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公开(公告)号：US20240256926A1
公开(公告)日：2024-08-01
申请号：US18632608
申请日：2024-04-11
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Emily Dodwell , Balachander Krishnamurthy , Rajat Malik , Ritwik Mitra
CPC classification number: G06N5/04 , G06F16/285 , G06N20/00
Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.
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公开(公告)号：US11983646B2
公开(公告)日：2024-05-14
申请号：US18172654
申请日：2023-02-22
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Emily Dodwell , Balachander Krishnamurthy , Rajat Malik , Ritwik Mitra
CPC classification number: G06N5/04 , G06F16/285 , G06N20/00
Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.
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公开(公告)号：US20230259796A1
公开(公告)日：2023-08-17
申请号：US18172654
申请日：2023-02-22
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Emily Dodwell , Balachander Krishnamurthy , Rajat Malik , Ritwik Mitra
CPC classification number: G06N5/04 , G06F16/285 , G06N20/00
Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning(ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.
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Patent Agency Ranking
公开(公告)号：US20210174222A1
公开(公告)日：2021-06-10
申请号：US16704965
申请日：2019-12-05
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Emily Dodwell , Balachander Krishnamurthy , Rajat Malik , Ritwik Mitra
Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.
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公开(公告)号：US11620542B2
公开(公告)日：2023-04-04
申请号：US16704965
申请日：2019-12-05
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Emily Dodwell , Balachander Krishnamurthy , Rajat Malik , Ritwik Mitra
Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.
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公开(公告)号：US11586950B2
公开(公告)日：2023-02-21
申请号：US16705520
申请日：2019-12-06
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Emily Dodwell , Balachander Krishnamurthy , Ritwik Mitra
IPC: G06F15/16 , G06N5/04 , G06Q30/0241 , G06N20/00
Abstract: Aspects of the disclosure include, for example, obtaining input data. Further embodiments include a determination of a fast path prediction for a first time period according to the input data based on a fast path model. Embodiments include providing instructions to deliver information to a user device according to the fast path prediction. Additional embodiments include obtaining additional input data. Embodiments include a determination of a slow path prediction for the first time period according to the input data and the additional input data based on a slow path model, retraining the fast path model according to the input data and the fast path prediction, and training the slow path model according to the slow path prediction. Embodiments include a determination of a fast path negative impact metric and determination of a slow path negative impact metric. Other embodiments are disclosed.
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公开(公告)号：US20220005077A1
公开(公告)日：2022-01-06
申请号：US16919457
申请日：2020-07-02
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Balachander Krishnamurthy , Subhabrata Majumdar , Ritwik Mitra , David Poole
Abstract: Aspects of the subject disclosure may include, for example, embodiments receiving a notification of actions, determining a potential bias metric for the actions in response to analyzing the actions using a machine learning application, determining the potential bias metric for the actions is above a potential bias threshold for the actions, and adjusting the actions to mitigate potential bias in the actions according to the potential bias metric being above the potential bias threshold using the machine learning application. Further embodiments can include determining a potential bias metric for the adjusted actions in response to analyzing the adjusted actions using the machine learning application, determining the potential bias metric for the adjusted actions is below the potential bias threshold for the actions, and providing a notification that indicates to implement the adjusted actions. Other embodiments are disclosed.
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公开(公告)号：US20210174223A1
公开(公告)日：2021-06-10
申请号：US16705520
申请日：2019-12-06
Applicant: AT&T Intellectual Property I, L.P.
Inventor： Emily Dodwell , Balachander Krishnamurthy , Ritwik Mitra
Abstract: Aspects of the disclosure include, for example, obtaining input data. Further embodiments include a determination of a fast path prediction for a first time period according to the input data based on a fast path model. Embodiments include providing instructions to deliver information to a user device according to the fast path prediction. Additional embodiments include obtaining additional input data. Embodiments include a determination of a slow path prediction for the first time period according to the input data and the additional input data based on a slow path model, retraining the fast path model according to the input data and the fast path prediction, and training the slow path model according to the slow path prediction. Embodiments include a determination of a fast path negative impact metric and determination of a slow path negative impact metric. Other embodiments are disclosed.
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