AI apparatus and operation method thereof
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Solution Overview
Problem
Users face difficulty in determining the appropriate laundry treatment apparatus for washing various textiles, as conventional methods require manual selection and setting of washing parameters, which can lead to damage and inefficiency, especially when dealing with mixed materials.
Innovation Solution
An AI apparatus that detects multiple laundry treatment apparatuses, analyzes the characteristics of the input laundry, and uses a washing course learning model to recommend the optimal apparatus and course, minimizing damage while maximizing cleaning power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection and setting of washing parameters is used, then user control over washing process is maintained, but user effort and time consumption increase significantly
Solution Approach 1:
The AI apparatus automatically detects laundry material composition and recommends optimal washing parameters without user intervention. The system analyzes laundry characteristics, compares them with stored washing course data, and autonomously selects the most suitable washing program, enabling the system to serve itself rather than requiring manual user configuration.
Solution Approach 2:
The patent replaces manual mechanical selection processes with an automated AI-based system. The AI apparatus uses sensors to detect laundry materials, processes this information through machine learning algorithms, and automatically determines washing parameters, substituting the mechanical interaction of manual button pressing and parameter setting with intelligent automated decision-making.
2Device complexity
If conventional washing courses are used for mixed materials, then washing process is simplified, but damage to delicate textiles increases
Solution Approach 1:
The AI apparatus applies different washing parameters to different material types detected in the laundry load. By analyzing the composition of mixed textiles and identifying the most delicate materials present, the system tailors washing conditions specifically suited for those sensitive fabrics, rather than applying a uniform washing approach to the entire load.
Solution Approach 2:
The system dynamically adjusts washing parameters such as water temperature, washing intensity, and cycle duration based on the detected laundry material composition. When delicate materials are identified, the AI automatically modifies these parameters to reduce mechanical stress and thermal exposure, thereby minimizing damage while still achieving effective cleaning.
3Adaptability or versatility
If multiple laundry treatment apparatuses are available, then washing options increase, but user difficulty in selecting the right apparatus increases
Solution Approach 1:
The AI apparatus acts as an intermediary between the user and multiple laundry treatment apparatuses. It detects the laundry materials, evaluates the capabilities of available washing machines, and automatically selects the most suitable apparatus from the group. This intermediary function eliminates the need for users to manually compare and select among multiple devices, as the AI performs this matching process autonomously.
Solution Approach 2:
The AI apparatus provides a universal interface that works across multiple different laundry treatment apparatuses. By creating a standardized method for detecting laundry characteristics and translating them into apparatus selection criteria, the system enables one AI device to control and optimize the operation of various washing machines with different capabilities and features.
Data Source
AI summary
Disclosed is an artificial intelligence (AI) apparatus comprising: a short-range communication module configured for sensing a plurality of laundry treatment apparatuses positioned around the AI apparatus; and a processor configured for: acquiring laundry information about laundry and characteristic information of each of the detected laundry treatment apparatuses; determining a laundry group corresponding to the laundry, based on the acquired laundry information; comparing characteristic information of the determined laundry group and characteristic information of each of the plurality of laundry treatment apparatuses with each other; and determining a laundry treatment apparatus for washing the laundry among the plurality of laundry treatment apparatuses based on the comparison result.