Machine-Learned Model Architecture for Generating Individual-Specific Instructions
Summary
USPTO published patent application US20260112505A1 filed by Bilikis J. Oladimeji, Reem A. Hussain, and Aaron C. Wacker on October 23, 2024, covering systems and methods for using generative language models to generate individual-specific health instructions. The invention maps a condition to an ontological representation, incorporates individual-specific information, determines comorbidity relationships, and generates prompts based on property data associated with condition and comorbidity concepts. CPC classifications G16H 50/70 and G16H 70/20 place this in health informatics.
“Systems and methods for using generative language models to generate individual-specific instructions are disclosed herein.”
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GovPing monitors USPTO Patent Applications - Health Informatics (G16H) for new healthcare & life sciences regulatory changes. Every update since tracking began is archived, classified, and available as free RSS or email alerts — 146 changes logged to date.
What changed
USPTO published patent application US20260112505A1 disclosing a machine-learned model architecture that uses generative language models to produce individual-specific health instructions. The system maps conditions to ontological concepts, incorporates patient-specific data, and determines whether individual factors represent comorbidities relative to the target condition, generating tailored prompts accordingly.
For entities developing AI-driven healthcare applications or patient-facing health information systems, this application indicates ongoing patent activity in personalized instruction generation using ontological reasoning. While the application remains pending and does not create immediate compliance obligations, it may be relevant for freedom-to-operate analyses in health AI development.
Archived snapshot
Apr 23, 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.
Machine-Learned Model Architecture for Generating Individual-Specific Instructions
Application US20260112505A1 Kind: A1 Apr 23, 2026
Inventors
Bilikis J. Oladimeji, Reem A. Hussain, Aaron C. Wacker
Abstract
Systems and methods for using generative language models to generate individual-specific instructions are disclosed herein. When receiving a request for instructions for a condition, the disclosed techniques map the condition to a condition concept of an ontological representation, map individual-specific information to an individual-specific concept of the ontological representation, determine from the ontological representation whether the individual-specific concept is a comorbidity concept with respect to the condition concept, and generate a prompt based on the request for instructions and on property data associated with the condition concept and the comorbidity concept. The disclosed techniques generate the requested instructions by applying the prompt to a generative language model.
CPC Classifications
G16H 50/70 G16H 70/20
Filing Date
2024-10-23
Application No.
18924533
Named provisions
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