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公开(公告)号：US20230222551A1
公开(公告)日：2023-07-13
申请号：US18124174
申请日：2023-03-21
Applicant: GOOGLE LLC
Inventor： Keith Bonawitz , Daniel Ramage , David Petrou
CPC classification number: G06Q30/0269 , H04L9/008 , G06N5/025 , G06Q30/0251 , H04L63/0428 , G06Q30/0261
Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
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公开(公告)号：US11610231B2
公开(公告)日：2023-03-21
申请号：US17388708
申请日：2021-07-29
Applicant: Google LLC
Inventor： Keith Bonawitz , Daniel Ramage , David Petrou
Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
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公开(公告)号：US11488054B2
公开(公告)日：2022-11-01
申请号：US15834001
申请日：2017-12-06
Applicant: Google LLC
Inventor： Keith Bonawitz
Abstract: The present disclosure provides systems and methods for distributed training of machine learning models. In one example, a computer-implemented method is provided for training machine-learned models. The method includes obtaining, by one or more computing devices, a plurality of regions based at least in part on temporal availability of user devices; selecting a plurality of available user devices within a region; and providing a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region. The method includes obtaining, from the plurality of selected user devices, updated machine-learned model data generated by the plurality of selected user devices through training of the current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices and generating an updated machine-learned model associated with the region based on the updated machine-learned model data.
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公开(公告)号：US12051092B2
公开(公告)日：2024-07-30
申请号：US18124174
申请日：2023-03-21
Applicant: GOOGLE LLC
Inventor： Keith Bonawitz , Daniel Ramage , David Petrou
IPC: G06Q30/0251 , G06N5/025 , H04L9/00 , H04L9/40
CPC classification number: G06Q30/0269 , G06N5/025 , G06Q30/0251 , G06Q30/0261 , H04L9/008 , H04L63/0428
Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
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公开(公告)号：US20230040555A1
公开(公告)日：2023-02-09
申请号：US17967437
申请日：2022-10-17
Applicant: Google LLC
Inventor： Keith Bonawitz
Abstract: The present disclosure provides systems and methods for distributed training of machine learning models. In one example, a computer-implemented method is provided for training machine-learned models. The method includes obtaining, by one or more computing devices, a plurality of regions based at least in part on temporal availability of user devices; selecting a plurality of available user devices within a region; and providing a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region. The method includes obtaining, from the plurality of selected user devices, updated machine-learned model data generated by the plurality of selected user devices through training of the current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices and generating an updated machine-learned model associated with the region based on the updated machine-learned model data.
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Patent Agency Ranking
公开(公告)号：US20200098011A1
公开(公告)日：2020-03-26
申请号：US16694691
申请日：2019-11-25
Applicant: Google LLC
Inventor： Keith Bonawitz , Daniel Ramage , David Petrou
Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
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公开(公告)号：US11087362B2
公开(公告)日：2021-08-10
申请号：US16694691
申请日：2019-11-25
Applicant: Google LLC
Inventor： Keith Bonawitz , Daniel Ramage , David Petrou
Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
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公开(公告)号：US12165024B2
公开(公告)日：2024-12-10
申请号：US17967437
申请日：2022-10-17
Applicant: Google LLC
Inventor： Keith Bonawitz
Abstract: The present disclosure provides systems and methods for distributed training of machine learning models. In one example, a computer-implemented method is provided for training machine-learned models. The method includes obtaining, by one or more computing devices, a plurality of regions based at least in part on temporal availability of user devices; selecting a plurality of available user devices within a region; and providing a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region. The method includes obtaining, from the plurality of selected user devices, updated machine-learned model data generated by the plurality of selected user devices through training of the current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices and generating an updated machine-learned model associated with the region based on the updated machine-learned model data.
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9.发明申请公开(公告)号：US20190171978A1
公开(公告)日：2019-06-06
申请号：US15834001
申请日：2017-12-06
Applicant: Google LLC
Inventor： Keith Bonawitz
IPC: G06N99/00
Abstract: The present disclosure provides systems and methods for distributed training of machine learning models. In one example, a computer-implemented method is provided for training machine-learned models. The method includes obtaining, by one or more computing devices, a plurality of regions based at least in part on temporal availability of user devices; selecting a plurality of available user devices within a region; and providing a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region. The method includes obtaining, from the plurality of selected user devices, updated machine-learned model data generated by the plurality of selected user devices through training of the current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices and generating an updated machine-learned model associated with the region based on the updated machine-learned model data.
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公开(公告)号：US20210357986A1
公开(公告)日：2021-11-18
申请号：US17388708
申请日：2021-07-29
Applicant: Google LLC
Inventor： Keith Bonawitz , Daniel Ramage , David Petrou
Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
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