Personalized laundry appliance
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Solution Overview
Problem
Existing laundry appliances lack intelligence and personalization, making it difficult for operators to select optimal settings, especially for diverse fabrics and loads, leading to poor results and underutilization of available settings.
Innovation Solution
Integration of machine learning models within laundry appliances that utilize sensors, such as spectral and tactile surface sensors, to determine fabric attributes and user preferences, allowing for personalized control of the laundry process based on real-time data and historical information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional laundry appliances with manual settings are used, then device complexity is low, but adaptability to different fabrics and loads is insufficient
Solution Approach 1:
The laundry appliance automatically detects fabric type, load characteristics, and soil level using sensors and machine learning models, then self-adjusts treatment parameters without user intervention. The system serves itself by making intelligent decisions based on sensor data, eliminating the need for users to manually select settings while achieving high adaptability across diverse laundry items.
Solution Approach 2:
Manual mechanical setting selection is replaced with electronic sensor-based detection and automated control systems. Optical sensors, spectral sensors, and machine learning algorithms substitute for traditional mechanical dials and buttons, enabling the appliance to automatically adapt to different fabrics and loads through intelligent analysis rather than manual configuration.
2Adaptability or versatility
If more settings and controls are added to laundry appliances, then adaptability improves, but ease of operation deteriorates
Solution Approach 1:
The appliance automatically selects optimal treatment settings by analyzing sensor data about the laundry load, fabric type, and soil level. Instead of presenting users with numerous manual options, the system self-determines the best parameters and applies them automatically, maintaining ease of operation while achieving high adaptability through intelligent automation.
Solution Approach 2:
Sensors continuously monitor the laundry load characteristics and provide feedback to the control system, which then automatically adjusts treatment parameters. This closed-loop feedback mechanism enables the appliance to adapt to different fabrics and loads dynamically without requiring users to understand or navigate complex setting options, preserving ease of operation while maximizing versatility.
3Extent of automation
If machine learning models and sensors are integrated, then automation and personalization improve, but device complexity increases
Solution Approach 1:
The machine learning model and sensor system serve multiple functions: detecting fabric type, analyzing load characteristics, determining soil level, and personalizing treatment parameters. By consolidating these diverse functions into a single integrated intelligent system, the appliance achieves high automation and personalization while managing device complexity through multi-functional design rather than separate dedicated systems for each function.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances automation and personalization of laundry processes, ensuring optimal treatment settings for various fabrics and loads, improving efficiency and user satisfaction by adapting to individual preferences and historical data.
Implementation Method 1
spectral sensors, and so on, are positioned to obtain information about the laundry items, for example by sensing the interior of the chamber or its contents or by sensing the laundry items as they are loaded into the chamber
Implementation Method 2
Sensor(s), for example touch sensors, spectral sensors, and so on, are positioned to obtain information about the laundry items
Implementation Method 3
control of the laundry process is based on user inputs, individual user preferences or other personalized data, such as provided by or accessed from a personal smart phone. The laundry process may also be controlled based on temperature sensing and other sensor data
Data Source
AI summary
Laundry appliances use machine learning models and/or personalization to provide better treatments. As one example, a laundry appliance has a chamber in which laundry items are placed for treatment. Sensor(s) are positioned to sense contents of the chamber or to sense the laundry items as they are loaded into the chamber. The machine learning model uses data from these sensors to determine various attributes of the laundry items and/or the treatment, such as the type of fabric and/or how dirty the items are, and the treatment process is controlled accordingly. Personalized data, such as an individual's preferences for laundry treatments or his sensitivities and allergies, may also be used to personalize the treatment process.