AI System for Analyzing Wearable Activity Tracker Health Data

ChangeBridge: Patent Grants - Diagnosis & Surgery (A61B)

Published April 7th, 2026

Detected April 7th, 2026

Summary

The USPTO granted Patent US12593990B2 to Aetna Inc. on April 7, 2026, covering a system and method for using deep learning algorithms to analyze health data collected from wearable devices such as activity trackers and smartwatches. The system processes input vectors containing monitored parameter data, embedding data from health records, and optionally social determinants and demographic data to generate health condition probability classifiers. Five inventors are named: Naiqian Zhi, Benjamin Wanamaker, Rajiv Bhan, and Sumeet Kumar.

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What changed

USPTO issued Patent US12593990B2 to Aetna Inc. on April 7, 2026, granting exclusive rights to an AI system that analyzes wearable activity tracker data combined with electronic health records using deep learning algorithms. The patent (20 claims) covers methods for detecting underlying health conditions by processing data from wearable devices, including parameters such as heart rate (A61B 5/02438), movement patterns (A61B 5/1118), and blood metrics (A61B 5/14551), alongside patient health record embeddings (G16H 10/60). The system can notify users directly via their wearable device or indirectly through their primary care provider when health conditions are detected.

This patent grant does not create immediate compliance obligations. However, health technology companies developing wearable-based health monitoring solutions should review the patent claims for potential licensing considerations or to assess whether their AI/ML-based health analytics systems may implicate similar methodologies. Healthcare insurers and providers implementing predictive health analytics using wearable data should consult intellectual property counsel to evaluate freedom-to-operate in this domain.

Source document (simplified)

← USPTO Patent Grants

System and methods utilizing artificial intelligence algorithms to analyze wearable activity tracker data

Grant US12593990B2 Kind: B2 Apr 07, 2026

Assignee

Aetna Inc.

Inventors

Naiqian Zhi, Benjamin Wanamaker, Rajiv Bhan, Sumeet Kumar

Abstract

A system and method are disclosed for monitoring health conditions based on data collected by a wearable device such as an activity tracker or a smart watch. Deep learning algorithms are configured to process an input vector that includes monitored parameter data collected by the wearable device as well as embedding data obtained from health records corresponding to a user account registered to the wearable device. In some embodiments, the input vector can also include social determinants data and/or demographic data. The output of the deep learning algorithms provides classifiers that represent probabilities that the user of the wearable device has an underlying health condition. If any underlying health condition is detected, then the user can be notified directly, via the wearable device or an associated application or technology, or indirectly, via a primary care provider associated with the user.

CPC Classifications

A61B 5/0205 A61B 5/0022 A61B 5/1118 A61B 5/14551 A61B 5/681 A61B 5/7264 A61B 5/02438 G06F 16/24522 G06N 3/04 G06N 3/08 G16H 10/60 G16H 20/30 G16H 40/67 G16H 50/70