Method for devising a schedule based on user input
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Solution Overview
Problem
Existing robotic floor-cleaning devices require users to manually set schedules, which can be time-consuming and inefficient, and lack the ability to automatically generate schedules based on historical usage data.
Innovation Solution
A method for a robotic floor-cleaning device to automatically devise a work schedule using historical data, incorporating a control unit that monitors usage times, stores data in a database, and uses machine learning techniques to suggest and adjust schedules through an input/output device, allowing for user approval and continuous updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually set schedules for robotic floor-cleaning devices, then scheduling flexibility is maintained, but user time and energy are consumed
Solution Approach 1:
The robotic floor-cleaning device automatically generates its own work schedule by analyzing historical cleaning data and usage patterns stored in its memory unit, eliminating the need for users to manually configure schedules. The device serves itself by autonomously determining optimal cleaning times and durations based on accumulated operational history.
Solution Approach 2:
The system performs preliminary data collection and analysis by continuously monitoring and storing cleaning effectiveness metrics, user interactions, and operational patterns before schedule generation is needed. This pre-accumulated historical data enables rapid automatic schedule creation without requiring real-time user input or manual configuration.
2Ease of operation
If automatic schedule generation is implemented, then user effort is reduced, but device complexity increases
Solution Approach 1:
The control unit performs multiple functions including data storage, historical pattern analysis, schedule generation, and user interaction management within a single integrated component. The memory unit serves both as operational memory and as a historical database for learning patterns, reducing the need for separate dedicated hardware components.
Solution Approach 2:
The system continuously monitors cleaning effectiveness, user manual adjustments, and operational outcomes, feeding this information back into the schedule generation algorithm. This feedback loop enables the device to refine and optimize schedules over time based on real-world performance data and user preferences.
3Measurement precision
If historical data is collected and analyzed, then schedule accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts and prioritizes only the most relevant features from historical data such as cleaning effectiveness metrics, user interaction patterns, and temporal usage trends, rather than processing complete raw datasets. This selective extraction reduces computational burden while maintaining schedule generation accuracy.
Solution Approach 2:
The device processes a subset of historical data that is sufficient for generating accurate schedules, rather than exhaustively analyzing every available data point. The system identifies and processes only the critical patterns needed for effective schedule creation, avoiding unnecessary computational overhead.
Data Source
AI summary
Some aspects include a schedule development method for a robotic floor-cleaning device that recognizes patterns in user input to automatically devise a work schedule.