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    Method and system for activity prediction, prefetching and preloading of computer assets by a client-device
    4.
    发明授权
    Method and system for activity prediction, prefetching and preloading of computer assets by a client-device 有权

    公开(公告)号：US11899733B2

    公开(公告)日：2024-02-13

    申请号：US17792965

    申请日：2020-01-14

    Applicant: Google LLC

    Inventor： Michael Shalai ,  Joseph Catalano ,  Bo Lin ,  Dustin Zelle ,  Rami Al-Rfou

    IPC: G06F7/00 ,  G06F16/957

    CPC classification number: G06F16/9574

    Abstract: A solution arranged to build or train a machine learning model (ML model) that can be uploaded to a server arranged to deploy the ML model to communicating devices. The ML model builder can build the ML model and a ML production pipeline. The ML production pipeline can train the ML model, convert the ML model to a web browser compatible format, and upload the converted ML model to the server. The ML model can receive as input a sequence of prior activities on one communicating device in the communicating devices, analyze the sequence of prior activities on the communicating device, predict a next activity on the communicating device based on the analysis of the sequence of prior activities, preemptively search a computer network based on the predicted next activity to find a computer asset, and preload the found computer asset to a storage in the communicating device.

    Method and System for Activity Prediction, Prefetching and Preloading of Computer Assets by A Client-Device
    6.
    发明申请
    Method and System for Activity Prediction, Prefetching and Preloading of Computer Assets by A Client-Device 有权

    公开(公告)号：US20230050882A1

    公开(公告)日：2023-02-16

    申请号：US17792965

    申请日：2020-01-14

    Applicant: Google LLC

    Inventor： Mikhail Shalai ,  Joseph Catalano ,  Bo Lin ,  Dustin Zelle ,  Rami Al-Rfou

    IPC: G06F16/957

    Abstract: A solution arranged to build or train a machine learning model and to upload the machine learning model to a server arranged to deploy the machine learning model to a plurality of communicating devices. The solution can include a machine learning model builder arranged to build the machine learning model and a machine learning production pipeline. The machine learning production pipeline can be arranged to train the machine learning model, convert the machine learning model to a web browser compatible format, and upload the converted machine learning model to the server. The machine learning model can be arranged to receive as input a sequence of one or more prior activities on one communicating device in the plurality of communicating devices, analyze the sequence of one or more prior activities on said one communicating device, predict a next activity on said one communicating device based on the analysis of the sequence of one or more prior activities, preemptively search a computer network based on the predicted next activity to find a computer asset, and preload the found computer asset to a storage in said one communicating device.

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