obtained by obtaining frequencies of words belonging to a certain interest dimension in a static user profile of a user, calculating a sum of the frequencies of the words belonging to the interest dimension as a score of the interest dimension, and forming a score vector with different scores to create a static interest model; and the dynamic interest model is obtained by obtaining frequencies of words belonging to a certain interest dimension in a document clicked in a search history of a user, calculating a sum of the frequencies of the words belonging to the interest dimension in the document as a score specific to the interest dimension in the document, forming a score vector specific to the document with different scores specific to different interest dimensions, and a sum of the score vectors specific to different documents to create a dynamic interest model. | 1. A search method, comprising: receiving a search request from a search client; extracting a user interest model from user personalized data according to the search request; obtaining a meta index of each member engine; selecting a member engine according to the meta index of each member engine, the search request, and the user interest model; and sending the search request to the selected member engine, so as to enable the selected member engine to complete searching; wherein the user interest model is a vector formed with scores given to each of several interest dimensions denoting user interests; the user interest model comprises a static interest model and a dynamic interest model: the static interest model is obtained by obtaining frequencies of words belonging to a certain interest dimension in a static user profile of a user, calculating a sum of the frequencies of the words belonging to the interest dimension as a score of the interest dimension, and forming a score vector with different scores to create a static interest model; and the dynamic interest model is obtained by obtaining frequencies of words belonging to a certain interest dimension in a document clicked in a search history of a user, calculating a sum of the frequencies of the words belonging to the interest dimension in the document as a score specific to the interest dimension in the document, forming a score vector specific to the document with different scores specific to different interest dimensions, and a sum of the score vectors specific to different documents to create a dynamic interest model. 7. The method according to claim 1 , wherein the extracting the user interest model from the user personalized data further comprises: normalizing the static interest model and the dynamic interest model respectively, calculating the sum of the static interest model and the dynamic interest model, and using the result as the user interest model; or weighted summing the static interest model and the dynamic interest model, normalizing the sum, and using the result as the interest model. | 0.82475 |
8,615,385 | 1 | 8 | 1. One or more non-transitory computer-readable media, comprising: one or more instructions that, when executed by a processor of a device, cause the processor to: select a location in a model having executable semantics, the selected location having one or more dependencies with one or more other locations in the model; detect at least one complexity in the model, the at least one complexity affecting the selected location, and interfering with a verification of the model; identify a source for the at least one complexity, the source located at one of the one or more other locations; and eliminate or reduce the source for the at least one complexity, thereby allowing successful verification of the model. | 1. One or more non-transitory computer-readable media, comprising: one or more instructions that, when executed by a processor of a device, cause the processor to: select a location in a model having executable semantics, the selected location having one or more dependencies with one or more other locations in the model; detect at least one complexity in the model, the at least one complexity affecting the selected location, and interfering with a verification of the model; identify a source for the at least one complexity, the source located at one of the one or more other locations; and eliminate or reduce the source for the at least one complexity, thereby allowing successful verification of the model. 8. The one or more non-transitory computer-readable media of claim 1 wherein the one or more instructions that, when executed by the processor of the device, cause the processor to eliminate or reduce the source for the at least one complexity include transforming the model. | 0.770451 |
9,280,326 | 7 | 10 | 7. A non-transitory computer readable medium configured to store instructions for generating a description of compiler selector rules from an architecture description, the compiler selector rules for use in a compiler that translates source code into machine instructions of a target processor, the instructions when executed by a processor cause the processor to: access a target processor architecture model of the target processor, the target processor architecture model written in a processor architecture description language, the target processor architecture model comprising semantic information and syntax information for the machine instructions, and description of non-terminals of the target processor; generate a plurality of semantic statements from semantic information included in the processor architecture model; apply, to said semantic information, at least one semantic transformation from a library of pre-defined semantic transformations to generate a single semantic statement from a sequence of at least two of said plurality of semantic statements; generate a plurality of basic rules that map from source code operations to machine instructions comprising: accessing rules that map from source code operations to semantic patterns, searching said semantic statements for matches, and mapping a sequence of two or more source code operations to a single machine instruction based on the accessed rules that matches from semantic statements to semantic patterns; and permute said basic rules with said non-terminals to generate a plurality of mappings that serve as said description of said compiler code selector rules. | 7. A non-transitory computer readable medium configured to store instructions for generating a description of compiler selector rules from an architecture description, the compiler selector rules for use in a compiler that translates source code into machine instructions of a target processor, the instructions when executed by a processor cause the processor to: access a target processor architecture model of the target processor, the target processor architecture model written in a processor architecture description language, the target processor architecture model comprising semantic information and syntax information for the machine instructions, and description of non-terminals of the target processor; generate a plurality of semantic statements from semantic information included in the processor architecture model; apply, to said semantic information, at least one semantic transformation from a library of pre-defined semantic transformations to generate a single semantic statement from a sequence of at least two of said plurality of semantic statements; generate a plurality of basic rules that map from source code operations to machine instructions comprising: accessing rules that map from source code operations to semantic patterns, searching said semantic statements for matches, and mapping a sequence of two or more source code operations to a single machine instruction based on the accessed rules that matches from semantic statements to semantic patterns; and permute said basic rules with said non-terminals to generate a plurality of mappings that serve as said description of said compiler code selector rules. 10. The computer readable medium of claim 7 , wherein the instructions for searching said semantic statements for matches to said semantic patters cause the processor to map a single source code operation to a single machine instruction. | 0.585664 |
10,072,939 | 4 | 8 | 4. The method of claim 3 , wherein the one or more contextual cues further comprise a geo-location of the electronic device. | 4. The method of claim 3 , wherein the one or more contextual cues further comprise a geo-location of the electronic device. 8. The method of claim 4 , the request for navigational information comprising a destination name, wherein a plurality of destinations each have the destination name, further comprising filtering the plurality of destinations as a function of the transit mode, the direction of transit, the speed of transit, and the geo-location to select a destination matching the destination name within a predefined range that the electronic device is approaching. | 0.815661 |
7,970,796 | 17 | 18 | 17. A computer readable medium comprising computer readable program code embodied therein for causing a computer system to execute a method for importing data from a document to a repository, comprising steps to: create a map file corresponding to a document type of the document, wherein the map file maps the document type of the document to a plurality of information fields stored in the repository for the document type; store the map file in a directory and provide a location of the directory to the repository; select data from the document; create a macro for the document, wherein the macro is dynamically inserted into the document from which data is imported when the document is loaded from the repository, and wherein the macro is configured to: access the map file at the directory location stored in the repository, determine the plurality of information fields corresponding to the document type of the document from which data is imported from the map file, and dynamically create a context menu, in response to the selection of data, comprising the plurality of information fields, wherein the context menu is a user interface that is created using the map file corresponding to the document type of the document; map the selected data to one of the plurality of fields in the repository by selecting the one of the plurality of information fields displayed in the context menu, insert a comment into the document by a user importing the data, wherein the comment indicates that the selected data is imported into the repository; and import the selected data from the document to the selected information field in the repository using the map file, wherein the selected data is copied to the map file, which acts as an intermediary storage file between the document and the repository, until the stored selected data is loaded from the map file into the repository at a later time. | 17. A computer readable medium comprising computer readable program code embodied therein for causing a computer system to execute a method for importing data from a document to a repository, comprising steps to: create a map file corresponding to a document type of the document, wherein the map file maps the document type of the document to a plurality of information fields stored in the repository for the document type; store the map file in a directory and provide a location of the directory to the repository; select data from the document; create a macro for the document, wherein the macro is dynamically inserted into the document from which data is imported when the document is loaded from the repository, and wherein the macro is configured to: access the map file at the directory location stored in the repository, determine the plurality of information fields corresponding to the document type of the document from which data is imported from the map file, and dynamically create a context menu, in response to the selection of data, comprising the plurality of information fields, wherein the context menu is a user interface that is created using the map file corresponding to the document type of the document; map the selected data to one of the plurality of fields in the repository by selecting the one of the plurality of information fields displayed in the context menu, insert a comment into the document by a user importing the data, wherein the comment indicates that the selected data is imported into the repository; and import the selected data from the document to the selected information field in the repository using the map file, wherein the selected data is copied to the map file, which acts as an intermediary storage file between the document and the repository, until the stored selected data is loaded from the map file into the repository at a later time. 18. The computer readable medium of claim 17 , further comprising computer readable program code embodied therein for causing a computer system to: load the document from a repository application comprising the repository. | 0.636066 |
7,908,234 | 10 | 11 | 10. The method of claim 3 , the generating a negative usefulness prediction value further comprising: determining the negative usefulness prediction value using a product, P, of a feature vector that identifies the features extracted from the given URL and a weighting vector that includes a weighting for each of the features extracted from the given URL to generate the negative usefulness prediction value using a formula:
1−[1/(1+e −P )]. | 10. The method of claim 3 , the generating a negative usefulness prediction value further comprising: determining the negative usefulness prediction value using a product, P, of a feature vector that identifies the features extracted from the given URL and a weighting vector that includes a weighting for each of the features extracted from the given URL to generate the negative usefulness prediction value using a formula:
1−[1/(1+e −P )]. 11. The method of claim 10 , the weighting vector including an intercept weighting that corresponds to the count of the positive URLs in the training set. | 0.958911 |
10,031,973 | 1 | 7 | 1. A method for identifying a sensor, from a plurality of sensors, to be deployed in a physical environment, the method comprising: storing sensor data and metadata of the plurality of sensors in a data store, wherein the sensor data is captured by the plurality of sensors, and wherein the metadata belongs to the plurality of sensors; deriving sensor information from at least one of the sensor data and the metadata, wherein the sensor information comprises at least one of thematic information, temporal information, and spatial information; creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information, wherein the sensor ontology is stored in the data store; receiving and decomposing one or more search queries into at least one of a basic query component and an inferred query component, wherein the basic query component is associated to the sensor data, and wherein the inferred query component is associated to the sensor information; identifying a subset of sensor data and a subset of sensor information, wherein the subset of sensor data and the subset of sensor information are matching with the at least one of the basic query component and the inferred query component; ranking the one or more search queries based upon frequency of receipt of the one or more search queries on a knowledge repository, wherein the one or more search queries are further ranked based on the identified subset of sensor data and the subset of sensor information, and wherein the sensor information is matching with the one or more search queries received on the knowledge repository; and executing at least one of the basic query component and the inferred query component on the data store in order to identify the sensor, and wherein the storing, the deriving, the creating, the receiving and decomposing, and the executing are performed by the processor using a set of instructions stored in a memory. | 1. A method for identifying a sensor, from a plurality of sensors, to be deployed in a physical environment, the method comprising: storing sensor data and metadata of the plurality of sensors in a data store, wherein the sensor data is captured by the plurality of sensors, and wherein the metadata belongs to the plurality of sensors; deriving sensor information from at least one of the sensor data and the metadata, wherein the sensor information comprises at least one of thematic information, temporal information, and spatial information; creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information, wherein the sensor ontology is stored in the data store; receiving and decomposing one or more search queries into at least one of a basic query component and an inferred query component, wherein the basic query component is associated to the sensor data, and wherein the inferred query component is associated to the sensor information; identifying a subset of sensor data and a subset of sensor information, wherein the subset of sensor data and the subset of sensor information are matching with the at least one of the basic query component and the inferred query component; ranking the one or more search queries based upon frequency of receipt of the one or more search queries on a knowledge repository, wherein the one or more search queries are further ranked based on the identified subset of sensor data and the subset of sensor information, and wherein the sensor information is matching with the one or more search queries received on the knowledge repository; and executing at least one of the basic query component and the inferred query component on the data store in order to identify the sensor, and wherein the storing, the deriving, the creating, the receiving and decomposing, and the executing are performed by the processor using a set of instructions stored in a memory. 7. The method of claim 1 , further comprising enriching the data store, via the processor, by, capturing new data, new metadata, and new sensor information associated to one or more new sensors, implementing a web crawling technique or information extraction technique to extract additional information about the plurality of sensors and the one or more new sensors from internet or an external resource, and capturing related sensor information associated to the search query, wherein the related sensor information is captured using one or more text matching techniques comprising hyperonymy, hyponymy, and meronymy. | 0.58745 |
7,580,960 | 16 | 29 | 16. A machine readable medium having data stored thereon, the data, when read, causing the following: accessing from a web server, via a publicly available network path, content in a first language, including content retrieved by crawling a web site hosted on the web server via following links to additional pages; dividing the content into one or more translatable components; identifying translated components generated previously by translating previous content in the first language to previous content in a second language; determining whether each of the translatable components in the first language has corresponding one of the translated components associated with the previous content in the second language; translating at least a portion of at least one translatable component in the first language that does not have a corresponding translated component in the second language from the first language to a second language; and generating an updated content in the second language based on the translated at least a portion of the at least one translatable component, wherein the updated content in the second language is synchronized with the accessed content in the first language, and the steps of translating and generating are performed independent of the web server. | 16. A machine readable medium having data stored thereon, the data, when read, causing the following: accessing from a web server, via a publicly available network path, content in a first language, including content retrieved by crawling a web site hosted on the web server via following links to additional pages; dividing the content into one or more translatable components; identifying translated components generated previously by translating previous content in the first language to previous content in a second language; determining whether each of the translatable components in the first language has corresponding one of the translated components associated with the previous content in the second language; translating at least a portion of at least one translatable component in the first language that does not have a corresponding translated component in the second language from the first language to a second language; and generating an updated content in the second language based on the translated at least a portion of the at least one translatable component, wherein the updated content in the second language is synchronized with the accessed content in the first language, and the steps of translating and generating are performed independent of the web server. 29. The medium according to claim 16 , wherein the accessing content comprises a step of replicating a session state via at least one cookie and updated session parameters. | 0.765027 |
7,975,223 | 1 | 2 | 1. A method of resolving a rejected content move in a document, comprising: causing a tracked movement of a portion of content of the document from a first location in the document to a second location in the document; receiving an indication of a rejection of the tracked movement of the portion of content from the first location in the document to the second location in the document; comparing a data representing a pre-move version of the portion of content with a data representing a post-move version of the portion of content to determine if the portion of content has been changed after it was moved to the second location in the document; providing a graphical representation of the pre-move version of the portion of content and a graphical representation the post-move version of the portion of content in a conflict resolution dialog that is displayed upon a determination that the post-move version of the portion of content is different than the pre-move version of the portion of content, wherein providing a graphical representation of the pre-move version of the portion of content and a graphical representation the post-move version of the portion of content in a conflict resolution dialog includes: preparing a first document for the pre-move version of the portion of content and preparing a second document for the post-move version of the portion of content and displaying the first and second documents in the conflict resolution dialog to allow a visual comparison of the pre-move version of the portion of content with the post-move version of the portion of content; receiving an indication, in the conflict resolution dialog, of a selection of one of the pre-move version of the portion of content or the post-move version of the portion of content simultaneously displayed in the conflict resolution dialog for moving the selected version back to the first location in the document; and moving the selected one of the pre-move version of the portion of content or the post-move version of the portion of content to the first location in the document. | 1. A method of resolving a rejected content move in a document, comprising: causing a tracked movement of a portion of content of the document from a first location in the document to a second location in the document; receiving an indication of a rejection of the tracked movement of the portion of content from the first location in the document to the second location in the document; comparing a data representing a pre-move version of the portion of content with a data representing a post-move version of the portion of content to determine if the portion of content has been changed after it was moved to the second location in the document; providing a graphical representation of the pre-move version of the portion of content and a graphical representation the post-move version of the portion of content in a conflict resolution dialog that is displayed upon a determination that the post-move version of the portion of content is different than the pre-move version of the portion of content, wherein providing a graphical representation of the pre-move version of the portion of content and a graphical representation the post-move version of the portion of content in a conflict resolution dialog includes: preparing a first document for the pre-move version of the portion of content and preparing a second document for the post-move version of the portion of content and displaying the first and second documents in the conflict resolution dialog to allow a visual comparison of the pre-move version of the portion of content with the post-move version of the portion of content; receiving an indication, in the conflict resolution dialog, of a selection of one of the pre-move version of the portion of content or the post-move version of the portion of content simultaneously displayed in the conflict resolution dialog for moving the selected version back to the first location in the document; and moving the selected one of the pre-move version of the portion of content or the post-move version of the portion of content to the first location in the document. 2. The method of claim 1 , prior to causing a tracked movement of a portion of content of the document from a first location in the document to a second location in the document, further comprising: receiving a document having one or more text items; tracking edits to any of the one or more text items; and displaying one or more graphical notations in the document to distinguish edited document content from non-edited document content. | 0.770397 |
8,589,157 | 1 | 11 | 1. A method for automatically generating a text message from an arbitrary speech input, comprising steps for: receiving a statistical language model trained using one or more sets of real-world text messages as training input; receiving a text message database constructed using a subset including some or all of the text messages in the one or more sets of real-world text messages received as the training input; receiving an arbitrary user speech input; evaluating the speech input relative to the probabilistic model and text message database to return a set of one or more probable speech recognition hypotheses corresponding to the arbitrary user speech input; identifying a set of one or more text messages from the text message database as probabilistic matches to one or more of the speech recognition hypotheses; ranking the probabilistically matching text messages in an order corresponding to a probabilistic confidence score associated with each speech recognition hypothesis; selecting the highest ranked text message to paraphrase the arbitrary user speech input; and transmitting the single selected text message to one or more recipients. | 1. A method for automatically generating a text message from an arbitrary speech input, comprising steps for: receiving a statistical language model trained using one or more sets of real-world text messages as training input; receiving a text message database constructed using a subset including some or all of the text messages in the one or more sets of real-world text messages received as the training input; receiving an arbitrary user speech input; evaluating the speech input relative to the probabilistic model and text message database to return a set of one or more probable speech recognition hypotheses corresponding to the arbitrary user speech input; identifying a set of one or more text messages from the text message database as probabilistic matches to one or more of the speech recognition hypotheses; ranking the probabilistically matching text messages in an order corresponding to a probabilistic confidence score associated with each speech recognition hypothesis; selecting the highest ranked text message to paraphrase the arbitrary user speech input; and transmitting the single selected text message to one or more recipients. 11. The method of claim 1 further comprising personalizing the text message database for a particular user by including text messages previously sent by the user in the text message database. | 0.8174 |
9,715,657 | 5 | 10 | 5. A medical diagnosis support apparatus comprising: at least one processor; and at least one memory, said processor and memory being operatively coupled to function as: a training data obtainment unit configured to obtain training data; a candidate creating unit configured to create a plurality of inference means candidates based on the training data; an inference performance evaluation unit configured to evaluate an accuracy or likelihood of the plurality of inference means candidates based on an inference result and correct diagnosis included in the training data; an information validity evaluation unit configured to evaluate the validity of information presented by each of the plurality of inference means candidates based on reason information and correct reason information included in the training data; and a selection unit configured to select an inference means from the plurality of inference means candidates based on (1) the accuracy or likelihood of the plurality of inference means candidates and (2) the validity of the information presented by each of the plurality of inference means candidates. | 5. A medical diagnosis support apparatus comprising: at least one processor; and at least one memory, said processor and memory being operatively coupled to function as: a training data obtainment unit configured to obtain training data; a candidate creating unit configured to create a plurality of inference means candidates based on the training data; an inference performance evaluation unit configured to evaluate an accuracy or likelihood of the plurality of inference means candidates based on an inference result and correct diagnosis included in the training data; an information validity evaluation unit configured to evaluate the validity of information presented by each of the plurality of inference means candidates based on reason information and correct reason information included in the training data; and a selection unit configured to select an inference means from the plurality of inference means candidates based on (1) the accuracy or likelihood of the plurality of inference means candidates and (2) the validity of the information presented by each of the plurality of inference means candidates. 10. The medical diagnosis support apparatus according to claim 5 , wherein said processor and memory further are operatively coupled to function as an additional data obtainment unit configured to obtain additional data, wherein the inference performance evaluation unit is operable to evaluate the performance of the plurality of inference means candidates based on the training data and the additional data, and the information validity evaluation unit is operable to evaluate the information presented by the plurality of inference means candidates based on the training data and the additional data. | 0.512136 |
9,854,049 | 11 | 16 | 11. A system for providing information to a user comprising: communications circuitry; and control circuitry configured to: receive, using the communications circuitry with user equipment associated with a first user, a communication transmitted by a second user; in response to receiving the communication that was transmitted by the second user, process, with the user equipment associated with the first user, text of the communication to identify a symbol of the communication that is subject to a plurality of candidate interpretations, the symbol including one or more words in the processed text of the communication; identify each candidate interpretation of the plurality of candidate interpretations; retrieve a profile of the user; compare an attribute of the profile to each candidate interpretation of the plurality of candidate interpretations; in response determining, based on the comparison, that the first user has accessed a first media asset that is associated with a first of the plurality of candidate interpretations more recently than a second media asset that is associated with a second of the plurality of candidate interpretations, select as a determined meaning of the symbol the first candidate interpretation; and update the profile to include information based on the determined meaning. | 11. A system for providing information to a user comprising: communications circuitry; and control circuitry configured to: receive, using the communications circuitry with user equipment associated with a first user, a communication transmitted by a second user; in response to receiving the communication that was transmitted by the second user, process, with the user equipment associated with the first user, text of the communication to identify a symbol of the communication that is subject to a plurality of candidate interpretations, the symbol including one or more words in the processed text of the communication; identify each candidate interpretation of the plurality of candidate interpretations; retrieve a profile of the user; compare an attribute of the profile to each candidate interpretation of the plurality of candidate interpretations; in response determining, based on the comparison, that the first user has accessed a first media asset that is associated with a first of the plurality of candidate interpretations more recently than a second media asset that is associated with a second of the plurality of candidate interpretations, select as a determined meaning of the symbol the first candidate interpretation; and update the profile to include information based on the determined meaning. 16. The system of claim 11 , wherein each candidate interpretation of the plurality of candidate interpretations corresponds to a media asset. | 0.874558 |
10,102,287 | 2 | 5 | 2. The method of claim 1 further comprising, generating a search creation interface on a display of a user electronic device, the search creation interface providing the set of graphical icons, the icon selection tool, and an icon weighting tool. | 2. The method of claim 1 further comprising, generating a search creation interface on a display of a user electronic device, the search creation interface providing the set of graphical icons, the icon selection tool, and an icon weighting tool. 5. The method of claim 2 , wherein the icon selection tool provides a drag and drop feature such that the user interaction with the icon selection tool comprises a user dragging and dropping the at least two selected graphical icons into a search creation workspace provided by the search creation interface. | 0.914776 |
9,453,741 | 11 | 15 | 11. A navigation system comprising: a user interface configured to receive a search term and a search range for searching an inverted term index; a control unit, coupled to the user interface, configured to: receive the inverted term index, extracted from a map data tree, including a nested spatial index with nodes, wherein the map data tree represents a physical geographic region and the nodes represent a physical geographic area within the map data tree; select one or more nodes within the nested spatial index corresponding to the search range; locate the search term in the inverted term index; and locate a location record linked to the nested spatial index and associated with the search term from within the one or more nodes corresponding to the search range for displaying on a device. | 11. A navigation system comprising: a user interface configured to receive a search term and a search range for searching an inverted term index; a control unit, coupled to the user interface, configured to: receive the inverted term index, extracted from a map data tree, including a nested spatial index with nodes, wherein the map data tree represents a physical geographic region and the nodes represent a physical geographic area within the map data tree; select one or more nodes within the nested spatial index corresponding to the search range; locate the search term in the inverted term index; and locate a location record linked to the nested spatial index and associated with the search term from within the one or more nodes corresponding to the search range for displaying on a device. 15. The system as claimed in claim 11 wherein the user interface is configured to sort location records according to a sort preference. | 0.802632 |
10,133,730 | 8 | 9 | 8. The computing system of claim 7 further comprising redefining the range of the content from which to determine the context of the language expression. | 8. The computing system of claim 7 further comprising redefining the range of the content from which to determine the context of the language expression. 9. The computing system of claim 8 further comprising modifying the action item based upon, at least in part, a new context of the language expression determined from the redefined range of the content. | 0.932032 |
8,145,632 | 21 | 27 | 21. A computer system, comprising: memory; one or more processors; one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including: instructions for identifying multiple resource identifiers in accordance with a first set of predefined criteria for selecting a respective document that satisfies user-specified search keywords, each resource identifier corresponding to a document at a respective data source; instructions for retrieving the corresponding document from the respective document source for at least one of the resource identifiers; instructions for identifying within the retrieved document a chunk by applying a second set of user-specified criteria to the retrieved document; and instructions for displaying the identified chunk and a link to the identified chunk within the document to the user, wherein the first set of predefined criteria requires that all the search keywords be found within an identified respective document, and the second set of predefined criteria requires that all the search keywords be found within an identified chunk. | 21. A computer system, comprising: memory; one or more processors; one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including: instructions for identifying multiple resource identifiers in accordance with a first set of predefined criteria for selecting a respective document that satisfies user-specified search keywords, each resource identifier corresponding to a document at a respective data source; instructions for retrieving the corresponding document from the respective document source for at least one of the resource identifiers; instructions for identifying within the retrieved document a chunk by applying a second set of user-specified criteria to the retrieved document; and instructions for displaying the identified chunk and a link to the identified chunk within the document to the user, wherein the first set of predefined criteria requires that all the search keywords be found within an identified respective document, and the second set of predefined criteria requires that all the search keywords be found within an identified chunk. 27. The computer system of claim 21 , further comprising: instructions for submitting the user-specified search keywords to a search engine; and instructions for receiving a set of search results from the search engine, wherein each search result includes an abbreviated document segment identified by the search engine as satisfying the search keywords and the abbreviated document segment is different from the identified chunk. | 0.622144 |
7,953,713 | 1 | 5 | 1. A management system for a managed system, said management system comprising: a computer hardware processor, said hardware processor computing: a plurality of data sources, each data source interfaces with said managed system using a data sensor that collects data and attaches a hierarchical semantic tag to said collected data, said hierarchical semantic tag conveying information of: hierarchy information; a set of ontology trees that capture semantics comprising: monitoring metric relationships data; business functions data; and application components data; and a set of rules that capture relationships between hierarchical semantic tag hierarchies, relationships between nodes in different trees, relationships between metrics and relationships between application and business context of components monitored by said data sources; and a core engine that receives said data including said hierarchical semantic tag, and that performs domain-independent processing on said data based upon at least a portion of said hierarchical semantic tag, said core engine further comprising: a base event generation and aggregation layer that processes said hierarchical semantic tag to generate base events; an event composition filtering and correlation layer, that filters and composes said base events based on pre-defined rules to generate composite events; and an authoring tool for receiving from a user new hierarchies, and associations between hierarchical semantic tags and data sources, wherein said hierarchical semantic tag is associated with monitoring data, and is used by said managed system to apply management-specific functions to said data at a data center. | 1. A management system for a managed system, said management system comprising: a computer hardware processor, said hardware processor computing: a plurality of data sources, each data source interfaces with said managed system using a data sensor that collects data and attaches a hierarchical semantic tag to said collected data, said hierarchical semantic tag conveying information of: hierarchy information; a set of ontology trees that capture semantics comprising: monitoring metric relationships data; business functions data; and application components data; and a set of rules that capture relationships between hierarchical semantic tag hierarchies, relationships between nodes in different trees, relationships between metrics and relationships between application and business context of components monitored by said data sources; and a core engine that receives said data including said hierarchical semantic tag, and that performs domain-independent processing on said data based upon at least a portion of said hierarchical semantic tag, said core engine further comprising: a base event generation and aggregation layer that processes said hierarchical semantic tag to generate base events; an event composition filtering and correlation layer, that filters and composes said base events based on pre-defined rules to generate composite events; and an authoring tool for receiving from a user new hierarchies, and associations between hierarchical semantic tags and data sources, wherein said hierarchical semantic tag is associated with monitoring data, and is used by said managed system to apply management-specific functions to said data at a data center. 5. The management system according to claim 1 , wherein said core engine includes one or more processing modules, and said hierarchical semantic tags are used for routing said data to said processing modules. | 0.661238 |
5,412,566 | 1 | 5 | 1. A variable replacement apparatus which replaces variable names in a text with corresponding variable values, each variable name formed by at least one character and each character in the variable name having a corresponding format, the apparatus comprising: variable name extracting means for extracting a variable name from the text; variable value acquisition means for obtaining a variable value corresponding to the variable name extracted by said variable name extracting means; variable name analyzing means for analyzing the corresponding format of each character forming the variable name; variable value converting means for converting the variable value obtained by said variable value acquisition means so that the variable value has a format which is determined in accordance with the corresponding format of each character forming the variable name; and variable replacing means for replacing the variable name in the text by the converted variable value. | 1. A variable replacement apparatus which replaces variable names in a text with corresponding variable values, each variable name formed by at least one character and each character in the variable name having a corresponding format, the apparatus comprising: variable name extracting means for extracting a variable name from the text; variable value acquisition means for obtaining a variable value corresponding to the variable name extracted by said variable name extracting means; variable name analyzing means for analyzing the corresponding format of each character forming the variable name; variable value converting means for converting the variable value obtained by said variable value acquisition means so that the variable value has a format which is determined in accordance with the corresponding format of each character forming the variable name; and variable replacing means for replacing the variable name in the text by the converted variable value. 5. The variable replacement apparatus as claimed in claim 1, wherein said variable name analyzing means analyzes the corresponding formats of respective characters forming the variable name, the corresponding formats including capital letters and small letters. | 0.740557 |
8,983,844 | 1 | 3 | 1. A method comprising: receiving, by a server device comprising a processor, a noise-reduced audio signal and a noise parameter from a user device, wherein the noise parameter provides information relating to how noise was reduced in the noise-reduced audio signal; selecting, by the server device, a first automatic speech recognition (ASR) model from a plurality of ASR models wherein: each ASR model of the plurality of ASR models is associated with a respective noise parameter model, and the first ASR model is selected based at least in part on a comparison between the noise parameter and the noise parameter model associated with the first ASR model; and performing, by the server device, ASR processing on the noise-reduced audio signal using the first ASR model to generate ASR results. | 1. A method comprising: receiving, by a server device comprising a processor, a noise-reduced audio signal and a noise parameter from a user device, wherein the noise parameter provides information relating to how noise was reduced in the noise-reduced audio signal; selecting, by the server device, a first automatic speech recognition (ASR) model from a plurality of ASR models wherein: each ASR model of the plurality of ASR models is associated with a respective noise parameter model, and the first ASR model is selected based at least in part on a comparison between the noise parameter and the noise parameter model associated with the first ASR model; and performing, by the server device, ASR processing on the noise-reduced audio signal using the first ASR model to generate ASR results. 3. The method of claim 1 , wherein the first ASR model is an acoustic model. | 0.898396 |
9,619,534 | 1 | 4 | 1. A method for creating or updating a data set stored as a record in a database, wherein a plurality of data sets are stored in the database, wherein each data set in the plurality of data sets is defined to include a plurality of fields corresponding to a plurality of predefined entities, the method comprising: searching through a plurality of documents for current information about the data set; upon locating a search result document, in the plurality of documents, containing the current information about the data set, copying and storing a data string having a plurality of tokens from content of the search result document containing the current information about the data set; extracting a sequence of tokens corresponding to the data string; recognizing a first set of tokens in the sequence of tokens as a first entity based on entity recognition probabilistic scoring derived from a machine evaluation of a training set of entities; recognizing a second set of tokens in the sequence of tokens as a second entity based on identifying the first entity as a first node in a tree-like structure and identifying the second entity as by a second node in the tree-like structure, the first node connected to the second node by an arc representing a probability that the first entity is followed by the second entity in a probable entity sequence, the first node connected to another node by another arc representing another probability that the first entity is followed by another entity in another probable entity sequence, the tree-like structure created by a machine evaluation of a training set of input strings; aligning one or more tokens of the first set of tokens as one of a plurality of probable entities using the probabilistic scoring of the first set of tokens and grammatical rules; assigning the aligned one or more tokens to one entity field of the plurality of predefined entity fields of the data set; and creating and storing a new record for the data set if none exists, or updating an existing record for the data set, using the assigned aligned one or more tokens. | 1. A method for creating or updating a data set stored as a record in a database, wherein a plurality of data sets are stored in the database, wherein each data set in the plurality of data sets is defined to include a plurality of fields corresponding to a plurality of predefined entities, the method comprising: searching through a plurality of documents for current information about the data set; upon locating a search result document, in the plurality of documents, containing the current information about the data set, copying and storing a data string having a plurality of tokens from content of the search result document containing the current information about the data set; extracting a sequence of tokens corresponding to the data string; recognizing a first set of tokens in the sequence of tokens as a first entity based on entity recognition probabilistic scoring derived from a machine evaluation of a training set of entities; recognizing a second set of tokens in the sequence of tokens as a second entity based on identifying the first entity as a first node in a tree-like structure and identifying the second entity as by a second node in the tree-like structure, the first node connected to the second node by an arc representing a probability that the first entity is followed by the second entity in a probable entity sequence, the first node connected to another node by another arc representing another probability that the first entity is followed by another entity in another probable entity sequence, the tree-like structure created by a machine evaluation of a training set of input strings; aligning one or more tokens of the first set of tokens as one of a plurality of probable entities using the probabilistic scoring of the first set of tokens and grammatical rules; assigning the aligned one or more tokens to one entity field of the plurality of predefined entity fields of the data set; and creating and storing a new record for the data set if none exists, or updating an existing record for the data set, using the assigned aligned one or more tokens. 4. The method of claim 1 , wherein the plurality of data sets store contact information including one or more entities, and wherein searching through the plurality of documents includes: searching through the plurality of documents for current information instances of the one or more entities. | 0.625 |
8,775,466 | 13 | 17 | 13. One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause performance of: extracting first meta-attributes based on a first data set of entries and first attributes of said first data set of entries; extracting second meta-attributes based on a second data set of entries and a second attributes of said first data set of entries; generating a first projection of the first data set of entries onto said first meta-attributes; generating a second projection of the second data set of entries onto said second meta-attributes; based on said first projection, said second projection, and data representing a relationship between said first data set of entries and said second data set of entries, generating data representing a relationship between said first meta-attributes and said second meta-attributes; and generating data representing a relationship between a third data set of entries and a fourth data set of entries, wherein generating data representing a relationship between a third data set of entries and a fourth data set of entries is based on: a third projection of said third data set of entries onto the first meta-attributes, a fourth projection of said fourth data set of entries onto the second meta-attributes, and said data representing said relationship between said first meta-attributes and second meta-attributes. | 13. One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause performance of: extracting first meta-attributes based on a first data set of entries and first attributes of said first data set of entries; extracting second meta-attributes based on a second data set of entries and a second attributes of said first data set of entries; generating a first projection of the first data set of entries onto said first meta-attributes; generating a second projection of the second data set of entries onto said second meta-attributes; based on said first projection, said second projection, and data representing a relationship between said first data set of entries and said second data set of entries, generating data representing a relationship between said first meta-attributes and said second meta-attributes; and generating data representing a relationship between a third data set of entries and a fourth data set of entries, wherein generating data representing a relationship between a third data set of entries and a fourth data set of entries is based on: a third projection of said third data set of entries onto the first meta-attributes, a fourth projection of said fourth data set of entries onto the second meta-attributes, and said data representing said relationship between said first meta-attributes and second meta-attributes. 17. The one or more non-transitory storage media of claim 13 , wherein data representing a relationship between said first data set of entries and said second data set of entries represents associations, each association of said associates being between a respective entry of said first data set of entries and a respective entry of said second data set of entries. | 0.73003 |
9,609,391 | 8 | 14 | 8. A system for providing information relating to media content, the system comprising: a hardware processor that is configured to: cause a plurality of images to be presented on a mobile device, wherein the plurality of images relate to media content being presented on a media presentation device; receive a user selection of an image from the plurality of images; cause a plurality of question terms to be presented in response to receiving the user selection of the image from the plurality of images; receive a user selection of a question term from the plurality of question terms, where the user selection of the question term indicates an entity type that is to be identified from the user-selected image; in response to receiving the user selection of the question term, identify an entity of the indicated entity type within the image using one or more image recognition techniques; generate a search query based at least in part on the identified entity; obtain a plurality of search results responsive to the generated search query; and cause at least a portion of the plurality of search results to be presented in response to receiving the user selection of the image. | 8. A system for providing information relating to media content, the system comprising: a hardware processor that is configured to: cause a plurality of images to be presented on a mobile device, wherein the plurality of images relate to media content being presented on a media presentation device; receive a user selection of an image from the plurality of images; cause a plurality of question terms to be presented in response to receiving the user selection of the image from the plurality of images; receive a user selection of a question term from the plurality of question terms, where the user selection of the question term indicates an entity type that is to be identified from the user-selected image; in response to receiving the user selection of the question term, identify an entity of the indicated entity type within the image using one or more image recognition techniques; generate a search query based at least in part on the identified entity; obtain a plurality of search results responsive to the generated search query; and cause at least a portion of the plurality of search results to be presented in response to receiving the user selection of the image. 14. The system of claim 8 , wherein the hardware processor is further configured to receive media content information relating to the media content, wherein the media content information includes information about one or more people appearing in the media content, wherein the user selection of the question term indicates that the entity to be identified in the image is a person, and wherein a person entity is identified within the image using one or more facial recognition techniques based on the one or more people appearing in the media content. | 0.625509 |
8,122,042 | 4 | 6 | 4. A method of determining relevance of a content identifier, comprising: receiving, at a direct answer computer system, a search query over a network; determining, at the direct answer computer system, one or more answer entities from one or more answer candidate snippets, wherein an answer candidate snippet comprises at least a portion of content available over the network for an answer candidate; determining, at the direct answer computer system, a content identifier for an answer candidate; determining a popularity for the content identifier and adjusting an indicator of the relevance for the content identifier in accordance with the popularity of the content identifier, wherein the popularity of the content identifier comprises a click count for the content identifier, a click count for a main web site for the content identifier, a count of references to the content identifier, and a count of references to the main web site; tokenizing, at the direct answer computer system, the content identifier; performing a comparison, at the direct answer computer system, between a vector of tokens for the content identifier and a vector of the one or more answer entities; adjusting an indicator of the relevance for the content identifier in accordance with the comparison; and send at least one answer candidate snippet for a response to the search query. | 4. A method of determining relevance of a content identifier, comprising: receiving, at a direct answer computer system, a search query over a network; determining, at the direct answer computer system, one or more answer entities from one or more answer candidate snippets, wherein an answer candidate snippet comprises at least a portion of content available over the network for an answer candidate; determining, at the direct answer computer system, a content identifier for an answer candidate; determining a popularity for the content identifier and adjusting an indicator of the relevance for the content identifier in accordance with the popularity of the content identifier, wherein the popularity of the content identifier comprises a click count for the content identifier, a click count for a main web site for the content identifier, a count of references to the content identifier, and a count of references to the main web site; tokenizing, at the direct answer computer system, the content identifier; performing a comparison, at the direct answer computer system, between a vector of tokens for the content identifier and a vector of the one or more answer entities; adjusting an indicator of the relevance for the content identifier in accordance with the comparison; and send at least one answer candidate snippet for a response to the search query. 6. The method of claim 4 , wherein an indicator of the relevance for the content identifier is a score for the content identifier that is increased when similar tokens from the content identifier are found in the answer entities from the one or more answer candidate snippets. | 0.501805 |
10,067,983 | 11 | 17 | 11. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to: analyze a plurality of communication logs associated with a query related to an information technology issue to determine one or more discourse relationships between the plurality of communication logs, wherein each of the plurality of communication logs comprises (i) a description of one or more actions taken in connection with the information technology issue, (ii) a status related to the one or more actions taken, and (iii) identification of a group carrying out the one or more actions; generate a hierarchical structure representing the plurality of communication logs and the one or more determined discourse relationships, wherein said generating the hierarchical structure comprises generating a discourse graph representing the plurality of communication logs and the one or more determined discourse relationships, wherein individual log entities form vertices of the discourse graph and the one or more determined discourse relationships form one or more directed edges in the discourse graph; derive a log reliability graph from the discourse graph by assigning each of the plurality of communication logs in the discourse graph a weight, wherein the weight indicates an amount contextual similarity of the communication log's description with the query; associate the query with one or more classified queries by (i) determining one or more patterns in the log reliability graph and (ii) comparing the one or more determined patterns to patterns associated with multiple historical log reliability graphs associated with classified queries; and determine one or more information technology issue categories applicable to the query based on said associating. | 11. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to: analyze a plurality of communication logs associated with a query related to an information technology issue to determine one or more discourse relationships between the plurality of communication logs, wherein each of the plurality of communication logs comprises (i) a description of one or more actions taken in connection with the information technology issue, (ii) a status related to the one or more actions taken, and (iii) identification of a group carrying out the one or more actions; generate a hierarchical structure representing the plurality of communication logs and the one or more determined discourse relationships, wherein said generating the hierarchical structure comprises generating a discourse graph representing the plurality of communication logs and the one or more determined discourse relationships, wherein individual log entities form vertices of the discourse graph and the one or more determined discourse relationships form one or more directed edges in the discourse graph; derive a log reliability graph from the discourse graph by assigning each of the plurality of communication logs in the discourse graph a weight, wherein the weight indicates an amount contextual similarity of the communication log's description with the query; associate the query with one or more classified queries by (i) determining one or more patterns in the log reliability graph and (ii) comparing the one or more determined patterns to patterns associated with multiple historical log reliability graphs associated with classified queries; and determine one or more information technology issue categories applicable to the query based on said associating. 17. The computer program product of claim 11 , wherein said determining one or more patterns comprises determining the one or more patterns from the perspective of group carrying out the one or more actions. | 0.775 |
9,684,639 | 6 | 9 | 6. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause: a repository receiving a first request to store a XML document; wherein a first process within a first session is running within said repository; wherein a second process within a second session is running within said repository; in response to receiving said first request, said first process validating said XML document based on a XML schema defined by one or more XML schema documents, wherein validating said XML document includes said first process storing, in a shared volatile memory, a compile-time generated static structures comprising validation data and specifically generated for XML document validation based on said XML schema, wherein said XML schema is registered with said repository; said repository receiving a subsequent request to store one or more XML documents associated with said XML schema; in response to receiving said subsequent request, said second process subsequently validating said one or more XML documents based on said XML schema; wherein subsequently validating said one or more XML document comprises: said second process copying from said shared volatile memory said compile-time generated static structures into private memory that is private to said second process, and said second process using said compile-time generated static structures that are stored in said private memory to validate said one or more XML documents. | 6. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause: a repository receiving a first request to store a XML document; wherein a first process within a first session is running within said repository; wherein a second process within a second session is running within said repository; in response to receiving said first request, said first process validating said XML document based on a XML schema defined by one or more XML schema documents, wherein validating said XML document includes said first process storing, in a shared volatile memory, a compile-time generated static structures comprising validation data and specifically generated for XML document validation based on said XML schema, wherein said XML schema is registered with said repository; said repository receiving a subsequent request to store one or more XML documents associated with said XML schema; in response to receiving said subsequent request, said second process subsequently validating said one or more XML documents based on said XML schema; wherein subsequently validating said one or more XML document comprises: said second process copying from said shared volatile memory said compile-time generated static structures into private memory that is private to said second process, and said second process using said compile-time generated static structures that are stored in said private memory to validate said one or more XML documents. 9. The non-transitory computer-readable medium of claim 6 , wherein generating validation structures includes dynamically creating a validation structure within said shared volatile memory, wherein a copy of a version of said validation structure is not stored in persistent storage before dynamically creating said validation structure. | 0.638412 |
8,761,351 | 1 | 6 | 1. A method comprising: receiving, by a processor, via an internet protocol (IP) network, information pertaining to an event, wherein: the information is indicative of speech; the information is formatted in accordance with a voice over internet protocol (VoIP); the information is indicative of being provided by a P25 land mobile radio, via a federally regulated land mobile radio system; providing the information for storage, wherein the stored information is available for access by at least one entity associated with an emergency operations center; automatically converting the VoIP formatted information to text; automatically formatting, by the processor, the text in accordance with a WebEOC emergency operations center log format; and providing the formatted text for storage, wherein the stored formatted text is available for access by at least one entity associated with the emergency operations center. | 1. A method comprising: receiving, by a processor, via an internet protocol (IP) network, information pertaining to an event, wherein: the information is indicative of speech; the information is formatted in accordance with a voice over internet protocol (VoIP); the information is indicative of being provided by a P25 land mobile radio, via a federally regulated land mobile radio system; providing the information for storage, wherein the stored information is available for access by at least one entity associated with an emergency operations center; automatically converting the VoIP formatted information to text; automatically formatting, by the processor, the text in accordance with a WebEOC emergency operations center log format; and providing the formatted text for storage, wherein the stored formatted text is available for access by at least one entity associated with the emergency operations center. 6. The method of claim 1 , further comprising providing a wireless priority service to an originator of the information. | 0.887218 |
8,677,231 | 10 | 11 | 10. The method of claim 8 , wherein said fragment is defined by a syntax defining the prescribed hierarchical structure of the electronic document. | 10. The method of claim 8 , wherein said fragment is defined by a syntax defining the prescribed hierarchical structure of the electronic document. 11. The method of claim 10 , wherein said syntax is XML schema. | 0.978454 |
9,514,116 | 1 | 17 | 1. A system comprising: at least one processor; and memory encoding computer executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method for integrating a gadget with a spreadsheet, the method comprising providing an Application Programming Interface (API) for the gadget to communicate with the spreadsheet; receiving a selection of a range of cells of the spreadsheet to bind to the gadget, wherein the selected range of cells comprises one or more cells of the spreadsheet; determining a binding between the selected range of cells of the spreadsheet and the gadget; determining an interaction with the selected range of cells; automatically providing a first notification to the gadget in response to the interaction; receiving a call from the gadget using the API; performing an operation involving the spreadsheet that relates to the received call; after performing the operation, receiving input to change the selected range of cells to adjust the binding to include the changed selected range of cells; and automatically providing a second notification to the gadget in response to the input. | 1. A system comprising: at least one processor; and memory encoding computer executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method for integrating a gadget with a spreadsheet, the method comprising providing an Application Programming Interface (API) for the gadget to communicate with the spreadsheet; receiving a selection of a range of cells of the spreadsheet to bind to the gadget, wherein the selected range of cells comprises one or more cells of the spreadsheet; determining a binding between the selected range of cells of the spreadsheet and the gadget; determining an interaction with the selected range of cells; automatically providing a first notification to the gadget in response to the interaction; receiving a call from the gadget using the API; performing an operation involving the spreadsheet that relates to the received call; after performing the operation, receiving input to change the selected range of cells to adjust the binding to include the changed selected range of cells; and automatically providing a second notification to the gadget in response to the input. 17. The method of claim 1 , further comprising determining when the call is out of date, wherein when it is determined that the call is out of date, preventing the call from being executed. | 0.757069 |
9,507,758 | 1 | 12 | 1. A method for collaborative matter management and analysis comprising: uploading a new document to a Master File in a computer system for collaborative matter management and analysis, wherein the Master File is a document repository for a matter; creating one or more metadata fields for said new document; extracting text from said document using text recognition; populating the one or more metadata fields with Players, document type and date associated with said new document; using said document type to determine if there are related documents and generating a structural hierarchy and placeholders for said related documents, wherein the structural hierarchy is based on timing; determining from said extracted text if said new document is an evidentiary document and adding a plurality of authentication metadata fields to said one or more metadata fields for authentication if said new document is evidentiary; determining from said extracted text if said new document is a version of an existing document in the system and associating said new document with said existing document upon positive determination; identifying all citations to evidentiary documents, Master File documents and legal authority documents in the new document; and converting said citations in said new document to hyperlinks to other documents in the system. | 1. A method for collaborative matter management and analysis comprising: uploading a new document to a Master File in a computer system for collaborative matter management and analysis, wherein the Master File is a document repository for a matter; creating one or more metadata fields for said new document; extracting text from said document using text recognition; populating the one or more metadata fields with Players, document type and date associated with said new document; using said document type to determine if there are related documents and generating a structural hierarchy and placeholders for said related documents, wherein the structural hierarchy is based on timing; determining from said extracted text if said new document is an evidentiary document and adding a plurality of authentication metadata fields to said one or more metadata fields for authentication if said new document is evidentiary; determining from said extracted text if said new document is a version of an existing document in the system and associating said new document with said existing document upon positive determination; identifying all citations to evidentiary documents, Master File documents and legal authority documents in the new document; and converting said citations in said new document to hyperlinks to other documents in the system. 12. The method of claim 1 , further comprising: posting said new document to a feed; and capturing collaborative discussions about said new document in said feed. | 0.906682 |
9,081,782 | 1 | 6 | 1. A computer system for dynamically generating a graphical memorabilia project, the system comprising: a computer processor used to generate the graphical memorabilia project, the memorabilia project comprising: a dynamic page layout template having a first well of a first well type and multiple wells of a second well type, wherein a first area of the template, which is fixed in location with respect to the template, is configured to accept an image and to prevent a background design element from being placed as a top layer of the memorabilia project in that first area, wherein the background design element comprises a virtual design that is configured to resemble a decorative element placed in a physical memorabilia project, wherein the first well of the first well type comprises the first area, wherein the wells of the second well type are divided into multiple well classes, and wherein wells of the second well type that are of the same well class are governed by similar pre-determined rules, such that a change to one of the wells that is of the second well type and of a first well class will cause a similar change to another well of the second well type and of the first well class. | 1. A computer system for dynamically generating a graphical memorabilia project, the system comprising: a computer processor used to generate the graphical memorabilia project, the memorabilia project comprising: a dynamic page layout template having a first well of a first well type and multiple wells of a second well type, wherein a first area of the template, which is fixed in location with respect to the template, is configured to accept an image and to prevent a background design element from being placed as a top layer of the memorabilia project in that first area, wherein the background design element comprises a virtual design that is configured to resemble a decorative element placed in a physical memorabilia project, wherein the first well of the first well type comprises the first area, wherein the wells of the second well type are divided into multiple well classes, and wherein wells of the second well type that are of the same well class are governed by similar pre-determined rules, such that a change to one of the wells that is of the second well type and of a first well class will cause a similar change to another well of the second well type and of the first well class. 6. The system of claim 1 , wherein the background design element is selected from a background kit having different background design elements, wherein some of the wells of the second well type are each configured to receive a different background design element, and wherein the background kit comprises multiple design and color palettes that are each selectable to apply the different background design elements to the page layout template in a different, coordinated manner. | 0.768635 |
9,147,212 | 1 | 3 | 1. A product location assistance method comprising: receiving retailer inventory data about a plurality of products, said inventory data comprising a location of each one of said products in a retail store; formatting said received retailer inventory data to include, for each one of said products, a programmatically searchable taxonomy including synonyms, slang, and phonetic data for said product, said taxonomy being formatted as plain text; receiving a call over a telecommunications network from a user using a mobile phone; receiving from said user an audio search request comprising an indication of a desired product, said desired product being a product in said plurality of products, during the call; converting said search request to a plain text indication of said desired product; identifying a location of said desired product in said retail store, said identification based at least in part on matching said converted plain text indication of said desired product to said plain text taxonomy for said desired product; sending over said telecommunications network an indication of said identified location of said desired product in said retail store. | 1. A product location assistance method comprising: receiving retailer inventory data about a plurality of products, said inventory data comprising a location of each one of said products in a retail store; formatting said received retailer inventory data to include, for each one of said products, a programmatically searchable taxonomy including synonyms, slang, and phonetic data for said product, said taxonomy being formatted as plain text; receiving a call over a telecommunications network from a user using a mobile phone; receiving from said user an audio search request comprising an indication of a desired product, said desired product being a product in said plurality of products, during the call; converting said search request to a plain text indication of said desired product; identifying a location of said desired product in said retail store, said identification based at least in part on matching said converted plain text indication of said desired product to said plain text taxonomy for said desired product; sending over said telecommunications network an indication of said identified location of said desired product in said retail store. 3. The method of claim 1 , wherein said indication of a desired product comprises voice data. | 0.731214 |
8,316,021 | 8 | 10 | 8. A method of receiving a search query from a user at a remote computer and returning a search results webpage, the method including the steps of: providing an input displayable on the remote computer; receiving a search query from the remote computer via the input; processing, via a processor, the search query; causing the search results webpage to be transmitted to the remote computer in response to the search query, the results webpage including respective links to, and respective website information relating to the content of, websites responsive to the search query; storing the website information; receiving a first feedback from the user relating to a perceived relevance of the website information included in the search results webpage relative to the search query; receiving a second feedback from the user relating to a perceived relevance of at least one of the websites visited by the user relative to the search query, the second user feedback received after the user visits at least one of the websites; receiving the first and second feedbacks; and amending the website information at least in part based on the first and second feedbacks. | 8. A method of receiving a search query from a user at a remote computer and returning a search results webpage, the method including the steps of: providing an input displayable on the remote computer; receiving a search query from the remote computer via the input; processing, via a processor, the search query; causing the search results webpage to be transmitted to the remote computer in response to the search query, the results webpage including respective links to, and respective website information relating to the content of, websites responsive to the search query; storing the website information; receiving a first feedback from the user relating to a perceived relevance of the website information included in the search results webpage relative to the search query; receiving a second feedback from the user relating to a perceived relevance of at least one of the websites visited by the user relative to the search query, the second user feedback received after the user visits at least one of the websites; receiving the first and second feedbacks; and amending the website information at least in part based on the first and second feedbacks. 10. The method of claim 8 wherein the step of causing the results webpage to be transmitted to the remote computer includes providing a feedback input displayable on the search results webpage. | 0.835043 |
9,612,996 | 11 | 16 | 11. A system comprising: one or more processors; and a non-transitory machine-readable medium comprising instructions stored therein, which when executed by the processors, cause the processors to perform operations comprising: receiving an indication of a request to provide a user with one or more suggestion items; identifying one or more contacts associated with the user at one or more social networking services; identifying social activity and interaction data relating to the user and one or more contacts of the user based on an affinity of the user for the one or more contacts and based on access rights associated with the social activity and interaction data; analyzing the social activity and interaction data; generating a plurality of n-grams in response to the analysis, each n-gram comprising a string of characters; and providing one or more suggestions for display as auto-complete suggestions to the user in response to the request based on the selected one or more n-grams of the plurality of n-grams, wherein the one or more suggestions are displayed with indicators corresponding to the identified one or more contacts. | 11. A system comprising: one or more processors; and a non-transitory machine-readable medium comprising instructions stored therein, which when executed by the processors, cause the processors to perform operations comprising: receiving an indication of a request to provide a user with one or more suggestion items; identifying one or more contacts associated with the user at one or more social networking services; identifying social activity and interaction data relating to the user and one or more contacts of the user based on an affinity of the user for the one or more contacts and based on access rights associated with the social activity and interaction data; analyzing the social activity and interaction data; generating a plurality of n-grams in response to the analysis, each n-gram comprising a string of characters; and providing one or more suggestions for display as auto-complete suggestions to the user in response to the request based on the selected one or more n-grams of the plurality of n-grams, wherein the one or more suggestions are displayed with indicators corresponding to the identified one or more contacts. 16. The system of claim 11 , wherein the one or more suggestions include recommended actions determined based on at least one of the one or more n-grams. | 0.830752 |
6,144,938 | 1 | 26 | 1. An apparatus for a voice user interface with personality, the apparatus comprising: logic that provides a voice user interface, the voice user interface outputting first voice signals, and the voice user interface recognizing speech signals; logic that provides a personality, the logic that provides the personality interfacing with the logic that provides the voice user interface to provide the voice user interface with a verbal personality; and a recognition grammar stored in a memory, the recognition grammar comprising multiple phrases that a virtual assistant with a personality can recognize when spoken by a user, and the recognition grammar being selected based on the personality of the virtual assistant. | 1. An apparatus for a voice user interface with personality, the apparatus comprising: logic that provides a voice user interface, the voice user interface outputting first voice signals, and the voice user interface recognizing speech signals; logic that provides a personality, the logic that provides the personality interfacing with the logic that provides the voice user interface to provide the voice user interface with a verbal personality; and a recognition grammar stored in a memory, the recognition grammar comprising multiple phrases that a virtual assistant with a personality can recognize when spoken by a user, and the recognition grammar being selected based on the personality of the virtual assistant. 26. The apparatus as recited in claim 1 wherein the logic that provides the personality comprises controlling the voice user interface in situations in which negative comments are needed. | 0.730548 |
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