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    DOCUMENT DIFFERENCES ANALYSIS AND PRESENTATION
    2.
    发明申请
    DOCUMENT DIFFERENCES ANALYSIS AND PRESENTATION 审中-公开

    公开(公告)号：US20180089155A1

    公开(公告)日：2018-03-29

    申请号：US15280442

    申请日：2016-09-29

    Applicant: Dropbox, Inc.

    Inventor： Amir Baron ,  Zach Johnston ,  Anat Danziger ,  Mark Kaplan ,  Omer Tamir ,  Julia Pfeiffenberger ,  Meir Morgenstern ,  Adam Fyne

    IPC: G06F17/22 ,  G06F17/30

    Abstract: The present technology pertains to displaying a version of a content item and an indication of differences between that version and another version of the content item. For example, a content management system can iterate through portions of a first version of a content item and attempt to match those portions with portions of a second version of the content item. The content management system can analyze these matches to determine differences between the respective portions and to classify and categorize the differences (e.g., do they represent a significant change or do they change the meaning of the content item). A client device can then represent a clean version of the content item in a first application and the differences and characteristics in a second application in coordination with the first application.

    DETECTING ANOMALOUS DIGITAL ACTIONS UTILIZING AN ANOMALOUS-DETECTION MODEL
    4.
    发明申请
    DETECTING ANOMALOUS DIGITAL ACTIONS UTILIZING AN ANOMALOUS-DETECTION MODEL 有权

    公开(公告)号：US20230007023A1

    公开(公告)日：2023-01-05

    申请号：US17364614

    申请日：2021-06-30

    Applicant: Dropbox, Inc.

    Inventor： Sarah Andrabi ,  Effi Goldstein ,  Omer Tamir ,  Boris Borshevsky

    IPC: H04L29/06 ,  G06N3/08

    Abstract: This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine-learning model to detect mass file deletions, mass file downloads, ransomware encryptions, or other anomalous digital events within a digital-content-synchronization platform. For example, the disclosed systems can monitor digital actions executed across a digital-content-synchronization platform in real (or near-real) time and use a machine-learning model to analyze features of such digital actions to distinguish and detect anomalous actions. Upon detection, the disclosed systems can alert a client device of the anomalous actions with an explanatory rationale and, in some cases, perform (or provide options to perform) a remedial action to neutralize or contain the anomalous actions. Furthermore, the disclosed systems can also modify the machine-learning model based on interactions received from an administrator device in response to the anomalous actions.

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