Cooking result inference system
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Solution Overview
Problem
Existing cooktop appliances lack reliable preset cooking options, often resulting in overcooking or undercooking due to user error or uncertainty, leading to unsatisfactory cooking outcomes and increased monitoring requirements.
Innovation Solution
A cooktop appliance system equipped with a camera assembly, mass sensor, and thermal sensor, utilizing a machine-learned model to generate an inferred cooking result based on image, mass, and temperature signals, providing a predicted appearance of food after recommended cooking time and temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If preset cooking options are provided, then user convenience is improved, but cooking reliability deteriorates due to user error and uncertainty
Solution Approach 1:
The system captures images of food at different cooking stages and provides feedback to the user about cooking progress and doneness, allowing users to adjust settings based on visual feedback rather than relying solely on preset timers that may not account for variations in food quantity or type
Solution Approach 2:
The patent replaces traditional mechanical timers and temperature controls with an image-based recognition system that uses computer vision to detect food characteristics and determine cooking status, substituting physical measurement mechanisms with optical sensing and AI analysis
2Reliability
If constant monitoring is required to avoid overcooking or undercooking, then cooking reliability is improved, but user convenience deteriorates
Solution Approach 1:
The system performs self-monitoring by automatically capturing images and analyzing cooking progress without requiring user intervention, with the AI model independently determining when food reaches desired doneness and notifying the user accordingly
Solution Approach 2:
The patent replaces manual monitoring with an automated image-based detection system that continuously or periodically captures visual data and uses machine learning to assess cooking status, eliminating the need for constant user observation
3Ease of operation
If preset cooking times and temperatures are used, then user convenience is improved, but manufacturing precision deteriorates due to inability to account for food variations
Solution Approach 1:
The system analyzes local characteristics of the food being cooked by examining specific visual features in captured images, such as color changes, texture development, and surface appearance, allowing customized cooking recommendations based on the actual state of that particular food item rather than applying generic presets
Solution Approach 2:
The AI model dynamically adjusts cooking parameters based on visual analysis of food characteristics, modifying recommended cooking time and temperature according to detected food type, size, color, and other visual parameters that indicate optimal cooking conditions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves cooking outcomes by reducing food wastage, enhancing cooking safety, and optimizing energy efficiency through accurate cooking time and temperature recommendations, ensuring proper food doneness and reduced monitoring needs.
Implementation Method 1
a camera assembly directed at a cooktop surface... accessing an image signal from the camera assembly. The image signal can include an image of the at least one object
Implementation Method 2
a mass sensor assembly configured to measure a mass of at least one object on the cooktop surface... accessing a mass signal from the mass sensor assembly. The mass signal can include the mass of the at least one object
Implementation Method 3
a thermal sensor assembly configured to measure a temperature of the at least one object... accessing a temperature signal from the thermal sensor assembly. The temperature signal can include the temperature of the at least one object
Data Source
AI summary
Systems, appliances, and methods for operating a cooking result inference system are provided herein. The cooking engagement system can include a controller in operable communication with a camera assembly, a mass sensor assembly, and a thermal sensor assembly. An image signal including an image of an object can be accessed. A mass signal including a mass of the object can be accessed from the mass sensor assembly. A temperature signal including a temperature of the object can be accessed from the thermal sensor assembly. An inferred cooking result can then be generated based on a machine-learned model that can perform operations on input including the image signal, the mass signal, and the temperature signal. The inferred cooking result can include an inferred image depicting the object as it is predicted to appear after a recommended cooking time at a recommended temperature.