Fecha de publicación: -- Fuente: WIPO "tomato" A system and method for AI-driven nutrition analysis and optimal meal scoring utilizes a multi-factor algorithm to evaluate meals based on ingredient quality, food processing level, nutrient density, macronutrient and micronutrient balance, glycemic impact, and meal timing. The system applies temporal metabolic weighting to adjust nutrient values based on circadian metabolic capacity and calculates dynamic nutrient synergies using interaction matrices that model non-linear relationships between nutrients. A knowledge graph database stores biological entities as nodes and physiological relationships as edges, enabling multi-hop queries for determining nutrient interaction effects. A deterministic logic layer comprising truth, law, and logic layers validates AI-generated recommendations against verified biological facts and physiological rules. The system generates personalized meal scores, actionable recommendations, and Protection Reports analyzing food brands and nutrition science literature, while enabling users to share results with designated individuals for accountability.