Artificial intelligence washing machine providing automatic washing course based on vision
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Solution Overview
Problem
Conventional home appliances require manual input of treatment information by users, which is inefficient and labor-intensive, and current artificial intelligence in home appliances only provides data processing without true learning capabilities.
Innovation Solution
A home appliance with an AI system that uses image recognition to automatically set optimal treatment information by learning from user interactions and object types, allowing for user-customized settings and minimizing the need for manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input of treatment information is required, then treatment accuracy can be ensured, but user effort and time consumption increase
Solution Approach 1:
The washing machine automatically captures images of laundry using an integrated camera, processes the images through AI algorithms to identify fabric type and color, and autonomously selects washing parameters without requiring manual user input. This self-service approach eliminates the need for users to manually input treatment information while maintaining accurate treatment selection.
Solution Approach 2:
The patent replaces the manual mechanical input system (user pressing buttons or typing) with an automated optical recognition system. The camera-based image capture and AI processing substitute for manual operation, automatically determining treatment information from visual characteristics of the laundry.
2Adaptability or versatility
If multiple treatment courses are provided, then treatment versatility is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal image recognition system that can identify multiple types of laundry (different fabrics, colors, patterns) using a single camera and AI processing unit. This multi-functional approach allows the system to handle diverse washing scenarios without requiring separate specialized sensors or control mechanisms for each laundry type.
Solution Approach 2:
The system maintains a library of pre-configured treatment courses with different parameters (temperature, water level, cycle time). Based on AI analysis of laundry images, the system dynamically selects and adjusts appropriate parameters from this library, enabling versatile treatment options without adding physical complexity to the hardware architecture.
3Extent of automation
If AI learning capabilities are implemented, then automation is improved, but computational requirements and energy consumption increase
Solution Approach 1:
The patent pre-trains AI models offline before deployment in the washing machine. The image processing algorithms and fabric recognition patterns are developed and optimized in advance using large datasets, then embedded in the device. This preliminary preparation reduces the computational burden during actual washing operations, lowering energy consumption while maintaining high automation levels.
Solution Approach 2:
The system performs basic image capture and processing only when needed (at the start of each washing cycle) rather than continuously. The AI model processes only the essential features required for laundry classification, avoiding excessive computation. This selective processing approach balances automation with energy efficiency.
Data Source
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AI summary
The present invention relates to home appliances treating the laundry or object received therein using artificial intelligence. Specifically, the home appliance may include an artificial intelligence-based laundry washing machine that recognizes a load based on vision and automatically provides a specific course. The home appliance include a processor (210) configured to: obtain a learning result from a learning operation using image information previously acquired using the camera (45) and treatment information previously acquired from user interface (100); and process image information currently acquired using the camera with respect to the learning result, thereby to generate and set current treatment information; and control the home appliance to treat the object based on the set current treatment information.