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    UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS
    11.
    发明申请
    UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS 有权

    公开(公告)号：US20220197961A1

    公开(公告)日：2022-06-23

    申请号：US17131488

    申请日：2020-12-22

    Applicant: Dropbox, Inc.

    Inventor： Jongmin Baek ,  Jiarui Ding ,  Ermo Wei ,  Scott McCrae

    IPC: G06F16/958 ,  G06F40/284 ,  G06N3/04 ,  G06N20/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model. Based on the digital connections, the disclosed systems can surface one or more digital content suggestions to a user interface of a client device.

    SELECTIVELY IDENTIFYING AND RECOMMENDING DIGITAL CONTENT ITEMS FOR SYNCHRONIZATION
    12.
    发明申请
    SELECTIVELY IDENTIFYING AND RECOMMENDING DIGITAL CONTENT ITEMS FOR SYNCHRONIZATION 审中-公开

    公开(公告)号：US20190332710A1

    公开(公告)日：2019-10-31

    申请号：US15964267

    申请日：2018-04-27

    Applicant: Dropbox, Inc.

    Inventor： Ermo Wei ,  Jialiang Li ,  Kaiyue Sun ,  Li Chen Koh ,  Mingye Xia ,  Yu Zhang ,  Yuyang Guo

    IPC: G06F17/30

    Abstract: One or more embodiments of a synchronization system facilitate selectivity synchronizing digital content items from a collection of digital content items to a local storage of a client device. In particular, one or more embodiments described herein collect and analyze recall data for users of a digital content management system with respect to digital content items to determine synchronization scores for the digital content items. One or more embodiments described herein further include selectively identifying a subset of the digital content items based on the synchronization scores to recommend for synchronization to a local storage of a client device.

    UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS
    15.
    发明公开
    UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS 审中-公开

    公开(公告)号：US20230169139A1

    公开(公告)日：2023-06-01

    申请号：US18153960

    申请日：2023-01-12

    Applicant: Dropbox, Inc.

    Inventor： Jongmin Baek ,  Jiarui Ding ,  Ermo Wei ,  Scott McCrae

    IPC: G06F16/958 ,  G06N20/00 ,  G06F40/284 ,  G06N3/045

    CPC classification number: G06F16/958 ,  G06F40/284 ,  G06N3/045 ,  G06N20/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model. Based on the digital connections, the disclosed systems can surface one or more digital content suggestions to a user interface of a client device.

    Utilizing a natural language model to determine a predicted activity event based on a series of sequential tokens
    16.
    发明授权
    Utilizing a natural language model to determine a predicted activity event based on a series of sequential tokens 有权

    公开(公告)号：US11567812B2

    公开(公告)日：2023-01-31

    申请号：US17065266

    申请日：2020-10-07

    Applicant: Dropbox, Inc.

    Inventor： Ranjitha Gurunath Kulkarni ,  Xingyu Xiang ,  Jongmin Baek ,  Ermo Wei

    IPC: G06F9/54 ,  G06F40/284 ,  G06N3/08 ,  G06N5/02

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that can leverage a natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity. In particular, the disclosed systems can tokenize activity event vectors to generate a series of sequential tokens that correspond to recent user activity of one or more user accounts. In addition, the disclosed systems can, for each candidate (e.g., hypothetical) user activity, augment the series of sequential tokens to include a corresponding token. Based on respective probability scores for each of the augmented series of sequential tokens, the disclosed systems can identify as the predicted user activity, a candidate user activity corresponding to one of the augmented series of sequential tokens associated with a highest probability score. Based on the predicted user activity, the disclosed systems can surface one or more suggestions to a client device.

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