USPTO Patent Grant: Liability Management Platform for Medical Bills

ChangeBridge: Patent Grants - Business Methods (G06Q)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted patent US12586106B2 for a computer-implemented method and system for managing liability related to medical bills for injured claimants. The platform utilizes a machine learning model to analyze electronic medical bills and generate findings and cost scenarios.

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What changed

The United States Patent and Trademark Office (USPTO) has granted patent US12586106B2 to Mitchell International, Inc. The patent covers a computer-implemented method for adjusting electronic medical bills for claimants injured in accidents. The system employs a trained machine learning model to process aggregated medical bills, generating findings (rationales for approval, denial, or repricing) and multiple cost-estimating scenarios.

This patent grant is a notice of intellectual property protection for a specific technology. It does not impose new regulatory obligations on healthcare providers or insurers. However, entities developing or utilizing similar liability management or medical bill adjustment platforms should be aware of this granted patent and its claims to ensure they do not infringe on the intellectual property rights of the assignee.

Source document (simplified)

← USPTO Patent Grants

Comprehensive liability management platform for calculation of multiple alternative scenarios

Grant US12586106B2 Kind: B2 Mar 24, 2026

Assignee

MITCHELL INTERNATIONAL, INC.

Inventors

Norman Tyrrell, Christopher Williamson

Abstract

A computer-implemented method for adjusting one or more electronic medical bills for a claimant injured in an accident comprises generating a user interface to be presented to a claims adjuster; receiving a first user input identifying a claimant; responsive to the first user input, retrieving and aggregating multiple electronic medical bills each having at least one line; generating one or more findings and multiple scenarios by providing the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios, wherein responsive to the inference input, the trained machine learning model outputs the one or more findings and the multiple scenarios, wherein the one or more findings represent rationales for approving, denying, or repricing, and wherein the multiple scenarios include cost estimates based on the one or more findings.