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    PERSONALIZED ENTITY REPOSITORY
    1.
    发明申请
    PERSONALIZED ENTITY REPOSITORY 有权

    公开(公告)号：US20250024237A1

    公开(公告)日：2025-01-16

    申请号：US18900067

    申请日：2024-09-27

    Applicant: GOOGLE LLC

    Inventor： Matthew Sharifi ,  Jorge Pereira ,  Dominik Roblek ,  Julian Odell ,  Cong Li ,  David Petrou

    IPC: H04W4/60 ,  G06F16/23 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/587 ,  G06F16/907 ,  G06F16/9535 ,  G06V20/62 ,  H04L67/50 ,  H04W4/029 ,  H04W4/18

    Abstract: Systems and methods are provided for a personalized entity repository. For example, a computing device comprises a personalized entity repository having fixed sets of entities from an entity repository stored at a server, a processor, and memory storing instructions that cause the computing device to identify fixed sets of entities that are relevant to a user based on context associated with the computing device, rank the fixed sets by relevancy, and update the personalized entity repository using selected sets determined based on the rank and on set usage parameters applicable to the user. In another example, a method includes generating fixed sets of entities from an entity repository, including location-based sets and topic-based sets, and providing a subset of the fixed sets to a client, the client requesting the subset based on the client's location and on items identified in content generated for display on the client.

    LEARNING TO SELECT VOCABULARIES FOR CATEGORICAL FEATURES
    2.
    发明公开
    LEARNING TO SELECT VOCABULARIES FOR CATEGORICAL FEATURES 审中-公开

    公开(公告)号：US20230146053A1

    公开(公告)日：2023-05-11

    申请号：US18076662

    申请日：2022-12-07

    Applicant: Google LLC

    Inventor： Cong Li ,  Jay Adams ,  Manas Joglekar ,  Pranav Khaitan ,  Quoc V. Le ,  Mei Chen

    IPC: G06F16/9035 ,  G06F40/242 ,  G06F11/34 ,  G06N20/00 ,  G06N3/08

    CPC classification number: G06F16/9035 ,  G06F40/242 ,  G06F11/3466 ,  G06N20/00 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, for each of one or more categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active during processing of inputs by a machine learning model. In one aspect, a method comprises: generating a batch of output sequences, each output sequence in the batch specifying, for each of the categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active; for each output sequence in the batch, determining a performance metric of the machine learning model on a machine learning task after the machine learning model has been trained to perform the machine learning task with only the respective vocabulary of categorical feature values of each categorical feature specified by the output sequence being active.

    Learning to select vocabularies for categorical features
    3.
    发明授权
    Learning to select vocabularies for categorical features 有权

    公开(公告)号：US11537664B2

    公开(公告)日：2022-12-27

    申请号：US16878912

    申请日：2020-05-20

    Applicant: Google LLC

    Inventor： Cong Li ,  Jay Adams ,  Manas Joglekar ,  Pranav Khaitan ,  Quoc V. Le ,  Mei Chen

    IPC: G06F16/9035 ,  G06F40/242 ,  G06F11/34 ,  G06N20/00 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, for each of one or more categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active during processing of inputs by a machine learning model. In one aspect, a method comprises: generating a batch of output sequences, each output sequence in the batch specifying, for each of the categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active; for each output sequence in the batch, determining a performance metric of the machine learning model on a machine learning task after the machine learning model has been trained to perform the machine learning task with only the respective vocabulary of categorical feature values of each categorical feature specified by the output sequence being active.

    Personalized entity repository
    4.
    发明授权
    Personalized entity repository 有权

    公开(公告)号：US11089457B2

    公开(公告)日：2021-08-10

    申请号：US16241704

    申请日：2019-01-07

    Applicant: Google LLC

    Inventor： Matthew Sharifi ,  Jorge Pereira ,  Dominik Roblek ,  Julian Odell ,  Cong Li ,  David Petrou

    IPC: H04W4/18 ,  H04W4/60 ,  G06F16/248 ,  G06F16/9535 ,  G06F16/2457 ,  H04W4/029 ,  G06F16/907 ,  G06F16/587 ,  G06K9/32 ,  H04L29/08 ,  G06F16/23

    Abstract: Systems and methods are provided for a personalized entity repository. For example, a computing device comprises a personalized entity repository having fixed sets of entities from an entity repository stored at a server, a processor, and memory storing instructions that cause the computing device to identify fixed sets of entities that are relevant to a user based on context associated with the computing device, rank the fixed sets by relevancy, and update the personalized entity repository using selected sets determined based on the rank and on set usage parameters applicable to the user. In another example, a method includes generating fixed sets of entities from an entity repository, including location-based sets and topic-based sets, and providing a subset of the fixed sets to a client, the client requesting the subset based on the client's location and on items identified in content generated for display on the client.

    LEARNING TO SELECT VOCABULARIES FOR CATEGORICAL FEATURES
    6.
    发明申请
    LEARNING TO SELECT VOCABULARIES FOR CATEGORICAL FEATURES 审中-公开

    公开(公告)号：US20200372076A1

    公开(公告)日：2020-11-26

    申请号：US16878912

    申请日：2020-05-20

    Applicant: Google LLC

    Inventor： Cong Li ,  Jay Adams ,  Manas Joglekar ,  Pranav Khaitan ,  Quoc V. Le ,  Mei Chen

    IPC: G06F16/9035 ,  G06F40/242 ,  G06N3/08 ,  G06N20/00 ,  G06F11/34

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, for each of one or more categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active during processing of inputs by a machine learning model. In one aspect, a method comprises: generating a batch of output sequences, each output sequence in the batch specifying, for each of the categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active; for each output sequence in the batch, determining a performance metric of the machine learning model on a machine learning task after the machine learning model has been trained to perform the machine learning task with only the respective vocabulary of categorical feature values of each categorical feature specified by the output sequence being active.

    PERSONALIZED ENTITY REPOSITORY
    7.
    发明申请
    PERSONALIZED ENTITY REPOSITORY 审中-公开

    公开(公告)号：US20200226187A1

    公开(公告)日：2020-07-16

    申请号：US16241704

    申请日：2019-01-07

    Applicant: Google LLC

    Inventor： Matthew Sharifi ,  Jorge Pereira ,  Dominik Roblek ,  Julian Odell ,  Cong Li ,  David Petrou

    IPC: G06F16/9535 ,  G06K9/32 ,  G06F16/587 ,  G06F16/907

    Abstract: Systems and methods are provided for a personalized entity repository. For example, a computing device comprises a personalized entity repository having fixed sets of entities from an entity repository stored at a server, a processor, and memory storing instructions that cause the computing device to identify fixed sets of entities that are relevant to a user based on context associated with the computing device, rank the fixed sets by relevancy, and update the personalized entity repository using selected sets determined based on the rank and on set usage parameters applicable to the user. In another example, a method includes generating fixed sets of entities from an entity repository, including location-based sets and topic-based sets, and providing a subset of the fixed sets to a client, the client requesting the subset based on the client's location and on items identified in content generated for display on the client.

    Learning to select vocabularies for categorical features
    10.
    发明授权
    Learning to select vocabularies for categorical features 有权

    公开(公告)号：US11714857B2

    公开(公告)日：2023-08-01

    申请号：US18076662

    申请日：2022-12-07

    Applicant: Google LLC

    Inventor： Cong Li ,  Jay Adams ,  Manas Joglekar ,  Pranav Khaitan ,  Quoc V. Le ,  Mei Chen

    IPC: G06F16/9035 ,  G06F40/242 ,  G06F11/34 ,  G06N20/00 ,  G06N3/08

    CPC classification number: G06F16/9035 ,  G06F11/3466 ,  G06F40/242 ,  G06N3/08 ,  G06N20/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, for each of one or more categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active during processing of inputs by a machine learning model. In one aspect, a method comprises: generating a batch of output sequences, each output sequence in the batch specifying, for each of the categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active; for each output sequence in the batch, determining a performance metric of the machine learning model on a machine learning task after the machine learning model has been trained to perform the machine learning task with only the respective vocabulary of categorical feature values of each categorical feature specified by the output sequence being active.

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