Beverage preparation machine with capsule recognition
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Solution Overview
Problem
Current beverage preparation machines face challenges in reliably identifying and adapting to different types of capsules, leading to potential errors in extraction parameters and inconsistent beverage preparation.
Innovation Solution
The machine incorporates a capsule recognition module with a sensor that determines a sample value representative of the capsule's properties, compares it to reference values, and selects the most likely type based on probability scores, allowing for accurate identification and adaptation of extraction parameters, even when certainty is not high by presenting multiple possible types to the user for selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a capsule recognition module with sensor and probability scoring is implemented, then capsule identification accuracy is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual capsule identification with an automated sensor-based recognition system. The sensor detects capsule properties and the control unit processes this data through probability scoring algorithms to automatically identify capsule types, eliminating the need for manual intervention and improving identification accuracy despite increased system complexity.
Solution Approach 2:
The recognition system implements feedback through probability scoring, where the sensor data is continuously compared against reference values and the results are used to adjust and refine capsule type identification. This feedback mechanism allows the system to learn from previous identifications and improve accuracy over time, justifying the increased device complexity.
2Manufacturing precision
If extraction parameters are adapted to specific capsule types, then beverage preparation quality is improved, but control system complexity increases
Solution Approach 1:
The control system dynamically adjusts extraction parameters based on the identified capsule type. Instead of using fixed parameters, the system modifies water temperature, flow rate, and extraction time according to the specific capsule characteristics detected by the recognition module, thereby improving beverage quality while managing control complexity through automated adaptation.
Solution Approach 2:
The patent changes multiple extraction parameters (temperature, pressure, flow rate, time) based on the identified capsule type. The control unit stores different parameter sets for different capsule types and automatically selects the appropriate parameters, improving beverage preparation quality without requiring complex real-time calculations during the extraction process itself.
3Reliability
If multiple reference values are compared with probability scoring, then identification reliability is improved, but processing time increases
Solution Approach 1:
The system compares the sensor data against multiple reference values (excessive action) to ensure reliable identification. By evaluating multiple possibilities and using probability scoring to rank them, the system ensures high identification reliability even if this requires additional processing time compared to simple single-reference matching.
Solution Approach 2:
The patent performs preliminary comparison of sensor data against all reference values before final capsule type determination. This preliminary action allows the system to pre-calculate probability scores and identify the most likely capsule types, enabling faster decision-making during actual beverage preparation while maintaining high reliability through comprehensive initial analysis.
Data Source
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AI summary
A beverage preparation machine using capsules and configured to automatically recognize a type of a capsule inserted in the machine in order to adapt the beverage preparation parameters to the recognized capsule type.