Feline Hypertrophic Cardiomyopathy Risk Prediction System
Summary
Mary Alexis Seguin filed US Patent Application US20260112500A1 for a system predicting feline hypertrophic cardiomyopathy risk. The system uses an ensemble of machine learning diagnostic models trained on medical data to screen new patient data, escalating to a knowledge-based diagnostic model when risk is indicated. The system includes a user interface that acquires additional clinician input and provides risk assessments with guidance for further testing and treatment. The application was filed October 15, 2025, published April 23, 2026, and covers CPC classifications G16H 50/30, G06N 20/20, G16H 10/20, G16H 20/00, G16H 50/20, and G16H 50/70.
This patent application covers health informatics and AI/ML-based diagnostic systems for veterinary cardiac disease, specifically targeting feline hypertrophic cardiomyopathy. Entities developing or licensing veterinary diagnostic software, AI-based health risk assessment tools, or feline healthcare technology may wish to review this application's claims for potential overlap with their own IP or product development roadmaps. The publication of this application establishes a priority date of October 15, 2025 for the disclosed methods.
“Systems and methods for assessing the risk of feline hypertrophic cardiomyopathy are described.”
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What changed
Mary Alexis Seguin filed US Patent Application US20260112500A1 disclosing systems and methods for assessing feline hypertrophic cardiomyopathy risk using an ensemble of machine learning diagnostic models trained on medical data, with a knowledge-based model providing detailed risk assessment and clinical decision support through a user interface.
Entities in the veterinary health technology, pet diagnostic software, or feline healthcare sectors should monitor this application's progression through examination. The disclosed dual-model architecture (ensemble ML screening followed by knowledge-based assessment) represents a specific approach to AI-assisted veterinary diagnostics that may be relevant to competitive IP landscapes or product development strategies in animal health technology.
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.
METHOD AND SYSTEM FOR PREDICTING FELINE HYPERTROPHIC CARDIOMYOPATHY DISEASE RISK
Application US20260112500A1 Kind: A1 Apr 23, 2026
Inventors
Mary Alexis SEGUIN
Abstract
Systems and methods for assessing the risk of feline hypertrophic cardiomyopathy are described. An ensemble of diagnostic models is trained on a first set of medical training data. A knowledge based diagnostic model is trained on a second set of medical training data. When new patient data is received, the ensemble of diagnostic models is used to determine whether a risk for feline hypertrophic cardiomyopathy is indicated for the new patient data. If the ensemble of diagnostic models indicates a risk for feline hypertrophic cardiomyopathy for the new patient data, the risk of feline hypertrophic cardiomyopathy is assessed using the knowledge based diagnostic model. The knowledge based diagnostic model interacts with a user interface is used to acquire additional information from a clinician. The user interface provides an assessment of the risk of feline hypertrophic cardiomyopathy for the new patient data, and guidance for further testing and treatment.
CPC Classifications
G16H 50/30 G06N 20/20 G16H 10/20 G16H 20/00 G16H 50/20 G16H 50/70
Filing Date
2025-10-15
Application No.
19358903
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