Cooked level determination
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Solution Overview
Problem
Existing cooking methods struggle to accurately determine the cooked level of food items beyond core temperature, particularly in terms of juiciness and tenderness, due to the complexity of measuring weight loss and the influence of environmental factors, and existing image processing techniques are not straightforward for consumer devices with resource constraints.
Innovation Solution
A cooking method that analyzes image data to identify the distribution and flow path of liquid leached out during cooking, using pixel intensity and geometric measurements to determine the cooked level without requiring complex AI models, suitable for consumer devices with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a temperature probe is used to measure core temperature, then the cooked level can be determined for safety, but it does not indicate other parameters such as juiciness and tenderness
Solution Approach 1:
The patent transitions from one-dimensional temperature measurement to two-dimensional image analysis. By capturing images of liquid leached out during cooking and analyzing their distribution patterns, the system extracts multiple parameters (area, shape, texture) that collectively indicate cooked level, juiciness, and tenderness, resolving the limitation of temperature-only measurement
2Measurement precision
If a scale is used to measure weight loss, then cooked level can be indicated, but a separation mechanism is needed to avoid leach-out liquid contributing to measured weight
Solution Approach 1:
The patent replaces the mechanical weighing system with an optical imaging system. Instead of measuring weight loss requiring physical separation of liquid, the system captures images of the liquid leached out and analyzes its distribution area and patterns, eliminating the need for mechanical separation mechanisms while providing additional visual information about cooking state
3Measurement precision
If image processing techniques are used to monitor cooking, then cooked level can be determined, but it requires complex AI models that are not suitable for consumer devices with resource constraints
Solution Approach 1:
The patent segments the image processing task into distinct, manageable components: liquid detection, area calculation, distribution pattern analysis, and cooked level determination. Each segment uses simple, resource-efficient algorithms rather than complex AI models, making the system suitable for consumer devices while maintaining measurement precision
Data Source
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Figure 4(a)~4(d)
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AI summary
In an embodiment, a cooking method (100) is described. The method comprises receiving (102) image data corresponding to a view of a food item during a cooking process implemented by a cooking apparatus. The method further comprises identifying (104) a region of interest in the view. The region of interest comprises an indication of liquid leached out from the food item as a result of the cooking process. The method further comprises determining (106) a cooked 5 level of the food item based on a distribution of the liquid on a surface in the view and a parameter value indicative of the cooked level of the food item. The parameter value is derived from a part of the image data that corresponds to the region of interest.