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9,894,460 | 2 | 4 | 2. The computer-implemented method of claim 1 , further comprising: receiving, by the computing device, a second orally-provided input that includes the second carrier phrase; and performing, by the computing device, the action by the second application program due to the second orally-provided input including the second carrier phrase. | 2. The computer-implemented method of claim 1 , further comprising: receiving, by the computing device, a second orally-provided input that includes the second carrier phrase; and performing, by the computing device, the action by the second application program due to the second orally-provided input including the second carrier phrase. 4. The computer-implemented method of claim 2 , further comprising: converting the first orally-provided query to text using a first language model due to the first orally-provided query including the first carrier phrase; and converting the second orally-provided query to text using a second language model due to the second orally-provided query including the second carrier phrase; wherein the first language model weights more heavily certain words than the second language model. | 0.511089 |
8,954,840 | 20 | 21 | 20. The article of manufacture of claim 14 , wherein the plurality of program elements comprises Java class file program elements. | 20. The article of manufacture of claim 14 , wherein the plurality of program elements comprises Java class file program elements. 21. The article of manufacture of claim 20 , the operations further comprising: converting a set of class files to generate the data stream input. | 0.5 |
9,792,714 | 15 | 18 | 15. A method of managing avatars, comprising: capturing an image of a subject and an image of a doll; identifying one or more facial expressions of the subject and a doll face in a video signal; generating avatar animation data based on the one or more facial expressions of the subject; incorporating the avatar animation data into an audio file associated with the video signal, and transferring the avatar animation data of the subject and audible content associated with the audio file to the doll face to obtain a doll animation. | 15. A method of managing avatars, comprising: capturing an image of a subject and an image of a doll; identifying one or more facial expressions of the subject and a doll face in a video signal; generating avatar animation data based on the one or more facial expressions of the subject; incorporating the avatar animation data into an audio file associated with the video signal, and transferring the avatar animation data of the subject and audible content associated with the audio file to the doll face to obtain a doll animation. 18. The method of claim 15 , wherein incorporating the avatar animation data into the audio file includes storing a link to timestamped facial motion data in a sound metadata field of the audio file. | 0.702096 |
8,401,854 | 1 | 15 | 1. A speech recognition method in which an entry corresponding to a speech input is selected from a list of entries, the method comprising: detecting the speech input; recognizing a phoneme sequence of the speech input; providing a list of fragments of entries in a list of entries, the fragments being based on a subword or phoneme level; and comparing the phoneme sequence of the recognized speech input to the list of fragments to generate a candidate list of best matching entries based on comparison scores, wherein a comparison score is calculated for a fragment when the recognized speech input is compared to the fragment, the comparison score being a measure of how well the recognized speech input fits to the fragment, wherein a score for one list entry is calculated based on the comparison scores of all the fragments that build the list entry, where the fragment is accompanied by different wildcards, each wildcard representing the part of the list entry not considered in the fragment of the list entry and each wildcard having a different weight when the recognized speech input is compared to the fragment. | 1. A speech recognition method in which an entry corresponding to a speech input is selected from a list of entries, the method comprising: detecting the speech input; recognizing a phoneme sequence of the speech input; providing a list of fragments of entries in a list of entries, the fragments being based on a subword or phoneme level; and comparing the phoneme sequence of the recognized speech input to the list of fragments to generate a candidate list of best matching entries based on comparison scores, wherein a comparison score is calculated for a fragment when the recognized speech input is compared to the fragment, the comparison score being a measure of how well the recognized speech input fits to the fragment, wherein a score for one list entry is calculated based on the comparison scores of all the fragments that build the list entry, where the fragment is accompanied by different wildcards, each wildcard representing the part of the list entry not considered in the fragment of the list entry and each wildcard having a different weight when the recognized speech input is compared to the fragment. 15. The method of claim 1 further comprising using the candidate list of best matching entries determined on the basis of fragments of the entries as a basis for a recognition step. | 0.805794 |
9,244,890 | 2 | 3 | 2. The electronic device according to claim 1 , wherein: the memory further stores dictionary data in which each headword and expository content of the headword is associated, and the processor is configured to further perform processing of: reading out from dictionary data stored in the memory an expository content having a headword to which the specified character string in the illustration is linked as the headword, and displaying the read-out expository content; when an expository content having, as a headword, a character string other than a character string highlighted with a highlighter is displayed on the display, highlighting the headword of the displayed expository content with a highlighter according to a user operation; and when the headword of the expository content is highlighted with a highlighter, highlighting a character string in an illustration from which a link with the headword originates with a highlighter. | 2. The electronic device according to claim 1 , wherein: the memory further stores dictionary data in which each headword and expository content of the headword is associated, and the processor is configured to further perform processing of: reading out from dictionary data stored in the memory an expository content having a headword to which the specified character string in the illustration is linked as the headword, and displaying the read-out expository content; when an expository content having, as a headword, a character string other than a character string highlighted with a highlighter is displayed on the display, highlighting the headword of the displayed expository content with a highlighter according to a user operation; and when the headword of the expository content is highlighted with a highlighter, highlighting a character string in an illustration from which a link with the headword originates with a highlighter. 3. The electronic device according to claim 2 , wherein the processor is configured to further perform processing of: highlighting an arbitrary character string in the displayed illustration with a highlighter of an arbitrary color according to a user operation, and when an expository content having a character string as a headword highlighted with a highlighter is displayed on the display, detecting the same character string as a character string highlighted with a highlighter in the displayed expository content and highlighting the same character string with a highlighter of the same color. | 0.530564 |
9,009,030 | 11 | 15 | 11. A computing device including one or more processors configured to execute instructions that cause the computing device to perform operations comprising: receiving a text string from a user via an application executing at the computing device; selecting at least a portion of the text string to obtain an input text; determining which of a plurality of text assistance services are appropriate to provide assistance to the user with respect to the input text to obtain one or more selected text assistance services, wherein each text assistance service is configured to provide the user with a different type of assistance with respect to the input text, wherein each text assistance service has different rules or standards defining its selection based on detected characteristics of various languages, and wherein obtaining the one or more selected text assistance services is automatically performed after both (i) a predetermined period of inactivity by the user and (ii) a length of the text string is greater than a predetermined length; obtaining a text content from each of the one or more selected text assistance services, each text content representing information to assist the user with respect to the input text; and outputting the text content from each of the one or more selected text assistance services. | 11. A computing device including one or more processors configured to execute instructions that cause the computing device to perform operations comprising: receiving a text string from a user via an application executing at the computing device; selecting at least a portion of the text string to obtain an input text; determining which of a plurality of text assistance services are appropriate to provide assistance to the user with respect to the input text to obtain one or more selected text assistance services, wherein each text assistance service is configured to provide the user with a different type of assistance with respect to the input text, wherein each text assistance service has different rules or standards defining its selection based on detected characteristics of various languages, and wherein obtaining the one or more selected text assistance services is automatically performed after both (i) a predetermined period of inactivity by the user and (ii) a length of the text string is greater than a predetermined length; obtaining a text content from each of the one or more selected text assistance services, each text content representing information to assist the user with respect to the input text; and outputting the text content from each of the one or more selected text assistance services. 15. The computing device of claim 11 , wherein the text content from each of the one or more text assistance services is output in a single window at the computing device. | 0.90273 |
7,533,069 | 11 | 18 | 11. A data processing system for mining data from a variety of sources for storing in a structured target data model, the system comprising: a processor; an outer parser running in said processor, wherein said outer parser parses a source format of a first source data received by said processor; an inner level parser embedded in said outer parser for parsing said first data source based on said parsed source format; an ontology description language in which the structured target data model is specified and which is also utilized by said inner level parser, wherein said inner level parser executes one or more statements in an order dictated by a content of said first source data, wherein said one or more statements are expressed in said ontology description language and access one or more data types and one or more data fields that are directly manipulated and assigned within said inner level parser using said ontology description language without explicit declarations therein; and a memory for storing a first collection of records created by said outer parser and said inner level parser, wherein said first collection of records conform to the structured target data model, and each of said records in said first collection of records are referenced and cross-referenced to each other, wherein said first collection of records are retrieved from the memory for further processing by the data processing system. | 11. A data processing system for mining data from a variety of sources for storing in a structured target data model, the system comprising: a processor; an outer parser running in said processor, wherein said outer parser parses a source format of a first source data received by said processor; an inner level parser embedded in said outer parser for parsing said first data source based on said parsed source format; an ontology description language in which the structured target data model is specified and which is also utilized by said inner level parser, wherein said inner level parser executes one or more statements in an order dictated by a content of said first source data, wherein said one or more statements are expressed in said ontology description language and access one or more data types and one or more data fields that are directly manipulated and assigned within said inner level parser using said ontology description language without explicit declarations therein; and a memory for storing a first collection of records created by said outer parser and said inner level parser, wherein said first collection of records conform to the structured target data model, and each of said records in said first collection of records are referenced and cross-referenced to each other, wherein said first collection of records are retrieved from the memory for further processing by the data processing system. 18. The system according to claim 11 further comprising: one or more post processing functions, wherein said one or more post processing functions are executed on said first collection of records prior to storing in said memory. | 0.743243 |
8,370,391 | 15 | 20 | 15. A computer-readable storage medium tangibly embodying computer-executable instructions configured to, in response to execution by at least one computing device, cause operations comprising: receiving an indication of an XML file or an XML stream to update in an input tree, and an update combinator, the update combinator including a function to apply to nodes matching the update combinator; optimizing a query to search for nodes matching the update combinator at least partly by skipping a search of a subtree associated with the input tree based on an XML schema associated with the input tree; searching for at least one node in the input tree matching the update combinator using the optimized search; cloning one or more portions of the input tree that include a matching node corresponding to the update combinator; streaming portions of the input tree respectively corresponding to a matched node, wherein only a portion of the input tree is streamed into memory at any given time; updating a cloned portion of the at least one node in the input tree by applying the function indicated by the update combinator; determining an amount of memory to be used based on a size of the input tree and an expected number of updates; and outputting an output tree comprising updated nodes. | 15. A computer-readable storage medium tangibly embodying computer-executable instructions configured to, in response to execution by at least one computing device, cause operations comprising: receiving an indication of an XML file or an XML stream to update in an input tree, and an update combinator, the update combinator including a function to apply to nodes matching the update combinator; optimizing a query to search for nodes matching the update combinator at least partly by skipping a search of a subtree associated with the input tree based on an XML schema associated with the input tree; searching for at least one node in the input tree matching the update combinator using the optimized search; cloning one or more portions of the input tree that include a matching node corresponding to the update combinator; streaming portions of the input tree respectively corresponding to a matched node, wherein only a portion of the input tree is streamed into memory at any given time; updating a cloned portion of the at least one node in the input tree by applying the function indicated by the update combinator; determining an amount of memory to be used based on a size of the input tree and an expected number of updates; and outputting an output tree comprising updated nodes. 20. The computer-readable storage medium of claim 15 , the operations further comprising storing the input tree in a memory, and utilizing artificial intelligence to determine whether the input tree should remain in the memory based on determining whether additional updating will be performed on the tree. | 0.5 |
9,558,400 | 1 | 6 | 1. A computer-implemented method comprising: identifying, using one or more computing devices, a field in a set of forms and strokes included in the field; determining, using the one or more computing devices, distance metrics between the strokes; creating, using the one or more computing devices, a hierarchical cluster for the strokes included in the field based on the distance metrics; receiving a search query from a first user, the search query including a first stroke extracted from the field of a form filled out by a second user; retrieving, using the one or more computing devices, a cluster from the hierarchical cluster and example strokes of the cluster similar to the search query; providing the example strokes to the first user; receiving, from the first user, a selection of a first example stroke from the provided example strokes; and responsive to receiving, from the first user, the selection of the first example stroke, retrieving a table listing a number of forms in the set of forms that were filled out by the second user and have strokes similar to the first example stroke. | 1. A computer-implemented method comprising: identifying, using one or more computing devices, a field in a set of forms and strokes included in the field; determining, using the one or more computing devices, distance metrics between the strokes; creating, using the one or more computing devices, a hierarchical cluster for the strokes included in the field based on the distance metrics; receiving a search query from a first user, the search query including a first stroke extracted from the field of a form filled out by a second user; retrieving, using the one or more computing devices, a cluster from the hierarchical cluster and example strokes of the cluster similar to the search query; providing the example strokes to the first user; receiving, from the first user, a selection of a first example stroke from the provided example strokes; and responsive to receiving, from the first user, the selection of the first example stroke, retrieving a table listing a number of forms in the set of forms that were filled out by the second user and have strokes similar to the first example stroke. 6. The method of claim 1 , wherein the hierarchical cluster includes at least one cluster level. | 0.864023 |
7,992,085 | 14 | 15 | 14. The method of claim 13 , wherein if an available display space in the user interface will not accommodate a display of all sections of information received for display in the user interface, displaying information from a number of sections that will fit in the user interface; collapsing any sections of information the display of which will not fit in the user interface; and providing a selectable control in the user interface, which when selected, causes a display of an associated collapsed section of information. | 14. The method of claim 13 , wherein if an available display space in the user interface will not accommodate a display of all sections of information received for display in the user interface, displaying information from a number of sections that will fit in the user interface; collapsing any sections of information the display of which will not fit in the user interface; and providing a selectable control in the user interface, which when selected, causes a display of an associated collapsed section of information. 15. The method of claim 14 , further comprising prioritizing the sections of information according to a preferred display order where a most preferred section is displayed first and a least preferred section is displayed last. | 0.5 |
8,219,543 | 1 | 10 | 1. A computer implemented method comprising: receiving a selection of an original content item; deriving a supplemental content item from the original content item; designating a unique identifier to the supplemental content item; inserting a reference including the unique identifier into a primary document; deriving an annotated supplemental content item from the supplemental content item; inserting another reference including a unique identifier of the annotated supplemental content item into the primary document, wherein the annotated supplemental content item includes at least one annotation and a subset of content from the supplemental content item; designating the another unique identifier for the annotated supplemental content item; and assembling the primary document, the supplemental content item and the annotated supplemental content item into a structured electronic document. | 1. A computer implemented method comprising: receiving a selection of an original content item; deriving a supplemental content item from the original content item; designating a unique identifier to the supplemental content item; inserting a reference including the unique identifier into a primary document; deriving an annotated supplemental content item from the supplemental content item; inserting another reference including a unique identifier of the annotated supplemental content item into the primary document, wherein the annotated supplemental content item includes at least one annotation and a subset of content from the supplemental content item; designating the another unique identifier for the annotated supplemental content item; and assembling the primary document, the supplemental content item and the annotated supplemental content item into a structured electronic document. 10. The method as recited in claim 1 , further comprising storing the structured electronic document on a portable memory medium. | 0.89196 |
9,471,943 | 1 | 8 | 1. A method comprising: providing a sponsored story unit to one or more client devices for initial presentation to one or more users of the social networking system connected to an acting user of the social networking system, the sponsored story unit including descriptions of a first action and a second action associated with the acting user; responsive to the initial presentation of the sponsored story unit to the one or more users of the social networking system, providing instructions to a client device associated with the acting user that cause the client device to present a notification to the acting user, the notification including information describing the presented sponsored story unit including the descriptions of the first and the second action associated with the acting user and including one or more settings for preventing inclusion of the first action associated with the acting user in one or more sponsored story units, wherein the one or more settings at least identify an action prevented from inclusion in the one or more sponsored story units based on the identity of a promoting user that provided the one or more sponsored story units; receiving one or more selected settings included in the notification from the client device associated with the acting user, the selected settings identifying the first action to be prevented from inclusion in the one or more sponsored story units; storing the one or more selected settings in a user profile associated with the acting user and maintained by the social networking system; and modifying the sponsored story unit for presentation based on the one or more selected settings, the modified sponsored story unit not including a description of the first action. | 1. A method comprising: providing a sponsored story unit to one or more client devices for initial presentation to one or more users of the social networking system connected to an acting user of the social networking system, the sponsored story unit including descriptions of a first action and a second action associated with the acting user; responsive to the initial presentation of the sponsored story unit to the one or more users of the social networking system, providing instructions to a client device associated with the acting user that cause the client device to present a notification to the acting user, the notification including information describing the presented sponsored story unit including the descriptions of the first and the second action associated with the acting user and including one or more settings for preventing inclusion of the first action associated with the acting user in one or more sponsored story units, wherein the one or more settings at least identify an action prevented from inclusion in the one or more sponsored story units based on the identity of a promoting user that provided the one or more sponsored story units; receiving one or more selected settings included in the notification from the client device associated with the acting user, the selected settings identifying the first action to be prevented from inclusion in the one or more sponsored story units; storing the one or more selected settings in a user profile associated with the acting user and maintained by the social networking system; and modifying the sponsored story unit for presentation based on the one or more selected settings, the modified sponsored story unit not including a description of the first action. 8. The method of claim 1 , wherein a setting, of the one or more settings for preventing inclusion of the first action associated with the acting user in one or more sponsored story units, identifies a type of user and prevents generation of one or more sponsored story units based on sponsored story requests received from the identified type of user. | 0.755216 |
9,110,995 | 7 | 8 | 7. A system, comprising: one or more computers programmed to perform operations comprising: while receiving a user input entered in a search engine query input field by a user, and before the user has submitted the user input as a search request: accessing, in a data processing system, historical data, the historical data associating each of a plurality of input-answer box pairs with a respective presentation value, each input-answer box pair associating a text input with an answer box, where a text input is associated with an answer box if the answer box was presented by a search engine in response to a query beginning with an actual input matching the text input, and where the presentation value for each input-answer box pair is derived from a number of times the answer box in the pair was presented by the search engine in response to queries beginning with actual inputs matching the text input in the pair divided by a total number of times queries beginning with actual inputs matching the text input in the pair were received by the search engine; identifying, in the data processing system and from the historical data, one or more candidate input-answer box pairs, where the text input for each candidate pair matches the user input; selecting, by the data processing system, a pair from the one or more candidate pairs, where the pair is selected according to the presentation value for each candidate pair; obtaining content for the answer box in the selected pair; and presenting the answer box to the user. | 7. A system, comprising: one or more computers programmed to perform operations comprising: while receiving a user input entered in a search engine query input field by a user, and before the user has submitted the user input as a search request: accessing, in a data processing system, historical data, the historical data associating each of a plurality of input-answer box pairs with a respective presentation value, each input-answer box pair associating a text input with an answer box, where a text input is associated with an answer box if the answer box was presented by a search engine in response to a query beginning with an actual input matching the text input, and where the presentation value for each input-answer box pair is derived from a number of times the answer box in the pair was presented by the search engine in response to queries beginning with actual inputs matching the text input in the pair divided by a total number of times queries beginning with actual inputs matching the text input in the pair were received by the search engine; identifying, in the data processing system and from the historical data, one or more candidate input-answer box pairs, where the text input for each candidate pair matches the user input; selecting, by the data processing system, a pair from the one or more candidate pairs, where the pair is selected according to the presentation value for each candidate pair; obtaining content for the answer box in the selected pair; and presenting the answer box to the user. 8. The system of claim 7 , further comprising receiving, for each candidate input-answer box pair, an indication of whether the candidate answer box in the pair was useful to users who submitted queries beginning with actual inputs matching the text input in the pair, wherein: selecting the candidate answer box is further based on the received indication. | 0.5 |
9,671,936 | 1 | 6 | 1. A non-transitory, computer-readable media having stored thereon instructions which, when executed by a smart device, cause the smart device to perform steps comprising: causing a content access interface to be displayed in a touch sensitive display of the smart device, the content access interface having a plurality of media interface elements wherein each of the plurality of media interface elements is representative of a media content that is accessible via use of a first controllable appliance and each of the plurality of media interface elements is selectable by a user to cause the smart device to transmit a command to cause the first controllable appliance to access the media content corresponding to the selected one of the plurality of media interface elements; and in response to the user interacting with an interactivity element displayed in the touch sensitive display, causing a first plurality of control interface elements to be temporarily added to the content access interface together with one or more of the plurality of media interface elements, wherein each of the first plurality of control interface elements is representative of volume operational function of a second controllable appliance and each of the first plurality of control interface elements is selectable by the user to cause the smart device to transmit a command to cause the second controllable appliance to perform a volume control function corresponding to the selected one of the first plurality of control interface elements. | 1. A non-transitory, computer-readable media having stored thereon instructions which, when executed by a smart device, cause the smart device to perform steps comprising: causing a content access interface to be displayed in a touch sensitive display of the smart device, the content access interface having a plurality of media interface elements wherein each of the plurality of media interface elements is representative of a media content that is accessible via use of a first controllable appliance and each of the plurality of media interface elements is selectable by a user to cause the smart device to transmit a command to cause the first controllable appliance to access the media content corresponding to the selected one of the plurality of media interface elements; and in response to the user interacting with an interactivity element displayed in the touch sensitive display, causing a first plurality of control interface elements to be temporarily added to the content access interface together with one or more of the plurality of media interface elements, wherein each of the first plurality of control interface elements is representative of volume operational function of a second controllable appliance and each of the first plurality of control interface elements is selectable by the user to cause the smart device to transmit a command to cause the second controllable appliance to perform a volume control function corresponding to the selected one of the first plurality of control interface elements. 6. The non-transitory, computer readable media as recited in claim 1 , wherein the control interactivity element comprises an element that is intended to be dragged across the touch sensitive display. | 0.890591 |
8,477,992 | 13 | 17 | 13. A method of controlling mail processing equipment of a mail processing system used to process one document of a plurality of documents, the one having minutiae extracted from an electronic file associated with the document, the method comprising the following steps: processing a printed representation of the document on mail processing equipment, the processing including at least capturing of an image of the printed representation of the document; extracting image minutiae from the captured image; comparing the image minutiae with the minutiae for positively identifying the document; and utilizing control data associated with the document to control operation of the mail processing equipment in response to positively identifying the document, wherein: the minutiae and the image minutiae are a plurality of document characteristics, the plurality of document characteristics are selected from a combination of two or more of font, pitch, key word, numerical sequence, symbol, phrase and coordinates of an area of interest on the document, the document characteristics individually are insufficient to uniquely identify the document from a second document, but the combination of the two or more document characteristics are sufficient to uniquely identify the one document from the second document, and the mail processing system is either an inserter system or sorter system. | 13. A method of controlling mail processing equipment of a mail processing system used to process one document of a plurality of documents, the one having minutiae extracted from an electronic file associated with the document, the method comprising the following steps: processing a printed representation of the document on mail processing equipment, the processing including at least capturing of an image of the printed representation of the document; extracting image minutiae from the captured image; comparing the image minutiae with the minutiae for positively identifying the document; and utilizing control data associated with the document to control operation of the mail processing equipment in response to positively identifying the document, wherein: the minutiae and the image minutiae are a plurality of document characteristics, the plurality of document characteristics are selected from a combination of two or more of font, pitch, key word, numerical sequence, symbol, phrase and coordinates of an area of interest on the document, the document characteristics individually are insufficient to uniquely identify the document from a second document, but the combination of the two or more document characteristics are sufficient to uniquely identify the one document from the second document, and the mail processing system is either an inserter system or sorter system. 17. A system configured to execute the steps of claim 13 . | 0.887597 |
7,885,120 | 1 | 2 | 1. A method for programming a memory cell of a memory from a first state to a second state, comprising the steps of: programming the memory cell and verifying the memory cell according to a first voltage level; and programming the memory cell and verifying the memory cell according to a first predetermined voltage level associated with the second state, wherein the first voltage level is less than the first predetermined voltage level and is associated with the second state. | 1. A method for programming a memory cell of a memory from a first state to a second state, comprising the steps of: programming the memory cell and verifying the memory cell according to a first voltage level; and programming the memory cell and verifying the memory cell according to a first predetermined voltage level associated with the second state, wherein the first voltage level is less than the first predetermined voltage level and is associated with the second state. 2. The method of claim 1 , further comprising: programming another memory cell of said memory and verifying said another memory cell according to a second voltage level; and programming said another memory cell and verifying said another memory cell according to a second predetermined voltage level, wherein the second voltage level is less than the second predetermined voltage level. | 0.5 |
8,671,353 | 19 | 21 | 19. A computer-implemented method comprising: receiving a selection of a primary keyword by a customer; causing a display of a hub associated with the primary keyword on a computer display; determining a quantity related to a countable event associated with the primary keyword and each of the secondary keywords, wherein the countable event comprises an order of an item corresponding to the secondary keyword following a selection of the primary keyword by at least one customer; determining a degree of association between the primary keyword and a plurality of secondary keywords based at least in part on the quantity determined for each of the secondary keywords; and causing a display of a plurality of nodes on the computer display, wherein each of the nodes corresponds to one of the plurality of secondary keywords, wherein a characteristic of each of the plurality of nodes is determined based at least in part on the degree of association between the primary keyword and the secondary keyword corresponding to each of the plurality of nodes. | 19. A computer-implemented method comprising: receiving a selection of a primary keyword by a customer; causing a display of a hub associated with the primary keyword on a computer display; determining a quantity related to a countable event associated with the primary keyword and each of the secondary keywords, wherein the countable event comprises an order of an item corresponding to the secondary keyword following a selection of the primary keyword by at least one customer; determining a degree of association between the primary keyword and a plurality of secondary keywords based at least in part on the quantity determined for each of the secondary keywords; and causing a display of a plurality of nodes on the computer display, wherein each of the nodes corresponds to one of the plurality of secondary keywords, wherein a characteristic of each of the plurality of nodes is determined based at least in part on the degree of association between the primary keyword and the secondary keyword corresponding to each of the plurality of nodes. 21. The method according to claim 19 , wherein the countable event further comprises an order of an item corresponding to the secondary keyword following an order of an item corresponding to the primary keyword by at least one customer. | 0.843709 |
9,990,919 | 8 | 13 | 8. The method according to claim 1 , further including training the discriminative statistical model from the deterministic formatting grammar module by segmenting formatted documents into formatted sentences. | 8. The method according to claim 1 , further including training the discriminative statistical model from the deterministic formatting grammar module by segmenting formatted documents into formatted sentences. 13. The method according to claim 8 , further including customizing the disambiguation for a particular user. | 0.526087 |
8,473,507 | 17 | 18 | 17. The computer-implemented method of claim 15 , further comprising: combining the updated first tokenized search suggestion and the second tokenized search suggestion with a Boolean OR operator if a scope of the updated first tokenized search suggestion is the same as a scope of the second tokenized search suggestion. | 17. The computer-implemented method of claim 15 , further comprising: combining the updated first tokenized search suggestion and the second tokenized search suggestion with a Boolean OR operator if a scope of the updated first tokenized search suggestion is the same as a scope of the second tokenized search suggestion. 18. The computer-implemented method of claim 17 , further comprising: combining the updated first tokenized search suggestion and the second tokenized search suggestion with a Boolean AND operator if the scope of the updated first tokenized search suggestion is different than the scope of the second tokenized search suggestion. | 0.5 |
8,832,047 | 6 | 7 | 6. The method of claim 1 , wherein making the determination comprises searching a data repository for document metadata matching that of the first electronic document. | 6. The method of claim 1 , wherein making the determination comprises searching a data repository for document metadata matching that of the first electronic document. 7. The method of claim 6 , wherein the matching document metadata comprises a filename and a securing user. | 0.5 |
9,330,311 | 15 | 17 | 15. A system comprising: a memory configured to store an image file representing an image comprising text; and a processing device coupled to the memory, the processing device configured to: determine a plurality of portions of the image respectively corresponding to a plurality of letters of the text; determine, for a first letter of the plurality of letters, a set of letter properties comprising at least one dependent letter property, wherein each of the at least one dependent letter property is dependent on another letter of the plurality of letters; and classify the first letter into one of plurality of letterform classes based on the set of letter properties. | 15. A system comprising: a memory configured to store an image file representing an image comprising text; and a processing device coupled to the memory, the processing device configured to: determine a plurality of portions of the image respectively corresponding to a plurality of letters of the text; determine, for a first letter of the plurality of letters, a set of letter properties comprising at least one dependent letter property, wherein each of the at least one dependent letter property is dependent on another letter of the plurality of letters; and classify the first letter into one of plurality of letterform classes based on the set of letter properties. 17. The system of claim 15 , wherein the at least one dependent letter property comprises at least one of an inter-letter property based, at least in part, on an adjacent letter or a letter-combination property based on a letter-combination of which the letter is a part. | 0.552805 |
9,202,127 | 1 | 4 | 1. A method of processing an image, the method comprising: generating a plurality of grayscale images from a multi-channel image, the plurality of grayscale images comprising a first grayscale image and a second grayscale image, the first grayscale image distinct from the second grayscale image; identifying at least one text region in the plurality of grayscale images; determining text region information from the at least one text region; and generating text information of the multi-channel image based on the text region information. | 1. A method of processing an image, the method comprising: generating a plurality of grayscale images from a multi-channel image, the plurality of grayscale images comprising a first grayscale image and a second grayscale image, the first grayscale image distinct from the second grayscale image; identifying at least one text region in the plurality of grayscale images; determining text region information from the at least one text region; and generating text information of the multi-channel image based on the text region information. 4. The method of claim 1 , wherein identifying the at least one text region includes: identifying at least one candidate text region in the plurality of grayscale images; and identifying the at least one text region in the identified at least one candidate text region. | 0.752302 |
9,462,351 | 11 | 12 | 11. A system for providing media content in multiple languages, the system comprising control circuitry configured to: receive, at a user device, a user selection of a preferred language; receive, at the user device, a user request to view media content; determine, at a server, whether the media content is available in the preferred language; and without requiring further user interaction, transmit, from the server to the user device, the media content in an alternate language based on determining the media content is not available in the preferred language. | 11. A system for providing media content in multiple languages, the system comprising control circuitry configured to: receive, at a user device, a user selection of a preferred language; receive, at the user device, a user request to view media content; determine, at a server, whether the media content is available in the preferred language; and without requiring further user interaction, transmit, from the server to the user device, the media content in an alternate language based on determining the media content is not available in the preferred language. 12. The system of claim 11 , wherein the media content is media guidance data related to a media program. | 0.897661 |
8,799,787 | 9 | 12 | 9. A method for performing a search for a resource in a virtual universe using selectable and modifiable user context objects, comprising: presenting a plurality of user context objects determined for an avatar that is online in the virtual universe, wherein the presenting of the plurality of user context objects to the avatar includes deriving the plurality of user context objects from all of the following: inventory items belonging to the avatar, teleportation history of the avatar, motion history of the avatar and social tagging behavior exhibited by a user of the avatar in the real world; receiving a user context object selection from the avatar, wherein the user context object selection contains one of the plurality of user context objects determined for the avatar and any desired modifications made to the selected user context object; receiving a query from the avatar; and performing a resource search for the query in accordance with the selected user context object; and providing results from the search to a robot avatar that is configured to allow the avatar to interact anonymously and semi-autonomously within the virtual universe in response to a modification made by the avatar to the selected user context object. | 9. A method for performing a search for a resource in a virtual universe using selectable and modifiable user context objects, comprising: presenting a plurality of user context objects determined for an avatar that is online in the virtual universe, wherein the presenting of the plurality of user context objects to the avatar includes deriving the plurality of user context objects from all of the following: inventory items belonging to the avatar, teleportation history of the avatar, motion history of the avatar and social tagging behavior exhibited by a user of the avatar in the real world; receiving a user context object selection from the avatar, wherein the user context object selection contains one of the plurality of user context objects determined for the avatar and any desired modifications made to the selected user context object; receiving a query from the avatar; and performing a resource search for the query in accordance with the selected user context object; and providing results from the search to a robot avatar that is configured to allow the avatar to interact anonymously and semi-autonomously within the virtual universe in response to a modification made by the avatar to the selected user context object. 12. The method according to claim 9 , further comprising providing advertisements along with search results in response to a modification made by the avatar to the selected user context object, wherein the advertisements relate to the selected user context object. | 0.546392 |
8,281,149 | 2 | 4 | 2. The method of claim 1 , wherein receiving the first representation of the access token from the IdP further comprises: generating an original token; modifying the original token to obtain a modified token; and providing the modified token to the IdP to obtain an access token for accessing the RP. | 2. The method of claim 1 , wherein receiving the first representation of the access token from the IdP further comprises: generating an original token; modifying the original token to obtain a modified token; and providing the modified token to the IdP to obtain an access token for accessing the RP. 4. The method of claim 2 , wherein the original token is generated cryptographically from a pre-image, the pre-image supports both fixed-format and free-format information structures. | 0.720183 |
9,842,162 | 14 | 16 | 14. A method, comprising: identifying, in at least one computing device, a plurality of categories in a taxonomy of a collection of items by matching at least one term in a user-provided unstructured search query with metadata associated with respective categories in the taxonomy; identifying, in the at least one computing device, a refinement in the user-provided unstructured search query that is associated with at least one of the plurality of categories by translating at least one keyword from the user-provided unstructured search query into at least one criterion for selecting items from the at least one of the plurality of categories, wherein the translation is configured to translate a plurality of synonyms into the at least one criterion; generating, in the at least one computing device, a confidence score for individual categories of the plurality of categories based at least in part on a matching of at least a portion of the user-provided unstructured search query with data associated with the individual categories of the plurality of categories, individual confidence scores being generated using a weighted combination of a plurality of factors, a first factor of the plurality of factors comprising a quality of text match of items in a respective category with the user-provided unstructured search query and a second factor of the plurality of factors comprising a number of refinements identified in the user-provided unstructured search query that are associated with the respective category, and the data associated with the respective category comprising a description of the respective category, and the quality of text match being based at least in part on a frequency that the at least a portion of the user-provided unstructured search query occurs within the description; selecting, in the at least one computing device, a first pool of items from the one of the plurality of categories when the respective confidence score meets a threshold; selecting, in the at least one computing device, a second pool of items from the collection of items when no confidence score meets the threshold; and generating, in the at least one computing device, a network page listing at least a portion of the first pool of items or the second pool of items that has been selected, the network page including a disambiguation tool when no confidence score meets the threshold, and the disambiguation tool providing a user interface for selecting one category from the plurality of categories. | 14. A method, comprising: identifying, in at least one computing device, a plurality of categories in a taxonomy of a collection of items by matching at least one term in a user-provided unstructured search query with metadata associated with respective categories in the taxonomy; identifying, in the at least one computing device, a refinement in the user-provided unstructured search query that is associated with at least one of the plurality of categories by translating at least one keyword from the user-provided unstructured search query into at least one criterion for selecting items from the at least one of the plurality of categories, wherein the translation is configured to translate a plurality of synonyms into the at least one criterion; generating, in the at least one computing device, a confidence score for individual categories of the plurality of categories based at least in part on a matching of at least a portion of the user-provided unstructured search query with data associated with the individual categories of the plurality of categories, individual confidence scores being generated using a weighted combination of a plurality of factors, a first factor of the plurality of factors comprising a quality of text match of items in a respective category with the user-provided unstructured search query and a second factor of the plurality of factors comprising a number of refinements identified in the user-provided unstructured search query that are associated with the respective category, and the data associated with the respective category comprising a description of the respective category, and the quality of text match being based at least in part on a frequency that the at least a portion of the user-provided unstructured search query occurs within the description; selecting, in the at least one computing device, a first pool of items from the one of the plurality of categories when the respective confidence score meets a threshold; selecting, in the at least one computing device, a second pool of items from the collection of items when no confidence score meets the threshold; and generating, in the at least one computing device, a network page listing at least a portion of the first pool of items or the second pool of items that has been selected, the network page including a disambiguation tool when no confidence score meets the threshold, and the disambiguation tool providing a user interface for selecting one category from the plurality of categories. 16. The method of claim 14 , wherein selecting the first pool of items from the one of the plurality of categories further comprises selecting the first pool of items from the individual categories when the respective confidence score meets the threshold and the respective confidence score is a maximum one of the confidence scores. | 0.63961 |
8,606,843 | 1 | 4 | 1. A computer-implemented method for processing convoy scenarios in a message driven process, said method comprising: determining if a subscription is associated with a convoy set by determining if a message associated with said subscription contains a convoy set identification property that is not set to a NULL value, which indicates that the message is a member of a group of related messages; searching existing convoy set instances for a convoy instance having said convoy set identification property; if searching the existing convoy set instances results in finding a convoy set instance having said convoy set identification property, determining if a convoy set instance identifier of said convoy set instance is the same as a subscription service instance identifier of said convoy set instance; if found same, setting said subscription service instance identifier to said convoy set instance identifier in a copy of a subscription property vector associated with the message; filling said subscription, wherein if a convoy set instance is not found having said convoy set identification property: determining that said subscription is allowed to initialize a new convoy set; and implementing a write lock on a first combination of said convoy set instance identifier and at least one property value associated with said message, said write lock permitting concurrent creation of multiple new convoy set instances having combinations that are different than the first combination. | 1. A computer-implemented method for processing convoy scenarios in a message driven process, said method comprising: determining if a subscription is associated with a convoy set by determining if a message associated with said subscription contains a convoy set identification property that is not set to a NULL value, which indicates that the message is a member of a group of related messages; searching existing convoy set instances for a convoy instance having said convoy set identification property; if searching the existing convoy set instances results in finding a convoy set instance having said convoy set identification property, determining if a convoy set instance identifier of said convoy set instance is the same as a subscription service instance identifier of said convoy set instance; if found same, setting said subscription service instance identifier to said convoy set instance identifier in a copy of a subscription property vector associated with the message; filling said subscription, wherein if a convoy set instance is not found having said convoy set identification property: determining that said subscription is allowed to initialize a new convoy set; and implementing a write lock on a first combination of said convoy set instance identifier and at least one property value associated with said message, said write lock permitting concurrent creation of multiple new convoy set instances having combinations that are different than the first combination. 4. The method of claim 1 , wherein concurrent creation of multiple new convoy set instances comprises executing at least two concurrent write accesses of a list of convoy set instances. | 0.77104 |
8,611,677 | 8 | 9 | 8. The method of claim 1 , further comprising calculating a subject distance for each image in an event within the hierarchical event representation, wherein the classifying each event is based upon the subject distances. | 8. The method of claim 1 , further comprising calculating a subject distance for each image in an event within the hierarchical event representation, wherein the classifying each event is based upon the subject distances. 9. The method of claim 8 , further comprising determining a location for each image in an event within the hierarchical event representation, wherein the classifying each event is based upon the locations. | 0.5 |
8,799,280 | 16 | 17 | 16. A computer-implemented process for providing personalized navigation for one or more individuals' use of a search engine, comprising: using a computer to perform the following process actions: identifying if a query submitted to the search engine is a personal navigational query (PNQ) using system-dependent measures of query similarity to identify said query, wherein a PNQ comprises a query designed to navigate to a particular site that was previously viewed by the individual or individuals or an information object (IO) that was previously viewed by the individual or individuals; and whenever said query is identified to be a PNQ, identifying the particular site or IO associated with said query, said identifying comprises at least one of, using syntactic or semantic measures of site or IO similarity with sites or IOs used during associated queries from said history to identify said particular site or IO, or using system-dependent measures of site or IO similarity with sites or lOs used during associated queries from said history to identify said particular site or IO; and personalizing results of the search based on knowledge of the identified site or IO. | 16. A computer-implemented process for providing personalized navigation for one or more individuals' use of a search engine, comprising: using a computer to perform the following process actions: identifying if a query submitted to the search engine is a personal navigational query (PNQ) using system-dependent measures of query similarity to identify said query, wherein a PNQ comprises a query designed to navigate to a particular site that was previously viewed by the individual or individuals or an information object (IO) that was previously viewed by the individual or individuals; and whenever said query is identified to be a PNQ, identifying the particular site or IO associated with said query, said identifying comprises at least one of, using syntactic or semantic measures of site or IO similarity with sites or IOs used during associated queries from said history to identify said particular site or IO, or using system-dependent measures of site or IO similarity with sites or lOs used during associated queries from said history to identify said particular site or IO; and personalizing results of the search based on knowledge of the identified site or IO. 17. The process of claim 16 , wherein, the process action of using system-dependent measures of query similarity to identify said query comprises at least one of the following actions: identifying a particular query whose search results rank higher than the same search results that were visited via the first query in a PNQ pair submitted to the search engine; or identifying a query where the individuals explicitly tell the search engine to map said query to a particular site or IO; or identifying a query having keywords which refer to static text content within the particular site or IO; the process action of using syntactic or semantic measures of site or IO similarity with sites or IOs used during associated queries from said history to identify said particular site or IO comprises an action of identifying sites or IOs having substantial overlap in their associated information links with one or more previous sites or IOs related to associated previous queries; the process action of identifying the particular site or IO associated with said query, further comprises a process action of using behavioral measures of site or IO similarity with sites or IOs used during associated queries from said history to identify said particular site or IO, said using behavioral measures comprises at least one of the following actions: identifying sites or IOs that the individuals interacted with in previous sessions that included an associated previous query or queries; or identifying sites or IOs that are informed by the individuals' post-click browsing patterns in a personal navigational trail; and the process action of using system-dependent measures of site or IO similarity with sites or IOs used during associated queries from said history to identify said particular site or IO comprises at least one of the following actions: identifying sites or IOs that are consistently returned in the same position in the search results for an associated previous query; or identifying sites or IOs that receive similar treatment by the search engine during crawling, indexing, or pre-processing operations; or identifying sites or IOs that receive similar treatment by the search engine at search time. | 0.5 |
9,351,109 | 15 | 20 | 15. A system for comprising: a mobile device configured to obtain location data indicative of a location of said mobile device, obtain additional data from one or more sensors of mobile device, said additional data comprising context information, the context information comprising at least one of information regarding whether the mobile device is moving, information regarding a surrounding environment of the mobile device, and information related to an activity that a user of the mobile device is engaged in— a computing device configured to: receive said additional data from the mobile device; and, process said additional data to obtain said context information and to determine enhanced location information for said mobile device, based at least in part on processing said location data in association with said context information, wherein the enhanced location information represents an improved location accuracy relative to the location information, and wherein processing said location data in association with said context information comprises further distinguishing the location data using the context information. | 15. A system for comprising: a mobile device configured to obtain location data indicative of a location of said mobile device, obtain additional data from one or more sensors of mobile device, said additional data comprising context information, the context information comprising at least one of information regarding whether the mobile device is moving, information regarding a surrounding environment of the mobile device, and information related to an activity that a user of the mobile device is engaged in— a computing device configured to: receive said additional data from the mobile device; and, process said additional data to obtain said context information and to determine enhanced location information for said mobile device, based at least in part on processing said location data in association with said context information, wherein the enhanced location information represents an improved location accuracy relative to the location information, and wherein processing said location data in association with said context information comprises further distinguishing the location data using the context information. 20. The system of claim 15 wherein the mobile device comprises a plurality of sensors, and wherein one or more of the plurality of sensors is a visual sensor configured to obtain visual data. | 0.5 |
9,372,687 | 13 | 14 | 13. A non-transitory computer readable medium storing instructions to customize a software application, the instructions, when executed by a computer processor, comprising functionality for: displaying, using a user interface of the online software application, a message inviting user contribution to a customizable component of the online software application; receiving, from an initial seed user via a network connection and in response to displaying the message, a structural specification of the customizable component suggested by the initial seed user according to a requirement based on an attribute of the initial seed user; determining that a new user of the online software application matches the attribute of the initial seed user; configuring, based on the structural specification of the customizable component suggested by the initial seed user, a first instantiation of a plurality of instantiations of the online software application for the new user to perform a pre-determined task according to the requirement; configuring, based on the structural specification of the customizable component, the plurality of instantiations of the online software application for a plurality of users to perform the pre-determined task; extracting, from the customizable component in each of the plurality of instantiations, a plurality of structured contents used by the plurality of users to further configure the plurality of instantiations for performing the pre-determined task; generating a statistical measure of the plurality of users using the plurality of instantiations to perform the pre-determined task, wherein the statistical measure comprises: a number of registered users, among the plurality of users, who have paid for the online software application; a number of users, among the plurality of users, who have performed the pre-determined task based on a same structured content; a number of users, among the plurality of users, who have performed the pre-determined task based on a shared pattern of the plurality of structured contents; and a number of times that the plurality of users have performed the pre-determined task based on the shared pattern; generating, in response to the statistical measure exceeding a pre-determined threshold, a suggested structured content to represent a portion of the plurality of structured contents that is qualified based on the statistical measure, wherein configuring the instantiation of the online software application for the new user is further based on the suggested structured content; and performing, using the first instantiation of the online software application and via the network connection, the pre-determined task for the new user. | 13. A non-transitory computer readable medium storing instructions to customize a software application, the instructions, when executed by a computer processor, comprising functionality for: displaying, using a user interface of the online software application, a message inviting user contribution to a customizable component of the online software application; receiving, from an initial seed user via a network connection and in response to displaying the message, a structural specification of the customizable component suggested by the initial seed user according to a requirement based on an attribute of the initial seed user; determining that a new user of the online software application matches the attribute of the initial seed user; configuring, based on the structural specification of the customizable component suggested by the initial seed user, a first instantiation of a plurality of instantiations of the online software application for the new user to perform a pre-determined task according to the requirement; configuring, based on the structural specification of the customizable component, the plurality of instantiations of the online software application for a plurality of users to perform the pre-determined task; extracting, from the customizable component in each of the plurality of instantiations, a plurality of structured contents used by the plurality of users to further configure the plurality of instantiations for performing the pre-determined task; generating a statistical measure of the plurality of users using the plurality of instantiations to perform the pre-determined task, wherein the statistical measure comprises: a number of registered users, among the plurality of users, who have paid for the online software application; a number of users, among the plurality of users, who have performed the pre-determined task based on a same structured content; a number of users, among the plurality of users, who have performed the pre-determined task based on a shared pattern of the plurality of structured contents; and a number of times that the plurality of users have performed the pre-determined task based on the shared pattern; generating, in response to the statistical measure exceeding a pre-determined threshold, a suggested structured content to represent a portion of the plurality of structured contents that is qualified based on the statistical measure, wherein configuring the instantiation of the online software application for the new user is further based on the suggested structured content; and performing, using the first instantiation of the online software application and via the network connection, the pre-determined task for the new user. 14. The non-transitory computer readable medium of claim 13 , the instructions, when executed by the computer processor, further comprising functionality for: identifying a first geographical region based on the attribute of the initial seed user, wherein the pre-determined task for the new user is performed within the first geographical region, wherein determining that the new user matches the attribute of the initial seed user comprises determining that the new user is within the first geographical region, and wherein the pre-determined task for the plurality of users is performed within the first geographical region. | 0.5 |
9,509,655 | 17 | 18 | 17. A computer-implemented system for identifying questionable content in a social network website, the social network website having therein content that has been posted in the social network website by a first user, the questionable content being identified in response to the first user performing steps of becoming a friend in the social network website to a second user, the system comprising: a first receiving element configured to receive the content that has been posted in the social network website by the first user; a second receiving element configured to receive an identification of the second user; a third receiving element configured to receive private information associated with the second user from a source of private information about the second user; a fourth receiving element configured to receive public information associated with the second user from a source of public information about the second user; an identifying element configured to identify, based upon the received content that has been posted in the social network website by the first user, the received private information and the received public information, at least a part of the content that has been posted in the social network website by the first user as the questionable content; and an indicating element configured to indicate, to the first user, the identified questionable content. | 17. A computer-implemented system for identifying questionable content in a social network website, the social network website having therein content that has been posted in the social network website by a first user, the questionable content being identified in response to the first user performing steps of becoming a friend in the social network website to a second user, the system comprising: a first receiving element configured to receive the content that has been posted in the social network website by the first user; a second receiving element configured to receive an identification of the second user; a third receiving element configured to receive private information associated with the second user from a source of private information about the second user; a fourth receiving element configured to receive public information associated with the second user from a source of public information about the second user; an identifying element configured to identify, based upon the received content that has been posted in the social network website by the first user, the received private information and the received public information, at least a part of the content that has been posted in the social network website by the first user as the questionable content; and an indicating element configured to indicate, to the first user, the identified questionable content. 18. The system of claim 17 , wherein the steps of becoming a friend in the social network website to a second user comprise the first user making a friendship request via the social network website to the second user. | 0.515625 |
8,341,081 | 14 | 17 | 14. The method of claim 1 , applying the second set of rules resulting in assigning respective scores to respective groups of name segments, the computer determining whether the on-line bank account is utilized for business purposes based at least in part upon the respective scores for respective groups of name segments. | 14. The method of claim 1 , applying the second set of rules resulting in assigning respective scores to respective groups of name segments, the computer determining whether the on-line bank account is utilized for business purposes based at least in part upon the respective scores for respective groups of name segments. 17. The method of claim 14 , the second set of rules comprising rule that assigns a score to a group of name segments based on whether the group appears within a database comprising phrases of business names or portions of business names. | 0.5 |
6,069,630 | 14 | 20 | 14. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for developing a communication interface for a data processing system, comprising the steps of: displaying a plurality of graphics wherein each graphic represents a file in authoring said web page; displaying links between objects; and generating each of said links, wherein each link is operable for connecting to one of a top, middle, and bottom of each graphic, and wherein a connection to said top represents a logical link to a location at the beginning of a file, a connection to a middle represents a logical link to a location within a middle of said file, and a link to said bottom indicates that all contents of said file are used in said web page. | 14. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for developing a communication interface for a data processing system, comprising the steps of: displaying a plurality of graphics wherein each graphic represents a file in authoring said web page; displaying links between objects; and generating each of said links, wherein each link is operable for connecting to one of a top, middle, and bottom of each graphic, and wherein a connection to said top represents a logical link to a location at the beginning of a file, a connection to a middle represents a logical link to a location within a middle of said file, and a link to said bottom indicates that all contents of said file are used in said web page. 20. The program product of claim 14 wherein said method steps further comprise the step of outputting contents of a file selected in response to user input. | 0.628571 |
7,689,927 | 25 | 29 | 25. A display system for managing a view-size of an electronic document, comprising: a display device; and a processor for: (a) rendering a user interface window; (b) rendering at least a portion of the electronic document in the user interface window; (c) storing a viewable document section corresponding to the view-size of the electronic document, wherein the viewable document section includes boundary information cumulative of only portions of the electronic document that have previously been displayed in the user interface window; (d) providing a first system that enables a user to change a displayed portion of the electronic document in the user interface window when at least a portion of information indicated by the stored boundary information in the viewable document section does not appear in the user interface window and the viewable document section also changes to include information indicative of a registering activity of any digitizing user-input at an outer portion of the user interface window, wherein the first system is not provided when all information indicated by the stored boundary information in the viewable document section appears in the user interface window and changes to the viewable document section with an additional input to include information indicative of a registering activity of any digitizing user-input at an outer portion of the user interface window that associates a user-input extending beyond the outer portion of the user interface window, while suppressing any scrolling view handle display or similar scroll bar display; and (e) providing a second system that enables a user to change a size of the user interface window, and responsive to the user interface window being enlarged, the stored boundary information in the viewable document section is adjusted based on any portion of the electronic document displayed for a first time within the user interface window. | 25. A display system for managing a view-size of an electronic document, comprising: a display device; and a processor for: (a) rendering a user interface window; (b) rendering at least a portion of the electronic document in the user interface window; (c) storing a viewable document section corresponding to the view-size of the electronic document, wherein the viewable document section includes boundary information cumulative of only portions of the electronic document that have previously been displayed in the user interface window; (d) providing a first system that enables a user to change a displayed portion of the electronic document in the user interface window when at least a portion of information indicated by the stored boundary information in the viewable document section does not appear in the user interface window and the viewable document section also changes to include information indicative of a registering activity of any digitizing user-input at an outer portion of the user interface window, wherein the first system is not provided when all information indicated by the stored boundary information in the viewable document section appears in the user interface window and changes to the viewable document section with an additional input to include information indicative of a registering activity of any digitizing user-input at an outer portion of the user interface window that associates a user-input extending beyond the outer portion of the user interface window, while suppressing any scrolling view handle display or similar scroll bar display; and (e) providing a second system that enables a user to change a size of the user interface window, and responsive to the user interface window being enlarged, the stored boundary information in the viewable document section is adjusted based on any portion of the electronic document displayed for a first time within the user interface window. 29. A display system according to claim 25 , wherein the electronic document includes electronic ink data. | 0.890269 |
9,953,274 | 10 | 17 | 10. A method for dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users, method comprising: defining a user characteristic, the user characteristic being an estimation that a user is a human user, wherein: when the user corresponds to the user characteristic, the user is estimated to be a human user, and when the user does not correspond to the user characteristic, the user is estimated to be a robot user; identifying a model that enhances the user characteristic definer by detecting when users alter activity to cause the ticket management system to falsely characterize the users, the model including at least one machine-learning algorithm, identifying, using the at least one machine-learning algorithm, an initial set of attributes based on the user characteristic, each attribute of the initial set of attributes being used to determine whether users correspond to the user characteristic, and at least one attribute of the initial set of attributes characterizing an engagement based on at least one of: interactions with the ticket management system, interactions with a social media system, membership to one or more groups, past event attendances, and information in user profiles; determining, for each user in a first set of users, a first value for each attribute in the initial set of attributes, the first value being indicative of an extent of the engagement; identifying a first group and a second group from amongst the first set of users based on the determined first values; identifying a collection of users previously determined to correspond to the user characteristic; automatically detecting whether a rate of increase of users in the collection of users meets or exceeds a threshold, the meeting or exceeding the threshold indicating a likelihood that one or more users have altered activity to cause the ticket management system to falsely characterize the one or more users as a human user; in response to detecting that the rate of increase meets or exceeds the threshold, automatically determining to modify the initial set of attributes, each attribute in the modified set of attributes being used to determine whether users correspond to the user characteristic; determining, for each user in a second set of users, a second value for each attribute in the modified set of attributes, the second value being indicative of an extent to which a user corresponds to the user characteristic; continuously detecting, using the at least one learning algorithm, whether or not at least one user has altered activity to cause the ticket management system to falsely characterize the at least one user; identifying a third group and a fourth group from amongst the second set of users based on the determined second values, wherein each user in the third group corresponds to the defined user characteristic, wherein each user in the fourth group does not correspond to the user characteristic, and wherein users in the second set of users are different from users in the first set of users; determining that ticket offerings are to be biased to favor users in the third group over users in the fourth group; generating a digital presentation for each user in the second set of users based on the determination that ticket offerings are to be biased to favor users in the third group over users in the fourth group, the digital presentation being different for users in the third group than for users in the fourth group; and transmitting the digital presentation to each user in the second set of users, the digital presentation causing an interface to be displayed when received at a user device associated with each user in the second set of users, the interface being configured to receive ticket requests when the interface is displayed at the user device associated with each user in the third group, and the interface being configured to not receive ticket requests when the interface is displayed at the user device associated with each user in the fourth group. | 10. A method for dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users, method comprising: defining a user characteristic, the user characteristic being an estimation that a user is a human user, wherein: when the user corresponds to the user characteristic, the user is estimated to be a human user, and when the user does not correspond to the user characteristic, the user is estimated to be a robot user; identifying a model that enhances the user characteristic definer by detecting when users alter activity to cause the ticket management system to falsely characterize the users, the model including at least one machine-learning algorithm, identifying, using the at least one machine-learning algorithm, an initial set of attributes based on the user characteristic, each attribute of the initial set of attributes being used to determine whether users correspond to the user characteristic, and at least one attribute of the initial set of attributes characterizing an engagement based on at least one of: interactions with the ticket management system, interactions with a social media system, membership to one or more groups, past event attendances, and information in user profiles; determining, for each user in a first set of users, a first value for each attribute in the initial set of attributes, the first value being indicative of an extent of the engagement; identifying a first group and a second group from amongst the first set of users based on the determined first values; identifying a collection of users previously determined to correspond to the user characteristic; automatically detecting whether a rate of increase of users in the collection of users meets or exceeds a threshold, the meeting or exceeding the threshold indicating a likelihood that one or more users have altered activity to cause the ticket management system to falsely characterize the one or more users as a human user; in response to detecting that the rate of increase meets or exceeds the threshold, automatically determining to modify the initial set of attributes, each attribute in the modified set of attributes being used to determine whether users correspond to the user characteristic; determining, for each user in a second set of users, a second value for each attribute in the modified set of attributes, the second value being indicative of an extent to which a user corresponds to the user characteristic; continuously detecting, using the at least one learning algorithm, whether or not at least one user has altered activity to cause the ticket management system to falsely characterize the at least one user; identifying a third group and a fourth group from amongst the second set of users based on the determined second values, wherein each user in the third group corresponds to the defined user characteristic, wherein each user in the fourth group does not correspond to the user characteristic, and wherein users in the second set of users are different from users in the first set of users; determining that ticket offerings are to be biased to favor users in the third group over users in the fourth group; generating a digital presentation for each user in the second set of users based on the determination that ticket offerings are to be biased to favor users in the third group over users in the fourth group, the digital presentation being different for users in the third group than for users in the fourth group; and transmitting the digital presentation to each user in the second set of users, the digital presentation causing an interface to be displayed when received at a user device associated with each user in the second set of users, the interface being configured to receive ticket requests when the interface is displayed at the user device associated with each user in the third group, and the interface being configured to not receive ticket requests when the interface is displayed at the user device associated with each user in the fourth group. 17. The method dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users as recited in claim 10 , wherein the attributes are indicative as to whether the users are fans of a performing entity. | 0.741379 |
4,819,271 | 12 | 13 | 12. The method of claim 11 comprising the further steps of: (q) concatenating the respective single sequences for successive segments in order to form a sequence of Markov models for the subject word; and (r) repeating steps (a) through (q) for one word after another in a vocabulary of words. | 12. The method of claim 11 comprising the further steps of: (q) concatenating the respective single sequences for successive segments in order to form a sequence of Markov models for the subject word; and (r) repeating steps (a) through (q) for one word after another in a vocabulary of words. 13. The method of claim 12 wherein step (d) includes the steps of: (s) selecting one of the strings for a given word and constructing a preliminary baseform of the given word formed of the sequence of fenemic Markov models corresponding to the labels in the selected string; and (t) computing arc probabilities and label output probabilities for the fenemic Markov models. | 0.5 |
7,545,982 | 1 | 11 | 1. A method of assessing fraud in a document using a computer system, comprising: providing a document to the computer system, wherein the document comprises at least one information field; and for at least one of the information fields of the document, comparing handwriting in the information field to at least one handwriting profile representation from at least two information fields of at least one other document, wherein at least one handwriting profile representation has been stored in a computer system; assessing fraud in the document using at least one comparison, wherein evidence of fraud comprises a failure of at least a portion of the handwriting in at least one of the information fields of the document to approximately match at least one handwriting profile representation. | 1. A method of assessing fraud in a document using a computer system, comprising: providing a document to the computer system, wherein the document comprises at least one information field; and for at least one of the information fields of the document, comparing handwriting in the information field to at least one handwriting profile representation from at least two information fields of at least one other document, wherein at least one handwriting profile representation has been stored in a computer system; assessing fraud in the document using at least one comparison, wherein evidence of fraud comprises a failure of at least a portion of the handwriting in at least one of the information fields of the document to approximately match at least one handwriting profile representation. 11. The method of claim 1 , wherein at least one of the documents comprises a payment instrument, and wherein the handwriting is handwriting from at least one account owner of an account of the payment instrument. | 0.848291 |
8,050,923 | 13 | 15 | 13. A method for enhancing an audio player, comprising: integrating a speech analysis engine with the audio player; verbally communicating a first search criterion to the speech analysis engine; using the speech analysis engine to identify a recorded communication session in response to the first search criterion by processing a select communication session with the speech analysis engine to generate a result, wherein the speech analysis engine uses a language model specific to a speaker when an identity of the speaker is known; using the speech analysis engine to cache a representation of the result, wherein the representation of the result comprises an indication of a match of the first search criterion in the select communication session and an indication of a certainty that the match is an actual match of the first search criterion; and translating the result into a visual representation. | 13. A method for enhancing an audio player, comprising: integrating a speech analysis engine with the audio player; verbally communicating a first search criterion to the speech analysis engine; using the speech analysis engine to identify a recorded communication session in response to the first search criterion by processing a select communication session with the speech analysis engine to generate a result, wherein the speech analysis engine uses a language model specific to a speaker when an identity of the speaker is known; using the speech analysis engine to cache a representation of the result, wherein the representation of the result comprises an indication of a match of the first search criterion in the select communication session and an indication of a certainty that the match is an actual match of the first search criterion; and translating the result into a visual representation. 15. The method of claim 13 , wherein translating the result into a visual representation comprises generating an icon having a characteristic that varies as a function of a confidence level in the match. | 0.5 |
10,025,782 | 1 | 3 | 1. A computer-based system for managing documents over a network, comprising: a storage device storing software instructions; and a memory coupled to one or more processors configured to execute the software instructions to perform operations including: receiving a first version of a document from a first client, creating, in a document log, an entry associated with the first version of the document, wherein the document log contains multiple entries, each entry corresponding to a different version of the document and identifying the date and storage location of such different version of the document, receiving, from a remote computer that is displaying in an interface a second version of the document, a request for information from one or more entries in the document log, wherein the remote computer is the first client or a second client different from the first client, and providing to the remote computer, instructions for displaying a display window and information from one or more entries in the document log, wherein the display window is displayed as superimposed over a portion of the interface associated with the one or more entries included in the request for information. | 1. A computer-based system for managing documents over a network, comprising: a storage device storing software instructions; and a memory coupled to one or more processors configured to execute the software instructions to perform operations including: receiving a first version of a document from a first client, creating, in a document log, an entry associated with the first version of the document, wherein the document log contains multiple entries, each entry corresponding to a different version of the document and identifying the date and storage location of such different version of the document, receiving, from a remote computer that is displaying in an interface a second version of the document, a request for information from one or more entries in the document log, wherein the remote computer is the first client or a second client different from the first client, and providing to the remote computer, instructions for displaying a display window and information from one or more entries in the document log, wherein the display window is displayed as superimposed over a portion of the interface associated with the one or more entries included in the request for information. 3. The system of claim 1 , wherein the display window displays information reflecting one or more differences in content between different versions of the document. | 0.710247 |
8,768,702 | 18 | 24 | 18. A method for speaking text of elements displayed by an electronic device, comprising: defining a plurality of elements with which speakable properties are associated; displaying the plurality of elements in a plurality of views, wherein each view is associated with a speakable order; generating a queue comprising the plurality of elements, wherein the order of the plurality of elements in the queue is set from the speakable order; pausing for a first timeout; identifying audio files associated with each of the plurality of elements of the queue, wherein the audio files correspond to text to speak for each element; sequentially playing back the identified audio files in the order of the queue; and pausing for a second timeout. | 18. A method for speaking text of elements displayed by an electronic device, comprising: defining a plurality of elements with which speakable properties are associated; displaying the plurality of elements in a plurality of views, wherein each view is associated with a speakable order; generating a queue comprising the plurality of elements, wherein the order of the plurality of elements in the queue is set from the speakable order; pausing for a first timeout; identifying audio files associated with each of the plurality of elements of the queue, wherein the audio files correspond to text to speak for each element; sequentially playing back the identified audio files in the order of the queue; and pausing for a second timeout. 24. The method of claim 18 , wherein the identified audio files are sequentially played back in the order of the queue without human intervention. | 0.726592 |
7,822,774 | 3 | 4 | 3. The one or more computer-readable media of claim 2 , wherein determining one or more related queries using the information associated with the directed graph comprises identifying the search query as a first node in the directed graph and identifying the one or more related queries as additional nodes linked to the first node in the directed graph. | 3. The one or more computer-readable media of claim 2 , wherein determining one or more related queries using the information associated with the directed graph comprises identifying the search query as a first node in the directed graph and identifying the one or more related queries as additional nodes linked to the first node in the directed graph. 4. The one or more computer-readable media of claim 3 , wherein the one or more related queries are ranked based on connections between the first node and a node corresponding with each related query. | 0.5 |
7,617,078 | 6 | 7 | 6. The system of claim 1 , wherein the data sources include one or more of: medical information, financial information, demographic information, billing information or combinations thereof. | 6. The system of claim 1 , wherein the data sources include one or more of: medical information, financial information, demographic information, billing information or combinations thereof. 7. The system of claim 6 , wherein the medical information includes one or more of: free text information, medical image information, laboratory information, prescription drug information, waveform information or combinations thereof. | 0.5 |
8,571,869 | 1 | 2 | 1. A method comprising: obtaining first data from a plurality of calls routed by a natural language call routing system that uses a first speech recognition model to recognize speech of the plurality of calls and a first action classification model to route calls based on the recognized speech, the first data comprising audio data from the plurality of calls, a first N-best list of word sequences generated by using the first speech recognition model to recognize at least a portion of the audio data and associated call classification data indicating how each of the plurality of calls were routed by the natural language call routing system; and modifying the first speech recognition model and the first action classification model based at least in part on the first data, including using the first N-best list of word sequences, to obtain a second speech recognition model and a second action classification model; and modifying the second speech recognition model and the second action classification model based at least in part on the first data to obtain a third speech recognition model and a third action classification model, wherein the modifying comprises modifying the second speech recognition model and the second action classification model by using the first N-best list or a second N-best list of word sequences generated by using the second speech recognition model to recognize at least the portion of the audio data. | 1. A method comprising: obtaining first data from a plurality of calls routed by a natural language call routing system that uses a first speech recognition model to recognize speech of the plurality of calls and a first action classification model to route calls based on the recognized speech, the first data comprising audio data from the plurality of calls, a first N-best list of word sequences generated by using the first speech recognition model to recognize at least a portion of the audio data and associated call classification data indicating how each of the plurality of calls were routed by the natural language call routing system; and modifying the first speech recognition model and the first action classification model based at least in part on the first data, including using the first N-best list of word sequences, to obtain a second speech recognition model and a second action classification model; and modifying the second speech recognition model and the second action classification model based at least in part on the first data to obtain a third speech recognition model and a third action classification model, wherein the modifying comprises modifying the second speech recognition model and the second action classification model by using the first N-best list or a second N-best list of word sequences generated by using the second speech recognition model to recognize at least the portion of the audio data. 2. The method of claim 1 , wherein said modifying comprises jointly modifying the first speech recognition model and the first action classification model by using an unisolated performance metric indicative of routing accuracy of the natural language call routing system. | 0.685912 |
7,940,273 | 11 | 12 | 11. A system comprising: one or more processors; and memory coupled to the processor, the memory comprising a Unicode deviation component that includes instructions that, when executed by the one or more processors, performs acts comprising: receiving an indication of at least one command relating to text that contains at least one first glyph; determining that the at least one first glyph maps to multiple corresponding Unicode representations, the multiple corresponding Unicode representations defining a collision; resolving the collision such that the at least one first glyph maps to only a single Unicode representation of the multiple corresponding Unicode representations; converting the at least one first glyph to the single corresponding Unicode representation in response to the command; performing the command on the single Unicode representation of the at least one first glyph; creating at least one further glyph for the single Unicode representation of the at least one first glyph; and mapping the at least one further glyph element to the at least one first glyph via a glyph substitution table. | 11. A system comprising: one or more processors; and memory coupled to the processor, the memory comprising a Unicode deviation component that includes instructions that, when executed by the one or more processors, performs acts comprising: receiving an indication of at least one command relating to text that contains at least one first glyph; determining that the at least one first glyph maps to multiple corresponding Unicode representations, the multiple corresponding Unicode representations defining a collision; resolving the collision such that the at least one first glyph maps to only a single Unicode representation of the multiple corresponding Unicode representations; converting the at least one first glyph to the single corresponding Unicode representation in response to the command; performing the command on the single Unicode representation of the at least one first glyph; creating at least one further glyph for the single Unicode representation of the at least one first glyph; and mapping the at least one further glyph element to the at least one first glyph via a glyph substitution table. 12. The system of claim 11 , wherein converting the glyph includes instructions reverse mapping the glyph through a character mapping table. | 0.641026 |
8,655,113 | 11 | 19 | 11. A computer-implemented system comprising: a processor coupled to a memory; a background pattern detector to identify, using the processor, a background pattern associated with a printed document; a validation module to, using the processor: based on the background pattern, determine identification of an electronic document corresponding to the printed document, determine, based on the identification, an electronic version of the electronic document; and an alert module to trigger, using the processor, an alert, if the determined electronic version of the electronic document is an invalid electronic version of the electronic document. | 11. A computer-implemented system comprising: a processor coupled to a memory; a background pattern detector to identify, using the processor, a background pattern associated with a printed document; a validation module to, using the processor: based on the background pattern, determine identification of an electronic document corresponding to the printed document, determine, based on the identification, an electronic version of the electronic document; and an alert module to trigger, using the processor, an alert, if the determined electronic version of the electronic document is an invalid electronic version of the electronic document. 19. The system of claim 11 , wherein the alert module is to trigger a visual alert or an audio alert. | 0.795547 |
8,386,467 | 9 | 11 | 9. A computer system that includes: a memory; at least one processor coupled to the memory, the at least one processor configured to: receive a query and a service level goal associated with the query that specifies a desired processing performance characteristic to be achieved by execution of the query, assign a confidence threshold parameter having a first value that specifies an optimizer confidence level that execution of a generated query plan for the query will incur a processing cost that will not exceed an estimated processing cost, generates a first query plan in accordance with the confidence threshold parameter, calculate an estimated processing cost of the first query plan that specifies an estimated processing performance characteristic for execution of the first query plan, determine whether the estimated processing cost is less than or equal to the service level goal; and lower the confidence threshold parameter to a second value when the determining determines that the estimated processing cost is not less than or equal to the service level goal; determines whether the second value of the confidence threshold parameter is less than a threshold boundary value; and generates a second query plan in accordance with the second value of the confidence threshold parameter when the determining the second value is not less than the threshold boundary value. | 9. A computer system that includes: a memory; at least one processor coupled to the memory, the at least one processor configured to: receive a query and a service level goal associated with the query that specifies a desired processing performance characteristic to be achieved by execution of the query, assign a confidence threshold parameter having a first value that specifies an optimizer confidence level that execution of a generated query plan for the query will incur a processing cost that will not exceed an estimated processing cost, generates a first query plan in accordance with the confidence threshold parameter, calculate an estimated processing cost of the first query plan that specifies an estimated processing performance characteristic for execution of the first query plan, determine whether the estimated processing cost is less than or equal to the service level goal; and lower the confidence threshold parameter to a second value when the determining determines that the estimated processing cost is not less than or equal to the service level goal; determines whether the second value of the confidence threshold parameter is less than a threshold boundary value; and generates a second query plan in accordance with the second value of the confidence threshold parameter when the determining the second value is not less than the threshold boundary value. 11. The system of claim 9 , wherein the at least one processor is further configured to determine the second value is less than the threshold boundary value and, responsive thereto, executes the first query plan. | 0.560166 |
7,890,499 | 15 | 16 | 15. The system of claim 10 , wherein the second user interface further displays a subject matter specific user interface element that allows users to select a subject matter specific criteria, or to sort the second search results according to a subject matter specific attribute. | 15. The system of claim 10 , wherein the second user interface further displays a subject matter specific user interface element that allows users to select a subject matter specific criteria, or to sort the second search results according to a subject matter specific attribute. 16. The system of claim 15 , wherein the operations further comprise, in response to receiving a user selection of the subject matter specific user interface element, removing second search results that have attributes which do not satisfy the selected subject matter specific criteria. | 0.5 |
7,552,420 | 23 | 29 | 23. A system for configuring an application, comprising: a processor; an application definition repository (ADR) storing a rule comprising a conditional expression, and an application object for configuring the application, the application object being associated with a plurality of options for configuring the application during runtime; an application context manager (ACM) comprising symbol lookup module, operatively connected to the ADR and configured to use the processor to: receive a request to configure the application from a client associated with the application, wherein the ACM is external to the client, and wherein the request comprises an application context describing a state of the application at runtime; parse the rule to extract a symbol and an operator; convert the conditional expression into a tree comprising the symbol and the operator; replace the symbol with a value from the application context, wherein the symbol lookup module identifies the value using a domain map to find a context data source publishing the value; and evaluate the tree to generate a result; and an application configuration engine (ACE) operatively connected to the ADR and the ACM, wherein the ACE is configured to use the processor to calculate a correct configuration for the application based on the result, select an option of the plurality of options based on the correct configuration, and send an instance of the application object encapsulating the option to the client in response to the request, wherein the application object is used by the client to set a configuration of the application to the correct configuration. | 23. A system for configuring an application, comprising: a processor; an application definition repository (ADR) storing a rule comprising a conditional expression, and an application object for configuring the application, the application object being associated with a plurality of options for configuring the application during runtime; an application context manager (ACM) comprising symbol lookup module, operatively connected to the ADR and configured to use the processor to: receive a request to configure the application from a client associated with the application, wherein the ACM is external to the client, and wherein the request comprises an application context describing a state of the application at runtime; parse the rule to extract a symbol and an operator; convert the conditional expression into a tree comprising the symbol and the operator; replace the symbol with a value from the application context, wherein the symbol lookup module identifies the value using a domain map to find a context data source publishing the value; and evaluate the tree to generate a result; and an application configuration engine (ACE) operatively connected to the ADR and the ACM, wherein the ACE is configured to use the processor to calculate a correct configuration for the application based on the result, select an option of the plurality of options based on the correct configuration, and send an instance of the application object encapsulating the option to the client in response to the request, wherein the application object is used by the client to set a configuration of the application to the correct configuration. 29. The system of claim 23 , wherein the application context comprises at least one selected from a group consisting of a version of the application and access rights of a user of the application. | 0.758621 |
8,146,156 | 57 | 58 | 57. A computer-implemented method, comprising: receiving, at an information-capturing system, a plurality of text capture operations from one or more rendered documents, each indicated text capture operation designating less than a whole page of text as captured; the information-capturing system analyzing the plurality of text capture operations to identify a product or service of likely interest; and the information-capturing system presenting information related to said identified product or service. | 57. A computer-implemented method, comprising: receiving, at an information-capturing system, a plurality of text capture operations from one or more rendered documents, each indicated text capture operation designating less than a whole page of text as captured; the information-capturing system analyzing the plurality of text capture operations to identify a product or service of likely interest; and the information-capturing system presenting information related to said identified product or service. 58. The method of claim 57 , further comprising facilitating a transaction for purchase of said product or service. | 0.5 |
6,134,553 | 8 | 10 | 8. A method for searching for documents in a hierarchically structured information space that includes a plurality of nodes of differing hierarchical levels, wherein a particular document region for a given node corresponds to all documents at nodes for which said given node constitutes a root, the method comprising the steps of: defining a first set of regions, said first set comprising pointers to a first plurality of nodes wherein the first plurality of nodes are logically related in accordance with a first criterion; defining a second set of regions, said second set comprising pointers to a second plurality of nodes wherein said second plurality of nodes are logically related in accordance with a second criterion; creating a third set of regions, said third set comprising pointers to a third plurality of nodes, wherein said third plurality of nodes are logically related to said first plurality of nodes and said second plurality of nodes in accordance with a third criterion; and searching said space in accordance with a search query and said third set of regions. | 8. A method for searching for documents in a hierarchically structured information space that includes a plurality of nodes of differing hierarchical levels, wherein a particular document region for a given node corresponds to all documents at nodes for which said given node constitutes a root, the method comprising the steps of: defining a first set of regions, said first set comprising pointers to a first plurality of nodes wherein the first plurality of nodes are logically related in accordance with a first criterion; defining a second set of regions, said second set comprising pointers to a second plurality of nodes wherein said second plurality of nodes are logically related in accordance with a second criterion; creating a third set of regions, said third set comprising pointers to a third plurality of nodes, wherein said third plurality of nodes are logically related to said first plurality of nodes and said second plurality of nodes in accordance with a third criterion; and searching said space in accordance with a search query and said third set of regions. 10. The method of claim 8 wherein said hierarchically structured information space is organized using file pathnames. | 0.634375 |
9,779,168 | 1 | 7 | 1. A method comprising: at a local device, while a search query is being formulated by a user in a search box of one or more applications displayed at the local device, automatically displaying, separate from the search query a set of one or more selected quick-picks, wherein the set of one or more selected quick-picks is selected based, at least in part, on a current context associated with displayed content in the one or more applications executing on the local device, wherein the displayed content is different and distinct from the search box and the search query; receiving, at the local device, user input that selects a particular quick-pick from the set of one or more selected quick-picks; in response to receiving, at the local device, the user input, adding, in the search box displayed at the local device, the particular quick-pick as a separate term to the search query and allowing the search query including the separate term to continue to be formulated by the user in the search box displayed at the local device; displaying a second set of one or more quick-picks in response to the user input that selects the particular quick-pick from the set of one or more selected quick-picks, wherein the second set of one or more quick-picks includes at least one additional quick-pick that: (a) was not in the set of one or more selected quick-picks, and (b) was selected to be a quick-pick based, at least in part, on an association between the at least one additional quick-pick and the particular quick-pick; receiving user input that deselects the particular quick-pick from the set of one or more selected quick-picks; and in response to the user input that deselects the particular quick-pick from the set of one or more selected quick-picks, automatically removing the separate term from the search query; wherein the method is performed by one or more computing devices. | 1. A method comprising: at a local device, while a search query is being formulated by a user in a search box of one or more applications displayed at the local device, automatically displaying, separate from the search query a set of one or more selected quick-picks, wherein the set of one or more selected quick-picks is selected based, at least in part, on a current context associated with displayed content in the one or more applications executing on the local device, wherein the displayed content is different and distinct from the search box and the search query; receiving, at the local device, user input that selects a particular quick-pick from the set of one or more selected quick-picks; in response to receiving, at the local device, the user input, adding, in the search box displayed at the local device, the particular quick-pick as a separate term to the search query and allowing the search query including the separate term to continue to be formulated by the user in the search box displayed at the local device; displaying a second set of one or more quick-picks in response to the user input that selects the particular quick-pick from the set of one or more selected quick-picks, wherein the second set of one or more quick-picks includes at least one additional quick-pick that: (a) was not in the set of one or more selected quick-picks, and (b) was selected to be a quick-pick based, at least in part, on an association between the at least one additional quick-pick and the particular quick-pick; receiving user input that deselects the particular quick-pick from the set of one or more selected quick-picks; and in response to the user input that deselects the particular quick-pick from the set of one or more selected quick-picks, automatically removing the separate term from the search query; wherein the method is performed by one or more computing devices. 7. The method of claim 1 , wherein the particular quick-pick corresponds to a non-textual item and the adding the particular quick-pick as the separate term to the search query includes adding a search criteria item associated with the non-textual item to the search query. | 0.553922 |
8,635,254 | 1 | 6 | 1. A method, performed by a server, for use in maintaining states of instances of a Web browser on a device that is configured for communicating with the server, the method comprising: receiving, from the device, a request for a first Web page, the request comprising a first URL-encoded session identifier (ID) associated with a first instance of the Web browser; storing, in the server, a first session object associated with the first URL-encoded session ID, the first session object containing data defining a state of the first instance of the Web browser, wherein the first instance of the Web browser comprises a first Web browser window; generating, in the server, the first Web page using the data in the first session object; receiving, from the device, a request for a second Web page, the request comprising a second URL-encoded session identifier (ID) associated with a second instance of the Web browser, wherein the second instance of the Web browser comprises a second Web browser window; storing, in the server, a second session object associated with the second session ID, the second session object containing data defining a state of the second instance of the Web browser; generating, in the server, the second Web page using the data in the second session object; and outputting, from the server to the device, the first Web page and the second Web page; and maintaining, via the first session object, a state of the first Web page such that the state of the first Web page is not affected when a state of the second Web page is changed in response to submission of information in the second Web page to the server, thereby preserving the state of the first instance of the Web browser; or maintaining, via the second session object, the state of the second Web page such that the state of the second Web page is not affected when the state of the first Web page is changed in response to submission of information in the first Web page to the server, thereby preserving the state of the second instance of the Web browser. | 1. A method, performed by a server, for use in maintaining states of instances of a Web browser on a device that is configured for communicating with the server, the method comprising: receiving, from the device, a request for a first Web page, the request comprising a first URL-encoded session identifier (ID) associated with a first instance of the Web browser; storing, in the server, a first session object associated with the first URL-encoded session ID, the first session object containing data defining a state of the first instance of the Web browser, wherein the first instance of the Web browser comprises a first Web browser window; generating, in the server, the first Web page using the data in the first session object; receiving, from the device, a request for a second Web page, the request comprising a second URL-encoded session identifier (ID) associated with a second instance of the Web browser, wherein the second instance of the Web browser comprises a second Web browser window; storing, in the server, a second session object associated with the second session ID, the second session object containing data defining a state of the second instance of the Web browser; generating, in the server, the second Web page using the data in the second session object; and outputting, from the server to the device, the first Web page and the second Web page; and maintaining, via the first session object, a state of the first Web page such that the state of the first Web page is not affected when a state of the second Web page is changed in response to submission of information in the second Web page to the server, thereby preserving the state of the first instance of the Web browser; or maintaining, via the second session object, the state of the second Web page such that the state of the second Web page is not affected when the state of the first Web page is changed in response to submission of information in the first Web page to the server, thereby preserving the state of the second instance of the Web browser. 6. The method of claim 1 , wherein each request comprises a HyperText Transfer Protocol (HTTP) command. | 0.911054 |
9,953,652 | 5 | 6 | 5. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, generating a transcription of at least a portion of an utterance, wherein the transcription comprises a sequence of tokens corresponding to at least a portion of a search query; identifying a first subset of the sequence of tokens as a reference to an entity in the transcription based at least partly on a first frequency with which at least a first token of the first subset occurs in a plurality of previously submitted search queries; identifying a second subset of the sequence of tokens as a reference to an attribute of the entity in the transcription based at least partly on a second frequency with which at least a second token of the second subset occurs with the first token in the plurality of previously submitted search queries, wherein the second token occurring with the first token comprises the second token and first token both occurring in a same search query of the plurality of previously submitted search queries; generating search terms using the reference to the entity and the reference to the attribute, wherein the search terms include the first subset and second subset, and wherein the search terms exclude a third subset of the sequence of tokens; and generating search results responsive to the search query by identifying an item in a product catalog using the search terms, wherein the product catalog indicates the item comprises an instance of the entity having the attribute. | 5. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, generating a transcription of at least a portion of an utterance, wherein the transcription comprises a sequence of tokens corresponding to at least a portion of a search query; identifying a first subset of the sequence of tokens as a reference to an entity in the transcription based at least partly on a first frequency with which at least a first token of the first subset occurs in a plurality of previously submitted search queries; identifying a second subset of the sequence of tokens as a reference to an attribute of the entity in the transcription based at least partly on a second frequency with which at least a second token of the second subset occurs with the first token in the plurality of previously submitted search queries, wherein the second token occurring with the first token comprises the second token and first token both occurring in a same search query of the plurality of previously submitted search queries; generating search terms using the reference to the entity and the reference to the attribute, wherein the search terms include the first subset and second subset, and wherein the search terms exclude a third subset of the sequence of tokens; and generating search results responsive to the search query by identifying an item in a product catalog using the search terms, wherein the product catalog indicates the item comprises an instance of the entity having the attribute. 6. The computer-implemented method of claim 5 , wherein the identifying the first subset comprises using a statistical model, and wherein the statistical model comprises a conditional random field model, a hidden Markov model, a maximum entropy Markov model, a structure prediction model, or a sequential model. | 0.728621 |
9,800,955 | 1 | 2 | 1. A method for a sign language translation and descriptive video service, the method comprising: extracting a character string in a text form from a caption of an original video; translating the character string to a machine language by: separating the character string based on a word space and a sentence identification symbol, separating the separated character string into morpheme units, and translating morpheme units to the machine language; matching the machine language with a sign language video in a database; and synchronizing the original video with the sign language video, and mixing the original video and the synchronized sign language video. | 1. A method for a sign language translation and descriptive video service, the method comprising: extracting a character string in a text form from a caption of an original video; translating the character string to a machine language by: separating the character string based on a word space and a sentence identification symbol, separating the separated character string into morpheme units, and translating morpheme units to the machine language; matching the machine language with a sign language video in a database; and synchronizing the original video with the sign language video, and mixing the original video and the synchronized sign language video. 2. The method of claim 1 , further comprising: inputting a Descriptive Video Service (DVS) voice for describing the original video by a user; and mixing the DVS voice by synchronizing with the original video. | 0.635088 |
8,234,248 | 10 | 12 | 10. The system of claim 9 , including a data store to store state documents, and where state data describing a current state of a database object is stored in a state document in the database. | 10. The system of claim 9 , including a data store to store state documents, and where state data describing a current state of a database object is stored in a state document in the database. 12. The system of claim 10 , where the output logic supplies data from the differences document to an external logic. | 0.669492 |
8,959,443 | 5 | 6 | 5. The method of claim 1 , the first database object and the second database object being part of a collection of at least two database objects, and the determining of the locations includes: if two database objects have data that is associated, two corresponding representations are located in a manner in which the two corresponding representations overlap one another. | 5. The method of claim 1 , the first database object and the second database object being part of a collection of at least two database objects, and the determining of the locations includes: if two database objects have data that is associated, two corresponding representations are located in a manner in which the two corresponding representations overlap one another. 6. The method of claim 5 , the determining of the locations further includes: if two database objects do not have associated data, two corresponding representations are located in a manner in which the two corresponding representations do not overlap one another. | 0.5 |
8,725,726 | 5 | 6 | 5. The method of claim 1 , further comprising: determining scores for the plurality of documents; and ranking the plurality of documents, in the list of documents, based on the scores for the plurality of documents. | 5. The method of claim 1 , further comprising: determining scores for the plurality of documents; and ranking the plurality of documents, in the list of documents, based on the scores for the plurality of documents. 6. The method of claim 5 , where the score, for one of the plurality of documents, is based on scores of documents that include links to the one of the plurality of documents. | 0.729102 |
8,429,601 | 12 | 13 | 12. The non-transitory medium of claim 11 , wherein the method further comprises: receiving a selection of a result item of the result displayed in the third querying-related window in the GUI; and in response to the selection, displaying in a fourth querying-related window in the same GUI one or more properties associated with the selected result item. | 12. The non-transitory medium of claim 11 , wherein the method further comprises: receiving a selection of a result item of the result displayed in the third querying-related window in the GUI; and in response to the selection, displaying in a fourth querying-related window in the same GUI one or more properties associated with the selected result item. 13. The non-transitory medium of claim 12 , wherein the method further comprises in response to an input from a user, displaying in a fifth querying-related window in the same GUI one or more parameters associated with the first query string. | 0.5 |
8,000,970 | 5 | 6 | 5. The method of claim 4 , further comprising: receiving said parsed VXML data; and providing said parsed VXML data to said associated service processor. | 5. The method of claim 4 , further comprising: receiving said parsed VXML data; and providing said parsed VXML data to said associated service processor. 6. The method of claim 5 , further comprising: executing said parsed VXML data to implement said telephony service. | 0.5 |
7,831,420 | 10 | 14 | 10. An apparatus comprising: a formants modifier comprising: a receiver configured to receive Mth order linear predictive coding (LPC) coefficients representative of an input speech signal and a scale factor; a first converter configured to convert the Mth order LPC coefficients to Mth order line spectral pairs (LSPs); a multiplier configured to multiply the Mth order LSPs by the scale factor to produce scaled Mth order LSPs; an extractor configured to remove any pairs of scaled LSPs with at least one coefficient above a frequency threshold to produce a Pth order set of LSPs, where P<M; a second converter configured to convert the Pth order set of scaled LSPs to a Pth order set of LPCs; an inserter configured to pad the Pth order set of LPCs with M-P zeros; a third converter configured to convert the Pth order set of LPCs padded with zeros to a second Mth order set of LSPs; a processor configured to process the second Mth order set of LSPs and at least a third set of Mth order LSPs of another frame; and a fourth converter configured to convert the processed LSPs to processed LPCs; and a synthesizer configured to re-synthesize speech using the processed LPCs. | 10. An apparatus comprising: a formants modifier comprising: a receiver configured to receive Mth order linear predictive coding (LPC) coefficients representative of an input speech signal and a scale factor; a first converter configured to convert the Mth order LPC coefficients to Mth order line spectral pairs (LSPs); a multiplier configured to multiply the Mth order LSPs by the scale factor to produce scaled Mth order LSPs; an extractor configured to remove any pairs of scaled LSPs with at least one coefficient above a frequency threshold to produce a Pth order set of LSPs, where P<M; a second converter configured to convert the Pth order set of scaled LSPs to a Pth order set of LPCs; an inserter configured to pad the Pth order set of LPCs with M-P zeros; a third converter configured to convert the Pth order set of LPCs padded with zeros to a second Mth order set of LSPs; a processor configured to process the second Mth order set of LSPs and at least a third set of Mth order LSPs of another frame; and a fourth converter configured to convert the processed LSPs to processed LPCs; and a synthesizer configured to re-synthesize speech using the processed LPCs. 14. The apparatus of claim 10 , wherein the processor is further configured to interpolate the second Mth order set of LSPs and at least a third set of Mth order LSPs of another frame of speech samples. | 0.5 |
8,200,597 | 8 | 12 | 8. A method for managing media contents, comprising: obtaining subtitle information according to a media ID of a media content to be marked; extracting subtitle content information in the obtained subtitle information, marking the subtitle content information chronologically to form multiple media content time segments, and classifying the multiple media content time segments according to defined subject contents to obtain multiple content clips of different subjects; marking start time and end time of each played content clip in media according to time information of the media content time segments, and obtaining multiple content clips which have start and end time information and different subjects; matching the content clips with concepts in an ontology library according to subjects of the content clips which have the start and end time information and different subjects, and marking the content clips through terms defined in the ontology library; extracting the subtitle content information in the obtained subtitle information, and recording an ID and time information of each subtitle content marked with the start time and end time; classifying contents in units of marked subtitle contents according to defined subjects, thus forming multiple content clips which have one or more subjects; and marking the start time and the end time of each played content clip in the media according to the time information. | 8. A method for managing media contents, comprising: obtaining subtitle information according to a media ID of a media content to be marked; extracting subtitle content information in the obtained subtitle information, marking the subtitle content information chronologically to form multiple media content time segments, and classifying the multiple media content time segments according to defined subject contents to obtain multiple content clips of different subjects; marking start time and end time of each played content clip in media according to time information of the media content time segments, and obtaining multiple content clips which have start and end time information and different subjects; matching the content clips with concepts in an ontology library according to subjects of the content clips which have the start and end time information and different subjects, and marking the content clips through terms defined in the ontology library; extracting the subtitle content information in the obtained subtitle information, and recording an ID and time information of each subtitle content marked with the start time and end time; classifying contents in units of marked subtitle contents according to defined subjects, thus forming multiple content clips which have one or more subjects; and marking the start time and the end time of each played content clip in the media according to the time information. 12. The method of claim 8 , further comprising: recording the content clips marked through the terms defined in the ontology library. | 0.804412 |
9,672,251 | 10 | 13 | 10. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining a plurality of seed facts, wherein each seed fact identifies a subject entity, an attribute possessed by the subject entity, and an object, and wherein the object is an attribute value of the attribute possessed by the subject entity; generating a plurality of patterns from the seed facts, wherein each of the plurality of patterns is a dependency pattern generated from a dependency parse, wherein a dependency parse of a text portion corresponds to a directed graph of vertices and edges, wherein each vertex represents a token in the text portion and each edge represents a syntactic relationship between tokens represented by vertices connected by the edge, wherein each vertex is associated with the token represented by the vertex and a part of speech tag, and wherein a dependency pattern corresponds to a sub-graph of a dependency parse with one or more of the vertices in the sub-graph having a token associated with the vertex replaced by a variable; applying the patterns to documents in a collection of documents to extract a plurality of candidate additional facts from the collection of documents, wherein applying the patterns to documents in the collection of documents comprises: applying the dependency patterns to documents from the collection of documents to identify matching sentences; generating an extraction from each matching sentence; and aggregating the extractions to generate the candidate additional facts; and selecting one or more additional facts from the plurality of candidate additional facts. | 10. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining a plurality of seed facts, wherein each seed fact identifies a subject entity, an attribute possessed by the subject entity, and an object, and wherein the object is an attribute value of the attribute possessed by the subject entity; generating a plurality of patterns from the seed facts, wherein each of the plurality of patterns is a dependency pattern generated from a dependency parse, wherein a dependency parse of a text portion corresponds to a directed graph of vertices and edges, wherein each vertex represents a token in the text portion and each edge represents a syntactic relationship between tokens represented by vertices connected by the edge, wherein each vertex is associated with the token represented by the vertex and a part of speech tag, and wherein a dependency pattern corresponds to a sub-graph of a dependency parse with one or more of the vertices in the sub-graph having a token associated with the vertex replaced by a variable; applying the patterns to documents in a collection of documents to extract a plurality of candidate additional facts from the collection of documents, wherein applying the patterns to documents in the collection of documents comprises: applying the dependency patterns to documents from the collection of documents to identify matching sentences; generating an extraction from each matching sentence; and aggregating the extractions to generate the candidate additional facts; and selecting one or more additional facts from the plurality of candidate additional facts. 13. The system of claim 10 , wherein generating the plurality of patterns from the seed facts comprises, for each of the seed facts: identifying sentences in the collection of documents that match the seed fact; identifying a respective minimal sub-graph of a dependency parse of each of the matching sentences, wherein the minimal sub-graph of a dependency parse is a smallest portion of the dependency parse that includes vertices representing head tokens of a subject entity, attribute, and object identified by the seed fact; and generating a respective dependency pattern from each minimal sub-graph by replacing the tokens associated with one or more of the vertices representing the head tokens of the subject entity, the attribute, or the object identified by the seed fact with a variable. | 0.5 |
5,560,060 | 5 | 8 | 5. An appliance according to claim 3, wherein an operating cycle comprises at least one pre-wash fill cycle, a main wash fill cycle, a rinse fill cycle, and a final rinse fill cycle. | 5. An appliance according to claim 3, wherein an operating cycle comprises at least one pre-wash fill cycle, a main wash fill cycle, a rinse fill cycle, and a final rinse fill cycle. 8. An appliance according to claim 5, wherein said controller varies the duration of at least one of the fill cycles as a function of liquid temperature. | 0.5 |
9,336,256 | 9 | 11 | 9. A computer-implemented method for data tokenization by one or more computing devices, the method comprising: receiving, by the one or more computing devices, a request directed to a tokenized database, wherein the tokenized database contains one or more tokenized data values and wherein the request does not include any tokenized data values; applying, by one or more computing devices, one or more rules to the request; rewriting, by the one or more computing devices, the request based on at least one of the one or more rules, wherein the rewritten request is configured to cause one or more non-tokenized data values specified in the request to be tokenized by a software agent resident on the tokenized database when data is added to the tokenized database as a result of the request and wherein the rewritten request is configured to cause the tokenized database to return non-tokenized data values when data is received from the tokenized database as a result of the request; and transmitting, by the one or more computing devices, the rewritten request to the tokenized database. | 9. A computer-implemented method for data tokenization by one or more computing devices, the method comprising: receiving, by the one or more computing devices, a request directed to a tokenized database, wherein the tokenized database contains one or more tokenized data values and wherein the request does not include any tokenized data values; applying, by one or more computing devices, one or more rules to the request; rewriting, by the one or more computing devices, the request based on at least one of the one or more rules, wherein the rewritten request is configured to cause one or more non-tokenized data values specified in the request to be tokenized by a software agent resident on the tokenized database when data is added to the tokenized database as a result of the request and wherein the rewritten request is configured to cause the tokenized database to return non-tokenized data values when data is received from the tokenized database as a result of the request; and transmitting, by the one or more computing devices, the rewritten request to the tokenized database. 11. The computer-implemented method of claim 9 , wherein applying one or more rules and rewriting the request comprises: selecting, by the one or more computing devices, an update rule when the database access request is a data update request, the data update request further comprising one or more new data values; and rewriting, by the one or more computing devices, the request to insert a tokenize command into the request, the tokenize command signaling to a software agent resident on the tokenized database to tokenize the one or more new data values prior to adding them to the tokenized database. | 0.703722 |
7,836,460 | 1 | 7 | 1. A method for operating a Web Services search method in a networked environment, wherein a Service Provider registers Web Services at a Service Broker, and wherein a Service Broker is inquired by a Service Consumer for offering Web Services, and a Service Provider provides an inquired Web Service to the Service Consumer, wherein the Service Broker maintains a service model comprising Web Service description data and wherein the service model is created by the Service Broker according to Web Service data offered by the Service Provider, and wherein the service model is used by the Service Broker to process inquiries issued by the Service Consumer, comprising: retrieving data mining input data relating to description details of the inquired Web Services from respective Service descriptions of the inquired Web Services; performing a data mining function on said data mining input data, wherein a clustering is performed with a distance calculation function including said description details, wherein the distance calculation includes the degree of interaction between said Web Services, and wherein the clustering yields a cluster model comprising a plurality of clusters and a mapping for each Web Service to one of said clusters, wherein Web Services having a similar semantic meaning are collected in a single cluster, yielding a data mining result; adding cluster information according to said data mining result to said service model resulting in an extended service model, thus associating each service with the cluster it is assigned to in the data mining result and storing this extended service model in a database; and offering search response data based on said extended service model in response to Web Services search operations. | 1. A method for operating a Web Services search method in a networked environment, wherein a Service Provider registers Web Services at a Service Broker, and wherein a Service Broker is inquired by a Service Consumer for offering Web Services, and a Service Provider provides an inquired Web Service to the Service Consumer, wherein the Service Broker maintains a service model comprising Web Service description data and wherein the service model is created by the Service Broker according to Web Service data offered by the Service Provider, and wherein the service model is used by the Service Broker to process inquiries issued by the Service Consumer, comprising: retrieving data mining input data relating to description details of the inquired Web Services from respective Service descriptions of the inquired Web Services; performing a data mining function on said data mining input data, wherein a clustering is performed with a distance calculation function including said description details, wherein the distance calculation includes the degree of interaction between said Web Services, and wherein the clustering yields a cluster model comprising a plurality of clusters and a mapping for each Web Service to one of said clusters, wherein Web Services having a similar semantic meaning are collected in a single cluster, yielding a data mining result; adding cluster information according to said data mining result to said service model resulting in an extended service model, thus associating each service with the cluster it is assigned to in the data mining result and storing this extended service model in a database; and offering search response data based on said extended service model in response to Web Services search operations. 7. The method of claim 1 , performed in order to register a new Web Service offered by a Web Service Provider, wherein the data mining result comprises the cluster ID of the cluster assigned to the new Web Service. | 0.50463 |
8,296,123 | 40 | 49 | 40. A system comprising: a plurality of machine translation resource servers, each machine translation resource server storing and operable to serve a partition of a collection of machine translation resource data for translation from a source language to a target language, the respective partitions together constituting the collection of machine translation resource data and each respective partition being less than the collection of machine translation resource data; and at least one translation server operable to receive source text in the source language to be translated into the target language, the translation server further operable to obtain machine translation resource data from the plurality of machine translation resource servers and to use the obtained machine translation resource data to translate the source text into the target language, wherein the machine translation resources servers comprise: a plurality of language model servers respectively storing and operable to serve different partitions of a language model for the target language, the respective partitions together constituting the entire language model and each respective partition being less than the whole of the language model; one or more replica language model servers for each of the plurality of language model servers; a plurality of translation model servers respectively storing and operable to serve different partitions of a translation model for translation between the target language and a human source language, the respective partitions together constituting the entire translation model and each respective partition being less than the whole of the translation model; and one or more replica translation model servers for each of the plurality of translation model servers, and wherein the translation server comprises: a plurality of translation front ends each operable to divide source text, in the source language to be translated into the target language, into a plurality of segments in the source language; a plurality of segment translation servers each operable to perform machine translation obtaining translation model data from the plurality of translation model servers and the replica translation model servers and obtaining language model data from the plurality of language model servers and the replica language model servers; and a load balancing module operable to assign the segments to one or more of the plurality of segment translation servers for translation based on translation load at the plurality of segment translation servers. | 40. A system comprising: a plurality of machine translation resource servers, each machine translation resource server storing and operable to serve a partition of a collection of machine translation resource data for translation from a source language to a target language, the respective partitions together constituting the collection of machine translation resource data and each respective partition being less than the collection of machine translation resource data; and at least one translation server operable to receive source text in the source language to be translated into the target language, the translation server further operable to obtain machine translation resource data from the plurality of machine translation resource servers and to use the obtained machine translation resource data to translate the source text into the target language, wherein the machine translation resources servers comprise: a plurality of language model servers respectively storing and operable to serve different partitions of a language model for the target language, the respective partitions together constituting the entire language model and each respective partition being less than the whole of the language model; one or more replica language model servers for each of the plurality of language model servers; a plurality of translation model servers respectively storing and operable to serve different partitions of a translation model for translation between the target language and a human source language, the respective partitions together constituting the entire translation model and each respective partition being less than the whole of the translation model; and one or more replica translation model servers for each of the plurality of translation model servers, and wherein the translation server comprises: a plurality of translation front ends each operable to divide source text, in the source language to be translated into the target language, into a plurality of segments in the source language; a plurality of segment translation servers each operable to perform machine translation obtaining translation model data from the plurality of translation model servers and the replica translation model servers and obtaining language model data from the plurality of language model servers and the replica language model servers; and a load balancing module operable to assign the segments to one or more of the plurality of segment translation servers for translation based on translation load at the plurality of segment translation servers. 49. The system of claim 40 , wherein: each segment translation server comprises: a first segment translation server cache operable to store language model data obtained from the plurality of language model servers and the replica language model servers for translating a segment, and a second segment translation server cache storing a selected portion of the language model in the plurality of language model servers and the replica language model servers. | 0.833212 |
8,249,867 | 7 | 15 | 7. A target speech extraction method for a microphone-array-based speech recognition system, comprising: separating mixed signals input through a plurality of microphone into sound-source signals by an ICA; extracting one target speech spoken for speech recognition from the separated sound-source signals; and recognizing a desired speech from the extracted target speech, wherein the extracting of the target speech comprises: extracting feature vector sequence X i from the separated sound-source signals; calculating an ith LLR (logarithm likelihood ratio) LLR i of the extracted feature vector sequence; calculating a maximum value using the LLR i ; comparing the maximum value with a predetermined threshold value; and determining the maximum value to be the target speech when the maximum value is larger than the threshold value. | 7. A target speech extraction method for a microphone-array-based speech recognition system, comprising: separating mixed signals input through a plurality of microphone into sound-source signals by an ICA; extracting one target speech spoken for speech recognition from the separated sound-source signals; and recognizing a desired speech from the extracted target speech, wherein the extracting of the target speech comprises: extracting feature vector sequence X i from the separated sound-source signals; calculating an ith LLR (logarithm likelihood ratio) LLR i of the extracted feature vector sequence; calculating a maximum value using the LLR i ; comparing the maximum value with a predetermined threshold value; and determining the maximum value to be the target speech when the maximum value is larger than the threshold value. 15. The target speech extraction method of claim 7 , wherein the extracting of the target speech comprises: in a case where the additional information for the target speech is not provided, performing primary speech recognition for the separated sound-source signals by using an HMM (hidden Markov model) as a speech-recognition acoustic model; calculating a closest HMM and a state column thereof for a sequence of the words obtained through the speech recognition; calculating LLR i by using the HMMs; calculating a maximum value by using the calculated LLRi; comparing the maximum value with a predetermined threshold value; and determining the maximum value to be the target speech if the maximum value is larger than the threshold value. | 0.5 |
10,061,866 | 1 | 4 | 1. A server that fulfills a literal query of a user, the server comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the server to: identify, for the literal query, at least two literal query results; generate result probabilities for the at least two literal query results based on results that were previously selected by other users who previously submitted the literal query, the result probabilities reflecting a probability that a corresponding literal query result matches an intent of the user in submitting the literal query; identify a sort order according to the result probabilities of the at least two literal query results; determines, for the literal query, an adjusted query; evaluate the adjusted query in order to identify, for the adjusted query, one or more adjusted query results; generate an interpreted probability for the adjusted query based on result probabilities of at least some of the adjusted query results, the interpreted probability reflecting a probability that the user intended the adjusted query; identify, within the sort order, an adjustment position that is between a first literal query result having a higher result probability than the interpreted probability, and a second literal query result having a lower result probability than the interpreted probability; and present the at least two literal query results and insert, at the adjustment position, an adjustment option describing the adjusted query. | 1. A server that fulfills a literal query of a user, the server comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the server to: identify, for the literal query, at least two literal query results; generate result probabilities for the at least two literal query results based on results that were previously selected by other users who previously submitted the literal query, the result probabilities reflecting a probability that a corresponding literal query result matches an intent of the user in submitting the literal query; identify a sort order according to the result probabilities of the at least two literal query results; determines, for the literal query, an adjusted query; evaluate the adjusted query in order to identify, for the adjusted query, one or more adjusted query results; generate an interpreted probability for the adjusted query based on result probabilities of at least some of the adjusted query results, the interpreted probability reflecting a probability that the user intended the adjusted query; identify, within the sort order, an adjustment position that is between a first literal query result having a higher result probability than the interpreted probability, and a second literal query result having a lower result probability than the interpreted probability; and present the at least two literal query results and insert, at the adjustment position, an adjustment option describing the adjusted query. 4. The server of claim 1 , wherein the memory stores further instructions that, when executed by the processor, cause the server to further: generate a query adjustment set that correlates literal queries and corresponding adjusted queries, each adjusted query having an interpreted probability higher than a result probability of at least one result of a corresponding literal query; and determining the adjusted query by reference to a previously generated query adjustment set. | 0.61165 |
8,255,218 | 1 | 7 | 1. A method performed by a computing device having one or more processors, memory, a microphone, and a display capable of displaying a graphical user interface (GUI), the method comprising: receiving a request from a user to activate a voice input capability of the computing device; activating the voice input capability; while the voice input capability is active, performing, by the computing device, the steps of: receiving vocal input from the user, wherein at least a portion of the vocal input is intended for input into a first input field of the GUI displayed on the display; during the receiving of the vocal input, receiving a selection by the user of a second input field of the GUI displayed on the display; identifying vocal input received prior to the selection of the second input field as first vocal input intended for the first input field; identifying vocal input received after the selection of the second input field as second vocal input intended for the second input field; detecting that vocal input from the user has concluded; and deactivating the voice input capability; graphically inputting a first text portion in the first input field, wherein the first text portion is based upon conversion of the first vocal input into textual characters by an automatic speech recognition (ASR) engine; and graphically inputting a second text portion in the second input field, wherein the second text portion is based upon conversion of the second vocal input into textual characters by the ASR engine. | 1. A method performed by a computing device having one or more processors, memory, a microphone, and a display capable of displaying a graphical user interface (GUI), the method comprising: receiving a request from a user to activate a voice input capability of the computing device; activating the voice input capability; while the voice input capability is active, performing, by the computing device, the steps of: receiving vocal input from the user, wherein at least a portion of the vocal input is intended for input into a first input field of the GUI displayed on the display; during the receiving of the vocal input, receiving a selection by the user of a second input field of the GUI displayed on the display; identifying vocal input received prior to the selection of the second input field as first vocal input intended for the first input field; identifying vocal input received after the selection of the second input field as second vocal input intended for the second input field; detecting that vocal input from the user has concluded; and deactivating the voice input capability; graphically inputting a first text portion in the first input field, wherein the first text portion is based upon conversion of the first vocal input into textual characters by an automatic speech recognition (ASR) engine; and graphically inputting a second text portion in the second input field, wherein the second text portion is based upon conversion of the second vocal input into textual characters by the ASR engine. 7. The method of claim 1 , wherein: if the vocal input received concurrent to receiving a selection by the user of a second input field of the GUI is not a pause within the vocal input, identifying, as a part of first vocal input intended for the first input field, a portion of the vocal input including the vocal input received concurrent to receiving the selection by the user of the second input and ending at a pause within the vocal input. | 0.614385 |
8,328,089 | 9 | 14 | 9. A communication device comprising: at least one input configured to record speech; and a processor configured to implement: responsive to detecting, at the communication device, an indication from a user of the communication device to record, recording at least a portion of a communication between the communication device and a second remote communication device, prompting the user to speak a contact name, and recording at least a portion of speech spoken by the user in response to the prompting as a recording of the contact name; automatically converting the recording of the at least a portion of the communication and the recording of the contact name into text; automatically extracting the contact name and at least one other type of contact information from the text; and storing the extracted contact name and the at least one other type of contact information in an entry of the contact database without manual entry of the extracted contact name and the at least one other type of contact information by the user of the communication device. | 9. A communication device comprising: at least one input configured to record speech; and a processor configured to implement: responsive to detecting, at the communication device, an indication from a user of the communication device to record, recording at least a portion of a communication between the communication device and a second remote communication device, prompting the user to speak a contact name, and recording at least a portion of speech spoken by the user in response to the prompting as a recording of the contact name; automatically converting the recording of the at least a portion of the communication and the recording of the contact name into text; automatically extracting the contact name and at least one other type of contact information from the text; and storing the extracted contact name and the at least one other type of contact information in an entry of the contact database without manual entry of the extracted contact name and the at least one other type of contact information by the user of the communication device. 14. The communication device according to claim 9 , wherein the extracting the contact name and the at least one other type of contact information from the text comprises: detecting a lack of a tag within the text; inferring information for the tag from other information collectable at the communication device about a second user of the second remote communication device; and assigning the inferred information to a field of the entry of the contact database, wherein the field is associated with the tag. | 0.518939 |
9,043,338 | 6 | 7 | 6. The method of claim 1 , wherein determining a rank score for each of the two or more distinct book content items comprises: representing the two or more distinct book content items as a graph in computer memory, where each of the distinct book content items is represented as a distinct node in the graph and where an edge exists in the graph between each pair of distinct nodes that represent distinct book content items that both include a similar image; and determining a rank score for each of the two or more distinct book content items based on the edges between the distinct nodes. | 6. The method of claim 1 , wherein determining a rank score for each of the two or more distinct book content items comprises: representing the two or more distinct book content items as a graph in computer memory, where each of the distinct book content items is represented as a distinct node in the graph and where an edge exists in the graph between each pair of distinct nodes that represent distinct book content items that both include a similar image; and determining a rank score for each of the two or more distinct book content items based on the edges between the distinct nodes. 7. The method of claim 6 , wherein the rank score for each distinct node is based on the edges connecting distinct nodes corresponding to distinct book content items that satisfy a search query. | 0.918487 |
8,676,815 | 12 | 13 | 12. The method of claim 9 , further comprising: collecting a topic thread in an online forum of the online forum system, wherein the topic thread comprises a topic post and a plurality of reply posts; stripping non-word tokens of the topic post and the plurality of reply posts; parsing remaining texts of the topic post and the plurality of reply posts into words of parsed posts; identifying and removing stop words in the parsed posts from the parsing; applying Porter stemming to the parsed posts to generate stemmed posts having stemmed words; combining the stemmed words into objects including selecting a subject of the topic thread as a title of the plurality of documents, combining text of the stemmed posts into the plurality of documents in order of respective submitted times; and merging the objects with at least a predetermined number of words into a document that facilitates populating the suffix tree document model. | 12. The method of claim 9 , further comprising: collecting a topic thread in an online forum of the online forum system, wherein the topic thread comprises a topic post and a plurality of reply posts; stripping non-word tokens of the topic post and the plurality of reply posts; parsing remaining texts of the topic post and the plurality of reply posts into words of parsed posts; identifying and removing stop words in the parsed posts from the parsing; applying Porter stemming to the parsed posts to generate stemmed posts having stemmed words; combining the stemmed words into objects including selecting a subject of the topic thread as a title of the plurality of documents, combining text of the stemmed posts into the plurality of documents in order of respective submitted times; and merging the objects with at least a predetermined number of words into a document that facilitates populating the suffix tree document model. 13. The method of claim 12 , further comprising: creating stopnodes in the suffix tree document model to retain at least a part of information related to the stop words. | 0.5 |
7,912,246 | 1 | 13 | 1. A method for determining age categories of people, comprising the following steps of: a) annotating a facial image database according to the demographics classes of the individual face, b) training a plurality of learning machines so that each learning machine outputs auxiliary demographics class information and age information of any given facial image, c) detecting and tracking a facial image from the input image frame, d) processing said facial image to extract image features, and e) processing said image features obtained from said facial image using classification techniques for determining age or age categories, whereby the age classes can be any partition based on age in multiple groups, wherein the method further comprises a step of determining the target outputs of the plurality of learning machines so that each learning machine maps a first input data, whose facial images belong to a first auxiliary demographics class, to first vector-valued points on a manifold in the space of facial images, whereas each learning machine maps a second input data, whose facial images do not belong to a first auxiliary demographics class, to second vector-valued points away from the manifold. | 1. A method for determining age categories of people, comprising the following steps of: a) annotating a facial image database according to the demographics classes of the individual face, b) training a plurality of learning machines so that each learning machine outputs auxiliary demographics class information and age information of any given facial image, c) detecting and tracking a facial image from the input image frame, d) processing said facial image to extract image features, and e) processing said image features obtained from said facial image using classification techniques for determining age or age categories, whereby the age classes can be any partition based on age in multiple groups, wherein the method further comprises a step of determining the target outputs of the plurality of learning machines so that each learning machine maps a first input data, whose facial images belong to a first auxiliary demographics class, to first vector-valued points on a manifold in the space of facial images, whereas each learning machine maps a second input data, whose facial images do not belong to a first auxiliary demographics class, to second vector-valued points away from the manifold. 13. The method according to claim 1 , wherein the method further comprises a step of arranging a plurality of classifiers in a manner of combining chains of serial classifiers in parallel, whereby the first classifier in each chain of serial classifiers performs a gross-level classification into gross-level classes by computing gross-level class information and the second classifier in each chain of serial classifiers performs a finer level classification that is specialized to one of the gross-level classes by computing the finer level class information. | 0.569124 |
9,792,204 | 7 | 10 | 7. A non-transitory computer readable medium having stored thereon instructions which when executed by a control processor cause the control processor to perform a method for automated test case augmentation, the method comprising: receiving, at an automated test augmentation system, a design model and model coverage gap information from a model-based development tool, the model coverage gap information indicating test criteria not exercised by a high-level requirements-based test case; translating the coverage gaps into machine-readable mathematical test objective expressions; developing a set of test objective operators by translating the machine-readable mathematical test objective expressions; localizing target operators for the identified coverage gaps within the design model; attaching the test objective operators to target operators of the design model to create a test model; augmenting the test model by propagating test objectives at the target operators to a test node operator of the design model; and a test generator executing the augmented test model to obtain the test cases to cover the coverage gaps and the causes for the model coverage gaps. | 7. A non-transitory computer readable medium having stored thereon instructions which when executed by a control processor cause the control processor to perform a method for automated test case augmentation, the method comprising: receiving, at an automated test augmentation system, a design model and model coverage gap information from a model-based development tool, the model coverage gap information indicating test criteria not exercised by a high-level requirements-based test case; translating the coverage gaps into machine-readable mathematical test objective expressions; developing a set of test objective operators by translating the machine-readable mathematical test objective expressions; localizing target operators for the identified coverage gaps within the design model; attaching the test objective operators to target operators of the design model to create a test model; augmenting the test model by propagating test objectives at the target operators to a test node operator of the design model; and a test generator executing the augmented test model to obtain the test cases to cover the coverage gaps and the causes for the model coverage gaps. 10. The non-transitory computer-readable medium of claim 7 , the instructions further causing the control processor to at least one of categorize and identify the coverage criteria. | 0.562802 |
9,710,944 | 21 | 22 | 21. The non-transitory machine-readable medium of claim 17 wherein the program further comprises a set of instructions for including the high-resolution images in the high-resolution version of the electronic document, wherein the set of instructions for including the high-resolution images comprises sets of instructions for: determining a maximum resolution needed to display each of the images on a high-resolution device; for each of the images that have a higher resolution than the maximum resolution needed to display the images on a high-resolution device: creating a version of the high-resolution image at the maximum resolution needed to display the image on the high-resolution device; and including the created version of the image in the high-resolution version of the electronic document. | 21. The non-transitory machine-readable medium of claim 17 wherein the program further comprises a set of instructions for including the high-resolution images in the high-resolution version of the electronic document, wherein the set of instructions for including the high-resolution images comprises sets of instructions for: determining a maximum resolution needed to display each of the images on a high-resolution device; for each of the images that have a higher resolution than the maximum resolution needed to display the images on a high-resolution device: creating a version of the high-resolution image at the maximum resolution needed to display the image on the high-resolution device; and including the created version of the image in the high-resolution version of the electronic document. 22. The non-transitory machine-readable medium of claim 21 , wherein the program further comprises a set of instructions for including the high-resolution image in the high-resolution version of the document for each of the images that does not have a higher resolution than the maximum resolution needed to display the image on a high-resolution device. | 0.5 |
7,475,018 | 2 | 8 | 2. The method of claim 1 , wherein the step of developing an initial domain model comprises at least one of the steps of defining an organizational scope, defining a content scope, defining a process scope, and defining economic factors relating to the domain. | 2. The method of claim 1 , wherein the step of developing an initial domain model comprises at least one of the steps of defining an organizational scope, defining a content scope, defining a process scope, and defining economic factors relating to the domain. 8. The method of claim 2 , wherein the step of defining economic factors further comprises developing an economic model for use in developing the estimates of costs and value associated with developing the knowledge map. | 0.770833 |
8,756,184 | 23 | 30 | 23. A non-transitory computer readable storage medium encoded with computer program instructions which when accessed by a computer cause the computer to load the program instructions to a memory therein creating a special purpose data structure causing the computer to operate as a specially programmed computer, executing a method of utilizing a user's predicted attributes, comprising: collecting, in the specially programmed computer, a plurality of sample user behaviors and a plurality of sample user attributes from a service that offers videos for viewing, wherein a sample user behavior is based on a sample user using the service and a sample user attribute is received from the sample user; training, in the specially programmed computer, a model to be able to produce the plurality of sample user attributes from the plurality of sample user behaviors; after training, inputting, in the specially programmed computer, the plurality of sample user behaviors into the trained model to produce predicted sample user attributes; comparing, in the specially programmed computer, the plurality of sample user attributes and the plurality of sample user behaviors to the predicted sample user attributes to determine associated probabilities for the predicted sample user attributes; determining, in the specially programmed computer, a real user behavior for a second user based on the second user using the service; predicting, in the specially programmed computer, in the specially programmed computer, using the model, that the second user has a predicted user attribute of an associated probability based on the user having the real user behavior, wherein the predicted user attribute of the associated probability is not known for the second user; and utilizing, in the specially programmed computer, that the second user has the predicted user attribute of the associated probability to improve the second user's experience using the service. | 23. A non-transitory computer readable storage medium encoded with computer program instructions which when accessed by a computer cause the computer to load the program instructions to a memory therein creating a special purpose data structure causing the computer to operate as a specially programmed computer, executing a method of utilizing a user's predicted attributes, comprising: collecting, in the specially programmed computer, a plurality of sample user behaviors and a plurality of sample user attributes from a service that offers videos for viewing, wherein a sample user behavior is based on a sample user using the service and a sample user attribute is received from the sample user; training, in the specially programmed computer, a model to be able to produce the plurality of sample user attributes from the plurality of sample user behaviors; after training, inputting, in the specially programmed computer, the plurality of sample user behaviors into the trained model to produce predicted sample user attributes; comparing, in the specially programmed computer, the plurality of sample user attributes and the plurality of sample user behaviors to the predicted sample user attributes to determine associated probabilities for the predicted sample user attributes; determining, in the specially programmed computer, a real user behavior for a second user based on the second user using the service; predicting, in the specially programmed computer, in the specially programmed computer, using the model, that the second user has a predicted user attribute of an associated probability based on the user having the real user behavior, wherein the predicted user attribute of the associated probability is not known for the second user; and utilizing, in the specially programmed computer, that the second user has the predicted user attribute of the associated probability to improve the second user's experience using the service. 30. The non-transitory computer readable storage medium of claim 23 , wherein the utilizing comprises transmitting a personalized recommendation to a user based on predicting that the second user has the predicted user attribute of the associated probability. | 0.622449 |
9,934,399 | 5 | 12 | 5. A computer-implemented method, comprising: receiving a request to access one or more resources of a resource hierarchy; determining a first representation of a security policy controlling access to the one or more resources, the first representation expressed using a first policy language; generating, in response to the request and based on the first representation, a second representation of the security policy in a second policy language, the second representation modifying a scope of authority granted by the first representation; retrieving one or more additional security policies for the request, the one or more additional security policies expressed in the second policy language; and evaluating the one or more additional security policies and the second representation to determine whether to grant access to the one or more resources. | 5. A computer-implemented method, comprising: receiving a request to access one or more resources of a resource hierarchy; determining a first representation of a security policy controlling access to the one or more resources, the first representation expressed using a first policy language; generating, in response to the request and based on the first representation, a second representation of the security policy in a second policy language, the second representation modifying a scope of authority granted by the first representation; retrieving one or more additional security policies for the request, the one or more additional security policies expressed in the second policy language; and evaluating the one or more additional security policies and the second representation to determine whether to grant access to the one or more resources. 12. The computer-implemented method of claim 5 , wherein generating the second representation further comprises: translating the security policy from the first security policy language, used to express the first representation, to the second security policy language. | 0.735644 |
9,430,531 | 1 | 6 | 1. A method comprising: receiving first activity information for a sender of a first link to at least one recipient collected by a collection resource at a Web site, wherein no personally identifiable information of the sender is collected in collecting the first activity information, and the personally identifiable information of the sender comprises an e-mail address of the sender; storing the first activity information at a storage server; receiving second activity information when a recipient accesses the first link sent by the sender corresponding to the first activity information stored at the storage server, wherein no personally identifiable information of the recipient collected in collecting the second activity information; using at least one processor, attempting to identify a first node representative of the sender in a social graph; when a first node representative of the sender in a social graph is not identified and after receiving the second activity information, creating a second node to represent the sender in the social graph; and based on at least information associated with the second node in the social graph, selecting a personalized digital content for delivery to the sender. | 1. A method comprising: receiving first activity information for a sender of a first link to at least one recipient collected by a collection resource at a Web site, wherein no personally identifiable information of the sender is collected in collecting the first activity information, and the personally identifiable information of the sender comprises an e-mail address of the sender; storing the first activity information at a storage server; receiving second activity information when a recipient accesses the first link sent by the sender corresponding to the first activity information stored at the storage server, wherein no personally identifiable information of the recipient collected in collecting the second activity information; using at least one processor, attempting to identify a first node representative of the sender in a social graph; when a first node representative of the sender in a social graph is not identified and after receiving the second activity information, creating a second node to represent the sender in the social graph; and based on at least information associated with the second node in the social graph, selecting a personalized digital content for delivery to the sender. 6. The method of claim 1 wherein the first activity information is generated by activity at a Web site. | 0.897 |
8,229,747 | 15 | 19 | 15. A non-transitory computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform a method comprising: receiving a speech input; constructing an unweighted grammar permitting all letter sequences that map to the speech input; generating keypad constraints using the unweighted grammar and a weighted statistical letter model trained on a database of words; receiving non-speech input constrained by the keypad constraints; and recognizing the speech input and the non-speech input using the unweighted grammar and the weighted statistical letter model. | 15. A non-transitory computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform a method comprising: receiving a speech input; constructing an unweighted grammar permitting all letter sequences that map to the speech input; generating keypad constraints using the unweighted grammar and a weighted statistical letter model trained on a database of words; receiving non-speech input constrained by the keypad constraints; and recognizing the speech input and the non-speech input using the unweighted grammar and the weighted statistical letter model. 19. The non-transitory computer-readable storage medium of claim 15 , the instructions further comprising: controlling the processor to generate a final letter string based on the database of words. | 0.5 |
9,471,581 | 11 | 17 | 11. A non-transitory computer-readable medium containing instructions for suggesting one or more autocompletions to a file name for a file to save, the instructions for execution by a computer system, the non-transitory computer-readable medium comprising: instructions for building an autocomplete dictionary based on text in a file by adding at least some text from the file to the autocomplete dictionary; instructions for receiving a request from a user to save the file; instructions for, in response to the request from the user to save the file, the computer system presenting a user interface element for specifying a name for the file to be saved, the user interface element configured to receive text entry; instructions for receiving text entry from the user in the user interface element; instructions for submitting at least a portion of the text entry of the user to the autocomplete dictionary in order to search the autocomplete dictionary; instructions for, in response to the search based on the portion of the text entry of the user, receiving from the autocomplete dictionary one or more proposed autocompletions, each of the one or more proposed autocompletions containing the portion of the text entry of the user as a prefix and at least one of the one or more proposed autocompletions containing text from the file; instructions for presenting, by the computer system, the one or more proposed autocompletions to the user. | 11. A non-transitory computer-readable medium containing instructions for suggesting one or more autocompletions to a file name for a file to save, the instructions for execution by a computer system, the non-transitory computer-readable medium comprising: instructions for building an autocomplete dictionary based on text in a file by adding at least some text from the file to the autocomplete dictionary; instructions for receiving a request from a user to save the file; instructions for, in response to the request from the user to save the file, the computer system presenting a user interface element for specifying a name for the file to be saved, the user interface element configured to receive text entry; instructions for receiving text entry from the user in the user interface element; instructions for submitting at least a portion of the text entry of the user to the autocomplete dictionary in order to search the autocomplete dictionary; instructions for, in response to the search based on the portion of the text entry of the user, receiving from the autocomplete dictionary one or more proposed autocompletions, each of the one or more proposed autocompletions containing the portion of the text entry of the user as a prefix and at least one of the one or more proposed autocompletions containing text from the file; instructions for presenting, by the computer system, the one or more proposed autocompletions to the user. 17. The non-transitory computer-readable medium of claim 11 , wherein the instructions for building the autocomplete dictionary only add text from the file to the autocomplete dictionary if the text exceeds a prominence threshold. | 0.743304 |
9,183,595 | 8 | 10 | 8. An apparatus according to claim 7 , wherein collecting the set of facts includes: for each fact of the set of facts, generating a link table of users of the population of users to which the fact has a reference, and wherein identifying the set of group facts includes: finding the facts of the set of facts for which the link table includes users of the group of users. | 8. An apparatus according to claim 7 , wherein collecting the set of facts includes: for each fact of the set of facts, generating a link table of users of the population of users to which the fact has a reference, and wherein identifying the set of group facts includes: finding the facts of the set of facts for which the link table includes users of the group of users. 10. An apparatus according to claim 8 , wherein collecting the set of facts includes: for each fact of the set of facts, using a force-based algorithm to produce a set of nodes of a graph representing the users of the population of users in the link table; wherein generating the set of questions from the set of group facts includes: for each group fact of the set of group facts, excluding the group fact in producing questions of the set of questions when a minimum distance between nodes representing users of the group of users and other nodes is less than an external distance threshold. | 0.549392 |
8,291,468 | 1 | 3 | 1. A method comprising: receiving, with an authentication device, authentication information from an endpoint device; authenticating, with the authentication device, the endpoint device based on the authentication information; based on the authentication, storing, with the authentication device, authorization information in accordance with a first proprietary authorization data model; applying, with the authentication device, an export translation policy to translate the authorization information stored in accordance the first proprietary authorization data model into translated authorization information that complies with a second Interface for Metadata Access Point (IF-MAP) authorization data model conforming to an IF-MAP standard, wherein the second IF-MAP authorization data model is different from the first proprietary authorization data model; and publishing, with the authentication device, the translated authorization information in accordance with an IF-MAP protocol to an intermediate storage device that implements the second IF-MAP authorization data model. | 1. A method comprising: receiving, with an authentication device, authentication information from an endpoint device; authenticating, with the authentication device, the endpoint device based on the authentication information; based on the authentication, storing, with the authentication device, authorization information in accordance with a first proprietary authorization data model; applying, with the authentication device, an export translation policy to translate the authorization information stored in accordance the first proprietary authorization data model into translated authorization information that complies with a second Interface for Metadata Access Point (IF-MAP) authorization data model conforming to an IF-MAP standard, wherein the second IF-MAP authorization data model is different from the first proprietary authorization data model; and publishing, with the authentication device, the translated authorization information in accordance with an IF-MAP protocol to an intermediate storage device that implements the second IF-MAP authorization data model. 3. The method of claim 1 , further comprising: presenting, with the authentication device, a user interface by which to receive data defining the export translation policy; receiving, with the authentication device, the data defining the export translation policy via the user interface; and storing, with the authentication device, the data defining the export translation policy. | 0.791347 |
10,140,977 | 12 | 17 | 12. A method comprising: obtaining, during operation of a computer-implemented dialogue system comprising a natural language understanding engine, data identifying (i) a first input conversational turn that was provided as input to the natural language understanding engine during a dialogue between a user and the computer-implemented dialogue system and (ii) a first annotation of the first input conversational turn generated by the natural language understanding engine, wherein the natural language understanding engine has been trained on a first set of training data comprising a plurality of training conversational turns; determining that the first annotation accurately characterized the first input conversational turn; determining, based on the training conversational turns in the first set of training data, that the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn; in response to determining that (i) the first annotation accurately characterized the first input conversational turn but (ii) the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn: obtaining one or more first paraphrases of the first input conversational turn; and generating, for each of the one or more first paraphrases, a respective first training example that identifies the first annotation as the correct annotation for the first paraphrase; and training the natural language understanding engine on at least the first training examples. | 12. A method comprising: obtaining, during operation of a computer-implemented dialogue system comprising a natural language understanding engine, data identifying (i) a first input conversational turn that was provided as input to the natural language understanding engine during a dialogue between a user and the computer-implemented dialogue system and (ii) a first annotation of the first input conversational turn generated by the natural language understanding engine, wherein the natural language understanding engine has been trained on a first set of training data comprising a plurality of training conversational turns; determining that the first annotation accurately characterized the first input conversational turn; determining, based on the training conversational turns in the first set of training data, that the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn; in response to determining that (i) the first annotation accurately characterized the first input conversational turn but (ii) the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn: obtaining one or more first paraphrases of the first input conversational turn; and generating, for each of the one or more first paraphrases, a respective first training example that identifies the first annotation as the correct annotation for the first paraphrase; and training the natural language understanding engine on at least the first training examples. 17. The method of claim 12 , further comprising obtaining a confidence score generated by the natural language understanding engine that represents a confidence that the first annotation is an accurate characterization of the first input conversational turn, and wherein determining that the first annotation accurately characterized the first input conversational turn comprises determining that the confidence score exceeds a threshold score. | 0.809442 |
8,135,577 | 21 | 24 | 21. A machine-readable non-transitory medium containing executable program instructions which cause a data processing system to perform operations comprising: converting original text to a Braille code; generating data for output to a Braille device wherein the output is representative of the original text; generating data for display of a Braille caption panel on a graphical user interface of a video display device, wherein the Braille caption panel includes the Braille code as being currently outputted to the Braille device and the original text; and generating data for display of the original text outside of the Braille caption panel on the graphical user interface. | 21. A machine-readable non-transitory medium containing executable program instructions which cause a data processing system to perform operations comprising: converting original text to a Braille code; generating data for output to a Braille device wherein the output is representative of the original text; generating data for display of a Braille caption panel on a graphical user interface of a video display device, wherein the Braille caption panel includes the Braille code as being currently outputted to the Braille device and the original text; and generating data for display of the original text outside of the Braille caption panel on the graphical user interface. 24. The machine-readable medium of claim 21 , further including data that cause the data processing system to perform operations comprising: automatically launching an accessibility service in response to receiving a connection to the Braille device. | 0.5 |
8,368,970 | 5 | 6 | 5. An image reader, comprising: a reading unit configured to receive light reflected froma document; and an automatic document feeder configured to supply the document to the reading unit, wherein the automatic document feeder has defined therein a document transfer channel along which the document travels within the automatic document feeder, the document transfer channel including a component having a surface that comes in contact with the document and that has a surface tension that is less than or equal to about 40 dyne-per-centimeter, and wherein the component of the document transfer channel has a surface roughness (Ra) that ranges from about 0.3 microns (μm) to about 30 μm. | 5. An image reader, comprising: a reading unit configured to receive light reflected froma document; and an automatic document feeder configured to supply the document to the reading unit, wherein the automatic document feeder has defined therein a document transfer channel along which the document travels within the automatic document feeder, the document transfer channel including a component having a surface that comes in contact with the document and that has a surface tension that is less than or equal to about 40 dyne-per-centimeter, and wherein the component of the document transfer channel has a surface roughness (Ra) that ranges from about 0.3 microns (μm) to about 30 μm. 6. The image reader according to claim 5 , wherein the surface of the component of the document transfer channel is etched. | 0.841495 |
8,894,489 | 16 | 20 | 16. A user interface method for use with a touch gesture user interface for implementing global or universal gestures and context-specific gestures for the control of user applications executing on a device comprising, the method comprising Recognizing at least one posture of a finger in contact with a tactile sensor, Recognizing a touch gesture from a plurality of possible touch gestures by recognizing specific changes within postures, the changes comprising at least one recognized change in the angle of finger contact with the tactile sensor, Assigning a user interface function to the recognized touch gesture, Directing executing software to perform a user interface operation associated with the recognized touch gesture, the user interface operation selected from a plurality of possible from a user interface operations, and the association from plurality of possible associations, the plurality of possible associations comprising at least a global type and a second type, the first type of association being common across the system and the second type of association being an application-specific association that is specific to an application, and Wherein the executing software is responsive to the recognized touch gesture. | 16. A user interface method for use with a touch gesture user interface for implementing global or universal gestures and context-specific gestures for the control of user applications executing on a device comprising, the method comprising Recognizing at least one posture of a finger in contact with a tactile sensor, Recognizing a touch gesture from a plurality of possible touch gestures by recognizing specific changes within postures, the changes comprising at least one recognized change in the angle of finger contact with the tactile sensor, Assigning a user interface function to the recognized touch gesture, Directing executing software to perform a user interface operation associated with the recognized touch gesture, the user interface operation selected from a plurality of possible from a user interface operations, and the association from plurality of possible associations, the plurality of possible associations comprising at least a global type and a second type, the first type of association being common across the system and the second type of association being an application-specific association that is specific to an application, and Wherein the executing software is responsive to the recognized touch gesture. 20. The method of claim 16 wherein the gesture is used to control the system. | 0.818396 |
7,624,113 | 1 | 15 | 1. A computer program product tangibly embodied in a computer-readable medium, the computer program product including executable instructions for performing operations for naming at least one of several data elements that can identify business information in a communication, the operations comprising: associating a data element with an aggregate data element that includes one or more data elements, wherein the data element is configured to have associated therewith an object class term that represents a logical grouping to which the data element belongs and a property term that represents a characteristic of the logical grouping, selecting a data type that defines values that are valid for being assigned to the data element, wherein the data type comprises a qualifier term that represents an additional semantic restriction describing the data type; assigning the qualifier term to a selected one of the property term and the object class term of the data element-based upon a name of the aggregate data element, wherein the aggregate data element is configured to have associated therewith an aggregate class term that semantically defines the logical grouping, and wherein the qualifier term is assigned to the object class term upon a determination that the qualifier term is identical with the aggregate class term, and wherein the qualifier term is assigned to the property term upon a determination that the qualifier term is not identical with the aggregate class term; and associating at least the object class term, the property term and the qualifier term assigned to one of the object class term and the property term into a name for the data element. | 1. A computer program product tangibly embodied in a computer-readable medium, the computer program product including executable instructions for performing operations for naming at least one of several data elements that can identify business information in a communication, the operations comprising: associating a data element with an aggregate data element that includes one or more data elements, wherein the data element is configured to have associated therewith an object class term that represents a logical grouping to which the data element belongs and a property term that represents a characteristic of the logical grouping, selecting a data type that defines values that are valid for being assigned to the data element, wherein the data type comprises a qualifier term that represents an additional semantic restriction describing the data type; assigning the qualifier term to a selected one of the property term and the object class term of the data element-based upon a name of the aggregate data element, wherein the aggregate data element is configured to have associated therewith an aggregate class term that semantically defines the logical grouping, and wherein the qualifier term is assigned to the object class term upon a determination that the qualifier term is identical with the aggregate class term, and wherein the qualifier term is assigned to the property term upon a determination that the qualifier term is not identical with the aggregate class term; and associating at least the object class term, the property term and the qualifier term assigned to one of the object class term and the property term into a name for the data element. 15. The computer program product of claim 1 , wherein the qualifier term is a variable qualifier term that is a place holder for another qualifier term. | 0.848303 |
8,458,315 | 8 | 9 | 8. The computer readable storage medium of claim 7 wherein the method further comprises causing the message broker to assign a priority to messages to the service provider. | 8. The computer readable storage medium of claim 7 wherein the method further comprises causing the message broker to assign a priority to messages to the service provider. 9. The computer readable storage medium of claim 8 wherein the assignment of the priority is based upon a certificate assigned to the vehicle-to-business software application. | 0.551282 |
8,539,380 | 11 | 13 | 11. A GPS-enabled wireless device, comprising: a memory and a processor that are respectively adapted to store and execute instructions that implement operations, the operations comprising: computing location coordinates of a user using the GPS-enabled wireless device; predicting a future activity of the user based on user context at the location coordinates; determining a direction in which the wireless device is pointed by the user and a field of view to query; searching websites disposed on a network using a search query to identify businesses within the field of view related to the predicted future activity, the search query being based at least on the location coordinates and coordinates for the field of view; retrieving a list of links to websites of businesses that satisfy the search query; querying the websites on the list of links to find information associated with the businesses; inferring business information obtained from the querying of the websites that is of interest to the user based upon historical data associated with the user and user preferences; and presenting the inferred business information to the user. | 11. A GPS-enabled wireless device, comprising: a memory and a processor that are respectively adapted to store and execute instructions that implement operations, the operations comprising: computing location coordinates of a user using the GPS-enabled wireless device; predicting a future activity of the user based on user context at the location coordinates; determining a direction in which the wireless device is pointed by the user and a field of view to query; searching websites disposed on a network using a search query to identify businesses within the field of view related to the predicted future activity, the search query being based at least on the location coordinates and coordinates for the field of view; retrieving a list of links to websites of businesses that satisfy the search query; querying the websites on the list of links to find information associated with the businesses; inferring business information obtained from the querying of the websites that is of interest to the user based upon historical data associated with the user and user preferences; and presenting the inferred business information to the user. 13. The GPS-enabled wireless device of claim 11 , wherein the context data comprises a time since an activity of the same type as the predicted future activity was carried out. | 0.687943 |
9,183,285 | 1 | 3 | 1. One or more computer-readable storage media device storing computer-readable instructions that, when executed, instruct one or more processors to perform operations comprising: providing recommended groupings of clustered data based at least in part on clustering data of a first data set; receiving an indication from a user that a first portion of the first data set is associated with a bucket, the indication based at least in part on an evaluation by the user of at least one of the recommended groupings; generating a classification model based at least in part on the indication, one or more data signatures based at least in part on one or more of units of data, input patterns of data, order and proximity of terms, or combinations thereof, and one or more bucket patterns based at least in part on one or more cluster patterns, cluster signatures, input data patterns, or a combination thereof; generating classified data based at least in part on applying the classification model to a second data set based at least in part on comparing a data signature of the one or more data signatures to a bucket pattern of the one or more bucket patterns; identifying a subset of data of the first data set, of the second data set, or a combination thereof; providing another recommended groupings of clustered data based at least in part on clustering data of the subset of data; receiving another indication from a user that a first portion of the subset of data is associated with another bucket, the another indication based at least in part on an another evaluation by the user of at least one of the another recommended groupings; generating another classification model based at least in part on the another indication; and generating another classified data based at least in part on applying the another classification model to a third data set. | 1. One or more computer-readable storage media device storing computer-readable instructions that, when executed, instruct one or more processors to perform operations comprising: providing recommended groupings of clustered data based at least in part on clustering data of a first data set; receiving an indication from a user that a first portion of the first data set is associated with a bucket, the indication based at least in part on an evaluation by the user of at least one of the recommended groupings; generating a classification model based at least in part on the indication, one or more data signatures based at least in part on one or more of units of data, input patterns of data, order and proximity of terms, or combinations thereof, and one or more bucket patterns based at least in part on one or more cluster patterns, cluster signatures, input data patterns, or a combination thereof; generating classified data based at least in part on applying the classification model to a second data set based at least in part on comparing a data signature of the one or more data signatures to a bucket pattern of the one or more bucket patterns; identifying a subset of data of the first data set, of the second data set, or a combination thereof; providing another recommended groupings of clustered data based at least in part on clustering data of the subset of data; receiving another indication from a user that a first portion of the subset of data is associated with another bucket, the another indication based at least in part on an another evaluation by the user of at least one of the another recommended groupings; generating another classification model based at least in part on the another indication; and generating another classified data based at least in part on applying the another classification model to a third data set. 3. The one or more computer-readable storage media device of claim 1 , wherein the indication comprises a selection of one or more inputs affirmatively associated with the bucket. | 0.559113 |
9,007,405 | 1 | 3 | 1. A method comprising: determining for an image-based document, by at least one of one or more computing systems, a color value of each pixel of a plurality of pixels; associating, by at least one of the one or more computing systems, a dominant color value to content within the image-based document, the content comprising at least text having a text color value within a predetermined deviation of the dominant color value; identifying, by at least one of the one or more computing systems, an image within the image-based document, the image comprising a group of pixels having a color value within the predetermined deviation of the dominant color value; determining, a layout of the text by identifying bounds of columns in a multicolumn presentation of the text within the image-based document, the determining based at least in part on the dominant color value; determining, by at least one of the one or more computing systems, a flow of the text between the columns by performing character recognition on a portion of the text in the columns; ordering, by at least one of the one or more computing systems, the columns based at least in part on the determined flow of the text; receiving, by at least one of the one or more computing systems, a zoom-in command associated with a selected column; zooming-in, by at least one of the one or more computing systems, on the selected column to initiate a column-viewing mode to enlarge the selected column such that the bounds of the selected column are enlarged to approximately span a width of a display space; and presenting, by at least one of the one or more computing systems, another column in line with the selected column based at least in part on the ordering of the columns to enable continuous scrolling through the columns in the column-viewing mode. | 1. A method comprising: determining for an image-based document, by at least one of one or more computing systems, a color value of each pixel of a plurality of pixels; associating, by at least one of the one or more computing systems, a dominant color value to content within the image-based document, the content comprising at least text having a text color value within a predetermined deviation of the dominant color value; identifying, by at least one of the one or more computing systems, an image within the image-based document, the image comprising a group of pixels having a color value within the predetermined deviation of the dominant color value; determining, a layout of the text by identifying bounds of columns in a multicolumn presentation of the text within the image-based document, the determining based at least in part on the dominant color value; determining, by at least one of the one or more computing systems, a flow of the text between the columns by performing character recognition on a portion of the text in the columns; ordering, by at least one of the one or more computing systems, the columns based at least in part on the determined flow of the text; receiving, by at least one of the one or more computing systems, a zoom-in command associated with a selected column; zooming-in, by at least one of the one or more computing systems, on the selected column to initiate a column-viewing mode to enlarge the selected column such that the bounds of the selected column are enlarged to approximately span a width of a display space; and presenting, by at least one of the one or more computing systems, another column in line with the selected column based at least in part on the ordering of the columns to enable continuous scrolling through the columns in the column-viewing mode. 3. The method as recited in claim 1 , further comprising: detecting, by at least one of the one or more computing systems, a non-text object within the selected column; and replacing, by at least one of the one or more computing systems, the non-text object in the column-viewing mode with a link that, when selected in the column-viewing mode, zooms in on the associated non-text object. | 0.688103 |
7,885,957 | 9 | 11 | 9. A method for displaying clusters, comprising: generating a plurality of clusters, which each comprise one or more documents and identifying concepts for the clusters based on the documents; selecting the cluster concepts that satisfy an acceptance criteria and forming spines from the clusters associated therewith; assigning at least one of the remaining clusters to one of the spines, which provides a best fit with the cluster concepts for that cluster; placing one or more of the spines into a display, wherein each placed spine is unique; identifying an anchor cluster with an open edge on at least one of the placed spines; and placing one or more of the spines not already in the display, comprising: determining a similarity between the non-placed spine and each anchor cluster and selecting the anchor cluster most similar; and setting the non-placed spine on the open edge of the anchor cluster. | 9. A method for displaying clusters, comprising: generating a plurality of clusters, which each comprise one or more documents and identifying concepts for the clusters based on the documents; selecting the cluster concepts that satisfy an acceptance criteria and forming spines from the clusters associated therewith; assigning at least one of the remaining clusters to one of the spines, which provides a best fit with the cluster concepts for that cluster; placing one or more of the spines into a display, wherein each placed spine is unique; identifying an anchor cluster with an open edge on at least one of the placed spines; and placing one or more of the spines not already in the display, comprising: determining a similarity between the non-placed spine and each anchor cluster and selecting the anchor cluster most similar; and setting the non-placed spine on the open edge of the anchor cluster. 11. A method according to claim 9 , further comprising: defining the acceptance criteria comprising at least one of a minimum cluster reference count, maximum cluster reference count, and user specified. | 0.507282 |
4,797,930 | 1 | 3 | 1. A speech producing apparatus comprising: input means for receiving a sequence of input data, said sequence of input data including a first part containing a sequence of phonological linguistic unit indicia and a second part including primary stress indicia indicative of primary stress, secondary stress indicia indicative of secondary stress, base pitch indicia indicative of a base pitch and rise/fall indicia indicative of a rising or falling intonation; control means connected to said input means for converting said sequence of input data into a sequence of speech synthesis control parameters including pitch control parameters for control of speech pitch by selection of one of a plurality of predetermined pitch patterns for each syllable grouping of phonological linguistic unit indicia in accordance with said second part of said sequence of input data, said control means including phonemic memory means for storing speech synthesis parameters corresponding to each of said phonological linguistic unit indicia, pitch parameter generating means for generating pitch parameters for syllable groupings of said sequence of phonological linguistic unit indicia dependent upon said second part of said sequence of input data, recall means operably associated with said phonemic memory means for recalling speech synthesis parameters corresponding to said sequence of phonological linguistic unit indicia, and concatenation means operably associated with said recall means and said pitch parameter generating means for combining said recalled speech synthesis parameters and said generated pitch parameters corresponding to syllable groupings of said sequence of phonological linguistic unit indicia; and speech synthesis means connected to said control means for generating one or more audible words of human language corresponding to said speech synthesis control parameters. | 1. A speech producing apparatus comprising: input means for receiving a sequence of input data, said sequence of input data including a first part containing a sequence of phonological linguistic unit indicia and a second part including primary stress indicia indicative of primary stress, secondary stress indicia indicative of secondary stress, base pitch indicia indicative of a base pitch and rise/fall indicia indicative of a rising or falling intonation; control means connected to said input means for converting said sequence of input data into a sequence of speech synthesis control parameters including pitch control parameters for control of speech pitch by selection of one of a plurality of predetermined pitch patterns for each syllable grouping of phonological linguistic unit indicia in accordance with said second part of said sequence of input data, said control means including phonemic memory means for storing speech synthesis parameters corresponding to each of said phonological linguistic unit indicia, pitch parameter generating means for generating pitch parameters for syllable groupings of said sequence of phonological linguistic unit indicia dependent upon said second part of said sequence of input data, recall means operably associated with said phonemic memory means for recalling speech synthesis parameters corresponding to said sequence of phonological linguistic unit indicia, and concatenation means operably associated with said recall means and said pitch parameter generating means for combining said recalled speech synthesis parameters and said generated pitch parameters corresponding to syllable groupings of said sequence of phonological linguistic unit indicia; and speech synthesis means connected to said control means for generating one or more audible words of human language corresponding to said speech synthesis control parameters. 3. A speech producing apparatus as claimed in claim 1, wherein: said phonological linguistic unit indicia correspond to allophones. | 0.777211 |
7,752,152 | 17 | 18 | 17. One or more processor-accessible storage media comprising processor-executable instructions of controlling a personal communication device that includes: a user data store module that stores information regarding a particular user's behavior with respect to the personal device; a predictive user model that receives a user input and employs statistical modeling of the information stored in the user data store module to predict a command/control, based at least in part on a past pattern of behavior by the user, to be automatically performed by the personal device; and an execution component module that automatically performs the command/control determined according to the predictive user model, wherein at least one of a parameter and a structure of the predictive model is updated in an online manner, and wherein the online update is performed according to a learning rate (β), wherein the learning rate is: β = ESS N + ESS , wherein N is the total number of individual observations and ESS is an equivalent sample size of training data. | 17. One or more processor-accessible storage media comprising processor-executable instructions of controlling a personal communication device that includes: a user data store module that stores information regarding a particular user's behavior with respect to the personal device; a predictive user model that receives a user input and employs statistical modeling of the information stored in the user data store module to predict a command/control, based at least in part on a past pattern of behavior by the user, to be automatically performed by the personal device; and an execution component module that automatically performs the command/control determined according to the predictive user model, wherein at least one of a parameter and a structure of the predictive model is updated in an online manner, and wherein the online update is performed according to a learning rate (β), wherein the learning rate is: β = ESS N + ESS , wherein N is the total number of individual observations and ESS is an equivalent sample size of training data. 18. The one or more processor-accessible storage media of claim 17 , wherein statistical modeling of the predictive user model is performed without regard to the substantive content of the user input. | 0.604743 |
7,831,614 | 19 | 20 | 19. A system for generating a structured query language (SQL) script based on a template, the system comprises: memory operable to store a data model, a plurality of instructions, and a plurality of template strings and the data model comprising a plurality of objects; and one or more processors operable to: select one object from the plurality of objects in the data model; automatically select, without user input, at least one first instruction based, at least in part, on a type of the selected object; select a first template string based on the selected at least one first instruction; select a second object from the plurality of objects in the data model; select at least one second instruction based, at least in part, on a type of the second object; select a second template string based on the selected at least one second instruction; automatically, and without user input, sort and concatenate the template strings from the selected objects in an order identified by the first and second instructions and based on the types of the first and second objects; and automatically generate at least a portion of the SQL script based on the sorted and concatenated first and second template strings in the order identified by the first and second instructions. | 19. A system for generating a structured query language (SQL) script based on a template, the system comprises: memory operable to store a data model, a plurality of instructions, and a plurality of template strings and the data model comprising a plurality of objects; and one or more processors operable to: select one object from the plurality of objects in the data model; automatically select, without user input, at least one first instruction based, at least in part, on a type of the selected object; select a first template string based on the selected at least one first instruction; select a second object from the plurality of objects in the data model; select at least one second instruction based, at least in part, on a type of the second object; select a second template string based on the selected at least one second instruction; automatically, and without user input, sort and concatenate the template strings from the selected objects in an order identified by the first and second instructions and based on the types of the first and second objects; and automatically generate at least a portion of the SQL script based on the sorted and concatenated first and second template strings in the order identified by the first and second instructions. 20. The system of claim 19 , one or more of the plurality of objects comprising a user-defined object. | 0.843077 |
8,254,629 | 1 | 4 | 1. A non-transitory computer-readable medium comprising program code, comprising: program code for determining a first plane between a first location of an object on a first frame from a plurality of frames and a second location of the object on a second frame from the plurality of frames; program code for determining a second plane between the second location and a third location of the object on a third frame from the plurality of frames; program code for calculating a difference between the first plane and the second plane; and program code for determining whether a change in direction of the object has occurred by at least comparing the difference to a predetermined threshold. | 1. A non-transitory computer-readable medium comprising program code, comprising: program code for determining a first plane between a first location of an object on a first frame from a plurality of frames and a second location of the object on a second frame from the plurality of frames; program code for determining a second plane between the second location and a third location of the object on a third frame from the plurality of frames; program code for calculating a difference between the first plane and the second plane; and program code for determining whether a change in direction of the object has occurred by at least comparing the difference to a predetermined threshold. 4. The non-transitory computer-readable medium of claim 1 , further comprising: program code for identifying the predetermined threshold as ninety degrees. | 0.793883 |
8,538,770 | 25 | 26 | 25. A method for controlling a computer to accomplish a diagnosis, comprising: creating an image database from a collection of images pertaining to a particular subject matter, the image database being accessible by the computer; crating a knowledge database including a plurality of findings-diagnosis links representing relationships between findings and diagnoses, said knowledge database including cross-references to said image database, for the purpose of assisting in the diagnostic process, the knowledge database also being accessible by the computer; receiving, through a user-interface adapted to the particular subject matter, a plurality of descriptive characteristics of a sample requiring diagnosis; the computer operating a diagnostic engine, responsive to said descriptive characteristics, the diagnostic engine using the findings-diagnosis links to automatically generate, from a plurality of possible diagnoses included within the knowledge database, a subset including a plurality of possible diagnoses consistent with the descriptive characteristics collected from the user; and automatically reorganizing an information space of said image database for concurrent display of a plurality of images related to the subset of possible diagnoses for user review via the user-interface. | 25. A method for controlling a computer to accomplish a diagnosis, comprising: creating an image database from a collection of images pertaining to a particular subject matter, the image database being accessible by the computer; crating a knowledge database including a plurality of findings-diagnosis links representing relationships between findings and diagnoses, said knowledge database including cross-references to said image database, for the purpose of assisting in the diagnostic process, the knowledge database also being accessible by the computer; receiving, through a user-interface adapted to the particular subject matter, a plurality of descriptive characteristics of a sample requiring diagnosis; the computer operating a diagnostic engine, responsive to said descriptive characteristics, the diagnostic engine using the findings-diagnosis links to automatically generate, from a plurality of possible diagnoses included within the knowledge database, a subset including a plurality of possible diagnoses consistent with the descriptive characteristics collected from the user; and automatically reorganizing an information space of said image database for concurrent display of a plurality of images related to the subset of possible diagnoses for user review via the user-interface. 26. The method according to claim 25 , wherein receiving a plurality of descriptive characteristics includes visual findings. | 0.711982 |
9,218,427 | 24 | 26 | 24. A network computer for managing data over a network, comprising: a transceiver that communicates over the network; a memory that stores at least instructions; and a processor device that executes instructions that perform actions, including: providing source data to the network computer from one or more separate data sources; providing a plurality of particular types of indices that are separate and optimized for one or more different content-types, wherein the particular types of indices include one or more of n-gram indices, temporal indices, or geo-spatial indices; generating a raw data graph from the source data, wherein the structure of the raw data graph is based on the structure of the source data; mapping one or more elements of the raw data graph to a concept graph; generating one or more concept instances based on at least the concept graph, the raw data graph, and the source data; generating one or more model-identifiers (MIDs) that correspond to the one or more concept instances, wherein the one or more MIDs include at least a path in the concept graph and one or more value keys that correspond to one or more portions of the source data; indexing values from the source data that correspond to the one or more MIDs in one or more indices that are selected from the plurality of indices based on a content-type of the source data that is associated with the one or more MIDs; and responsive to a query, generating a result set that includes one or more result MIDs based on one or more indices of the plurality of indices, wherein a content-type of at least one portion of the query is employed to select the one or more indices used to generate the result set. | 24. A network computer for managing data over a network, comprising: a transceiver that communicates over the network; a memory that stores at least instructions; and a processor device that executes instructions that perform actions, including: providing source data to the network computer from one or more separate data sources; providing a plurality of particular types of indices that are separate and optimized for one or more different content-types, wherein the particular types of indices include one or more of n-gram indices, temporal indices, or geo-spatial indices; generating a raw data graph from the source data, wherein the structure of the raw data graph is based on the structure of the source data; mapping one or more elements of the raw data graph to a concept graph; generating one or more concept instances based on at least the concept graph, the raw data graph, and the source data; generating one or more model-identifiers (MIDs) that correspond to the one or more concept instances, wherein the one or more MIDs include at least a path in the concept graph and one or more value keys that correspond to one or more portions of the source data; indexing values from the source data that correspond to the one or more MIDs in one or more indices that are selected from the plurality of indices based on a content-type of the source data that is associated with the one or more MIDs; and responsive to a query, generating a result set that includes one or more result MIDs based on one or more indices of the plurality of indices, wherein a content-type of at least one portion of the query is employed to select the one or more indices used to generate the result set. 26. The network computer of claim 24 , wherein the plurality of indices, further includes, at least one index that is optimized for a content-type of text, at least one index that is optimized for a content-type of time, and at least one index that is optimized for a content-type of geo-spatial information. | 0.507987 |
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