Laundry Scheduling Using User Preferences and Soil Levels
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Solution Overview
Problem
Current laundry scheduling systems do not provide an integrated schedule that considers user preferences and the characteristics of multiple laundry appliances, nor do they effectively address soil levels in laundry items, leading to inefficient laundry operations.
Innovation Solution
A laundry scheduling apparatus and method that uses machine learning or deep learning algorithms to analyze user preferences and appliance features, generating an integrated schedule for distributing laundry items across multiple appliances based on priority scores, and adjusting schedules based on soil levels and user availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple laundry apparatuses are used without integrated scheduling, then laundry capacity is increased, but laundry time is not optimized and operations become inefficient
Solution Approach 1:
The patent combines multiple laundry apparatuses into an integrated scheduling system that coordinates their operations. The server aggregates laundry logs from multiple devices and creates a unified schedule that optimizes the use of all apparatuses simultaneously, rather than operating them independently.
Solution Approach 2:
The system performs preliminary analysis of laundry logs and user preferences before generating schedules. It pre-processes laundry item information, soil levels, and appliance characteristics to create optimized schedules in advance, reducing overall laundry time through proactive planning.
2Ease of operation
If laundry scheduling is based on basic appliance functions only, then scheduling simplicity is maintained, but user preferences and laundry quality requirements are not met
Solution Approach 1:
The system incorporates user feedback from laundry logs and satisfaction information to continuously improve scheduling accuracy. It analyzes user preferences and appliance performance data to refine future schedules, ensuring both quality outcomes and operational simplicity.
Solution Approach 2:
The scheduling system dynamically adjusts operational parameters based on soil levels, laundry item characteristics, and appliance states. It modifies washing parameters such as water temperature, cycle duration, and detergent dosage to optimize laundry quality while maintaining ease of use through automated decision-making.
3Productivity
If laundry schedules are generated without considering soil levels, then scheduling speed is maintained, but laundry cleaning effectiveness is reduced
Solution Approach 1:
The system performs preliminary assessment of soil levels and laundry characteristics before schedule generation. It pre-analyzes laundry logs to determine appropriate washing parameters, ensuring cleaning effectiveness is built into the schedule from the outset rather than requiring post-hoc adjustments.
Data Source
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AI summary
Disclosed is a laundry scheduling apparatus. The apparatus includes a communication unit, an output unit, and a processor configured to pair with at least one washing machine via the communication unit, obtain laundry preference parameters of a user generated by learning based on at least one of a deep learning algorithm or a machine learning algorithm, using at least one of a laundry log of the user or laundry satisfaction information of the user as input data, generate laundry scheduling information by using washing machine information about the paired at least one washing machine, the laundry preference parameters, and laundry item information obtained via at least one of a user input unit, an interface unit, or a sensor, and cause the output unit to output the laundry scheduling information.