Beverage preparation machine with capsule recognition
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Solution Overview
Problem
There is a need to improve the reliability of beverage dispensing machines in automatically identifying and handling capsules, which is currently not efficiently addressed by existing technologies.
Innovation Solution
The machine incorporates a capsule recognition module equipped with a camera and a neural network computing device that captures images of the capsule and determines its type among predefined capsule types, allowing for precise adaptation of beverage preparation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a capsule recognition module with camera and neural network is added to the beverage preparation machine, then the measurement precision of capsule type identification is improved, but the device complexity increases
Solution Approach 1:
The patent replaces manual capsule identification with an automated optical recognition system. A camera captures images of the capsule, and a neural network computing device processes these images to automatically determine capsule type, eliminating the need for manual inspection and significantly improving identification precision while reducing operational complexity.
Solution Approach 2:
The patent introduces a capsule recognition module as an intermediary component between the capsule insertion and beverage preparation processes. This module acts as a mediator that automatically identifies capsule type and communicates this information to the control unit, enabling precise parameter adaptation without requiring direct user intervention or complex mechanical identification mechanisms.
2Productivity
If the machine automatically recognizes capsule type using image processing, then the productivity of beverage preparation is improved, but the use of energy increases due to camera and neural network operations
Solution Approach 1:
The patent performs capsule identification at the beginning of the beverage preparation process, before the actual brewing begins. The camera captures the capsule image and the neural network processes it during the machine's idle state or while water is being heated, so that by the time brewing starts, the capsule type is already known and parameters are pre-configured, maximizing productivity with minimal additional energy cost.
Solution Approach 2:
The neural network computing device is designed to autonomously process capsule images and determine capsule type without requiring continuous external control or supervision. The system self-manages the recognition process, activating the camera only when needed and automatically processing images, which optimizes energy consumption while maintaining high productivity.
Data Source
AI summary
Machine for preparing and dispensing a beverage, such as tea, coffee, hot chocolate, cold chocolate, milk, soup or baby food, comprising a capsule recognition module for recognizing a capsule inserted in said machine at a capsule recognition position, the capsule recognition module comprising a camera for capturing an image of at least part of said capsule in said capsule recognition position; wherein the capsule recognition module comprises a neural network computing device, said neural network computing device being configured to determine a type of said capsule amongst a plurality of predefined capsule types on the basis of an image of at least part of said capsule captured by said camera.