Intelligent content rating determination using multi-tiered machine learning

    公开(公告)号:US10671854B1

    公开(公告)日:2020-06-02

    申请号:US15948567

    申请日:2018-04-09

    Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for intelligent content rating determination. Example methods include determining presence of a first feature in a first frame of a video using an object recognition algorithm, determining presence of a second feature in an audio file associated with the video using an audio processing algorithm, and determining presence of a third feature in a text file associated with the video using a natural language processing algorithm. Certain embodiments may include generating a predicted content rating for the video using a machine learning model, where the predicted content rating is based at least in part on the first feature, the second feature, and the third feature, and using feedback data for the predicted content rating to retrain the machine learning model.

    Intelligent content rating determination using multi-tiered machine learning

    公开(公告)号:US11308332B1

    公开(公告)日:2022-04-19

    申请号:US16856744

    申请日:2020-04-23

    Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for intelligent content rating determination. Example methods include determining presence of a first feature in a first frame of a video using an object recognition algorithm, determining presence of a second feature in an audio file associated with the video using an audio processing algorithm, and determining presence of a third feature in a text file associated with the video using a natural language processing algorithm. Certain embodiments may include generating a predicted content rating for the video using a machine learning model, where the predicted content rating is based at least in part on the first feature, the second feature, and the third feature, and using feedback data for the predicted content rating to retrain the machine learning model.

    Customized video content summary generation and presentation

    公开(公告)号:US10455297B1

    公开(公告)日:2019-10-22

    申请号:US16116618

    申请日:2018-08-29

    Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for customized video content summary generation. Example methods may include determining a first segment of digital content including a first set of frames, first textual content, and first audio content. Example methods may include determining a first event that occurs in the first set of frames, determining a first theme of the first event, generating first metadata indicative of the first theme, and determining a meaning of a first sentence that occurs in the first textual content. Some methods may include determining a second theme of the first sentence, generating second metadata indicative of the second theme, determining that user preference data associated with an active user profile includes the first theme and the second theme, generating a video summary that includes a portion of the first segment of digital content, and presenting the video summary.

    Automated selection of color palettes for video content using artificial intelligence

    公开(公告)号:US11321877B1

    公开(公告)日:2022-05-03

    申请号:US17000585

    申请日:2020-08-24

    Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for automated selection of color palettes for video content. Example methods may include determining, by one or more computer processors coupled to memory, a first segment of video content, the first segment comprising a first set of frames, determining, using a first video processing algorithm, a first object that is present in the first set of frames, and determining, using a second video processing algorithm, a first semantic characteristic of the first segment. Some example methods may include generating a first vector representing the first object and the first semantic characteristic, and generating, using a first neural network and the first vector, a first color palette recommendation for the first segment. Selection of the first color palette recommendation may cause a color filter to be applied to the first set of frames.

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