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Systems and methods for intelligently implementing a machine learning-based digital threat mitigation service

  • US 10,623,423 B1
  • Filed: 06/06/2019
  • Issued: 04/14/2020
  • Est. Priority Date: 06/06/2019
  • Status: Active Grant
First Claim
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1. A machine learning-based system implementing a collisionless queue, the system comprising:

  • a distributed network of computers, a machine learning-based service being implemented by the distributed network of computers to;

    implement a review queue interface that includes a display of;

    (i) a review queue comprising a listing of a plurality of distinct review items;

    (ii) a current state for each of the plurality of distinct review items, wherein the current state is arranged proximate to each of the plurality of distinct review items; and

    (iii) for each review item of the plurality of distinct review items, a listing of distinct browser identifiers for each of one or more client browsers that are interacting with each respective review item;

    records to a persistent log of events client browser activity data of the one or more client browsers interacting with the plurality of distinct review items;

    computes a computed state for each of the plurality of distinct review items based at least on the client browser activity data, wherein computing the computed state includes;

    replaying the persistent log of events;

    identifying one or more instances in which the one or more client browsers is viewing one or more of the plurality of distinct review items based on the replay of the persistent log of events;

    identifying one or more instances in which the one or more client browsers has viewed away from the one or more of the plurality of distinct review items based on the replay of the persistent log of events;

    computes changes to the current state that is displayed for each of the plurality of distinct review items based on an assessment of the current state and the computed state for each of the plurality of distinct review items; and

    automatically updates a state of one or more of the plurality of distinct review items within the review queue interface based on identifying a difference between the current state and the computed state of the one or more of the plurality of distinct review items.

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