Laundry Appliance User Interface with Historical Cycle Learning
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Solution Overview
Problem
Conventional laundry appliances require users to manually input operating parameters for each cycle, even if the parameters fit a common pattern, leading to inefficient user interaction and cycle selection.
Innovation Solution
A laundry appliance with a user interface panel and a controller that determines the appliance's on state, obtains a list of favorite operating cycles based on historical data, and displays this list for user selection, thereby simplifying cycle selection for commonly used cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually input operating parameters for each cycle, then the appliance can operate with basic functionality, but user interaction efficiency deteriorates and cycle selection time increases
Solution Approach 1:
The controller proactively analyzes historical operating data and pre-prepares a customized list of favorite cycles before the user needs to select a cycle. This preliminary action of data analysis and cycle curation eliminates the need for users to manually re-input parameters for commonly used cycles, directly reducing cycle selection time while maintaining ease of operation.
Solution Approach 2:
The appliance serves itself by automatically learning from its own historical operation data and generating personalized cycle recommendations. The system autonomously identifies usage patterns and configures favorite cycles without requiring external input or manual programming by the user, thereby reducing both time and effort for cycle selection while improving operational ease.
2Adaptability or versatility
If the appliance provides full manual control for all parameters, then user customization capability is maximized, but user interface complexity increases
Solution Approach 1:
The user interface is segmented into two distinct modes: a simplified mode that displays only personalized favorite cycles for quick selection, and a full mode that provides access to all operating parameters when needed. This segmentation allows the interface to adapt its complexity level based on user needs, maintaining high adaptability while reducing perceived complexity during routine operations.
Solution Approach 2:
The user interface dynamically adjusts its displayed content based on the appliance's learning of user preferences. The system transitions between showing a condensed list of favorite cycles and the complete parameter set, making the interface complexity adaptive rather than static. This dynamic behavior preserves full customization capability while minimizing routine interaction complexity.
3Productivity
If the appliance stores and processes historical operating data, then cycle selection efficiency improves through pattern recognition, but data processing requirements and system complexity increase
Solution Approach 1:
The controller performs partial data processing by focusing only on the essential patterns needed for cycle selection rather than analyzing all possible operating parameters. The system extracts just enough information from historical data to identify usage patterns and generate favorite cycles, avoiding excessive data processing while still achieving improved cycle selection speed through intelligent pattern recognition.
Data Source
AI summary
A laundry appliance includes a tub positioned within a cabinet, a basket rotatably mounted within the tub and defining a chamber configured for receiving a load of clothes, a user interface panel mounted to the cabinet, and a controller in operative communication with the user interface panel. The controller is configured to determine the laundry appliance has been turned on, obtain a list of favorite operating cycles based on historical data related to operating cycles performed by the laundry appliance, and display the list of favorite operating cycles on the user interface panel for selection by a user through the user interface panel.