Beverage Maker Sensor Learning for Automatic Event Triggering
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Solution Overview
Problem
Existing beverage makers struggle with complex control processes that require significant user effort and time to program, and existing machine learning methods are inadequate for efficiently handling sensors that provide multiple measured values, leading to inefficiencies and errors.
Innovation Solution
A beverage maker equipped with both Category 1 sensors providing single measured values and Category 2 sensors providing multiple measured values, utilizing a control unit that learns patterns through machine learning to automatically trigger events based on user confirmations, reducing the need for repeated user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex control processes are programmed manually, then the beverage maker can perform specific tasks, but the programming process is time-consuming and error-prone
Solution Approach 1:
The beverage maker automatically learns control processes by observing user interactions and sensor patterns, eliminating the need for manual programming. The system self-configures by storing measured value patterns and their associated events in memory, enabling autonomous adaptation to user preferences and complex task automation.
2Measurement precision
If Category 2 sensors providing multiple measured values are used, then pattern recognition capability is improved, but the complexity of processing and assigning these values increases
Solution Approach 1:
The system creates simplified representations of complex sensor patterns by storing characteristic measured value combinations as templates in memory. When similar patterns are detected, the system matches them against stored templates, reducing processing complexity while maintaining high pattern recognition accuracy through template comparison rather than complex real-time analysis.
3Reliability
If manual assignment of sensor patterns to events is performed, then control accuracy is maintained, but user effort and time increase
Solution Approach 1:
The system performs preliminary learning by automatically observing and storing patterns of measured values that precede user-triggered events. By pre-processing and storing these patterns in memory during normal operation, the system maintains high control accuracy through learned associations while requiring minimal user effort beyond normal beverage preparation activities.
Data Source
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AI summary
A beverage maker and a method for operating a beverage maker are provided. The beverage maker comprises a sensor of a 1st category which provides only one individual measuring value S1 at a specific time, a sensor of a 2nd category which provides a plurality of measuring values S2 at a specific time, a data memory and a control unit. The control unit is configured to compare the measuring values S2 with target values which are stored in the data memory and, based on this comparison, to trigger or not to trigger at least one event. If the comparison does not lead to an event being triggered, detection of at least one measuring value S1 is effected by the control unit, the measuring value S1 being used to assign the measuring values S2 to at least one event and to store these measuring values S2 as target values for the assigned event in the data memory.