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8,880,390 | 14 | 16 | 14. A computer program embedded in a non-transitory computer-readable storage medium, when executed by one or more processors, for providing internet content, the computer program comprising: program instructions for receiving a plurality of features by a classifier that is operable to determine a probability of an availability of news for a sentence, wherein at least one of the features when found in the sentence increases a probability of the availability of news for the sentence, the sentence including one or more noun phrases and ending in a full stop; program instructions for determining by the classifier which sentences in a document are candidate sentences for having available news; program instructions for finding, for each candidate sentence, an associated news article when the associated news article exceeds a threshold of relevance to the candidate sentence, wherein finding the associated news article further includes: program instructions for performing, for each candidate sentence, a search on the candidate sentence to find potential news articles; program instructions for evaluating each potential news article against the candidate sentence; and program instructions for selecting a best potential news article based on the evaluation to be the associated news article when a score of the best potential news article exceeds a threshold score; program instructions for adding links in the document to the found associated news articles; and program instructions for displaying the document with the added links. | 14. A computer program embedded in a non-transitory computer-readable storage medium, when executed by one or more processors, for providing internet content, the computer program comprising: program instructions for receiving a plurality of features by a classifier that is operable to determine a probability of an availability of news for a sentence, wherein at least one of the features when found in the sentence increases a probability of the availability of news for the sentence, the sentence including one or more noun phrases and ending in a full stop; program instructions for determining by the classifier which sentences in a document are candidate sentences for having available news; program instructions for finding, for each candidate sentence, an associated news article when the associated news article exceeds a threshold of relevance to the candidate sentence, wherein finding the associated news article further includes: program instructions for performing, for each candidate sentence, a search on the candidate sentence to find potential news articles; program instructions for evaluating each potential news article against the candidate sentence; and program instructions for selecting a best potential news article based on the evaluation to be the associated news article when a score of the best potential news article exceeds a threshold score; program instructions for adding links in the document to the found associated news articles; and program instructions for displaying the document with the added links. 16. The computer program as recited in claim 14 , wherein the features are selected from a group consisting of a date, a verb in the past tense, a proper name in the sentence, a name of a celebrity, a name of a media outlet, a name of a crime, or a title. | 0.643855 |
7,912,288 | 1 | 5 | 1. A computer-implemented method of object detection and recognition comprising: receiving an image from an input device coupled to a processor and memory to undergo object detection and recognition; and generating, by the processor, a part label map for the received image, the part label map comprising, for each image element of the received image, a label indicating which of a plurality of parts that image element is assigned to, each part being a densely represented image area, wherein generating the part label map comprises at least: accessing a pre-specified classifier stored in the memory and configured to estimate a belief distribution over parts for each image element of the received image, the classifier formed during a training phase using a plurality of training images together with a mask for each training image indicating which pixels in the training image correspond to objects to be recognized and which correspond to background that is not required to be recognized, during the training phase, forming an initial part label map for a training image by dividing the image into a plurality of parts having a consistent pair-wise ordering such that the parts contiguously cover the image; using an inference algorithm stored in the memory to infer the part label map from a conditional random field by forcing a global part labeling which is substantially layout-consistent; and ensuring that the parts meet constraints related to image elements, the image elements being non-immediate neighbors. | 1. A computer-implemented method of object detection and recognition comprising: receiving an image from an input device coupled to a processor and memory to undergo object detection and recognition; and generating, by the processor, a part label map for the received image, the part label map comprising, for each image element of the received image, a label indicating which of a plurality of parts that image element is assigned to, each part being a densely represented image area, wherein generating the part label map comprises at least: accessing a pre-specified classifier stored in the memory and configured to estimate a belief distribution over parts for each image element of the received image, the classifier formed during a training phase using a plurality of training images together with a mask for each training image indicating which pixels in the training image correspond to objects to be recognized and which correspond to background that is not required to be recognized, during the training phase, forming an initial part label map for a training image by dividing the image into a plurality of parts having a consistent pair-wise ordering such that the parts contiguously cover the image; using an inference algorithm stored in the memory to infer the part label map from a conditional random field by forcing a global part labeling which is substantially layout-consistent; and ensuring that the parts meet constraints related to image elements, the image elements being non-immediate neighbors. 5. The computer-implemented method as claimed in claim 1 which is configured for detecting and recognizing images of partially occluded objects. | 0.827751 |
9,953,274 | 1 | 8 | 1. A ticket management system for dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users, the ticket management system comprising: one or more processors; and one or more memories coupled with the one or more processors, wherein the one or more processors and one or more memories comprising: a user characteristic definer that defines a user characteristic, the user characteristic being an estimation that a user is a human user, wherein: when the user corresponds to the user characteristic, the user is estimated to be a human user, and when the user does not correspond to the user characteristic, the user is estimated to be a robot user; a model engine that: identifies a model that enhances the user characteristic definer by detecting when users alter activity to cause the ticket management system to falsely characterize the users, the model including at least one machine-learning algorithm, automatically identifying, using the at least one machine-learning algorithm, an initial set of attributes based on the user characteristic, each attribute of the initial set of attributes being used to determine whether users correspond to the user characteristic, and at least one attribute of the initial set of attributes characterizing an engagement based on at least one of: interactions with the ticket management system, interactions with a social media system, membership to one or more groups, past event attendances, and information in user profiles; determines, for each user in a first set of users, a first value for each attribute in the initial set of attributes, the first value being indicative of an extent of the engagement; identifies a collection of users previously determined to correspond to the user characteristic; automatically detects whether a rate of increase of users in the collection of users meets or exceeds a threshold, the meeting or exceeding the threshold indicating a likelihood that one or more users have altered activity to cause the ticket management system to falsely characterize the one or more users as a human user; in response to detecting that the rate of increase meets or exceeds the threshold, automatically determines to modify the initial set of attributes, each attribute in the modified set of attributes being used to determine whether users correspond to the user characteristic; determines, for each user in a second set of users, a second value for each attribute in the modified set of attributes, the second value being indicative of an extent to which a user corresponds to the user characteristic; and continuously detecting, using the at least one learning algorithm, whether or not at least one user has altered activity to cause the ticket management system to falsely characterize the at least one user; a human user detector that: identifies a first group and a second group from amongst the first set of users based on the determined first values; and identifies a third group and a fourth group from amongst the second set of users based on the determined second values, wherein each user in the third group corresponds to the defined user characteristic, wherein each user in the fourth group does not correspond to the user characteristic, and wherein users in the second set of users are different from users in the first set of users; and a ticket engine that: determines that ticket offerings are to be biased to favor users in the third group over users in the fourth group; generates a digital presentation for each user in the second set of users based on the determination that ticket offerings are to be biased to favor users in the third group over users in the fourth group, the digital presentation being different for users in the third group than for users in the fourth group; and transmits the digital presentation to each user in the second set of users, the digital presentation causing an interface to be displayed when received at a user device associated with each user in the second set of users, the interface being configured to receive ticket requests when the interface is displayed at the user device associated with each user in the third group, and the interface being configured to not receive ticket requests when the interface is displayed at the user device associated with each user in the fourth group. | 1. A ticket management system for dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users, the ticket management system comprising: one or more processors; and one or more memories coupled with the one or more processors, wherein the one or more processors and one or more memories comprising: a user characteristic definer that defines a user characteristic, the user characteristic being an estimation that a user is a human user, wherein: when the user corresponds to the user characteristic, the user is estimated to be a human user, and when the user does not correspond to the user characteristic, the user is estimated to be a robot user; a model engine that: identifies a model that enhances the user characteristic definer by detecting when users alter activity to cause the ticket management system to falsely characterize the users, the model including at least one machine-learning algorithm, automatically identifying, using the at least one machine-learning algorithm, an initial set of attributes based on the user characteristic, each attribute of the initial set of attributes being used to determine whether users correspond to the user characteristic, and at least one attribute of the initial set of attributes characterizing an engagement based on at least one of: interactions with the ticket management system, interactions with a social media system, membership to one or more groups, past event attendances, and information in user profiles; determines, for each user in a first set of users, a first value for each attribute in the initial set of attributes, the first value being indicative of an extent of the engagement; identifies a collection of users previously determined to correspond to the user characteristic; automatically detects whether a rate of increase of users in the collection of users meets or exceeds a threshold, the meeting or exceeding the threshold indicating a likelihood that one or more users have altered activity to cause the ticket management system to falsely characterize the one or more users as a human user; in response to detecting that the rate of increase meets or exceeds the threshold, automatically determines to modify the initial set of attributes, each attribute in the modified set of attributes being used to determine whether users correspond to the user characteristic; determines, for each user in a second set of users, a second value for each attribute in the modified set of attributes, the second value being indicative of an extent to which a user corresponds to the user characteristic; and continuously detecting, using the at least one learning algorithm, whether or not at least one user has altered activity to cause the ticket management system to falsely characterize the at least one user; a human user detector that: identifies a first group and a second group from amongst the first set of users based on the determined first values; and identifies a third group and a fourth group from amongst the second set of users based on the determined second values, wherein each user in the third group corresponds to the defined user characteristic, wherein each user in the fourth group does not correspond to the user characteristic, and wherein users in the second set of users are different from users in the first set of users; and a ticket engine that: determines that ticket offerings are to be biased to favor users in the third group over users in the fourth group; generates a digital presentation for each user in the second set of users based on the determination that ticket offerings are to be biased to favor users in the third group over users in the fourth group, the digital presentation being different for users in the third group than for users in the fourth group; and transmits the digital presentation to each user in the second set of users, the digital presentation causing an interface to be displayed when received at a user device associated with each user in the second set of users, the interface being configured to receive ticket requests when the interface is displayed at the user device associated with each user in the third group, and the interface being configured to not receive ticket requests when the interface is displayed at the user device associated with each user in the fourth group. 8. The ticket management system for dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users as recited in claim 1 , wherein the attributes are indicative as to whether the users are fans of a performing entity. | 0.730983 |
8,897,495 | 13 | 14 | 13. A computer readable storage device for tracking a user, the computer readable storage medium having stored thereon computer executable instructions that, when executed on a computer, cause the computer to perform operations comprising: receiving a depth image; identifying an estimated location or position of a part of the user in the depth image; adjusting a model of the user based on the estimated location or position of the part of the user; and in response to a failure to identify a location or position of the part of the user in a second depth image, associating the part of the user with a portion of the second depth image based on a location or position of a default position of the part of the user. | 13. A computer readable storage device for tracking a user, the computer readable storage medium having stored thereon computer executable instructions that, when executed on a computer, cause the computer to perform operations comprising: receiving a depth image; identifying an estimated location or position of a part of the user in the depth image; adjusting a model of the user based on the estimated location or position of the part of the user; and in response to a failure to identify a location or position of the part of the user in a second depth image, associating the part of the user with a portion of the second depth image based on a location or position of a default position of the part of the user. 14. The computer readable storage device of claim 13 , further bearing computer-executable instructions that, when executed on the computer, cause the computer to perform operations comprising: mapping the model to an on-screen avatar. | 0.502119 |
8,942,963 | 1 | 10 | 1. A system, comprising: a memory configured to store a simulated model comprising a plurality of simulated components, each of the plurality of simulated components arranged in a component hierarchical graph such that a combination of the simulated components forms the simulated model; a processor that executes an inference engine configured to generate one or more redesign recommendations for at least one simulated component of the plurality of simulated components in the simulated model based on redesign recommendation rules and the at least one simulated component in the component hierarchical graph; and a display generator configured to display the redesign recommendations to a first user. | 1. A system, comprising: a memory configured to store a simulated model comprising a plurality of simulated components, each of the plurality of simulated components arranged in a component hierarchical graph such that a combination of the simulated components forms the simulated model; a processor that executes an inference engine configured to generate one or more redesign recommendations for at least one simulated component of the plurality of simulated components in the simulated model based on redesign recommendation rules and the at least one simulated component in the component hierarchical graph; and a display generator configured to display the redesign recommendations to a first user. 10. The system of claim 1 , wherein: each node of the component hierarchical graph comprises the at least one simulated component of the plurality of simulated components; and the each node of the component hierarchical graph represents a complete simulated model. | 0.779264 |
8,219,396 | 1 | 2 | 1. An apparatus for evaluating the performance of speech recognition comprising: a speech database configured to store a plurality of audio signal files corresponding to test speech signals; a driving unit configured to reproduce through a loud speaker the respective audio signal files of the test speech signals, the driving unit having the correct recognition results of the test speeches; a speech recognizer configured to execute a speech recognition of the reproduced audio signals in an actual environment where the speech recognizer is located to produce speech recognition results; and a performance evaluation module configured to evaluate the performance of the speech recognition by comparing the correct recognition results with the speech recognition results, wherein the performance evaluation module comprises: a speech-recognition evaluation unit configured to compare the correct recognition results of the test speeches and the speech recognition results and to produce a result corresponding to the accuracy of the speech recognition, and a speech-detection evaluation unit configured to obtain a plurality of cross-correlation coefficients between the test speech signals and respective speech sections, and to compare a maximum value of the cross-correlation coefficients with a predetermined threshold to calculate the performance of the speech detection. | 1. An apparatus for evaluating the performance of speech recognition comprising: a speech database configured to store a plurality of audio signal files corresponding to test speech signals; a driving unit configured to reproduce through a loud speaker the respective audio signal files of the test speech signals, the driving unit having the correct recognition results of the test speeches; a speech recognizer configured to execute a speech recognition of the reproduced audio signals in an actual environment where the speech recognizer is located to produce speech recognition results; and a performance evaluation module configured to evaluate the performance of the speech recognition by comparing the correct recognition results with the speech recognition results, wherein the performance evaluation module comprises: a speech-recognition evaluation unit configured to compare the correct recognition results of the test speeches and the speech recognition results and to produce a result corresponding to the accuracy of the speech recognition, and a speech-detection evaluation unit configured to obtain a plurality of cross-correlation coefficients between the test speech signals and respective speech sections, and to compare a maximum value of the cross-correlation coefficients with a predetermined threshold to calculate the performance of the speech detection. 2. The apparatus of claim 1 , wherein the speech recognizer comprises: a speech recognition unit configured to detect speech sections of the reproduced audio signals and performing the speech recognition on the detected speech sections; and a storage unit configured to store the speech recognition results and the detected speech sections of the reproduced audio signals. | 0.50134 |
9,268,668 | 20 | 26 | 20. One or more non-transitory computer-readable media storing instructions which, when executed by at least one processor, instruct the at least one processor to perform actions comprising: executing an application under test in a layout engine module, wherein the application under test comprises one or more instructions in one scripting language; initializing, in the layout engine module, a debugging session for the application under test, wherein the debugging session is configured to generate output data for the application under test, wherein the debugging session is based on a testing option to select a selected scripting language engine from among a plurality of scripting language engines, wherein the testing option to select the selected scripting language engine is selected using a user interface of testing options, and wherein each scripting language engine of the plurality of scripting language engines is configured to interpret the one scripting language; during the debugging session, determining a hierarchy view of the application under test, the hierarchy view including a plurality of objects associated with the application under test; accessing a class reference included in the hierarchy view; and modifying execution of the application under test within the debugging session, based at least partly on the class reference. | 20. One or more non-transitory computer-readable media storing instructions which, when executed by at least one processor, instruct the at least one processor to perform actions comprising: executing an application under test in a layout engine module, wherein the application under test comprises one or more instructions in one scripting language; initializing, in the layout engine module, a debugging session for the application under test, wherein the debugging session is configured to generate output data for the application under test, wherein the debugging session is based on a testing option to select a selected scripting language engine from among a plurality of scripting language engines, wherein the testing option to select the selected scripting language engine is selected using a user interface of testing options, and wherein each scripting language engine of the plurality of scripting language engines is configured to interpret the one scripting language; during the debugging session, determining a hierarchy view of the application under test, the hierarchy view including a plurality of objects associated with the application under test; accessing a class reference included in the hierarchy view; and modifying execution of the application under test within the debugging session, based at least partly on the class reference. 26. The one or more non-transitory computer-readable media of claim 20 , wherein the actions further comprise: sending the output data received from the layout engine module using the connection. | 0.741379 |
8,234,193 | 1 | 24 | 1. A computer-implemented method of enabling an organization to run a first promotion integrated with viral features associated with one or more social network-based platforms, comprising: receiving promotion information from the organization; generating one or more promotion applications responsive to the promotion information for integration with the one or more social network-based platforms, wherein the one or more promotion applications provide a way for a plurality of participants to actively interact with the first promotion and advertise the first promotion; receiving promotion entry data from the plurality of participants; storing the promotion entry data; using the viral features provided by the one or more social network-based platforms to increase awareness of at least one of a first group consisting of the promotion, the organization, a good or service associated with the promotion or organization, a third party organization, and the third party organization's good or service. | 1. A computer-implemented method of enabling an organization to run a first promotion integrated with viral features associated with one or more social network-based platforms, comprising: receiving promotion information from the organization; generating one or more promotion applications responsive to the promotion information for integration with the one or more social network-based platforms, wherein the one or more promotion applications provide a way for a plurality of participants to actively interact with the first promotion and advertise the first promotion; receiving promotion entry data from the plurality of participants; storing the promotion entry data; using the viral features provided by the one or more social network-based platforms to increase awareness of at least one of a first group consisting of the promotion, the organization, a good or service associated with the promotion or organization, a third party organization, and the third party organization's good or service. 24. The method of claim 1 , further comprising using a participant's social network based-platform profile data to suggest other promotions related to the participant's profile data. | 0.797778 |
7,752,534 | 1 | 3 | 1. A computer implemented process for customizing a display of a tag cloud, the computer implemented process comprising: displaying an interactive legend in conjunction with the display of the tag cloud, the interactive legend comprising a plurality of tag attributes, each tag attribute associated with a drop down menu comprising a plurality of display characteristics; responsive to a selection of a display characteristic from the drop down menu, mapping the display characteristic to a tag in the tag cloud, each display characteristic representing one of the plurality of tag attributes; modifying the tag cloud, wherein each tag is displayed in accordance with a display characteristic mapped to the tag by the interactive legend; and wherein the tag attributes are rearranged, added, or removed from the interactive legend. | 1. A computer implemented process for customizing a display of a tag cloud, the computer implemented process comprising: displaying an interactive legend in conjunction with the display of the tag cloud, the interactive legend comprising a plurality of tag attributes, each tag attribute associated with a drop down menu comprising a plurality of display characteristics; responsive to a selection of a display characteristic from the drop down menu, mapping the display characteristic to a tag in the tag cloud, each display characteristic representing one of the plurality of tag attributes; modifying the tag cloud, wherein each tag is displayed in accordance with a display characteristic mapped to the tag by the interactive legend; and wherein the tag attributes are rearranged, added, or removed from the interactive legend. 3. The computer implemented process of claim 1 wherein the plurality of tag attributes comprise: popularity of an item, last update of an item, frequency of updates to an item, age of an item, size of an item, most recently accessed, and whether there have been comments or replies related to an item. | 0.738715 |
8,775,468 | 11 | 12 | 11. The computer readable medium of claim 10 , further comprising computer program instructions for: storing each path and corresponding generated value expression in a table. | 11. The computer readable medium of claim 10 , further comprising computer program instructions for: storing each path and corresponding generated value expression in a table. 12. The computer readable medium of claim 11 , further comprising computer program instructions for: prior to the storing, compiling each generated value expression. | 0.962653 |
7,642,934 | 8 | 9 | 8. The method according to claim 1 , wherein, for at least some of the input members, said first character assignment further comprises a digit, and wherein subsequent to said altering, said first character assignment still comprises said digit. | 8. The method according to claim 1 , wherein, for at least some of the input members, said first character assignment further comprises a digit, and wherein subsequent to said altering, said first character assignment still comprises said digit. 9. The method according to claim 8 , further comprising detecting as at least a portion of said second input a vocal command, said vocal command being one of said digits of said first characters assignments. | 0.93511 |
7,574,349 | 1 | 14 | 1. A method for processing electronic mail comprising: computing a probability that a text string in an electronic mail message refers to an attachment as a function of a stored probability value for each of a plurality of sequences of words within the text string, the computing of the probability including computing a first probability that the text string refers to an attachment using a first set of stored probability values and, where the first probability exceeds a predetermined value, computing a second probability that the text string does not refer to an attachment using a second set of stored probability values, the probability that a text string in an electronic mail message refers to an attachment being computed as a function of the first and second probabilities; and where the electronic mail message lacks an attachment, prompting a user if the computed probability indicates that the text string refers to an attachment. | 1. A method for processing electronic mail comprising: computing a probability that a text string in an electronic mail message refers to an attachment as a function of a stored probability value for each of a plurality of sequences of words within the text string, the computing of the probability including computing a first probability that the text string refers to an attachment using a first set of stored probability values and, where the first probability exceeds a predetermined value, computing a second probability that the text string does not refer to an attachment using a second set of stored probability values, the probability that a text string in an electronic mail message refers to an attachment being computed as a function of the first and second probabilities; and where the electronic mail message lacks an attachment, prompting a user if the computed probability indicates that the text string refers to an attachment. 14. The method of claim 1 , further comprising adapting at least one stored probability value to the writing patterns of the user. | 0.88676 |
9,063,927 | 3 | 4 | 3. The method of claim 2 , wherein determining vowel/consonant ratio keyword feature information further comprises: counting the number of vowels in the message using the classifier; counting the number of consonants in the message using the classifier; and dividing the number of vowels in the message by the number of consonants in the message using the classifier. | 3. The method of claim 2 , wherein determining vowel/consonant ratio keyword feature information further comprises: counting the number of vowels in the message using the classifier; counting the number of consonants in the message using the classifier; and dividing the number of vowels in the message by the number of consonants in the message using the classifier. 4. The classifier of claim 3 , wherein the letter ‘y’ is excluded from the number of vowels in the message and from the number of consonants in the message. | 0.953012 |
9,269,056 | 6 | 8 | 6. A system for determining at least one combined forecast value of non-conventional energy resources for enabling adaptive forecasting of the non-conventional energy resources, the system comprising: a processor; an input/output (I/O) interface configured to read an adaptively selected historical dataset and a current dataset received from the one or more predictive forecast models and/or measurements, wherein the I/O interface is further configured to write the at least one combined forecast value; and a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules and data stored in the memory, and wherein the plurality of modules comprises: an adaptive forecast module configured for: 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 a 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 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 based on the evaluating of the one or more variants of the machine learning models trained on the historical dataset and the outputs of the each of the one or more variants of machine learning models trained on the historical dataset; and wherein the data comprises: a predictive forecast database configured for storing the historical dataset and the current dataset; a refined forecast database configured for storing the at least one combined forecast value. | 6. A system for determining at least one combined forecast value of non-conventional energy resources for enabling adaptive forecasting of the non-conventional energy resources, the system comprising: a processor; an input/output (I/O) interface configured to read an adaptively selected historical dataset and a current dataset received from the one or more predictive forecast models and/or measurements, wherein the I/O interface is further configured to write the at least one combined forecast value; and a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules and data stored in the memory, and wherein the plurality of modules comprises: an adaptive forecast module configured for: 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 a 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 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 based on the evaluating of the one or more variants of the machine learning models trained on the historical dataset and the outputs of the each of the one or more variants of machine learning models trained on the historical dataset; and wherein the data comprises: a predictive forecast database configured for storing the historical dataset and the current dataset; a refined forecast database configured for storing the at least one combined forecast value. 8. The system of claim 6 , 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, or combinations thereof. | 0.501538 |
9,886,703 | 13 | 14 | 13. The method of claim 12 , further comprising attaching a confidence factor to the subsequent ad request based on the geographical information associated with the first IP address. | 13. The method of claim 12 , further comprising attaching a confidence factor to the subsequent ad request based on the geographical information associated with the first IP address. 14. The method of claim 13 , wherein the geographical information associated with the first IP address includes data defining a geographic region and the confidence factor is dependent on the size of the geographic region. | 0.944137 |
9,396,724 | 5 | 6 | 5. The method of claim 1 , wherein building the speech to text decoder according to the previously obtained acoustic model, the respective template-based language model, the respective class-based language model and the respective lexicon-based language model for the given field, and the data samples further comprises: obtaining an expanded class-based language model for the given field by expanding the respective class-based language model using one or more sentences obtained through combining one or more templates from the respective template-based language model and one or more words from the respective lexicon-based language model; and building the speech to text decoder based on the expanded class-based language model and the lexicon-based language model for the given field, and the data samples. | 5. The method of claim 1 , wherein building the speech to text decoder according to the previously obtained acoustic model, the respective template-based language model, the respective class-based language model and the respective lexicon-based language model for the given field, and the data samples further comprises: obtaining an expanded class-based language model for the given field by expanding the respective class-based language model using one or more sentences obtained through combining one or more templates from the respective template-based language model and one or more words from the respective lexicon-based language model; and building the speech to text decoder based on the expanded class-based language model and the lexicon-based language model for the given field, and the data samples. 6. The method of claim 5 , wherein building the speech to text decoder according to the previously obtained acoustic model, the respective template-based language model, the respective class-based language model and the respective lexicon-based language model for the any given field, and the data samples, further comprises: performing weighted finite state transduction on the respective class-based language model in the given field, to obtain a respective class-based language model WFST (Weighted Finite State Transducer); performing weighted finite state transduction on the respective lexicon-based language model in the given field, to obtain a respective class vocabulary WFST; performing weighted finite state transduction on the respective class-based language model WFST and the respective class vocabulary WFST in the given field, to obtain a fused language model WFST; performing weighted finite state transduction on the data samples, to obtain a respective vocabulary WFST; performing weighted finite state transduction on the vocabulary WF ST and the fused language model WFST, to obtain a vocabulary language WFST; performing weighted finite state transduction on the acoustic model, to obtain an acoustic model WFST; performing weighted finite state transduction on the acoustic model WF ST and the vocabulary language WFST, to obtain an ultimate WFST; and taking the ultimate WFST as the speech-to-text decoder in the given field. | 0.664662 |
9,424,530 | 19 | 20 | 19. The computer program product according to claim 15 , wherein the computer readable program code is configured to apply the classifier and the calibration matrix by applying the classifier to the production dataset, thereby generating an intermediate classification, and applying the calibration matrix to the intermediate classification, thereby generating the production classification quantification. | 19. The computer program product according to claim 15 , wherein the computer readable program code is configured to apply the classifier and the calibration matrix by applying the classifier to the production dataset, thereby generating an intermediate classification, and applying the calibration matrix to the intermediate classification, thereby generating the production classification quantification. 20. The computer program product according to claim 19 , wherein the classification bias comprises a training classification bias, and wherein the intermediate classification has an intermediate classification bias relative to the expected classification, and wherein the production classification quantification has a production classification bias that is less than the intermediate classification bias. | 0.855357 |
10,134,131 | 1 | 12 | 1. A method, comprising: receiving, by a computing device, a target image of a target biological cell having a target phenotype; obtaining, by the computing device, a semantic embedding associated with the target image, wherein the semantic embedding associated with the target image is generated using a machine-learned, deep metric network model; obtaining, by the computing device for each of a plurality of candidate images of candidate biological cells each having a respective candidate phenotype, a semantic embedding associated with the respective candidate image, wherein the semantic embedding associated with the respective candidate image is generated using the machine-learned, deep metric network model; determining, by the computing device, a similarity score for each candidate image, wherein determining the similarity score for a respective candidate image comprises computing, by the computing device, a vector distance in a multi-dimensional space described by the semantic embeddings between the respective candidate image and the target image, and wherein the similarity score for each candidate image represents a degree of similarity between the target phenotype and the respective candidate phenotype; determining, by the computing device, a threshold similarity score; and determining, by the computing device, those candidate images having similarity scores that satisfy the threshold similarity score, wherein the target phenotype is a healthy phenotype, wherein the candidate images having similarity scores that satisfy the threshold similarity score have respective candidate phenotypes corresponding to the healthy phenotype, and wherein those candidate images having similarity scores that do not satisfy the threshold similarity score have respective candidate phenotypes corresponding to an unhealthy phenotype. | 1. A method, comprising: receiving, by a computing device, a target image of a target biological cell having a target phenotype; obtaining, by the computing device, a semantic embedding associated with the target image, wherein the semantic embedding associated with the target image is generated using a machine-learned, deep metric network model; obtaining, by the computing device for each of a plurality of candidate images of candidate biological cells each having a respective candidate phenotype, a semantic embedding associated with the respective candidate image, wherein the semantic embedding associated with the respective candidate image is generated using the machine-learned, deep metric network model; determining, by the computing device, a similarity score for each candidate image, wherein determining the similarity score for a respective candidate image comprises computing, by the computing device, a vector distance in a multi-dimensional space described by the semantic embeddings between the respective candidate image and the target image, and wherein the similarity score for each candidate image represents a degree of similarity between the target phenotype and the respective candidate phenotype; determining, by the computing device, a threshold similarity score; and determining, by the computing device, those candidate images having similarity scores that satisfy the threshold similarity score, wherein the target phenotype is a healthy phenotype, wherein the candidate images having similarity scores that satisfy the threshold similarity score have respective candidate phenotypes corresponding to the healthy phenotype, and wherein those candidate images having similarity scores that do not satisfy the threshold similarity score have respective candidate phenotypes corresponding to an unhealthy phenotype. 12. The method of claim 1 , further comprising: obtaining the candidate biological cells, wherein the candidate biological cells initially exhibit unhealthy phenotypes; treating the candidate biological cells with various concentrations of various candidate treatment compounds; recording the candidate images of the candidate biological cells; and transmitting the plurality candidate images to the computing device. | 0.874925 |
8,055,647 | 14 | 16 | 14. A computer program product for searching through multiple databases based on a search expression, the computer program product comprising: a computer readable medium; first program instructions to divide the search expression into multiple search expressions, wherein the first program instructions use a data distribution table to divide the search expression, wherein the data distribution table indicates how the records are distributed in each table of a plurality of tables, wherein ones of the plurality of tables correspond to ones of the multiple databases, and wherein the records correspond to a common key; second program instructions to determine respective search ranges for the multiple search expressions based in part on search rates through the respective search ranges; third program instructions to execute the multiple search expressions to form a multiple search expression output; and fourth program instructions to transmit the multiple search expression output to a memory, wherein the first, second, third, and fourth program instructions are recorded on the computer readable medium. | 14. A computer program product for searching through multiple databases based on a search expression, the computer program product comprising: a computer readable medium; first program instructions to divide the search expression into multiple search expressions, wherein the first program instructions use a data distribution table to divide the search expression, wherein the data distribution table indicates how the records are distributed in each table of a plurality of tables, wherein ones of the plurality of tables correspond to ones of the multiple databases, and wherein the records correspond to a common key; second program instructions to determine respective search ranges for the multiple search expressions based in part on search rates through the respective search ranges; third program instructions to execute the multiple search expressions to form a multiple search expression output; and fourth program instructions to transmit the multiple search expression output to a memory, wherein the first, second, third, and fourth program instructions are recorded on the computer readable medium. 16. The computer program product of claim 14 further comprising: fifth program instructions for creating the data distribution table, wherein the fifth program instructions comprise: instructions for designating a base table from the plurality of tables to be a core of an integrated search; instructions for dividing the base table into a number of base ranges for the common key such that the number of the distribution records in each range of the number of base ranges is approximately constant; instructions for, for each table other than the base table, calculating a second number of records corresponding to a base table key; and instructions for dividing each table other than the base table into corresponding ranges which correspond to the base ranges of the base table, wherein the corresponding ranges are based on an expected threshold amount of response time. | 0.565606 |
9,348,873 | 8 | 9 | 8. A tangible computer readable medium comprising computer readable instructions which, when executed, cause a processor to at least: determine a number of inbound links to a web site, the web site including posts that are associated with a topic; calculate elapsed times between time adjacent posts that are relevant to the topic; and determine a rank of the web site based on the number of inbound links and times between adjacent ones of the inbound links. | 8. A tangible computer readable medium comprising computer readable instructions which, when executed, cause a processor to at least: determine a number of inbound links to a web site, the web site including posts that are associated with a topic; calculate elapsed times between time adjacent posts that are relevant to the topic; and determine a rank of the web site based on the number of inbound links and times between adjacent ones of the inbound links. 9. The computer readable medium as defined in claim 8 , wherein the instructions further cause the processor to access at least a portion of the plurality of inbound links from a web site monitor. | 0.808219 |
9,292,798 | 14 | 15 | 14. The method of claim 1 , further comprising the step of: training the statistical model using a training data set. | 14. The method of claim 1 , further comprising the step of: training the statistical model using a training data set. 15. The method of claim 14 , wherein the training data set comprises a plurality of training data points, wherein each of the training data points comprises a set of covariates together with a known prediction. | 0.88758 |
9,436,680 | 8 | 9 | 8. The method according to claim 1 , further comprising: preparing a message file associated with the program product, wherein the message file includes at least a set of the character strings of the user-interface of the program product and respective identifiers within the message file that is uniquely associated with the character string. | 8. The method according to claim 1 , further comprising: preparing a message file associated with the program product, wherein the message file includes at least a set of the character strings of the user-interface of the program product and respective identifiers within the message file that is uniquely associated with the character string. 9. The method according to claim 8 , further comprising: preparing a master table, wherein the master table includes at least a set of the identifiers within the message file and the master identifiers uniquely associated with the identifier within the message file. | 0.946991 |
5,500,919 | 55 | 61 | 55. A text-to-speech processing apparatus according to claim 49, further comprising a monitor for displaying a graphical user interface, the graphical user interface comprising said command means, and wherein said process steps include steps to generate the graphical user interface display. | 55. A text-to-speech processing apparatus according to claim 49, further comprising a monitor for displaying a graphical user interface, the graphical user interface comprising said command means, and wherein said process steps include steps to generate the graphical user interface display. 61. A text-to-speech processing apparatus according to claim 55, wherein said graphical user interface includes means for displaying a state register which displays current status of said processing unit. | 0.918138 |
9,741,341 | 7 | 13 | 7. A system comprising: at least one processor; and one or more non-transitory computer-readable media storing executable instructions that, when executed by the at least one processor, cause the system to: characterize a speech input based on a dynamic noise adaptation (DNA) model reflecting effects of background noise; characterize the speech input based on a null noise DNA model reflecting a null noise mismatch condition; perform Bayesian model averaging using a first weighting of the DNA model and a second weighting of the null noise DNA model; re-weight the DNA model and the null noise DNA model by adjusting the first weighting to increase a probability of the DNA model predicted by the Bayesian model averaging when the DNA model is more likely than the null noise DNA model to best characterize the speech input; and perform recognition of the speech input using the re-weighted DNA model and the re-weighted null noise DNA model. | 7. A system comprising: at least one processor; and one or more non-transitory computer-readable media storing executable instructions that, when executed by the at least one processor, cause the system to: characterize a speech input based on a dynamic noise adaptation (DNA) model reflecting effects of background noise; characterize the speech input based on a null noise DNA model reflecting a null noise mismatch condition; perform Bayesian model averaging using a first weighting of the DNA model and a second weighting of the null noise DNA model; re-weight the DNA model and the null noise DNA model by adjusting the first weighting to increase a probability of the DNA model predicted by the Bayesian model averaging when the DNA model is more likely than the null noise DNA model to best characterize the speech input; and perform recognition of the speech input using the re-weighted DNA model and the re-weighted null noise DNA model. 13. The system of claim 7 , wherein the one or more non-transitory computer-readable media store executable instructions that, when executed by the at least one processor, cause the system to: model a transient component of a noise process of the speech input at each frequency band as zero mean and Gaussian; model a channel distortion of the speech input as a stochastically adapted parameter; approximate a noise posterior at each given frame of the speech input as Gaussian; and iteratively estimate a conditional posterior of a level of the background noise and a speech of the speech input for each speech Gaussian. | 0.500804 |
8,924,335 | 6 | 8 | 6. The method of claim 3 , wherein one or more of said aspects of the user interface pertain to any of priority of fields, color contrast, whitespace, alignment, field and/or element labels, redundancy, tool tips, progress indicators, and display resolution. | 6. The method of claim 3 , wherein one or more of said aspects of the user interface pertain to any of priority of fields, color contrast, whitespace, alignment, field and/or element labels, redundancy, tool tips, progress indicators, and display resolution. 8. The method of claim 6 , wherein one or more of said aspects of the user interface pertain to use of redundant headers in tables, sections and other portions of the user interface. | 0.953901 |
9,519,638 | 9 | 12 | 9. A computer program product comprising a non-transitory computer readable medium storing a computer readable program, wherein the computer readable program when executed causes the computer to perform steps comprising: receiving social feed data, the social feed data configured to cause a client device of a first user to display a social feed in a first language; determining a location of the client device displaying the social feed; receiving a user identifier of the first user; parsing the social feed data based on the user identifier to determine a social activity of the first user; parsing the social feed data based on relationships of the first user to identify a first portion of the social feed that has been acted on by one or more users having a particular type of relationship with the first user; determining a second language based on the location and the social activity of the first user; translating the social feed data that is associated with the first portion of the social feed so that the translated social feed data causes the client device to display the first portion translated from the first language into the second language; and transmitting the translated social feed data to the client device. | 9. A computer program product comprising a non-transitory computer readable medium storing a computer readable program, wherein the computer readable program when executed causes the computer to perform steps comprising: receiving social feed data, the social feed data configured to cause a client device of a first user to display a social feed in a first language; determining a location of the client device displaying the social feed; receiving a user identifier of the first user; parsing the social feed data based on the user identifier to determine a social activity of the first user; parsing the social feed data based on relationships of the first user to identify a first portion of the social feed that has been acted on by one or more users having a particular type of relationship with the first user; determining a second language based on the location and the social activity of the first user; translating the social feed data that is associated with the first portion of the social feed so that the translated social feed data causes the client device to display the first portion translated from the first language into the second language; and transmitting the translated social feed data to the client device. 12. The computer program product of claim 9 , wherein the client device includes at least one of a browser and a display application that displays the social feed and receives the translated social feed. | 0.810987 |
8,442,970 | 17 | 18 | 17. The method of claim 16 , further comprising: providing a second plurality of facets corresponding to the category after addition of the received plurality of characters; and adding a second facet to the expanded constructed query, the second facet being added in response to receiving a selection of the second facet from the provided second plurality of facets. | 17. The method of claim 16 , further comprising: providing a second plurality of facets corresponding to the category after addition of the received plurality of characters; and adding a second facet to the expanded constructed query, the second facet being added in response to receiving a selection of the second facet from the provided second plurality of facets. 18. The method of claim 17 , further comprising: providing a second plurality of facet values corresponding to the second facet value; and adding a second facet value to the expanded constructed query, the second facet value being added in response to receiving a selection of the second facet value from the provided second plurality of facet values. | 0.864896 |
8,762,433 | 1 | 2 | 1. An integration architecture for product data management, the integration architecture comprising: a processor in communication with a memory, the memory storing machine-readable instructions that when executed by the processor cause the processor to: establish a requirements component that manages and traces changes in requirements data and link the requirements component to a requirements system database; establish a software component that tracks software properties via a revision control repository database; establish a hardware component that tracks properties of at least one piece of an electronic device; establish a firmware component that tracks properties of a piece of firmware for controlling operation of the at least one piece of electronic device; establish a technical documents component that transmits technical documents between the technical documents component and an external documents database, wherein the stored technical documents support the software, hardware and firmware components; create a baseline model based on the software component, the hardware component, the firmware component, the technical documents component and the requirements component and store the baseline model in the requirements system database; and establish connections between said requirement components, said software component, said hardware component and said firmware component wherein a change in any one of the software, hardware or firmware components creates a change in requirements data stored in the requirements system database and managed by said requirements component and wherein said requirements component updates a property in at least one other component of the integration architecture. | 1. An integration architecture for product data management, the integration architecture comprising: a processor in communication with a memory, the memory storing machine-readable instructions that when executed by the processor cause the processor to: establish a requirements component that manages and traces changes in requirements data and link the requirements component to a requirements system database; establish a software component that tracks software properties via a revision control repository database; establish a hardware component that tracks properties of at least one piece of an electronic device; establish a firmware component that tracks properties of a piece of firmware for controlling operation of the at least one piece of electronic device; establish a technical documents component that transmits technical documents between the technical documents component and an external documents database, wherein the stored technical documents support the software, hardware and firmware components; create a baseline model based on the software component, the hardware component, the firmware component, the technical documents component and the requirements component and store the baseline model in the requirements system database; and establish connections between said requirement components, said software component, said hardware component and said firmware component wherein a change in any one of the software, hardware or firmware components creates a change in requirements data stored in the requirements system database and managed by said requirements component and wherein said requirements component updates a property in at least one other component of the integration architecture. 2. The integration architecture of claim 1 , wherein said firmware component further comprises: a plurality of connector components configured to connect said firmware component to an external requirements system database, an external repository database and the revision control repository database; a design artifacts component that manages files, properties, analyses, design and test data for a project design; a model component that manages an architecture framework and software based models; a code component that manages a collection of files to convert software files from a human readable form to a computer executable form; a working files component that manages properties and files including configuration files, libraries, settings and files needed to generate executable files; and a burn component for managing properties and files for executable files used to embed firmware code onto a programmable device. | 0.500541 |
9,213,684 | 23 | 26 | 23. A machine-readable storage medium having stored thereon instructions which, when executed by a processor on a server, cause the processor to: convert a plurality of resources in a document file into a plurality of files that are native to a browser; create a style sheet based on the document file, wherein an aggregate of the plurality of files together with the style sheet are configured to cause the browser to render an appearance of the document file; and generate, based on the document file, an invisible layer to be laid on the appearance, wherein the invisible layer enables actions to be performed on the document file. | 23. A machine-readable storage medium having stored thereon instructions which, when executed by a processor on a server, cause the processor to: convert a plurality of resources in a document file into a plurality of files that are native to a browser; create a style sheet based on the document file, wherein an aggregate of the plurality of files together with the style sheet are configured to cause the browser to render an appearance of the document file; and generate, based on the document file, an invisible layer to be laid on the appearance, wherein the invisible layer enables actions to be performed on the document file. 26. The medium of claim 23 , wherein the plurality of resources comprise an image, and wherein the processor is further caused to: extract the image from the document file; and down-sample the image if a size of the image is larger than a predetermined number. | 0.722814 |
8,627,276 | 32 | 47 | 32. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by a processor, cause the processor to: identify a plurality of entities having relationships therebetween; access a first entity from the plurality of entities; access a second entity from the plurality of entities; map the first entity to the second entity, the one or more instructions to map the first entity to the second entity including: one or more instructions to bi-directionally map a first part of the first entity and a second part of the second entity; determine, based on the mapping, if a graphical affordance, associated with at least one of an intermediate representation of a graphical model or code associated with the graphical model, is selected; and selectively identify, based on the determining, one or more portions of the graphical model or one or more portions of the at least one of the intermediate representation of the graphical model or the code associated with the graphical model, the one or more instructions to identify the one or more portions of the graphical model including: one or more instructions to receive information associated with browsing the at least one of the intermediate representation of the graphical model or the code associated with the graphical model; and one or more instructions to identify the one or more portions of the graphical model based on the received information associated with browsing the at least one of the intermediate representation of the graphical model or the code associated with the graphical model, and the one or more instructions to identify the one or more portions of the at least one of the intermediate representation of the graphical model or the code associated with the graphical model including: one or more instructions to receive information associated with selecting the graphical affordance of the graphical model; and one or more instructions to identify the at least one of the intermediate representation of the graphical model or the code associated with the graphical model based on the received information associated with selecting the graphical affordance of the graphical model. | 32. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by a processor, cause the processor to: identify a plurality of entities having relationships therebetween; access a first entity from the plurality of entities; access a second entity from the plurality of entities; map the first entity to the second entity, the one or more instructions to map the first entity to the second entity including: one or more instructions to bi-directionally map a first part of the first entity and a second part of the second entity; determine, based on the mapping, if a graphical affordance, associated with at least one of an intermediate representation of a graphical model or code associated with the graphical model, is selected; and selectively identify, based on the determining, one or more portions of the graphical model or one or more portions of the at least one of the intermediate representation of the graphical model or the code associated with the graphical model, the one or more instructions to identify the one or more portions of the graphical model including: one or more instructions to receive information associated with browsing the at least one of the intermediate representation of the graphical model or the code associated with the graphical model; and one or more instructions to identify the one or more portions of the graphical model based on the received information associated with browsing the at least one of the intermediate representation of the graphical model or the code associated with the graphical model, and the one or more instructions to identify the one or more portions of the at least one of the intermediate representation of the graphical model or the code associated with the graphical model including: one or more instructions to receive information associated with selecting the graphical affordance of the graphical model; and one or more instructions to identify the at least one of the intermediate representation of the graphical model or the code associated with the graphical model based on the received information associated with selecting the graphical affordance of the graphical model. 47. The computer-readable medium of claim 32 , where the second part includes a section of the generated report and the instructions further include: one or more instructions to graphically identify the first part based on selection of the section. | 0.636364 |
7,548,899 | 7 | 12 | 7. A method comprising: receiving, at a device, a first menu list of words or phrases, the received first menu list including a plurality of concepts associated with a natural language query to be formed; sending, from the device to a second device, a first selection of a first word or phrase from the first menu list, wherein the first selection identifies a concept for the natural language query to be formed; receiving a second menu list of words or phrases at the device, wherein the second menu list is based at least in part on the identified concept; sending, from the device to the second device, a second selection of a second word or phrase from the second menu list, the second selection identifying a first segment of the natural language query, wherein the natural language query is based at least in part on the first segment, and further wherein the natural language query does not include the identified concept; and receiving a response to the natural language query at the device. | 7. A method comprising: receiving, at a device, a first menu list of words or phrases, the received first menu list including a plurality of concepts associated with a natural language query to be formed; sending, from the device to a second device, a first selection of a first word or phrase from the first menu list, wherein the first selection identifies a concept for the natural language query to be formed; receiving a second menu list of words or phrases at the device, wherein the second menu list is based at least in part on the identified concept; sending, from the device to the second device, a second selection of a second word or phrase from the second menu list, the second selection identifying a first segment of the natural language query, wherein the natural language query is based at least in part on the first segment, and further wherein the natural language query does not include the identified concept; and receiving a response to the natural language query at the device. 12. A method as recited in claim 7 , wherein the second menu list is based at least in part on a profile that includes one or more of a selection history, a preference, content, or an application. | 0.769412 |
9,875,238 | 13 | 20 | 13. A system for establishing a setting for a telephony communication that will be conducted between a first telephony device that is used by an initiating party to initiate the setup of a telephony communication and a second telephony device that is used by a receiving party that will receive the telephony communication, comprising: an information obtaining unit comprising at least one processor that is configured to obtain information relating to at least one of the first telephony device and/or the initiating party that uses the first telephony device, and to obtain information relating to at least one of the second telephony device and/or the receiving party that uses the second telephony device; a determining unit comprising at least one processor that is configured to determine whether the initiating and receiving parties are likely to speak different languages based on the information obtained by the information obtaining unit; a query unit comprising at least one processor that is configured to cause a query to be posed to the initiating party when it is determined that the initiating and receiving parties are likely to speak different languages, the query asking if the initiating party would like a language translation to be performed as the telephony communication between the first and second telephony devices is conducted, wherein the query unit also receives input from the initiating party that is provided in response to the query; and a setting unit comprising at least one processor that is configured to establish a setting relating to a language translation for the telephony communication based on the input received by the query unit. | 13. A system for establishing a setting for a telephony communication that will be conducted between a first telephony device that is used by an initiating party to initiate the setup of a telephony communication and a second telephony device that is used by a receiving party that will receive the telephony communication, comprising: an information obtaining unit comprising at least one processor that is configured to obtain information relating to at least one of the first telephony device and/or the initiating party that uses the first telephony device, and to obtain information relating to at least one of the second telephony device and/or the receiving party that uses the second telephony device; a determining unit comprising at least one processor that is configured to determine whether the initiating and receiving parties are likely to speak different languages based on the information obtained by the information obtaining unit; a query unit comprising at least one processor that is configured to cause a query to be posed to the initiating party when it is determined that the initiating and receiving parties are likely to speak different languages, the query asking if the initiating party would like a language translation to be performed as the telephony communication between the first and second telephony devices is conducted, wherein the query unit also receives input from the initiating party that is provided in response to the query; and a setting unit comprising at least one processor that is configured to establish a setting relating to a language translation for the telephony communication based on the input received by the query unit. 20. The system of claim 13 , wherein query unit causes a query to be posed to the initiating party asking if the initiating party would like a translation to be performed between a first language and a second language as the telephony communication between the first and second telephony devices is conducted, wherein the identity of the first language stated in the query is based on information obtained from at least one of the first telephony device and the initiating party that uses the first telephony device, and wherein the identity of the second language stated in the query is based on information obtained from at least one of the second telephony device and the receiving party that uses the second telephony device. | 0.500685 |
9,081,588 | 1 | 3 | 1. A method comprising: executing, by a computing device, software that (a) is written in a first programming language, (b) calls one or more native interpretive functions that interpret one or more non-native functions written in a second programming language different from the first programming language to enable the computing device to execute the one or more non-native functions, and (c) calls one or more native functions written in the first programming language for execution by the computing device, wherein each of the one or more native interpretive functions is written in the first programming language; and profiling execution of the software by: identifying, based on execution of the one or more native interpretive functions, which of the one or more non-native functions is interpreted by the one or more native interpretive functions, resulting in an identified non-native function, obtaining profile information that describes one or more characteristics of how the identified non-native function executed on the computing device, identifying which of the one or more native functions is being executed, resulting in an identified native function, and obtaining additional profile information that describes one or more characteristics of how the identified native function executed on the computing device. | 1. A method comprising: executing, by a computing device, software that (a) is written in a first programming language, (b) calls one or more native interpretive functions that interpret one or more non-native functions written in a second programming language different from the first programming language to enable the computing device to execute the one or more non-native functions, and (c) calls one or more native functions written in the first programming language for execution by the computing device, wherein each of the one or more native interpretive functions is written in the first programming language; and profiling execution of the software by: identifying, based on execution of the one or more native interpretive functions, which of the one or more non-native functions is interpreted by the one or more native interpretive functions, resulting in an identified non-native function, obtaining profile information that describes one or more characteristics of how the identified non-native function executed on the computing device, identifying which of the one or more native functions is being executed, resulting in an identified native function, and obtaining additional profile information that describes one or more characteristics of how the identified native function executed on the computing device. 3. The method recited in claim 1 , further comprising: obtaining identifying information that identifies the one or more non-native functions; and employing the identifying information to associate the profile information with the one or more non-native functions. | 0.783607 |
9,350,913 | 17 | 18 | 17. A non-transitory computer readable storage medium storing one or more sequences of instructions executable by one or more processors to perform a set of operations comprising: capturing first image data of an object by a first camera of a computing device, the object including text displayed on a surface of the object; capturing second image data of the object by a second camera of the computing device; processing the first image data to recognize the text displayed on the surface of the object; determining a set of words from the recognized text; generating using the first image data and the second image data a three-dimensional representation of the object; displaying an interface that includes a selectable list of a subset of the set of words; and enabling selection of at least one word from one of the selectable list to be displayed with the three-dimensional representation of the object. | 17. A non-transitory computer readable storage medium storing one or more sequences of instructions executable by one or more processors to perform a set of operations comprising: capturing first image data of an object by a first camera of a computing device, the object including text displayed on a surface of the object; capturing second image data of the object by a second camera of the computing device; processing the first image data to recognize the text displayed on the surface of the object; determining a set of words from the recognized text; generating using the first image data and the second image data a three-dimensional representation of the object; displaying an interface that includes a selectable list of a subset of the set of words; and enabling selection of at least one word from one of the selectable list to be displayed with the three-dimensional representation of the object. 18. The non-transitory computer readable storage medium of claim 17 , further comprising instructions executed by the one or more processors to perform the set of operations of: acquiring a plurality of images, each image of the plurality of images including at least a portion of a representation of the object; and determining, based at least in part on the plurality of images, at least one of two or more views of the object, each view of the two of more views being acquired from a different point of view or physical dimensions of the object. | 0.551555 |
9,690,850 | 1 | 5 | 1. A method comprising: at a server device, receiving first formatted recipe data from a client device in communication with the server device, the first formatted recipe data comprising at least a plurality of words relating to a recipe; analyzing individual ones of the words relating to the recipe data to determine whether each may be found within one of three databases of known words, the three databases comprising: a database of known ingredient-related words, a database of known measurement-related words, and a database of known direction-related words; extracting nutrition information associated with at least the individual ones of the words relating to the recipe data as being found within the database of known ingredient-related words; organizing the extracted nutrition information and the characterized individual ones of the words relating to the recipe data into a second formatted recipe data; and providing the second formatted recipe data from the server device to the client device for nutrition logging performed thereat. | 1. A method comprising: at a server device, receiving first formatted recipe data from a client device in communication with the server device, the first formatted recipe data comprising at least a plurality of words relating to a recipe; analyzing individual ones of the words relating to the recipe data to determine whether each may be found within one of three databases of known words, the three databases comprising: a database of known ingredient-related words, a database of known measurement-related words, and a database of known direction-related words; extracting nutrition information associated with at least the individual ones of the words relating to the recipe data as being found within the database of known ingredient-related words; organizing the extracted nutrition information and the characterized individual ones of the words relating to the recipe data into a second formatted recipe data; and providing the second formatted recipe data from the server device to the client device for nutrition logging performed thereat. 5. The method of claim 1 , wherein the act of extracting nutrition information further comprises determining one or more nutritional values of the recipe data based at least in part on one or more component ingredients as determined by the act of analyzing. | 0.815638 |
9,002,764 | 12 | 16 | 12. A system for adding a hyperlink to a document including a person name, the system comprising: at least one processor; a memory coupled to the processor, the memory including instructions for: identifying a name in a document; determining a rarity indicator for the name, the rarity indicator representing a measure of how likely the name is to refer to more than one entity in a population, and wherein determining a rarity indicator for the name further comprises the formula:
P (nameUniqueness)=1/(( H*P (name))+1) wherein H denotes the size of the human population likely to be referenced in the document, P(name) is the name match probability score, and P(nameUniqueness) is the probability of name uniqueness; and defining a hyperlink for the name based on the rarity indicator. | 12. A system for adding a hyperlink to a document including a person name, the system comprising: at least one processor; a memory coupled to the processor, the memory including instructions for: identifying a name in a document; determining a rarity indicator for the name, the rarity indicator representing a measure of how likely the name is to refer to more than one entity in a population, and wherein determining a rarity indicator for the name further comprises the formula:
P (nameUniqueness)=1/(( H*P (name))+1) wherein H denotes the size of the human population likely to be referenced in the document, P(name) is the name match probability score, and P(nameUniqueness) is the probability of name uniqueness; and defining a hyperlink for the name based on the rarity indicator. 16. The system of claim 12 , wherein defining a hyperlink for the name based on the rarity indicator, comprises: identifying one or more non-person-name terms from the document; identifying one or more candidate records in a database based on at least a portion of the name; comparing the non-person-name terms for each of the candidate records to the non-person-name terms from the document; calculating one or more quantities, each based on the rarity indicator for the person name and the comparison of the non-person-name terms for one of the candidate records; and defining the hyperlink based on the one or more calculated quantities. | 0.50078 |
7,716,163 | 16 | 17 | 16. The method of claim 12 wherein determining whether to proceed with an update comprises determining whether an interval of time specified in the semantic category list file has elapsed, and, if so, then determining to perform an update. | 16. The method of claim 12 wherein determining whether to proceed with an update comprises determining whether an interval of time specified in the semantic category list file has elapsed, and, if so, then determining to perform an update. 17. The method of claim 16 wherein the interval of time comprises an update frequency element. | 0.981923 |
8,352,857 | 9 | 11 | 9. A document annotation or structuring system comprising: a reference identification and resolution module comprising a digital processor configured to identify reference text fragments in a document and associate referenced object text fragments in the document with the identified reference text fragments using a method comprising: identifying a set of candidate object text fragments from the document, each candidate object text fragment including at least a reference number, the set of candidate object text fragments including both object text fragments and reference text fragments, the identifying not differentiating between object text fragments and reference text fragments; deriving reference profiles from the set of candidate object text fragments, each reference profile including at least a reference number and an object type indicator; pairing reference profiles with object text fragments selected from the set of candidate object text fragments, each pair having a same reference number for the paired reference profile and the object text fragment, wherein the pairing includes: assigning likelihood scores to pairings of reference profiles and candidate object text fragments having matching reference numbers; and selecting the object text fragments for pairing with reference profiles based on the likelihood scores subject to a constraint that the pairings be one-to-one in which no reference profile is paired with more than one object text fragment and no object text fragment is paired with more than one reference profile; matching reference text fragments in the document with reference profiles by reference number and object type; and associating matched reference text fragments with object text fragments paired with the matching reference profiles; and a document annotation or structuring module configured to annotate or structure the document based on the reference text fragments and referenced object text fragments output by the reference identification and resolution module. | 9. A document annotation or structuring system comprising: a reference identification and resolution module comprising a digital processor configured to identify reference text fragments in a document and associate referenced object text fragments in the document with the identified reference text fragments using a method comprising: identifying a set of candidate object text fragments from the document, each candidate object text fragment including at least a reference number, the set of candidate object text fragments including both object text fragments and reference text fragments, the identifying not differentiating between object text fragments and reference text fragments; deriving reference profiles from the set of candidate object text fragments, each reference profile including at least a reference number and an object type indicator; pairing reference profiles with object text fragments selected from the set of candidate object text fragments, each pair having a same reference number for the paired reference profile and the object text fragment, wherein the pairing includes: assigning likelihood scores to pairings of reference profiles and candidate object text fragments having matching reference numbers; and selecting the object text fragments for pairing with reference profiles based on the likelihood scores subject to a constraint that the pairings be one-to-one in which no reference profile is paired with more than one object text fragment and no object text fragment is paired with more than one reference profile; matching reference text fragments in the document with reference profiles by reference number and object type; and associating matched reference text fragments with object text fragments paired with the matching reference profiles; and a document annotation or structuring module configured to annotate or structure the document based on the reference text fragments and referenced object text fragments output by the reference identification and resolution module. 11. The document annotation or structuring system as set forth in claim 9 , further comprising: labeling the reference profiles with labels representative of textual content of text fragments matching the reference number and object type of the reference profile, wherein the assigning of likelihood scores includes computing pairwise scoring components indicative of textual similarity between reference profile labels and textual content of the candidate object text fragments. | 0.578345 |
8,566,698 | 1 | 2 | 1. A computer-implemented method for processing forms on a personal computer while isolated from a computer server which has a document management system, comprising the steps of: storing Hypertext Markup Language (HTML) forms on the personal computer wherein the forms have data fields for data submission; receiving data as input to at least one first data field on a first HTML form while the personal computer is physically disconnected from the computer server; receiving a data submit command with respect to the first HTML form field input data; storing the first HTML form field input data on the personal computer in response to the received data submit command; wherein the HTML form field input data is stored in a first file on the personal computer in response to the received data submit command; after the personal computer is connected to a network having a data connection to the document management system, identifying second completed HTML forms including the first HTML form in response to a user command, second HTML form field input data associated with the second completed HTML forms being stored on the personal computer in second files; using a graphical user interface to indicate that the second HTML form field input data are to be sent from the second files on the personal computer to the document management system; converting each of the second files from a binary format into a character string; combining the character strings corresponding to the second files into a single data stream; sending the single data stream over the network to the document management system; wherein after being successfully sent to the document management system, the first HTML form and the stored first HTML form field input data are removed from the personal computer; wherein the document management system facilitates electronic capturing of documents and for storing and managing the documents. | 1. A computer-implemented method for processing forms on a personal computer while isolated from a computer server which has a document management system, comprising the steps of: storing Hypertext Markup Language (HTML) forms on the personal computer wherein the forms have data fields for data submission; receiving data as input to at least one first data field on a first HTML form while the personal computer is physically disconnected from the computer server; receiving a data submit command with respect to the first HTML form field input data; storing the first HTML form field input data on the personal computer in response to the received data submit command; wherein the HTML form field input data is stored in a first file on the personal computer in response to the received data submit command; after the personal computer is connected to a network having a data connection to the document management system, identifying second completed HTML forms including the first HTML form in response to a user command, second HTML form field input data associated with the second completed HTML forms being stored on the personal computer in second files; using a graphical user interface to indicate that the second HTML form field input data are to be sent from the second files on the personal computer to the document management system; converting each of the second files from a binary format into a character string; combining the character strings corresponding to the second files into a single data stream; sending the single data stream over the network to the document management system; wherein after being successfully sent to the document management system, the first HTML form and the stored first HTML form field input data are removed from the personal computer; wherein the document management system facilitates electronic capturing of documents and for storing and managing the documents. 2. The method of claim 1 wherein the forms are business forms. | 0.878431 |
7,860,971 | 1 | 7 | 1. A method for resisting spam webpages on a computing device installed with a web browser, the method comprising: receiving at the web browser a URL of a webpage; determining by a spam detection module installed on the computing device whether the webpage is spam by comparing the URL of the webpage with a spam list including spam URLs, the spam list being created by: dividing the spam URLs of the spam list into a plurality of sub chunks of spam URLs; indexing the spam URLs into a first level index and a second level index, the first level index maps a first set of hash values to ranges of sub chunks of spam URLs, and the second level index maps a second set of hash values to the remaining sub chunks of spam URLs in the plurality of sub chunks; the first set of hash values are created using a first hash function and the second set of hash values are created using a second hash function; and performing an anti-spam action on the computing device if the webpage is determined to be spam; wherein comparing the URL of the webpage with the spam comprises: computing a hash value for the URL of the webpage using a hash function; and matching the hash value of the webpage with the set of hash values of the spam URLs; wherein the spam list is further created by: computing the first set of hash values and the second set of hash values; sorting the spam URLs by their computed hash values; wherein each sub chunk having a sequential range of hash values defined by a lower bound and an upper bound. | 1. A method for resisting spam webpages on a computing device installed with a web browser, the method comprising: receiving at the web browser a URL of a webpage; determining by a spam detection module installed on the computing device whether the webpage is spam by comparing the URL of the webpage with a spam list including spam URLs, the spam list being created by: dividing the spam URLs of the spam list into a plurality of sub chunks of spam URLs; indexing the spam URLs into a first level index and a second level index, the first level index maps a first set of hash values to ranges of sub chunks of spam URLs, and the second level index maps a second set of hash values to the remaining sub chunks of spam URLs in the plurality of sub chunks; the first set of hash values are created using a first hash function and the second set of hash values are created using a second hash function; and performing an anti-spam action on the computing device if the webpage is determined to be spam; wherein comparing the URL of the webpage with the spam comprises: computing a hash value for the URL of the webpage using a hash function; and matching the hash value of the webpage with the set of hash values of the spam URLs; wherein the spam list is further created by: computing the first set of hash values and the second set of hash values; sorting the spam URLs by their computed hash values; wherein each sub chunk having a sequential range of hash values defined by a lower bound and an upper bound. 7. The method as recited in claim 1 , wherein receiving the URL of the webpage comprises determining if the URL as a link contained in a parent webpage that is being opened by the web browser is a spam URL. | 0.884009 |
9,336,495 | 1 | 3 | 1. A method for training a semantic indexing model comprising: providing a search engine with a first query; receiving a set of documents of a plurality of documents related to the first query from the search engine; generating, by at least one hardware processor, an expanded query by merging at least a portion of a subset of the set of the documents with the first query; and training the semantic indexing model based on the expanded query; wherein the training comprises presenting at least a portion of the plurality of documents to a user, receiving indications of which of the plurality of documents are relevant to the expanded query and which of the plurality of documents are irrelevant to the expanded query; wherein the training updates the model based on the expanded query, the documents that are relevant to the expanded query and the documents that are irrelevant to the expanded query; wherein the updating comprises modifying the model by computing the model such that ∑ ( q , d + , d - ) max ( 0 , 1 - f ( q ′ , d + ) + f ( q ′ , d - ) ) is minimized, where f is the model, q′ denotes the expanded query, d + denotes documents that are relevant to the query q′ and d − denotes documents that are irrelevant to the query q′. | 1. A method for training a semantic indexing model comprising: providing a search engine with a first query; receiving a set of documents of a plurality of documents related to the first query from the search engine; generating, by at least one hardware processor, an expanded query by merging at least a portion of a subset of the set of the documents with the first query; and training the semantic indexing model based on the expanded query; wherein the training comprises presenting at least a portion of the plurality of documents to a user, receiving indications of which of the plurality of documents are relevant to the expanded query and which of the plurality of documents are irrelevant to the expanded query; wherein the training updates the model based on the expanded query, the documents that are relevant to the expanded query and the documents that are irrelevant to the expanded query; wherein the updating comprises modifying the model by computing the model such that ∑ ( q , d + , d - ) max ( 0 , 1 - f ( q ′ , d + ) + f ( q ′ , d - ) ) is minimized, where f is the model, q′ denotes the expanded query, d + denotes documents that are relevant to the query q′ and d − denotes documents that are irrelevant to the query q′. 3. The method of claim 1 , wherein the receiving further comprises selecting the subset by applying a cosine distance between a vector denoting the first query and vectors denoting the documents in the set. | 0.768539 |
9,300,801 | 16 | 17 | 16. The method of claim 12 , which further comprises converting the agent recording to text. | 16. The method of claim 12 , which further comprises converting the agent recording to text. 17. The method of claim 16 , which further comprises performing linguistic analysis on the text of the agent recording to determine a personality type of the agent. | 0.940922 |
8,452,599 | 1 | 11 | 1. A message extraction system comprising: an image capture unit obtaining a set of images; a first features extraction unit communicatively coupled to the image capture unit to extract a first set of body features from the generated set of images; a words extraction unit communicatively coupled to the first features extraction unit to generate a first set of words from the first set of body features; a second features extraction unit communicatively coupled to the words extraction unit to generate a second set of body features from the generated first set of words; a features comparator communicatively coupled to the first features extraction unit and the second features extraction unit to determine whether the first set of body features matches the second set of body features and outputting the generated first set of words when the first set of body features matches the second set of body features; and a processor for coordinating the image capture unit, the first features extraction unit, the words extraction unit, the second features extraction unit, and the features comparator. | 1. A message extraction system comprising: an image capture unit obtaining a set of images; a first features extraction unit communicatively coupled to the image capture unit to extract a first set of body features from the generated set of images; a words extraction unit communicatively coupled to the first features extraction unit to generate a first set of words from the first set of body features; a second features extraction unit communicatively coupled to the words extraction unit to generate a second set of body features from the generated first set of words; a features comparator communicatively coupled to the first features extraction unit and the second features extraction unit to determine whether the first set of body features matches the second set of body features and outputting the generated first set of words when the first set of body features matches the second set of body features; and a processor for coordinating the image capture unit, the first features extraction unit, the words extraction unit, the second features extraction unit, and the features comparator. 11. The system of claim 1 wherein the features comparator is configured to output the first set of words to an electronic device, and the first set of words is configured to operate the electronic device. | 0.672026 |
8,321,199 | 70 | 71 | 70. The computer readable medium of claim 61 , wherein (B) comprises: (B)(1) selecting a visual characteristic based on the first feature; (B)(2) rendering the first data based on the first feature to produce a rendering of the first data, whereby the rendering has the selected visual characteristic. | 70. The computer readable medium of claim 61 , wherein (B) comprises: (B)(1) selecting a visual characteristic based on the first feature; (B)(2) rendering the first data based on the first feature to produce a rendering of the first data, whereby the rendering has the selected visual characteristic. 71. The computer readable medium of claim 70 , wherein the visual characteristic comprises a text formatting characteristic. | 0.957823 |
8,091,017 | 1 | 13 | 1. An apparatus for providing an immersive experience for reading the content of one or more source text units, comprising: a computer system comprising one or more processors, a display interface, at least one user input interface, and one or more data storage means for storing an operating system, at least one electronic document comprising one or more components, a database for holding a plurality of tables wherein each table includes at least information about the content of the one or more source text units and wherein the plurality of tables includes at least one unit table, at least one thread table, and at least one term table, and program instructions that, when implemented by the one or more processors, are configured to: generate one or more lists of one or more links based on information stored in one or more of the tables wherein at least one link is to at least one starting point for reading one or more of the plurality of components of said electronic document; generate and install one or more hypertext link anchors in one or more of said components of said electronic document based on the lists of links; generate and install one or more hypertext links to said link anchors, in one or more of said components of said electronic document; generate and install one or more indices in one or more components of said electronic document; generate and install hypertext links between said indices and one or more components of said electronic document. | 1. An apparatus for providing an immersive experience for reading the content of one or more source text units, comprising: a computer system comprising one or more processors, a display interface, at least one user input interface, and one or more data storage means for storing an operating system, at least one electronic document comprising one or more components, a database for holding a plurality of tables wherein each table includes at least information about the content of the one or more source text units and wherein the plurality of tables includes at least one unit table, at least one thread table, and at least one term table, and program instructions that, when implemented by the one or more processors, are configured to: generate one or more lists of one or more links based on information stored in one or more of the tables wherein at least one link is to at least one starting point for reading one or more of the plurality of components of said electronic document; generate and install one or more hypertext link anchors in one or more of said components of said electronic document based on the lists of links; generate and install one or more hypertext links to said link anchors, in one or more of said components of said electronic document; generate and install one or more indices in one or more components of said electronic document; generate and install hypertext links between said indices and one or more components of said electronic document. 13. The apparatus of claim 1 , wherein the one or more tables further includes a symbolic link table and the program instructions are further configured to: receive a user-supplied symbolic link anchor definition including a symbolic link name; validate the symbolic link name by comparing the symbolic link name to the symbolic link anchor definitions in the symbolic link table; insert a symbolic link anchor at a user-specified location in the one or more components; insert a symbolic hypertext link at a user-specified location in the one or more components wherein the hypertext link points to the symbolic link anchor location associated with the validated symbolic link name; and add said symbolic link anchor definition to the symbolic link table. | 0.519084 |
8,214,736 | 1 | 28 | 1. A method for identifying a critical textual passage and test method that influence the pagination of electronic documents such that the modification of text to said critical textual passage by said test method has the effect of decreasing or increasing the page count of the document, said method comprising the steps of: providing a document comprising a plurality of paragraphs and a plurality of page breaks; determining a first plurality of existent page break locations in the document; selecting a sub-portion of the document's text; selecting a test method for modifying said sub-portion of the document's text; performing said test method on said sub-portion of the document's text; recalculating changes in page break positions resulting from performing said test method; determining a second plurality of updated page break locations in the document; detecting a change in page break positions between said first plurality of page break locations and said second plurality of page break locations; recording the location of said sub-portion of document text and said test method used to create the change in page break locations. | 1. A method for identifying a critical textual passage and test method that influence the pagination of electronic documents such that the modification of text to said critical textual passage by said test method has the effect of decreasing or increasing the page count of the document, said method comprising the steps of: providing a document comprising a plurality of paragraphs and a plurality of page breaks; determining a first plurality of existent page break locations in the document; selecting a sub-portion of the document's text; selecting a test method for modifying said sub-portion of the document's text; performing said test method on said sub-portion of the document's text; recalculating changes in page break positions resulting from performing said test method; determining a second plurality of updated page break locations in the document; detecting a change in page break positions between said first plurality of page break locations and said second plurality of page break locations; recording the location of said sub-portion of document text and said test method used to create the change in page break locations. 28. The method of claim 1 , wherein said test method includes changing a line spacing metric of a line containing the said sub-portion. | 0.771186 |
8,601,031 | 1 | 20 | 1. A method comprising: receiving, via a communication module, a non-linear content identifier request for non-linear content, wherein the non-linear content identifier request comprises a linear content identifier associated with linear content selected by a viewer and location data associated with the viewer; identifying, via a matching module, linear content metadata associated with the linear content identifier of the non-linear content identifier request, the linear content metadata representing at least one metadata keyword; and identifying, via the matching module, a relationship between the at least one metadata keyword and at least one non-linear content keyword bid on by one or more advertisers associated with a time slot. | 1. A method comprising: receiving, via a communication module, a non-linear content identifier request for non-linear content, wherein the non-linear content identifier request comprises a linear content identifier associated with linear content selected by a viewer and location data associated with the viewer; identifying, via a matching module, linear content metadata associated with the linear content identifier of the non-linear content identifier request, the linear content metadata representing at least one metadata keyword; and identifying, via the matching module, a relationship between the at least one metadata keyword and at least one non-linear content keyword bid on by one or more advertisers associated with a time slot. 20. The method of claim 1 , wherein processing the at least one metadata keyword comprise removing a common metadata keyword. | 0.755859 |
9,678,998 | 10 | 11 | 10. The method of claim 1 , wherein obtaining a content record involves: determining a remote content-name-resolution server associated with at least a portion of the HSVLI; and sending, to the remote content-name-resolution server, a request for the content record associated with the portion of the HSVLI. | 10. The method of claim 1 , wherein obtaining a content record involves: determining a remote content-name-resolution server associated with at least a portion of the HSVLI; and sending, to the remote content-name-resolution server, a request for the content record associated with the portion of the HSVLI. 11. The method of claim 10 , further comprising: responsive to receiving the content record from the remote content-name-resolution server, storing the content record in association with the portion of the HSVLI. | 0.939704 |
10,089,982 | 15 | 16 | 15. The system of claim 11 , wherein the operations further include generating, based on the received user utterance, a graph comprising nodes and edges between some of the nodes, and wherein the multiple candidate transcriptions are determined, and the system is biased, using the generated graph, and wherein the nodes correspond to connections between terms, wherein the edges correspond to a candidate term that corresponds to a portion of audio data from the user utterance, and wherein the nodes or the edges, or both, are associated with probabilities indicating a determined confidence that the user utterance includes a particular term or connection between terms corresponding to the nodes or edges. | 15. The system of claim 11 , wherein the operations further include generating, based on the received user utterance, a graph comprising nodes and edges between some of the nodes, and wherein the multiple candidate transcriptions are determined, and the system is biased, using the generated graph, and wherein the nodes correspond to connections between terms, wherein the edges correspond to a candidate term that corresponds to a portion of audio data from the user utterance, and wherein the nodes or the edges, or both, are associated with probabilities indicating a determined confidence that the user utterance includes a particular term or connection between terms corresponding to the nodes or edges. 16. The system of claim 15 , wherein the operations further include ranking the determined multiple candidate transcriptions, wherein biasing the system comprises changing the ranking to prefer the candidate transcription associated with the new voice action. | 0.876195 |
9,740,736 | 1 | 2 | 1. A computer-implemented method of linking a collection of concepts to a collection of categories of content in an intelligent services engine, the method comprising: identifying at least one concept within the collection of concepts, wherein the at least one concept comprises a top-level word of a hierarchical set of related words; retrieving the collection of categories of content, wherein each category of content represents a general type of information offered by a content service; identifying, by a computing device, a logical relationship between the top-level word of the at least one concept and at least one category of the collection of categories of content; and based on the identified logical relationship, creating a mapping relationship between the at least one concept of the collection of concepts and the at least one category of the collection of categories of content. | 1. A computer-implemented method of linking a collection of concepts to a collection of categories of content in an intelligent services engine, the method comprising: identifying at least one concept within the collection of concepts, wherein the at least one concept comprises a top-level word of a hierarchical set of related words; retrieving the collection of categories of content, wherein each category of content represents a general type of information offered by a content service; identifying, by a computing device, a logical relationship between the top-level word of the at least one concept and at least one category of the collection of categories of content; and based on the identified logical relationship, creating a mapping relationship between the at least one concept of the collection of concepts and the at least one category of the collection of categories of content. 2. The computer-implemented method of claim 1 , wherein the collection of concepts comprises a first hierarchy, wherein the collection of categories of content comprises a second hierarchy, and wherein the first hierarchy is deeper than the second hierarchy. | 0.805136 |
9,996,624 | 1 | 7 | 1. A computer-implemented method executed by one or more processors, the method comprising: receiving, by the one or more processors, general search results that are responsive to a query; cross-referencing uniform resource locators (URLs) of the general search results with a site pattern of a publishing entity that is identified as publishing in-depth articles; determining, by the one or more processors, that one or more general search results to be provided in response to the query is an in-depth article search result based on the URL of the general search result corresponding to the site pattern of the publishing entity; and in response to determining that one or more in-depth article search results are to be provided in response to the query: obtaining, by the one or more processors, a topicality score for each in-depth article referenced by the one or more in-depth article search results responsive to the query, each topicality score indicating a degree of relevance of a respective in-depth article to the query; obtaining, by the one or more processors, a document score for each in-depth article referenced by the one or more in-depth article search results responsive to the query, each document score being based on a respective topicality score and a respective in-depth article score; selecting, by the one or more processors, one or more in-depth articles referenced by the one or more in-depth article search results responsive to the query based on respective document scores; and providing, by the one or more processors, the one or more in-depth article search results for display, each in-depth article search result representing an in-depth article of the one or more in-depth articles. | 1. A computer-implemented method executed by one or more processors, the method comprising: receiving, by the one or more processors, general search results that are responsive to a query; cross-referencing uniform resource locators (URLs) of the general search results with a site pattern of a publishing entity that is identified as publishing in-depth articles; determining, by the one or more processors, that one or more general search results to be provided in response to the query is an in-depth article search result based on the URL of the general search result corresponding to the site pattern of the publishing entity; and in response to determining that one or more in-depth article search results are to be provided in response to the query: obtaining, by the one or more processors, a topicality score for each in-depth article referenced by the one or more in-depth article search results responsive to the query, each topicality score indicating a degree of relevance of a respective in-depth article to the query; obtaining, by the one or more processors, a document score for each in-depth article referenced by the one or more in-depth article search results responsive to the query, each document score being based on a respective topicality score and a respective in-depth article score; selecting, by the one or more processors, one or more in-depth articles referenced by the one or more in-depth article search results responsive to the query based on respective document scores; and providing, by the one or more processors, the one or more in-depth article search results for display, each in-depth article search result representing an in-depth article of the one or more in-depth articles. 7. The method of claim 1 , wherein each in-depth article score is based on one or more sub-scores comprising at least one of an article score, a commercial score, an evergreen score, and an author score. | 0.502451 |
8,918,311 | 7 | 11 | 7. The system according to claim 1 , further comprising a parser component executed by the at least one processor and configured to generate the plurality of elements by executing at least one parse of the time-coded transcription information. | 7. The system according to claim 1 , further comprising a parser component executed by the at least one processor and configured to generate the plurality of elements by executing at least one parse of the time-coded transcription information. 11. The system according to claim 7 , wherein the parser component is configured to structure elements generated by the at least one parse into at least one parse tree. | 0.947434 |
8,706,680 | 1 | 5 | 1. An automated method that enables a user to prepare a report using a computer system having a processor, an input interface, and an output interface, said method comprising: entering context information into the input interface; displaying a single lexeme query and associated lexeme responses from a lexicon containing a plurality of such queries and associated responses on the computer system's output interface; allowing the user to select a response to the lexeme query from the display wherein the queries are displayed iteratively one-at-a-time in an order established by the system's coherence and predicance, and wherein the user is constrained to respond to the queries in the order they are presented by the system; determining whether the selected lexeme response instructs the system to move forward to the next section of the report, and if so, moving the system forward to the next section of the report; determining whether the selected lexeme is an active lexeme, and if so, executing the task associated with the active lexeme; determining whether the selected lexeme sets new predicants to be true, and if so, adding the predicants to a predicant list stored by the system; determining the next lexeme query in the order established by the coherence of the lexicon which contains one or more predicants matching the current predicant list and displaying this next lexeme query on the computer system's output interface; iterating this process until the report is complete; and concatenating and exporting the content of the selected lexeme responses in one or more styles. | 1. An automated method that enables a user to prepare a report using a computer system having a processor, an input interface, and an output interface, said method comprising: entering context information into the input interface; displaying a single lexeme query and associated lexeme responses from a lexicon containing a plurality of such queries and associated responses on the computer system's output interface; allowing the user to select a response to the lexeme query from the display wherein the queries are displayed iteratively one-at-a-time in an order established by the system's coherence and predicance, and wherein the user is constrained to respond to the queries in the order they are presented by the system; determining whether the selected lexeme response instructs the system to move forward to the next section of the report, and if so, moving the system forward to the next section of the report; determining whether the selected lexeme is an active lexeme, and if so, executing the task associated with the active lexeme; determining whether the selected lexeme sets new predicants to be true, and if so, adding the predicants to a predicant list stored by the system; determining the next lexeme query in the order established by the coherence of the lexicon which contains one or more predicants matching the current predicant list and displaying this next lexeme query on the computer system's output interface; iterating this process until the report is complete; and concatenating and exporting the content of the selected lexeme responses in one or more styles. 5. The method of claim 1 wherein the system chooses to display lexeme queries and responses based on coherence information. | 0.504032 |
8,966,439 | 1 | 9 | 1. A computer-implemented method of determining an answer to a query, the computer-implemented method comprising: receiving, at one or more computer processors, via a network, a user input in an imprecise syntax, wherein the user input in the imprecise syntax includes at least (i) a query requesting information determinable by a formula having a plurality of mathematical or scientific parameters, and (ii) one or more parameter values corresponding to the formula, and wherein the user input in the imprecise syntax is expressed using natural language and/or informal terminology provided by a user; analyzing, at one or more computer processors, the user input in the imprecise syntax to determine the formula with the one or more parameter values integrated into the formula; calculating, at one or more computer processors, an answer to the query using the determined formula with the one or more parameter values integrated into the formula; and causing, at one or more computer processors, an indication of the answer, represented as electronic display information, to be transmitted via the network. | 1. A computer-implemented method of determining an answer to a query, the computer-implemented method comprising: receiving, at one or more computer processors, via a network, a user input in an imprecise syntax, wherein the user input in the imprecise syntax includes at least (i) a query requesting information determinable by a formula having a plurality of mathematical or scientific parameters, and (ii) one or more parameter values corresponding to the formula, and wherein the user input in the imprecise syntax is expressed using natural language and/or informal terminology provided by a user; analyzing, at one or more computer processors, the user input in the imprecise syntax to determine the formula with the one or more parameter values integrated into the formula; calculating, at one or more computer processors, an answer to the query using the determined formula with the one or more parameter values integrated into the formula; and causing, at one or more computer processors, an indication of the answer, represented as electronic display information, to be transmitted via the network. 9. The computer-implemented method of claim 1 , further comprising: determining, at one or more processors, that one or more additional parameter values are required to calculate the answer; and causing, at one or more processors, a request, represented as electronic information, for the one or more additional parameter values to be transmitted via the network. | 0.767606 |
8,831,977 | 1 | 7 | 1. A method for implementing personalized dissemination of information, comprising: receiving, at a computer processor, personal information elements for a subject; applying, via a rules engine executing on the computer processor, business rules to the personal information elements, push data elements designated for publication by a source of the push data elements, and a database of information strings, the information strings comprising words and phrases that are commonly recognized across a defined populace, wherein the business rules are configured to use phonetics and rhyming schemes, the rhyming schemes including measure, meter formations, and rhythm; and in response to the applying: selecting one or more of the push data elements from the source, one or more of the personal information elements, and an information string from the database of information strings, constructing a contextually relevant output string relevant to the subject, the output string including portions of the selected information string, the selected data elements, and the selected personal information elements, wherein the information strings are altered according to phonetics and rhyming schemes selected in response to applying the business rules, and conveying the output string to the subject. | 1. A method for implementing personalized dissemination of information, comprising: receiving, at a computer processor, personal information elements for a subject; applying, via a rules engine executing on the computer processor, business rules to the personal information elements, push data elements designated for publication by a source of the push data elements, and a database of information strings, the information strings comprising words and phrases that are commonly recognized across a defined populace, wherein the business rules are configured to use phonetics and rhyming schemes, the rhyming schemes including measure, meter formations, and rhythm; and in response to the applying: selecting one or more of the push data elements from the source, one or more of the personal information elements, and an information string from the database of information strings, constructing a contextually relevant output string relevant to the subject, the output string including portions of the selected information string, the selected data elements, and the selected personal information elements, wherein the information strings are altered according to phonetics and rhyming schemes selected in response to applying the business rules, and conveying the output string to the subject. 7. The method of claim 1 , wherein the business rules include logic for identifying syntax and discourse structure of the information elements processed for use in creating the output string. | 0.709726 |
8,706,653 | 8 | 9 | 8. A method as claimed in claim 1 wherein using the probabilistic learning system to determine enhanced answers to the questions comprises propagating answers made by the judges through nodes in a logical component of the probabilistic learning system according to logical relations between the questions. | 8. A method as claimed in claim 1 wherein using the probabilistic learning system to determine enhanced answers to the questions comprises propagating answers made by the judges through nodes in a logical component of the probabilistic learning system according to logical relations between the questions. 9. A method as claimed in claim 8 wherein at least one node in the logical component represents all missing deductions that could lead to a specified enhanced answer. | 0.955376 |
7,853,629 | 1 | 3 | 1. A document imaging and management system, comprising: a device for imaging at least one coversheet and at least one document associated with the coversheet; a database including a plurality of rules for managing documents and tasks; at least one processor; software executing on said at least one processor for creating and storing a document profile unique to a particular document and creating a document key unique to the document before the document is imaged, wherein the document profile includes the document key unique to the document and routing instructions for the document, and wherein a coversheet of the document includes at least one computer readable identifier indicative of the document key; software executing on said at least one processor for reading the computer-readable identifier from coversheet and matching the imaged document to the stored document profile based on the document key; software executing on said at least one processor for routing the imaged document based on the stored document profile; software executing on said at least one processor for determining at least one task associated with the document based on the stored document profile and at least one of the plurality of rules; and error detection software executing on said processor for monitoring imaged documents received by the system and pending document keys for which document profiles have been created to detect unmatched imaged documents and unmatched pending document keys, wherein said error detection software stores the imaged document in an error storage if the imaged document is received but not matched to the document key. | 1. A document imaging and management system, comprising: a device for imaging at least one coversheet and at least one document associated with the coversheet; a database including a plurality of rules for managing documents and tasks; at least one processor; software executing on said at least one processor for creating and storing a document profile unique to a particular document and creating a document key unique to the document before the document is imaged, wherein the document profile includes the document key unique to the document and routing instructions for the document, and wherein a coversheet of the document includes at least one computer readable identifier indicative of the document key; software executing on said at least one processor for reading the computer-readable identifier from coversheet and matching the imaged document to the stored document profile based on the document key; software executing on said at least one processor for routing the imaged document based on the stored document profile; software executing on said at least one processor for determining at least one task associated with the document based on the stored document profile and at least one of the plurality of rules; and error detection software executing on said processor for monitoring imaged documents received by the system and pending document keys for which document profiles have been created to detect unmatched imaged documents and unmatched pending document keys, wherein said error detection software stores the imaged document in an error storage if the imaged document is received but not matched to the document key. 3. The system according to claim 1 , further comprising: software executing on said processor for reporting the at least one task. | 0.819444 |
9,870,405 | 1 | 2 | 1. A method, comprising: providing a search interface to a user for installation on a remote computer; receiving results of an Internet search query initiated by a user using the search interface, wherein the Internet search query comprises a number of search terms entered by the user using an input device and wherein the results comprise links to resources that meet criteria specified in the search query; evaluating the received results in relation to a personal profile of the user, wherein the personal profile includes a plurality of characteristics associated with the user and wherein the evaluating comprises comparing the plurality of characteristics to the results, wherein the plurality of characteristics comprise at least one characteristic derived from observing the user's behavioral patterns over a period of time and at least one characteristic declared by the user; evaluating the results based on attributes of the user; ranking the results to generate a resultant that reflects a ranking of the results in order of likely meaningfulness to the user based on the evaluation; and communicating the resultant to the user via the search interface such that the user cause to be displayed a web page associated with a selected one of the results by activating the selected one of the results via the search interface; wherein the plurality of characteristics includes at least one of a media type preferred by the user, a tag cloud associated with the user, a personal vocabulary of the user, and a rating of similar searches. | 1. A method, comprising: providing a search interface to a user for installation on a remote computer; receiving results of an Internet search query initiated by a user using the search interface, wherein the Internet search query comprises a number of search terms entered by the user using an input device and wherein the results comprise links to resources that meet criteria specified in the search query; evaluating the received results in relation to a personal profile of the user, wherein the personal profile includes a plurality of characteristics associated with the user and wherein the evaluating comprises comparing the plurality of characteristics to the results, wherein the plurality of characteristics comprise at least one characteristic derived from observing the user's behavioral patterns over a period of time and at least one characteristic declared by the user; evaluating the results based on attributes of the user; ranking the results to generate a resultant that reflects a ranking of the results in order of likely meaningfulness to the user based on the evaluation; and communicating the resultant to the user via the search interface such that the user cause to be displayed a web page associated with a selected one of the results by activating the selected one of the results via the search interface; wherein the plurality of characteristics includes at least one of a media type preferred by the user, a tag cloud associated with the user, a personal vocabulary of the user, and a rating of similar searches. 2. The method of claim 1 , wherein at least one of the characteristics is weighted more than another characteristic from the plurality of characteristics. | 0.848723 |
9,451,989 | 19 | 22 | 19. A medical implant assembly having first, second and third bone attachment structures with closures to capture a dynamic longitudinal connecting member wherein the assembly comprises: a) the connecting member, the connecting member comprises: i) a first end, a transition portion and a second end; ii) the first end includes a rod portion extending longitudinally from the transition portion and secured to the first and second bone attachment structures; iii) the second end includes a cord portion joined with the rod portion at the transition portion and extending longitudinally therefrom and entirely through the third bone attachment structure; and iv) a substantially elastic spacer portion positioned between the second and third bone attachment structures; and v) an elastic bumper surrounding and in slidable relation with the tensionable cord portion; and vi) a blocker having a releasable set screw secured to the cord portion near the second end, the releasable set screw being releasable such that the cord portion can be re-tensioned when the cord remains captured by the bone attachment closures. | 19. A medical implant assembly having first, second and third bone attachment structures with closures to capture a dynamic longitudinal connecting member wherein the assembly comprises: a) the connecting member, the connecting member comprises: i) a first end, a transition portion and a second end; ii) the first end includes a rod portion extending longitudinally from the transition portion and secured to the first and second bone attachment structures; iii) the second end includes a cord portion joined with the rod portion at the transition portion and extending longitudinally therefrom and entirely through the third bone attachment structure; and iv) a substantially elastic spacer portion positioned between the second and third bone attachment structures; and v) an elastic bumper surrounding and in slidable relation with the tensionable cord portion; and vi) a blocker having a releasable set screw secured to the cord portion near the second end, the releasable set screw being releasable such that the cord portion can be re-tensioned when the cord remains captured by the bone attachment closures. 22. The assembly according to claim 19 , wherein: a) the blocker engages the bumper in an overlapping relationship. | 0.744444 |
6,151,608 | 1 | 35 | 1. An automated computer-implemented method for migrating source data from at least one source to at least one destination table of a database having a schema without a user having to write computer code, the method comprising the steps of: defining patterns which describe format and content of the source data; applying the patterns to the source data to create transformed data; associating migration rules based on the schema with the patterns to generate a set of instructions that define migration paths; and loading the transformed data in a sequence into the at least one destination table based on the set of instructions, the at least one destination table having a defined format and destination fields, wherein the step of loading is automatically sequenced based on the migration rules so that referential integrity is maintained. | 1. An automated computer-implemented method for migrating source data from at least one source to at least one destination table of a database having a schema without a user having to write computer code, the method comprising the steps of: defining patterns which describe format and content of the source data; applying the patterns to the source data to create transformed data; associating migration rules based on the schema with the patterns to generate a set of instructions that define migration paths; and loading the transformed data in a sequence into the at least one destination table based on the set of instructions, the at least one destination table having a defined format and destination fields, wherein the step of loading is automatically sequenced based on the migration rules so that referential integrity is maintained. 35. The method as claimed in claim 1 wherein the source data is migrated across environments such as from a development environment to a test environment, a training environment or a production environment without changing setup parameters. | 0.52 |
7,856,598 | 12 | 13 | 12. A system, comprising: an al-gram logic configured to compute a set of al-grams having at least one al-gram composed of three selectively non-contiguous characters for a correctly spelled word; a data store that stores the computed al-grams for processes related to spell checking; an inverted index logic configured to compute an inverted index that relates an al-gram to one or more correctly spelled words; a search logic configured to receive a search word; the al-gram logic configured to compute a set of search word al-grams where the set of search word al-grams include at least one al-gram having at least three selectively non-contiguous characters, for the search word; the search logic being configured to select one or more candidate words based, at least in part, on comparing the set of al-grams to set of search word al-grams and to compute a set of candidate word al-grams for respective selected candidate words; and where the set of search word al-grams includes two copies of a contiguous liaoalphagram computed from the first three letters of the search word and where the set of candidate word al-grams includes two copies of a contiguous liaoalphagram computed from the first three letters of the candidate word; and where the search logic is configured to select one or more candidate words as suggested spellings for the query term, based at least in part on a number of common al-grams in the set of search word al-grams and the sets of candidate word al-grams. | 12. A system, comprising: an al-gram logic configured to compute a set of al-grams having at least one al-gram composed of three selectively non-contiguous characters for a correctly spelled word; a data store that stores the computed al-grams for processes related to spell checking; an inverted index logic configured to compute an inverted index that relates an al-gram to one or more correctly spelled words; a search logic configured to receive a search word; the al-gram logic configured to compute a set of search word al-grams where the set of search word al-grams include at least one al-gram having at least three selectively non-contiguous characters, for the search word; the search logic being configured to select one or more candidate words based, at least in part, on comparing the set of al-grams to set of search word al-grams and to compute a set of candidate word al-grams for respective selected candidate words; and where the set of search word al-grams includes two copies of a contiguous liaoalphagram computed from the first three letters of the search word and where the set of candidate word al-grams includes two copies of a contiguous liaoalphagram computed from the first three letters of the candidate word; and where the search logic is configured to select one or more candidate words as suggested spellings for the query term, based at least in part on a number of common al-grams in the set of search word al-grams and the sets of candidate word al-grams. 13. The system of claim 12 , the search logic being configured to select candidate correctly spelled words that receive a similarity score exceeding a threshold, the search logic being configured to compute the similarity score based, at least in part, on comparing the first set of al-grams to the set of search word al-grams. | 0.722881 |
8,661,050 | 1 | 7 | 1. A method comprising: receiving a search request from a search user; creating a network of entities, the network including relationships among the entities, wherein the entities include a search user entity representing the search user and one or more result entities; defining trust components and similarity components for the relationships among the entities, wherein the similarity components express relative similarity of the entities and the trust components express relative trust among the entities; identifying a plurality of search results, wherein the one or more result entities provide the plurality of search results and the plurality of search results are identified based on the search request; for the plurality of search results provided by the one or more result entities, determining, from the network, a set of relationships connecting the search user entity to the one or more result entities along one or more paths; determining, based on the set of relationships connecting the search user entity to the one or more result entities, relevance values for the plurality of search results, wherein the relevance values are determined based on both: individual similarity components that express relative similarity of individual entities along the one or more paths, and individual trust components that express relative trust among the individual entities along the one or more paths; ranking the plurality of search results based on the relevance values for the plurality of search results; and presenting, to the search user, individual search results from the plurality of search results according to the ranking, wherein at least the ranking is performed by a hardware computer processor. | 1. A method comprising: receiving a search request from a search user; creating a network of entities, the network including relationships among the entities, wherein the entities include a search user entity representing the search user and one or more result entities; defining trust components and similarity components for the relationships among the entities, wherein the similarity components express relative similarity of the entities and the trust components express relative trust among the entities; identifying a plurality of search results, wherein the one or more result entities provide the plurality of search results and the plurality of search results are identified based on the search request; for the plurality of search results provided by the one or more result entities, determining, from the network, a set of relationships connecting the search user entity to the one or more result entities along one or more paths; determining, based on the set of relationships connecting the search user entity to the one or more result entities, relevance values for the plurality of search results, wherein the relevance values are determined based on both: individual similarity components that express relative similarity of individual entities along the one or more paths, and individual trust components that express relative trust among the individual entities along the one or more paths; ranking the plurality of search results based on the relevance values for the plurality of search results; and presenting, to the search user, individual search results from the plurality of search results according to the ranking, wherein at least the ranking is performed by a hardware computer processor. 7. The method of claim 1 , wherein an individual result entity is an organization that provides one or more of the individual search results that are presented to the search user. | 0.836976 |
8,719,021 | 1 | 5 | 1. A speech recognition dictionary compilation assisting system, comprising: a computer processing apparatus; and a computer-readable storage medium having data stored thereon that includes a dictionary, a language model, an acoustic model, and a speech recognition dictionary compilation assisting program that is executable by the computer processing apparatus to cause the computer processing apparatus to operate as: a text analysis section that applies morphological analysis to input text data to produce analyzed text data comprising words of the input text data and pronunciation information for each word; a virtual speech recognition processing section that performs a speech recognition process on said analyzed text data received from the text analysis section by applying the dictionary and the language model to said analyzed text data thereby to generate virtual text data, and that compares a pronunciation information of the virtual text data with the pronunciation information of the analyzed text data to extract and output different points of the analyzed text data and the virtual text data, each different point comprising an element of the analyzed text data and a corresponding element of the virtual text data; and an update processing section that corrects at least one of the dictionary and the language model in accordance with the different points identified by the virtual speech recognition processing section, wherein for each different point, the pronunciation information corresponding to a word of the analyzed text data differs from a corresponding pronunciation information of the virtual text data. | 1. A speech recognition dictionary compilation assisting system, comprising: a computer processing apparatus; and a computer-readable storage medium having data stored thereon that includes a dictionary, a language model, an acoustic model, and a speech recognition dictionary compilation assisting program that is executable by the computer processing apparatus to cause the computer processing apparatus to operate as: a text analysis section that applies morphological analysis to input text data to produce analyzed text data comprising words of the input text data and pronunciation information for each word; a virtual speech recognition processing section that performs a speech recognition process on said analyzed text data received from the text analysis section by applying the dictionary and the language model to said analyzed text data thereby to generate virtual text data, and that compares a pronunciation information of the virtual text data with the pronunciation information of the analyzed text data to extract and output different points of the analyzed text data and the virtual text data, each different point comprising an element of the analyzed text data and a corresponding element of the virtual text data; and an update processing section that corrects at least one of the dictionary and the language model in accordance with the different points identified by the virtual speech recognition processing section, wherein for each different point, the pronunciation information corresponding to a word of the analyzed text data differs from a corresponding pronunciation information of the virtual text data. 5. The speech recognition dictionary compilation assisting system according to claim 1 , wherein said update processing section adds a word that has appeared in the analyzed text data to the dictionary in accordance to the different points of the analyzed text data and the virtual text data. | 0.73211 |
8,620,657 | 21 | 26 | 21. A speaker verification system comprising: at least one computer readable storage medium storing a voice print obtained from a user's utterance of at least one enrollment utterance; a receiver to receive a voice signal of the speaker uttering at least one challenge utterance, wherein the at least one challenge utterance includes at least one word that was not in the at least one enrollment utterance; and at least one controller, coupled to the memory and the receiver, configured to determine whether the speaker is the user based, at least in part, on voice signal and the voice print. | 21. A speaker verification system comprising: at least one computer readable storage medium storing a voice print obtained from a user's utterance of at least one enrollment utterance; a receiver to receive a voice signal of the speaker uttering at least one challenge utterance, wherein the at least one challenge utterance includes at least one word that was not in the at least one enrollment utterance; and at least one controller, coupled to the memory and the receiver, configured to determine whether the speaker is the user based, at least in part, on voice signal and the voice print. 26. The speaker verification system of claim 21 , wherein the at least one enrollment word is selected from an enrollment vocabulary, and wherein the receiver receives the voice signal of the speaker uttering at least one challenge utterance selected from a challenge vocabulary comprising a plurality of challenge words, and wherein the challenge vocabulary includes more words than the enrollment vocabulary. | 0.711268 |
8,023,636 | 14 | 15 | 14. The computer readable medium according to claim 8 , further comprising: a coaching code segment that provides instruction to the trainee based on at least the response to the customer portion. | 14. The computer readable medium according to claim 8 , further comprising: a coaching code segment that provides instruction to the trainee based on at least the response to the customer portion. 15. The interactive training method according to claim 14 , in which the coaching code segment comprises a determining code segment that determines a level of instruction to provide to the trainee prior to providing the instruction, the level of instruction being based on at least a determination of performance by the trainee. | 0.842459 |
8,170,866 | 14 | 15 | 14. A spoken dialog system having a speech model, the spoken dialog system comprising: a processor; a first module configured to control the processor to receive speech from a user; and a second module configured to control the processor to perform speech recognition on the speech using the speech model, wherein the speech model is generated by a method comprising: retrieving for an individual a calling list associated with the individual; identifying data of a social network associated with each number in the calling history; refining the data of the social network based on at least one parameter, to yield refined data of the social network; and creating, via a processor, a language model for the individual based on the refined data of the social network. | 14. A spoken dialog system having a speech model, the spoken dialog system comprising: a processor; a first module configured to control the processor to receive speech from a user; and a second module configured to control the processor to perform speech recognition on the speech using the speech model, wherein the speech model is generated by a method comprising: retrieving for an individual a calling list associated with the individual; identifying data of a social network associated with each number in the calling history; refining the data of the social network based on at least one parameter, to yield refined data of the social network; and creating, via a processor, a language model for the individual based on the refined data of the social network. 15. The spoken dialog system of claim 14 , wherein the data of the social network comprises at least one of data corresponding to an entity, a business name, and a person's name. | 0.64257 |
7,778,515 | 1 | 5 | 1. A system for linking content to viewing and/or shopping recommendations, comprising: an input device for selecting alpha numeric characters that are presented on a video display and that are within the video portion of a DVD content by highlighting the characters using the input device and pressing a selector key on the input device; means for receiving the alpha numeric characters; means responsive to the means for receiving for automatically accessing a source of recommended viewing and/or shopping. | 1. A system for linking content to viewing and/or shopping recommendations, comprising: an input device for selecting alpha numeric characters that are presented on a video display and that are within the video portion of a DVD content by highlighting the characters using the input device and pressing a selector key on the input device; means for receiving the alpha numeric characters; means responsive to the means for receiving for automatically accessing a source of recommended viewing and/or shopping. 5. The system of claim 1 , wherein the source of recommended viewing and/or shopping communicates with a WAN. | 0.68314 |
8,195,669 | 15 | 16 | 15. The non-transitory computer-readable storage media of claim 14 , wherein the process of maximizing a likelihood includes the use of a specific function that processes data describing content similarity relationships between the documents. | 15. The non-transitory computer-readable storage media of claim 14 , wherein the process of maximizing a likelihood includes the use of a specific function that processes data describing content similarity relationships between the documents. 16. The non-transitory computer-readable storage media of claim 15 , wherein the specific function utilizes: g ( y i , y j , x ) = - 1 2 S i , j ( y i - y j ) 2 , wherein S i,j is similarity between a first document d i and a second document d j . | 0.885 |
9,760,556 | 1 | 7 | 1. A system for linking electronic documents, comprising: a memory device that stores a set of instructions; at least one processor that executes the instructions to: receive annotations associated with source electronic documents, wherein at least some of the annotations include respective selections of text from the source electronic documents and text inputs received from users; generate snippets from the received annotations; determine content of selections of text of the annotations of the respective snippets; aggregate the generated snippets into clusters based at least in part on the determined content; generate an electronic document based on the clusters; generate links between the snippets and their respective source documents; and embed the generated links in the generated electronic document. | 1. A system for linking electronic documents, comprising: a memory device that stores a set of instructions; at least one processor that executes the instructions to: receive annotations associated with source electronic documents, wherein at least some of the annotations include respective selections of text from the source electronic documents and text inputs received from users; generate snippets from the received annotations; determine content of selections of text of the annotations of the respective snippets; aggregate the generated snippets into clusters based at least in part on the determined content; generate an electronic document based on the clusters; generate links between the snippets and their respective source documents; and embed the generated links in the generated electronic document. 7. The system of claim 1 , wherein the at least one processor executes the instructions to: aggregate the generated snippets into clusters further based at least in part on respective persons associated with source electronic documents from which the respective snippets are generated. | 0.501748 |
7,668,888 | 1 | 7 | 1. A computer-implemented method for providing, via a computer processor, at least one readable object that is readable by a search engine from at least one structured data object stored in a database, the method comprising: extracting, via the computer processor, the structured data object from the database, wherein the structured data object includes a hierarchical sequence of nodes related by at least one link and a plurality of content, and wherein at least one content of the plurality of content is nonreadable content that cannot be read by the search engine; mapping, via the computer processor, the structured data object into a generic data model according to the hierarchical sequence of nodes and content; and creating, via the computer processor, the readable object from the generic data model, wherein creating includes converting the nonreadable content of the structured data object into readable content for the search engine. | 1. A computer-implemented method for providing, via a computer processor, at least one readable object that is readable by a search engine from at least one structured data object stored in a database, the method comprising: extracting, via the computer processor, the structured data object from the database, wherein the structured data object includes a hierarchical sequence of nodes related by at least one link and a plurality of content, and wherein at least one content of the plurality of content is nonreadable content that cannot be read by the search engine; mapping, via the computer processor, the structured data object into a generic data model according to the hierarchical sequence of nodes and content; and creating, via the computer processor, the readable object from the generic data model, wherein creating includes converting the nonreadable content of the structured data object into readable content for the search engine. 7. The computer-implemented method of claim 1 , wherein the readable object comprises a document structure or a text document. | 0.845209 |
8,856,056 | 8 | 9 | 8. The sentiment calculator according to claim 1 , wherein the means for determining polarity derives polarity of syntactic constituents in head-complement, modifier-modified, and subject-predicate relations. | 8. The sentiment calculator according to claim 1 , wherein the means for determining polarity derives polarity of syntactic constituents in head-complement, modifier-modified, and subject-predicate relations. 9. The sentiment calculator according to claim 8 , wherein the means for determining a strength value derives strength of syntactic constituents by application of arithmetic operations. | 0.808884 |
8,577,865 | 9 | 14 | 9. A computer program product, the computer program product comprising a set of instructions in a machine-readable storage device for use in searching for documents, said documents having searchable parameters, said searchable parameters stored in more than one different data sources, the set of instructions for causing at least one machine to: receive a search statement, said search statement comprising at least a first search query, said first search query comprising a first search value and at least a first search parameter specifying a first data source to search, and a second search query, said second search query comprising a second search value and at least a second search parameter specifying a second data source to search; determine a search strategy based on the search statement, the search strategy comprising a search activity for each data source to be searched such that a first search activity and a second search activity for searching said first data source and searching said second data source are created, respectively; assign weights to said search activities, said weights based upon a type of a data source to be searched and a specificity of search value; search the first data source and second data source using the first and second search activities respectively, wherein an order in which the first and second search activities are performed is dictated by the weights assigned to the first and second search activities; and returning a final document search result from said search activities. | 9. A computer program product, the computer program product comprising a set of instructions in a machine-readable storage device for use in searching for documents, said documents having searchable parameters, said searchable parameters stored in more than one different data sources, the set of instructions for causing at least one machine to: receive a search statement, said search statement comprising at least a first search query, said first search query comprising a first search value and at least a first search parameter specifying a first data source to search, and a second search query, said second search query comprising a second search value and at least a second search parameter specifying a second data source to search; determine a search strategy based on the search statement, the search strategy comprising a search activity for each data source to be searched such that a first search activity and a second search activity for searching said first data source and searching said second data source are created, respectively; assign weights to said search activities, said weights based upon a type of a data source to be searched and a specificity of search value; search the first data source and second data source using the first and second search activities respectively, wherein an order in which the first and second search activities are performed is dictated by the weights assigned to the first and second search activities; and returning a final document search result from said search activities. 14. The computer program product as set forth in claim 9 , wherein at least one of the first data source and second data source comprises at least one of a table and a database. | 0.759511 |
9,157,855 | 20 | 32 | 20. An apparatus for classifying the material type of an unknown material into a probability distribution of multiple predetermined material types by using a collection of plural predetermined material classifiers, wherein each material type of the multiple predetermined material types is associated with a corresponding best performing classifier from the collection of plural predetermined material classifiers by a predesignated stored association, the apparatus comprising: an application unit configured to apply the plural material classifiers to the unknown material to obtain a list of candidate material types; a look up unit configured to look up a list of potential best performing classifiers using the list of candidate material types as a reference into the predesignated stored association; and an assigning unit configured to assign a respective probability that the unknown material belongs to a material type based on the list of potential best performing classifiers, wherein the application unit is further configured to obtain one or more feature vectors for the unknown material and to input a corresponding one of the feature vectors into each of the plural material classifiers so as to obtain an output representative of material type from each of the plural material classifiers, and wherein each of the application unit, the look up unit and the assigning unit is implemented using a processor. | 20. An apparatus for classifying the material type of an unknown material into a probability distribution of multiple predetermined material types by using a collection of plural predetermined material classifiers, wherein each material type of the multiple predetermined material types is associated with a corresponding best performing classifier from the collection of plural predetermined material classifiers by a predesignated stored association, the apparatus comprising: an application unit configured to apply the plural material classifiers to the unknown material to obtain a list of candidate material types; a look up unit configured to look up a list of potential best performing classifiers using the list of candidate material types as a reference into the predesignated stored association; and an assigning unit configured to assign a respective probability that the unknown material belongs to a material type based on the list of potential best performing classifiers, wherein the application unit is further configured to obtain one or more feature vectors for the unknown material and to input a corresponding one of the feature vectors into each of the plural material classifiers so as to obtain an output representative of material type from each of the plural material classifiers, and wherein each of the application unit, the look up unit and the assigning unit is implemented using a processor. 32. The apparatus according to claim 20 , wherein the predesignated stored association by which each material type is associated with a corresponding best performing classifier is based on theoretical behavior of the classifiers on the material types. | 0.679847 |
7,792,883 | 20 | 22 | 20. The method of claim 15 , including: determining key words corresponding to the location search query; and identifying geographical features that match the key words in the location search query. | 20. The method of claim 15 , including: determining key words corresponding to the location search query; and identifying geographical features that match the key words in the location search query. 22. The method of claim 20 , wherein the key words include one or more synonyms for one or more terms in the location search query. | 0.943437 |
8,381,236 | 1 | 7 | 1. A method to execute one or more functions of a new service module on a document personalization production system, the method comprising: establishing communication between an application framework of the document personalization production system and the new service module, wherein the new service module is located on a server and the application framework is configured to integrate the new service module in the document personalization production system without reprogramming a production manager of the document personalization production system; registering a machine of the document personalization production system to the application framework by communicating a name, a capability, a control system, and a metadata to the application framework in order to execute the new service module and determining the machine's operating parameters to execute the one or more functions of the new service module; the application framework providing one or more interfaces to enable the production manager to issue instructions and data transmission for executing the one or more functions of the new service module without reprogramming the production manager; and the application framework providing one or more plugins to execute the one or more functions of the new service module in the document personalization production system. | 1. A method to execute one or more functions of a new service module on a document personalization production system, the method comprising: establishing communication between an application framework of the document personalization production system and the new service module, wherein the new service module is located on a server and the application framework is configured to integrate the new service module in the document personalization production system without reprogramming a production manager of the document personalization production system; registering a machine of the document personalization production system to the application framework by communicating a name, a capability, a control system, and a metadata to the application framework in order to execute the new service module and determining the machine's operating parameters to execute the one or more functions of the new service module; the application framework providing one or more interfaces to enable the production manager to issue instructions and data transmission for executing the one or more functions of the new service module without reprogramming the production manager; and the application framework providing one or more plugins to execute the one or more functions of the new service module in the document personalization production system. 7. The method to execute a new service module on a document personalization production system, as in claim 1 , wherein the new service module audits system data of the document personalization production system. | 0.686012 |
9,361,286 | 1 | 2 | 1. A computer implemented method for dynamic report generation comprising: in a user interface presenting a report document, receiving a user interaction for modification of at least one report element in the report document; classifying the modification of the at least one report element by: receiving a unique identifier of the at least one report element; matching the unique identifier with the user interaction for modification of the at least one report element; and identifying a class based on the unique identifier and the user interaction for modification; identifying a visual effect based on the classified modification of the at least one report element; and updating the report document by applying the identified visual effect together with the modification of the at least one report element. | 1. A computer implemented method for dynamic report generation comprising: in a user interface presenting a report document, receiving a user interaction for modification of at least one report element in the report document; classifying the modification of the at least one report element by: receiving a unique identifier of the at least one report element; matching the unique identifier with the user interaction for modification of the at least one report element; and identifying a class based on the unique identifier and the user interaction for modification; identifying a visual effect based on the classified modification of the at least one report element; and updating the report document by applying the identified visual effect together with the modification of the at least one report element. 2. The method of claim 1 , wherein the at least one report element is selected from the group consisting of free cell, table, table cell, map, chart, and chart section. | 0.860465 |
9,852,728 | 8 | 9 | 8. A non-transitory computer-readable storage medium having stored thereon instructions, which when executed by a processor result in one or more operations configured for use in a text-to-speech (TTS) system, the operations comprising: identifying, using one or more processors, a word or phrase as a named entity; identifying a language of origin associated with the named entity; transliterating the named entity to a script associated with the language of origin; if the TTS system is operating in the language of origin, passing the transliterated script to the TTS system; and if the TTS system is not operating in the language of origin, generating a phoneme sequence in the language of origin using a grapheme to phoneme (G2P) converter. | 8. A non-transitory computer-readable storage medium having stored thereon instructions, which when executed by a processor result in one or more operations configured for use in a text-to-speech (TTS) system, the operations comprising: identifying, using one or more processors, a word or phrase as a named entity; identifying a language of origin associated with the named entity; transliterating the named entity to a script associated with the language of origin; if the TTS system is operating in the language of origin, passing the transliterated script to the TTS system; and if the TTS system is not operating in the language of origin, generating a phoneme sequence in the language of origin using a grapheme to phoneme (G2P) converter. 9. The non-transitory computer-readable storage medium of claim 8 , further comprising: if the TTS system is not operating in the language of origin, mapping the phoneme sequence to a sequence of target language phonemes. | 0.543388 |
8,150,872 | 1 | 9 | 1. A query system, comprising: a computing device communicatively coupled to a network and configured to receive audio input comprising a query and determine location information; and a server communicatively coupled to the computing device via the network, wherein the server is configured to: receive the query from the computing device; perform natural language processing on the query using lexicons and grammar rules to parse sentences and determine a meaning of the query, wherein the natural language processing comprises converting text in natural language form to text in searchable form using the lexicons and grammar rules to determine the meaning of the query; utilize location information to further determine the meaning of the query; perform a database look up based on the determined meaning of the query, wherein the database look up is provided with a context and environment for narrowing and streamlining the database look up utilizing the location information; and rank responses of the database lookup using an accuracy algorithm. | 1. A query system, comprising: a computing device communicatively coupled to a network and configured to receive audio input comprising a query and determine location information; and a server communicatively coupled to the computing device via the network, wherein the server is configured to: receive the query from the computing device; perform natural language processing on the query using lexicons and grammar rules to parse sentences and determine a meaning of the query, wherein the natural language processing comprises converting text in natural language form to text in searchable form using the lexicons and grammar rules to determine the meaning of the query; utilize location information to further determine the meaning of the query; perform a database look up based on the determined meaning of the query, wherein the database look up is provided with a context and environment for narrowing and streamlining the database look up utilizing the location information; and rank responses of the database lookup using an accuracy algorithm. 9. The query system of claim 1 , further comprising a semantic engine module executed on the server for converting the determined query to a formal database query. | 0.586294 |
7,594,270 | 1 | 8 | 1. A method of analyzing an event detected in a distributed computer system, comprising: at a receiving server machine in a security expert system, receiving information from said distributed computer system over a network, wherein said information comprises said event and wherein said event is detected by a device in said distributed computer system; storing said event in a database in said security expert system; at an expert system server machine in said security expert system: retrieving said event from said database; determining an attack validation value associated with said event; determining a target exposure value associated with a host targeted by said event; determining an attacker rating value associated with an attacker originating said event; and determining a threat rating for said event utilizing said attack validation value, said target exposure value, and said attacker rating value; and displaying said threat rating on a user interface for said security expert system. | 1. A method of analyzing an event detected in a distributed computer system, comprising: at a receiving server machine in a security expert system, receiving information from said distributed computer system over a network, wherein said information comprises said event and wherein said event is detected by a device in said distributed computer system; storing said event in a database in said security expert system; at an expert system server machine in said security expert system: retrieving said event from said database; determining an attack validation value associated with said event; determining a target exposure value associated with a host targeted by said event; determining an attacker rating value associated with an attacker originating said event; and determining a threat rating for said event utilizing said attack validation value, said target exposure value, and said attacker rating value; and displaying said threat rating on a user interface for said security expert system. 8. The method according to claim 1 , wherein the step of determining an attack validation value further comprises: determining a class rating value; determining a vulnerability to attack value for said host; and utilizing said class rating value and said vulnerability to attack value to calculate said attack validation value. | 0.630926 |
8,024,733 | 16 | 17 | 16. The computer-implemented method of claim 12 , further comprising: before running the batch component within the checkpoint interval, allocating resources to be used by the batch component during the checkpoint interval and locking the resources by the batch container. | 16. The computer-implemented method of claim 12 , further comprising: before running the batch component within the checkpoint interval, allocating resources to be used by the batch component during the checkpoint interval and locking the resources by the batch container. 17. The computer-implemented method of claim 16 , further comprising: upon completion of the checkpoint interval duration, unlocking the resources by the batch container. | 0.963472 |
7,698,642 | 1 | 7 | 1. A method comprising: receiving a request from a user, at a unified messaging server, for information, wherein the unified messaging server generates text prompts and audio prompts, wherein the unified messaging server is in communication with at least an email server and a voice mail server; obtaining a localization document, appropriate for the user, wherein the localization document contains rules that define a manner in which prompts are generated for a language; wherein the rules define which prompt file should be retrieved to generate a prompt for the user when at least one parameter representing user data input, received by the unified messaging system from the user, satisfies a particular rule; selecting, by applying the rules to the request received from the user, between text prompt files that apply to the email server and audio prompt files that apply to the voice mail server; selecting a plurality of prompt components from the determined files; and arranging the plurality of prompt components to generate the prompt to be provided to the user; in response to input from the user, the unified messaging server retrieving content from the email server or the voice mail server to be provided to the user. | 1. A method comprising: receiving a request from a user, at a unified messaging server, for information, wherein the unified messaging server generates text prompts and audio prompts, wherein the unified messaging server is in communication with at least an email server and a voice mail server; obtaining a localization document, appropriate for the user, wherein the localization document contains rules that define a manner in which prompts are generated for a language; wherein the rules define which prompt file should be retrieved to generate a prompt for the user when at least one parameter representing user data input, received by the unified messaging system from the user, satisfies a particular rule; selecting, by applying the rules to the request received from the user, between text prompt files that apply to the email server and audio prompt files that apply to the voice mail server; selecting a plurality of prompt components from the determined files; and arranging the plurality of prompt components to generate the prompt to be provided to the user; in response to input from the user, the unified messaging server retrieving content from the email server or the voice mail server to be provided to the user. 7. The method of claim 1 wherein the plurality of prompt components includes at least one reference to another prompt containing at least one prompt component. | 0.911568 |
8,401,838 | 17 | 18 | 17. The method of claim 11 , further comprising: employing as the at least one language database a plurality of different language databases including information from a plurality of different languages. | 17. The method of claim 11 , further comprising: employing as the at least one language database a plurality of different language databases including information from a plurality of different languages. 18. The method of claim 17 , further comprising: including with a first one of the different language databases information from a first language of the different languages; and including with a second one of the different language databases information from a second language of the different languages. | 0.874484 |
8,022,934 | 1 | 3 | 1. A method of enabling input on a handheld electronic device having an input apparatus, an output apparatus, and a memory having stored therein a plurality of language objects, at least some of the language objects each having a frequency value associated therewith, the input apparatus including a plurality of input members, at least some of the input members each having a plurality of linguistic element assigned thereto, the method comprising: detecting an ambiguous input; determining that at least a first language object and a second language object each correspond with at least a portion of the ambiguous input, the first language object being associated with a first frequency value, the second language object being associated with a second frequency value; detecting an occurrence of a predetermined input; responsive to the occurrence of the predetermined input, initiating the storing of a revised frequency value that effectively alters the frequency value with which one of the first language object and the second language object is associated; detecting another ambiguous input; determining that at least a third language object and a fourth language object each correspond with at least a portion of the another ambiguous input, one of the third language object and the fourth language object being associated with a frequency value higher than the frequency value with which the other of the third language object and the fourth language object is associated; making a determination that the third and fourth language objects are in a special category, the special category including word objects stored in the memory that correspond to a particular ambiguous input sequence and are of a same length; detecting another occurrence of the predetermined input; and responsive to said making a determination, maintaining the one of the third language object and the fourth language object associated with a frequency value higher than the frequency value with which the other of the third language object and the fourth language object is associated despite said detecting another occurrence of the predetermined input. | 1. A method of enabling input on a handheld electronic device having an input apparatus, an output apparatus, and a memory having stored therein a plurality of language objects, at least some of the language objects each having a frequency value associated therewith, the input apparatus including a plurality of input members, at least some of the input members each having a plurality of linguistic element assigned thereto, the method comprising: detecting an ambiguous input; determining that at least a first language object and a second language object each correspond with at least a portion of the ambiguous input, the first language object being associated with a first frequency value, the second language object being associated with a second frequency value; detecting an occurrence of a predetermined input; responsive to the occurrence of the predetermined input, initiating the storing of a revised frequency value that effectively alters the frequency value with which one of the first language object and the second language object is associated; detecting another ambiguous input; determining that at least a third language object and a fourth language object each correspond with at least a portion of the another ambiguous input, one of the third language object and the fourth language object being associated with a frequency value higher than the frequency value with which the other of the third language object and the fourth language object is associated; making a determination that the third and fourth language objects are in a special category, the special category including word objects stored in the memory that correspond to a particular ambiguous input sequence and are of a same length; detecting another occurrence of the predetermined input; and responsive to said making a determination, maintaining the one of the third language object and the fourth language object associated with a frequency value higher than the frequency value with which the other of the third language object and the fourth language object is associated despite said detecting another occurrence of the predetermined input. 3. The method of claim 1 , further comprising: making, as at least a portion of said determination, an additional determination that the frequency values associated with the third and fourth language objects are at least one of: both above a predetermined threshold, and of a difference in magnitude below another predetermined threshold. | 0.763636 |
8,396,879 | 13 | 15 | 13. A system comprising: one or more devices comprising: means for calculating a first ranking score for a user and a second ranking score for a comment authored by the user, where the means for calculating includes: means for representing the user as a first node in a graph, means for representing the comment as a second node in the graph, means for transferring a reputation of the user to the comment, the means for transferring the reputation of the user to the comment including: means for adding a first link, in the graph, from the first node to the second node based on first relationship between the first node and the second node, means for transferring an indication of quality of the comment to the user, the means for transferring the indication of the quality of the comment to the user including: means for adding a second link, in the graph, from the second node to the first node based on the first relationship, means for transferring a reputation of another user to the user, means for causing an indication of quality of the comment to be conveyed to the first user, the means for causing the indication of the quality of the comment to be conveyed to the first user including: means for assigning a first initial value to the first node, the first initial value being calculated for the first user based on signals associated with the first user, means for assigning a second initial value to the second node, the second initial value being calculated for the comment based on signals associated with the comment, means for running iterations of a graph algorithm using values of the first node and second node to obtain the first ranking score and the second ranking score, the iterations being run until: values of the nodes converge or a number of iterations has been performed, and at least one of: means for providing a list of users, the user being placed in the list of users at a location based on the first ranking score, or means for providing a list of comments, the comment being placed in the list of comments at a location based on the second ranking score. | 13. A system comprising: one or more devices comprising: means for calculating a first ranking score for a user and a second ranking score for a comment authored by the user, where the means for calculating includes: means for representing the user as a first node in a graph, means for representing the comment as a second node in the graph, means for transferring a reputation of the user to the comment, the means for transferring the reputation of the user to the comment including: means for adding a first link, in the graph, from the first node to the second node based on first relationship between the first node and the second node, means for transferring an indication of quality of the comment to the user, the means for transferring the indication of the quality of the comment to the user including: means for adding a second link, in the graph, from the second node to the first node based on the first relationship, means for transferring a reputation of another user to the user, means for causing an indication of quality of the comment to be conveyed to the first user, the means for causing the indication of the quality of the comment to be conveyed to the first user including: means for assigning a first initial value to the first node, the first initial value being calculated for the first user based on signals associated with the first user, means for assigning a second initial value to the second node, the second initial value being calculated for the comment based on signals associated with the comment, means for running iterations of a graph algorithm using values of the first node and second node to obtain the first ranking score and the second ranking score, the iterations being run until: values of the nodes converge or a number of iterations has been performed, and at least one of: means for providing a list of users, the user being placed in the list of users at a location based on the first ranking score, or means for providing a list of comments, the comment being placed in the list of comments at a location based on the second ranking score. 15. The system of claim 13 , where at least one first relationship of the first relationships corresponds to a relationship between a user and a comment that the user authored. | 0.854545 |
8,280,823 | 84 | 85 | 84. The method of claim 83 , wherein to satisfy the search criteria, the parsed resume associated with each said at least one matching resume includes an expected salary that falls within the required salary range. | 84. The method of claim 83 , wherein to satisfy the search criteria, the parsed resume associated with each said at least one matching resume includes an expected salary that falls within the required salary range. 85. The method of claim 84 , wherein the expected salary falls within the required salary range when: the expected salary is greater than or equal to the minimum required salary, and the expected salary is less than or equal to the maximum required salary. | 0.938668 |
9,876,867 | 11 | 14 | 11. The one or more non-transitory, computer-readable storage medium of claim 10 , wherein to identify the social information comprises to identify, from the pool of social networking sites used by the user, one or more social networking sites on which the friend is interconnected with the user. | 11. The one or more non-transitory, computer-readable storage medium of claim 10 , wherein to identify the social information comprises to identify, from the pool of social networking sites used by the user, one or more social networking sites on which the friend is interconnected with the user. 14. The one or more non-transitory, computer-readable storage medium of claim 11 , wherein to identify the social information comprises to identify social information that is (i) available to the friend and (ii) not accessed by the friend. | 0.918151 |
9,767,478 | 15 | 20 | 15. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions, which, when executed by one or more processors, cause the one or more processors to: determine, for a first time period, a first quantity of links associated with a document; determine, for a second time period, a second quantity of links associated with the document, the second time period being subsequent to the first time period, and the second quantity of links being less than the first quantity of links; identify a first weight associated with each link of the second quantity of links; determine, based on the first weight, a second weight; determine, based on the determined second weight and the second quantity of links being less than the first quantity of links, that the document is stale; and process, based on determining that the document is stale, the document. | 15. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions, which, when executed by one or more processors, cause the one or more processors to: determine, for a first time period, a first quantity of links associated with a document; determine, for a second time period, a second quantity of links associated with the document, the second time period being subsequent to the first time period, and the second quantity of links being less than the first quantity of links; identify a first weight associated with each link of the second quantity of links; determine, based on the first weight, a second weight; determine, based on the determined second weight and the second quantity of links being less than the first quantity of links, that the document is stale; and process, based on determining that the document is stale, the document. 20. The non-transitory computer-readable medium of claim 15 , where the one or more instructions to determine the second weight further include: one or more instructions to combine the first weight to create the second weight, and the one or more instructions to process the document include: one or more instructions to determine, based on the second weight, a score associated with the document. | 0.601406 |
9,690,849 | 2 | 4 | 2. The method of claim 1 , wherein the first cluster of conceptually-related portions of text is generated by aggregating one or more financial documents according to an assigned key value. | 2. The method of claim 1 , wherein the first cluster of conceptually-related portions of text is generated by aggregating one or more financial documents according to an assigned key value. 4. The method of claim 2 , wherein the assigned key value is a sector for a given financial document. | 0.967628 |
10,055,461 | 1 | 3 | 1. A computer-implemented method comprising: receiving, by a distributed search system, a collection of training data comprising a plurality of training instances that each identify a respective first document selected by a particular user when the first document was identified in search results provided by the search system to the particular user in response to particular search query issued by the particular user; partitioning the collection of training data over a plurality of computing devices of the distributed search system; generating, by the distributed search system, a ranking model that produces a likelihood that a particular user will select a particular document when identified by one or more search results provided in response to a particular search query submitted by the particular user, including processing, by each computing device of the plurality of computing devices, training instances assigned to the computing device, including: selecting, by the computing device, a candidate condition, wherein the candidate condition specifies values for one or more user features, one or more query features, and one or more document features, sending, by the computing device, to each other computing device of the plurality of computing devices, a request to compute local statistics for the candidate condition, receiving, by the computing device from each other computing device of one or more other computing devices, respective computed statistics for the candidate condition computed by the other computing device using values of local training instances assigned to the other computing device, computing, by the computing device, a weight for the candidate condition according to the computed statistics received from the one or more other computing devices for the candidate condition; determining, by the computing device, that a new rule comprising the candidate condition and the computed weight should be added to the ranking model, and in response, adding the new rule to the ranking model and providing, by the computing device, to each other computing device of the plurality of computing devices, an indication that the new rule comprising the candidate condition and the computed weight should be added to the ranking model; receiving a search query submitted by a first user; obtaining a plurality of search results that satisfy the search query, wherein each search result identifies a respective document of a plurality of documents; determining one or more features of the first user and one or more features of the search query submitted by the first user; using the one or more features of the first user and the one or more features of the search query as input to the ranking model to compute, for each document identified by the search results, a respective likelihood that the first user will select the document when provided in response to the search query; and ranking the plurality of search results based on a respective computed likelihood for each document, the computed likelihood for each document being a likelihood that the first user will select the document when provided in response to the search query. | 1. A computer-implemented method comprising: receiving, by a distributed search system, a collection of training data comprising a plurality of training instances that each identify a respective first document selected by a particular user when the first document was identified in search results provided by the search system to the particular user in response to particular search query issued by the particular user; partitioning the collection of training data over a plurality of computing devices of the distributed search system; generating, by the distributed search system, a ranking model that produces a likelihood that a particular user will select a particular document when identified by one or more search results provided in response to a particular search query submitted by the particular user, including processing, by each computing device of the plurality of computing devices, training instances assigned to the computing device, including: selecting, by the computing device, a candidate condition, wherein the candidate condition specifies values for one or more user features, one or more query features, and one or more document features, sending, by the computing device, to each other computing device of the plurality of computing devices, a request to compute local statistics for the candidate condition, receiving, by the computing device from each other computing device of one or more other computing devices, respective computed statistics for the candidate condition computed by the other computing device using values of local training instances assigned to the other computing device, computing, by the computing device, a weight for the candidate condition according to the computed statistics received from the one or more other computing devices for the candidate condition; determining, by the computing device, that a new rule comprising the candidate condition and the computed weight should be added to the ranking model, and in response, adding the new rule to the ranking model and providing, by the computing device, to each other computing device of the plurality of computing devices, an indication that the new rule comprising the candidate condition and the computed weight should be added to the ranking model; receiving a search query submitted by a first user; obtaining a plurality of search results that satisfy the search query, wherein each search result identifies a respective document of a plurality of documents; determining one or more features of the first user and one or more features of the search query submitted by the first user; using the one or more features of the first user and the one or more features of the search query as input to the ranking model to compute, for each document identified by the search results, a respective likelihood that the first user will select the document when provided in response to the search query; and ranking the plurality of search results based on a respective computed likelihood for each document, the computed likelihood for each document being a likelihood that the first user will select the document when provided in response to the search query. 3. The method of claim 1 , wherein the one or more features of the search query include a language of the query and one or more terms of the query. | 0.937394 |
9,665,499 | 13 | 14 | 13. A computer system for facilitating memory access, said computer system comprising: a memory; and a processor in communications with the memory, wherein the computer system is configured to perform a method, said method comprising: providing a first partition within a system configuration, the first partition configured to support an operating system (OS) designed for a first address translation architecture, wherein configuration of the first partition to support the OS designed for the first address translation architecture is indicated in configuration information in a configuration data structure, and wherein the first partition is not configured to support an OS designed for a second address translation architecture; providing a second partition within the system configuration, the second partition configured to support the OS designed for the second address translation architecture, wherein configuration of the second partition to support the OS designed for the second address translation architecture is indicated in the configuration information in the configuration data structure, wherein the second partition is not configured to support the OS designed for the first address translation architecture, and wherein the first address translation architecture is structurally different from the second address translation architecture; based on obtaining, as part of an address translation request of the first partition or second partition, an address for translation, determining, based on the configuration information in the configuration data structure, an address translation architecture to use to translate the address; and translating the address via the determined address translation architecture. | 13. A computer system for facilitating memory access, said computer system comprising: a memory; and a processor in communications with the memory, wherein the computer system is configured to perform a method, said method comprising: providing a first partition within a system configuration, the first partition configured to support an operating system (OS) designed for a first address translation architecture, wherein configuration of the first partition to support the OS designed for the first address translation architecture is indicated in configuration information in a configuration data structure, and wherein the first partition is not configured to support an OS designed for a second address translation architecture; providing a second partition within the system configuration, the second partition configured to support the OS designed for the second address translation architecture, wherein configuration of the second partition to support the OS designed for the second address translation architecture is indicated in the configuration information in the configuration data structure, wherein the second partition is not configured to support the OS designed for the first address translation architecture, and wherein the first address translation architecture is structurally different from the second address translation architecture; based on obtaining, as part of an address translation request of the first partition or second partition, an address for translation, determining, based on the configuration information in the configuration data structure, an address translation architecture to use to translate the address; and translating the address via the determined address translation architecture. 14. The computer system of claim 13 , wherein the first address translation architecture is for handling address translation requests of the first partition and the second address translation architecture is for handling address translation requests of the second partition. | 0.654912 |
9,002,003 | 15 | 20 | 15. The system according to claim 11 wherein said cryptographic operation comprises combining a current selection with all previous selections to generate a first intermediate value, operating on said first intermediate value to generate a second intermediate value, sending said second intermediate value to one of said at least one other entity, receiving a third intermediate value generated by said other entity using said second intermediate value and information private to said other entity; and using said third intermediate value to generate a selection value used to determine said second plurality of choices. | 15. The system according to claim 11 wherein said cryptographic operation comprises combining a current selection with all previous selections to generate a first intermediate value, operating on said first intermediate value to generate a second intermediate value, sending said second intermediate value to one of said at least one other entity, receiving a third intermediate value generated by said other entity using said second intermediate value and information private to said other entity; and using said third intermediate value to generate a selection value used to determine said second plurality of choices. 20. The system according to claim 15 wherein said cryptographic engine is further configured for applying the inverse of said random value to said third intermediate value to derive a third point and rendering said third point to generate said selection value. | 0.903057 |
9,934,309 | 20 | 23 | 20. A non-transitory computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more computing devices, cause the computing devices to perform actions including: storing unstructured data in an unstructured data store, wherein at least some of the unstructured data remains unstructured in the unstructured data store; receiving a structured query in a structured query language from an application; identifying a value of a field in data stored in the unstructured data store, based on an extraction rule that specifies where to find a subportion of text within a segment of the data; generating a second query in a second query language associated with the unstructured data store, based on the structured query; causing execution of the second query against the unstructured data stored in the unstructured data store; receiving a result of execution of the second query against the unstructured data stored in the unstructured data store; and causing an indication of the result to be provided to the application for output to a user. | 20. A non-transitory computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more computing devices, cause the computing devices to perform actions including: storing unstructured data in an unstructured data store, wherein at least some of the unstructured data remains unstructured in the unstructured data store; receiving a structured query in a structured query language from an application; identifying a value of a field in data stored in the unstructured data store, based on an extraction rule that specifies where to find a subportion of text within a segment of the data; generating a second query in a second query language associated with the unstructured data store, based on the structured query; causing execution of the second query against the unstructured data stored in the unstructured data store; receiving a result of execution of the second query against the unstructured data stored in the unstructured data store; and causing an indication of the result to be provided to the application for output to a user. 23. A non-transitory computer-readable storage medium as recited in claim 20 , wherein the unstructured data store does not include a relational database. | 0.917998 |
9,609,073 | 1 | 5 | 1. A method comprising: logging actions taken by users of a social networking system; determining one or more story generators based on a view requested by a client device of a viewing user of the social networking system; accessing a plurality of logged actions of the viewing user or one or more other users connected to the viewing user in the social networking system; selecting one or more of the logged actions based on a relevance of each of the logged actions to the viewing user; generating a plurality of candidate stories from the logged actions using the one or more story generators, each of the plurality of candidate stories being associated with a story type of a plurality of story types, where two or more candidate stories of the plurality of candidate stories are associated with a same logged action; generating an affinity for each of the plurality of candidate stories, wherein each affinity comprises a measure of the relevance of a candidate story of the plurality of candidate stories to the viewing user; generating a ranking of the plurality of candidate stories based on the affinity generated for each the plurality of stories; identifying the two or more candidate stories that are associated with the same logged action; responsive to the identifying, updating the ranking by removing a subset of the two or more candidate stories from the ranking; selecting one or more of the plurality of candidate stories as selected stories for the view requested by the client device of the viewing user based on the updated ranking; and sending the requested view comprising displayable representations of the selected stories to the client device for display to the viewing user. | 1. A method comprising: logging actions taken by users of a social networking system; determining one or more story generators based on a view requested by a client device of a viewing user of the social networking system; accessing a plurality of logged actions of the viewing user or one or more other users connected to the viewing user in the social networking system; selecting one or more of the logged actions based on a relevance of each of the logged actions to the viewing user; generating a plurality of candidate stories from the logged actions using the one or more story generators, each of the plurality of candidate stories being associated with a story type of a plurality of story types, where two or more candidate stories of the plurality of candidate stories are associated with a same logged action; generating an affinity for each of the plurality of candidate stories, wherein each affinity comprises a measure of the relevance of a candidate story of the plurality of candidate stories to the viewing user; generating a ranking of the plurality of candidate stories based on the affinity generated for each the plurality of stories; identifying the two or more candidate stories that are associated with the same logged action; responsive to the identifying, updating the ranking by removing a subset of the two or more candidate stories from the ranking; selecting one or more of the plurality of candidate stories as selected stories for the view requested by the client device of the viewing user based on the updated ranking; and sending the requested view comprising displayable representations of the selected stories to the client device for display to the viewing user. 5. The method of claim 1 , wherein updating the ranking further comprises: removing candidate stories associated with logged actions having one or more types not associated with the requested view. | 0.815543 |
8,280,827 | 2 | 23 | 2. A multilevel semiotic and fuzzy logic user and metadata interface apparatus according to claim 1 , further comprising: (d) a logic layer operably connected to store said fuzzy logic descriptor set module, and the logic layer further stores a user preference engine that defines a user profile based on the addressing of a user of a level of said multilevel semiotic means. | 2. A multilevel semiotic and fuzzy logic user and metadata interface apparatus according to claim 1 , further comprising: (d) a logic layer operably connected to store said fuzzy logic descriptor set module, and the logic layer further stores a user preference engine that defines a user profile based on the addressing of a user of a level of said multilevel semiotic means. 23. A multilevel semiotic and fuzzy logic user and metadata interface apparatus according to claim 2 , further comprising information output means operable so that said user profiles are readable externally. | 0.947833 |
5,442,547 | 13 | 14 | 13. A dictionary production aiding apparatus as recited in claim 12, wherein said first language is English. | 13. A dictionary production aiding apparatus as recited in claim 12, wherein said first language is English. 14. A dictionary producing aiding apparatus as recited in claim 13, wherein said available parts of speech include noun and verb, and said estimate means includes means for estimating a location at which said second form is different from said first form, when said target morpheme is a verb, and means for estimating a location at which said second form is different from said first form, when said target morpheme is a noun. | 0.879661 |
8,469,712 | 2 | 3 | 2. The method according to claim 1 , further comprising outputting as said indication a visual output. | 2. The method according to claim 1 , further comprising outputting as said indication a visual output. 3. The method according to claim 2 , further comprising outputting as said indication an indication disposed in a window on a display of said output apparatus. | 0.945918 |
8,447,760 | 27 | 31 | 27. A non-transitory computer storage medium having instructions stored thereon that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising: determining a respective strength of relationship score between each candidate document in a group of candidate documents and each of the first documents by aggregating user selection data for multiple users, the first documents and the candidate documents being in a corpus of web documents, the user selection data indicating, for each of the multiple users, whether the user viewed the candidate document during a window of time after the first document is presented to the user on a search results web page in response to a query, wherein the strength of relationship score is a probability that the candidate document will be viewed given that the first document has been presented to a user on a search results web page in response to a query; calculating an aggregate strength of relationship score for each candidate document from the respective strength of relationship scores for the candidate document; and selecting the one or more second documents from the candidate documents according to the aggregate strength of relationship scores for the candidate documents. | 27. A non-transitory computer storage medium having instructions stored thereon that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising: determining a respective strength of relationship score between each candidate document in a group of candidate documents and each of the first documents by aggregating user selection data for multiple users, the first documents and the candidate documents being in a corpus of web documents, the user selection data indicating, for each of the multiple users, whether the user viewed the candidate document during a window of time after the first document is presented to the user on a search results web page in response to a query, wherein the strength of relationship score is a probability that the candidate document will be viewed given that the first document has been presented to a user on a search results web page in response to a query; calculating an aggregate strength of relationship score for each candidate document from the respective strength of relationship scores for the candidate document; and selecting the one or more second documents from the candidate documents according to the aggregate strength of relationship scores for the candidate documents. 31. The non-transitory computer storage medium of claim 27 , wherein: the one or more second documents are associated with a natural language; and determining a respective strength of relationship score between each candidate document and each of the first documents further includes scaling the strength of relationship score by a percentage of the multiple users who viewed the candidate document and are associated with the natural language. | 0.608466 |
10,042,549 | 8 | 10 | 8. The method of claim 1 , further comprising: in response to determining that the initial portion of the gesture is detected at a location corresponding to a second region of the electronic document, selecting a second dynamic disambiguation threshold used to determine whether to perform navigation or annotation. | 8. The method of claim 1 , further comprising: in response to determining that the initial portion of the gesture is detected at a location corresponding to a second region of the electronic document, selecting a second dynamic disambiguation threshold used to determine whether to perform navigation or annotation. 10. The method of claim 8 , wherein determining whether the initial portion of the gesture is detected at a location corresponding to the second region of the electronic document comprises determining whether the initial portion is detected at a location not corresponding to text of the electronic document. | 0.911798 |
7,627,816 | 1 | 2 | 1. A method, in a data processing system, for generating an electronic document, comprising: receiving textual content of the electronic document; receiving a selection of a term in the textual content; receiving a dictionary definition of the selected term; storing the dictionary definition in a transient document associated electronic dictionary that is linked to the electronic document; storing the electronic document and the transient document associated electronic dictionary in association with one another wherein, in response to electronic distribution of the electronic document to one or more recipient computer systems, the transient document associated electronic dictionary is automatically distributed along with the electronic document, by virtue of the electronic document and transient document associated electronic dictionary being stored in association with one another, to the one or more recipient computer systems; receiving the electronic document and the associated transient document associated dictionary at a recipient computing device; loading the transient document associated dictionary to augment a local permanent dictionary of the recipient computing device; outputting a request, at the recipient computing device, requesting that a user indicate whether the transient document associated dictionary is to augment the local permanent dictionary of the recipient computing device permanently or temporarily; permanently adding definitions from the transient document associated dictionary to the local permanent dictionary of the recipient computing device in response to the user indicating that the transient document associated dictionary is to augment the local permanent dictionary permanently; and preventing the definitions from the transient document associated dictionary from permanently being added to the local permanent dictionary of the recipient computing device in response to the user indicating that the transient document associated dictionary is to augment the local permanent dictionary temporarily such that the definitions from the transient document associated dictionary are only used with the associated electronic document and not with other electronic documents. | 1. A method, in a data processing system, for generating an electronic document, comprising: receiving textual content of the electronic document; receiving a selection of a term in the textual content; receiving a dictionary definition of the selected term; storing the dictionary definition in a transient document associated electronic dictionary that is linked to the electronic document; storing the electronic document and the transient document associated electronic dictionary in association with one another wherein, in response to electronic distribution of the electronic document to one or more recipient computer systems, the transient document associated electronic dictionary is automatically distributed along with the electronic document, by virtue of the electronic document and transient document associated electronic dictionary being stored in association with one another, to the one or more recipient computer systems; receiving the electronic document and the associated transient document associated dictionary at a recipient computing device; loading the transient document associated dictionary to augment a local permanent dictionary of the recipient computing device; outputting a request, at the recipient computing device, requesting that a user indicate whether the transient document associated dictionary is to augment the local permanent dictionary of the recipient computing device permanently or temporarily; permanently adding definitions from the transient document associated dictionary to the local permanent dictionary of the recipient computing device in response to the user indicating that the transient document associated dictionary is to augment the local permanent dictionary permanently; and preventing the definitions from the transient document associated dictionary from permanently being added to the local permanent dictionary of the recipient computing device in response to the user indicating that the transient document associated dictionary is to augment the local permanent dictionary temporarily such that the definitions from the transient document associated dictionary are only used with the associated electronic document and not with other electronic documents. 2. The method of claim 1 , further comprising: identifying one or more terms in the textual content for the electronic document; and accentuating, in a display of the electronic document, the one or more terms in the textual content for the electronic document, wherein the one or more terms are terms that are either not defined in a local permanent dictionary or are defined in both the local permanent dictionary and the transient document associated electronic dictionary. | 0.501048 |
8,458,276 | 13 | 14 | 13. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: evaluate, by one or more processors, a plurality of messages, each message associated with an author; log, by the one or more processors, for each message, information associated with the author, information associated with one or more designated recipients of the message, and time information associated with the message; determine, by the one or more processors, correlation values for one or more sets of the designated recipients based on at least a portion of the logged information; and determine, by the one or more processors, an association amongst a plurality of users over time, the determining being based on the correlation values, at least one of the plurality of users comprising at least one of the designated recipients. | 13. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: evaluate, by one or more processors, a plurality of messages, each message associated with an author; log, by the one or more processors, for each message, information associated with the author, information associated with one or more designated recipients of the message, and time information associated with the message; determine, by the one or more processors, correlation values for one or more sets of the designated recipients based on at least a portion of the logged information; and determine, by the one or more processors, an association amongst a plurality of users over time, the determining being based on the correlation values, at least one of the plurality of users comprising at least one of the designated recipients. 14. The media of claim 13 , wherein the correlation values are determined based on a desired profile or mathematical curve. | 0.856308 |
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