Generative Model for Visual Doneness Prediction in Smart Oven Control
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Solution Overview
Problem
Existing methods for determining food doneness are subjective and lack precise definitions, leading to inconsistencies in cooking times and results.
Innovation Solution
A smart oven system that captures raw images of food items and uses a generative model, such as a Generative Adversarial Network (GAN) or Variational AutoEncoder (VAE), to generate synthesized images of the food at different levels of doneness, allowing users to select a desired level, and then adjusts the cooking process accordingly using a doneness detection model to ensure precise cooking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective terms (light, medium, dark) are used to define doneness levels, then the cooking process is simple to operate, but the measurement precision and consistency of doneness levels deteriorate
Solution Approach 1:
The patent transforms the subjective doneness assessment into an objective parameter-based system by using color space values (L*, a*, b*) and chroma calculations to quantify doneness levels. This allows precise measurement while maintaining user-friendly operation through visual image comparison.
Solution Approach 2:
The patent replaces subjective human judgment with an automated image processing and analysis system that uses computer vision algorithms to objectively determine doneness levels, eliminating the inconsistency inherent in human perception while keeping the interface simple.
2Manufacturing precision
If synthesized images are generated using a generative model, then the manufacturing precision of doneness prediction is improved, but the device complexity increases
Solution Approach 1:
The patent uses a generative model to create synthesized images that copy and represent the visual appearance of food at different doneness levels. These synthetic images serve as reference templates for comparison, enabling precise prediction without requiring complex physical measurement devices.
Solution Approach 2:
The patent introduces synthesized images as an intermediary between the raw food image and the doneness determination. The generative model creates these intermediate representations that bridge the gap between visual appearance and doneness classification, simplifying the overall system architecture.
3Measurement precision
If a doneness detection model is used to determine current doneness level in real-time, then the measurement precision is improved, but the loss of time for processing increases
Solution Approach 1:
The patent pre-generates a library of synthesized images representing different doneness levels before the cooking process begins. During cooking, the system only needs to compare the current food image against these pre-prepared references, significantly reducing processing time while maintaining high precision.
Solution Approach 2:
The patent focuses the doneness detection on specific critical features (color space values, chroma) rather than analyzing the entire image in detail. This partial analysis approach achieves sufficient precision for cooking applications while minimizing processing time.
Data Source
AI summary
Controlling a heating process is provided. An image of a raw food item is captured. Using a generative model, synthesized images 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 is received. The food item is cooked to the levels of doneness corresponding to the one of the synthesized cooked images.