Filling Device Container Classification Using Machine Learning
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Solution Overview
Problem
Conventional filling devices, such as coffee machines, do not effectively monitor the correct container size or type, leading to potential overflows when users employ their own cups or glasses, which are not predefined in the machine's settings.
Innovation Solution
Integration of a camera and a machine learning classifier using trained algorithms to identify and classify containers based on their characteristics, allowing the control unit to adjust operations such as limiting beverage selection, preventing incorrect filling, or issuing warnings, utilizing optical, capacitive, or acoustic sensors and employing methods like convolutional neural networks for image recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional filling devices do not monitor container size or type, then the device operation is simple and cost-effective, but beverage overflow occurs when users employ their own cups or glasses
Solution Approach 1:
The patent replaces complex mechanical monitoring systems with optical sensors (cameras) and machine learning algorithms. Instead of using mechanical measurement devices to detect container size and type, the system uses image processing and neural networks to classify containers, thereby preventing overflow while maintaining simplicity.
Solution Approach 2:
The system enables the filling device to automatically identify and adapt to different container types without user intervention. The machine learning classifier autonomously categorizes containers and adjusts dispensing parameters, eliminating the need for manual container registration or complex user setup procedures.
2Measurement precision
If a camera and machine learning classifier are integrated to identify containers, then container recognition accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent makes the camera system serve multiple functions: it not only identifies container types but also estimates container volume, detects liquid levels, and recognizes user gestures. This multi-functionality justifies the added complexity by consolidating multiple sensing requirements into a single sensor system.
Solution Approach 2:
The system uses optical copies (images) of containers instead of physical measurements. The camera captures visual information that is processed by machine learning algorithms to infer container characteristics, replacing the need for physical contact sensors or complex measurement mechanisms.
3Reliability
If the control unit limits beverage selection based on detected container type, then overflow is prevented, but user flexibility and ease of operation are reduced
Solution Approach 1:
The system implements feedback by detecting the container type and automatically adjusting the available beverage options accordingly. The control unit receives information from the classifier and dynamically modifies the user interface to enable or disable specific beverages, creating a closed-loop system that adapts to the detected container.
Solution Approach 2:
The system performs preliminary classification of the container before the user makes a beverage selection. By pre-identifying the container type and calculating its volume capacity, the system can proactively limit beverage choices to those appropriate for the detected container, preventing overflow before it occurs.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Prevents malfunctions by accurately identifying and adapting to various container types, preventing overflows and ensuring correct beverage dispensing, while allowing for future adaptation to new container types through learning algorithms and external resources.
Implementation Method 1
A reflection light barrier with a beam source and a sensor, such as a photodiode, is used therefor
Implementation Method 2
a camera which is configured to take an image of a container
Data Source
AI summary
A method for preventing a malfunction of a filling device when a container is filled by a user with a beverage. The method includes providing a filling device comprising a control unit which controls the filling device, a camera which takes an image of a container currently being used with the filling device and which outputs the image to a classifier which then uses a trained learning algorithm to analyze the output of the image of the container provided by the camera. The trained learning algorithm of the classifier analyzes which container is currently being used based on characteristics of the container so as to classify the container into a predefined class. The predefined class is then employed by the control unit to prevent the malfunction of the filling device.