Machine Learning Blood Pressure Monitoring Using Digital Twin Technology
Summary
The USPTO granted Twin Health, Inc. Patent US12588823B2 for a machine learning-based blood pressure monitoring system using digital twin technology. The platform generates short-term and long-term blood pressure predictions based on patient-reported nutrition data, sensor readings, and lab test results. The system updates a digital twin of the patient's metabolic profile to enable continuous health monitoring. The patent includes 17 claims and was applied for on April 28, 2021.
What changed
The USPTO issued Patent US12588823B2 to Twin Health, Inc., covering a patient health management platform that uses machine-learned metabolic models to predict blood pressure. The system implements both short-term prediction models (generating daily predictions based on nutrition, sensor, and lab data) and long-term prediction models (for extended time period predictions). The platform generates predictions of diastolic and systolic blood pressure and maintains a digital twin of the patient's metabolic profile.
Patent grants do not impose compliance obligations on regulated entities. Healthcare technology companies and medical device manufacturers developing AI-based patient monitoring systems may review this patent to understand existing intellectual property in the digital health space and assess potential licensing needs or design-around opportunities.
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Mar 31, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Virtually monitoring blood pressure levels in a patient using machine learning and digital twin technology
Grant US12588823B2 Kind: B2 Mar 31, 2026
Assignee
Twin Health, Inc.
Inventors
James Wilson, Frederick Hadley, Terrence Chun Yin Poon, Jahangir Mohammed
Abstract
A patient health management platform implements a machine-learned metabolic model to generate a prediction of a patient's blood pressure. The platform implements a short-term prediction model to generate a daily prediction of the patient's blood pressure based on nutrition data reported by the patient and sensor data and lab test data collected for the patient. The platform implements a long-term prediction model generate a prediction of the patient's blood pressure during an extended time period based on sensor data and lab test data collected for the patient. Using the short-term prediction model, the long-term prediction model, or both, the patient health management platform generates predictions of the patient's diastolic and systolic blood pressure and updates a digital twin of the patient's metabolic profile.
CPC Classifications
A61B 5/021 A61B 5/7267 A61B 5/7275 A61B 5/742 G16H 10/40 G16H 40/67 G16H 50/20 G16H 50/30 G16H 50/70
Filing Date
2021-04-28
Application No.
17243473
Claims
17
Named provisions
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