中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

38 records (took 0.018 seconds)

    Localized Learning From A Global Model
    23.
    发明申请
    Localized Learning From A Global Model 有权

    公开(公告)号：US20210042666A1

    公开(公告)日：2021-02-11

    申请号：US17082730

    申请日：2020-10-28

    Applicant: Google LLC

    Inventor： Daniel Ramage ,  Jeremy Gillmor Kahn

    IPC: G06N20/00 ,  H04L29/06

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a global model for a particular activity, the global model derived based on input data representing multiple observations associated with the particular activity performed by a collection of users; determining, using the global model, expected data representing an expected observation associated with the particular activity performed by a particular user; receiving, by a computing device operated by the particular user, particular data representing an actual observation associated with the particular activity performed by the particular user; determining, by the computing device and using (i) the expected data and (ii) the particular data, residual data of the particular user; and deriving a local model of the particular user based on the residual data.

    SYSTEMS AND METHODS FOR PRIVATE LOCAL SPONSORED CONTENT
    26.
    发明申请
    SYSTEMS AND METHODS FOR PRIVATE LOCAL SPONSORED CONTENT 审中-公开

    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

    IPC: G06Q30/02 ,  H04L9/00 ,  H04L29/06 ,  G06N5/02

    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.

    Training User-Level Differentially Private Machine-Learned Models
    27.
    发明申请
    Training User-Level Differentially Private Machine-Learned Models 审中-公开

    公开(公告)号：US20190227980A1

    公开(公告)日：2019-07-25

    申请号：US15877196

    申请日：2018-01-22

    Applicant: Google LLC

    Inventor： Hugh Brendan McMahan ,  Kunal Talwar ,  Li Zhang ,  Daniel Ramage

    IPC: G06F15/18 ,  G06F8/65 ,  G06K9/62

    Abstract: Systems and methods for learning differentially private machine-learned models are provided. A computing system can include one or more server computing devices comprising one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the one or more server computing devices to perform operations. The operations can include selecting a subset of client computing devices from a pool of available client computing devices; providing a machine-learned model to the selected client computing devices; receiving, from each selected client computing device, a local update for the machine-learned model; determining a differentially private aggregate of the local updates; and determining an updated machine-learned model based at least in part on the data-weighted average of the local updates.

    On-device machine learning platform
    28.
    发明授权
    On-device machine learning platform 有权

    公开(公告)号：US12282869B2

    公开(公告)日：2025-04-22

    申请号：US17874967

    申请日：2022-07-27

    Applicant: Google LLC

    Inventor： Pannag Sanketi ,  Wolfgang Grieskamp ,  Daniel Ramage ,  Hrishikesh Aradhye

    IPC: G06N5/048 ,  G06F21/62 ,  G06N20/00

    Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Patent Agency Ranking