USPTO Patent Grant: AI for Genomic Data Treatment Recommendations

ChangeBridge: Patent Grants - Health Informatics (G16H)

Published March 24th, 2026

Detected March 24th, 2026

Summary

The USPTO has granted a patent (US12586686B1) to PointHealth AI, Inc. for methods and systems using artificial intelligence and reinforcement learning to recommend patient treatment options based on genomic data and medical history. The patent covers a system that integrates a rules engine and an AI model to provide personalized treatment recommendations.

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What changed

The United States Patent and Trademark Office (USPTO) has issued patent US12586686B1 to PointHealth AI, Inc. This patent covers novel methods, systems, and computer program products for generating treatment recommendations using genomic data and artificial intelligence. Specifically, the patented technology employs a rules engine and a reinforcement learning AI model to analyze a patient's diagnosis, genomic profile, and treatment history to suggest personalized treatment options, potentially modified by biomarker-based rules.

While this is a patent grant and not a regulatory rule imposing direct compliance obligations, it signifies innovation in the health informatics space. Companies developing or utilizing AI for personalized medicine, particularly those incorporating genomic data, should be aware of this patented technology. Compliance officers in healthcare and pharmaceutical sectors should monitor the commercialization and adoption of such AI-driven treatment recommendation systems, as they may influence future clinical practice guidelines or data handling requirements.

Source document (simplified)

← USPTO Patent Grants

Treatment recommendations using genomic data and reinforcement learning

Grant US12586686B1 Kind: B1 Mar 24, 2026

Assignee

PointHealth AI, Inc.

Inventors

Rachel Mara Gollub

Abstract

Methods, systems, apparatuses, devices, and computer program products are described. A system may use a rules engine and a reinforcement learning artificial intelligence (AI) model to recommend treatment options for a patient. In some examples, the AI model may be trained for a specific diagnosis. The system may receive patient information including the patient's diagnosis, genomic profile (e.g., partial or full genomic information), and treatment history. The system may input the genomic profile into the rules engine to determine any relevant treatment modifications for the user based on biomarkers in the genomic profile. The system may additionally input the patient information into the AI model to determine a set of treatment option recommendations and corresponding confidence metrics. The system may send the treatment option recommendations (e.g., which in some cases may be modified based on the output of the rules engine) to a user device for display.