UTILIZING A NATURAL LANGUAGE MODEL TO DETERMINE A PREDICTED ACTIVITY EVENT BASED ON A SERIES OF SEQUENTIAL TOKENS

    公开(公告)号:US20230161648A1

    公开(公告)日:2023-05-25

    申请号:US18156275

    申请日:2023-01-18

    Applicant: Dropbox, Inc.

    CPC classification number: G06F9/542 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.

    MALWARE DETECTION AND CONTENT ITEM RECOVERY

    公开(公告)号:US20230139473A1

    公开(公告)日:2023-05-04

    申请号:US18147468

    申请日:2022-12-28

    Applicant: Dropbox, Inc.

    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for malware detection and content item recovery. For example, a content management system can receive information describing changes made to content items stored on a user device. The content management system can analyze the information to determine if the described changes are related to malicious software on the user device. When the changes are related to malicious software, the content management system can determine which content items are effected by the malicious software and/or determine when the malicious software first started making changes to the user device. The content management system can recover effected content items associated with the user device by replacing the effected versions of the content items with versions of the content items that existed immediately before the malicious software started making changes to the user device.

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