Meal Recommendation System Using Learned User Health Data
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Solution Overview
Problem
Existing meal recommendation techniques struggle to suggest meal categories that a user may not recognize as suitable, even though they align with the user's preferences.
Innovation Solution
A meal recommendation apparatus and method that utilize physical information and health conditions of a user, combined with a learned model based on data from multiple users, to generate and output suitable meal menu recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a recommendation system recommends food categories based on frequent consumption history, then the recommendation reflects user and family preferences, but the user cannot recognize the suitability of the recommendation
Solution Approach 1:
The patent introduces an intermediary component that analyzes the relationship between users and food items to generate explanations. This intermediary process examines consumption patterns, dietary restrictions, and preferences to create rationale that bridges the gap between automated recommendations and user understanding, making the recommendations transparent and recognizable to users.
2Ease of manufacture
If the system uses only user and family consumption history, then the recommendation is simple to generate, but it cannot recommend meals that conform to user preferences that the user does not recognize
Solution Approach 1:
The patent extends the recommendation approach by adding a new dimension of analysis - examining the relationships between multiple users and their consumption patterns together. Instead of analyzing individual user history in isolation, the system analyzes the multi-user context to discover preferences and patterns that individual analysis would miss, thereby improving adaptability without proportionally increasing complexity.
Data Source
AI summary
In order to recommend a meal menu suitable for a user, a meal recommendation apparatus (1) includes: a reception section (11) for receiving physical information or a health condition of a subject user and a request pertaining to a meal menu; a generation section (12) for generating response information including information pertaining to a meal menu which corresponds to the physical information or health condition of the subject user based on the request and the health condition of the subject user using a learned model which has learned pieces of physical information or health conditions of a plurality of second users and meal order histories of the plurality of second users; and an output section (13) for outputting the response information.