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7,644,069 | 1 | 7 | 1. A computer-implemented search ranking method suitable for a file system, comprising: receiving a query via a computer; calculating final relevance scores of individual file items via said computer with respect to said query at least partially in accordance with energy scores of individual nodes on a current file system energy tree, and outputting a list of search results based on said final relevance scores; and updating said energy scores of said individual nodes on said file system energy tree via said computer in response to an operation on said file system performed by a user, wherein said file system energy tree has a tree structure corresponding to that of said file system, and said individual nodes thereof respectively correspond to the individual file items in said file system. | 1. A computer-implemented search ranking method suitable for a file system, comprising: receiving a query via a computer; calculating final relevance scores of individual file items via said computer with respect to said query at least partially in accordance with energy scores of individual nodes on a current file system energy tree, and outputting a list of search results based on said final relevance scores; and updating said energy scores of said individual nodes on said file system energy tree via said computer in response to an operation on said file system performed by a user, wherein said file system energy tree has a tree structure corresponding to that of said file system, and said individual nodes thereof respectively correspond to the individual file items in said file system. 7. The method according to claim 1 , wherein said file system energy tree is initialized such that the individual nodes have equal energy scores. | 0.833333 |
8,782,050 | 1 | 7 | 1. A method of sorting a plurality of documents to be accessed by a plurality of readers or groups of readers, said method comprising the steps of developing a list of interest areas represented by said plurality of readers, developing a hierarchical index of subject matter referred to in said plurality of documents, each entry in said hierarchical index having at least one of an index term and an associated code, assigning a limited number of index terms and associated codes of said hierarchical index to each document of said plurality of documents, assigning a limited number of contexts or potential uses for which a document of said plurality of documents is particularly suited and associated codes to selected ones of said plurality of documents, assigning at least one of said interest areas to each document of said plurality of documents, and assembling a plurality of hierarchical indices of subject matter for respective interest areas from index terms and associated codes assigned to documents in each of said interest areas, and sorting respective documents of said plurality of documents in accordance with a respective one of said plurality of hierarchical indices for respective interest areas and said contexts or potential uses. | 1. A method of sorting a plurality of documents to be accessed by a plurality of readers or groups of readers, said method comprising the steps of developing a list of interest areas represented by said plurality of readers, developing a hierarchical index of subject matter referred to in said plurality of documents, each entry in said hierarchical index having at least one of an index term and an associated code, assigning a limited number of index terms and associated codes of said hierarchical index to each document of said plurality of documents, assigning a limited number of contexts or potential uses for which a document of said plurality of documents is particularly suited and associated codes to selected ones of said plurality of documents, assigning at least one of said interest areas to each document of said plurality of documents, and assembling a plurality of hierarchical indices of subject matter for respective interest areas from index terms and associated codes assigned to documents in each of said interest areas, and sorting respective documents of said plurality of documents in accordance with a respective one of said plurality of hierarchical indices for respective interest areas and said contexts or potential uses. 7. The method as recited in claim 1 , including the further step of designating a document as a key article. | 0.668712 |
8,312,034 | 1 | 7 | 1. A computer system comprising: a processor; a computer readable memory encoded with logic that when executed by the processor is operable to perform operations comprising: deriving concept terms by extracting significant terms from search text and inferring relevant terms from the extracted significant terms using a concept matrix, the concept matrix derived from a plurality of sample documents using singular value decomposition of a term-document matrix, the concept matrix identifying a latent pattern of word usage in the plurality of sample documents around a concept, the concept matrix further providing a term-term similarity between the search text and terms in the plurality of sample documents; and generating a query for a search engine, the query comprising a search expression having at least one of the derived concept terms. | 1. A computer system comprising: a processor; a computer readable memory encoded with logic that when executed by the processor is operable to perform operations comprising: deriving concept terms by extracting significant terms from search text and inferring relevant terms from the extracted significant terms using a concept matrix, the concept matrix derived from a plurality of sample documents using singular value decomposition of a term-document matrix, the concept matrix identifying a latent pattern of word usage in the plurality of sample documents around a concept, the concept matrix further providing a term-term similarity between the search text and terms in the plurality of sample documents; and generating a query for a search engine, the query comprising a search expression having at least one of the derived concept terms. 7. The computer system of claim 1 , wherein the concept matrix comprises a compressed matrix derived by compressing a term by document matrix using singular value decomposition, the term by document matrix derived from the plurality of sample documents using latent semantic analysis and comprising weighted term frequencies. | 0.5 |
8,407,233 | 1 | 9 | 1. A method, using a processor, of calculating relevance among words based on a relevance of each word in a document, the method comprising: generating statistical information associated with relevance among words by calculating a crossing frequency of words associated with a number of times of each of cross-word being appeared in a document, an appearance frequency of a word, or a word-word combination frequency associated with an appearance and a non-appearance of a combination of a first word and a second word, wherein the appearance frequency is a number of times that a word appears and frequency information is generated based on one of the appearance frequency or the crossing frequency, or the word-word combination frequency to provide the statistical information, the calculation being performed by the processor according to word-word or word-document classification; standardizing the statistical information by applying a parameter to the calculated statistical information, wherein the standardizing the statistical information comprises generating a combination probability distribution of a random variable corresponding to a pair of words and standardizing the statistical information based on the word-word combination frequency, wherein the word-word combination frequency associated with the pair of words is a number of documents that include all words in the pair, a number of documents that do not include any word in the pair, and a number of documents that include one of the words in the pair, and wherein the random variable is defined in a point space of columns and rows that comprise appearance or non-appearance points of the word; determining, by the processor, the relevance among the words as a numerical value based on the standardization; and providing the numerical value associated with the relevance among words to a search system. | 1. A method, using a processor, of calculating relevance among words based on a relevance of each word in a document, the method comprising: generating statistical information associated with relevance among words by calculating a crossing frequency of words associated with a number of times of each of cross-word being appeared in a document, an appearance frequency of a word, or a word-word combination frequency associated with an appearance and a non-appearance of a combination of a first word and a second word, wherein the appearance frequency is a number of times that a word appears and frequency information is generated based on one of the appearance frequency or the crossing frequency, or the word-word combination frequency to provide the statistical information, the calculation being performed by the processor according to word-word or word-document classification; standardizing the statistical information by applying a parameter to the calculated statistical information, wherein the standardizing the statistical information comprises generating a combination probability distribution of a random variable corresponding to a pair of words and standardizing the statistical information based on the word-word combination frequency, wherein the word-word combination frequency associated with the pair of words is a number of documents that include all words in the pair, a number of documents that do not include any word in the pair, and a number of documents that include one of the words in the pair, and wherein the random variable is defined in a point space of columns and rows that comprise appearance or non-appearance points of the word; determining, by the processor, the relevance among the words as a numerical value based on the standardization; and providing the numerical value associated with the relevance among words to a search system. 9. The method of claim 1 , wherein the standardizing the statistical information further comprises: setting the word as a column of a discrete random variable set to indicate each independent point in a point space where a random variable is defined; and setting the word as a row of the discrete random variable set to indicate a discrete random variable, wherein the discrete random variable is generated based on the crossing frequency associated with words comprising a first word and a second word that is a number of documents that include both the first word and the second word. | 0.5 |
8,336,078 | 1 | 6 | 1. A method for managing role-based access in a multi-customer computing environment, the method comprising: receiving, by a computing device, a request from an actor to take action within the multi-customer computing environment; determining, by the computing device, a role from one or more roles for the actor based on an identification of the actor, wherein each role is assigned a plurality of context parameters, each role is used by a plurality of customers, and the role that is determined can have a first policy element for the actor and a second policy element for a different actor, the first policy element and the second policy element are not the same; receiving, by the computing device, one value for each of the one or more context parameters assigned to the role based on the identification of the actor; determining, by the computing device, a role scope for the role based on the one value of each of the one or more context parameters assigned to the actor; determining, by the computing device, an actor-role scope value based on the role scope and the one value of each of the one or more context parameters assigned to the role; determining, by the computing device, a policy type based on the request from the actor and the actor's role and the one or more context parameters assigned to actor; populating, by the computing device, policy elements of the policy type to form a policy instance with one or more values from the one or more context parameters assigned to the role; and providing to the actor, by the computing device, an access permission for the first policy element or the second policy element so the actor can take action within the multi-customer computing environment based on the policy instance. | 1. A method for managing role-based access in a multi-customer computing environment, the method comprising: receiving, by a computing device, a request from an actor to take action within the multi-customer computing environment; determining, by the computing device, a role from one or more roles for the actor based on an identification of the actor, wherein each role is assigned a plurality of context parameters, each role is used by a plurality of customers, and the role that is determined can have a first policy element for the actor and a second policy element for a different actor, the first policy element and the second policy element are not the same; receiving, by the computing device, one value for each of the one or more context parameters assigned to the role based on the identification of the actor; determining, by the computing device, a role scope for the role based on the one value of each of the one or more context parameters assigned to the actor; determining, by the computing device, an actor-role scope value based on the role scope and the one value of each of the one or more context parameters assigned to the role; determining, by the computing device, a policy type based on the request from the actor and the actor's role and the one or more context parameters assigned to actor; populating, by the computing device, policy elements of the policy type to form a policy instance with one or more values from the one or more context parameters assigned to the role; and providing to the actor, by the computing device, an access permission for the first policy element or the second policy element so the actor can take action within the multi-customer computing environment based on the policy instance. 6. The method of claim 1 , wherein said actor comprises a user, a system account, a system application, a computing device, or any combination thereof. | 0.650463 |
8,949,707 | 1 | 2 | 1. An adaptive document displaying apparatus comprising: a summary information creating unit to create various lengths of summary information for a first component of a plurality of components included in a document to adjust an amount of contents of the document to be displayed on a display device, the various lengths of summary information being provided to be displayed on the display device replacing the first component so one length of information among the various lengths of summary information is displayed instead of the first component; a document tree creating unit to analyze each of the plurality of components included in the document and to calculate a priority for each of the plurality of components based on attributes of each respective component, a user's profile, and attributes of the display device; and a document converting unit to convert the document into another document, which includes the one length of summary information and does not include the first component, to be displayed on the display device, wherein the one length of summary information is selected among the various lengths of summary information using the calculated priority of the first component and the attributes of the display device, wherein at least one of the summary information creating unit and the document converting unit use one or more processors to create the various lengths of summary information or to convert the document. | 1. An adaptive document displaying apparatus comprising: a summary information creating unit to create various lengths of summary information for a first component of a plurality of components included in a document to adjust an amount of contents of the document to be displayed on a display device, the various lengths of summary information being provided to be displayed on the display device replacing the first component so one length of information among the various lengths of summary information is displayed instead of the first component; a document tree creating unit to analyze each of the plurality of components included in the document and to calculate a priority for each of the plurality of components based on attributes of each respective component, a user's profile, and attributes of the display device; and a document converting unit to convert the document into another document, which includes the one length of summary information and does not include the first component, to be displayed on the display device, wherein the one length of summary information is selected among the various lengths of summary information using the calculated priority of the first component and the attributes of the display device, wherein at least one of the summary information creating unit and the document converting unit use one or more processors to create the various lengths of summary information or to convert the document. 2. The adaptive document displaying apparatus as claimed in claim 1 , wherein the attribute of the display device includes at least one of a size and a shape of a display screen of the display device. | 0.84252 |
7,536,521 | 8 | 9 | 8. The computer storage device of claim 6 wherein the media access circuit remaps a writing of data associated with an instruction from a block designated by the instruction to a different block determined by the access monitor circuit to be live and having lower latency. | 8. The computer storage device of claim 6 wherein the media access circuit remaps a writing of data associated with an instruction from a block designated by the instruction to a different block determined by the access monitor circuit to be live and having lower latency. 9. The computer storage device of claim 8 wherein the different block is a dead block closer to a disk read head than the block designated by the instruction. | 0.655022 |
9,799,350 | 13 | 17 | 13. The method of claim 10 , wherein the performing of the utterance verification includes: separately calculating word-specific confidence scores of words of sections in which the events have occurred and words for which no event has occurred; calculating confidence scores in units of sentences or utterances based on the calculated word-specific confidence scores; calculating sentence-specific confidence scores to which the events are applied without separating words from each other using section information of the sections in which the events have occurred and estimated values of a plurality of feature parameters; analyzing sentence structures and meanings of speech recognition result sentences and calculating confidence scores of sentences; and comparing the calculated confidence scores with a preset threshold value and determining whether or not to accept the speech recognition result sentences according to comparison results. | 13. The method of claim 10 , wherein the performing of the utterance verification includes: separately calculating word-specific confidence scores of words of sections in which the events have occurred and words for which no event has occurred; calculating confidence scores in units of sentences or utterances based on the calculated word-specific confidence scores; calculating sentence-specific confidence scores to which the events are applied without separating words from each other using section information of the sections in which the events have occurred and estimated values of a plurality of feature parameters; analyzing sentence structures and meanings of speech recognition result sentences and calculating confidence scores of sentences; and comparing the calculated confidence scores with a preset threshold value and determining whether or not to accept the speech recognition result sentences according to comparison results. 17. The method of claim 13 , wherein the analyzing of the sentence structures and meanings and the calculating of the confidence scores of the sentences include analyzing the sentence structures and meanings of the speech recognition result sentences using a morpheme analyzer. | 0.798984 |
8,332,327 | 9 | 14 | 9. A method comprising: receiving, by a processor, service guide information associated with a plurality of content items; receiving, by the processor, a user selection of a content item of the plurality of content items; determining, by the processor, to transmit a pricing information request corresponding to the selected content item; receiving, by the processor, a pricing information response in response to the pricing information request, wherein the pricing information response includes a legal text and at least one of a legal text identifier and user consent information; and determining, by the processor, to render the legal text on a display. | 9. A method comprising: receiving, by a processor, service guide information associated with a plurality of content items; receiving, by the processor, a user selection of a content item of the plurality of content items; determining, by the processor, to transmit a pricing information request corresponding to the selected content item; receiving, by the processor, a pricing information response in response to the pricing information request, wherein the pricing information response includes a legal text and at least one of a legal text identifier and user consent information; and determining, by the processor, to render the legal text on a display. 14. The method of claim 9 , wherein the response to the pricing information request is received from a service provisioning system. | 0.78871 |
8,433,611 | 28 | 29 | 28. The one or more disk devices of claim 23 , wherein calculating one of the scores is further based on content of a second different document that contains a hyperlink to the content item. | 28. The one or more disk devices of claim 23 , wherein calculating one of the scores is further based on content of a second different document that contains a hyperlink to the content item. 29. The one or more disk devices of claim 28 , wherein the content of the second different document that contains the hyperlink to the content item comprises anchor text of the hyperlink. | 0.5 |
8,064,573 | 1 | 7 | 1. An interactive voice response (IVR) system for providing acknowledgement prompts, the system configured to: receive an input audio stream over a voice channel from a user; identify, from the input audio stream, a keyword, a confidence value associated with the keyword, and an input volume; select an acknowledgement prompt and an output volume for the selected acknowledgement prompt based at least in part on the confidence value and the input volume; and output the selected acknowledgement prompt at the selected output volume to the user. | 1. An interactive voice response (IVR) system for providing acknowledgement prompts, the system configured to: receive an input audio stream over a voice channel from a user; identify, from the input audio stream, a keyword, a confidence value associated with the keyword, and an input volume; select an acknowledgement prompt and an output volume for the selected acknowledgement prompt based at least in part on the confidence value and the input volume; and output the selected acknowledgement prompt at the selected output volume to the user. 7. The IVR system according to claim 1 , wherein the selected output volume is louder than the input volume if the confidence value is below a predetermined threshold. | 0.58867 |
7,769,591 | 25 | 31 | 25. A tangible computer readable medium having stored thereon computer-executable instructions that, if executed by a computing device, cause the computing device to perform a method comprising: receiving, at the local device, a speech input; identifying keywords in the speech input; establishing communications between the local device and a remote system, wherein the communications comprise: high bandwidth communications configured to return data supporting audio or video output at the local device, and low bandwidth communications configured to return data supporting the remote control signals; determining, at the local device, whether the local device is capable of processing the speech input based on whether one or more keywords are identified in the speech input; if the local device is capable of processing the speech input, then processing the speech input at the local device, generating corresponding local control signals, and transmitting the local control signals to the primary functionality component to direct an action in the primary functionality component; and if the local device is not capable of processing the speech input, then extracting feature parameters from the speech input for processing at the remote system, sending the feature parameters to the remote system for processing by storing an acoustic model of the feature parameters and recognizing a command based on a previously stored acoustic model associated with the local device to address specific characteristics of the feature parameter, receiving remote control signals from the remote system responsive to the feature parameters via the low bandwidth communications, and sending the remote control signals to the primary functionality component. | 25. A tangible computer readable medium having stored thereon computer-executable instructions that, if executed by a computing device, cause the computing device to perform a method comprising: receiving, at the local device, a speech input; identifying keywords in the speech input; establishing communications between the local device and a remote system, wherein the communications comprise: high bandwidth communications configured to return data supporting audio or video output at the local device, and low bandwidth communications configured to return data supporting the remote control signals; determining, at the local device, whether the local device is capable of processing the speech input based on whether one or more keywords are identified in the speech input; if the local device is capable of processing the speech input, then processing the speech input at the local device, generating corresponding local control signals, and transmitting the local control signals to the primary functionality component to direct an action in the primary functionality component; and if the local device is not capable of processing the speech input, then extracting feature parameters from the speech input for processing at the remote system, sending the feature parameters to the remote system for processing by storing an acoustic model of the feature parameters and recognizing a command based on a previously stored acoustic model associated with the local device to address specific characteristics of the feature parameter, receiving remote control signals from the remote system responsive to the feature parameters via the low bandwidth communications, and sending the remote control signals to the primary functionality component. 31. The computer program product of claim 25 , wherein the method further comprises: generating a first speech output. | 0.795139 |
8,572,099 | 9 | 10 | 9. The computer-implemented method of claim 1 , wherein the request to identify interests for the users of the social network comprises information received from an advertiser that identifies one or more keywords to use to target ads to the users of the social network; and the method further comprising selecting the labels based on the one or more keywords identified by the advertiser. | 9. The computer-implemented method of claim 1 , wherein the request to identify interests for the users of the social network comprises information received from an advertiser that identifies one or more keywords to use to target ads to the users of the social network; and the method further comprising selecting the labels based on the one or more keywords identified by the advertiser. 10. The computer-implemented method of claim 9 , wherein the one or more identified keywords are provided as part of one or more advertising word groups comprising categories of related keywords. | 0.881675 |
8,266,656 | 17 | 18 | 17. The method of claim 16 , wherein the all selection includes a second search parameter. | 17. The method of claim 16 , wherein the all selection includes a second search parameter. 18. The method of claim 17 , wherein the second search parameter is a time parameter. | 0.5 |
9,110,874 | 1 | 2 | 1. A document conversion apparatus for converting a format of a structured document using a template, the document conversion apparatus comprising: a computer processor; and a memory that stores at least one computer program executed by the computer processor, the at least one computer program comprising program code that, when executed by the computer processor, implements: a first determining unit configured to determine whether a binary format is specified as an output format of the structured document in the template; a command acquiring unit configured to acquire a processing command for the structured document from the template used for converting the format of the structured document in a case where the first determining unit determines that the binary format is specified as the output format of the structured document; a second determining unit configured to, in a case where the processing command acquired by the command acquiring unit is a command for outputting a part of the structured document, determine whether an output format of the part of the structured document is specified in the template; and a writing unit configured to, in a case where the output format of the part of the structured document is specified in the template, perform binary conversion associated beforehand with the output format on the part of the structured document and write a result of the binary conversion in a file for storing output data, and in a case where the output format of the part of the structured document is not specified in the template, write the part of the structured document in the file. | 1. A document conversion apparatus for converting a format of a structured document using a template, the document conversion apparatus comprising: a computer processor; and a memory that stores at least one computer program executed by the computer processor, the at least one computer program comprising program code that, when executed by the computer processor, implements: a first determining unit configured to determine whether a binary format is specified as an output format of the structured document in the template; a command acquiring unit configured to acquire a processing command for the structured document from the template used for converting the format of the structured document in a case where the first determining unit determines that the binary format is specified as the output format of the structured document; a second determining unit configured to, in a case where the processing command acquired by the command acquiring unit is a command for outputting a part of the structured document, determine whether an output format of the part of the structured document is specified in the template; and a writing unit configured to, in a case where the output format of the part of the structured document is specified in the template, perform binary conversion associated beforehand with the output format on the part of the structured document and write a result of the binary conversion in a file for storing output data, and in a case where the output format of the part of the structured document is not specified in the template, write the part of the structured document in the file. 2. The document conversion apparatus according to claim 1 , wherein the at least one computer program further comprises program code that, when executed by the computer processor, implements: a processing unit configured to receive a structured document including a first document element and a second document element, the processing unit configured to process a template; a converting unit configured to perform a first binary conversion algorithm on the first document element and to perform a second binary conversion algorithm on the second document element, wherein the second binary conversion algorithm is different from the first binary conversion algorithm; and an output unit configured to output the structured document in the binary format. | 0.534035 |
8,615,664 | 1 | 7 | 1. A system, comprising: an inference data acquisition module configured to acquire inference data that indicate an inferred mental state of an authoring user in connection with a particular item of an electronic message, the inference data derived based, at least in part, on at least one physical characteristic of the authoring user; one or more sensors configured to sense the at least one physical characteristic of the authoring user in connection with the particular item of the electronic message; a source identity acquisition module configured to acquire source identity data providing at least one identity of one or more sources that provide a basis, at least in part, for the inference data, the one or more sources including at least the one or more sensors; an inference data association module configured to associate the inference data with the particular item, the inference data association module including at least an inference data inclusion module configured to include the inference data into the electronic message; and a source identity association module configured to associate the source identity data with the particular item, the source identity association module including at least a source identity inclusion module configured to include into the electronic message one or more identities of the one or more sensors, the one or more sensors having been used to derive, at least in part, the inference data acquired by the inference data acquisition module; and wherein the electronic message thereby includes at least a data pair that includes at least the inference data that indicate the inferred mental state of the authoring user in connection with the particular item and the one or more identities of the one or more sensors used to derive, at least in part, the inference data. | 1. A system, comprising: an inference data acquisition module configured to acquire inference data that indicate an inferred mental state of an authoring user in connection with a particular item of an electronic message, the inference data derived based, at least in part, on at least one physical characteristic of the authoring user; one or more sensors configured to sense the at least one physical characteristic of the authoring user in connection with the particular item of the electronic message; a source identity acquisition module configured to acquire source identity data providing at least one identity of one or more sources that provide a basis, at least in part, for the inference data, the one or more sources including at least the one or more sensors; an inference data association module configured to associate the inference data with the particular item, the inference data association module including at least an inference data inclusion module configured to include the inference data into the electronic message; and a source identity association module configured to associate the source identity data with the particular item, the source identity association module including at least a source identity inclusion module configured to include into the electronic message one or more identities of the one or more sensors, the one or more sensors having been used to derive, at least in part, the inference data acquired by the inference data acquisition module; and wherein the electronic message thereby includes at least a data pair that includes at least the inference data that indicate the inferred mental state of the authoring user in connection with the particular item and the one or more identities of the one or more sensors used to derive, at least in part, the inference data. 7. The system of claim 1 , wherein the one or more sensors configured to sense the at least one physical characteristic of the authoring user in connection with the particular item of the electronic message comprise: an electroencephalography (EEG) device. | 0.868583 |
7,617,232 | 1 | 12 | 1. A method of developing terminology for use within an organization, said method comprising: receiving data representing a term for building a glossary from a user; receiving data representing one or more projects to be associated with the term in the organization from the user; receiving data representing a definition associated with the data representing the term used in association with the one or more projects; defining a relationship between the received data representing the term and the received data representing the one or more projects, said relationship defining at least one of the following: a project may include zero to many terms, a term may appear in zero to many projects, and a term may not appear in the same project more than once; linking the received data representing the term with the received data representing the one or more projects in a central memory area accessible by a computing device; automatically extracting the received data representing the definition associated with the data representing the term; and automatically generating the glossary for the one or more projects, said generated glossary including the term having the extracted definition linked to the one or more projects provided from the user. | 1. A method of developing terminology for use within an organization, said method comprising: receiving data representing a term for building a glossary from a user; receiving data representing one or more projects to be associated with the term in the organization from the user; receiving data representing a definition associated with the data representing the term used in association with the one or more projects; defining a relationship between the received data representing the term and the received data representing the one or more projects, said relationship defining at least one of the following: a project may include zero to many terms, a term may appear in zero to many projects, and a term may not appear in the same project more than once; linking the received data representing the term with the received data representing the one or more projects in a central memory area accessible by a computing device; automatically extracting the received data representing the definition associated with the data representing the term; and automatically generating the glossary for the one or more projects, said generated glossary including the term having the extracted definition linked to the one or more projects provided from the user. 12. The method of claim 1 wherein one or more computer storage media have computer-executable instructions to perform the method recited in claim 1 . | 0.853057 |
9,980,016 | 9 | 11 | 9. A computing system comprising: at least one processor; and memory storing instructions executable by the at least one processor, wherein the instructions, when executed, configure the computing system to provide: a speech recognizer configured to perform speech recognition, on an audio portion of a video signal, to generate a transcription of the audio portion; a recognized content analysis component configured to analyze the transcription of the audio portion and identify a keyword included in the transcription; a supplemental content selection component configured to select a supplemental content item from a collection of supplemental content items based on the keyword included in the transcription; and a display component configured to simultaneously display video, corresponding to the video signal, and text corresponding to the transcription, the displayed text including the keyword included in the transcription and at least one hyperlink that links the keyword to the selected supplemental content item. | 9. A computing system comprising: at least one processor; and memory storing instructions executable by the at least one processor, wherein the instructions, when executed, configure the computing system to provide: a speech recognizer configured to perform speech recognition, on an audio portion of a video signal, to generate a transcription of the audio portion; a recognized content analysis component configured to analyze the transcription of the audio portion and identify a keyword included in the transcription; a supplemental content selection component configured to select a supplemental content item from a collection of supplemental content items based on the keyword included in the transcription; and a display component configured to simultaneously display video, corresponding to the video signal, and text corresponding to the transcription, the displayed text including the keyword included in the transcription and at least one hyperlink that links the keyword to the selected supplemental content item. 11. The computing system of claim 9 , wherein the supplemental content selection component selects the supplemental content item in response to a determination that the keyword is a word to which the supplemental content item is to be assigned. | 0.568905 |
8,773,733 | 1 | 13 | 1. A digital image capture device, comprising: an image sensor for capturing a digital image; an optical system for forming an image of a scene onto the image sensor; a data processing system; a storage memory; and a program memory communicatively connected to the data processing system and storing instructions configured to cause the data processing system to implement a method for extracting textual information from a document containing text characters, wherein the method includes: capturing a plurality of digital images of the document using the image sensor, wherein all of the digital images include substantially the same image content; automatically analyzing each of the captured digital images using an optical character recognition process to determine extracted textual data for each captured digital image; and merging the extracted textual data for the captured digital images to determine the textual information for the document, wherein differences between the extracted textual data for corresponding portions of the captured digital images are analyzed to determine the textual information for corresponding portions of the document; and storing the textual information in the storage memory. | 1. A digital image capture device, comprising: an image sensor for capturing a digital image; an optical system for forming an image of a scene onto the image sensor; a data processing system; a storage memory; and a program memory communicatively connected to the data processing system and storing instructions configured to cause the data processing system to implement a method for extracting textual information from a document containing text characters, wherein the method includes: capturing a plurality of digital images of the document using the image sensor, wherein all of the digital images include substantially the same image content; automatically analyzing each of the captured digital images using an optical character recognition process to determine extracted textual data for each captured digital image; and merging the extracted textual data for the captured digital images to determine the textual information for the document, wherein differences between the extracted textual data for corresponding portions of the captured digital images are analyzed to determine the textual information for corresponding portions of the document; and storing the textual information in the storage memory. 13. The digital image capture device of claim 1 , further including using an image alignment process to align the captured digital images before they are analyzed using the optical character recognition process. | 0.866456 |
7,756,313 | 12 | 16 | 12. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for computer aided detection of anatomical abnormalities in medical images, said method comprising the steps of: providing a plurality of abnormality candidates and features of said abnormality candidates; and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(w T x+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more computationally complex features are used for each successive stage of said cascade of classifiers, wherein each stage of said cascade solves a linear program formulated using a training error rate ξ equivalent to max {0,1−y(w T x+b)} and an l 1 -norm equivalent to ∥w∥ t =Σ|w 1 | summed over all features, wherein said linear program is equivalent to the system represented by min u , v , ξ λ ∑ j = 1 d ( u j + v j ) + μ l + ∑ i ∈ C + ξ i + 1 - μ l - ∑ i ∈ C - ξ i , y ( ∑ j X ij ( u j - v j ) + b ) + ξ i ≥ 1 , such that ξ i ≥ 0 , i = 1 , … , l , u j , v j ≥ 0 , j = 1 , … , d , ( 1 ) wherein λ>0 is a regularization parameter, {x i , y i }, i=1, . . . , l denotes the abnormality candidates, y denotes a label indicating whether or not a candidate associated with a feature vector is a true positive, X denotes a feature matrix of d features wherein each row represents candidate feature vector x, and each column specifies a feature, l + is the number of positive candidates and l − the number of negative candidates, C + and C − contain, respectively, the sets of indices of positive candidates and negative candidates, 0≦μ≦1 is a tuning parameter for combining the false negative rate and false positive rate, and w j =u j −v j . | 12. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for computer aided detection of anatomical abnormalities in medical images, said method comprising the steps of: providing a plurality of abnormality candidates and features of said abnormality candidates; and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(w T x+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more computationally complex features are used for each successive stage of said cascade of classifiers, wherein each stage of said cascade solves a linear program formulated using a training error rate ξ equivalent to max {0,1−y(w T x+b)} and an l 1 -norm equivalent to ∥w∥ t =Σ|w 1 | summed over all features, wherein said linear program is equivalent to the system represented by min u , v , ξ λ ∑ j = 1 d ( u j + v j ) + μ l + ∑ i ∈ C + ξ i + 1 - μ l - ∑ i ∈ C - ξ i , y ( ∑ j X ij ( u j - v j ) + b ) + ξ i ≥ 1 , such that ξ i ≥ 0 , i = 1 , … , l , u j , v j ≥ 0 , j = 1 , … , d , ( 1 ) wherein λ>0 is a regularization parameter, {x i , y i }, i=1, . . . , l denotes the abnormality candidates, y denotes a label indicating whether or not a candidate associated with a feature vector is a true positive, X denotes a feature matrix of d features wherein each row represents candidate feature vector x, and each column specifies a feature, l + is the number of positive candidates and l − the number of negative candidates, C + and C − contain, respectively, the sets of indices of positive candidates and negative candidates, 0≦μ≦1 is a tuning parameter for combining the false negative rate and false positive rate, and w j =u j −v j . 16. The computer readable program storage device of claim 12 , wherein each classifier of said cascade of classifiers uses a single set of features. | 0.876461 |
10,019,515 | 2 | 3 | 2. The method of claim 1 , further comprising: grouping the set of attributes by the one or more sentiments prior to displaying the set of attributes in the context of the topic. | 2. The method of claim 1 , further comprising: grouping the set of attributes by the one or more sentiments prior to displaying the set of attributes in the context of the topic. 3. The method of claim 2 , wherein the one or more sentiments comprise at least one of: a positive sentiment; a negative sentiment; a neutral sentiment; and an unknown sentiment. | 0.5 |
8,566,314 | 7 | 8 | 7. The method of claim 6 further comprising applying an event tag to the selected portion of the data. | 7. The method of claim 6 further comprising applying an event tag to the selected portion of the data. 8. The method of claim 7 wherein applying an event tag to the selected portion of the data comprises one of: (a) manually applying an event tag to the selected portion of the data; or (b) automatically applying an event tag to the selected portion of the data. | 0.5 |
8,214,346 | 26 | 29 | 26. A computer program product with instructions recorded on a non-transitory computer readable storage medium, which, when executed by a processor, cause the processor to carry out a method for classifying an electronic document, the computer program product comprising: instructions for analyzing, with a computing device, author-generated classification information regarding a document and assigning a set of first taxonomic nouns to characterize the document based upon the author-generated classification information; instructions for examining, with a computing device, a user-generated tag from a client computer characterizing a portion of the document and assigning a set of second taxonomic nouns to characterize the document based upon the user-generated tag characterization; instructions for identifying, with a computing device, a method of access through which the document has been accessed from a content provider and assigning at set of third taxonomic nouns to characterize the document based upon the method of access; instructions for evaluating, with a computing device, attributes related to the method of access and assigning a set of fourth taxonomic nouns to characterize the document based upon the attributes related to the method of access; instructions for processing, with a computing device, the document to extract a set of fifth taxonomic nouns to characterize the document based upon a predetermined pattern rule; instructions for aggregating, with a computing device, the taxonomic nouns to determine at least one term vector that represents the document; and instructions for categorizing, with a computing device, the document based upon the taxonomic nouns, the author-generated classification information, and at least one of the term vectors. | 26. A computer program product with instructions recorded on a non-transitory computer readable storage medium, which, when executed by a processor, cause the processor to carry out a method for classifying an electronic document, the computer program product comprising: instructions for analyzing, with a computing device, author-generated classification information regarding a document and assigning a set of first taxonomic nouns to characterize the document based upon the author-generated classification information; instructions for examining, with a computing device, a user-generated tag from a client computer characterizing a portion of the document and assigning a set of second taxonomic nouns to characterize the document based upon the user-generated tag characterization; instructions for identifying, with a computing device, a method of access through which the document has been accessed from a content provider and assigning at set of third taxonomic nouns to characterize the document based upon the method of access; instructions for evaluating, with a computing device, attributes related to the method of access and assigning a set of fourth taxonomic nouns to characterize the document based upon the attributes related to the method of access; instructions for processing, with a computing device, the document to extract a set of fifth taxonomic nouns to characterize the document based upon a predetermined pattern rule; instructions for aggregating, with a computing device, the taxonomic nouns to determine at least one term vector that represents the document; and instructions for categorizing, with a computing device, the document based upon the taxonomic nouns, the author-generated classification information, and at least one of the term vectors. 29. The computer program product of claim 26 , wherein the attributes related to the method of access include at least one of a network type used to access the document, a Web site from which the document was accessed, an electronic correspondence to which the document was associated, a time of day the document was accessed, a referrer who directed a user to the document, and a document category used by a repository storing the document to describe the document. | 0.718259 |
8,090,678 | 1 | 12 | 1. A computer-implemented method of extracting information from co-occurring Hyper Text Mark-up Language (HTML) structured documents, the method comprising: presenting a list of web sites to a user; receiving one or more of the web sites selected from the user for data extraction; collecting a plurality of co-occurring different HTML structured documents for each of the selected web sites at a computer comprising a processor; forming a plurality of clusters comprising different subsets of the co-occurring HTML structured documents, wherein: each cluster comprises a different HTML structured document of the plurality of co-occurring HTML structured documents as a centroid document and other HTML structured documents of the plurality of co-occurring HTML structured documents that achieve a threshold of similarity with respect to the centroid document, the clusters are formed by comparing each co-occurring HTML structured document to each centroid document of each cluster based on relative structural similarity of HTML data structure of each co-occurring HTML structured document with respect to HTML data structure of each centroid document of each cluster, an alignment algorithm is used to determine the co-occurring HTML structured documents that achieve the threshold of similarity with respect to each centroid document by comparing structured locations of data fields for storing data elements within each centroid document and structured locations of corresponding data fields for storing data elements within each of the co-occurring HTML structured documents, the co-occurring HTML structured documents are compared to each centroid document based on similarity of structured locations of corresponding data fields within the HTML data structures without regard to content of data elements stored in the corresponding data fields within the HTML data structures, and the relative structural similarity of a particular co-occurring HTML structured document with respect to a particular centroid document is penalized when the co-occurring HTML structured document includes a data field that is within the particular centroid document in a different structured location; displaying a list of clusters; displaying the centroid document of a particular cluster selected from the list of clusters; marking a data element on the centroid document of the particular cluster; identifying a data element on each of the other HTML structured documents of the particular cluster that is stored within a data field having a structured location that corresponds to the structured location of the data field storing the marked data element within the centroid document of the particular cluster; and providing a user interface displaying content of data elements identified from the other HTML structured documents of the particular cluster on a computer display. | 1. A computer-implemented method of extracting information from co-occurring Hyper Text Mark-up Language (HTML) structured documents, the method comprising: presenting a list of web sites to a user; receiving one or more of the web sites selected from the user for data extraction; collecting a plurality of co-occurring different HTML structured documents for each of the selected web sites at a computer comprising a processor; forming a plurality of clusters comprising different subsets of the co-occurring HTML structured documents, wherein: each cluster comprises a different HTML structured document of the plurality of co-occurring HTML structured documents as a centroid document and other HTML structured documents of the plurality of co-occurring HTML structured documents that achieve a threshold of similarity with respect to the centroid document, the clusters are formed by comparing each co-occurring HTML structured document to each centroid document of each cluster based on relative structural similarity of HTML data structure of each co-occurring HTML structured document with respect to HTML data structure of each centroid document of each cluster, an alignment algorithm is used to determine the co-occurring HTML structured documents that achieve the threshold of similarity with respect to each centroid document by comparing structured locations of data fields for storing data elements within each centroid document and structured locations of corresponding data fields for storing data elements within each of the co-occurring HTML structured documents, the co-occurring HTML structured documents are compared to each centroid document based on similarity of structured locations of corresponding data fields within the HTML data structures without regard to content of data elements stored in the corresponding data fields within the HTML data structures, and the relative structural similarity of a particular co-occurring HTML structured document with respect to a particular centroid document is penalized when the co-occurring HTML structured document includes a data field that is within the particular centroid document in a different structured location; displaying a list of clusters; displaying the centroid document of a particular cluster selected from the list of clusters; marking a data element on the centroid document of the particular cluster; identifying a data element on each of the other HTML structured documents of the particular cluster that is stored within a data field having a structured location that corresponds to the structured location of the data field storing the marked data element within the centroid document of the particular cluster; and providing a user interface displaying content of data elements identified from the other HTML structured documents of the particular cluster on a computer display. 12. The method of claim 1 , further comprising using an alignment algorithm to extract the data element identified on each of the other HTML structured documents that corresponds to the marked data element on the centroid document of the particular cluster. | 0.831586 |
9,020,865 | 17 | 18 | 17. The non-transitory computer-readable storage medium of claim 15 , storing additional instructions which, when executed by the one or more processors, cause the one or more processors to perform steps of: initializing one or more elements of the network model; identifying a plurality of state-and-event-specific words for the particular event in contents of the plurality of event-and-time-specific texts; and generating a modified network model based on the plurality of state-and-event-specific words and the plurality of event-and-time-specific texts. | 17. The non-transitory computer-readable storage medium of claim 15 , storing additional instructions which, when executed by the one or more processors, cause the one or more processors to perform steps of: initializing one or more elements of the network model; identifying a plurality of state-and-event-specific words for the particular event in contents of the plurality of event-and-time-specific texts; and generating a modified network model based on the plurality of state-and-event-specific words and the plurality of event-and-time-specific texts. 18. The non-transitory computer-readable storage medium of claim 17 , storing additional instructions which, when executed by the one or more processors, cause the one or more processors to perform steps of: determining unknown parameters of the modified network model using a Baum-Welch algorithm; wherein the Baum-Welch algorithm computes maximum likelihood estimates and posterior mode estimates for the unknown parameters of the modified network model using training data. | 0.5 |
6,014,559 | 1 | 8 | 1. A method of delivering a voice mail notification to a subscriber of a voice mail system to indicate that a voice mail message is waiting in a subscriber mailbox, comprising the steps of receiving within a cellular phone network a voice mail notification from a voice mail system that indicates a voice mail message is waiting for a subscriber, and forwarding, without subscriber intervention, the voice mail notification through the cellular phone network to a private base station used by the subscriber to indicate a voice mail message is waiting in a subscriber mailbox of the voice mail system, and including the step of incorporating within the voice mail notification forwarded to the private base station a calling number of the calling party that left the voice mail message, a name of the calling party, if known, and an index of the voice mail messages waiting in the subscriber mailbox. | 1. A method of delivering a voice mail notification to a subscriber of a voice mail system to indicate that a voice mail message is waiting in a subscriber mailbox, comprising the steps of receiving within a cellular phone network a voice mail notification from a voice mail system that indicates a voice mail message is waiting for a subscriber, and forwarding, without subscriber intervention, the voice mail notification through the cellular phone network to a private base station used by the subscriber to indicate a voice mail message is waiting in a subscriber mailbox of the voice mail system, and including the step of incorporating within the voice mail notification forwarded to the private base station a calling number of the calling party that left the voice mail message, a name of the calling party, if known, and an index of the voice mail messages waiting in the subscriber mailbox. 8. A method according to claim 1 including the step of querying a home location register to determine the location of a subscriber private base station which is to receive the voice mail notification. | 0.744898 |
8,516,012 | 1 | 7 | 1. A method, executed on a processor, the method comprising: defining a plurality of translating references for an object; generating a common information model (CIM) with a processor of a system, the CIM comprising one or more functional object attributes of the object; generating a first instantiation of a user information model (UIM), the first instantiation of the UIM comprising one or more user-associated attributes of the object; interfacing with the CIM using the first instantiation of the UIM; translating one or more user-associated attributes of the first instantiation of the UIM to the one or more functional object attributes of the CIM using the plurality of translating references; generating a second instantiation of a user information model (UIM); interfacing with the CIM using the second instantiation of the UIM; translating one or more user-associated attributes of the second instantiation of the UIM to the one or more functional object attributes of the CIM using the plurality of translating references; and providing at least a portion of the CIM. | 1. A method, executed on a processor, the method comprising: defining a plurality of translating references for an object; generating a common information model (CIM) with a processor of a system, the CIM comprising one or more functional object attributes of the object; generating a first instantiation of a user information model (UIM), the first instantiation of the UIM comprising one or more user-associated attributes of the object; interfacing with the CIM using the first instantiation of the UIM; translating one or more user-associated attributes of the first instantiation of the UIM to the one or more functional object attributes of the CIM using the plurality of translating references; generating a second instantiation of a user information model (UIM); interfacing with the CIM using the second instantiation of the UIM; translating one or more user-associated attributes of the second instantiation of the UIM to the one or more functional object attributes of the CIM using the plurality of translating references; and providing at least a portion of the CIM. 7. The method of claim 1 , wherein the object is a radio. | 0.853093 |
7,698,263 | 1 | 8 | 1. A method for providing a search, the method comprising: displaying, by a computer, a media guide comprising at least one display element comprising an action card configured to display a plurality of actions that can be taken with respect to a selected program, one of the plurality of actions comprising a search action; displaying, in response to a user selecting a user selectable element corresponding to the search action on the action card, a search card, the search card configured to display a plurality of search options consisting of the following: an upcoming episodes search option, a related programs search option, a related personalities search option, and a related key word search option; and performing, in response to the user selecting a user selectable element corresponding to the related programs search option, a search comprising searching for one of the following: a current showing of the selected program that is in a same genre as the selected program and a future showing of another program that is in the same genre as the selected program; and displaying a results of the search. | 1. A method for providing a search, the method comprising: displaying, by a computer, a media guide comprising at least one display element comprising an action card configured to display a plurality of actions that can be taken with respect to a selected program, one of the plurality of actions comprising a search action; displaying, in response to a user selecting a user selectable element corresponding to the search action on the action card, a search card, the search card configured to display a plurality of search options consisting of the following: an upcoming episodes search option, a related programs search option, a related personalities search option, and a related key word search option; and performing, in response to the user selecting a user selectable element corresponding to the related programs search option, a search comprising searching for one of the following: a current showing of the selected program that is in a same genre as the selected program and a future showing of another program that is in the same genre as the selected program; and displaying a results of the search. 8. The method of claim 1 , wherein displaying the media guide comprises displaying the media guide in response to a received first input. | 0.911954 |
7,831,582 | 37 | 38 | 37. A system, comprising: one or more computer systems, each comprising at least a memory configured to store program instructions and a processor configured to execute program instructions, wherein the program instructions are executable by the one or more computer systems to implement: a web services interface configured to receive, according to a web services protocol, indications of navigation traffic directed to respective ones of a plurality of online content sources; a traffic analysis engine configured to receive said indications from said web services interface; and a search engine configured to identify a result set including one or more of said plurality of online content sources, wherein each of the included one or more online content sources satisfies a keyword query including one or more keywords; wherein said traffic analysis engine is further configured, for a given one of the online content sources included in said result set, to identify and generate corresponding representations of one or more aggregate paths including said given online content source, wherein each of the one or more aggregate paths includes one or more navigation paths among said plurality of online content sources as reflected by said indications of navigation traffic, wherein each of the one or more navigation paths is indicative of an access request by one or more users that originates from a corresponding originating one of said online content sources to access a corresponding destination one of said online content sources, wherein each such access request indicated by a navigation path occurs prior to generation of representations of said one or more aggregate paths, wherein a representation of a given one of said one or more aggregate paths is indicative of multiple ones of said online content sources; and wherein said search engine is further configured to detect a selection of a particular online content source from one of said identified aggregate paths, wherein prior to said detecting, said particular online content source does not satisfy said keyword query; and wherein in response to detecting said selection of said particular online content source, said search engine is further configured to associate said one or more keywords included in said keyword query with said particular online content source, such that after said one or more keywords are associated with said particular online content source, said particular online content source satisfies said keyword query. | 37. A system, comprising: one or more computer systems, each comprising at least a memory configured to store program instructions and a processor configured to execute program instructions, wherein the program instructions are executable by the one or more computer systems to implement: a web services interface configured to receive, according to a web services protocol, indications of navigation traffic directed to respective ones of a plurality of online content sources; a traffic analysis engine configured to receive said indications from said web services interface; and a search engine configured to identify a result set including one or more of said plurality of online content sources, wherein each of the included one or more online content sources satisfies a keyword query including one or more keywords; wherein said traffic analysis engine is further configured, for a given one of the online content sources included in said result set, to identify and generate corresponding representations of one or more aggregate paths including said given online content source, wherein each of the one or more aggregate paths includes one or more navigation paths among said plurality of online content sources as reflected by said indications of navigation traffic, wherein each of the one or more navigation paths is indicative of an access request by one or more users that originates from a corresponding originating one of said online content sources to access a corresponding destination one of said online content sources, wherein each such access request indicated by a navigation path occurs prior to generation of representations of said one or more aggregate paths, wherein a representation of a given one of said one or more aggregate paths is indicative of multiple ones of said online content sources; and wherein said search engine is further configured to detect a selection of a particular online content source from one of said identified aggregate paths, wherein prior to said detecting, said particular online content source does not satisfy said keyword query; and wherein in response to detecting said selection of said particular online content source, said search engine is further configured to associate said one or more keywords included in said keyword query with said particular online content source, such that after said one or more keywords are associated with said particular online content source, said particular online content source satisfies said keyword query. 38. The system as recited in claim 37 , wherein said given online content source included in said result set satisfies said keyword query if said given online content source is associated with each keyword included in said keyword query. | 0.662393 |
4,488,243 | 15 | 16 | 15. The apparatus of claim 13 wherein the parameter .alpha. is approximately 0.7 if ##EQU9## and the parameter .alpha. is approximately 2.0 if ##EQU10## where .beta. is the average distance expected between comparisons of speech patterns from the same acoustic class. | 15. The apparatus of claim 13 wherein the parameter .alpha. is approximately 0.7 if ##EQU9## and the parameter .alpha. is approximately 2.0 if ##EQU10## where .beta. is the average distance expected between comparisons of speech patterns from the same acoustic class. 16. The apparatus of claim 15 wherein the average distance .beta. is in the range of about 0.6 and 0.7. | 0.5 |
9,043,206 | 1 | 7 | 1. A system by which a user inputs information into a record, comprising: a processor; a main memory in communication with the processor via a communication infrastructure and storing instructions that, when executed by the processor, cause the processor to: (a) receive an input utterance from an input device, wherein the input utterance includes at least one word; (b) retrieve a program code that includes one or more markers, each of which is associated with a certain utterance and correspond to a computer-implemented task; (c) assess the program code for the presence of one or more markers for which the certain utterance with which it is associated is the input utterance; (d) if the input utterance is associated with one or more markers, perform the computer-implemented task corresponding to the one or more markers associated with the input utterance; (e) access a set of template hierarchies from a database, wherein each template hierarchy of the set includes at least one template and where each template includes one or more markers, each of which is associated with a certain utterance and correspond to a computer-implemented task; (f) compare the at least one word of the input utterance to at least one term of the at least one template of template hierarchy in the set of template hierarchies; (g) determine whether the at least one word of the input utterance matches the at least one term of the at least one template of the template hierarchy; (h) calculate a score based on the match between the at least one word of the input utterance and the at least one term of the at least one template of the template hierarchy; (i) repeat steps (f)-(h) until there are no more words of the input utterance for said compare step; (j) populate the at least one template with at least one data element corresponding to the at least one term of the at least one template of the template hierarchy to obtain one or more populated templates; (k) compute a total score of each populated template of the one or more populated templates based on the match between all words of the input utterance to each populated template of the one or more populated templates; (l) select at least one populated template with a high total score; (m) ascertain by the processor whether the high total score of the selected template meets a threshold score and, if so, perform computer-implemented task corresponding to the one or more markers associated with the template; (n) establish by the processor whether there is any template that scores at or above the threshold score; (o) if no template scores at or above the threshold score record the utterance as a sequence of words; and (p) communicate the sequence of words to the user. | 1. A system by which a user inputs information into a record, comprising: a processor; a main memory in communication with the processor via a communication infrastructure and storing instructions that, when executed by the processor, cause the processor to: (a) receive an input utterance from an input device, wherein the input utterance includes at least one word; (b) retrieve a program code that includes one or more markers, each of which is associated with a certain utterance and correspond to a computer-implemented task; (c) assess the program code for the presence of one or more markers for which the certain utterance with which it is associated is the input utterance; (d) if the input utterance is associated with one or more markers, perform the computer-implemented task corresponding to the one or more markers associated with the input utterance; (e) access a set of template hierarchies from a database, wherein each template hierarchy of the set includes at least one template and where each template includes one or more markers, each of which is associated with a certain utterance and correspond to a computer-implemented task; (f) compare the at least one word of the input utterance to at least one term of the at least one template of template hierarchy in the set of template hierarchies; (g) determine whether the at least one word of the input utterance matches the at least one term of the at least one template of the template hierarchy; (h) calculate a score based on the match between the at least one word of the input utterance and the at least one term of the at least one template of the template hierarchy; (i) repeat steps (f)-(h) until there are no more words of the input utterance for said compare step; (j) populate the at least one template with at least one data element corresponding to the at least one term of the at least one template of the template hierarchy to obtain one or more populated templates; (k) compute a total score of each populated template of the one or more populated templates based on the match between all words of the input utterance to each populated template of the one or more populated templates; (l) select at least one populated template with a high total score; (m) ascertain by the processor whether the high total score of the selected template meets a threshold score and, if so, perform computer-implemented task corresponding to the one or more markers associated with the template; (n) establish by the processor whether there is any template that scores at or above the threshold score; (o) if no template scores at or above the threshold score record the utterance as a sequence of words; and (p) communicate the sequence of words to the user. 7. The system of claim 1 , wherein the input utterance associated with one or more markers in step d corresponds to the computer implemented task of executing a sequence of instructions. | 0.903627 |
10,013,729 | 8 | 14 | 8. A computer program product comprising a non-transitory computer-readable storage medium storing computer-executable code comprising instructions for: storing a plurality of events and user interactions performed by users of a social networking system with the plurality of events; associating a set of events of the plurality of events with a category; selecting a set of users associated with the set of events, comprising, for each event from the set of events: identifying users performing user interactions with the event, for each identified user, determining a measure of user interactions of the user with the event, wherein the measure of user interactions of the user with the event is based on a frequency of interaction of the user with the event, and including the user in the set of users, responsive to the measure of user interactions of the user with the event exceeding a threshold; selecting a set of candidate events associated with the set of users, based on user interactions of users from the set of users with the candidate events determining whether each candidate event is associated with the category based on keyword occurrences in content associated with each candidate event; and providing information describing a particular event to a user for performing an action, the information provided based on the category. | 8. A computer program product comprising a non-transitory computer-readable storage medium storing computer-executable code comprising instructions for: storing a plurality of events and user interactions performed by users of a social networking system with the plurality of events; associating a set of events of the plurality of events with a category; selecting a set of users associated with the set of events, comprising, for each event from the set of events: identifying users performing user interactions with the event, for each identified user, determining a measure of user interactions of the user with the event, wherein the measure of user interactions of the user with the event is based on a frequency of interaction of the user with the event, and including the user in the set of users, responsive to the measure of user interactions of the user with the event exceeding a threshold; selecting a set of candidate events associated with the set of users, based on user interactions of users from the set of users with the candidate events determining whether each candidate event is associated with the category based on keyword occurrences in content associated with each candidate event; and providing information describing a particular event to a user for performing an action, the information provided based on the category. 14. The computer program product of claim 8 , wherein the computer-executable code further comprises instructions for: responsive to determining that a candidate event is associated with the category, adding the candidate event to the set of events. | 0.675781 |
8,271,399 | 13 | 14 | 13. The computer program product of claim 11 , wherein the scheme further includes a group number. | 13. The computer program product of claim 11 , wherein the scheme further includes a group number. 14. The computer program product of claim 13 , further comprising instructions for adding a separator to at least a first document in each group before printing the documents from the sorted document stream. | 0.5 |
9,686,288 | 8 | 9 | 8. The method of claim 1 , wherein rewriting the script program comprises inserting a run-time check into the script program. | 8. The method of claim 1 , wherein rewriting the script program comprises inserting a run-time check into the script program. 9. The method of claim 8 , wherein the run-time check comprises one or more of a group consisting of a security check and a user warning. | 0.5 |
7,548,462 | 10 | 15 | 10. A method for double programming a multi-level-cell (MLC) in a multi-bit-cell (MBC) of a charge trapping memory, the charge trapping memory having an array of charge trapping memory cells connecting to a plurality of word lines, each word line is connected to a plurality of charge trapping memory cells, each charge trapping memory cell having a first trapping site and a second trapping site, comprising: receiving a data pattern including a plurality of programming levels; programming a plurality of charge trapping cells along a word line, comprising: during a pre-program phase, conducting a pre-program operation to increase threshold voltages and a pre-program-verify operation to verify the increased threshold voltages of a plurality of charge trapping memory cells along the word line, the pre-program verify operation verifying the charge trapping memory cells to a pre-program-verify levels; and during a post-program phase, conducting a post-programming operation to farther increase threshold voltages and a post-program-verify operation to verify the farther increased threshold voltages of the plurality of charge trapping cells along the word line, the post-program verify operation verifying the charge trapping memory cells to corresponding predetermined program-verify levels; wherein the first program-verify levels are less than the corresponding predetermined program-verify levels. | 10. A method for double programming a multi-level-cell (MLC) in a multi-bit-cell (MBC) of a charge trapping memory, the charge trapping memory having an array of charge trapping memory cells connecting to a plurality of word lines, each word line is connected to a plurality of charge trapping memory cells, each charge trapping memory cell having a first trapping site and a second trapping site, comprising: receiving a data pattern including a plurality of programming levels; programming a plurality of charge trapping cells along a word line, comprising: during a pre-program phase, conducting a pre-program operation to increase threshold voltages and a pre-program-verify operation to verify the increased threshold voltages of a plurality of charge trapping memory cells along the word line, the pre-program verify operation verifying the charge trapping memory cells to a pre-program-verify levels; and during a post-program phase, conducting a post-programming operation to farther increase threshold voltages and a post-program-verify operation to verify the farther increased threshold voltages of the plurality of charge trapping cells along the word line, the post-program verify operation verifying the charge trapping memory cells to corresponding predetermined program-verify levels; wherein the first program-verify levels are less than the corresponding predetermined program-verify levels. 15. The method of claim 10 , wherein the first programming and first program-verify operations during the first programming phase are conducted in parallel, comprising: simultaneously programming the plurality of charge trapping cells along the word line to a first pre-program level, a second pre-program level, and a third pre-program level; and simultaneously verifying the plurality of charge trapping cells along the word line to a first pre-program-verify level, a second pre-program-verify level and a third pre-program-verify level. | 0.713071 |
8,818,788 | 1 | 7 | 1. A method for analyzing sentiment, comprising: at a first computer: dividing a collection of text into a plurality of sentiment segments; tokenizing words or phrases in the plurality of sentiment segments; performing a frequency analysis on tokenized words or phrases in each sentiment segment of the plurality of sentiment segments; performing a scaling operation to size individual sentiment segments based on results from the frequency analysis; for each tokenized word or phrase in each sentiment segment of the plurality of sentiment segments, subtracting a first number of the tokenized word or phrase in the sentiment segment from a second number of the tokenized word or phrase in at least one other sentiment segment of the plurality of sentiment segments, thereby producing, for each sentiment segment of the plurality of sentiment segments, a list of words or phrases that apply specifically to the sentiment segment; and providing the list of words or phrases that apply specifically to the sentiment segment to a second computer over a network connection. | 1. A method for analyzing sentiment, comprising: at a first computer: dividing a collection of text into a plurality of sentiment segments; tokenizing words or phrases in the plurality of sentiment segments; performing a frequency analysis on tokenized words or phrases in each sentiment segment of the plurality of sentiment segments; performing a scaling operation to size individual sentiment segments based on results from the frequency analysis; for each tokenized word or phrase in each sentiment segment of the plurality of sentiment segments, subtracting a first number of the tokenized word or phrase in the sentiment segment from a second number of the tokenized word or phrase in at least one other sentiment segment of the plurality of sentiment segments, thereby producing, for each sentiment segment of the plurality of sentiment segments, a list of words or phrases that apply specifically to the sentiment segment; and providing the list of words or phrases that apply specifically to the sentiment segment to a second computer over a network connection. 7. The method of claim 1 , wherein the plurality of sentiment segments comprises a positive sentiment and a negative sentiment. | 0.674359 |
9,619,515 | 1 | 2 | 1. A system for expanding a search, the system comprising: a processor and executable instructions accessible on a computer-readable medium that, when executed, cause the processor to perform operations comprising: calculate a diversity index for a plurality of query terms included in a plurality of other queries associated with a query, the diversity index being a measure of diversity among the plurality of query terms, the diversity index relating to differences among the plurality of query terms; compare the diversity index to a threshold value; and expand the query with one or more of the plurality of query terms based on the comparison. | 1. A system for expanding a search, the system comprising: a processor and executable instructions accessible on a computer-readable medium that, when executed, cause the processor to perform operations comprising: calculate a diversity index for a plurality of query terms included in a plurality of other queries associated with a query, the diversity index being a measure of diversity among the plurality of query terms, the diversity index relating to differences among the plurality of query terms; compare the diversity index to a threshold value; and expand the query with one or more of the plurality of query terms based on the comparison. 2. The system of claim 1 , wherein the expanding the query is responsive to the diversity index being less than the threshold value. | 0.507463 |
6,122,614 | 2 | 3 | 2. The invention according to claim 1 wherein said written text is at least temporarily synchronized to said voice dictation file, said manual editing means comprises: means for sequentially comparing a copy of said written text with said transcribed file resulting in a sequential list of unmatched words culled from said copy of said written text, said sequential list having a beginning, an end and a current unmatched word, said current unmatched word being successively advanced from said beginning to said end; means for incrementally searching for said current unmatched word contemporaneously within a first buffer associated with the speech recognition program containing said written text and a second buffer associated with said sequential list; and means for correcting said current unmatched word in said second buffer, said correcting means including means for displaying said current unmatched word in a manner substantially visually isolated from other text in said copy of said written text and means for playing a portion of said synchronized voice dictation recording from said first buffer associated with said current unmatched word. | 2. The invention according to claim 1 wherein said written text is at least temporarily synchronized to said voice dictation file, said manual editing means comprises: means for sequentially comparing a copy of said written text with said transcribed file resulting in a sequential list of unmatched words culled from said copy of said written text, said sequential list having a beginning, an end and a current unmatched word, said current unmatched word being successively advanced from said beginning to said end; means for incrementally searching for said current unmatched word contemporaneously within a first buffer associated with the speech recognition program containing said written text and a second buffer associated with said sequential list; and means for correcting said current unmatched word in said second buffer, said correcting means including means for displaying said current unmatched word in a manner substantially visually isolated from other text in said copy of said written text and means for playing a portion of said synchronized voice dictation recording from said first buffer associated with said current unmatched word. 3. The invention according to claim 2 wherein said correcting means further includes means for alternatively viewing said current unmatched word in context within said copy of said written text. | 0.5 |
8,600,969 | 4 | 5 | 4. The non-transitory computer readable medium of claim 3 , wherein the history collection unit is operable to collect at least one of text data and metadata corresponding to the content. | 4. The non-transitory computer readable medium of claim 3 , wherein the history collection unit is operable to collect at least one of text data and metadata corresponding to the content. 5. The non-transitory computer readable medium of claim 4 , wherein the keyword extraction unit is operable to extract the keyword from the text data and the metadata. | 0.5 |
7,623,715 | 15 | 16 | 15. The system of claim 14 wherein the step of constructing the character segmented features list further comprises: back tracking to trace operations in the act of comparing to identify correspondence between a feature in the input phrase and a prototype feature in a character set in the reference phrase; and locating segmentation points between features in adjacent character feature sets. | 15. The system of claim 14 wherein the step of constructing the character segmented features list further comprises: back tracking to trace operations in the act of comparing to identify correspondence between a feature in the input phrase and a prototype feature in a character set in the reference phrase; and locating segmentation points between features in adjacent character feature sets. 16. The system of claim 15 wherein the step of translating further comprises: placing features from the character segmented feature list on the image of the input phrase; detecting segmentation points between features of adjacent segmented feature sets on the image of the input phrase; and cutting the image of the input phrase at the segmentation points to create the character images. | 0.5 |
8,341,185 | 1 | 6 | 1. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, determine local context data that indicates one or more of temporal, spatial, environmental or activity circumstances of a consumer who uses the apparatus to obtain network services; cause, at least in part, transmission of the local context data to a service via a network; determine whether data that indicates a network resource is received in response to the transmission of the local context data; and if the data that indicates the network resource is received in response to the transmission of the local context data, then cause, at least in part, presentation of data that indicates the network resource on a display of the apparatus, wherein the apparatus is a user device. | 1. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, determine local context data that indicates one or more of temporal, spatial, environmental or activity circumstances of a consumer who uses the apparatus to obtain network services; cause, at least in part, transmission of the local context data to a service via a network; determine whether data that indicates a network resource is received in response to the transmission of the local context data; and if the data that indicates the network resource is received in response to the transmission of the local context data, then cause, at least in part, presentation of data that indicates the network resource on a display of the apparatus, wherein the apparatus is a user device. 6. An apparatus of claim 1 , wherein the apparatus is further caused to: cause, at least in part, transmission of a query message to one of the network services; and receive the local context data in response to the transmission of the query message to the one of the network services. | 0.731132 |
9,442,744 | 1 | 12 | 1. A method implemented by a computing device comprising: parsing source content during the process of building an application to identify resources that were modified; updating localization files having a file format designated for translations into one or more selected languages selected for the application based on the resources that are identified as modified in the build such that the localization file reflects updates as resources are updated for each build; creating dynamic resource files based on the updated localization files that incorporate available translations for the resources; compiling the dynamic resource files into compiled language specific resource files for each of the one or more selected languages; discarding the dynamic resource files that are created for the build of the application when the build is complete; and producing a multilingual resource package for the application configured to contain the compiled language specific resource files for the application. | 1. A method implemented by a computing device comprising: parsing source content during the process of building an application to identify resources that were modified; updating localization files having a file format designated for translations into one or more selected languages selected for the application based on the resources that are identified as modified in the build such that the localization file reflects updates as resources are updated for each build; creating dynamic resource files based on the updated localization files that incorporate available translations for the resources; compiling the dynamic resource files into compiled language specific resource files for each of the one or more selected languages; discarding the dynamic resource files that are created for the build of the application when the build is complete; and producing a multilingual resource package for the application configured to contain the compiled language specific resource files for the application. 12. A method as described in claim 1 , wherein the dynamic resource files are intermediary files that are created by transforming the localization files into a programming language specific format that compiles multilingual resources into the build. | 0.68401 |
7,855,799 | 28 | 29 | 28. A method of controlling automated printing of electronic documents implemented at least in part on a computing system, comprising: receiving an electronic file comprising a plurality of electronic documents; displaying the electronic file; receiving a first input identifying a first text item located in a first area of a first page in the file; receiving a second input identifying the first text item as a first document delineator; storing the first text item and the identification of the first text item as a first document delineator; receiving a third input identifying a second text item located in a second area of the page; receiving a fourth input identifying the second text item as a second document delineator; storing the second text item and the identification of the second text item as a second document delineator; identifying the first page as the beginning of a first document; identify the first text item located in the first area of a second page in the electronic file; confirming the existence of the second text item in the second area of the second page; and identifying the second page in the electronic file as the beginning of a second document. | 28. A method of controlling automated printing of electronic documents implemented at least in part on a computing system, comprising: receiving an electronic file comprising a plurality of electronic documents; displaying the electronic file; receiving a first input identifying a first text item located in a first area of a first page in the file; receiving a second input identifying the first text item as a first document delineator; storing the first text item and the identification of the first text item as a first document delineator; receiving a third input identifying a second text item located in a second area of the page; receiving a fourth input identifying the second text item as a second document delineator; storing the second text item and the identification of the second text item as a second document delineator; identifying the first page as the beginning of a first document; identify the first text item located in the first area of a second page in the electronic file; confirming the existence of the second text item in the second area of the second page; and identifying the second page in the electronic file as the beginning of a second document. 29. The method of claim 28 , wherein receiving a second input identifying the first text item as a first document delineator comprises receiving a second input identifying the first text item as static. | 0.89346 |
7,805,711 | 1 | 3 | 1. A method of implementing an I/O redirection provider in a management system comprising a Common Information Model Object Manager (“CIMOM”), the method comprising: for each of a number of script files, determining whether a class corresponding to the script file exists; responsive to a determination that a class corresponding to the script file does not exist, selectively using at least one template to generate a new schema for the script file; and compiling the new schema within the CIMOM. | 1. A method of implementing an I/O redirection provider in a management system comprising a Common Information Model Object Manager (“CIMOM”), the method comprising: for each of a number of script files, determining whether a class corresponding to the script file exists; responsive to a determination that a class corresponding to the script file does not exist, selectively using at least one template to generate a new schema for the script file; and compiling the new schema within the CIMOM. 3. The method of claim 1 further comprising awaiting receipt by the I/O redirection provider of a CIMOM request. | 0.891051 |
8,874,430 | 1 | 5 | 1. A method for preparing a multi-lingual personal identification card, comprising: receiving, by a computer processor, a multi-lingual text comprising Latin-based characters in a Latin-based language and non-Latin-based characters in a non-Latin-based language, wherein the multi-lingual text comprises a name of a holder of the personal identification card in the Latin-based language and the non-Latin-based language; converting, by the computer processor, the non-Latin-based characters in the multi-lingual text to index values to produce a pseudo text, wherein each of the non-Latin-based characters has a Unicode value two byte in length, wherein the index values are a single byte in length, wherein the conversion is based on a predefined mapping that converts the Unicode values of the non-Latin-based characters to index values having fewer digits than the corresponding Unicode values of the non-Latin-based characters, wherein the predefined mapping allocates at least 55 consecutive digital numbers for the index values, wherein the pseudo text includes the index values in co-existence with the Latin-based characters in a Latin-based language; receiving vector data for a personal image which includes a facial image, a finger print, or a combination of both of the holder of the personal identification card; and encoding the pseudo text and the vector data in a matrix-code symbol. | 1. A method for preparing a multi-lingual personal identification card, comprising: receiving, by a computer processor, a multi-lingual text comprising Latin-based characters in a Latin-based language and non-Latin-based characters in a non-Latin-based language, wherein the multi-lingual text comprises a name of a holder of the personal identification card in the Latin-based language and the non-Latin-based language; converting, by the computer processor, the non-Latin-based characters in the multi-lingual text to index values to produce a pseudo text, wherein each of the non-Latin-based characters has a Unicode value two byte in length, wherein the index values are a single byte in length, wherein the conversion is based on a predefined mapping that converts the Unicode values of the non-Latin-based characters to index values having fewer digits than the corresponding Unicode values of the non-Latin-based characters, wherein the predefined mapping allocates at least 55 consecutive digital numbers for the index values, wherein the pseudo text includes the index values in co-existence with the Latin-based characters in a Latin-based language; receiving vector data for a personal image which includes a facial image, a finger print, or a combination of both of the holder of the personal identification card; and encoding the pseudo text and the vector data in a matrix-code symbol. 5. The method of claim 1 , wherein the Latin-based language comprises English, French, Spanish, German, or Italian. | 0.779693 |
9,473,883 | 1 | 10 | 1. A method comprising: receiving, by a processor of a mobile device, a request or intent, from an application running on the mobile device, for one of a plurality of location service authorization types for the application, wherein in a first location service authorization type allows location data to be sent by a location service to the application when the application is not in use and a second location service authorization type allows location data to be sent by the service to the application only when the application is displayed on a display screen of the mobile device; presenting, by the processor, an authorization dialog on the display screen of the mobile device; receiving user input, through the authorization dialog, authorizing location service for the application; and enforcing, by the processor, the location service authorization type for the application, the enforcing including determining whether to provide the location data to the application based on the location service authorization type for the application. | 1. A method comprising: receiving, by a processor of a mobile device, a request or intent, from an application running on the mobile device, for one of a plurality of location service authorization types for the application, wherein in a first location service authorization type allows location data to be sent by a location service to the application when the application is not in use and a second location service authorization type allows location data to be sent by the service to the application only when the application is displayed on a display screen of the mobile device; presenting, by the processor, an authorization dialog on the display screen of the mobile device; receiving user input, through the authorization dialog, authorizing location service for the application; and enforcing, by the processor, the location service authorization type for the application, the enforcing including determining whether to provide the location data to the application based on the location service authorization type for the application. 10. The method of claim 1 , further comprising: displaying a settings pane on the display screen including a user interface element for specifying the authorization type for the application; receiving input through the user interface element, the input specifying a new authorization type for the application; and replacing the authorization type with the new authorization type. | 0.598517 |
9,342,581 | 20 | 21 | 20. The system according to claim 15 , wherein the database management system kernel to populate the determined members of the persistent database object with the default values comprises: a database management system kernel to populate the determined members of the persistent database object with the default values before transferring the persistent database object to a requesting application. | 20. The system according to claim 15 , wherein the database management system kernel to populate the determined members of the persistent database object with the default values comprises: a database management system kernel to populate the determined members of the persistent database object with the default values before transferring the persistent database object to a requesting application. 21. The system according to claim 20 , wherein the database management system kernel to read the persistent database object from the database comprises: a database management system kernel to transfer the persistent database object to the requesting application after the populating of the determined members of the persistent database object with the default values. | 0.5 |
8,375,017 | 1 | 3 | 1. A computerized method of automatically identifying keywords relevant to a document invisible to search engines comprising: analyzing at a computer the document invisible to search engine crawlers to obtain a keyword starter set from the document, the keyword starter set obtained by: (1) applying at said computer an automated parser to the document to obtain keywords; and (2) applying a frequency prominence analysis to the keywords to select one or more frequently occurring keywords to add to the keyword starter set; expanding at the computer the keyword starter set by applying a computerized taxonomy to the keyword starter set to form a keyword super set; applying at the computer a keyword stop list to keywords in the keyword super set to remove keywords included in the keyword stop list; refining at the computer the keyword super set to form a keyword final set by applying keyword demand data to the keyword super set to remove one or more additional keywords from the keyword super set, wherein the demand data reflects the frequency of use of the keywords as search terms in internet search engines; adding at the computer the keyword final set to a web page for accessing the document; storing the document invisible to search engines for retrieval via the web page for accessing the document; adding the web page with the keyword final set to a web site to facilitate location by internet search engines of the web page for accessing the document according to the keywords added to the web page; and providing internet users with access via the web page to the document invisible to search engines. | 1. A computerized method of automatically identifying keywords relevant to a document invisible to search engines comprising: analyzing at a computer the document invisible to search engine crawlers to obtain a keyword starter set from the document, the keyword starter set obtained by: (1) applying at said computer an automated parser to the document to obtain keywords; and (2) applying a frequency prominence analysis to the keywords to select one or more frequently occurring keywords to add to the keyword starter set; expanding at the computer the keyword starter set by applying a computerized taxonomy to the keyword starter set to form a keyword super set; applying at the computer a keyword stop list to keywords in the keyword super set to remove keywords included in the keyword stop list; refining at the computer the keyword super set to form a keyword final set by applying keyword demand data to the keyword super set to remove one or more additional keywords from the keyword super set, wherein the demand data reflects the frequency of use of the keywords as search terms in internet search engines; adding at the computer the keyword final set to a web page for accessing the document; storing the document invisible to search engines for retrieval via the web page for accessing the document; adding the web page with the keyword final set to a web site to facilitate location by internet search engines of the web page for accessing the document according to the keywords added to the web page; and providing internet users with access via the web page to the document invisible to search engines. 3. The method of claim 1 , wherein the keyword starter set comprises a plurality of keywords in a section title of the document. | 0.880819 |
8,209,481 | 11 | 17 | 11. A computer-implemented system of realizing an associative memory capable of storing a set of documents and retrieving one or more of said stored documents similar to an inputted query document, said method comprising: coding each of said stored document or a part of it through a corresponding feature vector; arranging said feature vector in a matrix; and generating a query feature vector based on the query document. | 11. A computer-implemented system of realizing an associative memory capable of storing a set of documents and retrieving one or more of said stored documents similar to an inputted query document, said method comprising: coding each of said stored document or a part of it through a corresponding feature vector; arranging said feature vector in a matrix; and generating a query feature vector based on the query document. 17. The system of claim 11 , wherein the matrix formed by the feature vector representing the documents to be searched is stored column-wise, and wherein said method further comprises: for those columns of the matrix where the query feature vector bit is set, performing a processor supported bitwise operation between the columns of the matrix to obtain one or more result columns, the values in the result columns representing the similarity between the query and the stored documents. | 0.598185 |
9,240,187 | 18 | 19 | 18. Non-transitory computer-readable storage storing executable code that, when executed by one or more processors, causes the one or more processors to perform a process comprising: obtaining audio data corresponding to an utterance; obtaining marker data corresponding to a first portion of a plurality of portions of an audio presentation, the audio presentation presented contemporaneously with capture of the utterance; generating a transcription of the utterance by performing automatic speech recognition on at least a portion of the audio data; determining an action to be taken responsive to the utterance based at least partly on the transcription and the marker data; and performing the action. | 18. Non-transitory computer-readable storage storing executable code that, when executed by one or more processors, causes the one or more processors to perform a process comprising: obtaining audio data corresponding to an utterance; obtaining marker data corresponding to a first portion of a plurality of portions of an audio presentation, the audio presentation presented contemporaneously with capture of the utterance; generating a transcription of the utterance by performing automatic speech recognition on at least a portion of the audio data; determining an action to be taken responsive to the utterance based at least partly on the transcription and the marker data; and performing the action. 19. The non-transitory computer-readable storage of claim 18 , wherein the executable code causes the one or more processors to obtain the audio data and the marker data via a network connection with a client computing device separate from the one or more processors. | 0.5 |
9,158,858 | 1 | 4 | 1. A method for managing an Extensible Markup Language (XML) Document Management (XDM) Server history, the method comprising the steps of: receiving, by the XDM Server, XML Documents and Filtering Rules within a filter body in an XCAP request from a first XDM Client Device, wherein the Filtering Rules define operation information performed on specific XML Documents to store as history information and further define when to store the history information of the XML Documents; storing the XML Documents and the Filtering Rules on the XDM Server; and when a second XDM Client Device has access to perform one or more operations on the XML Documents stored in the XDM Server, storing, by the XDM Server, the history information of the XML Documents according to the Filtering Rules by the XDM Server and the one or more operations performed. | 1. A method for managing an Extensible Markup Language (XML) Document Management (XDM) Server history, the method comprising the steps of: receiving, by the XDM Server, XML Documents and Filtering Rules within a filter body in an XCAP request from a first XDM Client Device, wherein the Filtering Rules define operation information performed on specific XML Documents to store as history information and further define when to store the history information of the XML Documents; storing the XML Documents and the Filtering Rules on the XDM Server; and when a second XDM Client Device has access to perform one or more operations on the XML Documents stored in the XDM Server, storing, by the XDM Server, the history information of the XML Documents according to the Filtering Rules by the XDM Server and the one or more operations performed. 4. The method for managing an XDM Server history as claimed in claim 1 , wherein the Filtering Rules include history preferences based on events and time according to an XML Configuration Access Protocol (XCAP). | 0.80991 |
9,201,869 | 8 | 12 | 8. A method for use in operating a computer-based tool for converting data from a first form to a second form, comprising the steps of: identifying a set of data to be converted from said first form to said second form; representing each one of the plurality of schema as one or more target features; compiling an index corresponding to the plurality of schema, wherein the index comprises a mapping for each target feature to each of the plurality of schema containing the target feature; representing the set of data to be converted as one or more source features; accessing a plurality of schema that are each based on external knowledge of at least one subject matter area independent of analysis of a particular data set to be converted, each one of the plurality of schema including one or more conversion rules for use in converting data within a corresponding context of a subject matter area of the schema; determining a subset of the plurality of schema for which a similarity metric is to be calculated, wherein the subset of the plurality of schema comprises less than the entirety of the plurality of schema; calculating a similarity metric for the set of data to be converted relative to the subset of the plurality of schema, wherein the similarity metric is at least partially based on commonality between the target features and the source features; selecting at least one selected schema of the plurality of schema at least partially based on the similarity metric; and using an included conversion rule of the sat least one selected schema in a process for converting the set of data from the first form to the second form. | 8. A method for use in operating a computer-based tool for converting data from a first form to a second form, comprising the steps of: identifying a set of data to be converted from said first form to said second form; representing each one of the plurality of schema as one or more target features; compiling an index corresponding to the plurality of schema, wherein the index comprises a mapping for each target feature to each of the plurality of schema containing the target feature; representing the set of data to be converted as one or more source features; accessing a plurality of schema that are each based on external knowledge of at least one subject matter area independent of analysis of a particular data set to be converted, each one of the plurality of schema including one or more conversion rules for use in converting data within a corresponding context of a subject matter area of the schema; determining a subset of the plurality of schema for which a similarity metric is to be calculated, wherein the subset of the plurality of schema comprises less than the entirety of the plurality of schema; calculating a similarity metric for the set of data to be converted relative to the subset of the plurality of schema, wherein the similarity metric is at least partially based on commonality between the target features and the source features; selecting at least one selected schema of the plurality of schema at least partially based on the similarity metric; and using an included conversion rule of the sat least one selected schema in a process for converting the set of data from the first form to the second form. 12. The method according to claim 8 , wherein the selecting step is performed without reference to any particular subject matter or context of the data to be converted or the subject matter or context of the plurality of schema. | 0.684211 |
10,061,769 | 2 | 3 | 2. The machine translation method according to claim 1 , wherein the method further comprising: changing an output form of the forward-translated sentence corresponding to the selected backward-translated sentence in accordance with which of a voice information via the microphone and a text information via the text input device is received as the translation-source sentence. | 2. The machine translation method according to claim 1 , wherein the method further comprising: changing an output form of the forward-translated sentence corresponding to the selected backward-translated sentence in accordance with which of a voice information via the microphone and a text information via the text input device is received as the translation-source sentence. 3. The machine translation method according to claim 2 , wherein the information output device has a speaker, a display, and a processor; and wherein the machine translation method further comprises: outputting, when the translation-source sentence is received via the microphone, the forward-translated sentence corresponding to the selected backward-translated sentence via the speaker; and outputting, when the translation-source sentence is received via the input device, the forward-translated sentence corresponding to the selected backward-translated sentence via the display. | 0.5 |
9,218,812 | 7 | 11 | 7. A server capable of communicating with a vehicular device that performs dialogs with a vehicular driver, comprising: a communication portion which communicates with the vehicular device; and a controller which controls the communication portion; wherein the controller provides control such that, when trigger information initiating a dialog process is generated in the vehicular device or in the server, the controller sends information, indicative of the type of a first dialog forming a starting point of the dialog process and information indicative of the type of a second dialog forming an ending point of the dialog process to the vehicular device, the type of the second dialog being different from the type of the first dialog. | 7. A server capable of communicating with a vehicular device that performs dialogs with a vehicular driver, comprising: a communication portion which communicates with the vehicular device; and a controller which controls the communication portion; wherein the controller provides control such that, when trigger information initiating a dialog process is generated in the vehicular device or in the server, the controller sends information, indicative of the type of a first dialog forming a starting point of the dialog process and information indicative of the type of a second dialog forming an ending point of the dialog process to the vehicular device, the type of the second dialog being different from the type of the first dialog. 11. The server according to claim 7 , wherein the communication portion sends information indicative of the type of the second dialog to the vehicular device, the type of the second dialog being different depending on whether or not there is a fellow passenger. | 0.684783 |
8,156,101 | 5 | 6 | 5. The method of claim 1 wherein the structured data is stored in a relational database. | 5. The method of claim 1 wherein the structured data is stored in a relational database. 6. The method of claim 5 further comprising retrieving the identified subset of unstructured data prior to the performing step, wherein the retrieving step comprises retrieving the identified subset of unstructured data from the relational database. | 0.5 |
7,499,591 | 1 | 6 | 1. A method of classifying a document comprising: using a processor (or computer) to perform the steps of: classifying said document using output from one or more of said classifier engines based on a comparison of one or more metrics for each classifier engine; using a set of training documents to determine a precision value for each classifier engine; identifying a highest precision value and a second highest precision value; determining if said highest precision value is greater than said second highest precision value by a predetermined amount; if so, using output from the classifier engine having said highest precision value to classify said document; and if not, generating for each classifier engine, a list of probabilities of said document being classified by each classifier engine into each one of a group of possible classes, summing said probabilities for each class, and classifying said document into the class with the largest sum of probabilities. | 1. A method of classifying a document comprising: using a processor (or computer) to perform the steps of: classifying said document using output from one or more of said classifier engines based on a comparison of one or more metrics for each classifier engine; using a set of training documents to determine a precision value for each classifier engine; identifying a highest precision value and a second highest precision value; determining if said highest precision value is greater than said second highest precision value by a predetermined amount; if so, using output from the classifier engine having said highest precision value to classify said document; and if not, generating for each classifier engine, a list of probabilities of said document being classified by each classifier engine into each one of a group of possible classes, summing said probabilities for each class, and classifying said document into the class with the largest sum of probabilities. 6. The method of claim 1 further comprising finding confidence values for each classifier engine and multiplying each classifier engine's probabilities by the corresponding classifier engine confidence value prior to summing said probabilities for each class. | 0.645205 |
10,027,688 | 1 | 6 | 1. A method of detecting at least one malicious and/or botnet-related domain name, comprising: performing processing associated with collecting at least one domain name by monitoring Domain Name System (DNS) traffic in at least one network; performing processing associated with obtaining, during a time period, information about the at least one domain name, comprising determining if the at least one domain name is in at least one domain name white list; wherein the obtained information further comprises statistics related to the at least one domain name comprising a total number of queries to the at least one domain name during the time period and a total number of distinct source IP addresses that queried the at least one domain name during the time period; responsive to determining that the at least one domain name is not in the at least one domain name white list, performing processing associated with automatically obtaining, using at least one Internet search engine, search results for the at least one domain name; performing processing associated with analyzing the search results to determine whether at least one search result associated with the at least one domain name comprises a known malware site; and performing processing associated with classifying the at least one domain name as at least one of malicious, suspicious, and legitimate based on the analyzed search results. | 1. A method of detecting at least one malicious and/or botnet-related domain name, comprising: performing processing associated with collecting at least one domain name by monitoring Domain Name System (DNS) traffic in at least one network; performing processing associated with obtaining, during a time period, information about the at least one domain name, comprising determining if the at least one domain name is in at least one domain name white list; wherein the obtained information further comprises statistics related to the at least one domain name comprising a total number of queries to the at least one domain name during the time period and a total number of distinct source IP addresses that queried the at least one domain name during the time period; responsive to determining that the at least one domain name is not in the at least one domain name white list, performing processing associated with automatically obtaining, using at least one Internet search engine, search results for the at least one domain name; performing processing associated with analyzing the search results to determine whether at least one search result associated with the at least one domain name comprises a known malware site; and performing processing associated with classifying the at least one domain name as at least one of malicious, suspicious, and legitimate based on the analyzed search results. 6. The method of claim 1 , wherein the obtained information further comprises performing at least one reverse lookup on the at least one domain name. | 0.833705 |
9,633,658 | 6 | 7 | 6. A system according to claim 1 , further comprising: a confidence determination module configured to determine the identified transcribed speech utterance as having a low confidence score by applying a threshold to the assigned confidence score and designating the identified transcribed speech utterance as having the low confidence score when the confidence score falls below the threshold. | 6. A system according to claim 1 , further comprising: a confidence determination module configured to determine the identified transcribed speech utterance as having a low confidence score by applying a threshold to the assigned confidence score and designating the identified transcribed speech utterance as having the low confidence score when the confidence score falls below the threshold. 7. A system according to claim 6 , wherein the low confidence score represents a high likelihood that the transcribed speech utterance incorrectly represents the corresponding speech utterance. | 0.5 |
8,352,277 | 13 | 16 | 13. The method of claim 11 further including a step: providing an interactive electronic character who provides suggestions for queries which the user can articulate. | 13. The method of claim 11 further including a step: providing an interactive electronic character who provides suggestions for queries which the user can articulate. 16. The method of claim 13 wherein the interactive electronic agent provides responses adjusted for a context experienced by the user. | 0.775168 |
8,789,011 | 15 | 18 | 15. A computer program product for modeling an arbitrarily complex environment, the computer program product comprising at least one non-transitory computer readable medium storing instructions executable by a computer communicatively connected to networked devices for: defining a data model having a set of data structures, the set of data structures comprising: a components data structure, a relationships data structure, and a blueprints data structure, wherein a component in the components data structure represents a logical or physical entity in the arbitrarily complex environment, wherein a relationship in the relationships data structure represents an association or dependency between two or more components in the arbitrarily complex environment, wherein a blueprint in the blueprints data structure represents a container for the two or more components and the association or dependency between the two or more components, wherein the data model comprises a set of blueprints for modeling the arbitrarily complex environment or a subset thereof; storing the components data structure, the relationships data structure, and the blueprints data structure corresponding to the data model in a table schema that includes a plurality of linked tables; and making a change to the components data structure, the relationships data structure, or the blueprints data structure, wherein the table schema is not altered by the change. | 15. A computer program product for modeling an arbitrarily complex environment, the computer program product comprising at least one non-transitory computer readable medium storing instructions executable by a computer communicatively connected to networked devices for: defining a data model having a set of data structures, the set of data structures comprising: a components data structure, a relationships data structure, and a blueprints data structure, wherein a component in the components data structure represents a logical or physical entity in the arbitrarily complex environment, wherein a relationship in the relationships data structure represents an association or dependency between two or more components in the arbitrarily complex environment, wherein a blueprint in the blueprints data structure represents a container for the two or more components and the association or dependency between the two or more components, wherein the data model comprises a set of blueprints for modeling the arbitrarily complex environment or a subset thereof; storing the components data structure, the relationships data structure, and the blueprints data structure corresponding to the data model in a table schema that includes a plurality of linked tables; and making a change to the components data structure, the relationships data structure, or the blueprints data structure, wherein the table schema is not altered by the change. 18. The computer program product of claim 15 , wherein the data model further comprises a hierarchy of component and relationship types. | 0.749077 |
8,291,319 | 16 | 18 | 16. The computer program product of claim 13 , wherein the instruction steps include click-through instruction steps and core instruction steps, and wherein the click-through instruction steps and core instruction steps are labeled for identification. | 16. The computer program product of claim 13 , wherein the instruction steps include click-through instruction steps and core instruction steps, and wherein the click-through instruction steps and core instruction steps are labeled for identification. 18. The computer program product of claim 16 , wherein in response to determining a current solution the user is browsing, a textual relevance of the core instruction steps of this currently browsed solution is determined in order to recommend other relevant solutions. | 0.539384 |
8,880,440 | 1 | 2 | 1. A computer-implemented method of combining text mining services that rely upon different taxonomies, the method comprising: applying a first text mining service relying upon a first taxonomy, to a knowledge base comprising a plurality of documents according to a first iterative instance generation process to generate a plurality of instances of an entity type of the first taxonomy; applying a second text mining service relying upon a second taxonomy, to the knowledge base according to a second iterative instance generation process to generate a plurality of instances of an entity type of the second taxonomy; utilizing the plurality of instances of the first taxonomy and the plurality of instances of the second taxonomy to construct an instance-matching procedure; applying the first text mining service to a target text document smaller than the knowledge base to produce a plurality of first taxonomy entity types; applying the second text mining service to the target text document to produce a plurality of second taxonomy entity types; creating a mapping of the plurality of first taxonomy entity types to the plurality of the second taxonomy entity types based at least in part on the instance-matching procedure; using the mapping to create a merged result that compares a result of applying the first text mining service to the target text document, with a result of applying the second text mining service to the target text document; and displaying the merged result to a user. | 1. A computer-implemented method of combining text mining services that rely upon different taxonomies, the method comprising: applying a first text mining service relying upon a first taxonomy, to a knowledge base comprising a plurality of documents according to a first iterative instance generation process to generate a plurality of instances of an entity type of the first taxonomy; applying a second text mining service relying upon a second taxonomy, to the knowledge base according to a second iterative instance generation process to generate a plurality of instances of an entity type of the second taxonomy; utilizing the plurality of instances of the first taxonomy and the plurality of instances of the second taxonomy to construct an instance-matching procedure; applying the first text mining service to a target text document smaller than the knowledge base to produce a plurality of first taxonomy entity types; applying the second text mining service to the target text document to produce a plurality of second taxonomy entity types; creating a mapping of the plurality of first taxonomy entity types to the plurality of the second taxonomy entity types based at least in part on the instance-matching procedure; using the mapping to create a merged result that compares a result of applying the first text mining service to the target text document, with a result of applying the second text mining service to the target text document; and displaying the merged result to a user. 2. The method of claim 1 wherein: the first taxonomy comprises a first metadata and the second taxonomy comprises a second metadata; and creating the mapping further comprises comparing the first metadata and the second metadata. | 0.85741 |
7,707,032 | 1 | 2 | 1. A method for matching speech data used to determine the similarity between an input speech data and a sample speech data, the method comprising: segmenting the input speech data into a plurality of input speech frames; segmenting the sample speech data into a plurality of sample speech frames; building a matching matrix, wherein each element of the matching matrix corresponds to one of the input speech frames and one of the sample speech frames and indicates a distance value between the corresponding input speech frame and the corresponding sample speech frame; determining a minimum value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a plurality of minimum distance values of the respective rows of elements of the matching matrix, determining a second least value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a plurality of second least distance values of the respective rows of elements of the matching matrix; summing up the minimum distance values and the second least distance value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a row score, determined by: row score = ∑ j = 1 r min r ⊗ C [ MM ( i , j ) ] + ∑ j = 1 r min r ⊗ C - i c [ MM ( i , j ) ] ; determining a minimum value of the distance values indicated in each column of elements of the matching matrix, thereby obtaining a plurality of another minimum distance values of the respective columns of elements of the matching matrix, determining a second least value of the distance values indicated in each column of elements of the matching matrix, thereby obtaining a plurality of second least distance values of the respective columns of elements of the matching matrix; summing up the another minimum distance values and the second least value of the indicated distance values in each column of elements of the matching matrix distance values, thereby obtaining a column score, wherein: column score = ∑ i = 1 e min j ∈ R [ MM ( i , j ) ] + ∑ i = 1 e min j ∈ R - j k [ MM ( i , j ) ] ; calculating a matching score obtained by combining the distance row score and the column score; and determining whether the input speech data and the sample speech data are similar according to the matching score. | 1. A method for matching speech data used to determine the similarity between an input speech data and a sample speech data, the method comprising: segmenting the input speech data into a plurality of input speech frames; segmenting the sample speech data into a plurality of sample speech frames; building a matching matrix, wherein each element of the matching matrix corresponds to one of the input speech frames and one of the sample speech frames and indicates a distance value between the corresponding input speech frame and the corresponding sample speech frame; determining a minimum value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a plurality of minimum distance values of the respective rows of elements of the matching matrix, determining a second least value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a plurality of second least distance values of the respective rows of elements of the matching matrix; summing up the minimum distance values and the second least distance value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a row score, determined by: row score = ∑ j = 1 r min r ⊗ C [ MM ( i , j ) ] + ∑ j = 1 r min r ⊗ C - i c [ MM ( i , j ) ] ; determining a minimum value of the distance values indicated in each column of elements of the matching matrix, thereby obtaining a plurality of another minimum distance values of the respective columns of elements of the matching matrix, determining a second least value of the distance values indicated in each column of elements of the matching matrix, thereby obtaining a plurality of second least distance values of the respective columns of elements of the matching matrix; summing up the another minimum distance values and the second least value of the indicated distance values in each column of elements of the matching matrix distance values, thereby obtaining a column score, wherein: column score = ∑ i = 1 e min j ∈ R [ MM ( i , j ) ] + ∑ i = 1 e min j ∈ R - j k [ MM ( i , j ) ] ; calculating a matching score obtained by combining the distance row score and the column score; and determining whether the input speech data and the sample speech data are similar according to the matching score. 2. The method as claimed in claim 1 , wherein the distance values indicated in the matching matrix are obtained by the dynamic timing warping (DTW) calculation. | 0.768786 |
8,538,842 | 1 | 7 | 1. A method comprising the steps of: receiving, over a network, a brand name, wherein the brand name comprises at least one token; generating, using at least one computing device, a plurality of domain names, wherein the plurality of domain names comprises at least one brand domain name comprising the brand name and a top level domain name, and wherein the plurality of domain names further comprises at least one qualified brand domain name comprising the brand name, at least one qualifying term, and the top level domain name; creating, using the at least one computing device, a domain name portfolio, wherein each of the plurality of domain names is checked using the WHOIS protocol to determine if the respective domain name is registered, wherein domain names that are not registered and domain names that are registered to an owner of the brand are inserted into the domain name portfolio; registering, using the at least one computing device, each of the domain names in the domain name portfolio that is not registered to the brand owner, whereby each of the domain names in the domain name portfolio is registered to the brand owner; parking, using the at least one computing device, each of the domain names in the domain name portfolio with a domain name parking service; tracking, using the at least one computing device, network traffic, for each of the domain names in the domain name portfolio over an analysis period; and determining, using the at least one computing device, a first role of brand index, wherein the role of brand index is determined using the ratio of network traffic for the at least one brand domain name to a total of the network traffic for all domain names in the domain name portfolio. | 1. A method comprising the steps of: receiving, over a network, a brand name, wherein the brand name comprises at least one token; generating, using at least one computing device, a plurality of domain names, wherein the plurality of domain names comprises at least one brand domain name comprising the brand name and a top level domain name, and wherein the plurality of domain names further comprises at least one qualified brand domain name comprising the brand name, at least one qualifying term, and the top level domain name; creating, using the at least one computing device, a domain name portfolio, wherein each of the plurality of domain names is checked using the WHOIS protocol to determine if the respective domain name is registered, wherein domain names that are not registered and domain names that are registered to an owner of the brand are inserted into the domain name portfolio; registering, using the at least one computing device, each of the domain names in the domain name portfolio that is not registered to the brand owner, whereby each of the domain names in the domain name portfolio is registered to the brand owner; parking, using the at least one computing device, each of the domain names in the domain name portfolio with a domain name parking service; tracking, using the at least one computing device, network traffic, for each of the domain names in the domain name portfolio over an analysis period; and determining, using the at least one computing device, a first role of brand index, wherein the role of brand index is determined using the ratio of network traffic for the at least one brand domain name to a total of the network traffic for all domain names in the domain name portfolio. 7. The method of claim 1 wherein the at least one qualifying term is received over the network. | 0.920701 |
9,697,490 | 12 | 15 | 12. A method, comprising: receiving, via a set of one or more interfaces, review data associated with a plurality of entities, wherein each of the entities is associated with a particular industry, and wherein the review data comprises review data collected, over a network from a plurality of disparate, external review websites; wherein the review data is collected by a plurality of instances of different types of helpers that are executed to obtain, over the network, information from the plurality of disparate, external review websites, wherein each type of helper is configured with instructions to fetch review data from a particular type of source, wherein, for a first external review website for which review data is available via an Application Programming Interface (API), an instance of a first helper configured with instructions to obtain review data from the first external review website using the API is executed, and wherein, for a second external review website for which review data is not available via an API, an instance of a second helper configured with instructions to scrape review data from the second external review website is executed; and wherein at least some of the received review data is received from a data store configured to store heterogeneous data records, and wherein the data store includes review data from different external review websites that is stored in heterogeneous record formats; generating, using a set of one or more processors, and from at least a portion of the received review data collected, over the network, from the plurality of disparate, external review websites, at least one online review benchmark for the particular industry, including by determining at least one of an online review volume benchmark and an online review distribution benchmark, wherein: the online review volume benchmark is determined at least in part by counting a first number of reviews associated with at least some entities in the particular industry on the plurality of disparate, external review websites; and the online review distribution benchmark is determined at least in part by determining, for at least the first and second external reviews website included in the plurality of disparate, external review websites, a respective proportion of reviews associated with at least some entities in the particular industry on the respective external review websites; storing the generated at least one online review benchmark for the particular industry; comparing, at a time subsequent to generating the at least one online review benchmark for the particular industry, review data associated with a first entity in the particular industry to the stored industry benchmark, including by comparing at least one of an online review volume of the first entity to the online review volume benchmark for the particular industry and an online review distribution of the first entity to the online review distribution benchmark of the particular industry; determining, based at least in part on the comparison of the review data associated with the first entity in the particular industry to the stored industry benchmark, that an adjustment to the online review distribution of the first entity should be performed, wherein the adjustment includes increasing a number of reviews associated with the first entity on one or more external review websites; modeling an impact that additional reviews on the one or more external review websites would have for the first entity, wherein modeling the impact includes: running a first simulation in which a first volume of additional positive reviews are obtained on the first external review website; determining a first modeled online reputation score based at least in part on the first simulation; running a second simulation in which a second volume of additional positive reviews are obtained on the second external review website, wherein the first volume and the second volume are different; and determining a second modeled online reputation score based at least in part on the second simulation; determining, based at least in part on the first and second modeled online reputation scores, that additional reviews associated with the first external review website should be requested from one or more potential reviewers; in response to determining that additional reviews associated with the first external review website should be requested: identifying, in a list of potential reviewers, individuals in the list of potential reviewers that have accounts with the first external review website for which it has been determined that additional reviews should be requested, wherein the identified individuals are identified based at least in part on an evaluation of corresponding email addresses associated with the identified individuals; and facilitating transmission of review requests to the identified individuals, wherein facilitating transmission of the review requests includes facilitating transmission, over the network, of an electronic message to an individual included in the identified individuals, wherein the electronic message includes a link to the first external review website; and at a time subsequent to transmission of the electronic message, performing a follow-up action based at least in part on a determination of whether the individual has performed at least one of opening the electronic message and clicking on the link included in the electronic message. | 12. A method, comprising: receiving, via a set of one or more interfaces, review data associated with a plurality of entities, wherein each of the entities is associated with a particular industry, and wherein the review data comprises review data collected, over a network from a plurality of disparate, external review websites; wherein the review data is collected by a plurality of instances of different types of helpers that are executed to obtain, over the network, information from the plurality of disparate, external review websites, wherein each type of helper is configured with instructions to fetch review data from a particular type of source, wherein, for a first external review website for which review data is available via an Application Programming Interface (API), an instance of a first helper configured with instructions to obtain review data from the first external review website using the API is executed, and wherein, for a second external review website for which review data is not available via an API, an instance of a second helper configured with instructions to scrape review data from the second external review website is executed; and wherein at least some of the received review data is received from a data store configured to store heterogeneous data records, and wherein the data store includes review data from different external review websites that is stored in heterogeneous record formats; generating, using a set of one or more processors, and from at least a portion of the received review data collected, over the network, from the plurality of disparate, external review websites, at least one online review benchmark for the particular industry, including by determining at least one of an online review volume benchmark and an online review distribution benchmark, wherein: the online review volume benchmark is determined at least in part by counting a first number of reviews associated with at least some entities in the particular industry on the plurality of disparate, external review websites; and the online review distribution benchmark is determined at least in part by determining, for at least the first and second external reviews website included in the plurality of disparate, external review websites, a respective proportion of reviews associated with at least some entities in the particular industry on the respective external review websites; storing the generated at least one online review benchmark for the particular industry; comparing, at a time subsequent to generating the at least one online review benchmark for the particular industry, review data associated with a first entity in the particular industry to the stored industry benchmark, including by comparing at least one of an online review volume of the first entity to the online review volume benchmark for the particular industry and an online review distribution of the first entity to the online review distribution benchmark of the particular industry; determining, based at least in part on the comparison of the review data associated with the first entity in the particular industry to the stored industry benchmark, that an adjustment to the online review distribution of the first entity should be performed, wherein the adjustment includes increasing a number of reviews associated with the first entity on one or more external review websites; modeling an impact that additional reviews on the one or more external review websites would have for the first entity, wherein modeling the impact includes: running a first simulation in which a first volume of additional positive reviews are obtained on the first external review website; determining a first modeled online reputation score based at least in part on the first simulation; running a second simulation in which a second volume of additional positive reviews are obtained on the second external review website, wherein the first volume and the second volume are different; and determining a second modeled online reputation score based at least in part on the second simulation; determining, based at least in part on the first and second modeled online reputation scores, that additional reviews associated with the first external review website should be requested from one or more potential reviewers; in response to determining that additional reviews associated with the first external review website should be requested: identifying, in a list of potential reviewers, individuals in the list of potential reviewers that have accounts with the first external review website for which it has been determined that additional reviews should be requested, wherein the identified individuals are identified based at least in part on an evaluation of corresponding email addresses associated with the identified individuals; and facilitating transmission of review requests to the identified individuals, wherein facilitating transmission of the review requests includes facilitating transmission, over the network, of an electronic message to an individual included in the identified individuals, wherein the electronic message includes a link to the first external review website; and at a time subsequent to transmission of the electronic message, performing a follow-up action based at least in part on a determination of whether the individual has performed at least one of opening the electronic message and clicking on the link included in the electronic message. 15. The method of claim 12 further comprising determining that the first entity does not have an account on a particular external review website. | 0.846398 |
9,213,707 | 1 | 8 | 1. A computer implemented method for interrelating a plurality of source data files and providing access to said interrelated source data files, said computer implemented method comprising: providing an interrelated data integration application comprising an interlinear sort component and an interrelated data access component executable by at least one processor, wherein said interrelated data integration application is configured to sort and access a plurality of records in said source data files according to a graphical representation of a lineage relationship between said source data files defined in a configuration language; providing a parsing component executable by at least one processor, said parsing component configured to compile said configuration language and generate file descriptors usable by said interlinear sort component and said interrelated data access component of said interrelated data integration application, said configuration language configured to define: said lineage relationship between said source data files, each of said source data files containing one or more of said records, wherein said source data files are graphically related to each other in a tree structure using an array of symbols; one or more adopt key fields, wherein a common one of said one or more adopt key fields is configured to relate each child file containing one or more child file records to a corresponding parent file containing one or more parent file records in said tree structure; one or more order key fields configured to define ordering criteria for said records of one or more of said source data files; and one or more predetermined subprograms configured to process instances of one or more of said records from said source data files, a start point of one of a sequence and a subsequence of said records of said source data files, and an end point of said one of said sequence and said subsequence of said records of said source data files; sorting said each of said source data files based on one or more of said lineage relationship, said one or more order key fields, and said one or more adopt key fields defined in said configuration language, and attaching a position number to each of said records of said each of said source data files, by said interlinear sort component of said interrelated data integration application; accessing said records in said source data files reordered by said interlinear sort component based on said lineage relationship between said source data files, and using said position number to determine access of a subsequent one of said records, by said interrelated data access component of said interrelated data integration application; and outputting on a user device responsive to a user request at runtime a set of records comprising a common lineage relationship. | 1. A computer implemented method for interrelating a plurality of source data files and providing access to said interrelated source data files, said computer implemented method comprising: providing an interrelated data integration application comprising an interlinear sort component and an interrelated data access component executable by at least one processor, wherein said interrelated data integration application is configured to sort and access a plurality of records in said source data files according to a graphical representation of a lineage relationship between said source data files defined in a configuration language; providing a parsing component executable by at least one processor, said parsing component configured to compile said configuration language and generate file descriptors usable by said interlinear sort component and said interrelated data access component of said interrelated data integration application, said configuration language configured to define: said lineage relationship between said source data files, each of said source data files containing one or more of said records, wherein said source data files are graphically related to each other in a tree structure using an array of symbols; one or more adopt key fields, wherein a common one of said one or more adopt key fields is configured to relate each child file containing one or more child file records to a corresponding parent file containing one or more parent file records in said tree structure; one or more order key fields configured to define ordering criteria for said records of one or more of said source data files; and one or more predetermined subprograms configured to process instances of one or more of said records from said source data files, a start point of one of a sequence and a subsequence of said records of said source data files, and an end point of said one of said sequence and said subsequence of said records of said source data files; sorting said each of said source data files based on one or more of said lineage relationship, said one or more order key fields, and said one or more adopt key fields defined in said configuration language, and attaching a position number to each of said records of said each of said source data files, by said interlinear sort component of said interrelated data integration application; accessing said records in said source data files reordered by said interlinear sort component based on said lineage relationship between said source data files, and using said position number to determine access of a subsequent one of said records, by said interrelated data access component of said interrelated data integration application; and outputting on a user device responsive to a user request at runtime a set of records comprising a common lineage relationship. 8. The computer implemented method of claim 1 , wherein said file descriptors are configured to describe a user specified order for said each of said source data files and assign said one or more predetermined subprograms to perform computation of aggregated values from said records in said source data files. | 0.822044 |
7,996,345 | 45 | 47 | 45. The method of claim 41 , wherein the road attribute is road safety. | 45. The method of claim 41 , wherein the road attribute is road safety. 47. The method of claim 45 , wherein at least one of the plurality of conditions is from a group consisting of time of day and day of week. | 0.636126 |
7,489,819 | 1 | 3 | 1. A method for recognizing handwriting on a paper form using a digital pen, the method comprising: accessing a stroke collection file comprising data corresponding to a set of pen strokes marked by a user on the paper form using the digital pen; associating a field on the paper form with a predetermined lexical inference type; determining a subset of pen strokes in the stroke collection file corresponding to the field, wherein determining comprises: associating the field with an area on the paper form corresponding to a first set of coordinates; accessing a second set of coordinates corresponding to a particular stroke from the stroke collection file; and comparing the second set of coordinates to the first set of coordinates to determine whether the particular stroke was written within the area associated with the field; based on the lexical inference type, dynamically updating an inference lexicon associated with the field; and performing handwriting recognition on the subset of pen strokes based on the dynamically updated inference lexicon. | 1. A method for recognizing handwriting on a paper form using a digital pen, the method comprising: accessing a stroke collection file comprising data corresponding to a set of pen strokes marked by a user on the paper form using the digital pen; associating a field on the paper form with a predetermined lexical inference type; determining a subset of pen strokes in the stroke collection file corresponding to the field, wherein determining comprises: associating the field with an area on the paper form corresponding to a first set of coordinates; accessing a second set of coordinates corresponding to a particular stroke from the stroke collection file; and comparing the second set of coordinates to the first set of coordinates to determine whether the particular stroke was written within the area associated with the field; based on the lexical inference type, dynamically updating an inference lexicon associated with the field; and performing handwriting recognition on the subset of pen strokes based on the dynamically updated inference lexicon. 3. The method of claim 1 , further comprising associating the predetermined lexical inference type with a list of lexical inferences expected to be written in the field by the user. | 0.781401 |
8,515,954 | 11 | 15 | 11. A client system, for processing query information, comprising: one or more processors; and memory storing one or more programs to be executed by the one or more processors; the one or more programs comprising instructions for: receiving a partial search query from a user; prior to receiving a search request from the user on a complete search query that includes the partial search query, sending the partial search query to a server system; receiving from the server system, a set of historical complete search queries, the set of historical complete search queries corresponding to the partial search query and ordered in accordance with a ranking criterion; receiving from the server system, prior to receiving a user selection of one of the set of historical complete search queries, predicted search results corresponding to one or more search queries of the set of historical complete search queries; and displaying at least a subset of the set of historical complete search queries and at least a subset of the predicted search results. | 11. A client system, for processing query information, comprising: one or more processors; and memory storing one or more programs to be executed by the one or more processors; the one or more programs comprising instructions for: receiving a partial search query from a user; prior to receiving a search request from the user on a complete search query that includes the partial search query, sending the partial search query to a server system; receiving from the server system, a set of historical complete search queries, the set of historical complete search queries corresponding to the partial search query and ordered in accordance with a ranking criterion; receiving from the server system, prior to receiving a user selection of one of the set of historical complete search queries, predicted search results corresponding to one or more search queries of the set of historical complete search queries; and displaying at least a subset of the set of historical complete search queries and at least a subset of the predicted search results. 15. The system of claim 11 , wherein the predicted search results are received prior to receiving from the user the search request on the complete search query that includes the partial search query. | 0.628731 |
9,558,158 | 5 | 7 | 5. The method of claim 1 , further comprising: identifying an account to be associated with a current translation job. | 5. The method of claim 1 , further comprising: identifying an account to be associated with a current translation job. 7. The method of claim 5 , further comprising: producing a billing record reflecting the current translation job. | 0.544355 |
7,792,575 | 13 | 15 | 13. The language processing function measuring device according to claim 10 that is equipped with a search condition data acceptor for accepting the search conditions from an operator, and wherein the word extractor searches the word databases and extracts the word data indicating words belonging to or relating to a predetermined category, and, satisfying desired word attribute, based upon the search condition data accepted by the search condition data acceptor. | 13. The language processing function measuring device according to claim 10 that is equipped with a search condition data acceptor for accepting the search conditions from an operator, and wherein the word extractor searches the word databases and extracts the word data indicating words belonging to or relating to a predetermined category, and, satisfying desired word attribute, based upon the search condition data accepted by the search condition data acceptor. 15. The language processing function measuring device according to claim 13 wherein the search conditions are composed of multiple logical formula for search conditions. | 0.64346 |
8,543,565 | 11 | 14 | 11. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: training a lexical association model between a question and a first set of possible answers; training a semantic association model between the question and a second set of possible answers; receiving a user question containing query words; parsing the user question syntactically and semantically, to yield a parsed user question; expanding the query words based on the lexical association model and the semantic association model, to yield expanded query words; weighting the expanded query words according to importance when answering the user question, to yield a weighted expanded query words; and returning an answer based on a score calculated based on the following equation: score ( q , a ) = ∑ w ∈ q , a docf ( w , q , a ) * qf ( w ) + ∑ ne ∈ NE ( qp ) , qp ∈ q docf ( ne , q , a ) * λ sem ( qp , ne ) + ∑ v ∈ Exp ( w ) , w ∈ q , v ∈ a docf ( v , q , a ) * λ lex ( w , v ) wherein docf represents a normalized frequency distribution function, w represents individual query words, q represents individual questions, a represents individual answers, NE represents a set of named entity tags, ne represents individual named entity tags, qp represents a question phrase, v represents an answer word, w represents a query word, λsem represents a semantic association parameter, λlex represents a lexical association parameter, and qf represents the weighted expanded query words. | 11. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: training a lexical association model between a question and a first set of possible answers; training a semantic association model between the question and a second set of possible answers; receiving a user question containing query words; parsing the user question syntactically and semantically, to yield a parsed user question; expanding the query words based on the lexical association model and the semantic association model, to yield expanded query words; weighting the expanded query words according to importance when answering the user question, to yield a weighted expanded query words; and returning an answer based on a score calculated based on the following equation: score ( q , a ) = ∑ w ∈ q , a docf ( w , q , a ) * qf ( w ) + ∑ ne ∈ NE ( qp ) , qp ∈ q docf ( ne , q , a ) * λ sem ( qp , ne ) + ∑ v ∈ Exp ( w ) , w ∈ q , v ∈ a docf ( v , q , a ) * λ lex ( w , v ) wherein docf represents a normalized frequency distribution function, w represents individual query words, q represents individual questions, a represents individual answers, NE represents a set of named entity tags, ne represents individual named entity tags, qp represents a question phrase, v represents an answer word, w represents a query word, λsem represents a semantic association parameter, λlex represents a lexical association parameter, and qf represents the weighted expanded query words. 14. The system of claim 11 , wherein a plurality of answers are returned. | 0.851626 |
8,150,736 | 18 | 23 | 18. One or more tangible computer-readable media comprising instructions that are executable by a computing device having a processor to cause the computing device to perform a method, the method comprising: receiving a request for a web page, the request including a locale identifier value, the locale identifier value referencing a geographic location associated with a referral website and a language associated with a webpage of the referral website containing a link used to generate the request; with the processor, retrieving a version of marketing information identified by processing the locale identifier value included in the request for the web page; with the processor, generating the requested web page to include information representative of the retrieved version of the marketing information; and transmitting the generated web page. | 18. One or more tangible computer-readable media comprising instructions that are executable by a computing device having a processor to cause the computing device to perform a method, the method comprising: receiving a request for a web page, the request including a locale identifier value, the locale identifier value referencing a geographic location associated with a referral website and a language associated with a webpage of the referral website containing a link used to generate the request; with the processor, retrieving a version of marketing information identified by processing the locale identifier value included in the request for the web page; with the processor, generating the requested web page to include information representative of the retrieved version of the marketing information; and transmitting the generated web page. 23. The one or more tangible computer-readable media of claim 18 , wherein the retrieval of the version of the marketing information is performed through a SQL procedure to access a database that includes the version of the marketing information. | 0.600649 |
8,452,598 | 22 | 24 | 22. A navigation device for providing advertisements in conjunction with telematics services, wherein the navigation device comprises one or more processors configured to: identify a current location associated with the navigation device; retrieve information indicative of a preference of a user of the telematics services; identify one or more advertisements that have an affinity to the current location and that have a target audience that matches the information the information indicative of the preference of the user; and generate an output to present the one or more advertisements, wherein the information indicative of the preference of the user is based on long-term shared knowledge used to interpret natural language utterances from the user that relate to a navigation service in conjunction with telematics services, and wherein the long-term shared knowledge includes information indicative of the user's demographics. | 22. A navigation device for providing advertisements in conjunction with telematics services, wherein the navigation device comprises one or more processors configured to: identify a current location associated with the navigation device; retrieve information indicative of a preference of a user of the telematics services; identify one or more advertisements that have an affinity to the current location and that have a target audience that matches the information the information indicative of the preference of the user; and generate an output to present the one or more advertisements, wherein the information indicative of the preference of the user is based on long-term shared knowledge used to interpret natural language utterances from the user that relate to a navigation service in conjunction with telematics services, and wherein the long-term shared knowledge includes information indicative of the user's demographics. 24. The navigation device of claim 22 , wherein the one or more processors are further configured to receive information relating to the one or more advertisements over a data channel. | 0.853503 |
8,423,352 | 17 | 18 | 17. A data processing system for enhancing language detection in short communications, the data processing system comprising: a storage device including a storage medium, wherein the storage device stores computer usable program code; and a processor, wherein the processor executes the computer usable program code, and wherein the computer usable program code comprises: computer usable code for storing a short communication in an element of a line cache accessible to an application executing in a data processing system, the element being an element in a set of elements in the line cache; computer usable code for assembling a compound text from contents of a subset of the elements of the line cache; computer usable code for receiving a language identifier (language ID) for the compound text from a language detection algorithm; computer usable code for storing the language ID in a language cache element of a language ID cache accessible to the application, the language ID cache including a set of language cache elements; and computer usable code for determining, using contents of a subset of language cache elements, a language of the short communication. | 17. A data processing system for enhancing language detection in short communications, the data processing system comprising: a storage device including a storage medium, wherein the storage device stores computer usable program code; and a processor, wherein the processor executes the computer usable program code, and wherein the computer usable program code comprises: computer usable code for storing a short communication in an element of a line cache accessible to an application executing in a data processing system, the element being an element in a set of elements in the line cache; computer usable code for assembling a compound text from contents of a subset of the elements of the line cache; computer usable code for receiving a language identifier (language ID) for the compound text from a language detection algorithm; computer usable code for storing the language ID in a language cache element of a language ID cache accessible to the application, the language ID cache including a set of language cache elements; and computer usable code for determining, using contents of a subset of language cache elements, a language of the short communication. 18. The data processing system of claim 17 , further comprising: computer usable code for receiving a confidence level from the language detection algorithm; computer usable code for storing the confidence level relative to the short communication; computer usable code for determining whether the confidence level is at least equal to a threshold confidence level; and computer usable code for setting a current language indicator of the short communication to be the language ID responsive to confidence level being at least equal to the threshold confidence level. | 0.688119 |
8,484,552 | 1 | 7 | 1. A method in a computing system for generating an extensible stylesheet, the method comprising: receiving a target file in a markup language, the target file including a plurality of dynamic objects; receiving a structure tree, each node of the structure tree corresponding to one of the dynamic objects in the target file; creating a data structure by associating each of the dynamic objects in the target file with at least one element in at least one source file by a meta-tag; and generating, from the data structure, the extensible stylesheet in reference to the target file, wherein the stylesheet, when applied to the target file, controls visual aspects of how the target file is displayed; wherein said associating each of the dynamic objects in the target file with at least one element in at least one source file by a meta-tag comprises: copying each of the dynamic objects into a corresponding one of the elements in the at least one source file, or creating or updating an identifier to link each of the dynamic objects with the corresponding one of the elements in the at least one source file; and traversing the structure tree to obtain related information for one or more of the meta-tags, wherein the related information includes an association between the at least one dynamic object and the corresponding element in the at least one source file. | 1. A method in a computing system for generating an extensible stylesheet, the method comprising: receiving a target file in a markup language, the target file including a plurality of dynamic objects; receiving a structure tree, each node of the structure tree corresponding to one of the dynamic objects in the target file; creating a data structure by associating each of the dynamic objects in the target file with at least one element in at least one source file by a meta-tag; and generating, from the data structure, the extensible stylesheet in reference to the target file, wherein the stylesheet, when applied to the target file, controls visual aspects of how the target file is displayed; wherein said associating each of the dynamic objects in the target file with at least one element in at least one source file by a meta-tag comprises: copying each of the dynamic objects into a corresponding one of the elements in the at least one source file, or creating or updating an identifier to link each of the dynamic objects with the corresponding one of the elements in the at least one source file; and traversing the structure tree to obtain related information for one or more of the meta-tags, wherein the related information includes an association between the at least one dynamic object and the corresponding element in the at least one source file. 7. The method of claim 1 , wherein said associating each of the dynamic objects in the target file with at least one element comprises: copying the at least one element from the at least one source file to each of the dynamic objects via one of the nodes in the structure tree; and traversing the structure tree to obtain related information for the meta-tag of each of the dynamic objects, wherein the related information includes an association between each of the dynamic objects and the at least one element in the at least one source file. | 0.5 |
9,788,055 | 1 | 19 | 1. A computer-implemented method, comprising: at an electronic device that includes a processor and memory, automatically and without user interaction at the electronic device: streaming a media program to a first client device for display on the first client device; receiving a content search request associated with the media program displayed on the first client device, wherein the content search request is received from a second client device that is communicatively coupled to the first client device; obtaining an image of what is being displayed on the first client device by capturing screen display data associated with the media program displayed on the first client device; in response to the content search request: after obtaining the image of what is being displayed on the first client device: analyzing the obtained image for one or more predefined indicators of a program information overlay including information about the media program, wherein the program information overlay is distinct from the media program; in response to the analysis, determining whether the one or more predefined indicators are present in the obtained image; and in response to determining that the obtained image includes the one or more predefined indicators, extracting text displayed on the program information overlay in the obtained image, wherein the extracted text is associated with the media program; generating search terms from the extracted text; performing an Internet search based on at least some of the generated search terms to identify content associated with the media program; and transmitting the results of the Internet search to the second screen client device for concurrent display thereon when the media program is displayed on the first client device. | 1. A computer-implemented method, comprising: at an electronic device that includes a processor and memory, automatically and without user interaction at the electronic device: streaming a media program to a first client device for display on the first client device; receiving a content search request associated with the media program displayed on the first client device, wherein the content search request is received from a second client device that is communicatively coupled to the first client device; obtaining an image of what is being displayed on the first client device by capturing screen display data associated with the media program displayed on the first client device; in response to the content search request: after obtaining the image of what is being displayed on the first client device: analyzing the obtained image for one or more predefined indicators of a program information overlay including information about the media program, wherein the program information overlay is distinct from the media program; in response to the analysis, determining whether the one or more predefined indicators are present in the obtained image; and in response to determining that the obtained image includes the one or more predefined indicators, extracting text displayed on the program information overlay in the obtained image, wherein the extracted text is associated with the media program; generating search terms from the extracted text; performing an Internet search based on at least some of the generated search terms to identify content associated with the media program; and transmitting the results of the Internet search to the second screen client device for concurrent display thereon when the media program is displayed on the first client device. 19. The method of claim 1 , wherein the Internet search is selected from a group consisting of a general Internet search, a targeted search for associated news items, a targeted search for associated images, a targeted search for associated Internet-accessible media content, and a targeted search for associated social media content. | 0.616972 |
7,974,989 | 1 | 8 | 1. A computerized method for enhancing keyword expansion for promoting digital content, comprising: accessing a plurality of search engines using at least one input seed keyword, each of the plurality of search engines returns organic results respective of the at least one input seed keyword; for each organic result returned by each of the plurality of search engines, retrieving, using a plurality of keyword suggestion sources, a list of key terms and their scores; merging the list of key terms and their scores from each of the plurality of keyword suggestion sources into a merged list respective of each of the at least one input seed keyword and scores thereof; combining the scores for each of the key terms in the merged list into a single combined score; associating the single combined score with a respective input seed keyword; and reporting the single combined score for each of the key terms for the at least one input seed keyword and the merged list resulting from access of the plurality of search engines and keyword suggestion sources, wherein the merged list contains the keyword expansion. | 1. A computerized method for enhancing keyword expansion for promoting digital content, comprising: accessing a plurality of search engines using at least one input seed keyword, each of the plurality of search engines returns organic results respective of the at least one input seed keyword; for each organic result returned by each of the plurality of search engines, retrieving, using a plurality of keyword suggestion sources, a list of key terms and their scores; merging the list of key terms and their scores from each of the plurality of keyword suggestion sources into a merged list respective of each of the at least one input seed keyword and scores thereof; combining the scores for each of the key terms in the merged list into a single combined score; associating the single combined score with a respective input seed keyword; and reporting the single combined score for each of the key terms for the at least one input seed keyword and the merged list resulting from access of the plurality of search engines and keyword suggestion sources, wherein the merged list contains the keyword expansion. 8. The computerized method according to claim 1 , further comprises: merging results within an individual seed keyword using the at least one input seed keyword. | 0.550279 |
9,305,085 | 15 | 16 | 15. A computer program product comprising: at least one processor; and a computer readable storage medium having computer readable program code embodied therewith and executable by the at least one processor, the computer readable program code comprising: computer readable program code configured to receive a query; computer readable program code configured to automatically classify the query as subjective or objective; computer readable program code configured, to thereupon calculate, for discussion threads of the query, at least one of: a subjectivity score and an objectivity score; wherein to calculate comprises: applying a maximum entropy model; and incorporating, with respect to at least one discussion thread, at least one member taken from the group consisting of: a number of posts in a discussion thread; average number of words in posts, presence of a predetermined pattern among posts, a number of authors of posts within a discussion thread, average depth of each post, maximum depth of a post, and length of at least one reply; and computer readable program code configured to determine a degree of relevance to the query of at least one of: the discussion threads, and at least one post in the at least one discussion thread; wherein to determine the degree of relevance of the query to the discussion threads comprises: iteratively determining a relevance score with respect to each post in a discussion thread and then accepting a maximum relevance score with respect to a post in a discussion thread; determining a penalty or reward regulizer with respect to choosing a predetermined number of posts for calculating a relevance score of a thread; and including at least one of: a subjectivity score of the thread and an objectivity score of the thread; and computer readable program code configured to rank the discussion threads based on the calculating and determining of a degree of relevance of the query to the discussion threads. | 15. A computer program product comprising: at least one processor; and a computer readable storage medium having computer readable program code embodied therewith and executable by the at least one processor, the computer readable program code comprising: computer readable program code configured to receive a query; computer readable program code configured to automatically classify the query as subjective or objective; computer readable program code configured, to thereupon calculate, for discussion threads of the query, at least one of: a subjectivity score and an objectivity score; wherein to calculate comprises: applying a maximum entropy model; and incorporating, with respect to at least one discussion thread, at least one member taken from the group consisting of: a number of posts in a discussion thread; average number of words in posts, presence of a predetermined pattern among posts, a number of authors of posts within a discussion thread, average depth of each post, maximum depth of a post, and length of at least one reply; and computer readable program code configured to determine a degree of relevance to the query of at least one of: the discussion threads, and at least one post in the at least one discussion thread; wherein to determine the degree of relevance of the query to the discussion threads comprises: iteratively determining a relevance score with respect to each post in a discussion thread and then accepting a maximum relevance score with respect to a post in a discussion thread; determining a penalty or reward regulizer with respect to choosing a predetermined number of posts for calculating a relevance score of a thread; and including at least one of: a subjectivity score of the thread and an objectivity score of the thread; and computer readable program code configured to rank the discussion threads based on the calculating and determining of a degree of relevance of the query to the discussion threads. 16. The computer program product according to claim 15 , wherein said computer readable program code is configured to determine a degree of relevance to the query of both of: the discussion threads, and at least one post in the discussion threads. | 0.5 |
10,152,471 | 1 | 13 | 1. A method for performing normalization of at least one piece of unstructured data, comprising steps of: (a) a computing device determining whether a predetermined parsing rule is applicable to at least some of the unstructured data; (a1) where the predetermined parsing rule is determined to be applicable to at least some of the unstructured data, the computing device parsing or supporting other device to parse at least some unstructured data under the parsing rule to extract individual fields (a2) where the predetermined parsing rule is determined not to be applicable to at least some of the unstructured data, the computing device performs or supports other device to perform a process of determining a new parsing rule by referring to the unstructured data, and parses or supports other device to parse the at least some unstructured data under the new parsing rule to extract the individual fields; (b) the computing device selecting or supporting other device to select names of items corresponding to individual fields extracted from the unstructured data through the parsing and verifying or supporting other device to verify a validity of data types corresponding to the individual fields; (c1) the computing device creating or supporting other device to create information on transfoiriiation of the individual fields of the unstructured data to a respective desired format for each of the individual fields by referring to the names of the items and the data types by: (c2) the computing device creating or supporting other device to create candidates of the information on transformation by referring to the names of items and the types of data and displaying or supporting other device to display the created candidates to a user; (c3) the computing device, if the information on the transformation is detected to be selected among the created candidates, transforming or supporting other device to transform the unstructured data by referring to the information on the transformation; and (d) the computing device creating or supporting other device to create a program code for the normalization based on the information on the transformation; and (e) the computing device delivering the created code to a database; and instructing the database to store the created code and to normalize or support a server connected with the database to normalize at least one of the unstructured data according to the created code. | 1. A method for performing normalization of at least one piece of unstructured data, comprising steps of: (a) a computing device determining whether a predetermined parsing rule is applicable to at least some of the unstructured data; (a1) where the predetermined parsing rule is determined to be applicable to at least some of the unstructured data, the computing device parsing or supporting other device to parse at least some unstructured data under the parsing rule to extract individual fields (a2) where the predetermined parsing rule is determined not to be applicable to at least some of the unstructured data, the computing device performs or supports other device to perform a process of determining a new parsing rule by referring to the unstructured data, and parses or supports other device to parse the at least some unstructured data under the new parsing rule to extract the individual fields; (b) the computing device selecting or supporting other device to select names of items corresponding to individual fields extracted from the unstructured data through the parsing and verifying or supporting other device to verify a validity of data types corresponding to the individual fields; (c1) the computing device creating or supporting other device to create information on transfoiriiation of the individual fields of the unstructured data to a respective desired format for each of the individual fields by referring to the names of the items and the data types by: (c2) the computing device creating or supporting other device to create candidates of the information on transformation by referring to the names of items and the types of data and displaying or supporting other device to display the created candidates to a user; (c3) the computing device, if the information on the transformation is detected to be selected among the created candidates, transforming or supporting other device to transform the unstructured data by referring to the information on the transformation; and (d) the computing device creating or supporting other device to create a program code for the normalization based on the information on the transformation; and (e) the computing device delivering the created code to a database; and instructing the database to store the created code and to normalize or support a server connected with the database to normalize at least one of the unstructured data according to the created code. 13. The method of claim 1 , wherein the information on transformation includes at least one piece of transformation option information and transformation function information. | 0.884868 |
9,311,366 | 7 | 11 | 7. An interactive object retrieval system, comprising: a filtering module, comprising: a preliminary filtering module, selecting a plurality of data records of a first category complied with a time and space searching condition from an object database; and a similarity comparison module, coupled to the preliminary filtering module, receiving the data records of the first category from the preliminary filtering module, comparing a similarity between the query and each of the data records of the first category to obtain a plurality of data records of a second category wherein a time information and a location information are corresponding to each of the data records of the second category, and dividing the data records of the second category into a plurality of groups, wherein the data records of the second category in a same group correspond to a same location information, and a time difference between the time information corresponding to the data records of the second category in the same group does not exceed a predetermined value, wherein the similarity comparison module respectively selects the data records of the second category having a highest similarity with the query from each of the groups, and takes all of the data records of the second category which are selected as a plurality of searching results; and an interactive module, coupled to the filtering module, receiving at least one user input corresponding to at least one of the searching results, and providing a user interface, wherein each of the at least one user input corresponds to a similarity decreasing operation or a similarity increasing operation, wherein the filtering module determines a display manner of each of the searching results on the user interface according to the at least one user input and a similarity between the query and each of the searching results, wherein for each of the at least one user input, the similarity comparison module determines whether the user input corresponds to the similarity decreasing operation or the similarity increasing operation, so as to change a display size of one of the searching results on the user interface and to change the similarity between the query and the one of the searching results wherein the one of the searching results is corresponding to the user input, and selects the searching results that have the similarity with the query higher than a threshold value from all of the searching results to serve as a plurality of formal searching results. | 7. An interactive object retrieval system, comprising: a filtering module, comprising: a preliminary filtering module, selecting a plurality of data records of a first category complied with a time and space searching condition from an object database; and a similarity comparison module, coupled to the preliminary filtering module, receiving the data records of the first category from the preliminary filtering module, comparing a similarity between the query and each of the data records of the first category to obtain a plurality of data records of a second category wherein a time information and a location information are corresponding to each of the data records of the second category, and dividing the data records of the second category into a plurality of groups, wherein the data records of the second category in a same group correspond to a same location information, and a time difference between the time information corresponding to the data records of the second category in the same group does not exceed a predetermined value, wherein the similarity comparison module respectively selects the data records of the second category having a highest similarity with the query from each of the groups, and takes all of the data records of the second category which are selected as a plurality of searching results; and an interactive module, coupled to the filtering module, receiving at least one user input corresponding to at least one of the searching results, and providing a user interface, wherein each of the at least one user input corresponds to a similarity decreasing operation or a similarity increasing operation, wherein the filtering module determines a display manner of each of the searching results on the user interface according to the at least one user input and a similarity between the query and each of the searching results, wherein for each of the at least one user input, the similarity comparison module determines whether the user input corresponds to the similarity decreasing operation or the similarity increasing operation, so as to change a display size of one of the searching results on the user interface and to change the similarity between the query and the one of the searching results wherein the one of the searching results is corresponding to the user input, and selects the searching results that have the similarity with the query higher than a threshold value from all of the searching results to serve as a plurality of formal searching results. 11. The interactive object retrieval system as claimed in claim 7 , further comprising: a geographic information module, coupled to the filtering module, providing a map, wherein the filtering module connects the location information corresponding to each of the formal searching results on the map according to the time information corresponding to each of the formal searching results, so as to display an object moving track on the map. | 0.653239 |
6,047,286 | 11 | 21 | 11. A system for indexing information, comprising: a memory configured to store an index to the information; and a processor configured to receive a first signal representing a query and having a phrase corresponding to a concatenation of adjacent portions of the information, and to process the first signal so as to generate a second signal representing an index entry for the phrase to be stored as part of the index within the memory; wherein the processor is further configured to process the first signal to identify one or more locations at which the phrase occurs within the information, to measure an amount of time required to process the first signal to identify the one or more locations, and to generate the second signal if the measured time exceeds the threshold. | 11. A system for indexing information, comprising: a memory configured to store an index to the information; and a processor configured to receive a first signal representing a query and having a phrase corresponding to a concatenation of adjacent portions of the information, and to process the first signal so as to generate a second signal representing an index entry for the phrase to be stored as part of the index within the memory; wherein the processor is further configured to process the first signal to identify one or more locations at which the phrase occurs within the information, to measure an amount of time required to process the first signal to identify the one or more locations, and to generate the second signal if the measured time exceeds the threshold. 21. A system according to claim 11, further comprising: a plurality of server stations configured to store the information; a plurality of client stations configured to generate queries; and a communications network configured to interconnect the processor, the client stations and the server stations; wherein the first signal is received by the processor from one of the client stations via the network, and the processor is further configured to process the first signal using the index to identify one or more locations at which the phrase occurs within the information stored at the plurality of server stations and to generate a third signal for the one client station representing the identified one or more locations. | 0.5 |
9,129,041 | 1 | 2 | 1. A computer-implemented method for updating a concept-mapping function that facilitates evaluating a qualitative search term applied to an attribute during query processing, the method comprising: extracting a value for the attribute from each data item in a set of data items; and updating the concept-mapping function based on the extracted attribute values, wherein the concept-mapping function associates a given value for the attribute with a numerical compatibility index that indicates a compatibility between the given value and the qualitative search term as applied to the attribute, wherein the qualitative search term comprises one or more words that express a quality of the attribute value; wherein updating the concept-mapping function comprises, determining boundaries for the concept-mapping function based the extracted attribute values, and within the determined boundaries, using a pre-specified distribution to update the concept-mapping function; and wherein determining the boundaries for the concept-mapping function includes: computing a mean attribute value and a standard deviation for the extracted attribute values; and using the mean attribute value and the standard deviation to determine the boundaries for the concept-mapping. | 1. A computer-implemented method for updating a concept-mapping function that facilitates evaluating a qualitative search term applied to an attribute during query processing, the method comprising: extracting a value for the attribute from each data item in a set of data items; and updating the concept-mapping function based on the extracted attribute values, wherein the concept-mapping function associates a given value for the attribute with a numerical compatibility index that indicates a compatibility between the given value and the qualitative search term as applied to the attribute, wherein the qualitative search term comprises one or more words that express a quality of the attribute value; wherein updating the concept-mapping function comprises, determining boundaries for the concept-mapping function based the extracted attribute values, and within the determined boundaries, using a pre-specified distribution to update the concept-mapping function; and wherein determining the boundaries for the concept-mapping function includes: computing a mean attribute value and a standard deviation for the extracted attribute values; and using the mean attribute value and the standard deviation to determine the boundaries for the concept-mapping. 2. The computer-implemented method of claim 1 , wherein updating the concept-mapping function includes using a pre-specified distribution obtained from a domain expert or a user. | 0.866165 |
8,576,097 | 42 | 49 | 42. One or more non-transitory computer-readable media having stored thereon executable instructions configured to, when executed, cause an apparatus at least to: receive a code word; determine a hierarchy level in a tree structure, wherein the tree structure relates to at least one of a transform block size, a prediction block size, or a block size, and wherein the hierarchy level represents division of a block into smaller blocks; determine a mapping to obtain a syntax element on the basis of said hierarchy level, wherein the syntax element relates to at least one of a coded block pattern, a transform split pattern, or motion partition information; and update the mapping. | 42. One or more non-transitory computer-readable media having stored thereon executable instructions configured to, when executed, cause an apparatus at least to: receive a code word; determine a hierarchy level in a tree structure, wherein the tree structure relates to at least one of a transform block size, a prediction block size, or a block size, and wherein the hierarchy level represents division of a block into smaller blocks; determine a mapping to obtain a syntax element on the basis of said hierarchy level, wherein the syntax element relates to at least one of a coded block pattern, a transform split pattern, or motion partition information; and update the mapping. 49. The one or more non-transitory computer-readable media according to claim 42 , having stored thereon further executable instructions configured to, when executed, cause the apparatus to: use at least two sorting tables, wherein each of said at least two sorting tables comprises a set of code words and a set of syntax elements; use the hierarchy level to select a sorting table from said at least two sorting tables; use said code word to select the syntax element; and swap the syntax element with another syntax element of said set of syntax elements in the selected sorting table. | 0.5 |
9,576,188 | 6 | 7 | 6. The method of claim 1 , wherein: each of said query images comprises a unique state of image properties as compared with said gallery images. | 6. The method of claim 1 , wherein: each of said query images comprises a unique state of image properties as compared with said gallery images. 7. The method of claim 6 , wherein: said image properties comprise at least one of viewing aspect, illumination, texture, and configuration. | 0.5 |
8,229,952 | 1 | 7 | 1. A method to query a database of physical tables based on a logical database schema including logical tables mapped to the physical tables and on an abstraction layer comprising a plurality of objects mapped to the logical tables and one or more properties associating one or more of the plurality of objects to one or more others of the plurality of objects, the method comprising: receiving a query comprising a first plurality of objects of the abstraction layer, and a first one or more properties associating one of the first plurality of objects with another one of the plurality of objects, the query comprising two or more instances of a first one of the first plurality of objects; modifying a dynamic representation of the logical database schema to include an alias of the first one of the plurality of objects; modifying the query to include the alias; identifying one or more functional dependencies of the abstraction layer within a reverse chain or a direct chain of the modified query, or relating to initial objects of the modified query; editing the modified dynamic representation by removing joins of the modified dynamic representation of logical database schema associated with non-identified functional dependencies of the modified query; and generating a database query based on the modified query and the edited dynamic representation. | 1. A method to query a database of physical tables based on a logical database schema including logical tables mapped to the physical tables and on an abstraction layer comprising a plurality of objects mapped to the logical tables and one or more properties associating one or more of the plurality of objects to one or more others of the plurality of objects, the method comprising: receiving a query comprising a first plurality of objects of the abstraction layer, and a first one or more properties associating one of the first plurality of objects with another one of the plurality of objects, the query comprising two or more instances of a first one of the first plurality of objects; modifying a dynamic representation of the logical database schema to include an alias of the first one of the plurality of objects; modifying the query to include the alias; identifying one or more functional dependencies of the abstraction layer within a reverse chain or a direct chain of the modified query, or relating to initial objects of the modified query; editing the modified dynamic representation by removing joins of the modified dynamic representation of logical database schema associated with non-identified functional dependencies of the modified query; and generating a database query based on the modified query and the edited dynamic representation. 7. A method according to claim 1 , wherein generating the database query comprises: determining a path through the edited dynamic representation based on the modified query; and generating Structured Query Language statements based on the determined path. | 0.831572 |
8,583,609 | 12 | 17 | 12. A method of generating and storing a plurality of definitions to be applied to a plurality of electronic files as metadata, by execution of computer readable program code by at least one processor of at least one computer system, comprising: providing a multi-level database comprising a plurality of terms, wherein each term of the plurality of terms is included in the multi-level database once and only once, said plurality of terms comprising a plurality of first-level terms having a first common theme and a plurality of second-level terms having a second common theme, said multi-level database further comprising a plurality of numeric first-level term codes and a plurality of numeric second-level term codes, wherein each first-level term is assigned a numeric first-level term code of the plurality of numeric first-level term codes, and each second-level term is assigned a numeric second-level term code of the plurality of numeric second-level term codes, wherein the multi-level database is divided into a plurality of sections including a first section and a second section, said first section comprising a plurality of first-level tables, a plurality of numeric first-level table codes, and a first node table, wherein each first-level table is assigned a numeric first-level table code of the plurality of numeric first-level table codes, and the plurality of first-level terms and the plurality of numeric first-level term codes are located in the plurality of first-level tables, said second section comprising a plurality of second-level tables, a plurality of numeric second-level table codes, and a second node table, wherein each second-level table of the plurality of second-level tables is assigned a numeric second-level table code of the plurality of numeric second-level table codes, and the plurality of second-level terms and the plurality of numeric second-level term codes are located in the plurality of second-level tables, wherein the first section communicates with the second section through the first node table and the second node table; forming a first code string in the first node table, by joining together at least one numeric first-level term code from a first-level table of the plurality of first-level tables, at least one numeric first-level table code, and first-level code language describing the location and relations of said first-level table, using at least one of the processors; forming a second code string in the second node table, by joining together at least one numeric second-level term code from a second-level table of the plurality of second-level tables, at least one numeric second-level table code, and second-level code language describing the location and relations of said second-level table, using at least one of the processors; forming a combined code string in the second node table, by joining together the first code string and the second code string, using at least one of the processors; creating a plurality of definitions, each definition comprising a final code string, wherein each final code string is formed in a final node table of the multi-level database, using at least one of the processors; and joining each definition of the plurality of definitions with a defined word and phrase of a plurality of defined words and phrases, using at least one of the processors, wherein the plurality of definitions and the plurality of defined words and phrases function as an industry-specific electronic dictionary. | 12. A method of generating and storing a plurality of definitions to be applied to a plurality of electronic files as metadata, by execution of computer readable program code by at least one processor of at least one computer system, comprising: providing a multi-level database comprising a plurality of terms, wherein each term of the plurality of terms is included in the multi-level database once and only once, said plurality of terms comprising a plurality of first-level terms having a first common theme and a plurality of second-level terms having a second common theme, said multi-level database further comprising a plurality of numeric first-level term codes and a plurality of numeric second-level term codes, wherein each first-level term is assigned a numeric first-level term code of the plurality of numeric first-level term codes, and each second-level term is assigned a numeric second-level term code of the plurality of numeric second-level term codes, wherein the multi-level database is divided into a plurality of sections including a first section and a second section, said first section comprising a plurality of first-level tables, a plurality of numeric first-level table codes, and a first node table, wherein each first-level table is assigned a numeric first-level table code of the plurality of numeric first-level table codes, and the plurality of first-level terms and the plurality of numeric first-level term codes are located in the plurality of first-level tables, said second section comprising a plurality of second-level tables, a plurality of numeric second-level table codes, and a second node table, wherein each second-level table of the plurality of second-level tables is assigned a numeric second-level table code of the plurality of numeric second-level table codes, and the plurality of second-level terms and the plurality of numeric second-level term codes are located in the plurality of second-level tables, wherein the first section communicates with the second section through the first node table and the second node table; forming a first code string in the first node table, by joining together at least one numeric first-level term code from a first-level table of the plurality of first-level tables, at least one numeric first-level table code, and first-level code language describing the location and relations of said first-level table, using at least one of the processors; forming a second code string in the second node table, by joining together at least one numeric second-level term code from a second-level table of the plurality of second-level tables, at least one numeric second-level table code, and second-level code language describing the location and relations of said second-level table, using at least one of the processors; forming a combined code string in the second node table, by joining together the first code string and the second code string, using at least one of the processors; creating a plurality of definitions, each definition comprising a final code string, wherein each final code string is formed in a final node table of the multi-level database, using at least one of the processors; and joining each definition of the plurality of definitions with a defined word and phrase of a plurality of defined words and phrases, using at least one of the processors, wherein the plurality of definitions and the plurality of defined words and phrases function as an industry-specific electronic dictionary. 17. The method of claim 12 , wherein the plurality of electronic files includes health care data, further comprising segregating sensitive data of the health care data from non-sensitive data of the health care data, using at least one of the processors. | 0.923263 |
9,619,812 | 9 | 12 | 9. A method of engaging an audience in a conversational advertisement, wherein the method is performed by a computing system having a processor and a memory, the method comprising: identifying, at a server computer, a conversational advertisement to present to an audience via a client device in response to a received indication to present an advertisement, wherein the conversational advertisement is selected from multiple conversational advertisements and wherein the identified conversational advertisement provides a verbal interface such that the identified conversational advertisement provides responses, to verbal input provided to the verbal interface, that are based on an identified meaning of the verbal input to the verbal interface; orchestrating, through a first webpage, at least two initial interactions between the audience and the conversational advertisement, wherein one of the at least two initial interactions comprises: conveying, at the server computer, a first message of the conversational advertisement to the client device to be presented to the audience; and receiving, at the server computer, audio data from the client device representing a verbal response by the audience to the first message of the conversational advertisement, wherein the audio data is received through the member of the audience interacting with the first webpage; identifying, at the server computer, based on the spoken words from the audio data representing the verbal response of the audience to the first message, a particular point in a conversation with the member of the audience; and processing, at the server computer, the spoken words from the audio data representing the verbal response of the audience to the first message; determining, at the server computer, a response to convey to the audience via the client device, wherein the response to convey to the audience is determined based at least in part on the spoken words from the audio data representing the verbal response by the audience to the first message, and converting, at the server computer, the response to convey to the audience to response audio data to be played to the audience; wherein the response to convey to the member of the audience is presented on a second webpage different from the first webpage such that the response to convey to the member of the audience continues the conversational advertisement on the second webpage, picking up from the identified particular point in the conversation with the member of the audience such that the conversation with the member of the audience continues where the member of the audience left off from the at least two initial interactions with the conversational advertisement orchestrated through the first webpage. | 9. A method of engaging an audience in a conversational advertisement, wherein the method is performed by a computing system having a processor and a memory, the method comprising: identifying, at a server computer, a conversational advertisement to present to an audience via a client device in response to a received indication to present an advertisement, wherein the conversational advertisement is selected from multiple conversational advertisements and wherein the identified conversational advertisement provides a verbal interface such that the identified conversational advertisement provides responses, to verbal input provided to the verbal interface, that are based on an identified meaning of the verbal input to the verbal interface; orchestrating, through a first webpage, at least two initial interactions between the audience and the conversational advertisement, wherein one of the at least two initial interactions comprises: conveying, at the server computer, a first message of the conversational advertisement to the client device to be presented to the audience; and receiving, at the server computer, audio data from the client device representing a verbal response by the audience to the first message of the conversational advertisement, wherein the audio data is received through the member of the audience interacting with the first webpage; identifying, at the server computer, based on the spoken words from the audio data representing the verbal response of the audience to the first message, a particular point in a conversation with the member of the audience; and processing, at the server computer, the spoken words from the audio data representing the verbal response of the audience to the first message; determining, at the server computer, a response to convey to the audience via the client device, wherein the response to convey to the audience is determined based at least in part on the spoken words from the audio data representing the verbal response by the audience to the first message, and converting, at the server computer, the response to convey to the audience to response audio data to be played to the audience; wherein the response to convey to the member of the audience is presented on a second webpage different from the first webpage such that the response to convey to the member of the audience continues the conversational advertisement on the second webpage, picking up from the identified particular point in the conversation with the member of the audience such that the conversation with the member of the audience continues where the member of the audience left off from the at least two initial interactions with the conversational advertisement orchestrated through the first webpage. 12. The method of claim 9 , wherein identifying, at the server computer, a conversational advertisement to present to the audience includes identifying, at the server computer, a conversational advertisement associated with an input of the audience on the client device. | 0.793262 |
10,083,205 | 6 | 8 | 6. The method of claim 4 , further comprising: receiving, by the processing device, a user selection of one of the new application cards; launching, by the processing device, a native application indicated by an access mechanism of the set of access mechanisms; and setting, by the processing device, the native application to a state indicated by the access mechanism. | 6. The method of claim 4 , further comprising: receiving, by the processing device, a user selection of one of the new application cards; launching, by the processing device, a native application indicated by an access mechanism of the set of access mechanisms; and setting, by the processing device, the native application to a state indicated by the access mechanism. 8. The method of claim 6 , wherein the access mechanism is an application resource identifier. | 0.745946 |
7,590,541 | 10 | 13 | 10. The system of claim 1 , the data store includes information relating to at least one of a device, system, process, and sub-process within the industrial automation environment. | 10. The system of claim 1 , the data store includes information relating to at least one of a device, system, process, and sub-process within the industrial automation environment. 13. The system of claim 10 , further comprising an updating component that automatically updates devices and their associated content in the data store based upon detecting the addition or removal of a device from a network, process or system. | 0.5 |
4,221,061 | 1 | 2 | 1. An aid for teaching word pronunciation comprising: a set of alphabetical letters in material form, said letters being arrangeable to form words; at least one of said letters having a structural distinction from other letters in said set, in addition to conventional differences of alphabetic configuration; said structural distinction selected from a group of at least three structural distinctions, each distinction denoting a particular pronunciation of said letter in the formed word; one of said distinctions being that the letter is transparent, to denote that the letter is silent in the formed word; another of said distinctions being that the letter is of a greater height than the other letters, to denote that the letter is to be pronounced with a long vowel sound; still another of said distinctions being that the letter is in the shape of an object, to denote that the letter is to be pronounced as in the word for the depicted object. | 1. An aid for teaching word pronunciation comprising: a set of alphabetical letters in material form, said letters being arrangeable to form words; at least one of said letters having a structural distinction from other letters in said set, in addition to conventional differences of alphabetic configuration; said structural distinction selected from a group of at least three structural distinctions, each distinction denoting a particular pronunciation of said letter in the formed word; one of said distinctions being that the letter is transparent, to denote that the letter is silent in the formed word; another of said distinctions being that the letter is of a greater height than the other letters, to denote that the letter is to be pronounced with a long vowel sound; still another of said distinctions being that the letter is in the shape of an object, to denote that the letter is to be pronounced as in the word for the depicted object. 2. A teaching aid in accordance with claim 1 wherein said set of alphabetical letters includes the letter A shaped to represent an apple to denote its pronunciation as being that of the letter A in the word APPLE. | 0.709809 |
9,971,577 | 4 | 6 | 4. A method for code conversion, comprising: acquiring an initialization node of a code tree to be compiled, and scanning all child nodes under the initialization node of the code tree; recording a total number of the lines of the child nodes; creating a natural language stack according to the total number of the lines; acquiring a natural statement value, which is preset in a child node, and resolving the natural statement value into a natural phrase to be converted; storing into the natural language stack the natural phrase to be converted and popping-up the natural phrases to be converted according to the sequence of the natural language stack; traversing a preset comparison table of reference phrases and natural languages, wherein if the comparison table has a reference phrase same as the natural phrase to be converted, then the natural phrase to be converted will be converted into a syntax phrase corresponding to the reference phrase in the comparison table; resolving and converting a next child node, when the natural language stack is empty, and ending the conversion when both the child node and the natural language stack are empty; creating a syntax phrase stack, which is used to store the converted syntax phrase; creating a syntax statement stack, whenever the natural language stack is empty and the child node has no child nodes on the next layer; popping-up the converted syntax phrase stored in the syntax phrase stack, and joining the syntax phrases into a statement which is pushed into the syntax statement stack; ending the popping-up when all the child nodes of the code tree are converted and the natural language stack is empty; and forming a code file from the statements formed by joining all the syntax phrases corresponding to all the child nodes of the code tree, wherein the creating of the syntax statement stack includes: reading the child node; pushing-in a nested starting syntax symbol before the conversion starts if the child node is a first child node; and pushing-in a nested ending syntax symbol after the conversion ends if the child node is the last one. | 4. A method for code conversion, comprising: acquiring an initialization node of a code tree to be compiled, and scanning all child nodes under the initialization node of the code tree; recording a total number of the lines of the child nodes; creating a natural language stack according to the total number of the lines; acquiring a natural statement value, which is preset in a child node, and resolving the natural statement value into a natural phrase to be converted; storing into the natural language stack the natural phrase to be converted and popping-up the natural phrases to be converted according to the sequence of the natural language stack; traversing a preset comparison table of reference phrases and natural languages, wherein if the comparison table has a reference phrase same as the natural phrase to be converted, then the natural phrase to be converted will be converted into a syntax phrase corresponding to the reference phrase in the comparison table; resolving and converting a next child node, when the natural language stack is empty, and ending the conversion when both the child node and the natural language stack are empty; creating a syntax phrase stack, which is used to store the converted syntax phrase; creating a syntax statement stack, whenever the natural language stack is empty and the child node has no child nodes on the next layer; popping-up the converted syntax phrase stored in the syntax phrase stack, and joining the syntax phrases into a statement which is pushed into the syntax statement stack; ending the popping-up when all the child nodes of the code tree are converted and the natural language stack is empty; and forming a code file from the statements formed by joining all the syntax phrases corresponding to all the child nodes of the code tree, wherein the creating of the syntax statement stack includes: reading the child node; pushing-in a nested starting syntax symbol before the conversion starts if the child node is a first child node; and pushing-in a nested ending syntax symbol after the conversion ends if the child node is the last one. 6. The method according to claim 4 , wherein the method further comprises: popping-up sequentially the statements from the syntax statement stack; adding at a tail of each statement natural semantics corresponding to the statement and a corresponding noting symbol; coding the code tree and adding the coding result to the tail of the noting symbol, displaying the entire code file if the syntax statement stack is empty, wherein the popping-up sequentially the statements comprises: determining a number of indent spaces of the code and sequentially popping-up the statements according to a number of layers of the nested starting syntax symbol and the nested ending syntax symbol. | 0.598351 |
9,146,989 | 11 | 14 | 11. A computer-readable non-transitory storage medium configured with data and with instructions that when executed by at least one processor in a cloud computing environment and/or a cloud storage environment causes the processor(s) to perform a process for digital good library comparison, the process comprising: obtaining a first dataset, namely, first electronic organizational data and first electronic history data associated with a first library of digital goods; obtaining a second dataset, namely, second electronic organizational data and second electronic history data associated with a second library of digital goods; automatically comparing at least a portion of the first dataset with at least a portion of the second dataset; reporting at least one of the following results: a shared multiple natural languages presence, a shared genre frequency change, a shared artist frequency change, a shared digital good frequency change, a shared outlier presence, a shared recommendable goods presence; and wherein the process operates in a cloud computing environment and/or a cloud storage environment to perform at least one of the obtaining, comparing, or reporting steps, and the process reports at least one of the following shared recommendable goods presence results: at least a specified percent of library entries in one dataset support a recommendation of library entry(ies) in the other dataset under an automated recommendation system, wherein the specified percent is a predetermined percent; at least a specified percent of library entries in one dataset support a recommendation of library entry(ies) in the other dataset under an automated recommendation system, wherein the specified percent is determined at least in part by comparing at least a portion of the first dataset with at least a portion of the second dataset; at least a specified number of library entries in one dataset support a recommendation of library entry(ies) in the other dataset under an automated recommendation system, wherein the specified number is a predetermined number; at least a specified number of library entries in one dataset support a recommendation of library entry(ies) in the other dataset under an automated recommendation system, wherein the specified number is determined at least in part by comparing at least a portion of the first dataset with at least a portion of the second dataset. | 11. A computer-readable non-transitory storage medium configured with data and with instructions that when executed by at least one processor in a cloud computing environment and/or a cloud storage environment causes the processor(s) to perform a process for digital good library comparison, the process comprising: obtaining a first dataset, namely, first electronic organizational data and first electronic history data associated with a first library of digital goods; obtaining a second dataset, namely, second electronic organizational data and second electronic history data associated with a second library of digital goods; automatically comparing at least a portion of the first dataset with at least a portion of the second dataset; reporting at least one of the following results: a shared multiple natural languages presence, a shared genre frequency change, a shared artist frequency change, a shared digital good frequency change, a shared outlier presence, a shared recommendable goods presence; and wherein the process operates in a cloud computing environment and/or a cloud storage environment to perform at least one of the obtaining, comparing, or reporting steps, and the process reports at least one of the following shared recommendable goods presence results: at least a specified percent of library entries in one dataset support a recommendation of library entry(ies) in the other dataset under an automated recommendation system, wherein the specified percent is a predetermined percent; at least a specified percent of library entries in one dataset support a recommendation of library entry(ies) in the other dataset under an automated recommendation system, wherein the specified percent is determined at least in part by comparing at least a portion of the first dataset with at least a portion of the second dataset; at least a specified number of library entries in one dataset support a recommendation of library entry(ies) in the other dataset under an automated recommendation system, wherein the specified number is a predetermined number; at least a specified number of library entries in one dataset support a recommendation of library entry(ies) in the other dataset under an automated recommendation system, wherein the specified number is determined at least in part by comparing at least a portion of the first dataset with at least a portion of the second dataset. 14. The configured medium of claim 11 , wherein the process automatically compares a first portion of the first dataset with a second portion of the second dataset, a portion of a dataset being less than the entire dataset, and each compared portion is specified by specifying at least one of the following: a playlist; a genre; an artist; a category; a date range. | 0.720521 |
8,825,447 | 4 | 5 | 4. The automatic correlation accelerator tool of claim 3 , wherein the operations further comprise automatically, without human intervention, causing the system to execute the correlated and parameterized script, wherein execution of the correlated and parameterized script results in testing performance of the system. | 4. The automatic correlation accelerator tool of claim 3 , wherein the operations further comprise automatically, without human intervention, causing the system to execute the correlated and parameterized script, wherein execution of the correlated and parameterized script results in testing performance of the system. 5. The automatic correlation accelerator tool of claim 4 , wherein the operations further comprise generating performance testing output for the system based on the execution of the correlated and parameterized script. | 0.5 |
9,639,173 | 1 | 6 | 1. A human interface device, comprising: a text input unit comprising a plurality of physical buttons; a pointer location input unit comprising a motion detection area formed on a surface of the physical buttons, the pointer location input unit receiving information related to a pointer location from a user motion on the motion detection area, wherein the pointer location indicates a location of a pointer displayed on a digital device connected to the human interface device via wired or wireless connection; a first button configured to activate or deactivate a pointer location input mode and to execute at least one function at the pointer location; a communication unit configured to transmit the information related to the pointer location to the digital device; and a foldable cover coupled to the text input unit to protect the text input unit, wherein the first button is provided in an area separate from the text input unit, wherein the first button comprises a capacitive touch sensor which detects a user touch input, the pointer location input mode being temporarily activated while the user touch input is maintained without mechanically pressing the first button, wherein the at least one function at the pointer location is executed when the first button is mechanically pressed, wherein the pointer location input mode is permanently activated without maintaining the user touch input while the first button is locked. | 1. A human interface device, comprising: a text input unit comprising a plurality of physical buttons; a pointer location input unit comprising a motion detection area formed on a surface of the physical buttons, the pointer location input unit receiving information related to a pointer location from a user motion on the motion detection area, wherein the pointer location indicates a location of a pointer displayed on a digital device connected to the human interface device via wired or wireless connection; a first button configured to activate or deactivate a pointer location input mode and to execute at least one function at the pointer location; a communication unit configured to transmit the information related to the pointer location to the digital device; and a foldable cover coupled to the text input unit to protect the text input unit, wherein the first button is provided in an area separate from the text input unit, wherein the first button comprises a capacitive touch sensor which detects a user touch input, the pointer location input mode being temporarily activated while the user touch input is maintained without mechanically pressing the first button, wherein the at least one function at the pointer location is executed when the first button is mechanically pressed, wherein the pointer location input mode is permanently activated without maintaining the user touch input while the first button is locked. 6. The human interface device of claim 1 , wherein, after the foldable cover is folded, the foldable cover is mounted on a bottom surface of the human interface device to adjust inclination of the human interface device. | 0.552846 |
8,782,074 | 21 | 22 | 21. A non-transitory computer-readable storage medium having stored thereon instructions for causing a computer to perform a method for identifying previously-submitted queries related to web page content served by a content server, the method comprising: storing a plurality of previously-submitted queries with corresponding popularity values; responsive to a link-selection request by a user for a web page of a media web site provided by the content server, receiving, by an information server of an electronic marketplace, an automatically generated request from the content server to provide information about an item to be displayed on a user device based at least in part on the web page content to be displayed on the media web site, the media content server being different than the information server of the electronic marketplace, the web page content in the generated request having been derived by the media content server; responsive to receiving the automatically generated request, identifying phrases of the content served by the content server that match at least one of the previously-submitted queries; selecting, based at least in part on a length of the identified phrases and the popularity values of corresponding queries, at least one of the identified phrases; and providing said selected at least one of the identified phrases as representative of the web page content served by the content server. | 21. A non-transitory computer-readable storage medium having stored thereon instructions for causing a computer to perform a method for identifying previously-submitted queries related to web page content served by a content server, the method comprising: storing a plurality of previously-submitted queries with corresponding popularity values; responsive to a link-selection request by a user for a web page of a media web site provided by the content server, receiving, by an information server of an electronic marketplace, an automatically generated request from the content server to provide information about an item to be displayed on a user device based at least in part on the web page content to be displayed on the media web site, the media content server being different than the information server of the electronic marketplace, the web page content in the generated request having been derived by the media content server; responsive to receiving the automatically generated request, identifying phrases of the content served by the content server that match at least one of the previously-submitted queries; selecting, based at least in part on a length of the identified phrases and the popularity values of corresponding queries, at least one of the identified phrases; and providing said selected at least one of the identified phrases as representative of the web page content served by the content server. 22. The non-transitory computer-readable storage medium of claim 21 , wherein the method further comprises removing at least one noise word from the web page content. | 0.627803 |
8,909,708 | 14 | 21 | 14. A system to confirm authorship of documents, comprising: at least one processor; memory; and at least one program stored in the memory and executable by the at least one processor, the at least one program comprising instructions to: access a first document hosted on a first website of a first domain, the first document being indirectly linked to a second document through at least one link in a chain of links, a respective link in the chain of links including a first predefined authorship attribute asserting authorship of a respective document including the respective link by a respective entity associated with a respective target document of the respective link; and conditionally confirm authorship of the first document by an entity associated with the second document when the second document includes a second link to the first website of the first domain, the second link including a second predefined authorship attribute indicating that the entity associated with the second document is an author of or contributor to content at the first website of the first domain. | 14. A system to confirm authorship of documents, comprising: at least one processor; memory; and at least one program stored in the memory and executable by the at least one processor, the at least one program comprising instructions to: access a first document hosted on a first website of a first domain, the first document being indirectly linked to a second document through at least one link in a chain of links, a respective link in the chain of links including a first predefined authorship attribute asserting authorship of a respective document including the respective link by a respective entity associated with a respective target document of the respective link; and conditionally confirm authorship of the first document by an entity associated with the second document when the second document includes a second link to the first website of the first domain, the second link including a second predefined authorship attribute indicating that the entity associated with the second document is an author of or contributor to content at the first website of the first domain. 21. The system of claim 14 , wherein the second document is hosted on a second domain that is distinct from the first domain. | 0.87721 |
8,041,918 | 17 | 18 | 17. The system of claim 15 , wherein the first thread is further configured to push the second reference on a first mark stack. | 17. The system of claim 15 , wherein the first thread is further configured to push the second reference on a first mark stack. 18. The system of claim 17 , further comprising: a second mark stack; and a second thread associated with the second mark stack. | 0.5 |
7,747,942 | 8 | 9 | 8. The method according to claim 5 , wherein the program application is written in COBOL. | 8. The method according to claim 5 , wherein the program application is written in COBOL. 9. The method according to claim 8 , wherein the start data includes an occurs clause. | 0.5 |
9,443,511 | 5 | 6 | 5. The method of claim 1 , wherein selecting the first label comprises: grouping the one or more sound models into one or more sets of sound models based on the one or more labels; for each set of the one or more sets of sound models, calculating a sum of the similarity values of sound models in the set to determine a largest sum; and selecting a particular label associated with a set of the one or more sets having the largest sum. | 5. The method of claim 1 , wherein selecting the first label comprises: grouping the one or more sound models into one or more sets of sound models based on the one or more labels; for each set of the one or more sets of sound models, calculating a sum of the similarity values of sound models in the set to determine a largest sum; and selecting a particular label associated with a set of the one or more sets having the largest sum. 6. The method of claim 5 , wherein the confidence level is determined based on a difference between the largest sum and a second largest sum of the sums determined for the one or more sets of sound models. | 0.5 |
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