Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

Abstract: In some implementations, the techniques described herein relate to a method including: parsing, by a processor, a generated text to identify statements included within a generated text; querying, by the processor, a remote data source to identify sources for each statement in the statements; determining, by the processor, trustworthiness values for each statement, a trustworthiness value for a given statement determined by computing trustworthiness labels for each source corresponding to a given statement: generating, by the processor, a label for the generated text based on an aggregated trustworthiness of each of the statements; and displaying, by the processor, the generated text and the label within a user interface displayed to a user.

Abstract: The present teaching relates to communication related to predictions. A communication chain is created with multiple chain units, each operating independently to provide prediction services related to a prompt with embedded information therein associated with predictions for the prompt to enable prediction services. A request is received for a requested operation related to a query prompt on one of prediction solicitation, prediction entry, verification entry, and prediction access. If a chain unit for the query prompt, the request is directed to the chain unit to perform the requested operation. If not, a new chain unit is created for the query prompt and the request is forwarded to the new chain unit to carry out the requested operation.

Abstract: Disclosed are systems and methods for determining a beta value of at least one candidate relative to a class. The method comprises receiving historical data for the at least one candidate and historical data for the class; determining a first set of regression coefficients for the historical data for the at least one candidate, and a second set of regression coefficients for the historical data for the class; and calculating the beta value for the at least one candidate relative to the class based on the first set of regression coefficients and the second sets of regression coefficients. The method may further include generating an indication that the beta value for the candidate is one of positive, negative, or zero; and transmitting to at least one display window of a graphical user interface (GUI) a graphical symbol corresponding to the indication of the beta value for the candidate.

Abstract: In some aspects, the techniques described herein relate to a method including obtaining purchase history data for a plurality of users within a group, the purchase history corresponding to at least one product; determining a first product availability corresponding to the at least one product based on the purchase history data; transmitting, to a User Equipment (UE) of a user of the plurality of users, a notification including the first product availability; obtaining updated purchase history data, the updated purchase history data corresponding to the at least one product; determining a second product availability corresponding to the at least one product based on the updated purchase history data; determining a product availability change based on the first product availability and the second product availability; and modifying user rights of the user based on the product availability change.

Abstract: Methods are disclosed that include receiving electronic content data associated with an electronic content provider comprising: a content campaign identification associated with a virtual token, and provider-identified platform identifications that identify platforms on which the content campaign may be initiated; receiving input from the user comprising: a user identification, a virtual token identification, a campaign identification that identifies the content campaign associated with the virtual token, and user-identified platform identifications that identify the platforms on which the user consents to be targeted by the content campaign; determining, based on the received electronic content data and user data, the content campaign and platforms the user consents to; and generating a notification to initiate a content campaign targeting the user on the platforms on which the user consents to be targeted; and outputting the generated notification to the platforms on which the user consents to be targeted.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

Abstract: The present teaching related to estimating quantiles. An input is received that specifies one or more quantiles to be determined from a sample obtained by sampling from a full data set. The one or more quantile estimates are indicative of corresponding quantiles by rank in the full data set within an accuracy range. The one or more quantile estimates are then determined based on the sample with a probability estimated to represent a confidence in that the one or more quantile estimates are indicative of corresponding quantiles by rank in the full data set within the accuracy range. A decision may then be made based on at least some of the one or more quantile estimates, the accuracy range, and the confidence.

Abstract: A method includes receiving inputs from a plurality of data providers reflecting the plurality of data providers' respective parameters for providing message channel data; receiving inputs from a plurality of data recipients reflecting the data recipients' respective parameters for receiving message channel data; identifying, using a matching engine, message channel data that matches the parameters of at least one of the plurality of data providers and the parameters of at least one of the data recipients; processing the message channel data in accordance with the parameters of the data providers, the processing step comprising segmenting the message channel data into permitted data segments and non-permitted data segments based on the data providers' parameters; delivering the permitted data segments to the identified data recipients in accordance with their respective parameters; and initiating a request for compensation of the identified data providers in accordance with their respective parameters.

Abstract: Methods are disclosed that include receiving electronic content data associated with an electronic content provider comprising: a content campaign identification associated with a virtual token, and provider-identified platform identifications that identify platforms on which the content campaign may be initiated; receiving input from the user comprising: a user identification, a virtual token identification, a campaign identification that identifies the content campaign associated with the virtual token, and user-identified platform identifications that identify the platforms on which the user consents to be targeted by the content campaign; determining, based on the received electronic content data and user data, the content campaign and platforms the user consents to; and generating a notification to initiate a content campaign targeting the user on the platforms on which the user consents to be targeted; and outputting the generated notification to the platforms on which the user consents to be targeted.

Abstract: The present teaching relates to communication related to predictions. A communication chain is created with multiple chain units, each operating independently to provide prediction services related to a prompt with embedded information therein associated with predictions for the prompt to enable prediction services. A request is received for a requested operation related to a query prompt on one of prediction solicitation, prediction entry, verification entry, and prediction access. If a chain unit for the query prompt, the request is directed to the chain unit to perform the requested operation. If not, a new chain unit is created for the query prompt and the request is forwarded to the new chain unit to carry out the requested operation.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

Abstract: Disclosed are a method and apparatus for limiting the number of results returned by a data store in response to a query. Upon receiving an initial query, the data store returns a page of results that includes a subset of the data items within the data store satisfying the conditions of the query. The data store also provides a marker indicating the extent of the set of data items. If a subsequent query that requests additional results which satisfy the same query conditions and that includes the marker is received, the data store returns a page of results that includes a subset of data items that are disjoint from the initial subset, and provides an updated marker which indicates the extent of the union of the initial and subsequent subsets. If still further results are desired from the data store, an additional query containing the updated marker may be submitted.

Abstract: Disclosed are systems and methods for initiating communication between users of a user group based on machine learning techniques. The disclosed systems and methods provide a novel framework for automating communication scheduling and communication initiation based on user communication objectives and machine learning techniques. The disclosed framework operates by leveraging available user provided communication parameters, user provided objectives, and various real-time data associated with the users, and using the aforementioned data as inputs for machine learning models, in order to schedule communication between the users, automatically initiate communication between the users, or transmit communication notifications to the users.

Abstract: Disclosed are systems and methods for initiating communication between users of a user group based on machine learning techniques. The disclosed systems and methods provide a novel framework for automating communication scheduling and communication initiation based on user communication objectives and machine learning techniques. The disclosed framework operates by leveraging available user provided communication parameters, user provided objectives, and various real-time data associated with the users, and using the aforementioned data as inputs for machine learning models, in order to schedule communication between the users, automatically initiate communication between the users, or transmit communication notifications to the users.

Abstract: The present teaching relates to method, system, medium, and implementations for characterizing data with categorical classes and the number of observations for each of the categorical classes. Each categorical class is associated with a category value. The categorical classes are arranged in a first order based on category values. A total of observations is determined based on the numbers of observations for each categorical class. A bound of the average value of the data is estimated based on the categorical classes, the total of observations, and the numbers of observations for the categorical classes in accordance with a dot product of a probability vector and a categorical class vector comprising the category values of the categorical classes.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.

Abstract: Disclosed are a method and apparatus for limiting the number of results returned by a data store in response to a query. Upon receiving an initial query, the data store returns a page of results that includes a subset of the data items within the data store satisfying the conditions of the query. The data store also provides a marker indicating the extent of the set of data items. If a subsequent query that requests additional results which satisfy the same query conditions and that includes the marker is received, the data store returns a page of results that includes a subset of data items that are disjoint from the initial subset, and provides an updated marker which indicates the extent of the union of the initial and subsequent subsets. If still further results are desired from the data store, an additional query containing the updated marker may be submitted.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for privacy-aware machine learning using an improved data encoding that withholds more information. The disclosed systems and methods encode a corpus of data and encode each query used in searching and generating query results from the corpus of encoded data.