GENERATING IMPROVED DIGITAL TRANSCRIPTS UTILIZING DIGITAL TRANSCRIPTION MODELS THAT ANALYZE DYNAMIC MEETING CONTEXTS

    公开(公告)号:US20250070994A1

    公开(公告)日:2025-02-27

    申请号:US18941541

    申请日:2024-11-08

    Applicant: Dropbox, Inc.

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for improving digital transcripts of a meeting based on user information. For example, a digital transcription system creates a digital transcription model to automatically transcribe audio from a meeting based on documents associated with meeting participants, event details, user features, and other meeting context data. In one or more embodiments, the digital transcription model creates a digital lexicon based on the user information, which the digital transcription system uses to generate the digital transcript. In some embodiments, the digital transcription model trains and utilizes a digital transcription neural network to generate the digital transcript.

    Generating customized meeting insights based on user interactions and meeting media

    公开(公告)号:US11689379B2

    公开(公告)日:2023-06-27

    申请号:US16587408

    申请日:2019-09-30

    Applicant: Dropbox, Inc.

    Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating meeting insights based on media data and device input data. For example, in one or more embodiments, the disclosed system utilizes analyzes media data including audio data or video data and inputs to client devices associated with a meeting to determine a portion of the meeting (e.g., a portion of the media data) that is relevant for a user. In response to determining a relevant portion of the meeting, the system generates an electronic message including content related to the relevant portion of the meeting. The system then provides the electronic message to a client device of the user. For instance, in one or more embodiments, the system generates a meeting summary, meeting highlights, or action items related to the media data to provide to the client device of the user. In one or more embodiments, the system also uses the summary, highlights, or action items to train a machine-learning model for use with future meetings.

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