Google Inc.

Wei-Hong Chuang

Min-Hsuan Tsai

Ji Zhang

Chenjie Gu

Jianfeng Yang

Honglu Zhou

Hassan Akbari

Yuanzhen Li

Andrei Kapishnikov

Marco Ziccardi

Rahul Sunil Bhalerao

Ye Xia

Madhuri Shanbhogue

Mojtaba Seyedhosseini

A method includes obtaining first data including a first identifier of a first product determine in association with a content item based on first metadata of the content item. The method further includes obtaining a first confidence value associated with the first product and the content item. The method further includes obtaining second data including a second identifier of the first product and a second confidence value. The method further includes providing the first data and the second data to a trained machine learning model. The method further includes obtaining a third confidence value from the trained machine learning model associated with the first product. The method further includes adjusting second metadata of the content item in view of the third confidence value.

Product identification in media items

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

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.

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Mountain View, CA, United States of America

Wei-Hong Chuang