Product Dispenser Parameter Optimization Using Usage Data Feedback
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Solution Overview
Problem
Automated product dispensers face challenges in optimizing dispensing parameters such as sheet length and user sensor reliability to align with varying user needs and behaviors, leading to inefficiencies and waste.
Innovation Solution
Implementing a system that adjusts dispensing parameters based on product usage data, using counters to track aggregate product dispensed and unique users, and refining user sensor capabilities to distinguish between genuine and false triggers, allowing for iterative optimization of dispensing parameters to minimize waste and maximize user sensor reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the dispenser uses a fixed dispensing parameter, then the device complexity is reduced, but the adaptability to varying user needs deteriorates
Solution Approach 1:
The dispenser automatically monitors its own usage data and adjusts dispensing parameters without external intervention. The system self-optimizes by tracking product dispensed, unique users, and sensor triggers, then autonomously modifies parameters like sheet length and sensor sensitivity to adapt to changing usage patterns.
Solution Approach 2:
The system implements a feedback loop where usage data from sensors and dispensing operations is continuously collected and analyzed. This feedback mechanism enables the dispenser to detect patterns in user behavior and automatically adjust dispensing parameters to optimize performance and reduce waste.
2Ease of operation
If the dispenser increases the amount of product dispensed per cycle, then user satisfaction improves, but product waste increases
Solution Approach 1:
The dispensing parameter is transformed from a static fixed value to a dynamic variable that automatically adjusts based on real-time usage data. The system modifies sheet length, dispense frequency, and sensor sensitivity dynamically to match actual user needs, ensuring optimal product delivery without waste.
Solution Approach 2:
The system changes key dispensing parameters including sheet length, dispense duration, and sensor trigger sensitivity based on analyzed usage patterns. By adjusting these parameters dynamically, the dispenser delivers appropriate product amounts that satisfy users while minimizing excess dispensing and waste.
3Loss of substance
If the dispenser decreases the amount of product dispensed per cycle, then product waste is reduced, but user satisfaction deteriorates
Solution Approach 1:
The system dynamically adjusts dispensing parameters based on real-time usage data, transforming fixed parameters into adaptive variables. This ensures the dispenser delivers optimal product amounts that satisfy users while minimizing waste, avoiding the trade-off between the two objectives.
4Measurement precision
If the dispenser increases sensor sensitivity to detect all user triggers, then detection precision improves, but false trigger rate increases
Solution Approach 1:
The system adjusts sensor sensitivity as a variable parameter based on usage pattern analysis. By dynamically modifying sensitivity thresholds and trigger criteria, the system achieves optimal detection precision while filtering out false triggers, resolving the contradiction between detecting all genuine triggers and avoiding false activations.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for optimizing a value of a dispensing parameter of a product dispenser based at least in part on product usage data. The dispensing parameter may include any suitable adjustable parameter of the product dispenser including, but not limited to, a dispense duration, a volume of product (e.g., a shot size), a sheet length, a capability of a user sensor to distinguish between genuine and false triggers of the sensor, a delay setting that specifies a delay time between successive dispenses, or the like. The value of the dispensing parameter may be optimized to align an actual performance outcome with a target performance outcome. The target performance outcome may include, for example, minimizing product usage per user, maximizing user sensor reliability (e.g., a percentage of dispenses that occur in response to genuine user-initiated triggering events), and so forth.