Appliance for processing food and method of operating the same
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Solution Overview
Problem
Existing methods for operating cooking ovens lack advanced automation and self-learning capabilities, leading to inefficiencies in food processing and recognition.
Innovation Solution
A method that captures images of food items using a camera, extracts characteristic features, assigns categories, and generates datasets for automatic operation, allowing for self-learning and pattern recognition to optimize cooking processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If image capture and feature extraction methods are implemented for food recognition, then automation level and productivity are improved, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The camera system serves multiple functions: capturing food images for recognition, monitoring cooking progress, and potentially guiding users on proper food placement. This multi-functionality justifies the added complexity by providing multiple benefits from a single component addition.
Solution Approach 2:
The system automatically captures images, extracts features, recognizes food types, and determines cooking parameters without user intervention. The appliance serves itself by making autonomous decisions about cooking processes based on visual analysis, reducing the need for manual programming by users.
2Measurement precision
If multiple characteristic features are extracted from food images, then measurement precision and recognition accuracy are improved, but processing time and loss of time increase
Solution Approach 1:
The system extracts a comprehensive set of features including color, shape, texture, and size characteristics - more features than minimally necessary. This excessive feature extraction ensures high recognition accuracy by capturing all relevant visual aspects of the food, accepting the trade-off of increased processing time.
Solution Approach 2:
The system performs feature extraction and food recognition before the cooking process begins. By completing the analysis in advance, the system determines optimal cooking parameters upfront, allowing the actual cooking to proceed without delays from ongoing analysis.
3Adaptability or versatility
If a database of food categories and processing programs is built and stored, then adaptability and versatility are improved, but device complexity and storage requirements increase
Solution Approach 1:
A comprehensive database of food categories and associated processing programs is pre-built and stored in the appliance's memory before use. This preliminary preparation allows the system to quickly match recognized foods with appropriate cooking instructions without requiring complex real-time decision algorithms, simplifying the processing logic.
Solution Approach 2:
The database acts as an intermediary between food recognition and cooking execution. Instead of implementing complex real-time decision-making logic, the system uses the pre-stored database as a mediator to translate recognized food types into appropriate cooking parameters, simplifying the control architecture.
Data Source
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AI summary
The present application in particular is related to a method of operating a cooking appliance, in which a food category of a food item automatically can be assigned based on features extracted from an image of the food item. For improving assignment, the method is provided with self learning performance.