Meal Feedback Loop Using ML for Dynamic Nutrient Optimization
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Solution Overview
Problem
Existing meal preparation methods fail to optimize for a wide range of phenotypes and dynamic nutritional factors over time, leading to suboptimal nutritional outcomes.
Innovation Solution
An apparatus and method utilizing a feedback loop with a processor and memory to retrieve nutrition data, generate nutrient and optimization scores, and create an edible chain through machine-learning, iteratively updating based on user inputs to continuously optimize meals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional meal preparation methods are used, then simplicity and ease of operation are maintained, but nutritional optimization for diverse phenotypes and dynamic factors is insufficient
Solution Approach 1:
The system integrates multiple functions into a single platform: retrieving nutrition data from databases, generating nutrient target scores, creating optimization scores, building edible chains through machine learning, and processing user feedback. This multi-functional approach enables comprehensive nutritional optimization across diverse phenotypes without requiring separate systems for each function.
Solution Approach 2:
The system implements continuous feedback loops where user inputs and consumption data are processed to iteratively update the edible chain and refine future meal recommendations. This feedback mechanism allows the system to adapt to individual nutritional needs and preferences over time, improving personalization while managing complexity through automated learning.
2Adaptability or versatility
If static meal plans are used, then ease of operation is maintained, but ability to account for changing nutritional factors over time is limited
Solution Approach 1:
The system pre-generates edible chains and stores them in databases before user interaction. These pre-computed chains contain ranked edible elements based on nutritional optimization criteria. When users interact with the system, they receive pre-prepared recommendations rather than requiring real-time computation, thus enabling dynamic adaptation without excessive computational time loss.
Solution Approach 2:
The system transitions from static meal plans to dynamic edible chains that can be updated and refined over time. The machine learning model continuously learns from user feedback and consumption patterns, allowing the recommendations to adapt to changing nutritional factors while maintaining efficient response times through incremental updates rather than complete recalculation.
3Measurement precision
If comprehensive nutrition data analysis is performed, then measurement precision of nutritional needs is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system introduces an intermediary machine learning model that processes comprehensive nutrition data and translates it into structured edible chains with ranked elements. This intermediary layer handles the complexity of analyzing multiple nutritional factors, phenotypes, and preferences, while presenting simplified, actionable recommendations to users. The model acts as a mediator between raw data complexity and user-friendly output.
Solution Approach 2:
The system segments the comprehensive nutrition analysis into distinct components: retrieving base nutrition data, generating nutrient target scores, creating optimization scores, and building ranked edible chains. Each segment handles a specific aspect of the analysis, making the overall complex process more manageable and allowing parallel processing of different nutritional dimensions.
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.