Electronic Apparatus for Food Group Classification via Nutrient Analysis
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Solution Overview
Problem
Users face difficulties in determining and inputting comprehensive food information daily, and existing systems struggle to recommend specific food groups based on nutritional ingredients, making it cumbersome to provide personalized dietary guidance.
Innovation Solution
An electronic apparatus that classifies food groups using nutritional ingredient information, candidate food probability, and food group capacity modules, filtering probability values below a threshold, and provides user-specific eating habit scores and guide information for recommended food groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the user manually inputs all food information, then comprehensive food data can be obtained, but the operation becomes cumbersome and time-consuming
Solution Approach 1:
The system automatically identifies and classifies food groups by analyzing nutritional ingredient information without requiring manual user input. The electronic apparatus processes food information autonomously using algorithms that match nutritional data against database records, enabling the system to serve itself in data collection and classification tasks.
Solution Approach 2:
The patent replaces manual mechanical input operations with automated information processing systems. Optical character recognition, image recognition, or natural language processing technologies substitute for manual typing, while algorithmic classification replaces manual categorization, dramatically reducing user effort while maintaining data accuracy.
2Measurement precision
If specific food names are recommended, then precise dietary guidance can be provided, but adaptability to different dietary guidelines and food groups is reduced
Solution Approach 1:
The system segments food information into hierarchical levels: specific food names, food groups, and nutritional ingredient categories. This segmentation allows the system to provide recommendations at different granularities - sometimes recommending specific foods when appropriate, and other times recommending broader food groups to maintain flexibility across different dietary guidelines and cultural contexts.
Solution Approach 2:
The recommendation system dynamically adjusts between specific food names and food group categories based on contextual factors such as the user's dietary guidelines, cultural preferences, and available food options. The system flexibly switches between recommendation levels to optimize both precision and adaptability for each user situation.
3Measurement precision
If comprehensive food information is collected, then accurate dietary analysis can be performed, but the system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential nutritional ingredient information needed for food group classification rather than collecting and processing all possible food attributes. By focusing on key nutritional components, the system achieves accurate dietary analysis while minimizing data collection complexity and processing requirements.
Solution Approach 2:
The system performs preliminary classification of food information into standard food groups using established nutritional databases and classification systems before conducting detailed dietary analysis. This preliminary organization simplifies subsequent processing steps and reduces the complexity of the overall analysis system by working with pre-structured data.
Data Source
AI summary
An electronic apparatus and a controlling method thereof are provided. The electronic apparatus includes memory storing one or more computer programs, and one or more processors communicatively coupled to the memory, wherein the one or more computer programs include computer-executable instructions executed by the one or more processors and wherein the one or more processors configured to acquire nutritional ingredient information corresponding to food information in case of acquiring the food information, acquire candidate food probability information based on the nutritional ingredient information, and acquire final food group capacity information corresponding to the food information based on the nutritional ingredient information and the candidate food probability information.