Tailored nutritional supplements for athletic performance

Abstract

A personalized supplement formulation system integrates biological data, structured qualitative feedback, and machine learning algorithms to generate individualized nutritional protocols. The system receives biological inputs such as genetic single nucleotide polymorphism (SNP) data, blood biomarkers (e.g., ferritin, vitamin D, B12), lipid profiles, and urinary metabolites. A digital user profile is created by organizing these inputs and calculating derived indices relevant to supplementation. Structured qualitative feedback, including dietary restrictions, perceived wellness, and training goals, is normalized and processed alongside the biological data. A trained machine learning engine analyzes combined inputs to output a tailored supplement formulation specifying ingredient selection, dosage, delivery format, and timing. Instructions are transmitted to a manufacturing system capable of producing the custom formulation. The system supports periodic re-evaluation and iteration based on new biological samples or user-reported feedback, enabling dynamic personalization over time and improving efficacy, compliance, and outcome tracking in athletic and wellness domains.

Dependencies are inferred from the claim text.