Feedback Loop Meal Optimization Using Machine Learning
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Solution Overview
Problem
Existing meal optimization methods fail to account for dynamic changes in nutritional needs over time and across various phenotypes, leading to suboptimal meal preparation.
Innovation Solution
A feedback loop system utilizing a processor and machine-learning model to continuously collect and analyze nutrition data, generate optimization scores, and adjust meal plans based on target nutrient scores, ensuring optimal nutritional balance for individual phenotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional meal preparation methods are used, then meal preparation is simple and quick, but nutritional optimization for individual phenotypes and dynamic changes over time is not achieved
Solution Approach 1:
The system implements a feedback loop where nutrition data is continuously collected from users, processed through machine learning models to generate optimization scores, and used to dynamically adjust meal recommendations. This closed-loop feedback mechanism enables the system to adapt to changing nutritional needs over time and across different phenotypes, resolving the contradiction between adaptability and complexity by making the complexity productive rather than wasteful.
Solution Approach 2:
The patent transforms static meal preparation into a dynamic system that continuously adapts to changing conditions. The machine learning models are trained on historical nutrition data and continuously updated, allowing the system to evolve its recommendations based on temporal changes in user needs, phenotypic variations, and nutritional requirements, thereby achieving high adaptability without requiring complete system redesign.
2Measurement precision
If personalized meal optimization is implemented, then nutritional precision for individual phenotypes is improved, but data processing requirements and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive nutrition databases and phenotypic data before actual meal optimization is needed. This pre-computation phase stores learned patterns and relationships, allowing the system to make rapid, precise recommendations during actual use without requiring excessive real-time computational resources, thus balancing precision with energy efficiency.
Solution Approach 2:
The patent uses machine learning models to create simplified representations (copies) of complex nutritional relationships and phenotypic patterns. Instead of processing raw nutritional data from scratch for each meal recommendation, the system uses trained models that capture essential patterns, enabling precise personalized recommendations with reduced computational overhead by working with compressed, learned representations rather than full data sets.
Data Source
AI summary
The present disclosure is generally directed to an apparatus for using a feedback loop to optimize meals, may include at least a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to retrieve nutrition data from a database. The processor may be configured to generate an optimization score, wherein generating the optimization score may include training an optimization machine-learning model, wherein the optimization machine-learning model is trained with optimization training data, inputting a nutrient quantity to the optimization machine-learning model to output a target nutrient score, and generating an optimization score as a function of the nutrition data and the target nutrient score.