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.

    Summarizing content of live media programs

    公开(公告)号:US11687576B1

    公开(公告)日:2023-06-27

    申请号:US17466899

    申请日:2021-09-03

    CPC classification number: G06F16/345 H04N21/4882

    Abstract: Summaries of media programs that are in progress are generated based on content of the media programs that has already been transmitted to listeners or viewers. The content is transcribed into text, and contextual features regarding the media program such as topics, identities of speakers or interactions received from listeners are identified. The transcribed content and the contextual features are provided as multi-modal inputs to a model that is trained to generate a summary of the media program in response to such inputs. Summaries of media programs that are then in progress are transmitted to devices of listeners who may be interested in joining one of the media programs and displayed in a menu or user interface or announced to the listeners.

    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.

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