Eye Tracking Machine Learning System for Physiological Assessment

ChangeBridge: Patent Apps - Diagnosis & Surgery (A61B)

Published September 29th, 2025

Detected April 2nd, 2026

Summary

RightEye LLC filed USPTO patent application US20260090750A1 for a system using eye tracking and machine learning to automatically assess physiological states. The invention captures eye movement data, processes it through an ML model trained to identify patterns associated with physiological characteristics, and outputs both the physiological state and recommended interventions. Application No. 19343212 was published April 2, 2026.

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What changed

RightEye LLC's patent application describes an eye tracking system that captures user eye movements as data and feeds this to a machine-learning model trained to identify associations between eye movement patterns and physiological characteristics. The model outputs the user's physiological state, which is then used to determine and output an intervention. The system uses CPC classifications A61B 5/165, A61B 5/163, and A61B 5/7264.

This is a published patent application that has not yet been granted. No compliance deadlines, required actions, or penalties are associated with this document. Entities developing similar eye tracking or physiological assessment technologies should review this application to understand potential patent claims and Freedom to Operate considerations. The filing date of September 29, 2025 establishes priority for the claimed inventions.

Source document (simplified)

← USPTO Patent Applications

SYSTEMS AND METHODS FOR AUTOMATICALLY ASSESSING A PHYSIOLOGICAL STATE

Application US20260090750A1 Kind: A1 Apr 02, 2026

Assignee

RightEye, LLC

Inventors

Melissa HUNFALVAY, Adam Todd GROSS, Takumi BOLTE

Abstract

Automatically assessing a physiological state of a user may include capturing, by an eye tracking device, eye movement of a user as eye movement data, providing the eye movement data to a machine-learning model trained to identify associations between one or more patterns in the eye movement data and one or more characteristics of one or more physiological states, outputting, by the machine-learning model, the physiological state of the user based on the identified associations, determining an intervention for the user based on the output physiological state, and outputting the intervention.