Personalized Meal Menu Recommendation Through Nutrient-Health Analysis
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Solution Overview
Problem
Existing dietary advice systems fail to consider individual differences, necessitating pre-prepared databases and limiting personalized health management.
Innovation Solution
An information processing system that includes a measured value storage unit, intake storage unit, analysis unit, meal menu storage unit, and meal menu recommendation unit to analyze nutrient intake and recommend personalized meal menus based on individual health goals and nutrient relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-prepared advice databases are used, then the system can provide dietary advice, but it cannot consider individual differences
Solution Approach 1:
The system dynamically generates personalized advice by analyzing individual user data (measured values, intake information) rather than relying on static pre-prepared databases. The analysis unit processes user-specific information to generate customized recommendations in real-time, enabling adaptation to individual differences without requiring complex manual configuration.
Solution Approach 2:
The system performs self-analysis by automatically processing user-provided data (measured values, nutrient intake) to generate personalized advice. The analysis unit independently evaluates individual characteristics and generates appropriate recommendations without requiring external intervention or manual database selection, allowing the system to serve itself in generating customized advice.
2Ease of operation
If pre-prepared advice databases are used, then the system can operate simply, but it cannot personalize for individual needs
Solution Approach 1:
The system incorporates feedback loops where user measured values and intake information are continuously analyzed to generate and refine personalized advice. The analysis unit processes user data, generates recommendations, and the system can iterate based on user responses and outcomes, maintaining ease of use while achieving personalization through automated feedback-driven adaptation.
3Measurement precision
If the system analyzes nutrient intake relationships, then personalized advice can be generated, but data processing complexity increases
Solution Approach 1:
The system transforms raw user data (measured values, intake information) into meaningful analysis results by applying appropriate processing parameters and methods. The analysis unit changes the state of data from raw form to analyzed form, extracting relationships between nutrients and measured values through systematic parameter transformation, thereby achieving precise analysis without excessive complexity.
Data Source
AI summary
To manage health in consideration of the individual difference. An information processing system according to the present invention includes: a measured value storage unit configured to store a measured value linked to a goal to be achieved by a user; an intake storage unit configured to store nutrients intakes of the user; an analysis unit configured to analyze a relationship between the nutrients intake and the measured value to specify nutrients that contribute to improving the measured value (improvement factor) and nutrients that contribute to deteriorating the measured value (deterioration factor); a meal menu storage unit configured to store information specifying the nutrient content in a meal menu; and a meal menu recommendation unit configured to modify the meal menu by at least one of increasing the improvement factor content and decreasing of the deterioration factor content, and recommend the modified meal menu.