Generative Model Doneness Prediction for Precision Cooking Control
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Solution Overview
Problem
Existing cooking methods lack precision in determining food doneness, as terms like 'light', 'medium', and 'well-done' are subjective and do not provide clear definitions, leading to inconsistencies in cooking times and results.
Innovation Solution
A smart oven uses a generative model, such as a Generative Adversarial Network (GAN) or Variational AutoEncoder (VAE), to generate images of food at different doneness levels, allowing users to select a desired level, and then utilizes a doneness detection model to control the heating system for precise cooking based on real-time image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional subjective doneness terms (light, medium, well-done) are used, then cooking instructions can be provided, but measurement precision and consistency are poor due to lack of exact definitions
Solution Approach 1:
The patent replaces subjective human judgment and traditional temperature-based mechanical measurement with computer vision-based optical measurement. A camera captures images of the food, and machine learning models analyze visual features to objectively determine doneness levels, eliminating the ambiguity of subjective terms while maintaining relatively simple cooking control.
2Ease of operation
If generative models are used to create synthesized images of cooked food, then user ability to select desired doneness level is improved, but device complexity increases due to additional computational requirements
Solution Approach 1:
The patent creates synthesized images that are visual copies or representations of what the food will look like at different doneness levels. These generated images serve as virtual prototypes, allowing users to select their preferred outcome before cooking begins, thereby improving ease of operation while the computational complexity is managed through efficient model deployment.
Solution Approach 2:
The system performs preliminary image generation and display to the user before the actual cooking process begins. Users can preview and select their desired doneness level in advance, allowing the cooking system to be pre-configured with target parameters, which streamlines the subsequent cooking and monitoring processes.
3Manufacturing precision
If real-time image analysis is used to monitor cooking progress, then cooking precision is improved, but processing time and computational load increase
Solution Approach 1:
The system performs partial image analysis by focusing on key visual features that are most indicative of doneness changes, rather than analyzing every pixel in full detail. This selective analysis approach maintains high cooking precision while reducing computational time and processing load during real-time monitoring.
Solution Approach 2:
Instead of continuous full-frame analysis, the system uses periodic sampling of images at strategically chosen time intervals during cooking. The doneness detection model evaluates these sampled images to track cooking progress, maintaining precision while minimizing total processing time and computational resource consumption.
Data Source
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Figure 3A(A)~3A(C)
AI summary
Controlling a heating process (500, 600) is provided. An image (302) of a raw food item is captured. Using a generative model (212), synthesized images (304) of the cooked food are generated at different levels of doneness based on the raw image. A selection of one of the synthesized cooked images (304) is received. The food item is cooked to the level of doneness corresponding to the one of the synthesized cooked images (304).