Patent ID: 11916927
Assignee: SIFT SCIENCE, INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 0:
1. A machine learning-based method for accelerating a disposition of an inbound digital dispute event, the method comprising:
identifying, by one or more computers, a digital dispute event and a digital event processor associated with a digital event that occurred between a target online user and a subscriber to a digital threat mitigation service;
routing, by the one or more computers, the digital dispute event to one of (1) a subscriber-specific machine learning-based dispute scoring model that is trained on historical dispute response data of the subscriber and (2) a subscriber-agnostic machine learning-based dispute scoring model that is trained on historical dispute response data of a plurality of distinct subscribers that involve the digital event processor based on the digital dispute event satisfying a routing protocol of a hierarchical digital dispute routing matrix, wherein:
the hierarchical digital dispute routing matrix includes a plurality of distinct routing protocols in a predetermined routing sequence that prioritizes a routing of the digital dispute event to the subscriber-specific machine learning-based dispute scoring model over the subscriber-agnostic machine learning-based dispute scoring model;
the digital dispute event is routed to the subscriber-specific machine learning-based dispute scoring model when the digital threat mitigation service determines that the subscriber has historically prevailed in historical digital dispute events analogous to the digital dispute event, and
the digital dispute event is routed to the subscriber-agnostic machine learning-based dispute scoring model when the digital threat mitigation service determines that the subscriber has historically underperformed in the historical digital dispute events analogous to the digital dispute event;

computing, by the one of the subscriber-specific machine learning-based dispute scoring model and the subscriber-agnostic machine learning-based dispute scoring model, a preliminary machine learning-based dispute inference based on one or more features extracted from the digital dispute event, wherein:
the preliminary machine learning-based dispute inference relates to a probability of the subscriber prevailing against the digital dispute event based on each piece of evidence data of a service-proposed corpus of evidence data being available to include in a dispute response artifact;

generating the dispute response artifact based on the digital dispute event, wherein the generating includes installing one or more obtainable pieces of evidence data associated with the digital event into one or more distinct dispute response sections of the dispute response artifact;
computing, by a target one of the subscriber-specific machine learning-based dispute scoring model and the subscriber-agnostic machine learning-based dispute scoring model, an updated machine learning-based dispute inference for the digital dispute event based on a current state of the evidence data included in the dispute response artifact; and
transmitting, by the one or more computers, the dispute response artifact to a target entity based on the updated machine learning-based dispute inference satisfying a dispute response submittal criterion.