GENERATING AND MAINTAINING COMPOSITE ACTIONS UTILIZING LARGE LANGUAGE MODELS

    公开(公告)号:US20250111149A1

    公开(公告)日:2025-04-03

    申请号:US18478066

    申请日:2023-09-29

    Applicant: Dropbox, Inc.

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating composite actions for a user account. In particular, in one or more embodiments, the disclosed systems determine a set of tasks performable by the user account using software tools on a client device. In some embodiments, the disclosed systems generate a task initialization prompt to provide to a large language model. Additionally, in some implementations, the disclosed systems generate a composite action comprising a hybridized combination of the set of tasks performable by the user account along with a set of content items relevant to the set of tasks. Moreover, in some embodiments, the disclosed systems provide access to the composite action and the set of content items via a user interface of the client device. Furthermore, in some implementations, the disclosed systems generate and insert predicted content into a content item without user input.

    SYNTHESIZING VISUALIZATIONS FOR CONTENT COLLECTIONS

    公开(公告)号:US20230394714A1

    公开(公告)日:2023-12-07

    申请号:US17938244

    申请日:2022-10-05

    Applicant: Dropbox, Inc.

    CPC classification number: G06T11/00 G06F16/164 G06F16/168

    Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing synthetic visualizations representative of content collections within a content management system. In some cases, the disclosed systems generate a synthetic visualization based on content features that indicate relevance of content items with respect to a user account to emphasize more relevant content items within the synthetic visualization and/or to represent descriptive content attributes of the content items. For example, the disclosed systems can generate a synthetic phrase that represents a content collection and can further generate a synthetic visualization from the synthetic phrase utilizing a synthetic visualization machine learning model.

    Synthesizing visualizations for content collections

    公开(公告)号:US12277622B2

    公开(公告)日:2025-04-15

    申请号:US17938244

    申请日:2022-10-05

    Applicant: Dropbox, Inc.

    Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing synthetic visualizations representative of content collections within a content management system. In some cases, the disclosed systems generate a synthetic visualization based on content features that indicate relevance of content items with respect to a user account to emphasize more relevant content items within the synthetic visualization and/or to represent descriptive content attributes of the content items. For example, the disclosed systems can generate a synthetic phrase that represents a content collection and can further generate a synthetic visualization from the synthetic phrase utilizing a synthetic visualization machine learning model.

    GENERATING AND MAINTAINING COMPOSITE ACTIONS UTILIZING LARGE LANGUAGE MODELS

    公开(公告)号:US20250111148A1

    公开(公告)日:2025-04-03

    申请号:US18478061

    申请日:2023-09-29

    Applicant: Dropbox, Inc.

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating composite actions for a user account. In particular, in one or more embodiments, the disclosed systems determine a set of tasks performable by the user account using software tools on a client device. In some embodiments, the disclosed systems generate a task initialization prompt to provide to a large language model. Additionally, in some implementations, the disclosed systems generate a composite action comprising a hybridized combination of the set of tasks performable by the user account along with a set of content items relevant to the set of tasks. Moreover, in some embodiments, the disclosed systems provide access to the composite action and the set of content items via a user interface of the client device. Furthermore, in some implementations, the disclosed systems generate and insert predicted content into a content item without user input.

    ORGANIZING MEDIA CONTENT ITEMS UTILIZING DETECTED SCENE TYPES

    公开(公告)号:US20240193207A1

    公开(公告)日:2024-06-13

    申请号:US18158326

    申请日:2023-01-23

    Applicant: Dropbox, Inc.

    CPC classification number: G06F16/783 G06F16/7335

    Abstract: This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that can detect scene types across various portions of media content and display collections that organize segments (or portions) of media content (e.g., videos or images) according to the detected scene types for the media content files. For example, the disclosed systems can automatically identify content segments of media content that belong to one or more identified scene types and display the content segments organized by the different scene types. In order to determine the scene types for the content segments of the media content files, the disclosed systems can utilize machine learning that determines relevancies between data of the media content files and the scene types. Furthermore, the disclosed systems can display, within a GUI, the groupings of media content segments organized by the different scene types.

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