Multi-purpose smart rice cookers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional rice cookers lack the ability to optimize cooking conditions for various rice mixtures, leading to potential over- or under-cooking and failing to suggest optimal combinations, resulting in suboptimal taste and nutrition.
Innovation Solution
A rice cooker assembly equipped with machine learning models that classify different types of food using cameras, determining the appropriate cooking mode, temperature-time curve, and mixture ratios based on nutrition and taste, and adjusting the cooking process dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional rice cookers use fixed cooking modes, then the device complexity is low, but the adaptability to different rice mixtures is poor
Solution Approach 1:
The rice cooker automatically identifies rice types and determines optimal cooking parameters without user input. The camera captures images of the rice, the machine learning model classifies the rice type and mixture ratio, and the system automatically adjusts cooking temperature and time, making the complex adaptation process transparent to the user.
Solution Approach 2:
The patent replaces manual selection of cooking modes with an automated vision-based identification system. Instead of users mechanically selecting from fixed modes, a camera captures images and a machine learning model automatically determines the appropriate cooking parameters based on rice appearance characteristics.
2Measurement precision
If conventional rice cookers lack food identification capability, then the device complexity is low, but the measurement precision of food types is zero
Solution Approach 1:
The patent replaces manual food identification with an automated vision system. A camera captures images of the rice, and a machine learning model processes these images to accurately identify rice types and mixture ratios, achieving high measurement precision without requiring user expertise.
Solution Approach 2:
The machine learning model acts as an intermediary between the camera and the cooking control system. It processes the visual information captured by the camera and translates it into actionable cooking parameters, enabling accurate food identification and automatic parameter adjustment.
3Manufacturing precision
If conventional rice cookers use blind execution of cooking instructions, then the ease of operation is high, but the cooking precision is poor
Solution Approach 1:
The system uses feedback from image analysis to dynamically adjust cooking parameters. The camera monitors the rice during cooking, the machine learning model analyzes the changes, and the system adjusts temperature and time in real-time to achieve optimal cooking results based on the actual rice type and state.
Solution Approach 2:
The patent transforms static cooking instructions into dynamic, adaptive cooking control. Instead of following fixed pre-programmed sequences, the system continuously adjusts cooking parameters based on real-time analysis of rice characteristics and cooking progress, achieving high precision while maintaining simplicity for the user.
4Adaptability or versatility
If conventional rice cookers lack mixture optimization capability, then the device complexity is low, but the nutrition value optimization is poor
Solution Approach 1:
The rice cooker automatically determines optimal rice mixtures and cooking parameters based on the detected rice types. The system self-adjusts the cooking process to optimize nutrition value and taste without requiring users to manually configure settings or have nutritional knowledge.
Data Source
AI summary
A rice cooker assembly uses machine learning models to identify and classify different types of food stored. The rice cooker has a chamber including different compartments for storing different types of food. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model classifies the different types of food stored. The rice cooker determines a mixture of different types of food based on nutrition value and/or taste. The rice cooker creates the mixture and controls the cooking process accordingly. The one or more machine learning models may be resident in the rice cooker or it may be accessed via a network.