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8,935,274 | 15 | 17 | 15. An apparatus, comprising: memory element configured to store data; a processor operable to execute instructions associated with the data; a network sensor configured to: receive data propagating in a network environment; identify in real time a first user who generated the data and a second user who consumed the data; evaluate one or more fields in the data, wherein the evaluating includes searching and filtering for nouns and noun phrases; and a central engine configured to interface with the memory element, the processor, and the network sensor, the central engine being configured to: identify selected terms within the nouns and noun phrases, wherein the selected terms correspond to one or more topics, wherein the selected terms are tagged with respective expertise tags; associate the first user and the second user with the expertise tags and add the selected terms to a first personal vocabulary of the first user and a second personal vocabulary of the second user, wherein the first personal vocabulary is associated with expertise of the first user in the one or more topics and the second personal vocabulary is associated with expertise of the second user in the one or more topics, wherein the selected terms are assigned a higher weight in the first personal vocabulary than in the second personal vocabulary. | 15. An apparatus, comprising: memory element configured to store data; a processor operable to execute instructions associated with the data; a network sensor configured to: receive data propagating in a network environment; identify in real time a first user who generated the data and a second user who consumed the data; evaluate one or more fields in the data, wherein the evaluating includes searching and filtering for nouns and noun phrases; and a central engine configured to interface with the memory element, the processor, and the network sensor, the central engine being configured to: identify selected terms within the nouns and noun phrases, wherein the selected terms correspond to one or more topics, wherein the selected terms are tagged with respective expertise tags; associate the first user and the second user with the expertise tags and add the selected terms to a first personal vocabulary of the first user and a second personal vocabulary of the second user, wherein the first personal vocabulary is associated with expertise of the first user in the one or more topics and the second personal vocabulary is associated with expertise of the second user in the one or more topics, wherein the selected terms are assigned a higher weight in the first personal vocabulary than in the second personal vocabulary. 17. The apparatus of claim 15 , wherein the expertise tags are provided in respective profiles for the first user and the second user. | 0.801187 |
8,060,501 | 7 | 8 | 7. The method of claim 6 , where identifying that the list is present within the document further comprises: identifying a list that is explicitly defined, within the document, by at least one predefined list identifier. | 7. The method of claim 6 , where identifying that the list is present within the document further comprises: identifying a list that is explicitly defined, within the document, by at least one predefined list identifier. 8. The method of claim 7 , where the at least one predefined list identifier includes hyper-text markup language tags. | 0.5 |
8,756,611 | 6 | 7 | 6. A system, comprising: a memory; and a processor programmed to: monitor application context changes comprising runtime changes to values of referenced application definitions associated with instantiated applications, where the referenced application definitions represent application context dependencies for the instantiated applications; evaluate, in response to each of a plurality of application context changes to the values of the referenced application definitions, relationship context dependency properties that define dynamic runtime-assignable information comprising inter-relationships between the instantiated applications and application associations with application resources used by the instantiated applications, where the processor is programmed to: evaluate a context object associated with each instantiated application, where each context object comprises a configuration object of another instantiated application; scan the referenced application definitions associated with each instantiated application for context-based dynamic relationship definitions; identify variables referenced within the context-based dynamic relationship definitions associated with each instantiated application; and identify any referenced variables that depend upon context objects of at least one other instantiated application; determine that at least one relationship context dependency property that is used by at least one of the instantiated applications has changed as a result of an application context change; and update during runtime the at least one relationship context dependency property based upon the application context change, where the at least one of the instantiated applications continues execution using the updated at least one relationship context dependency property. | 6. A system, comprising: a memory; and a processor programmed to: monitor application context changes comprising runtime changes to values of referenced application definitions associated with instantiated applications, where the referenced application definitions represent application context dependencies for the instantiated applications; evaluate, in response to each of a plurality of application context changes to the values of the referenced application definitions, relationship context dependency properties that define dynamic runtime-assignable information comprising inter-relationships between the instantiated applications and application associations with application resources used by the instantiated applications, where the processor is programmed to: evaluate a context object associated with each instantiated application, where each context object comprises a configuration object of another instantiated application; scan the referenced application definitions associated with each instantiated application for context-based dynamic relationship definitions; identify variables referenced within the context-based dynamic relationship definitions associated with each instantiated application; and identify any referenced variables that depend upon context objects of at least one other instantiated application; determine that at least one relationship context dependency property that is used by at least one of the instantiated applications has changed as a result of an application context change; and update during runtime the at least one relationship context dependency property based upon the application context change, where the at least one of the instantiated applications continues execution using the updated at least one relationship context dependency property. 7. The system of claim 6 , where the plurality of application context changes comprise application instantiation changes and application resource changes. | 0.825792 |
7,698,626 | 10 | 12 | 10. A system comprising: memory to store personal information relating to a user, the personal information including at least one of a geographical location of the user, information provided by the user when registering an account, or information based on a browsing history of the user; and one or more devices comprising: means for receiving a first web page requested by the user; means for identifying a word or phrase in the first web page based on a number of times the word or phrase occurs in the first web page and a frequency with which the word or phrase occurs in a language of the first web page; means for determining one or more terms based on the personal information relating to the user; means for forming a search query by concatenating the one or more terms with the word or phrase; means for identifying an additional web page based on a search performed by a search engine using the search query; means for modifying the first web page by embedding a reference to the additional web page into the first web page in-line with the identified word or phrase; and means for transmitting the modified first web page to the user. | 10. A system comprising: memory to store personal information relating to a user, the personal information including at least one of a geographical location of the user, information provided by the user when registering an account, or information based on a browsing history of the user; and one or more devices comprising: means for receiving a first web page requested by the user; means for identifying a word or phrase in the first web page based on a number of times the word or phrase occurs in the first web page and a frequency with which the word or phrase occurs in a language of the first web page; means for determining one or more terms based on the personal information relating to the user; means for forming a search query by concatenating the one or more terms with the word or phrase; means for identifying an additional web page based on a search performed by a search engine using the search query; means for modifying the first web page by embedding a reference to the additional web page into the first web page in-line with the identified word or phrase; and means for transmitting the modified first web page to the user. 12. The system of claim 10 , where the means for identifying a word or phrase in the first web page comprise means for identifying a word or phrase in the first web page relating to one or more of celebrity names, commercial or consumer products, or company names. | 0.5 |
8,990,124 | 11 | 12 | 11. The method of claim 8 , wherein determining a feature vector for each review from the plurality of reviews comprises determining a value for at least one text feature based on the text of each review. | 11. The method of claim 8 , wherein determining a feature vector for each review from the plurality of reviews comprises determining a value for at least one text feature based on the text of each review. 12. The method of claim 11 , wherein the text features comprise one of text-statistics features, syntactic analysis features, conformity based features, sentiment based features, or product features. | 0.5 |
9,472,207 | 13 | 18 | 13. An apparatus, comprising: a wearable device, comprising: a processor; a support vector machine; and a memory comprising computer-readable instructions which when executed by the processor cause the processor and the support vector machine to perform the steps comprising: receiving an audio input from a first person comprising spoken words through a microphone communicatively coupled to the processor; sampling the audio input into a sample of a predetermined length of time; applying by the support vector machine an algorithm to the sample; determining, by the support vector machine, an emotional content of the sample by accessing a database comprising audio samples with predetermined emotional content and determining a closest emotional match to the sample from the predetermined emotional content such that the determining the closest emotional match trains the algorithm to optimize accuracy in determining the closest emotional match for subsequent samples; and outputting, by the support vector machine, the closest emotional match to the emotional content of the sample for use by a second person having an autism spectrum disorder. | 13. An apparatus, comprising: a wearable device, comprising: a processor; a support vector machine; and a memory comprising computer-readable instructions which when executed by the processor cause the processor and the support vector machine to perform the steps comprising: receiving an audio input from a first person comprising spoken words through a microphone communicatively coupled to the processor; sampling the audio input into a sample of a predetermined length of time; applying by the support vector machine an algorithm to the sample; determining, by the support vector machine, an emotional content of the sample by accessing a database comprising audio samples with predetermined emotional content and determining a closest emotional match to the sample from the predetermined emotional content such that the determining the closest emotional match trains the algorithm to optimize accuracy in determining the closest emotional match for subsequent samples; and outputting, by the support vector machine, the closest emotional match to the emotional content of the sample for use by a second person having an autism spectrum disorder. 18. The apparatus of claim 13 , wherein the vibratory pattern comprises four different patterns corresponding to neutral, happy, sad, and angry emotions. | 0.689024 |
8,589,869 | 1 | 4 | 1. A method, implemented in a computer system, of determining a result according to a formula, comprising: electronically receiving a user input in an imprecise syntax, wherein the user input includes (i) an indication of a desired formula having a plurality of mathematical or scientific parameters, and (ii) an indication of one or more respective values of one or more parameters in the plurality of mathematical or scientific parameters, and wherein the imprecise syntax is expressed using natural language and terms; after receiving the user input in the imprecise syntax, determining, at the computer system, one or more possible formulas corresponding to the indication of the desired formula, wherein the one or more possible formulas are expressed in mathematical or scientific symbols; calculating, at the computer system, one or more results according to the one or more possible formulas with the one or more parameters set to the respective one or more values; and generating electronic display information to display at least one of the one or more possible formulas and at least one of the one or more results on a display device. | 1. A method, implemented in a computer system, of determining a result according to a formula, comprising: electronically receiving a user input in an imprecise syntax, wherein the user input includes (i) an indication of a desired formula having a plurality of mathematical or scientific parameters, and (ii) an indication of one or more respective values of one or more parameters in the plurality of mathematical or scientific parameters, and wherein the imprecise syntax is expressed using natural language and terms; after receiving the user input in the imprecise syntax, determining, at the computer system, one or more possible formulas corresponding to the indication of the desired formula, wherein the one or more possible formulas are expressed in mathematical or scientific symbols; calculating, at the computer system, one or more results according to the one or more possible formulas with the one or more parameters set to the respective one or more values; and generating electronic display information to display at least one of the one or more possible formulas and at least one of the one or more results on a display device. 4. A method according to claim 1 , further comprising: electronically transmitting the electronic display information to a user computer. | 0.869524 |
10,043,521 | 16 | 20 | 16. At least one non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device to provide user dependent key phrase enrollment by: receiving, via a microphone, an audio input representing a user defined key phrase and converting the audio input to received audio data representative of the audio input; determining a sequence of most probable audio units corresponding to the received audio data, wherein each audio unit of the most probable audio units corresponds to a frame of a plurality of frames of the audio data; processing the sequence of most probable audio units to eliminate at least one audio unit from the sequence of most probable audio units to generate a final sequence of audio units by determining a first silence audio unit of the sequence and a number of silence audio units immediately temporally following the first silence audio unit, wherein the first silence audio unit and the number of silence audio units are between non-silence audio units of the sequence, and eliminating the first silence audio unit and the immediately temporally following silence audio units in response to the total number of consecutive silence audio units not exceeding a threshold; generating a key phrase recognition model representing the user defined key phrase based on the final sequence of audio units, the key phrase recognition model comprising a single start state based rejection model, a key phrase model, and a transition from the single start state based rejection model to the key phrase model, wherein the single start state based rejection model includes a single rejection state having a plurality of rejection model self loops, wherein the key phrase model comprises a plurality of states having transitions therebetween, the plurality of states including a final state of the key phrase model, and wherein the plurality of states of the key phrase model correspond to the final sequence of audio units; receiving a further audio input for evaluation by the key phrase recognition model; generating a time series of scores of audio units based on a time series of feature vectors representative of the further audio input; scoring the key phrase recognition model based on the time series of scores of audio units to generate a rejection likelihood score and a key phrase likelihood score; and recognizing that the further audio input corresponds to the user defined key phrase based on the rejection likelihood score and the key phrase likelihood score. | 16. At least one non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device to provide user dependent key phrase enrollment by: receiving, via a microphone, an audio input representing a user defined key phrase and converting the audio input to received audio data representative of the audio input; determining a sequence of most probable audio units corresponding to the received audio data, wherein each audio unit of the most probable audio units corresponds to a frame of a plurality of frames of the audio data; processing the sequence of most probable audio units to eliminate at least one audio unit from the sequence of most probable audio units to generate a final sequence of audio units by determining a first silence audio unit of the sequence and a number of silence audio units immediately temporally following the first silence audio unit, wherein the first silence audio unit and the number of silence audio units are between non-silence audio units of the sequence, and eliminating the first silence audio unit and the immediately temporally following silence audio units in response to the total number of consecutive silence audio units not exceeding a threshold; generating a key phrase recognition model representing the user defined key phrase based on the final sequence of audio units, the key phrase recognition model comprising a single start state based rejection model, a key phrase model, and a transition from the single start state based rejection model to the key phrase model, wherein the single start state based rejection model includes a single rejection state having a plurality of rejection model self loops, wherein the key phrase model comprises a plurality of states having transitions therebetween, the plurality of states including a final state of the key phrase model, and wherein the plurality of states of the key phrase model correspond to the final sequence of audio units; receiving a further audio input for evaluation by the key phrase recognition model; generating a time series of scores of audio units based on a time series of feature vectors representative of the further audio input; scoring the key phrase recognition model based on the time series of scores of audio units to generate a rejection likelihood score and a key phrase likelihood score; and recognizing that the further audio input corresponds to the user defined key phrase based on the rejection likelihood score and the key phrase likelihood score. 20. The machine readable medium of claim 16 , further comprising instructions that, in response to being executed on the computing device, cause the computing device to provide user dependent key phrase enrollment by: generating a second final sequence of audio units corresponding to a second received audio input, wherein the key phrase recognition model further comprises a second transition from the single start based rejection state model to a second key phrase model, wherein the second key phrase model comprises a plurality of second states having second transitions therebetween, the plurality of second states including the final state of the key phrase model shared with the second key phrase model, wherein the plurality of second states of the second key phrase model correspond to the second final sequence of audio units. | 0.5 |
7,542,971 | 6 | 7 | 6. The method of claim 1 , wherein the language model is a composite language model. | 6. The method of claim 1 , wherein the language model is a composite language model. 7. The method of claim 6 , wherein the composite language model is comprised of at least one of an actor based model, a domain based model and a genre based model. | 0.5 |
7,865,873 | 1 | 6 | 1. A computer-implemented method of defining object classes, comprising: on a computer, executing the method of: programming the computer with a browser-based classification editor having a first tier functionality and a second tier functionality; wherein said first tier functionality is operable to enable a user of said computer to create a new class having a first group; select and associate one or more attributes with said first group; and define and modify one or more conditions to said first group, wherein said first group comprises objects having said one or more attributes that meet at least one of said one or more conditions; and wherein said second tier functionality is operable to enable said user to manipulate an expression comprising said first group. | 1. A computer-implemented method of defining object classes, comprising: on a computer, executing the method of: programming the computer with a browser-based classification editor having a first tier functionality and a second tier functionality; wherein said first tier functionality is operable to enable a user of said computer to create a new class having a first group; select and associate one or more attributes with said first group; and define and modify one or more conditions to said first group, wherein said first group comprises objects having said one or more attributes that meet at least one of said one or more conditions; and wherein said second tier functionality is operable to enable said user to manipulate an expression comprising said first group. 6. The computer-implemented method according to claim 1 , wherein upon creation of a second group said first tier functionality is operable to display a default expression comprising names of said first group and said second group. | 0.5 |
9,298,703 | 17 | 20 | 17. The system of claim 1 , wherein the incentive is rewarded only if the user feedback is approved. | 17. The system of claim 1 , wherein the incentive is rewarded only if the user feedback is approved. 20. The system of claim 17 , wherein the user feedback is approved once the user feedback is accepted as accurately defining the word or phrase. | 0.726236 |
9,613,007 | 8 | 9 | 8. A method as described in claim 1 , wherein the non-rectangular frame is configured with multiple columns, and automatically fitting the one or more anchored text elements within the multi-column non-rectangular frame includes splitting the one or more anchored text elements into multiple portions responsive to a determination that the one or more anchored text elements do not fit at a bottom of one of the columns. | 8. A method as described in claim 1 , wherein the non-rectangular frame is configured with multiple columns, and automatically fitting the one or more anchored text elements within the multi-column non-rectangular frame includes splitting the one or more anchored text elements into multiple portions responsive to a determination that the one or more anchored text elements do not fit at a bottom of one of the columns. 9. A method as described in claim 8 , wherein automatically fitting the one or more anchored text elements within the multi-column non-rectangular frame further includes fitting a first of the multiple portions at the bottom of the one column and fitting a second of the multiple portions at a bottom of a next said column. | 0.5 |
8,266,082 | 12 | 13 | 12. The context inference system according to claim 11 , wherein the context inference process comprises: receiving and collecting the at least one context information and the service information by using the information receiving unit and the information collection module; inferring the context based on the user preference information, the at least one context information, and the service information by using the inference module; and generating the recommendation information according to the context by using the inference module. | 12. The context inference system according to claim 11 , wherein the context inference process comprises: receiving and collecting the at least one context information and the service information by using the information receiving unit and the information collection module; inferring the context based on the user preference information, the at least one context information, and the service information by using the inference module; and generating the recommendation information according to the context by using the inference module. 13. The context inference system according to claim 12 , wherein the mobile device further comprises a display unit, the context operation platform further comprises a graphic guiding interface module, and the context inference process further comprises displaying the recommendation information in the display unit by using the graphic guiding interface module. | 0.5 |
9,275,639 | 1 | 7 | 1. A system having a client-server architecture for Automatic Speech Recognition (ASR) applications, comprising: a) client-side including: a.1) a client being part of distributed front end for converting acoustic waves to feature vectors representing the properties of said acoustic waves using Digital Signal Processing (DSP); a.2) Voice Activity Detection for separating between speech and non-speech acoustic signals and for environmental compensation; a.3) adaptor for WebSockets to pipeline chunked feature vectors to the server side; b) a server side including: b.1) A web layer utilizing HTTP protocols and including a Web Server having a Servlet Container which contains dedicated servlet for managing voice recognition sessions that include transferring WebSockets' packets to messages and returning responses and diagnostic message to client, said layer being an access point for corporate information systems, and a point of integration with another web application, which are used for load balancing and. authentication and authorization of clients; b.2) an intermediate layer for transport based on Message-Oriented Middleware (MOM) being a message broker, to which all other server parts are connected, for feature vector streaming, for communication and integration, and for load balancing between said client side and said server side; b.3) a recognition server and an adaptation server both connected to said intermediate layer, said adaptation server being connected via an adaptation channel, said recognition server interacts with said client side via a recognition channel and a Distributed Frontend (DFE); b.4) a Speech processing server consisting of a server part of distributed front end, a Recognition Server and an Adaptation Server; b.5) a Recognition Server for instantiation of a recognition channel per client; b.6)an Adaptation Server for adaptation acoustic and linguistic models for each speaker; b.7) a Bidirectional communication channel between Speech processing server and client side via distributed frontend said recognition channel; and b.8) a Persistent layer for storing, a Language Knowledge Base connected to said Speech processing server, and includes a dictionary, acoustic models, statistical language models and language patterns. | 1. A system having a client-server architecture for Automatic Speech Recognition (ASR) applications, comprising: a) client-side including: a.1) a client being part of distributed front end for converting acoustic waves to feature vectors representing the properties of said acoustic waves using Digital Signal Processing (DSP); a.2) Voice Activity Detection for separating between speech and non-speech acoustic signals and for environmental compensation; a.3) adaptor for WebSockets to pipeline chunked feature vectors to the server side; b) a server side including: b.1) A web layer utilizing HTTP protocols and including a Web Server having a Servlet Container which contains dedicated servlet for managing voice recognition sessions that include transferring WebSockets' packets to messages and returning responses and diagnostic message to client, said layer being an access point for corporate information systems, and a point of integration with another web application, which are used for load balancing and. authentication and authorization of clients; b.2) an intermediate layer for transport based on Message-Oriented Middleware (MOM) being a message broker, to which all other server parts are connected, for feature vector streaming, for communication and integration, and for load balancing between said client side and said server side; b.3) a recognition server and an adaptation server both connected to said intermediate layer, said adaptation server being connected via an adaptation channel, said recognition server interacts with said client side via a recognition channel and a Distributed Frontend (DFE); b.4) a Speech processing server consisting of a server part of distributed front end, a Recognition Server and an Adaptation Server; b.5) a Recognition Server for instantiation of a recognition channel per client; b.6)an Adaptation Server for adaptation acoustic and linguistic models for each speaker; b.7) a Bidirectional communication channel between Speech processing server and client side via distributed frontend said recognition channel; and b.8) a Persistent layer for storing, a Language Knowledge Base connected to said Speech processing server, and includes a dictionary, acoustic models, statistical language models and language patterns. 7. A system according to claim 1 , in which the web server includes: a) a first Message Queue Adaptor for sending the features vector via a Message queue to a second Message Queue Adaptor on the voice decoding channel, which includes a filter that checks changes in the features vector and generates additional features from said changes; and b) a searcher module which decodes the acoustic signals and operates in a shared and re-enterable search space for avoiding synchronization. | 0.538241 |
8,010,341 | 1 | 6 | 1. A system for processing information, comprising: a processor; a module that is configured to apply a set of labels to a set of components using a probabilistic model; a module that is configured to incorporate prototypical information in said probabilistic model by augmenting said probabilistic model with a conditional probability of the prototypical information; and a module that is configured to determine whether said prototypical information is to be used in said probabilistic model based on a determination of at least one component in said set of components corresponding to a component in said prototypical information. | 1. A system for processing information, comprising: a processor; a module that is configured to apply a set of labels to a set of components using a probabilistic model; a module that is configured to incorporate prototypical information in said probabilistic model by augmenting said probabilistic model with a conditional probability of the prototypical information; and a module that is configured to determine whether said prototypical information is to be used in said probabilistic model based on a determination of at least one component in said set of components corresponding to a component in said prototypical information. 6. The system according to claim 1 , wherein said probabilistic model is a Hidden Markov Model. | 0.731638 |
8,195,602 | 12 | 14 | 12. A tangible computer-readable medium encoded with instructions that, in response to execution by a computing device, cause the computing device to perform operations comprising: receiving one or more natural language queries from a user; determining whether servicing a given natural language query needs data stored in a relational data store or a multi-dimensional data store, wherein the relational data store stores fact data and the multi-dimensional data store data store stores aggregated fact data, formed via a segmented aggregation process involving aggregation along multiple dimensions in a determined sequence, in a multi-dimensional data structure, and wherein the multi-dimensional data store is configured to communicate bi-directionally with the relational data store; in response to determining that servicing the given natural language query needs data stored in the relational data store, automatically routing the given natural language query to the relational data store, so that data is accessed from the relational data store and forwarded to the user; and in response to determining that servicing the given natural language query needs data stored in the multi-dimensional data store formed via the segmented aggregation process, automatically routing the given natural language query to the multi-dimensional data store, so that aggregated fact data can be accessed and forwarded to the user. | 12. A tangible computer-readable medium encoded with instructions that, in response to execution by a computing device, cause the computing device to perform operations comprising: receiving one or more natural language queries from a user; determining whether servicing a given natural language query needs data stored in a relational data store or a multi-dimensional data store, wherein the relational data store stores fact data and the multi-dimensional data store data store stores aggregated fact data, formed via a segmented aggregation process involving aggregation along multiple dimensions in a determined sequence, in a multi-dimensional data structure, and wherein the multi-dimensional data store is configured to communicate bi-directionally with the relational data store; in response to determining that servicing the given natural language query needs data stored in the relational data store, automatically routing the given natural language query to the relational data store, so that data is accessed from the relational data store and forwarded to the user; and in response to determining that servicing the given natural language query needs data stored in the multi-dimensional data store formed via the segmented aggregation process, automatically routing the given natural language query to the multi-dimensional data store, so that aggregated fact data can be accessed and forwarded to the user. 14. The tangible computer-readable medium of claim 12 , wherein the multi-dimensional data store is generated by calculating aggregated fact data from the fact data according to a multi-dimensional data aggregation process, and storing the aggregated fact data in the multi-dimensional data store. | 0.5 |
7,720,845 | 1 | 39 | 1. A computer-implemented method, comprising: generating a first representation of a set of one or more documents of a plurality of documents, wherein the first representation represents a state of the set of one or more documents before at least one change is made to at least one document of said plurality of documents; after said at least one change is made to at least one document of said plurality of documents, performing the steps of: causing a currently-stale view or currently-stale count for at least one current query to be displayed on a user interface; wherein the currently-stale view or currently-stale count is based at least in part on a result set of at least one previously-executed query that was executed on said plurality of documents before said at least one change was made; generating a second representation of the set of one or more documents of the plurality of documents, wherein the second representation represents a state of the set of one or more documents after said at least one change was made, whereby said first representation and said second representation together represent at least all of the documents affected by said at least one change; comparing the first and second representations outputting an updated view or count by, to form a third representation that includes representations of differences between said first and second representations; while said currently-stale view or currently-stale count is displayed, on said user interface utilizing said third representation to update said currently-stale view or currently-stale count on said user interface to reflect said at least one change; wherein the step of comparing is performed by one or more processors. | 1. A computer-implemented method, comprising: generating a first representation of a set of one or more documents of a plurality of documents, wherein the first representation represents a state of the set of one or more documents before at least one change is made to at least one document of said plurality of documents; after said at least one change is made to at least one document of said plurality of documents, performing the steps of: causing a currently-stale view or currently-stale count for at least one current query to be displayed on a user interface; wherein the currently-stale view or currently-stale count is based at least in part on a result set of at least one previously-executed query that was executed on said plurality of documents before said at least one change was made; generating a second representation of the set of one or more documents of the plurality of documents, wherein the second representation represents a state of the set of one or more documents after said at least one change was made, whereby said first representation and said second representation together represent at least all of the documents affected by said at least one change; comparing the first and second representations outputting an updated view or count by, to form a third representation that includes representations of differences between said first and second representations; while said currently-stale view or currently-stale count is displayed, on said user interface utilizing said third representation to update said currently-stale view or currently-stale count on said user interface to reflect said at least one change; wherein the step of comparing is performed by one or more processors. 39. The computer-implemented method according to claim 1 , further comprising: performing continuous rather than discrete querying. | 0.818056 |
6,134,532 | 50 | 51 | 50. A method of selecting advertisements in a computer environment, comprising: providing a database of electronic advertisements; converting an observed behavior of a user computing device in the computer environment to a behavior vector, modifying a profile vector indicative of the user with the behavior vector; comparing the modified profile vector to a plurality of entity vectors, the entity vectors indicative of the electronic advertisements, so as to identify at least one entity vector closely associated with the observed behavior; accessing the electronic database with the identified entity vector; and selecting at least one electronic advertisement to communicate to the user computing device. | 50. A method of selecting advertisements in a computer environment, comprising: providing a database of electronic advertisements; converting an observed behavior of a user computing device in the computer environment to a behavior vector, modifying a profile vector indicative of the user with the behavior vector; comparing the modified profile vector to a plurality of entity vectors, the entity vectors indicative of the electronic advertisements, so as to identify at least one entity vector closely associated with the observed behavior; accessing the electronic database with the identified entity vector; and selecting at least one electronic advertisement to communicate to the user computing device. 51. The method as defined in claim 50, wherein the selecting includes selecting an electronic advertisement that is under-selected according to a selection schedule and to inhibit the selection of an electronic advertisement that is over-selected according to the selection schedule. | 0.779251 |
8,996,629 | 1 | 7 | 1. A computer-implemented method for generating a stream of content for each of a plurality of channels, the method comprising: generating, with one or more processors, a model for a user comprising an interest of the user and prior interaction of the user with heterogeneous data sources; computing, with the one or more processors, an interestingness score for each content item received from the heterogeneous data sources by summing properties of each content item over single-attribute properties using the model and based upon interestingness of each content item to the user and an extent to which the content item's popularity has increased within a geographic area associated with the user; categorizing, with the one or more processors, content items received from the heterogeneous data sources by annotating each content item with a dynamic feature including the interestingness score; identifying, with the one or more processors, a first channel category for the user based on a historical trend and the prior interaction of the user with the heterogeneous data sources, the historical trend including a change in a number of content items categorized under the first channel category; receiving an input through a user interface specifying a second channel category; querying the content items based on the first channel category, the second channel category and at least one channel attribute; in response to the query, receiving candidate content items that include the first channel category, the second channel category and the at least one channel attribute and comparing the interestingness score for each candidate content item with a threshold for the first channel category and the second channel category to determine the candidate content items that have an interestingness score that exceeds the threshold; and generating the stream of content from the candidate content items that have an interestingness score that exceeds the threshold. | 1. A computer-implemented method for generating a stream of content for each of a plurality of channels, the method comprising: generating, with one or more processors, a model for a user comprising an interest of the user and prior interaction of the user with heterogeneous data sources; computing, with the one or more processors, an interestingness score for each content item received from the heterogeneous data sources by summing properties of each content item over single-attribute properties using the model and based upon interestingness of each content item to the user and an extent to which the content item's popularity has increased within a geographic area associated with the user; categorizing, with the one or more processors, content items received from the heterogeneous data sources by annotating each content item with a dynamic feature including the interestingness score; identifying, with the one or more processors, a first channel category for the user based on a historical trend and the prior interaction of the user with the heterogeneous data sources, the historical trend including a change in a number of content items categorized under the first channel category; receiving an input through a user interface specifying a second channel category; querying the content items based on the first channel category, the second channel category and at least one channel attribute; in response to the query, receiving candidate content items that include the first channel category, the second channel category and the at least one channel attribute and comparing the interestingness score for each candidate content item with a threshold for the first channel category and the second channel category to determine the candidate content items that have an interestingness score that exceeds the threshold; and generating the stream of content from the candidate content items that have an interestingness score that exceeds the threshold. 7. The method of claim 1 , wherein the user interface is provided for the user to define a new channel. | 0.836508 |
8,760,726 | 3 | 4 | 3. The method of claim 1 , additionally comprising the step of curing said printed polymer overlayer. | 3. The method of claim 1 , additionally comprising the step of curing said printed polymer overlayer. 4. The method of claim 3 , wherein said curing comprises UV curing. | 0.5 |
7,587,664 | 1 | 2 | 1. A computer implemented method for profiling a user based on the user's activity, the method comprising: assigning one or more topics to each of a plurality of documents based at least in part upon content contained in the documents; maintaining an affinity variable associated with the user for each of one or more of the topics assigned to a document attributed to the user, wherein the affinity variable is a calculated value linking the user to each of the one or more topics assigned to the documents, the affinity variable being calculated using a mathematical function; determining whether a first affinity variable for the user for a given topic has reached a threshold; associating the user with the given topic for the first affinity variable which reaches the threshold; and updating the affinity variable for a first topic for each document created by the user to which the first topic is assigned to maintain the affinity variable, the maintaining including weighting each document created by the user based upon one or more factors including a number of documents to which the first topic is assigned, a period of time over which the documents were created by the user, and a closeness of each document to the first topic. | 1. A computer implemented method for profiling a user based on the user's activity, the method comprising: assigning one or more topics to each of a plurality of documents based at least in part upon content contained in the documents; maintaining an affinity variable associated with the user for each of one or more of the topics assigned to a document attributed to the user, wherein the affinity variable is a calculated value linking the user to each of the one or more topics assigned to the documents, the affinity variable being calculated using a mathematical function; determining whether a first affinity variable for the user for a given topic has reached a threshold; associating the user with the given topic for the first affinity variable which reaches the threshold; and updating the affinity variable for a first topic for each document created by the user to which the first topic is assigned to maintain the affinity variable, the maintaining including weighting each document created by the user based upon one or more factors including a number of documents to which the first topic is assigned, a period of time over which the documents were created by the user, and a closeness of each document to the first topic. 2. The method of claim 1 , wherein the step of maintaining the affinity variable for the user comprises adjusting the affinity variable based upon affinity variables associated with a plurality of second users for the first topic. | 0.670487 |
8,412,746 | 1 | 22 | 1. A system, implemented in a data processing system, for managing and querying a plurality of inter-related data sources, the system comprising: an interface to the plurality of inter-related data sources of diverse types, wherein each of the plurality of inter-related data sources has a corresponding schema that describes a corresponding data structure of a corresponding data source; a schema generator communicating with said interface, wherein the schema generator generates a federated schema that describes structures of the plurality of inter-related data sources and inter-relationships of the plurality of inter-related data sources, and wherein the schema generator further modifies the federated schema over time as the plurality of inter-related data sources undergo changes; a query generator communicating with said schema generator, wherein the query generator generates a query for the federated schema; and a storage device in communication with the data processing system, the storage device operable to store the query. | 1. A system, implemented in a data processing system, for managing and querying a plurality of inter-related data sources, the system comprising: an interface to the plurality of inter-related data sources of diverse types, wherein each of the plurality of inter-related data sources has a corresponding schema that describes a corresponding data structure of a corresponding data source; a schema generator communicating with said interface, wherein the schema generator generates a federated schema that describes structures of the plurality of inter-related data sources and inter-relationships of the plurality of inter-related data sources, and wherein the schema generator further modifies the federated schema over time as the plurality of inter-related data sources undergo changes; a query generator communicating with said schema generator, wherein the query generator generates a query for the federated schema; and a storage device in communication with the data processing system, the storage device operable to store the query. 22. The system of claim 1 wherein said schema generator modifies the federated schema such that at least one of the plurality of inter-related data sources appears to be removed. | 0.779156 |
7,958,451 | 1 | 11 | 1. A computer memory device containing processor readable instructions for causing an information processor to display a graphical user interface, the graphical user interface comprising: a first object representing a first information source that provides content over the Internet; a plurality of base word labels, each base word label comprising text representing guidepost data displayed in the graphical user interface; a plurality of display objects, each display object corresponding to one of the base word labels, wherein the first object and the plurality of display objects are displayed relative to an observation point arbitrarily fixed in a visual display space according to the input of a user, the first object and the plurality of display objects being displayed at specific display locations depending on a correlation between the content available from the first information source, guidepost data being represented, and the observation point arbitrarily fixed in a visual display space according to the input of the user so that when input from the user is received, the display locations of the first object and the plurality of display objects are altered in the graphical user interface to maintain the correlation between the content available from the first information source and the guidepost data being represented and displayed in the graphical user interface, wherein a brightness of a display object indicates a relevance of the information source to the guidepost data, the most relevant being the brightest and, wherein activity of the first information source is indicated by a rotation speed of the first object. | 1. A computer memory device containing processor readable instructions for causing an information processor to display a graphical user interface, the graphical user interface comprising: a first object representing a first information source that provides content over the Internet; a plurality of base word labels, each base word label comprising text representing guidepost data displayed in the graphical user interface; a plurality of display objects, each display object corresponding to one of the base word labels, wherein the first object and the plurality of display objects are displayed relative to an observation point arbitrarily fixed in a visual display space according to the input of a user, the first object and the plurality of display objects being displayed at specific display locations depending on a correlation between the content available from the first information source, guidepost data being represented, and the observation point arbitrarily fixed in a visual display space according to the input of the user so that when input from the user is received, the display locations of the first object and the plurality of display objects are altered in the graphical user interface to maintain the correlation between the content available from the first information source and the guidepost data being represented and displayed in the graphical user interface, wherein a brightness of a display object indicates a relevance of the information source to the guidepost data, the most relevant being the brightest and, wherein activity of the first information source is indicated by a rotation speed of the first object. 11. The computer memory device according to claim 1 , wherein each of the display objects is displayed lengthwise radiating from a common center. | 0.88056 |
8,677,485 | 12 | 14 | 12. A system for detecting an anomaly in a network, the system comprising: a number of agents configured to collect flow data from a number of devices; a number of monitors configured to create a number of sketch-sets using the collected flow data; and an aggregator configured to: combine the number of sketch-sets received from the number of monitors into a number of common sketch-sets; identify a first sketch-set among the number of common sketch-sets above a first threshold value as the anomaly; eliminate a second sketch-set among the number of common sketch- sets below a second threshold value; and send a request, to the number of monitors, to create a number of finer sketch-sets for a third sketch-set among the number of common sketch-sets below the first threshold value and above the second threshold value. | 12. A system for detecting an anomaly in a network, the system comprising: a number of agents configured to collect flow data from a number of devices; a number of monitors configured to create a number of sketch-sets using the collected flow data; and an aggregator configured to: combine the number of sketch-sets received from the number of monitors into a number of common sketch-sets; identify a first sketch-set among the number of common sketch-sets above a first threshold value as the anomaly; eliminate a second sketch-set among the number of common sketch- sets below a second threshold value; and send a request, to the number of monitors, to create a number of finer sketch-sets for a third sketch-set among the number of common sketch-sets below the first threshold value and above the second threshold value. 14. The system of claim 12 , wherein the aggregator is further configured to: combine the number of finer sketch-sets received from the number of monitors into a number of common finer sketch-sets; identify a first common finer sketch-set among the number of common finer sketch-sets above the first threshold value as the anomaly; and eliminate a second common finer sketch- set among the number of common finer sketch-sets below a second threshold value. | 0.5 |
8,296,254 | 1 | 5 | 1. A data flow analyzing apparatus comprising: an analysis rule storage which stores analysis rules having definitions of data types; a data flow analyzer which performs data flow analysis on an analysis target program as a target of analysis by using the analysis rules stored in the analysis rule storage to thereby output unsafe data-including procedures as detected points; an analysis rule candidate generator which generates candidates of analysis rules by selecting a point from among the detected points and generating a control flow graph of the procedure at the point and extracting procedure calls from nodes which are reachable in tracing back the control flow graph from the location of the point; and an analysis rule candidate output which outputs the analysis rule candidates generated by the analysis rule candidate generator to a predetermined output. | 1. A data flow analyzing apparatus comprising: an analysis rule storage which stores analysis rules having definitions of data types; a data flow analyzer which performs data flow analysis on an analysis target program as a target of analysis by using the analysis rules stored in the analysis rule storage to thereby output unsafe data-including procedures as detected points; an analysis rule candidate generator which generates candidates of analysis rules by selecting a point from among the detected points and generating a control flow graph of the procedure at the point and extracting procedure calls from nodes which are reachable in tracing back the control flow graph from the location of the point; and an analysis rule candidate output which outputs the analysis rule candidates generated by the analysis rule candidate generator to a predetermined output. 5. A data flow analyzing apparatus according to claim 1 , further comprising: an analysis rule additional acceptor which accepts analysis rule candidates to be added as analysis rules from the analysis rule candidates output from the analysis rule candidate output; and an additional rule setter which sets the analysis rule candidates accepted by the analysis rule additional acceptor so as to be added as analysis rules. | 0.5 |
9,134,810 | 3 | 7 | 3. The method of claim 2 , wherein the visual indication of a first key of the virtual keyboard of a subsequent candidate input character of a first set of predicted input characters having a higher predictive rank provides a greater emphasis than the visual indication of a second key of the virtual keyboard of a subsequent candidate input character of a second set of predicted input characters having a lower predictive rank. | 3. The method of claim 2 , wherein the visual indication of a first key of the virtual keyboard of a subsequent candidate input character of a first set of predicted input characters having a higher predictive rank provides a greater emphasis than the visual indication of a second key of the virtual keyboard of a subsequent candidate input character of a second set of predicted input characters having a lower predictive rank. 7. The method of claim 3 , wherein the first key of the virtual keyboard is displayed using more than one type of visual emphasis while the second key of the virtual keyboard is displayed using only one type of visual emphasis. | 0.57963 |
8,346,536 | 21 | 22 | 21. The system of claim 19 , wherein when the user specifies that the source language term translates to the target language term, the user also specifies additional information corresponding to the translation, and the specified additional information is stored along with the translation in the vote store. | 21. The system of claim 19 , wherein when the user specifies that the source language term translates to the target language term, the user also specifies additional information corresponding to the translation, and the specified additional information is stored along with the translation in the vote store. 22. The system of claim 21 , wherein when the specified additional information is a context associated with the translation. | 0.649718 |
9,613,145 | 20 | 22 | 20. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining selection data identifying a term selected by a user from a document displayed to the user, the term comprising one or more adjacent words; obtaining context data comprising one or more other words in the document; determining, from the context data and the selection data, a type of contextual search presentation to request from a search engine for the selected term, comprising: determining whether or not the context data and the selection data satisfy one or more criteria for presenting any of one or more types of special case contextual search presentations, wherein each type of special case contextual search presentation includes a formatted presentation of a different type of content; in response to determining that the context data and the selection data satisfy one or more criteria for presenting a first type of special case contextual search presentation: obtaining, from the search engine, a first special case contextual search presentation for the term identified by the selection data that includes a first type of content corresponding to the first type of special case contextual presentation; in response to determining that the context data and the selection data do not satisfy the criteria for presenting any of the types of special case contextual search presentations: obtaining, from the search engine, a default contextual search presentation for the selected term that includes a second type of content corresponding to the default contextual search presentation; and providing the first special case contextual search presentation or the default contextual search presentation for presentation to the user. | 20. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining selection data identifying a term selected by a user from a document displayed to the user, the term comprising one or more adjacent words; obtaining context data comprising one or more other words in the document; determining, from the context data and the selection data, a type of contextual search presentation to request from a search engine for the selected term, comprising: determining whether or not the context data and the selection data satisfy one or more criteria for presenting any of one or more types of special case contextual search presentations, wherein each type of special case contextual search presentation includes a formatted presentation of a different type of content; in response to determining that the context data and the selection data satisfy one or more criteria for presenting a first type of special case contextual search presentation: obtaining, from the search engine, a first special case contextual search presentation for the term identified by the selection data that includes a first type of content corresponding to the first type of special case contextual presentation; in response to determining that the context data and the selection data do not satisfy the criteria for presenting any of the types of special case contextual search presentations: obtaining, from the search engine, a default contextual search presentation for the selected term that includes a second type of content corresponding to the default contextual search presentation; and providing the first special case contextual search presentation or the default contextual search presentation for presentation to the user. 22. The system of claim 20 , wherein the one or more types of special case contextual search presentations comprise an entity type of contextual search presentation that includes a knowledge panel, and wherein a knowledge panel is a formatted presentation of content relevant to an entity. | 0.66 |
8,086,619 | 43 | 45 | 43. The system of claim 42 , where, when formulating the search query refinement suggestion, the processor is further to: normalize the term vectors; and form the clusters based on distances of each of the normalized term vectors from a common origin. | 43. The system of claim 42 , where, when formulating the search query refinement suggestion, the processor is further to: normalize the term vectors; and form the clusters based on distances of each of the normalized term vectors from a common origin. 45. The system of claim 43 , where the processor is further to: assign a relevance score to the at least one search result document, where, when formulating the search query refinement suggestion, the processor is further to: rank the clusters based on the relevance score and a number of identified search documents in the clusters. | 0.557181 |
9,009,133 | 1 | 8 | 1. A computer-implemented method for searching an unstructured and uncharacterized collection of articles stored in at least one computer readable medium, the method comprising: receiving by the computer at least two user specified categories whose definitions are not constrained; receiving by the computer at least one user specified token associated with each of the at least two categories, wherein the user specified tokens are different for each category; generating a boolean query associated with the combination of the at least two categories and the user specified tokens; executing by the computer the boolean query against the unstructured and uncharacterized collection of articles including unstructured text to identify at least one article that includes the user tokens from the at least two categories and generating for any identified at least one article an identifier associated with the article, wherein the article identifier comprises information that points to the article; and creating a result set comprising the at least one article identifier. | 1. A computer-implemented method for searching an unstructured and uncharacterized collection of articles stored in at least one computer readable medium, the method comprising: receiving by the computer at least two user specified categories whose definitions are not constrained; receiving by the computer at least one user specified token associated with each of the at least two categories, wherein the user specified tokens are different for each category; generating a boolean query associated with the combination of the at least two categories and the user specified tokens; executing by the computer the boolean query against the unstructured and uncharacterized collection of articles including unstructured text to identify at least one article that includes the user tokens from the at least two categories and generating for any identified at least one article an identifier associated with the article, wherein the article identifier comprises information that points to the article; and creating a result set comprising the at least one article identifier. 8. The computer-implemented method of claim 1 , further comprising providing the result set to a user interface. | 0.918723 |
9,646,614 | 8 | 9 | 8. A method for speaker identification, comprising: at a device having one or more processors and memory: receiving a spoken utterance; generating a first phoneme-independent representation based on the spoken utterance; decomposing the first phoneme-independent representation into at least one content-independent characteristic unit; comparing the at least one content-independent characteristic unit to at least one content-independent recognition distribution value associated with a registered user of the device, the at least one content-independent recognition distribution value previously generated by: generating a second phoneme-independent representation based on speech from the registered user; and decomposing the second phoneme-independent representation into a content-independent recognition unit, the at least one content-independent recognition distribution value based on the content-independent recognition unit; and determining that the spoken utterance is spoken by the registered user if the at least one content-independent characteristic unit is within a threshold limit of the at least one content-independent recognition distribution value. | 8. A method for speaker identification, comprising: at a device having one or more processors and memory: receiving a spoken utterance; generating a first phoneme-independent representation based on the spoken utterance; decomposing the first phoneme-independent representation into at least one content-independent characteristic unit; comparing the at least one content-independent characteristic unit to at least one content-independent recognition distribution value associated with a registered user of the device, the at least one content-independent recognition distribution value previously generated by: generating a second phoneme-independent representation based on speech from the registered user; and decomposing the second phoneme-independent representation into a content-independent recognition unit, the at least one content-independent recognition distribution value based on the content-independent recognition unit; and determining that the spoken utterance is spoken by the registered user if the at least one content-independent characteristic unit is within a threshold limit of the at least one content-independent recognition distribution value. 9. The method of claim 8 , further comprising: generating the at least one content-independent characteristic unit from a singular value matrix of a singular value decomposition of the first phoneme-independent representation. | 0.753813 |
9,606,986 | 10 | 11 | 10. A method for processing discourse input comprising: at an electronic device with a processor and memory storing one or more programs for execution by the processor: receiving a discourse input from a user; determining a text string corresponding to the discourse input, wherein determining the text string comprises: determining, using a first language model, a conditional probability of a candidate word in the discourse input given one or more words in the discourse input; and applying a weight to the conditional probability of the candidate word given the one or more words to obtain a weighted conditional probability of the candidate word given the one or more words, wherein the weight is based on a conditional probability of the candidate word given one or more classes associated with the one or more words, and wherein the text string is based on the weighted conditional probability of the candidate word; and generating an output based on the text string. | 10. A method for processing discourse input comprising: at an electronic device with a processor and memory storing one or more programs for execution by the processor: receiving a discourse input from a user; determining a text string corresponding to the discourse input, wherein determining the text string comprises: determining, using a first language model, a conditional probability of a candidate word in the discourse input given one or more words in the discourse input; and applying a weight to the conditional probability of the candidate word given the one or more words to obtain a weighted conditional probability of the candidate word given the one or more words, wherein the weight is based on a conditional probability of the candidate word given one or more classes associated with the one or more words, and wherein the text string is based on the weighted conditional probability of the candidate word; and generating an output based on the text string. 11. The method of claim 10 , wherein the weight is further based on a probability of the candidate word within a corpus. | 0.857482 |
7,840,524 | 1 | 13 | 1. A method related to cluster analysis of a world wide web having identifiable web pages and hyperlink relationships made up of Universal Resource Locaters with pointers, wherein objects are related to the world wide web, direct non-semantic relationships relate to hyperlink relationships, and indirect non-semantic relationships relate to a series of hyperlink relationships between objects, comprising: crawling webpages on the world wide web for information used to define a set of objects to be indexed and to collect information about the direct non-semantic relationships, wherein Universal Resource Locators that either point to or point away from one or more of the web pages are crawled; defining the set of objects to be indexed, wherein each object in the set of objects has an identification and wherein a plurality of the objects in the set of objects have direct and indirect non-semantic relationships; generating, using a computer processor, a numerical representation for the set of objects in the form of a series of arrays representing each of said objects in the set of objects based upon each of said object's direct non-semantic relationships, if any, with other of said objects in the set of objects, wherein generating the numerical representation for the set of objects accounts for a plurality of direct non-semantic relationships and includes quantifying the accounted for direct non-semantic relationships, wherein the quantifying includes weighting some of the direct non-semantic relationships differently than others; generating, using a computer processor, a scalar value for each of said objecting the set of objects, wherein said scalar value accounts for direct and indirect non-semantic relationships that exist with other said objects in the set of objects and generating the scalar value includes: quantifying, for each of said objects in the set of objects that has one or more of the indirect non-semantic relationships, said object's indirect non-semantic relationships with other objects in the set of objects, wherein a.) some of the indirect non-semantic relationships contribute greater value to the scalar value than others, b.) a plurality of different types of indirect relationships, when present, contribute to the scalar value, and c.) quantifying said object's indirect non-semantic relationships includes accounting for at least the following three indirect non-semantic relationship patterns for a given object A when present: i) B cites f and f cites A, ii) B cites f, f cites e, and e cites A, and iii) B cites f, f cites e, e cites d, and d cites A, wherein B, d, e, and fare objects in the set of objects and said accounting for indirect non-semantic relationships uses weights that are calculated using one or more of said objects'quantity of outbound direct relationships; storing the generated scalar values in one or more computer memories as an index; receiving search commands wherein the search commands are received from an input device, wherein the received search commands include one or more search terms; identifying a resultant set of said objects that are associated with one or more search terms using at least a word index and the received search commands; determining a rank for objects in the resultant set of objects using said scalar values as a factor in determining the rank; and sending, for use by a display device, information for displaying identities of two or more objects in the resultant set of objects using the rank as a factor in determining an order of display. | 1. A method related to cluster analysis of a world wide web having identifiable web pages and hyperlink relationships made up of Universal Resource Locaters with pointers, wherein objects are related to the world wide web, direct non-semantic relationships relate to hyperlink relationships, and indirect non-semantic relationships relate to a series of hyperlink relationships between objects, comprising: crawling webpages on the world wide web for information used to define a set of objects to be indexed and to collect information about the direct non-semantic relationships, wherein Universal Resource Locators that either point to or point away from one or more of the web pages are crawled; defining the set of objects to be indexed, wherein each object in the set of objects has an identification and wherein a plurality of the objects in the set of objects have direct and indirect non-semantic relationships; generating, using a computer processor, a numerical representation for the set of objects in the form of a series of arrays representing each of said objects in the set of objects based upon each of said object's direct non-semantic relationships, if any, with other of said objects in the set of objects, wherein generating the numerical representation for the set of objects accounts for a plurality of direct non-semantic relationships and includes quantifying the accounted for direct non-semantic relationships, wherein the quantifying includes weighting some of the direct non-semantic relationships differently than others; generating, using a computer processor, a scalar value for each of said objecting the set of objects, wherein said scalar value accounts for direct and indirect non-semantic relationships that exist with other said objects in the set of objects and generating the scalar value includes: quantifying, for each of said objects in the set of objects that has one or more of the indirect non-semantic relationships, said object's indirect non-semantic relationships with other objects in the set of objects, wherein a.) some of the indirect non-semantic relationships contribute greater value to the scalar value than others, b.) a plurality of different types of indirect relationships, when present, contribute to the scalar value, and c.) quantifying said object's indirect non-semantic relationships includes accounting for at least the following three indirect non-semantic relationship patterns for a given object A when present: i) B cites f and f cites A, ii) B cites f, f cites e, and e cites A, and iii) B cites f, f cites e, e cites d, and d cites A, wherein B, d, e, and fare objects in the set of objects and said accounting for indirect non-semantic relationships uses weights that are calculated using one or more of said objects'quantity of outbound direct relationships; storing the generated scalar values in one or more computer memories as an index; receiving search commands wherein the search commands are received from an input device, wherein the received search commands include one or more search terms; identifying a resultant set of said objects that are associated with one or more search terms using at least a word index and the received search commands; determining a rank for objects in the resultant set of objects using said scalar values as a factor in determining the rank; and sending, for use by a display device, information for displaying identities of two or more objects in the resultant set of objects using the rank as a factor in determining an order of display. 13. The method of claim 1 wherein the use of the one or more of said object's quantity of outbound direct relationships is as a divisor in a ratio. | 0.919319 |
9,516,154 | 3 | 9 | 3. The apparatus of claim 1 , wherein the management unit is further configured to incorporate each of the conversational messages associated with all numbers of the one contact into a single conversational content associated with the one contact. | 3. The apparatus of claim 1 , wherein the management unit is further configured to incorporate each of the conversational messages associated with all numbers of the one contact into a single conversational content associated with the one contact. 9. The apparatus of claim 3 , wherein the management unit is further configured to preserve the conversational information associated with the one contact by making the conversational messages associated with the all numbers of the one contact corresponding to a same conversational identification. | 0.712909 |
9,552,411 | 1 | 9 | 1. A method implemented at least in part by a computer, the method comprising: in response to receiving user input from an input device of a client device, providing one or more text suggestions from a first dataset, the first dataset and a second dataset including a respective set of phrases to be used for text suggestions and stored at the client device, the user input including a character, the first dataset comprising trending data, the second dataset different from the first dataset, the second dataset comprising phrases that have been inputted by user input on the client device; in response to the user selecting one of the text suggestions from the first dataset from the input device of the client device, adding the selected text suggestion to the second dataset; in response to receiving a new first dataset from a service, deleting the first dataset from the client device; and assigning the new first dataset a weight that is less than a weight assigned to the second dataset such that suggestions from the new first dataset are suggested after higher weighted suggestions available from the second dataset when the suggestions from the second dataset and the suggestions from the new first dataset are equally probable. | 1. A method implemented at least in part by a computer, the method comprising: in response to receiving user input from an input device of a client device, providing one or more text suggestions from a first dataset, the first dataset and a second dataset including a respective set of phrases to be used for text suggestions and stored at the client device, the user input including a character, the first dataset comprising trending data, the second dataset different from the first dataset, the second dataset comprising phrases that have been inputted by user input on the client device; in response to the user selecting one of the text suggestions from the first dataset from the input device of the client device, adding the selected text suggestion to the second dataset; in response to receiving a new first dataset from a service, deleting the first dataset from the client device; and assigning the new first dataset a weight that is less than a weight assigned to the second dataset such that suggestions from the new first dataset are suggested after higher weighted suggestions available from the second dataset when the suggestions from the second dataset and the suggestions from the new first dataset are equally probable. 9. The method of claim 1 , wherein at least one of the phrases includes a new name. | 0.875375 |
10,110,847 | 1 | 11 | 1. A program image creation method that creates a program image based on input voice input information, image selection information, avatar selection information, decoration selection information, and interactive selection information, the program image creation method comprising: an image processing step of setting a description image based on the image selection information, the description image appears in the created program image; a voice processing step of synchronizing a voice from the voice input information with the created program image; an avatar processing step of combining an avatar that is set based on the avatar selection information with the description image, the avatar appearing in a predetermined location of the description image within the created program image; a decoration processing step of combining a decoration material that is set based on the decoration selection information with the description image, the decoration material appearing in the created program image; an interactive processing step of setting two or more hyperlinks based on the interactive selection information, the two or more hyperlinks appearing in the created program image and the two or more hyperlinks are selectable, wherein the the two or more hyperlinks are in the form of first and second selectable buttons separate from the avatar, wherein the first selectable button is associated with a first auxiliary screen and the second selectable button is associated with a second auxiliary screen, wherein the description image includes a background portion and a foreground portion, the foreground portion being overlaid on the background portion, wherein the first and second selectable buttons are displayed in the foreground portion, and when the first selectable button is selected, the foreground portion displaying the second selectable button is replaced by the first auxiliary screen, when the second selectable button is selected, the foreground portion displaying the first selectable button is replaced by the second auxiliary screen, and the background portion of the description image is unchanged; and displaying the created program image on a display device, the created program image including a reproduction player displayed at a bottom of the description image. | 1. A program image creation method that creates a program image based on input voice input information, image selection information, avatar selection information, decoration selection information, and interactive selection information, the program image creation method comprising: an image processing step of setting a description image based on the image selection information, the description image appears in the created program image; a voice processing step of synchronizing a voice from the voice input information with the created program image; an avatar processing step of combining an avatar that is set based on the avatar selection information with the description image, the avatar appearing in a predetermined location of the description image within the created program image; a decoration processing step of combining a decoration material that is set based on the decoration selection information with the description image, the decoration material appearing in the created program image; an interactive processing step of setting two or more hyperlinks based on the interactive selection information, the two or more hyperlinks appearing in the created program image and the two or more hyperlinks are selectable, wherein the the two or more hyperlinks are in the form of first and second selectable buttons separate from the avatar, wherein the first selectable button is associated with a first auxiliary screen and the second selectable button is associated with a second auxiliary screen, wherein the description image includes a background portion and a foreground portion, the foreground portion being overlaid on the background portion, wherein the first and second selectable buttons are displayed in the foreground portion, and when the first selectable button is selected, the foreground portion displaying the second selectable button is replaced by the first auxiliary screen, when the second selectable button is selected, the foreground portion displaying the first selectable button is replaced by the second auxiliary screen, and the background portion of the description image is unchanged; and displaying the created program image on a display device, the created program image including a reproduction player displayed at a bottom of the description image. 11. The program image creation method according to claim 1 , wherein the first and second selectable buttons are based on answers to a question. | 0.803815 |
9,159,324 | 1 | 2 | 1. A method for identifying a speaker with a mobile device, the method comprising: capturing, via a microphone on the mobile device, audio data comprising a speech signal; inferring, via the mobile device, a context of a user of the mobile device; identifying, via the mobile device, a social graph based at least partly on the inferred context, the social graph comprising a list of potential speakers; and identifying, via the mobile device, a speaker determined to have vocally contributed to the speech signal, the speaker identification based at least partly on the identified social graph. | 1. A method for identifying a speaker with a mobile device, the method comprising: capturing, via a microphone on the mobile device, audio data comprising a speech signal; inferring, via the mobile device, a context of a user of the mobile device; identifying, via the mobile device, a social graph based at least partly on the inferred context, the social graph comprising a list of potential speakers; and identifying, via the mobile device, a speaker determined to have vocally contributed to the speech signal, the speaker identification based at least partly on the identified social graph. 2. The method of claim 1 , wherein inferring the context of the user is based at least partly on a location of the user. | 0.84252 |
7,645,294 | 17 | 26 | 17. The spinal fixation system of claim 1 , wherein the connecting plate defines an opening at an end and a spanning portion extending from the end. | 17. The spinal fixation system of claim 1 , wherein the connecting plate defines an opening at an end and a spanning portion extending from the end. 26. The spinal fixation system of claim 17 , wherein the opening defined by the connecting plate is open-ended. | 0.740654 |
10,007,720 | 1 | 5 | 1. A method comprising: receiving, over a network by a networked system, a communication that is a part of a conversation involving one or more users, the networked system being a participant in the conversation; analyzing, by one or more hardware processors of the networked system, the communication, the analyzing including parsing key terms from the communication; identifying, by the networked system, a sentiment of a user among the one or more users based on the parsed key terms, the identified sentiment of the user identifying a level of commitment readiness among at least two sequentially increasing levels of commitment readiness, the at least two sequentially increasing levels including a penultimate level that indicates readiness to consider multiple options and including an ultimate level that indicates readiness to commit to one option among the multiple options; based on the identified level of commitment readiness among the at least two sequentially increasing levels of commitment readiness, determining whether the networked system is to respond to the communication; and in response to a determination to respond, generating, by the networked system, a customized response based on the identified level of commitment readiness among the at least two sequentially increasing levels of commitment readiness and transmitting the customized response, over the network, to a device of the user. | 1. A method comprising: receiving, over a network by a networked system, a communication that is a part of a conversation involving one or more users, the networked system being a participant in the conversation; analyzing, by one or more hardware processors of the networked system, the communication, the analyzing including parsing key terms from the communication; identifying, by the networked system, a sentiment of a user among the one or more users based on the parsed key terms, the identified sentiment of the user identifying a level of commitment readiness among at least two sequentially increasing levels of commitment readiness, the at least two sequentially increasing levels including a penultimate level that indicates readiness to consider multiple options and including an ultimate level that indicates readiness to commit to one option among the multiple options; based on the identified level of commitment readiness among the at least two sequentially increasing levels of commitment readiness, determining whether the networked system is to respond to the communication; and in response to a determination to respond, generating, by the networked system, a customized response based on the identified level of commitment readiness among the at least two sequentially increasing levels of commitment readiness and transmitting the customized response, over the network, to a device of the user. 5. The method of claim 1 , further comprising: inferring a goal using the parsed key terms from the communication; and modifying a set of options to best meet the goal, the set of options being provided in the customized response. | 0.676966 |
7,627,882 | 45 | 47 | 45. The method of claim 39 , further comprising the steps of: displaying an operating menu; and displaying at least one user-link in the operating menu. | 45. The method of claim 39 , further comprising the steps of: displaying an operating menu; and displaying at least one user-link in the operating menu. 47. The method of claim 45 , further comprising the step of setting a reminder for an upcoming television program. | 0.5 |
9,524,175 | 9 | 11 | 9. A non-transitory computer-readable storage medium storing program instructions executable by on one or more processors to implement a tool configured to: determine, for an overloaded operation invocation identified in source code of a computer program, whether the source code includes, as an argument to the invocation, an expression whose type is context-dependent; and in response to a determination that the source code includes, as an argument to the invocation, an expression whose type is context-dependent, determine, at an overload resolver to which the expression is provided as input, (a) whether each argument of the invocation, including the expression, is compatible with a type of a corresponding parameter indicated in one or more invocable operation declarations identified from the source code, and (b) whether a particular invocable operation declaration of the one or more invocable operation declarations is identifiable as the most specific invocable operation declaration for the invocation based at least in part on one or more specificity criteria; in response to a determination that (a) each argument of the invocation is compatible with a type of a corresponding parameter indicated in one or more invocable operation declarations in the source code, and (b) a particular invocable operation declaration of the one or more invocable operation declarations is identifiable as the most specific invocable operation declaration for the invocation, generate executable instructions for the invocation in accordance with the particular invocable operation declaration; and in response to a determination that none of the plurality of invocable operation declarations is identifiable as the most specific invocable operation declaration for the invocation, generate an error indication; wherein the error is generated even though each argument of the invocation is compatible with a type of a corresponding parameter indicated in a plurality of invocable operation declarations in the source code. | 9. A non-transitory computer-readable storage medium storing program instructions executable by on one or more processors to implement a tool configured to: determine, for an overloaded operation invocation identified in source code of a computer program, whether the source code includes, as an argument to the invocation, an expression whose type is context-dependent; and in response to a determination that the source code includes, as an argument to the invocation, an expression whose type is context-dependent, determine, at an overload resolver to which the expression is provided as input, (a) whether each argument of the invocation, including the expression, is compatible with a type of a corresponding parameter indicated in one or more invocable operation declarations identified from the source code, and (b) whether a particular invocable operation declaration of the one or more invocable operation declarations is identifiable as the most specific invocable operation declaration for the invocation based at least in part on one or more specificity criteria; in response to a determination that (a) each argument of the invocation is compatible with a type of a corresponding parameter indicated in one or more invocable operation declarations in the source code, and (b) a particular invocable operation declaration of the one or more invocable operation declarations is identifiable as the most specific invocable operation declaration for the invocation, generate executable instructions for the invocation in accordance with the particular invocable operation declaration; and in response to a determination that none of the plurality of invocable operation declarations is identifiable as the most specific invocable operation declaration for the invocation, generate an error indication; wherein the error is generated even though each argument of the invocation is compatible with a type of a corresponding parameter indicated in a plurality of invocable operation declarations in the source code. 11. The non-transitory computer-readable storage medium as recited in claim 9 , wherein the tool comprises one of: a compiler, or an interpreter. | 0.946296 |
9,613,007 | 12 | 13 | 12. A method as described in claim 10 , wherein the non-rectangular frame is configured with multiple columns, and fitting the anchored text element within the non-rectangular frame further includes splitting the anchored text element into multiple portions responsive to a determination that the anchored text element will not fit entirely at a bottom of one of the columns. | 12. A method as described in claim 10 , wherein the non-rectangular frame is configured with multiple columns, and fitting the anchored text element within the non-rectangular frame further includes splitting the anchored text element into multiple portions responsive to a determination that the anchored text element will not fit entirely at a bottom of one of the columns. 13. A method as described in claim 12 , wherein fitting the anchored text element within the non-rectangular frame further includes fitting a first of the multiple portions at the bottom of the one column and fitting a second of the multiple portions in a next said column. | 0.5 |
9,928,060 | 1 | 2 | 1. A method for tracking changes in a Javascript object notation structure, the method comprising: adjusting, by one or more computer processors, a Javascript object notation structure to comprise a tag on at least one object and a tag on at least one array; receiving, by one or more computer processors, data indicating a first set of at least one change to the Javascript object notation structure; adjusting, by one or more computer processors, the tags in the Javascript object notation structure to include the first set of the at least one change in the Javascript object notation structure; receiving, by one or more computer processor, data indicating the first set of the at least one change to the Javascript object notation structure is complete; and displaying, by one or more computer processors, the first set of the at least one change to the Javascript object notation structure based upon the adjusted tags. | 1. A method for tracking changes in a Javascript object notation structure, the method comprising: adjusting, by one or more computer processors, a Javascript object notation structure to comprise a tag on at least one object and a tag on at least one array; receiving, by one or more computer processors, data indicating a first set of at least one change to the Javascript object notation structure; adjusting, by one or more computer processors, the tags in the Javascript object notation structure to include the first set of the at least one change in the Javascript object notation structure; receiving, by one or more computer processor, data indicating the first set of the at least one change to the Javascript object notation structure is complete; and displaying, by one or more computer processors, the first set of the at least one change to the Javascript object notation structure based upon the adjusted tags. 2. The method of claim 1 , further comprising: changing, by one or more computer processors, the Javascript object notation structure based upon the first set of the at least one completed changes to the Javascript object notation structure. | 0.806581 |
9,558,270 | 7 | 10 | 7. The method of claim 1 , comprising: receiving a search query; providing a set of search results based upon the search query, the set of search results comprising the first search result; and providing a tag suggestion as the first tag. | 7. The method of claim 1 , comprising: receiving a search query; providing a set of search results based upon the search query, the set of search results comprising the first search result; and providing a tag suggestion as the first tag. 10. The method of claim 7 , the receiving a tag action comprising receiving the tag action based upon a one-click user input associated with the tag suggestion, and the tagging the first tag based upon the one-click user input. | 0.5 |
8,494,834 | 10 | 17 | 10. A system for local, computer-aided translation using remotely-generated translation predictions comprising: a remote translation server comprising: a remote translation memory providing access to a stored translation, a remote translation engine determining that the translation stored in the remote translation memory is useful in translating a first portion of a document, and a transmitter transmitting the stored translation; and a local machine comprising: a receiver receiving the stored translation, and a translation agent: determining, prior to receiving a communication from the remote translation memory indicating that the remote translation memory stores an updated version of the stored translation, and prior to receiving a request from a translator for the translation of the first portion of the document, that the remote translation memory stores an updated version of the stored translation, identifying the updated version of the stored translation as useful in translating a second portion of the document, wherein the identification occurs before a translation of the second portion of the local document is generated, and using the updated version of the stored translation to generate a translation of the second portion of the document, the generation of the translation being in response to the identification of the utility of the updated version of the stored translation in translating the second portion of the document. | 10. A system for local, computer-aided translation using remotely-generated translation predictions comprising: a remote translation server comprising: a remote translation memory providing access to a stored translation, a remote translation engine determining that the translation stored in the remote translation memory is useful in translating a first portion of a document, and a transmitter transmitting the stored translation; and a local machine comprising: a receiver receiving the stored translation, and a translation agent: determining, prior to receiving a communication from the remote translation memory indicating that the remote translation memory stores an updated version of the stored translation, and prior to receiving a request from a translator for the translation of the first portion of the document, that the remote translation memory stores an updated version of the stored translation, identifying the updated version of the stored translation as useful in translating a second portion of the document, wherein the identification occurs before a translation of the second portion of the local document is generated, and using the updated version of the stored translation to generate a translation of the second portion of the document, the generation of the translation being in response to the identification of the utility of the updated version of the stored translation in translating the second portion of the document. 17. The system of claim 10 , wherein the translation agent further comprises a means for determining whether the updated version of the stored translation makes the received translation obsolete. | 0.52439 |
7,870,001 | 1 | 2 | 1. A method comprising: communicating with a network server via a network; receiving, at a computing device, a selection of a word or phrase; converting, via the computing device, the word or phrase into at least one unique key value using a conversion table, the conversion table comprising at least one language key for at least one language and at least one text phrase corresponding to the language key and the unique key value; and communicating the unique key value to the network server over the network. | 1. A method comprising: communicating with a network server via a network; receiving, at a computing device, a selection of a word or phrase; converting, via the computing device, the word or phrase into at least one unique key value using a conversion table, the conversion table comprising at least one language key for at least one language and at least one text phrase corresponding to the language key and the unique key value; and communicating the unique key value to the network server over the network. 2. The method of claim 1 , wherein the conversion table further comprising a subset of information corresponding to information contained on a server conversion table. | 0.546196 |
7,496,559 | 26 | 34 | 26. A computerized search system, comprising: at least one processor configured to generate for display: a single search interface configured for display on a display device, the single search interface comprising at least: an email search interface having at least a first email-specific attribute search field selected from the group comprising a date field, a from field, and a sender field; and a file search interface having at least a first file-specific attribute search field selected from the group comprising a file name field, a file type field, a date field, a file size field, and a path field; a Web history search interface having Web page-specific attribute search fields, including one or more of a date field, a title field, a size field, and a search term field; an email attachment search interface having email attachment-specific attribute search fields including one or more of a name field, a date field, a size field, and an extension field; a favorites search interface used to search Web pages designated by a user as favorite Web pages; at least one index comprising data regarding emails and files; one or more memories configured to store the at least one index; and an apparatus configured to access the at least one index in order to perform incremental searching of the emails and files as the user enters characters into at least one of the email-specific search fields and the file-specific search fields, wherein the processor executes the apparatus. | 26. A computerized search system, comprising: at least one processor configured to generate for display: a single search interface configured for display on a display device, the single search interface comprising at least: an email search interface having at least a first email-specific attribute search field selected from the group comprising a date field, a from field, and a sender field; and a file search interface having at least a first file-specific attribute search field selected from the group comprising a file name field, a file type field, a date field, a file size field, and a path field; a Web history search interface having Web page-specific attribute search fields, including one or more of a date field, a title field, a size field, and a search term field; an email attachment search interface having email attachment-specific attribute search fields including one or more of a name field, a date field, a size field, and an extension field; a favorites search interface used to search Web pages designated by a user as favorite Web pages; at least one index comprising data regarding emails and files; one or more memories configured to store the at least one index; and an apparatus configured to access the at least one index in order to perform incremental searching of the emails and files as the user enters characters into at least one of the email-specific search fields and the file-specific search fields, wherein the processor executes the apparatus. 34. The computerized search system as defined in claim 26 , further comprising an email attachment search interface having email attachment specific attribute fields. | 0.823404 |
9,710,755 | 1 | 2 | 1. A method comprising: training a machine learning algorithm to create a predictive model; in a set of distinct records in a database system, the database system comprising a first database cluster H and a second database cluster L, for each record of the set of distinct records, using the predictive model to calculate a probability of the record being accessed; for each record of the set of distinct records, if the probability of the record being accessed as calculated is greater than a threshold value, then placing the record in the first database cluster H; for each record of the set of distinct records, if the probability of the record being accessed as calculated is not greater than the threshold value, then placing the record in the second database cluster L; receiving a request from a requester for at least one record of the set of distinct records; and presenting the at least one record from the set of distinct records to the requester in response to the request; wherein the method is implemented via execution of computer instructions configured to run on one or more processing modules and configured to be stored on one or more non-transitory memory storage modules; and wherein training the machine learning algorithm comprises: for each record in the set of distinct records, inputting a training feature vector associated with the record into the machine learning algorithm, the training feature vector associated with the record comprising a list of characteristics of the record; for each record in the set of distinct records, inputting a cost vector associated with the record into the machine learning algorithm, the cost vector associated with the record configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of distinct records to create the predictive model. | 1. A method comprising: training a machine learning algorithm to create a predictive model; in a set of distinct records in a database system, the database system comprising a first database cluster H and a second database cluster L, for each record of the set of distinct records, using the predictive model to calculate a probability of the record being accessed; for each record of the set of distinct records, if the probability of the record being accessed as calculated is greater than a threshold value, then placing the record in the first database cluster H; for each record of the set of distinct records, if the probability of the record being accessed as calculated is not greater than the threshold value, then placing the record in the second database cluster L; receiving a request from a requester for at least one record of the set of distinct records; and presenting the at least one record from the set of distinct records to the requester in response to the request; wherein the method is implemented via execution of computer instructions configured to run on one or more processing modules and configured to be stored on one or more non-transitory memory storage modules; and wherein training the machine learning algorithm comprises: for each record in the set of distinct records, inputting a training feature vector associated with the record into the machine learning algorithm, the training feature vector associated with the record comprising a list of characteristics of the record; for each record in the set of distinct records, inputting a cost vector associated with the record into the machine learning algorithm, the cost vector associated with the record configured to train the machine learning algorithm to reduce a probability of a false negative prediction for the record; and iteratively operating the machine learning algorithm on each record in the set of distinct records to create the predictive model. 2. The method of claim 1 wherein the machine learning algorithm is a cost-insensitive machine learning algorithm. | 0.886318 |
8,645,390 | 43 | 44 | 43. The non-transitory computer readable storage medium of claim 40 , wherein each search context is associated with a respective group of users and a respective class of search queries. | 43. The non-transitory computer readable storage medium of claim 40 , wherein each search context is associated with a respective group of users and a respective class of search queries. 44. The non-transitory computer readable storage medium of claim 43 , wherein the respective class for a particular search query is determined in accordance with a number of search terms in the particular search query. | 0.528139 |
7,966,341 | 9 | 10 | 9. The method of claim 8 wherein processing the query based on the plurality of dates includes including, in search results for the query, items associated with each of the plurality of dates. | 9. The method of claim 8 wherein processing the query based on the plurality of dates includes including, in search results for the query, items associated with each of the plurality of dates. 10. The method of claim 9 wherein: processing the query based on the plurality of dates includes ranking search results for the query based, at least in part, on scores generated for each of the plurality of dates; and the scores are based on content of said queries previously received by the search engine. | 0.5 |
9,501,661 | 1 | 14 | 1. A system to execute within a host organization, wherein the system comprises: a processor and a memory to execute instructions at the system; a search index stored on disk within the system comprised of a plurality of individual search index files, each of the individual search index files being accessible as a random access file, the search index having information stored therein, wherein at least one of the individual search index files constitutes a term dictionary or a term index type file having internal structure which allows a portion of the individual search index file to be updated, encrypted, and/or decrypted without affecting the internal structure of the individual search index file; wherein the search index stores both customer data and non-customer data organized into sub-blocks, wherein sub-blocks having customer data therein do not contain non-customer data and wherein sub-blocks having non-customer data therein do not contain customer data; a file input/output (TO) layer to encrypt the information being written into the individual search index file and to decrypt the information being read from the individual search index file, wherein the file IO layer encrypts and decrypts only a portion of the individual search index file in reply to an operation without requiring decryption or encryption of the individual search index file in its entirety; and a query interface to execute the operation against the information stored in the memory in its decrypted form. | 1. A system to execute within a host organization, wherein the system comprises: a processor and a memory to execute instructions at the system; a search index stored on disk within the system comprised of a plurality of individual search index files, each of the individual search index files being accessible as a random access file, the search index having information stored therein, wherein at least one of the individual search index files constitutes a term dictionary or a term index type file having internal structure which allows a portion of the individual search index file to be updated, encrypted, and/or decrypted without affecting the internal structure of the individual search index file; wherein the search index stores both customer data and non-customer data organized into sub-blocks, wherein sub-blocks having customer data therein do not contain non-customer data and wherein sub-blocks having non-customer data therein do not contain customer data; a file input/output (TO) layer to encrypt the information being written into the individual search index file and to decrypt the information being read from the individual search index file, wherein the file IO layer encrypts and decrypts only a portion of the individual search index file in reply to an operation without requiring decryption or encryption of the individual search index file in its entirety; and a query interface to execute the operation against the information stored in the memory in its decrypted form. 14. The system of claim 1 : wherein the individual search index file and storage of the information within the search index is implemented via the plurality of individual search index files as stored on disk within the system which are selectively retrieved and/or updated during the process of adding index documents or executing operations including search queries, add operations, delete operations, and update operations; and wherein the query interface is operable to submit queries, including the operation, against the search index. | 0.685531 |
8,401,252 | 1 | 11 | 1. A method for processing video data, comprising: detecting human faces in a plurality of video frames in said video data using a processor; for at least one detected human face, identifying a face-specific set of video frames using said processor, irrespective of whether said detected human face is present in said face-specific set of video frames in a substantially temporally continuous manner; grouping video frames in said face-specific set of video frames into a plurality of face tracks using said processor, wherein each face track contains corresponding one or more video frames having at least a substantial temporal continuity therebetween; using said processor, merging two or more of said plurality of face tracks that are disjoint in time using a face recognition method based on a Bayesian Network based classifier, wherein the Bayesian Network based classifier is constructed based on a ratio of a plurality of Bayesian networks and each of said Bayesian networks is a probability distribution representation derived from dependencies among video input variables that statistically depend upon each other; and enabling a user to view on an electronic display face-specific video segments of said at least one detected human face in said video data based on said merging of temporally disjoint face tracks. | 1. A method for processing video data, comprising: detecting human faces in a plurality of video frames in said video data using a processor; for at least one detected human face, identifying a face-specific set of video frames using said processor, irrespective of whether said detected human face is present in said face-specific set of video frames in a substantially temporally continuous manner; grouping video frames in said face-specific set of video frames into a plurality of face tracks using said processor, wherein each face track contains corresponding one or more video frames having at least a substantial temporal continuity therebetween; using said processor, merging two or more of said plurality of face tracks that are disjoint in time using a face recognition method based on a Bayesian Network based classifier, wherein the Bayesian Network based classifier is constructed based on a ratio of a plurality of Bayesian networks and each of said Bayesian networks is a probability distribution representation derived from dependencies among video input variables that statistically depend upon each other; and enabling a user to view on an electronic display face-specific video segments of said at least one detected human face in said video data based on said merging of temporally disjoint face tracks. 11. The method of claim 1 , further comprising: enabling said user to input a textual description of said face-specific video segments associated with said at least one detected human face using said processor. | 0.743276 |
8,606,728 | 1 | 5 | 1. A computer-implemented method comprising: calculating one or more types of suggestion scores for each of a plurality of training examples, wherein each type of suggestion score is based at least in part on a plurality of computed predictions for each training example by generated by a plurality of different trained models, including weighting each type of suggestion score by an accuracy of the trained model that generated the prediction; calculating an overall suggestion score for each training example based at least in part on a combination of the one or more types of suggestion scores for each training example; ranking the training examples by the corresponding overall suggestion scores; and providing one or more highest-ranked training examples as a set of suggested training examples. | 1. A computer-implemented method comprising: calculating one or more types of suggestion scores for each of a plurality of training examples, wherein each type of suggestion score is based at least in part on a plurality of computed predictions for each training example by generated by a plurality of different trained models, including weighting each type of suggestion score by an accuracy of the trained model that generated the prediction; calculating an overall suggestion score for each training example based at least in part on a combination of the one or more types of suggestion scores for each training example; ranking the training examples by the corresponding overall suggestion scores; and providing one or more highest-ranked training examples as a set of suggested training examples. 5. The method of claim 1 , wherein one of the one or more types of suggestion scores is a sparseness score, wherein the sparseness score for a particular training example in the training examples is based on comparing a count of training examples for a particular category or feature space of each training example to a threshold. | 0.712544 |
8,064,954 | 1 | 5 | 1. A method for a communication device comprising a microphone, a speaker, an input device, a display, and an antenna, said method comprising: a function implementing step in which one or more specific functions are implemented; wherein said communication device implements a voice communicating function and a multiple language function; wherein a voice communication is implemented by utilizing said microphone and said speaker when said voice communicating function is implemented in said step; and wherein a 1st language data and a 2nd language data are pre-stored in said communication device, wherein said 1st language data indicates a 1st language and said 2nd language data indicates a 2nd language, wherein said communication device functions under a 1st language mode or a 2nd language mode when said multiple language function is implemented in said step, wherein said multiple language function is the functionality enabling the user to manipulate said communication device with the user's preferred language by displaying the user interface with the language selected by the user, wherein when said 1st language mode is selected by the user, a first command which is the command for the user to manipulate said communication device in a first manner and a second command which is the command for the user to manipulate said communication device in a second manner are displayed by utilizing said 1st language data, wherein when said 2nd language mode is selected by the user, said first command which is the command for the user to manipulate said communication device in said first manner and said second command which is the command for the user to manipulate said communication device in said second manner are displayed by utilizing said 2nd language data, and wherein when said communication device is powered on after being powered off, said first command and said second command after said communication device is powered on are automatically displayed with said 1st language if said 1st language mode was selected by the user before said communication device was powered off, and said first command and said second command after said communication device is powered on are automatically displayed with said 2nd language if said 2nd language mode was selected by the user before said communication device was powered off. | 1. A method for a communication device comprising a microphone, a speaker, an input device, a display, and an antenna, said method comprising: a function implementing step in which one or more specific functions are implemented; wherein said communication device implements a voice communicating function and a multiple language function; wherein a voice communication is implemented by utilizing said microphone and said speaker when said voice communicating function is implemented in said step; and wherein a 1st language data and a 2nd language data are pre-stored in said communication device, wherein said 1st language data indicates a 1st language and said 2nd language data indicates a 2nd language, wherein said communication device functions under a 1st language mode or a 2nd language mode when said multiple language function is implemented in said step, wherein said multiple language function is the functionality enabling the user to manipulate said communication device with the user's preferred language by displaying the user interface with the language selected by the user, wherein when said 1st language mode is selected by the user, a first command which is the command for the user to manipulate said communication device in a first manner and a second command which is the command for the user to manipulate said communication device in a second manner are displayed by utilizing said 1st language data, wherein when said 2nd language mode is selected by the user, said first command which is the command for the user to manipulate said communication device in said first manner and said second command which is the command for the user to manipulate said communication device in said second manner are displayed by utilizing said 2nd language data, and wherein when said communication device is powered on after being powered off, said first command and said second command after said communication device is powered on are automatically displayed with said 1st language if said 1st language mode was selected by the user before said communication device was powered off, and said first command and said second command after said communication device is powered on are automatically displayed with said 2nd language if said 2nd language mode was selected by the user before said communication device was powered off. 5. The method of claim 1 , wherein a directory name, which indicates the name of a directory in which one or more data are operable to be stored, is displayed by the language corresponding to the current language mode. | 0.773859 |
8,556,937 | 1 | 10 | 1. A method for spinal stabilization, comprising: coupling a first end of a connecting member to a head of a bone anchor implanted in a first vertebra to anchor the first end of the connecting member to the first vertebra, the bone anchor having a rod receiving portion with opposed arms and a first spinal rod disposed in the rod receiving portion; threadably engaging a distal portion of a set screw with each of the opposed arms of the bone anchor, the threaded set screw extending through an opening in the first end of the connecting member; and threadably engaging a washer with a proximal portion of the set screw, the first end of the connecting member being located between the threaded washer and a proximal end of the opposed arms on the rod receiving portion of the bone anchor. | 1. A method for spinal stabilization, comprising: coupling a first end of a connecting member to a head of a bone anchor implanted in a first vertebra to anchor the first end of the connecting member to the first vertebra, the bone anchor having a rod receiving portion with opposed arms and a first spinal rod disposed in the rod receiving portion; threadably engaging a distal portion of a set screw with each of the opposed arms of the bone anchor, the threaded set screw extending through an opening in the first end of the connecting member; and threadably engaging a washer with a proximal portion of the set screw, the first end of the connecting member being located between the threaded washer and a proximal end of the opposed arms on the rod receiving portion of the bone anchor. 10. The method of claim 1 , wherein the first end of the connecting plate has a distal surface that bears against the proximal end of the opposed arms of the rod receiving portion of the bone anchor. | 0.5 |
9,854,330 | 18 | 23 | 18. A method comprising: generating a fingerprint data using a television; matching primary data generated from the fingerprint data with targeted data, based on a relevancy factor, using a relevancy-matching server; locating in a storage at least one of the targeted data, a reference to the targeted data and a metadata of the targeted data using the relevancy-matching server, wherein the primary data is any one of a content identification data and a content identification history; constraining an executable environment in a security sandbox of a mobile device; executing a sandboxed application in the executable environment of the mobile device; associating the mobile device with the television based on: executing a sandbox-reachable service on the television; automatically announcing, through the television, the sandbox-reachable service to a discovery module executing at least one of: on a pairing server external to the mobile device and as part of an extension of the security sandbox executing on the mobile device; identifying the sandbox-reachable service offered through the television based on receiving, through the discovery module, the automatic announcement to enable the sandboxed application of the mobile device reach the sandbox-reachable service by communicating an identification data of the television via the sandbox-reachable service when a shared computer network is determined to be commonly associated with the mobile device and the television; and establishing bidirectional communication between the mobile device and the television based on the sandboxed application reaching the sandbox-reachable service to render the primary data available across the sandbox-reachable service and the sandboxed application; processing an embedded object within the sandboxed application; and executing the embedded object through the sandboxed application to cause rendering of the targeted data therethrough, wherein the identification data of the television comprises at least one of a GUID, an alphanumeric name, a hardware address associated with the television, a public address associated with an automatic content identification service of the television, and a private address associated with the automatic content identification service of the television when the shared computer network is determined to be commonly associated with the mobile device and the television. | 18. A method comprising: generating a fingerprint data using a television; matching primary data generated from the fingerprint data with targeted data, based on a relevancy factor, using a relevancy-matching server; locating in a storage at least one of the targeted data, a reference to the targeted data and a metadata of the targeted data using the relevancy-matching server, wherein the primary data is any one of a content identification data and a content identification history; constraining an executable environment in a security sandbox of a mobile device; executing a sandboxed application in the executable environment of the mobile device; associating the mobile device with the television based on: executing a sandbox-reachable service on the television; automatically announcing, through the television, the sandbox-reachable service to a discovery module executing at least one of: on a pairing server external to the mobile device and as part of an extension of the security sandbox executing on the mobile device; identifying the sandbox-reachable service offered through the television based on receiving, through the discovery module, the automatic announcement to enable the sandboxed application of the mobile device reach the sandbox-reachable service by communicating an identification data of the television via the sandbox-reachable service when a shared computer network is determined to be commonly associated with the mobile device and the television; and establishing bidirectional communication between the mobile device and the television based on the sandboxed application reaching the sandbox-reachable service to render the primary data available across the sandbox-reachable service and the sandboxed application; processing an embedded object within the sandboxed application; and executing the embedded object through the sandboxed application to cause rendering of the targeted data therethrough, wherein the identification data of the television comprises at least one of a GUID, an alphanumeric name, a hardware address associated with the television, a public address associated with an automatic content identification service of the television, and a private address associated with the automatic content identification service of the television when the shared computer network is determined to be commonly associated with the mobile device and the television. 23. The method of claim 18 , comprising: determining the mobile device to be associated with a user based on a unique identifier that is unlikely to change. | 0.815603 |
7,493,252 | 1 | 8 | 1. A method of mining a collection of data, comprising: receiving the collection of data, the collection of data comprising key words, wherein a key word comprises a coherent character string; converting the collection of data into labeled data by grouping various types of data into a same format and assigning a label indicating a category of item contents, such that the labeled data is in analyzable condition for concept extraction, and wherein the labeled data comprises the label and a clause comprising the item contents; assigning a category to the key words, wherein the category references a concept so that the key words can be handled as concepts with a meaning; separating the clauses into pairs comprising an independent word and an attached word; assigning categories to the separated clauses using syntactic patterns and a category dictionary; generating, by syntactic analysis, a syntactic tree of a sentence comprising the separated clauses; receiving a syntactically analyzed sentence as input, identifying mutually dependent relationships between or among the categorized key words, according to at least one rule defining mutually dependent relationships between or among categorized key words; grouping the identified mutually dependent relationships into groups of related mutually dependent relationships; and extracting the key words with mutually dependent relationships in the same sentence as labeled data with concepts, wherein the step of extracting key words comprises using a mutually dependent relationship extraction rule comprising a string of categories of arbitrary length to be extracted; searching for unique concepts, a unique concept being a concept whose statistical characteristic is distinguished beyond a threshold with the set to which it belongs; creating and keeping statistical information; visually displaying the statistical information; and presenting a distribution of differences of the unique concepts. | 1. A method of mining a collection of data, comprising: receiving the collection of data, the collection of data comprising key words, wherein a key word comprises a coherent character string; converting the collection of data into labeled data by grouping various types of data into a same format and assigning a label indicating a category of item contents, such that the labeled data is in analyzable condition for concept extraction, and wherein the labeled data comprises the label and a clause comprising the item contents; assigning a category to the key words, wherein the category references a concept so that the key words can be handled as concepts with a meaning; separating the clauses into pairs comprising an independent word and an attached word; assigning categories to the separated clauses using syntactic patterns and a category dictionary; generating, by syntactic analysis, a syntactic tree of a sentence comprising the separated clauses; receiving a syntactically analyzed sentence as input, identifying mutually dependent relationships between or among the categorized key words, according to at least one rule defining mutually dependent relationships between or among categorized key words; grouping the identified mutually dependent relationships into groups of related mutually dependent relationships; and extracting the key words with mutually dependent relationships in the same sentence as labeled data with concepts, wherein the step of extracting key words comprises using a mutually dependent relationship extraction rule comprising a string of categories of arbitrary length to be extracted; searching for unique concepts, a unique concept being a concept whose statistical characteristic is distinguished beyond a threshold with the set to which it belongs; creating and keeping statistical information; visually displaying the statistical information; and presenting a distribution of differences of the unique concepts. 8. The method of claim 1 wherein the mutually dependent relationship extraction rule are provided manually by a user for each of the identified groups of mutually dependent relationships in response to the user being presented a display of all the groups of mutually dependent relationships, at a GUI. | 0.553412 |
7,761,461 | 19 | 20 | 19. A computer-readable medium containing a program which, when executed by a processor, performs a process of logically representing relationships between data elements defined according to a first physical representation of data, the process comprising: retrieving a logical representation of the data, the logical representation abstractly describing a second physical representation of the data, wherein the second physical representation of the data is generated from the first physical representation of the data; on the basis of the relationships between the data elements defined according to the first physical representation of the data, determining corresponding relationships between corresponding data structures defined according to the second physical representation of the data; generating logical relationships abstractly describing the determined corresponding relationships, each logical relationship defining a path between data structures of the second physical representation; associating the generated logical relationships with the logical representation of the data; and storing the associations and the generated logical relationships on one or more computer-readable storage media. | 19. A computer-readable medium containing a program which, when executed by a processor, performs a process of logically representing relationships between data elements defined according to a first physical representation of data, the process comprising: retrieving a logical representation of the data, the logical representation abstractly describing a second physical representation of the data, wherein the second physical representation of the data is generated from the first physical representation of the data; on the basis of the relationships between the data elements defined according to the first physical representation of the data, determining corresponding relationships between corresponding data structures defined according to the second physical representation of the data; generating logical relationships abstractly describing the determined corresponding relationships, each logical relationship defining a path between data structures of the second physical representation; associating the generated logical relationships with the logical representation of the data; and storing the associations and the generated logical relationships on one or more computer-readable storage media. 20. The computer-readable medium of claim 19 , wherein the logical representation comprises a plurality of logical field specifications, and wherein associating comprises including the generated logical relationships with respective logical field specifications. | 0.5 |
8,655,879 | 1 | 8 | 1. A method comprising: receiving a plurality of bookmarks associated with a video, wherein each bookmark in the plurality of bookmarks has a first place marker and a second place marker; determining, via a processor, an aggregated first mark and an aggregated second mark based at least in part on the first place marker and the second place marker of each bookmark in the plurality of bookmarks, to yield an aggregated bookmark defining an aggregated video segment; and normalizing metadata associated with the aggregated video segment for locating the aggregated video segment independent of playback device type. | 1. A method comprising: receiving a plurality of bookmarks associated with a video, wherein each bookmark in the plurality of bookmarks has a first place marker and a second place marker; determining, via a processor, an aggregated first mark and an aggregated second mark based at least in part on the first place marker and the second place marker of each bookmark in the plurality of bookmarks, to yield an aggregated bookmark defining an aggregated video segment; and normalizing metadata associated with the aggregated video segment for locating the aggregated video segment independent of playback device type. 8. The method of claim 1 , wherein determining the aggregated first mark and the aggregated second mark further comprises: using a first threshold distance between the first place marker of each bookmark in the plurality of bookmarks to yield a first threshold value for each bookmark; using a second threshold distance between the second place marker of each bookmark in the plurality of bookmarks to yield a second threshold value for each bookmark; and determining if each bookmark in the plurality of bookmarks is associated with a similar video segment based at least in part on the first threshold value and the second threshold value. | 0.5 |
8,374,885 | 12 | 16 | 12. A computer system comprising at least one processor and a nontransitory computer-readable storage medium, the non-transitory computer-readable storage medium storing an executable program which directs the processor in performing a computer-implemented method for automatically assessing credibility of a particular website, the computer-implemented method comprising: classifying the particular website based on subject matter of the particular website, the particular website comprising a plurality of elements that produce a presentation of the website when rendered, each element of the plurality of elements defined by at least one attribute; identifying a set of credibility scoring rules based on the classification of the particular website, said set of credibility scoring rules for computing credibility of the particular website based on encoded preferences of a primary demographic of users for websites of the same classification as the particular website; for each particular element of the plurality of elements, producing a credibility score identifying whether the particular element when rendered for display according to the at least one attribute defined for that particular element increases credibility of the particular website by attracting more visitors to the particular website or decreases credibility of the particular website by discouraging visitors to the particular website, wherein producing the credibility score for a particular element comprises (i) selecting a particular credibility scoring rule from the identified set of credibility scoring rules that quantifies a credibility impact of the particular element and (ii) passing the at least one attribute defined for the particular element to the particular credibility scoring rule in order to generate a credibility score as output; and presenting credibility of the particular website by (i) rendering each particular element of the plurality of elements according to the at least one attribute that is defined for each particular element thereby producing a display of the particular website and (ii) overlaying each particular element with the credibility score that is computed for that particular element. | 12. A computer system comprising at least one processor and a nontransitory computer-readable storage medium, the non-transitory computer-readable storage medium storing an executable program which directs the processor in performing a computer-implemented method for automatically assessing credibility of a particular website, the computer-implemented method comprising: classifying the particular website based on subject matter of the particular website, the particular website comprising a plurality of elements that produce a presentation of the website when rendered, each element of the plurality of elements defined by at least one attribute; identifying a set of credibility scoring rules based on the classification of the particular website, said set of credibility scoring rules for computing credibility of the particular website based on encoded preferences of a primary demographic of users for websites of the same classification as the particular website; for each particular element of the plurality of elements, producing a credibility score identifying whether the particular element when rendered for display according to the at least one attribute defined for that particular element increases credibility of the particular website by attracting more visitors to the particular website or decreases credibility of the particular website by discouraging visitors to the particular website, wherein producing the credibility score for a particular element comprises (i) selecting a particular credibility scoring rule from the identified set of credibility scoring rules that quantifies a credibility impact of the particular element and (ii) passing the at least one attribute defined for the particular element to the particular credibility scoring rule in order to generate a credibility score as output; and presenting credibility of the particular website by (i) rendering each particular element of the plurality of elements according to the at least one attribute that is defined for each particular element thereby producing a display of the particular website and (ii) overlaying each particular element with the credibility score that is computed for that particular element. 16. The computer-implemented method of claim 12 , wherein presenting the action comprises displaying the action as text that is overlaid the specific element during presentation of the credibility of the particular website. | 0.5 |
5,537,628 | 8 | 9 | 8. A method for handling text using a code page that is different than a native code page used in a document into which the text is pasted, comprising the steps of: (a) producing a piece table by scanning characters comprising the document to develop an array of character positions and an array of data records, said characters in the document being referenced in the array of character positions by a sequence of character position coordinates, said array of character positions being divided into a plurality of pieces, each piece comprising characters of text that are disposed adjacent to each other in the document and which have common properties; (b) including in each record of the array of data records: (i) a file number for a corresponding piece of the array of character positions, said file number indicating a file in which the characters referenced in the piece are stored; and (ii) a file position in said file where said characters are to be found; (c) producing a file control block for any file that is opened to paste text into the document, said file control block including code page identifier data indicating a default code page for the text stored in said file, and thus, for the text referenced by any of the pieces; (d) providing a data block for each file that is opened to paste text into the document, said data block including a specifier for an exception code page to be used for any run of text in the file that has a different code page than the default code page for the file in which the run of text is stored, so that the default code page for the file in which the run of text is stored and the data block for the run of text are checked to determine the code page to be applied to all of the characters in said run of text; and (e) when the code page used by any run of text to be displayed is different than the native code page, translating the code page for the characters in the run of text to be displayed to the native code page using a closest available mapping, said code page for the run of text being retained if the document is saved to a file, thereby ensuring that a reference to the code page for any text pasted into the document is not omitted from the file to which the document is saved. | 8. A method for handling text using a code page that is different than a native code page used in a document into which the text is pasted, comprising the steps of: (a) producing a piece table by scanning characters comprising the document to develop an array of character positions and an array of data records, said characters in the document being referenced in the array of character positions by a sequence of character position coordinates, said array of character positions being divided into a plurality of pieces, each piece comprising characters of text that are disposed adjacent to each other in the document and which have common properties; (b) including in each record of the array of data records: (i) a file number for a corresponding piece of the array of character positions, said file number indicating a file in which the characters referenced in the piece are stored; and (ii) a file position in said file where said characters are to be found; (c) producing a file control block for any file that is opened to paste text into the document, said file control block including code page identifier data indicating a default code page for the text stored in said file, and thus, for the text referenced by any of the pieces; (d) providing a data block for each file that is opened to paste text into the document, said data block including a specifier for an exception code page to be used for any run of text in the file that has a different code page than the default code page for the file in which the run of text is stored, so that the default code page for the file in which the run of text is stored and the data block for the run of text are checked to determine the code page to be applied to all of the characters in said run of text; and (e) when the code page used by any run of text to be displayed is different than the native code page, translating the code page for the characters in the run of text to be displayed to the native code page using a closest available mapping, said code page for the run of text being retained if the document is saved to a file, thereby ensuring that a reference to the code page for any text pasted into the document is not omitted from the file to which the document is saved. 9. The method of claim 8, further comprising the steps of determining if a run of text being saved to a file and previously identified as using an exception code page uses a code page that is the same as the default code page for said file to which the run of text is being saved; and if not, omitting the specifier for said run of text from the data block of said file so that an exception code page for the run of text is not indicated. | 0.831797 |
8,250,018 | 1 | 4 | 1. An expert system for aiding engineering personnel in a contact lens manufacturing, comprising: a user interface for receiving a query from a user regarding an element of business entity's industrial environment in which the user works in a contact lens manufacturing related capacity; a database containing information describing aspects of elements of the business entity's industrial environment; the inference engine subsystem receives the query via the user interface and obtains solution information from the database by matching keyterms in the query, wherein the user interface outputs the solution information to the user; an inference engine matches keyterms in the query with keyterms associated with rules to select a rule from the database; and the inference engine obtains a further question from the database associated with the selected rule; the user interface prompts the user to answer the further question; and the inference engine determines if solution information associated with a user's answer to the further question is available in the database. | 1. An expert system for aiding engineering personnel in a contact lens manufacturing, comprising: a user interface for receiving a query from a user regarding an element of business entity's industrial environment in which the user works in a contact lens manufacturing related capacity; a database containing information describing aspects of elements of the business entity's industrial environment; the inference engine subsystem receives the query via the user interface and obtains solution information from the database by matching keyterms in the query, wherein the user interface outputs the solution information to the user; an inference engine matches keyterms in the query with keyterms associated with rules to select a rule from the database; and the inference engine obtains a further question from the database associated with the selected rule; the user interface prompts the user to answer the further question; and the inference engine determines if solution information associated with a user's answer to the further question is available in the database. 4. The expert system claimed in claim 1 , wherein the inference engine subsystem determines whether query relates to a knowledge-based solution or an expert consultation-based solution by matching keyterms in the query. | 0.5 |
5,587,918 | 1 | 3 | 1. A circuit pattern comparison apparatus for extracting portions matched to a designated predetermined search pattern from an object of comparison represented by a circuit network having a set of nodes and node-to-node arcs or links, comprising: search pattern editing means for schematically describing said designated predetermined search pattern having a plurality of nodes; comparison order designating means for designating a comparison order of the nodes contained in the designated predetermined search pattern; search code synthesizing means for synthesizing at least one search code by subjecting, to code conversion, said designated predetermined search pattern schematically described by said search pattern editing means; comparing means for comparing the designated predetermined search pattern to the object of comparison utilizing the at least one search code; and extracting means for extracting at least one portion of the object of comparison matched to said designated predetermined search pattern. | 1. A circuit pattern comparison apparatus for extracting portions matched to a designated predetermined search pattern from an object of comparison represented by a circuit network having a set of nodes and node-to-node arcs or links, comprising: search pattern editing means for schematically describing said designated predetermined search pattern having a plurality of nodes; comparison order designating means for designating a comparison order of the nodes contained in the designated predetermined search pattern; search code synthesizing means for synthesizing at least one search code by subjecting, to code conversion, said designated predetermined search pattern schematically described by said search pattern editing means; comparing means for comparing the designated predetermined search pattern to the object of comparison utilizing the at least one search code; and extracting means for extracting at least one portion of the object of comparison matched to said designated predetermined search pattern. 3. A circuit pattern comparison apparatus according to claim 1, wherein said search pattern editing means includes means for schematically designating, in said designated predetermined search pattern, a given pattern to be extracted by repeating comparison. | 0.830026 |
7,533,335 | 10 | 15 | 10. A computer-readable storage medium for representing fields in a markup language document, comprising: inputting an application document that has been generated by a word-processing application that uses a non-markup language file format that is specific to the application; determining properties relating to one or more fields used within the application document, wherein the field comprises unique properties are defined by the application; determining whether the field is one of a complex field and a simple field; writing the properties into at least one of a markup language element, an attribute, and a value, wherein the field is designated with a simple field markup language element when the field is determined to be a simple field; and storing the properties in the markup language document such that the fields of the application document are substantially maintained when the markup language document is parsed by an application that is different from the application used to generate the application document. | 10. A computer-readable storage medium for representing fields in a markup language document, comprising: inputting an application document that has been generated by a word-processing application that uses a non-markup language file format that is specific to the application; determining properties relating to one or more fields used within the application document, wherein the field comprises unique properties are defined by the application; determining whether the field is one of a complex field and a simple field; writing the properties into at least one of a markup language element, an attribute, and a value, wherein the field is designated with a simple field markup language element when the field is determined to be a simple field; and storing the properties in the markup language document such that the fields of the application document are substantially maintained when the markup language document is parsed by an application that is different from the application used to generate the application document. 15. The computer-readable storage medium of claim 10 , wherein the simple field markup language element used to designate the field includes a fldSimple element when the field is determined to be a simple field. | 0.786004 |
9,342,581 | 15 | 18 | 15. A system comprising: program code comprising: an object management system to call a constructor to register an interface to a description of a persistent class; and a database management system kernel to access the registered interface to determine an internal structure of the persistent class, to process an instance of the persistent class based on the determined internal structure, wherein the instance of the persistent class is a persistent database object, to determine, based on the determined internal structure, members of the persistent database object that are filled with default values, to store the persistent database object in a database, to reduce storage demands on the database by removing the default values from the persistent database object before the storing of the persistent database object in the database, to read the persistent database object from the database after the storing of the persistent database object in the database, and to, after the reading of the persistent database object from the database, populate the determined members of the persistent database object with the default values of the persistent database object that were removed from the persistent database object before the storing of the persistent database object to reduce storage demands on the database; and at least one processor to execute the program code. | 15. A system comprising: program code comprising: an object management system to call a constructor to register an interface to a description of a persistent class; and a database management system kernel to access the registered interface to determine an internal structure of the persistent class, to process an instance of the persistent class based on the determined internal structure, wherein the instance of the persistent class is a persistent database object, to determine, based on the determined internal structure, members of the persistent database object that are filled with default values, to store the persistent database object in a database, to reduce storage demands on the database by removing the default values from the persistent database object before the storing of the persistent database object in the database, to read the persistent database object from the database after the storing of the persistent database object in the database, and to, after the reading of the persistent database object from the database, populate the determined members of the persistent database object with the default values of the persistent database object that were removed from the persistent database object before the storing of the persistent database object to reduce storage demands on the database; and at least one processor to execute the program code. 18. The system according to claim 15 , the database management system kernel further to define a key associated with one or more members of the instance based on the determined internal structure. | 0.753149 |
8,065,246 | 1 | 7 | 1. A storage medium storing instructions executable to implement a sum-of-exponentials function optimization method including the operations of: constructing an upper bound using a double majorization bounding process to a sum-of-exponentials function including at least one summation of exponentials; optimizing the constructed upper bound respective to parameters of the exponentials of the at least one summation of exponentials to generate optimized parameters; and outputting the optimized sum-of-exponentials function represented at least by the optimized parameters. | 1. A storage medium storing instructions executable to implement a sum-of-exponentials function optimization method including the operations of: constructing an upper bound using a double majorization bounding process to a sum-of-exponentials function including at least one summation of exponentials; optimizing the constructed upper bound respective to parameters of the exponentials of the at least one summation of exponentials to generate optimized parameters; and outputting the optimized sum-of-exponentials function represented at least by the optimized parameters. 7. The storage medium as set forth in claim 1 , wherein the summation of exponentials is of the form ∑ k = 1 K ⅇ β k T x and the operation of constructing an upper bound using a double majorization bounding process comprises constructing an upper bound to an expectation of the sum-of-exponentials function under the form E Q [ ∑ k = 1 K ⅇ β k T x ] , into an expectation having the form E Q [e β k T x ] where Q(β k ) denotes a probability density function (pdf) over the parameters β and E Q [ . . . ] denotes an expectation with respect to Q. | 0.635827 |
7,730,094 | 9 | 12 | 9. A computer comprising a processor controlling operation of the computer according to computer readable instructions stored in a memory, wherein, upon execution of the computer readable instructions by the processor, the computer performs a method for determining access rights to a range of objects by a range of users through scope information, comprising: receiving, from a first user, request to access a first resource; determining that a scope of a first access control metadata element encompasses the first resource, wherein the first access control metadata element includes: a first set of one or more XML statements that define the scope of the first access control metadata element by selecting a plurality of resources, the scope of the first access control metadata element encompassing the first resource and at least one other resource; a second set of one or more XML statements that define access rights for the plurality of resources within the scope of the first access control metadata element; and a third set of one or more XML statements that define a plurality of users to which the first access control metadata element applies; determining that a scope of a second access control metadata element encompasses the first resource, wherein the second access control metadata element includes: a first set of one or more XML access control-related statements that define the scope of the second access control metadata element by selecting one or more resources, the scope of the second access control metadata element encompassing the first resource; and a second set of one or more XML statements that define access rights for the plurality of resources within the scope of the second access control metadata element; and a third set of one or more XML statements that define a plurality of users to which the second access control metadata element applies; determining that the first user is among the plurality of users to which the first access control metadata element applies as defined by the third set of one or more XML statements of the first access control metadata element; determining that the first user is among the plurality of users to which the second access control metadata element applies as defined by the third set of one or more XML statements of the second access control metadata element; determining that a first access right defined in the first access control metadata element conflicts with a second access right defined in the second access control metadata element; in response to determining that the first access right conflicts with the second access right, determining whether the first access right supersedes the second access right; and in response to determining that the first access right supersedes the second access right, applying the first access right to the access request from the first user. | 9. A computer comprising a processor controlling operation of the computer according to computer readable instructions stored in a memory, wherein, upon execution of the computer readable instructions by the processor, the computer performs a method for determining access rights to a range of objects by a range of users through scope information, comprising: receiving, from a first user, request to access a first resource; determining that a scope of a first access control metadata element encompasses the first resource, wherein the first access control metadata element includes: a first set of one or more XML statements that define the scope of the first access control metadata element by selecting a plurality of resources, the scope of the first access control metadata element encompassing the first resource and at least one other resource; a second set of one or more XML statements that define access rights for the plurality of resources within the scope of the first access control metadata element; and a third set of one or more XML statements that define a plurality of users to which the first access control metadata element applies; determining that a scope of a second access control metadata element encompasses the first resource, wherein the second access control metadata element includes: a first set of one or more XML access control-related statements that define the scope of the second access control metadata element by selecting one or more resources, the scope of the second access control metadata element encompassing the first resource; and a second set of one or more XML statements that define access rights for the plurality of resources within the scope of the second access control metadata element; and a third set of one or more XML statements that define a plurality of users to which the second access control metadata element applies; determining that the first user is among the plurality of users to which the first access control metadata element applies as defined by the third set of one or more XML statements of the first access control metadata element; determining that the first user is among the plurality of users to which the second access control metadata element applies as defined by the third set of one or more XML statements of the second access control metadata element; determining that a first access right defined in the first access control metadata element conflicts with a second access right defined in the second access control metadata element; in response to determining that the first access right conflicts with the second access right, determining whether the first access right supersedes the second access right; and in response to determining that the first access right supersedes the second access right, applying the first access right to the access request from the first user. 12. The computer of claim 9 , wherein the first resource comprises a file stored on a storage device. | 0.777533 |
4,783,758 | 29 | 31 | 29. In a method for automated linguistic expression substitution on a digital data processor according to claim 22, the improvement in which the evaluating step further comprises the steps of responding to a disparity signal in a selected first range of disparity signal values for determining the alternate expression signal corresponding to a disparity signal to be exclusively substitutable for the suspect expression signal corresponding to said disparity signal and for producing a signal indicative thereof. | 29. In a method for automated linguistic expression substitution on a digital data processor according to claim 22, the improvement in which the evaluating step further comprises the steps of responding to a disparity signal in a selected first range of disparity signal values for determining the alternate expression signal corresponding to a disparity signal to be exclusively substitutable for the suspect expression signal corresponding to said disparity signal and for producing a signal indicative thereof. 31. In a method for automated linguistic expression substitution on a digital data processor according to claim 29, the improvement in which the evaluating step further comprises the step of responding to a disparity signal value in a selected second range of disparity signal values and producing an output signal indicative thereof, said second disparity signal value range having a lower value bound larger than a lower value bound of said first disparity signal value range. | 0.5 |
7,496,560 | 25 | 26 | 25. The method of claim 21 , further comprising storing the user-personalized library of content in a memory for later retrieval by the user. | 25. The method of claim 21 , further comprising storing the user-personalized library of content in a memory for later retrieval by the user. 26. The method of claim 25 , further comprising enabling the user to store and retrieve multiple user-personalized libraries. | 0.5 |
9,043,352 | 34 | 46 | 34. A computer-implemented method for searching established document object relationships, the method comprising: accessing, using a server, one or more link relationships stored on a memory device, wherein the one or more link relationships comprise a record, each record having a pointer to a first document object, a pointer to a second document object and information identifying one or more attributes describing the relationship between the first document object and the second document object, wherein the link relationships are stored separately from the first document object and the second document object; prompting, using a server, selection of one or more first link references, wherein the first link references represent particular document objects; searching established link relationships with the selected one or more first link references, wherein the searching includes identifying one or more first link relationships that the selected one or more first link references participate in; and transmitting, for display, information describing the one or more identified first link relationships. | 34. A computer-implemented method for searching established document object relationships, the method comprising: accessing, using a server, one or more link relationships stored on a memory device, wherein the one or more link relationships comprise a record, each record having a pointer to a first document object, a pointer to a second document object and information identifying one or more attributes describing the relationship between the first document object and the second document object, wherein the link relationships are stored separately from the first document object and the second document object; prompting, using a server, selection of one or more first link references, wherein the first link references represent particular document objects; searching established link relationships with the selected one or more first link references, wherein the searching includes identifying one or more first link relationships that the selected one or more first link references participate in; and transmitting, for display, information describing the one or more identified first link relationships. 46. The method of claim 34 wherein the established link relationships are filed according to one or more link relationship attributes. | 0.760714 |
9,104,710 | 11 | 12 | 11. The method according to claim 10 , wherein said applying step further comprises the step of: retrieving the matching feature pair from said correlation index. | 11. The method according to claim 10 , wherein said applying step further comprises the step of: retrieving the matching feature pair from said correlation index. 12. The method according to claim 11 , wherein said method for data correlation further comprises the steps of: using said computed correlation score to rank each retrieved feature. | 0.5 |
7,672,935 | 13 | 16 | 13. An article of manufacture, comprising a machine-accessible storage medium including data that, when accessed by a machine, cause the machine to perform a method comprising: receiving a request, at a lightweight directory access protocol (LDAP) directory server, to retrieve data from a LDAP repository communicably coupled to the LDAP server, wherein the request is in the form of a filter that is a logical expression including search terms related to the data; evaluating, by the LDAP server, the filter in terms of statistical data being tracked by the LDAP server; adding, by the LDAP server, statistical data related to the filter to a set of filter tracking data maintained by the LDAP server for one or more filters, wherein the filter tracking data includes at least one of an access frequency of each filter, an evaluation time of each filter, a time that a request for each filter is received, system load when the request for each filter is received, and a number of entries processed for each filter; generating, by the LDAP server, one or more LDAP indices for each of the one or more filters; selecting, by the LDAP server, a defined number of the one or more LDAP indices with a highest dynamic ranking to maintain in the LDAP repository, wherein the dynamic ranking is determined from the statistical data determined for the filter associated with each potential LDAP index; and deleting, by the LDAP server, one or more remaining LDAP indices that are not selected for maintenance; wherein the filter tracking data is updated each time the LDAP server receives another request, and the selecting and deleting the one or more remaining LDAP indices is repeated using updated dynamic rankings based on the updated filter tracking data on an on-going basis. | 13. An article of manufacture, comprising a machine-accessible storage medium including data that, when accessed by a machine, cause the machine to perform a method comprising: receiving a request, at a lightweight directory access protocol (LDAP) directory server, to retrieve data from a LDAP repository communicably coupled to the LDAP server, wherein the request is in the form of a filter that is a logical expression including search terms related to the data; evaluating, by the LDAP server, the filter in terms of statistical data being tracked by the LDAP server; adding, by the LDAP server, statistical data related to the filter to a set of filter tracking data maintained by the LDAP server for one or more filters, wherein the filter tracking data includes at least one of an access frequency of each filter, an evaluation time of each filter, a time that a request for each filter is received, system load when the request for each filter is received, and a number of entries processed for each filter; generating, by the LDAP server, one or more LDAP indices for each of the one or more filters; selecting, by the LDAP server, a defined number of the one or more LDAP indices with a highest dynamic ranking to maintain in the LDAP repository, wherein the dynamic ranking is determined from the statistical data determined for the filter associated with each potential LDAP index; and deleting, by the LDAP server, one or more remaining LDAP indices that are not selected for maintenance; wherein the filter tracking data is updated each time the LDAP server receives another request, and the selecting and deleting the one or more remaining LDAP indices is repeated using updated dynamic rankings based on the updated filter tracking data on an on-going basis. 16. The article of manufacture of claim 13 , further comprising: generating the one or more LDAP indices for the one or more filters with the highest request frequencies. | 0.770889 |
9,269,056 | 1 | 4 | 1. A method for determining at least one combined forecast value of non-conventional energy resources for enabling adaptive forecasting of the non-conventional energy resources, the method comprising: selecting a historical dataset comprising a first set of forecast values received from one or more predictive forecast models and a first set of actual values received from one or more measurements of the non-conventional energy resources; generating one or more variants of machine learning models to model performance of the one or more predictive forecast models by training the one or more variants of the machine learning models on the historical dataset; receiving a current dataset comprising a second set of forecast values derived from the one or more predictive forecast models and a second set of actual values derived from the one or more measurements of the non-conventional energy resources; correlating the current dataset with the historical dataset to adaptively obtain a filtered historical dataset; selecting the one or more variants of the machine learning models trained on the historical dataset and evaluating them on the filtered historical dataset to assign weights to each of the one or more variants of the machine learning models and their outputs; and deriving a statistical model in the form of an optimal combination function to determine at least one combined forecast value by combining weights assigned to the each of the one or more variants of the machine learning models trained based on the evaluating of the one or more variants of the machine learning models on the filtered historical dataset and the outputs of the each of the one or more variants of machine learning models trained on the historical dataset, wherein the selecting, the generating, the receiving, the correlating, the evaluating and the deriving are performed by a processor using computer-readable instructions stored in the memory. | 1. A method for determining at least one combined forecast value of non-conventional energy resources for enabling adaptive forecasting of the non-conventional energy resources, the method comprising: selecting a historical dataset comprising a first set of forecast values received from one or more predictive forecast models and a first set of actual values received from one or more measurements of the non-conventional energy resources; generating one or more variants of machine learning models to model performance of the one or more predictive forecast models by training the one or more variants of the machine learning models on the historical dataset; receiving a current dataset comprising a second set of forecast values derived from the one or more predictive forecast models and a second set of actual values derived from the one or more measurements of the non-conventional energy resources; correlating the current dataset with the historical dataset to adaptively obtain a filtered historical dataset; selecting the one or more variants of the machine learning models trained on the historical dataset and evaluating them on the filtered historical dataset to assign weights to each of the one or more variants of the machine learning models and their outputs; and deriving a statistical model in the form of an optimal combination function to determine at least one combined forecast value by combining weights assigned to the each of the one or more variants of the machine learning models trained based on the evaluating of the one or more variants of the machine learning models on the filtered historical dataset and the outputs of the each of the one or more variants of machine learning models trained on the historical dataset, wherein the selecting, the generating, the receiving, the correlating, the evaluating and the deriving are performed by a processor using computer-readable instructions stored in the memory. 4. The method of claim 1 , wherein the one or more variants of the machine learning models include Artificial Neural Networks (ANNs), basis function models, kernel methods, support vector machines, decision trees, variation methods, distribution sampling methods, ensemble methods, graphical models, search methods, or combinations thereof. | 0.5 |
9,189,470 | 12 | 14 | 12. The method of claim 9 , wherein the topic label comprises at least two dimensions, wherein the second data set comprises background data, and wherein the method further comprises: generating the modified topic label by eliminating a dimension from the at least two dimensions; and obtaining the background data that is related to the modified topic label. | 12. The method of claim 9 , wherein the topic label comprises at least two dimensions, wherein the second data set comprises background data, and wherein the method further comprises: generating the modified topic label by eliminating a dimension from the at least two dimensions; and obtaining the background data that is related to the modified topic label. 14. The method of claim 12 , wherein comparing the first data set to the background data further comprises determining whether a feature of the extracted features is more frequent in the first data set than the background data. | 0.5 |
8,615,478 | 1 | 2 | 1. A method comprising: electronically developing a kernel based supervised classifier; determining a kernel based supervised classifier; choosing the number of kernels to equal the number of dimensions in a data space plus one; electronically determining a non-binary affinity measure between two data points using said supervised classifier; and electronically providing a visualization of the relationships between data points using said non-binary affinity measure. | 1. A method comprising: electronically developing a kernel based supervised classifier; determining a kernel based supervised classifier; choosing the number of kernels to equal the number of dimensions in a data space plus one; electronically determining a non-binary affinity measure between two data points using said supervised classifier; and electronically providing a visualization of the relationships between data points using said non-binary affinity measure. 2. The method of claim 1 including developing a tree based supervised classifier. | 0.804348 |
8,812,602 | 1 | 29 | 1. A method of identifying conversations of a social network system having relevance to a first file, comprising: identifying a plurality of conversations within the social network system, wherein the plurality of conversations each have a relationship with the first file, wherein the social network system provides a platform for storing and sharing conversation, and each conversation includes a conversation and associated information; generating, by a system server, a list of inquiries based on the plurality of conversations, wherein the list of inquiries includes search terms used in a search that identified the first file and the plurality of conversations, thereby establishing the relationship between the first file and the plurality of conversations by text analysis or filtering; providing, by the system server, the list of inquiries to at least one sender of the first file, wherein the sender is provided with write-privilege to the first file; receiving from the at least one sender at least one response to the list of inquiries; selecting a subset of the plurality of conversations based on the at least one response; storing information related to the selected subset of the plurality of conversations for access if the first file is selected; providing, by the system server, the selected subset of the plurality of conversations to a user that selects the first file; and identifying the at least one sender to the user. | 1. A method of identifying conversations of a social network system having relevance to a first file, comprising: identifying a plurality of conversations within the social network system, wherein the plurality of conversations each have a relationship with the first file, wherein the social network system provides a platform for storing and sharing conversation, and each conversation includes a conversation and associated information; generating, by a system server, a list of inquiries based on the plurality of conversations, wherein the list of inquiries includes search terms used in a search that identified the first file and the plurality of conversations, thereby establishing the relationship between the first file and the plurality of conversations by text analysis or filtering; providing, by the system server, the list of inquiries to at least one sender of the first file, wherein the sender is provided with write-privilege to the first file; receiving from the at least one sender at least one response to the list of inquiries; selecting a subset of the plurality of conversations based on the at least one response; storing information related to the selected subset of the plurality of conversations for access if the first file is selected; providing, by the system server, the selected subset of the plurality of conversations to a user that selects the first file; and identifying the at least one sender to the user. 29. The method of claim 1 , wherein the list of inquiries is provided to a plurality of senders, and responses received from each of the plurality of senders are compiled for selecting the subset of the plurality of conversations. | 0.831378 |
7,584,414 | 1 | 2 | 1. A method of exporting a report generated by an application to an alternate format, the method comprising the steps of: (a) generating at least one report including data from a database; (b) generating at least one report document from the at least one report, the at least one report document having a plurality of rows, at least one of the plurality of rows containing no report document objects therein, and at least one of the plurality of rows containing one or more report document objects therein; (c) establishing a row pointer to each one of the plurality of rows in an iterative fashion; (d) during the time that the row pointer is established with respect to each one of the plurality of rows, determining if the row pointed to by the row pointer does not include any report document objects contained therein and, if so, then immediately serializing that row to an extensible markup language document before the row pointer is established to another one of the plurality of rows; and (e) during the time that the row pointer is established with respect to each one of the plurality of rows, determining if a row pointed to by the row pointer includes one or more report document objects and, if so, then converting the one or more report document objects to a corresponding number of extensible markup language schema definition objects and then immediately serializing and de-allocating the extensible markup language schema definition objects to the extensible markup language document before the row pointer is established to another one of the plurality of rows. | 1. A method of exporting a report generated by an application to an alternate format, the method comprising the steps of: (a) generating at least one report including data from a database; (b) generating at least one report document from the at least one report, the at least one report document having a plurality of rows, at least one of the plurality of rows containing no report document objects therein, and at least one of the plurality of rows containing one or more report document objects therein; (c) establishing a row pointer to each one of the plurality of rows in an iterative fashion; (d) during the time that the row pointer is established with respect to each one of the plurality of rows, determining if the row pointed to by the row pointer does not include any report document objects contained therein and, if so, then immediately serializing that row to an extensible markup language document before the row pointer is established to another one of the plurality of rows; and (e) during the time that the row pointer is established with respect to each one of the plurality of rows, determining if a row pointed to by the row pointer includes one or more report document objects and, if so, then converting the one or more report document objects to a corresponding number of extensible markup language schema definition objects and then immediately serializing and de-allocating the extensible markup language schema definition objects to the extensible markup language document before the row pointer is established to another one of the plurality of rows. 2. The method of claim 1 , wherein the report document contains at least one report document object type of at least one of values, names, and styles. | 0.666667 |
5,579,466 | 27 | 28 | 27. A method in a computer system for implementing a rich text edit field in a dialog window, the computer system having an application program having code for inserting and displaying data in document structures, the method comprising the computer-implemented steps of: creating a dialog window; allocating space in the dialog window for the rich text edit field; allocating a document structure for storing data that is placed in the rich text edit field; associating the allocated document structure with the rich text edit field; initializing the allocated document structure according to initial format characteristics; displaying the dialog window with the rich text edit field; in response to receiving user input of data for the rich text edit field, invoking the code for inserting and displaying data; and under control of the invoked code for inserting and displaying data, inserting the received data in the allocated document structure associated with the rich text edit field, such that the received data is displayed in the rich text edit field according to the initial format characteristics. | 27. A method in a computer system for implementing a rich text edit field in a dialog window, the computer system having an application program having code for inserting and displaying data in document structures, the method comprising the computer-implemented steps of: creating a dialog window; allocating space in the dialog window for the rich text edit field; allocating a document structure for storing data that is placed in the rich text edit field; associating the allocated document structure with the rich text edit field; initializing the allocated document structure according to initial format characteristics; displaying the dialog window with the rich text edit field; in response to receiving user input of data for the rich text edit field, invoking the code for inserting and displaying data; and under control of the invoked code for inserting and displaying data, inserting the received data in the allocated document structure associated with the rich text edit field, such that the received data is displayed in the rich text edit field according to the initial format characteristics. 28. The method of claim 27, further comprising the steps of: without closing the dialog window, determining a modified format for the rich text edit field; and re-displaying the data in the rich text edit field according to the modified format. | 0.658263 |
8,359,202 | 10 | 11 | 10. The method of claim 1 , wherein the first character has a first mood and a second mood. | 10. The method of claim 1 , wherein the first character has a first mood and a second mood. 11. The method of claim 10 , wherein a voice model associated with the first mood for the first character differs from a voice model associated with the second mood for the first character. | 0.743207 |
7,636,883 | 1 | 9 | 1. A computer implemented method for annotating a displayable electronic document at a graphical user interface (GUI), the displayable electronic document comprising any one of a displayable image or text, the GUI comprising a GUI display, the method comprising the steps of: responsive to a first GUI action, selecting the displayable electronic document to be annotated, the displayable electronic document to be annotated selected from displayable electronic documents stored in a displayable electronic document database; and displaying the displayable electronic image at the GUI display; responsive to a second GUI action, selecting an annotation form from a plurality of annotation forms, the selection based on one or more predetermined parameters, the annotation form comprising one or more user prompts for annotation data; and displaying the selected annotation form at the GUI display, the displayed annotation form comprising one or more user prompts, the user prompts prompting a user for annotation data; responsive to a third user GUI action comprising moving a cursor to a position within the displayed electronic image, selecting the position of the cursor within the displayed image independent of content of the displayable electronic document, creating information associating the annotation form with the selected position of the selected displayable electronic document to be annotated; responsive to an event associated with the annotation form, performing by a computer program, an analytic action on the displayed electronic image by way of a first annotation runtime program, the analytic action analyzing characteristics of the displayed electronic image to produce an analytic result; receiving user provided annotation data associated with one of the one or more of said displayed user prompts; and storing in an annotation database each of the received annotation data, the selected position within the selected displayable electronic document and the produced analytic result; and responsive to a user fourth GUI action subsequent to said storing the annotation data, the user fourth GUI action selecting said position within the selected displayable electronic document, performing the steps comprising: finding in the annotation database, the annotation data; and displaying the found annotation data. | 1. A computer implemented method for annotating a displayable electronic document at a graphical user interface (GUI), the displayable electronic document comprising any one of a displayable image or text, the GUI comprising a GUI display, the method comprising the steps of: responsive to a first GUI action, selecting the displayable electronic document to be annotated, the displayable electronic document to be annotated selected from displayable electronic documents stored in a displayable electronic document database; and displaying the displayable electronic image at the GUI display; responsive to a second GUI action, selecting an annotation form from a plurality of annotation forms, the selection based on one or more predetermined parameters, the annotation form comprising one or more user prompts for annotation data; and displaying the selected annotation form at the GUI display, the displayed annotation form comprising one or more user prompts, the user prompts prompting a user for annotation data; responsive to a third user GUI action comprising moving a cursor to a position within the displayed electronic image, selecting the position of the cursor within the displayed image independent of content of the displayable electronic document, creating information associating the annotation form with the selected position of the selected displayable electronic document to be annotated; responsive to an event associated with the annotation form, performing by a computer program, an analytic action on the displayed electronic image by way of a first annotation runtime program, the analytic action analyzing characteristics of the displayed electronic image to produce an analytic result; receiving user provided annotation data associated with one of the one or more of said displayed user prompts; and storing in an annotation database each of the received annotation data, the selected position within the selected displayable electronic document and the produced analytic result; and responsive to a user fourth GUI action subsequent to said storing the annotation data, the user fourth GUI action selecting said position within the selected displayable electronic document, performing the steps comprising: finding in the annotation database, the annotation data; and displaying the found annotation data. 9. The method according to claim 1 , wherein the selecting the annotation form step is performed by an element consisting of any one of a GUI widget, a second runtime program or an extensible DHTML engine. | 0.806238 |
9,251,244 | 16 | 17 | 16. The non-transitory computer readable storage medium of claim 14 , further comprising instructions for: providing one or more logs that contain information about the search criteria and the set of results returned by the search engine. | 16. The non-transitory computer readable storage medium of claim 14 , further comprising instructions for: providing one or more logs that contain information about the search criteria and the set of results returned by the search engine. 17. The non-transitory computer readable storage medium of claim 16 , further comprising instructions for: analyzing the one or more logs to deter mine whether synonyms should be added to a search term in the search criteria. | 0.5 |
9,164,965 | 8 | 11 | 8. A method for visualizing a system comprising: obtaining a context managed entity from a client system, wherein the context managed entity corresponds to software executing on a computer system; querying a managed entity repository to obtain a plurality of managed entities linked to the context managed entity, wherein the plurality of managed entities comprises a software managed entity, a first hardware managed entity, and a second hardware managed entity; generating a node in a topology model object for the plurality of managed entities; generating a plurality of link objects in the topology model object for a plurality of relationships between the plurality of managed entities, wherein the plurality of link objects comprises a containment link object; and rendering a topology graph comprising each node and each link object in the topology model object by arranging the nodes into tiers; and expanding the containment link object in the topology graph to show a containment link between the first hardware managed entity nesting as a hardware component within the second hardware managed entity. | 8. A method for visualizing a system comprising: obtaining a context managed entity from a client system, wherein the context managed entity corresponds to software executing on a computer system; querying a managed entity repository to obtain a plurality of managed entities linked to the context managed entity, wherein the plurality of managed entities comprises a software managed entity, a first hardware managed entity, and a second hardware managed entity; generating a node in a topology model object for the plurality of managed entities; generating a plurality of link objects in the topology model object for a plurality of relationships between the plurality of managed entities, wherein the plurality of link objects comprises a containment link object; and rendering a topology graph comprising each node and each link object in the topology model object by arranging the nodes into tiers; and expanding the containment link object in the topology graph to show a containment link between the first hardware managed entity nesting as a hardware component within the second hardware managed entity. 11. The method of claim 8 , wherein the managed entity repository comprises a managed entity table, an associations table, and a dynamic state table. | 0.641827 |
6,154,212 | 1 | 10 | 1. A method of providing a network interface comprising the steps of: generating a network view portion of the network interface using at least one of a plurality of software components programmed in a first programming language, each of at least a subset of the components programmed in the first programming language including a network base class for managing view operations and interactions with a database, and a display class for managing display operations; and generating a user interface portion of the network interface using at least one of a plurality of software components programmed in a second programming language at a higher level than the first programming language, each of at least a subset of the components programmed in the second programming language including a standard interface corresponding to a given one of the network base classes, and a special interface associated with a particular type of network view. | 1. A method of providing a network interface comprising the steps of: generating a network view portion of the network interface using at least one of a plurality of software components programmed in a first programming language, each of at least a subset of the components programmed in the first programming language including a network base class for managing view operations and interactions with a database, and a display class for managing display operations; and generating a user interface portion of the network interface using at least one of a plurality of software components programmed in a second programming language at a higher level than the first programming language, each of at least a subset of the components programmed in the second programming language including a standard interface corresponding to a given one of the network base classes, and a special interface associated with a particular type of network view. 10. The method of claim 1 wherein the network view portion of the network interface includes a view providing a conformal warping of a two-dimensional network map onto a three-dimensional object. | 0.841721 |
9,406,029 | 1 | 2 | 1. At least one non-transitory machine-readable medium comprising a set of instructions that, in response to being executed on a computing device, cause the computing device to: identify, in response to a triggering event associated with a workload of a storage system, a proposed solution comprising one or more actions to be performed; predict, for the proposed solution, a value of an output metric for the workload using a mapping function for the output metric based on a plurality of input metric values for a foreground workload and a plurality of input metric values for a set of background workloads of the storage system, the mapping function produced using a machine learning algorithm; and generate an evaluation value for the proposed solution based on the predicted value of the output metric. | 1. At least one non-transitory machine-readable medium comprising a set of instructions that, in response to being executed on a computing device, cause the computing device to: identify, in response to a triggering event associated with a workload of a storage system, a proposed solution comprising one or more actions to be performed; predict, for the proposed solution, a value of an output metric for the workload using a mapping function for the output metric based on a plurality of input metric values for a foreground workload and a plurality of input metric values for a set of background workloads of the storage system, the mapping function produced using a machine learning algorithm; and generate an evaluation value for the proposed solution based on the predicted value of the output metric. 2. The at least one non-transitory machine-readable medium of claim 1 , comprising a set of instructions that, in response to being executed on the computing device, cause the computing device to: predict, for the proposed solution, respective values of each of a plurality of output metrics for the workload using respective mapping functions for each of the plurality of output metrics; and generate the evaluation value for the proposed solution based on the respective predicted values of each of the plurality of output metrics. | 0.5 |
9,146,967 | 1 | 9 | 1. A method of processing a query in a multi-stage query processing system having one or more processors and memory storing one or more programs for execution by the one or more processors to perform the method comprising: performing a first stage processing of a query, including: retrieving a first set of document identifiers from an index in response to one or more query terms; generating a first set of relevancy scores for a first set of compressed documents corresponding to at least a subset of the first set of document identifiers based on one or more of: presence of query terms, term frequency, and document popularity; and storing the first set of relevancy scores in the memory; performing a second stage processing of the query, including: generating a second set of relevancy scores for the documents in the first set of compressed documents based on one or more of: a list of token positions for one or more query terms in the query, distances between query terms in the documents, attributes of tokens in the documents, and text that appears around a query term used in a document of the first set of documents; and storing the second set of relevancy scores in the memory; reading the first and second set of relevancy scores from the memory, and generating an ordered list of documents for further processing based on the first and second set of relevancy scores; automatically generating additional query terms from the documents in the ordered list of documents; formulating a new query using the additional query terms; processing the new query to retrieve a second set of document identifiers from the index and to generate a third set of relevancy scores based at least in part on the additional query terms; and using the third set of relevancy scores to select a set of top documents for presentation to the user. | 1. A method of processing a query in a multi-stage query processing system having one or more processors and memory storing one or more programs for execution by the one or more processors to perform the method comprising: performing a first stage processing of a query, including: retrieving a first set of document identifiers from an index in response to one or more query terms; generating a first set of relevancy scores for a first set of compressed documents corresponding to at least a subset of the first set of document identifiers based on one or more of: presence of query terms, term frequency, and document popularity; and storing the first set of relevancy scores in the memory; performing a second stage processing of the query, including: generating a second set of relevancy scores for the documents in the first set of compressed documents based on one or more of: a list of token positions for one or more query terms in the query, distances between query terms in the documents, attributes of tokens in the documents, and text that appears around a query term used in a document of the first set of documents; and storing the second set of relevancy scores in the memory; reading the first and second set of relevancy scores from the memory, and generating an ordered list of documents for further processing based on the first and second set of relevancy scores; automatically generating additional query terms from the documents in the ordered list of documents; formulating a new query using the additional query terms; processing the new query to retrieve a second set of document identifiers from the index and to generate a third set of relevancy scores based at least in part on the additional query terms; and using the third set of relevancy scores to select a set of top documents for presentation to the user. 9. The method of claim 1 , wherein retrieving the first set of document identifiers comprises using the index to produce a list of token positions for the one or more query terms and accessing a map to produce a set of documents identifiers corresponding to the token positions. | 0.569659 |
4,520,501 | 41 | 45 | 41. The apparatus of claim 39, wherein the presenting means comprises means for presenting the patterns as illuminated areas on a display. | 41. The apparatus of claim 39, wherein the presenting means comprises means for presenting the patterns as illuminated areas on a display. 45. The apparatus of claim 41, wherein the display comprises means for producing electrical signals representative of the patterns displayed thereon for driving another presenting means. | 0.5 |
8,412,782 | 1 | 2 | 1. An email client system for improving efficiency of email discussion for a conversation among a plurality of authors within an email client operating at a user computer having at least a processing device, a user input device and a display, said email client system comprising: an email tag filter for automatically filtering a special tag in email contents, said special tag being inserted at a tag composition component of an email client, extracting original email contents, and identifying tag related information which includes at least an author name corresponding to the extracted original email contents; a content style setting component for automatically receiving the extracted original email contents and tag related information, keeping the tag related information fed by the email tag filter, identifying which part of the extracted original email contents is written by which author according to the tag information, and assigning a style to corresponding extracted original email contents using a user customization style for each author, wherein contents for each of said plurality of authors is assigned a different style; and a display generation component for generating and displaying the email discussion in a single generated email for a conversation among a plurality of authors wherein the email discussion is generated in a single email by organizing extracted original email contents with a different style for each of said plurality of authors. | 1. An email client system for improving efficiency of email discussion for a conversation among a plurality of authors within an email client operating at a user computer having at least a processing device, a user input device and a display, said email client system comprising: an email tag filter for automatically filtering a special tag in email contents, said special tag being inserted at a tag composition component of an email client, extracting original email contents, and identifying tag related information which includes at least an author name corresponding to the extracted original email contents; a content style setting component for automatically receiving the extracted original email contents and tag related information, keeping the tag related information fed by the email tag filter, identifying which part of the extracted original email contents is written by which author according to the tag information, and assigning a style to corresponding extracted original email contents using a user customization style for each author, wherein contents for each of said plurality of authors is assigned a different style; and a display generation component for generating and displaying the email discussion in a single generated email for a conversation among a plurality of authors wherein the email discussion is generated in a single email by organizing extracted original email contents with a different style for each of said plurality of authors. 2. The email system of claim 1 , wherein: said email tag filter is implemented by a general string matching algorithm using scripts on the email client. | 0.91224 |
3,946,157 | 4 | 5 | 4. A speech recognition system for controlling a machine, said system receiving words, said words being classified in a reduced plurality of three through twelve phoneme classes, said system including: a. input means for receiving said words; b. first means responsive to said words indicating short-time energy values of said words, said first means producing a first output signal representing rising temporal energy flanks, nearly constant energy levels and zero levels of said words; c. second means responsive to said words and producing a second output signal representing a steepness of said rising temporal energy flanks exceeding a determined limit value; d. third means responsive to said words producing at least three output signals representing said short-time energy values distributed in at least three frequency ranges; e. first logic means i. responsive to said first output signal segmenting said words into phonetic elements, indicated by both the rising temporal energy flanks and the ends of the nearly constant energy levels, and indicating an end of each word only by a zero level which exceeds a determined value of time duration producing a third output signal, ii. responsive to said second output signal separating a class of the plosive phonetic elements from the class of the fricative phonetic elements and producing a fourth output signal, iii. responsive to said three output signals for detecting the plosive class, the fricative class and at least one vowel class and producing a fifth, sixth and seventh output signal; f. second logic means detecting a sequence of the occurrence of said phonetic elements within one word; and g. output means for controlling a machine as result of the detected words. | 4. A speech recognition system for controlling a machine, said system receiving words, said words being classified in a reduced plurality of three through twelve phoneme classes, said system including: a. input means for receiving said words; b. first means responsive to said words indicating short-time energy values of said words, said first means producing a first output signal representing rising temporal energy flanks, nearly constant energy levels and zero levels of said words; c. second means responsive to said words and producing a second output signal representing a steepness of said rising temporal energy flanks exceeding a determined limit value; d. third means responsive to said words producing at least three output signals representing said short-time energy values distributed in at least three frequency ranges; e. first logic means i. responsive to said first output signal segmenting said words into phonetic elements, indicated by both the rising temporal energy flanks and the ends of the nearly constant energy levels, and indicating an end of each word only by a zero level which exceeds a determined value of time duration producing a third output signal, ii. responsive to said second output signal separating a class of the plosive phonetic elements from the class of the fricative phonetic elements and producing a fourth output signal, iii. responsive to said three output signals for detecting the plosive class, the fricative class and at least one vowel class and producing a fifth, sixth and seventh output signal; f. second logic means detecting a sequence of the occurrence of said phonetic elements within one word; and g. output means for controlling a machine as result of the detected words. 5. A speech recognition apparatus related to claim 4, wherein said third means embody additional circuit means to determine a class of nasal phonetic elements. | 0.85 |
10,121,286 | 31 | 34 | 31. A system for synchronizing an annotated 3D computer-aided design (CAD) model and a 2D drawing, the system comprising: a communications interface configured to receive a 2D drawing based on an annotated 3D CAD model of a physical part or assembly; and a processing circuit configured to: identify supplemental content in the 2D drawing that is not included in the annotated 3D CAD model; and modify the annotated 3D CAD model to include 2D drawing parameters that include the supplemental content from the 2D drawing, wherein the 2D drawing parameters provide instructions for reproducing the 2D drawing including the supplemental content, such that the 2D drawing is not saved. | 31. A system for synchronizing an annotated 3D computer-aided design (CAD) model and a 2D drawing, the system comprising: a communications interface configured to receive a 2D drawing based on an annotated 3D CAD model of a physical part or assembly; and a processing circuit configured to: identify supplemental content in the 2D drawing that is not included in the annotated 3D CAD model; and modify the annotated 3D CAD model to include 2D drawing parameters that include the supplemental content from the 2D drawing, wherein the 2D drawing parameters provide instructions for reproducing the 2D drawing including the supplemental content, such that the 2D drawing is not saved. 34. The system of claim 31 , wherein the processing circuit is configured to use the 2D drawing parameters within the annotated 3D CAD model to re-create the 2D drawing including the supplemental content. | 0.711048 |
9,959,300 | 13 | 14 | 13. A computer system comprising: a computer processor; and a non-transitory computer-readable storage medium storing computer instructions executed by the computer processor and causing the computer processor to perform operations comprising: providing a first dialog control specific to an application executed on the user device to a display on the user device; outputting an indexing preferences dialog control to the display of the user device in addition to the first dialog control and responsive to an interruption of normal processing of an operating system associated with displaying the first dialog control, the indexing preferences dialog control being a different dialog control than the first dialog control and displayed with the first dialog control; receiving, from the indexing preferences dialog control displayed on the user device, a user selection of indexing preferences specific to a file type associated with the application, the indexing preferences including: options for indexing events, each event comprising a user interaction with an article of the file type using the application; an indication to index events associated with articles of the file type; and an indication as to a maximum storage space an indexed event may occupy; and generating an index of the events based at least in part on the indexing preferences selected by the user, wherein at least some of the events are indexed and stored in real time upon the occurrences of the events. | 13. A computer system comprising: a computer processor; and a non-transitory computer-readable storage medium storing computer instructions executed by the computer processor and causing the computer processor to perform operations comprising: providing a first dialog control specific to an application executed on the user device to a display on the user device; outputting an indexing preferences dialog control to the display of the user device in addition to the first dialog control and responsive to an interruption of normal processing of an operating system associated with displaying the first dialog control, the indexing preferences dialog control being a different dialog control than the first dialog control and displayed with the first dialog control; receiving, from the indexing preferences dialog control displayed on the user device, a user selection of indexing preferences specific to a file type associated with the application, the indexing preferences including: options for indexing events, each event comprising a user interaction with an article of the file type using the application; an indication to index events associated with articles of the file type; and an indication as to a maximum storage space an indexed event may occupy; and generating an index of the events based at least in part on the indexing preferences selected by the user, wherein at least some of the events are indexed and stored in real time upon the occurrences of the events. 14. The system of claim 13 , wherein the indexing preferences include an indication of content within an article to index. | 0.5 |
8,606,807 | 3 | 4 | 3. The method of claim 2 , further comprising, for each of the existing tags associated with the tag type, displaying within a first display area of the main window resources representing a number of document pages that have been tagged by each existing tag. | 3. The method of claim 2 , further comprising, for each of the existing tags associated with the tag type, displaying within a first display area of the main window resources representing a number of document pages that have been tagged by each existing tag. 4. The method of claim 3 , further comprising: displaying in a second display area of the main window a list of one or more communities in which the selected tag has been published, wherein each of the one or more communities can be selectable or unselectable. | 0.5 |
4,437,155 | 7 | 8 | 7. The method as claimed in claim 6 wherein said means for linking said segments of data in an age chain comprises means for storing a segment descriptor table having therein a segment descriptor corresponding to each data segment resident in said cache store, each segment descriptor including a backward age link field pointing to the segment descriptor for the next least recently accessed data segment in the cache store and a forward age link field pointing to the next most recently accessed data segment in said cache store, said step of assigning an age which is intermediate the most recently and least recently accessed ages comprising adjusting the age links in said chain so as to link the formed segment descriptor in said age chain as the next most recently accessed relative to the last segment descriptor assigned said intermediate age. | 7. The method as claimed in claim 6 wherein said means for linking said segments of data in an age chain comprises means for storing a segment descriptor table having therein a segment descriptor corresponding to each data segment resident in said cache store, each segment descriptor including a backward age link field pointing to the segment descriptor for the next least recently accessed data segment in the cache store and a forward age link field pointing to the next most recently accessed data segment in said cache store, said step of assigning an age which is intermediate the most recently and least recently accessed ages comprising adjusting the age links in said chain so as to link the formed segment descriptor in said age chain as the next most recently accessed relative to the last segment descriptor assigned said intermediate age. 8. The improvement as claimed in claim 7 wherein said step of assigning an age which is intermediate the most recently and least recently accessed ages comprises adjusting the age links in the segment descriptors for the next most recently accessed and next least recently accessed data segments, relative to the data segment being assigned said intermediate age, whereby an age field in the segment descriptor for said next most recently accessed and next least recently accessed data segments point to each other and they are adjacent entries in said age chain. | 0.5 |
9,948,586 | 8 | 11 | 8. A computer program product for information sharing, the computer program product comprising: one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices, the program instructions comprising: program instructions to receive, by a computer, user created content from a user; program instructions to extract information, comprising one or more types of information, by a computer, from the user created content, wherein at least one type of information is semantic concepts; program instructions to determine, by a computer, one or more messaging platforms on which to share the user created content, based on at least a correlation between: the extracted information; and historical data correlating the extracted information with what messaging platforms the user created content was sent to; program instructions to notify the user of the one or more determined messaging platforms; program instructions to receive a selection from the user of one of the one or more determined messaging platforms; and program instructions to transmit the user created content to the selected messaging platform. | 8. A computer program product for information sharing, the computer program product comprising: one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices, the program instructions comprising: program instructions to receive, by a computer, user created content from a user; program instructions to extract information, comprising one or more types of information, by a computer, from the user created content, wherein at least one type of information is semantic concepts; program instructions to determine, by a computer, one or more messaging platforms on which to share the user created content, based on at least a correlation between: the extracted information; and historical data correlating the extracted information with what messaging platforms the user created content was sent to; program instructions to notify the user of the one or more determined messaging platforms; program instructions to receive a selection from the user of one of the one or more determined messaging platforms; and program instructions to transmit the user created content to the selected messaging platform. 11. The computer program product of claim 8 , further comprising program instructions to update the historical data with information related to the user selected messaging platform and the extracted information. | 0.758581 |
9,182,952 | 1 | 23 | 1. A method comprising: capturing an image of a scene that includes a portion of a diagram representing functional blocks, wherein the functional blocks include at least a first functional block associated with a first computer operation; applying functional block recognition rules to image data of the image to recognize the functional blocks; determining whether the functional blocks comply with functional block syntax rules, wherein the functional block syntax rules indicate a hierarchy of operations associated with the functional blocks; and computer-generating a functional graph corresponding to the diagram based on the functional blocks complying with the functional block syntax rules, wherein the functional graph includes a graphical representation of the functional blocks. | 1. A method comprising: capturing an image of a scene that includes a portion of a diagram representing functional blocks, wherein the functional blocks include at least a first functional block associated with a first computer operation; applying functional block recognition rules to image data of the image to recognize the functional blocks; determining whether the functional blocks comply with functional block syntax rules, wherein the functional block syntax rules indicate a hierarchy of operations associated with the functional blocks; and computer-generating a functional graph corresponding to the diagram based on the functional blocks complying with the functional block syntax rules, wherein the functional graph includes a graphical representation of the functional blocks. 23. The method of claim 1 , wherein the image is captured at a mobile device, and further comprising generating, at a computer that is distinct from the mobile device, program code corresponding to the functional graph. | 0.834091 |
9,619,079 | 12 | 13 | 12. The non-transitory computer readable storage medium of claim 8 , wherein the executable program instructions further cause the electronic device to: if it is determined that the electronic device is in the first state, display information on the touch-sensitive display in a first display state, wherein the first display state is a regular power mode; and if it is determined that the electronic device is in the second state, enter into a second display state different from the first display state, wherein the second display state is a low-power mode. | 12. The non-transitory computer readable storage medium of claim 8 , wherein the executable program instructions further cause the electronic device to: if it is determined that the electronic device is in the first state, display information on the touch-sensitive display in a first display state, wherein the first display state is a regular power mode; and if it is determined that the electronic device is in the second state, enter into a second display state different from the first display state, wherein the second display state is a low-power mode. 13. The non-transitory computer readable storage medium of claim 12 , wherein the executable program instructions further cause the electronic device to: in response to detecting the first change in the received data relating to device movement and the received data relating to device orientation, change from the second display state to the first display state. | 0.592135 |
7,917,496 | 7 | 8 | 7. The method as recited in claim 6 , wherein the first word and the second word co-occur in one or more blocks of text in the plurality of documents, and wherein the count is based at least in part on a third quantity of blocks of text in the plurality of documents in which the first word and the second word co-occur. | 7. The method as recited in claim 6 , wherein the first word and the second word co-occur in one or more blocks of text in the plurality of documents, and wherein the count is based at least in part on a third quantity of blocks of text in the plurality of documents in which the first word and the second word co-occur. 8. The method as recited in claim 7 , wherein the first quantity of occurrences of the first word for the plurality of documents comprises a quantity of blocks of text that contain the first word, and the second quantity of occurrences of the second word for the plurality of documents comprises a quantity of blocks of text that contain the second word; wherein the count accounts for an overcorrelation of the first word and the second word in same blocks of text in the plurality of documents. | 0.5 |
9,953,331 | 1 | 3 | 1. A non-transitory computer readable medium having instructions which, when executed by a processor, causes the processor to perform a process for extending attributes for a predictive analysis engine, the process comprising: defining one or more extended attributes of a database entity, the database entity corresponding to a database table, the database table comprising one or more original attribute columns and one or more unused extension columns for the database entity, the one or more unused extension columns providing extensibility of the database entity for the one or more extended attributes not defined in the one or more original attribute columns of the database table of the database entity; receiving from an interface a definition of the one or more extended attributes for the database entity; modifying a metadata schema of the database table using the one or more extended attributes, comprising: modifying a first version of the database entity to a second version of the database entity, the second version of the database entity comprising the one or more extended attributes that do not exist in the first version, by: reviewing the one or more unused extension columns for the first version of the database entity, identifying unused extension columns from among the one or more unused extension columns of the database table for the first version of the database entity, and editing the metadata schema for the database table to map the identified unused extension columns from the database table to the one or more extended attributes to generate the second version of the database entity within the database table; recognizing, by the predictive analysis engine, the one or more extended attributes of the database entity; and executing the predictive analysis engine to generate recommendations based at least in part on the one or more extended attributes by using one or more new rules or one or more new models, the one or more new rules or the one or more new models comprise the one or more extended attributes. | 1. A non-transitory computer readable medium having instructions which, when executed by a processor, causes the processor to perform a process for extending attributes for a predictive analysis engine, the process comprising: defining one or more extended attributes of a database entity, the database entity corresponding to a database table, the database table comprising one or more original attribute columns and one or more unused extension columns for the database entity, the one or more unused extension columns providing extensibility of the database entity for the one or more extended attributes not defined in the one or more original attribute columns of the database table of the database entity; receiving from an interface a definition of the one or more extended attributes for the database entity; modifying a metadata schema of the database table using the one or more extended attributes, comprising: modifying a first version of the database entity to a second version of the database entity, the second version of the database entity comprising the one or more extended attributes that do not exist in the first version, by: reviewing the one or more unused extension columns for the first version of the database entity, identifying unused extension columns from among the one or more unused extension columns of the database table for the first version of the database entity, and editing the metadata schema for the database table to map the identified unused extension columns from the database table to the one or more extended attributes to generate the second version of the database entity within the database table; recognizing, by the predictive analysis engine, the one or more extended attributes of the database entity; and executing the predictive analysis engine to generate recommendations based at least in part on the one or more extended attributes by using one or more new rules or one or more new models, the one or more new rules or the one or more new models comprise the one or more extended attributes. 3. The non-transitory computer readable medium of claim 1 , wherein the one or more new rules identify characteristics of customers that are more likely to lead to a sale of a particular product. | 0.865145 |
7,831,584 | 2 | 3 | 2. The method of claim 1 wherein said (a), (b), (c), and (d) categories of said ranking database are adaptively weighted. | 2. The method of claim 1 wherein said (a), (b), (c), and (d) categories of said ranking database are adaptively weighted. 3. The method of claim 2 wherein an adaptive weighting is reduced where the amount of data in one of said categories is less than a threshold amount. | 0.506623 |
7,774,291 | 11 | 13 | 11. A knowledge management computer program product, the computer program product comprising a computer usable storage medium having computer-readable program code embodied in the medium, the computer-readable program code comprising: computer-readable program code that is configured to provide an associative memory system; and computer-readable program code that is configured to observe associations among user queries, results of user queries and user evaluations of results of user queries for a plurality of users, into the associative memory system. | 11. A knowledge management computer program product, the computer program product comprising a computer usable storage medium having computer-readable program code embodied in the medium, the computer-readable program code comprising: computer-readable program code that is configured to provide an associative memory system; and computer-readable program code that is configured to observe associations among user queries, results of user queries and user evaluations of results of user queries for a plurality of users, into the associative memory system. 13. A knowledge management computer program product according to claim 11 wherein the user queries are defined by a plurality of entities that are stored in the knowledge base, the results of user queries are defined by associations among the entities that are stored in the knowledge base and wherein the evaluations of results of user queries are defined by positive and/or negative evaluations of the associations among the entities. | 0.5 |
8,078,554 | 14 | 19 | 14. A method for knowledge-based interpretable predictive modeling of patients, the method comprising: training, with machine training using training data for a plurality of previous lung cancer patients, a graphic model to predict survivability of lung cancer based on relationships between variables from a lung cancer expert, the training data including previous patient values for the variables, the variables including the survivability; applying, with a processor, current patient values of the variables for a current lung cancer patient to the graphic model, the graphic model configured to predict even with one of the variables not having a current patient value as a function of the relationships; displaying a representation of the graphic model, the representation showing the variables and the relationships remaining after training; and displaying the survivability for the current lung cancer patient predicted by the graphic model, wherein the variables comprise tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and the survivability. | 14. A method for knowledge-based interpretable predictive modeling of patients, the method comprising: training, with machine training using training data for a plurality of previous lung cancer patients, a graphic model to predict survivability of lung cancer based on relationships between variables from a lung cancer expert, the training data including previous patient values for the variables, the variables including the survivability; applying, with a processor, current patient values of the variables for a current lung cancer patient to the graphic model, the graphic model configured to predict even with one of the variables not having a current patient value as a function of the relationships; displaying a representation of the graphic model, the representation showing the variables and the relationships remaining after training; and displaying the survivability for the current lung cancer patient predicted by the graphic model, wherein the variables comprise tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and the survivability. 19. The method of claim 14 further comprising outputting confidence information with the survivability. | 0.820557 |
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