Gradient-Boosted Nutrient Prediction for Score-Based Diets

The patent introduces an image recognition system as an intermediary between traditional nutrition labels and consumers. The system captures images of food items and automatically processes nutrition information, converting complex label data into simplified visual representations with traffic light color coding and health score ratings. This intermediary layer translates inaccessible nutrition data into easily understandable formats without requiring consumers to interpret complex label formats themselves.

The patent transforms nutrition information from traditional tabular parameters into visual parameters through image processing. Nutrition facts are converted into color-coded visual indicators (green/yellow/red traffic lights), numerical health scores, and graphical representations. This parameter transformation changes the state of nutrition information from text-heavy tables to visually intuitive displays that are easier to comprehend at a glance.

The patent enables self-service nutrition analysis through automated image recognition technology. Instead of requiring manual implementation of detailed labeling systems across the food supply chain, the system allows consumers to independently scan and analyze nutrition information from existing food labels or packaging images. The AI automatically extracts, processes, and interprets nutrition data, providing detailed analysis without human intervention in the data collection process.

The patent replaces manual mechanical processes of nutrition label creation and verification with automated optical recognition and AI processing. Instead of requiring physical label design, manual data entry, and administrative processing, the system uses image capture devices and machine learning algorithms to automatically extract and analyze nutrition information, dramatically reducing implementation time and complexity.

The patent segments the nutrient prediction process into multiple independent machine learning models, each specialized for predicting specific nutrient quantities (carbohydrates, proteins, fats, vitamins, minerals). Instead of using one complex model to predict all nutrients simultaneously, the system divides the prediction task into separate specialized models that can be trained and executed independently, reducing overall computational complexity while maintaining high accuracy for each nutrient type.

The patent implements a hierarchical prediction approach where the system first predicts macro-nutrients (carbohydrates, proteins, fats) and then uses those predictions to inform subsequent predictions of micronutrients (vitamins, minerals). This partial action strategy breaks down the complex multi-nutrient prediction into sequential stages, where each stage builds upon previous results, managing computational complexity by processing nutrients in ordered groups rather than all at once.