Drip Coffee Machine Learning Extraction for Barista Recipe Replication
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Solution Overview
Problem
Existing drip coffee technologies do not enable the extraction of coffee that replicates the specific recipe and acidity/concentration of a barista's method without direct interaction or visitation, making it difficult for users to enjoy coffee that matches the desired taste and aroma of a particular barista's drip coffee.
Innovation Solution
A drip coffee machine equipped with a machine learning model that learns and replicates the recipe of a barista by sensing the acidity and concentration of extracted coffee, adjusting parameters such as water supply speed, temperature, and spray angle to match the reference values, allowing for re-extraction until the desired taste and aroma are achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a machine learning model is introduced to learn and replicate barista's recipe, then coffee extraction accuracy matches barista's taste, but device complexity increases
Solution Approach 1:
The system creates a digital copy of the barista's extraction method by learning from sensor data collected during the barista's manual extraction process. The machine learning model replicates the barista's technique by analyzing and storing the relationship between water supply parameters and coffee extraction outcomes, enabling automated reproduction of the authentic taste without requiring the barista's physical presence.
Solution Approach 2:
The patent replaces the barista's manual mechanical operations with an automated machine learning-based control system. Sensors that detect water supply speed, temperature, and spray angle replace the barista's tactile and visual monitoring, while the machine learning model substitutes the barista's decision-making process, automatically adjusting parameters to replicate the desired extraction outcome.
2Manufacturing precision
If multiple sensors and machine learning components are added to replicate barista's method, then coffee taste and aroma accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The system performs self-learning by automatically collecting sensor data during the barista's extraction process and autonomously training the machine learning model without requiring user intervention. The machine then independently adjusts water supply parameters based on the learned patterns, eliminating the need for users to manually configure complex settings or understand the underlying extraction science.
Solution Approach 2:
The system performs preliminary learning and parameter optimization during an initial calibration phase where the barista demonstrates their technique. This preliminary action captures the essential extraction patterns and stores them in the machine learning model, so that subsequent operations can automatically reproduce the authentic taste without requiring repeated adjustments or user expertise.
3Reliability
If the system performs multiple re-extractions to match reference acidity and concentration, then coffee quality consistency improves, but productivity decreases
Solution Approach 1:
The system implements a feedback mechanism where sensors continuously monitor the extracted coffee's acidity and concentration, comparing these measurements against reference values. The machine learning model uses this feedback to automatically adjust water supply parameters for subsequent extractions, progressively refining the output to match the desired quality standards and reducing the number of re-extraction cycles needed.
Solution Approach 2:
The system performs preliminary parameter optimization by pre-calculating the optimal water supply speed, temperature, and spray angle settings based on the learned barista technique and reference coffee characteristics. This preliminary preparation enables the machine to achieve consistent quality outcomes in fewer extraction cycles, minimizing the need for repeated adjustments and improving overall productivity.
Data Source
AI summary
According to the present disclosure, when drip coffee is extracted, it may learn drip coffee recipe information of a barista to be imitated and extract the drip coffee based on the learned reference acidity and reference concentration. The extracted drip coffee may be compared with the learned reference acidity and the reference concentration and evaluated, and when the acidity and concentration of the drip coffee are matched with the reference acidity and the reference concentration, the drip coffee having the same acidity and concentration as the drip coffee may be extracted. Alternatively, when the acidity and concentration of the drip coffee are not matched with the reference acidity and the reference concentration, drip coffee having the same or/and similar acidity and concentration as or/and to the reference acidity and the reference concentration may be extracted by changing extraction conditions of the drip coffee through reinforcement learning.