GAMIFIED ANNOTATIONS DURING LIVESTREAM

    公开(公告)号:US20240375012A1

    公开(公告)日:2024-11-14

    申请号:US18315103

    申请日:2023-05-10

    Inventor: Chen Yao

    Abstract: Non-players can vote and comment on uploaded video clips or livestreams of video games played by video game players. The video game players that upload the clips or do the livestreaming and that then receive the most votes can be awarded digital awards. The non-players providing the best comments can also be awarded digital awards. This incentivizes these behaviors, providing ample training data in the process so that the votes and comments can be used to train a model to make inferences related to video game video. For instance, the model may be trained to provide auto-generated comments in real time as a second video game is played, where the comments are in video game domain-specific language.

    GAMIFIED ANNOTATIONS
    2.
    发明申请

    公开(公告)号:US20240375018A1

    公开(公告)日:2024-11-14

    申请号:US18315053

    申请日:2023-05-10

    Inventor: Chen Yao

    Abstract: Non-players can vote and comment on uploaded video clips or livestreams of video games played by video game players. The video game players that upload the clips or do the livestreaming and that then receive the most votes can be awarded digital awards. The non-players providing the best comments can also be awarded digital awards. This incentivizes these behaviors, providing ample training data in the process so that the votes and comments can be used to train a model to make inferences related to video game video. For instance, the model may be trained to provide auto-generated comments in real time as a second video game is played, where the comments are in video game domain-specific language.

    AI HIGHLIGHT DETECTION USING CASCADED FILTERING OF CAPTURED CONTENT

    公开(公告)号:US20240412515A1

    公开(公告)日:2024-12-12

    申请号:US18208200

    申请日:2023-06-09

    Inventor: Chen Yao

    Abstract: A device, system, and method of training for application highlight detection. A first of set one or more of unimodal modules is configured to generate interest features from application data. A second set of unimodal modules is configured to generate refined interest features from application data with interest features and a multimodal neural network trained with a machine learning algorithm to classify application highlights from the application data with the interest features and the refined interest features.

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