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    TIME MARKING CHAPTERS IN MEDIA ITEMS AT A PLATFORM USING MACHINE-LEARNING
    1.
    发明公开
    TIME MARKING CHAPTERS IN MEDIA ITEMS AT A PLATFORM USING MACHINE-LEARNING 审中-公开

    公开(公告)号：US20230421855A1

    公开(公告)日：2023-12-28

    申请号：US18244625

    申请日：2023-09-11

    申请人： Google LLC

    发明人： Chenjie Gu ,  Wei-Hong Chuang ,  Min-Hsuan Tsai ,  Jianfeng Yang ,  Ji Zhang ,  Honglu Zhou ,  Hassan Akbari

    IPC分类号： H04N21/472 ,  G11B27/34

    CPC分类号： H04N21/47217 ,  G11B27/34

    摘要： Methods and systems for time marking of media items at a platform using machine-learning are provided herein. An indication of a identified media item is provided as input to a machine-learning model and one or more outputs of the machine-learning model is obtained. The one or more obtained outputs comprise time marks identifying each of the plurality of content segments of the media item. Each of the plurality of content segments is associated with a segment start indicator for a timeline of the media item. A resulting duration is determined of a combination of the plurality of content segments for which the time marks were obtained from the one or more of outputs of the machine-learning model. Responsive to determining that the resulting duration is less than the duration of the media item, one or more further inputs is provided to the machine learning model.

    Time marking chapters in media items at a platform using machine-learning
    3.
    发明授权
    Time marking chapters in media items at a platform using machine-learning 有权

    公开(公告)号：US11758233B1

    公开(公告)日：2023-09-12

    申请号：US17835547

    申请日：2022-06-08

    申请人： Google LLC

    发明人： Chenjie Gu ,  Wei-Hong Chuang ,  Min-Hsuan Tsai ,  Jianfeng Yang ,  Ji Zhang ,  Honglu Zhou ,  Hassan Akbari

    IPC分类号： H04N21/472 ,  G11B27/34

    CPC分类号： H04N21/47217 ,  G11B27/34

    摘要： Methods and systems for time marking of media items at a platform using machine-learning are provided herein. A media item is provided to users of a platform. An indication of the identified media item is provided as input to a machine-learning model that is trained using different feature types of historical media items to predict a plurality of content segments of a given media item each depicting, to the one or more users, a distinct section of the media item. One or more outputs of the machine-learning model are obtained comprising time marks identifying each of the plurality of content segments of the media item. Each of the plurality of content segments are associated with a segment start indicator for a timeline of the media item. The media item and an indication of each segment start indicator is provided for presentation to at least one user.