AI Meal Plan Generation with Nutritional Vector Substitution
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Solution Overview
Problem
Existing meal planning systems fail to provide personalized and optimized nutrition intake plans that consider individual preferences and health conditions, particularly for vulnerable groups such as pregnant females, lactating women, and senior individuals.
Innovation Solution
A meal plan generating method and apparatus that utilizes an evolution algorithm and artificial intelligence to create personalized meal plans based on user-specific diet information, including health conditions, preferences, and genetic data, while ensuring nutritional balance and substitution options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing meal planning systems are used, then meal plans can be generated quickly, but they fail to provide personalized and optimized nutrition intake plans that consider individual preferences and health conditions
Solution Approach 1:
The system segments the meal plan generation process into multiple stages: initial parameter collection (diet information, health conditions, preferences), evolution algorithm-based optimization, and substitution option generation. This segmentation allows comprehensive personalization while maintaining efficiency by processing different aspects in dedicated modules.
Solution Approach 2:
The system performs preliminary actions by collecting all necessary user information (diet information, health conditions, preferences) before generating meal plans. The evolution algorithm pre-processes and optimizes meal plan parameters based on these inputs, ensuring personalized results are generated efficiently without requiring repeated adjustments.
2Manufacturing precision
If comprehensive diet information and evolution algorithm are used, then optimized and personalized meal plans are generated, but the system complexity increases
Solution Approach 1:
The evolution algorithm acts as an intermediary between user input data and meal plan generation. It processes diet information, health conditions, and preferences through automated optimization routines, translating complex requirements into optimized meal plans without requiring users to directly manage the complexity of the optimization process.
Solution Approach 2:
The system performs self-service by automatically collecting user information, processing it through the evolution algorithm, and generating optimized meal plans without requiring external intervention. The algorithm autonomously adjusts meal plan parameters to meet nutritional requirements and user preferences, reducing the need for manual system configuration.
3Reliability
If personalized meal plans are generated considering multiple factors, then nutrition intake is optimized, but the time required for meal plan generation increases
Solution Approach 1:
The evolution algorithm employs periodic action by iteratively processing meal plan parameters through defined cycles of evaluation and optimization. Each iteration refines the meal plan based on nutritional requirements and user preferences, converging toward an optimized solution within a predetermined number of iterations, thus balancing accuracy with time efficiency.
Data Source
AI summary
The present invention may comprise a method, performed by an electronic device, for suggesting meal plans, comprising, determining a first element in the first meal plan to be replaced and a first nutritional vector of the first element, obtaining a candidate set of elements, obtaining candidate nutritional vectors for some or all the candidate elements in the candidate set, determining each similarity score based on each obtained candidate nutritional vector and the first nutritional vector, obtaining a second meal plan based on a second element which has a highest similarly score, wherein the first element in the first meal plan is replaced by the second element to form the second meal plan.