AI Voice Laundry Course Selection for Mixed Fabric Loads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Washing machines face difficulties in determining the optimal laundry course when multiple types of laundry are inputted simultaneously, leading to conflicting recommendations and incomplete contaminant removal, as they struggle to distinguish between different laundry items and contaminants through speech recognition.
Innovation Solution
A washing machine and server system that utilize speech recognition to extract 'cloth-words' and 'stain-words' to determine suitable laundry courses, configure separate washing units, and provide guidance for effective contaminant removal, allowing for optimal laundry course selection and tip information based on the type of laundry and contaminant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the washing machine selects separate laundry courses for different kinds of laundry, then the washing quality for each type is improved, but the total washing time increases and multiple washing operations are required
Solution Approach 1:
The washing machine is divided into multiple independent washing units (first washing unit and second washing unit), each capable of operating with different laundry courses simultaneously. This segmentation allows different types of laundry to be washed in parallel, improving overall efficiency while maintaining optimal washing quality for each type.
Solution Approach 2:
The system transitions from sequential washing operations to parallel operations by adding a spatial dimension (multiple washing units). This allows the washing machine to handle multiple laundry types simultaneously rather than one after another, reducing total washing time while maintaining quality.
2Reliability
If the washing machine notifies the user to remove conflicting laundry, then the washing quality is maintained, but the ease of operation decreases
Solution Approach 1:
The washing machine automatically detects conflicting laundry types through speech recognition, identifies the appropriate washing units and courses, and configures the washing operation without requiring user intervention. The system serves itself by resolving conflicts autonomously, maintaining washing quality while improving ease of operation.
Solution Approach 2:
The system uses speech recognition to receive laundry information from the user, processes this information to identify conflicts, and automatically adjusts the washing configuration. This feedback loop allows the system to adapt to user needs while maintaining optimal washing quality without requiring manual user input for conflict resolution.
3Adaptability or versatility
If the washing machine uses speech recognition to identify laundry types, then the adaptability improves, but the measurement precision decreases due to difficulty in distinguishing similar terms
Solution Approach 1:
The speech recognition system dynamically adjusts its processing based on the input. When similar terms are detected (e.g., different names for the same laundry type), the system uses contextual analysis and comparison with stored laundry course information to resolve ambiguities, maintaining high accuracy while preserving adaptability to various user expressions.
Solution Approach 2:
The system introduces an intermediate processing layer that translates diverse speech inputs into standardized laundry type classifications. This intermediary process helps bridge the gap between varied user expressions and precise laundry course selection, improving both adaptability and measurement precision.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention relates to a washing machine and a server system that recommend a laundry course and washing tip information in consideration of information on a kind of laundry and a degree of contamination inputted by using artificial intelligence through speech recognition, and a method for controlling such washing machine and server system. The present invention extracts a cloth-word indicating a kind of clothes of laundry and a stain-word indicating a kind of contaminant. Then, the present invention determines a laundry course in consideration of the cloth-word. Then, washing tip information on the stain-word is searched in a pre-stored database and the searched washing tip information is provided to a user. As a result, the user can be notified of a washing method capable of effectively removing the contaminant.