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公开(公告)号:US12105694B2
公开(公告)日:2024-10-01
申请号:US18298353
申请日:2023-04-10
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Prince Paulraj , Shilpi Harpavat , Weiping Liu , Shreyash Taywade , Arjun Coimbatore Nagarasan , Yukun Zeng , Prabhu Gururaj
IPC: G06F7/00 , G06F16/22 , G06N5/04 , G06N20/00 , G06Q30/016 , G06Q30/0201 , G06Q40/02
CPC classification number: G06F16/2272 , G06N5/04 , G06N20/00 , G06Q30/016 , G06Q30/0201 , G06Q40/02
Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.
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公开(公告)号:US20230143392A1
公开(公告)日:2023-05-11
申请号:US18149787
申请日:2023-01-04
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Paul Ireifej , Mohammad Omar Khalid Mirza , Prince Paulraj , Heather Wighton , Christopher Kim , Stephen Grandinetti , Mger Babayan
CPC classification number: G06F16/168 , G06F16/256 , G06F16/258
Abstract: Aspects of the subject disclosure may include, for example, receiving input data via a transformation UI, generating transformation configuration data, causing the transformation UI to present transformation object data per the transformation configuration data, where the transformation object data identifies data objects each including an input and output field name and a data type, detecting, from the transformation UI, an instruction defining a mapping for the input data, including a modification to the output field name of a data object such that the input field name of the data object is mapped to the modified output field name, based on the detecting the instruction, modifying the first transformation configuration data per the mapping to derive second transformation configuration data, performing a transformation of the input data based on the second transformation configuration data, and causing the transformation UI to present a transformation output. Other embodiments are disclosed.
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公开(公告)号:US20220327326A1
公开(公告)日:2022-10-13
申请号:US17226908
申请日:2021-04-09
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Lauren Savage , Mark Austin , Prince Paulraj , Ana Armenta , James Pratt
Abstract: An example method includes receiving data to be provided to an application using a scoring model for calculating a score, determining that the data is incompatible with a current feature set of the scoring model applied by the application, receiving a next best model of features in response to the determining that the data is incompatible with the current feature set, executing the application to calculate the score with the data and the features of the next best model, and generating an output in accordance with the score.
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公开(公告)号:US12244619B2
公开(公告)日:2025-03-04
申请号:US17480170
申请日:2021-09-21
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Maisam Shahid Wasti , Sai Sharath Japa , Ana Armenta , Prince Paulraj
Abstract: Aspects of the subject disclosure may include, for example, monitoring a first activity undertaken by a communication device during a first communication session, generating, based on the monitoring, first data that indicates an amount of time that is spent on the first activity, comparing, based on the generating, the first data to a threshold, and identifying, based on at least the comparing, an action to take when the amount of time that is spent on the first activity exceeds the threshold. Other embodiments are disclosed.
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公开(公告)号:US20250021541A1
公开(公告)日:2025-01-16
申请号:US18902429
申请日:2024-09-30
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Prince Paulraj , Shilpi Harpavat , Weiping Liu , Shreyash Taywade , Arjun Coimbatore Nagarasan , Yukun Zeng , Prabhu Gururaj
IPC: G06F16/22 , G06N5/04 , G06N20/00 , G06Q30/016 , G06Q30/0201 , G06Q40/02
Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.
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公开(公告)号:US20240095579A1
公开(公告)日:2024-03-21
申请号:US17949787
申请日:2022-09-21
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Prince Paulraj , Antoine Diffloth , James Pratt
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A processing system including at least one processor may obtain a request from a first entity to train a machine learning model, access at least one data feature of at least a second entity, and train the machine learning model on behalf of the first entity in accordance with the at least one data feature of the at least the second entity to generate a trained machine learning model, where the at least one data feature of the at least the second entity is a restricted data feature that is inaccessible to the first entity. The processing system may then provide the trained machine learning model to the first entity.
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公开(公告)号:US20230216968A1
公开(公告)日:2023-07-06
申请号:US17566886
申请日:2021-12-31
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Elijah Hall , Prince Paulraj , Ana Armenta , Surya Murali
Abstract: A processing system may maintain a communication graph that includes nodes representing a plurality of phone numbers including a first phone number and edges between the nodes representing a plurality of communications between the plurality of phone numbers and may generate at least one vector via a graph embedding process applied to the communication graph, the at least one vector representing features of at least a portion of the communication graph. The processing system may then apply the at least one vector to a prediction model that is implemented by the processing system and that is configured to predict whether the first phone number is associated with a type of network activity associated with a telecommunication network and may implement a remedial action in response to an output of the prediction model indicating that the first phone number is associated with the type of network activity.
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公开(公告)号:US20220394049A1
公开(公告)日:2022-12-08
申请号:US17338646
申请日:2021-06-03
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Edmond Abrahamian , Maisam Shahid Wasti , Andrew Campbell , Ana Armenta , Prince Paulraj
IPC: H04L29/06 , G06N20/00 , G06F40/134
Abstract: A method for detecting threat pathways using sequence graphs includes constructing a sequence graph from a set of data containing information about activities in a telecommunications service provider network, where the sequence graph represents a subset of the activities that occurs as a sequence, providing an embedding of the sequence graph as input to a machine learning model, wherein the machine learning model has been trained to detect when an input embedding of a sequence graph is likely to indicate a threat activity, determining, based on an output of the machine learning model, whether the subset of the activities is indicative of the threat activity, and initiating a remedial action to mitigate the threat activity.
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公开(公告)号:US20220366430A1
公开(公告)日:2022-11-17
申请号:US17321279
申请日:2021-05-14
Applicant: AT&T Intellectual Property I, L.P.
Inventor: Ryan Steckel , Ana Armenta , Prince Paulraj , Chih Chien Huang
Abstract: Data stream based event sequence anomaly detection for mobility customer fraud analysis is presented herein. A system obtains a sequence of events comprising respective modalities of communication that correspond to a subscriber identity associated with a communication service—the sequence of events having occurred within a defined period. Based on defined classifiers representing respective fraudulent sequences of events, the system determines, via a group of machine learning models corresponding to respective machine learning processes, whether the sequence of events satisfies a defined condition with respect to likelihood of representing a fraudulent sequence of events of the respective fraudulent sequences of events. In response to the sequence of events being determined to satisfy the defined condition, the system sends, via a user interface of the system, a notification indicating that the sequence of events has been determined to represent the fraudulent sequence of events.
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公开(公告)号:US20170046497A1
公开(公告)日:2017-02-16
申请号:US14823213
申请日:2015-08-11
Applicant: AT&T INTELLECTUAL PROPERTY I, L.P. , AT&T MOBILITY II LLC
Inventor: Vc Ramesh , Michael G. Branam , Philip Edward Brown , Lee Callaway , Halim Damerdji , Shilpi Harpavat , Azeddine Kasmi , Terri A. Lewis , Sunil Nakrani , Tung Nguyen , Maruthi Nori , Prince Paulraj , Homayoun Torab , Christopher L. Tsai
CPC classification number: G06F19/3418 , G06F16/2455
Abstract: One or more simulations are generated for in-home monitoring. The simulations model sensory detection of a user's physical activities using a different number and/or a different combination of sensors. Each different simulation may thus be associated with an accuracy and a cost, depending on the number and/or combination of sensors. The simulations thus present a range of sensory configurations that balance accuracy and affordability, from which an optimum sensory solution may be determined for the in-home monitoring.
Abstract translation: 生成一个或多个模拟用于家庭内监控。 模拟使用不同数量和/或不同的传感器组合来模拟用户身体活动的感觉检测。 因此,根据传感器的数量和/或组合,每个不同的模拟可以与精度和成本相关联。 因此,模拟物呈现一系列平衡精度和负担能力的感官配置,从而可以确定家庭内监测的最佳感觉解决方案。
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