Predictive health recommendation system using large data models
Summary
The USPTO granted Patent US12597526B2 to Included Health Inc. covering systems and methods for generating predictive data models to provide personalized health action recommendations. The patent describes acquiring service requests, identifying user features, segmenting users, generating recommended actions, determining expected values, and ranking outputs. The patent contains 20 claims and falls under CPC classifications G16H 50/70, G16H 10/60, G16H 20/00, and G06N 20/00 (health informatics and machine learning).
What changed
USPTO issued Patent US12597526B2 to Included Health Inc. for methods of generating personalized action recommendations using predictive data models applied to health services. The patent covers acquiring user requests, identifying user features, assigning users to segments, generating recommended actions with expected values, and ranking outputs based on calculated values. The technology incorporates machine learning (G06N 20/00) and health informatics (G16H classifications) for segment-based personalization.
For competitors in health technology, predictive analytics, or personalized medicine, this patent establishes IP rights that may restrict development of similar predictive recommendation systems. Healthcare technology companies developing comparable solutions should review their products for potential infringement. The patent holder gains exclusive rights to practice the claimed methods in the United States for the duration of the patent term.
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Apr 7, 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.
Systems and methods for generating predictive data models using large data sets to provide personalized action recommendations
Grant US12597526B2 Kind: B2 Apr 07, 2026
Assignee
Included Health Inc.
Inventors
Eric Carlson, Ramakrishna Soma, Molong Li, Jacob David Rifkin, Zachary Taylor, Peyton Rose
Abstract
Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.
CPC Classifications
G16H 50/70 G16H 10/60 G16H 20/00 G06N 20/00
Filing Date
2023-09-27
Application No.
18373650
Claims
20
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