Laundry treating apparatus, control method of laundry treatment apparatus and online system including the same
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Solution Overview
Problem
Current laundry treating apparatuses lack convenience in selecting customized courses and options, requiring manual input and failing to adapt to user-specific conditions such as weather, time zone, and environment, and often rely on insufficient use history or external server connections for recommendations.
Innovation Solution
A laundry treating apparatus equipped with a communication module and a controller using deep learning to compute and recommend courses and options based on user-specific data, including weather and area information, without the need for continuous external server connection, utilizing a storage unit for use histories and a computing unit for parallel processing to determine optimal settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual selection of courses and options is required, then users can customize laundry settings according to their needs, but user convenience deteriorates due to repeated manual input
Solution Approach 1:
The system automatically collects user information through the camera to identify the user and retrieves their preferences from the server without manual input. The laundry treating apparatus autonomously determines and sets appropriate courses and options based on the identified user's stored preferences, eliminating the need for repeated manual customization while maintaining personalized service.
Solution Approach 2:
User information and preferences are pre-stored on the server during registration. When a user needs laundry service, the system quickly retrieves pre-stored information rather than requiring real-time manual input. This preliminary preparation of data enables immediate personalized service delivery.
2Adaptability or versatility
If use history is collected for recommendations, then personalized service improves, but system complexity increases due to data collection and processing requirements
Solution Approach 1:
The server acts as an intermediary that stores and manages user information and use histories. The laundry treating apparatus communicates with the server to retrieve necessary data rather than maintaining complex local databases and processing systems. This distributes complexity to the server while keeping the local apparatus relatively simple.
Solution Approach 2:
The patent replaces complex mechanical data handling systems with optical recognition technology. The camera captures user images and the system uses image recognition algorithms to identify users and retrieve their information, substituting traditional manual data entry and complex local storage systems with automated optical recognition and cloud-based data retrieval.
3Measurement precision
If external server connection is required for recommendations, then accurate personalized recommendations can be provided, but reliability deteriorates when server connection is unavailable
Solution Approach 1:
User information and preferences are pre-loaded and stored locally in the laundry treating apparatus during initial setup or previous connections. When the server is unavailable, the system can still operate by retrieving information from local storage, ensuring continuous service availability while maintaining personalized recommendations.
Solution Approach 2:
The system prepares backup data storage locally to cushion against server unavailability. By maintaining local copies of essential user information and preferences, the system ensures that personalized recommendations can continue even when the external server connection is interrupted or unavailable.
4Adaptability or versatility
If multiple manipulation buttons are provided for course selection, then user control flexibility improves, but ease of operation deteriorates due to complicated manipulation
Solution Approach 1:
The system automatically determines the appropriate course and options based on the identified user's stored preferences, eliminating the need for users to manually navigate through multiple buttons and settings. The apparatus self-configures the laundry parameters, providing full control flexibility through automation rather than manual selection.
Data Source
Figure 1(a)~1(d)
Figure 2
Figure 3(a)~3(b)
AI summary
A laundry treating apparatus (20) is disclosed in which a recommendation formula is previously stored to compute and determine a course and option to be recommended.