Gaming Display Capture via Mirror and Machine Learning
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Solution Overview
Problem
Existing gaming technologies lack efficient methods to accurately capture and analyze information from diverse gaming device displays, including mechanical and hybrid systems, which hinders real-time monitoring and data collection for gameplay metrics and user interactions.
Innovation Solution
A camera-based system that captures images of gaming device displays, utilizing image processing and machine learning algorithms to determine game elements, values, and user interactions, enabling continuous monitoring and data collection across various types of gaming devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a camera is placed at the edge of the display and oriented to point at the display, then the camera can capture the display, but the captured image becomes significantly distorted
Solution Approach 1:
A mirror is introduced as an intermediary element to redirect the camera's view of the display. The mirror positioned at a 45-degree angle reflects the display image to the camera, allowing the camera to capture the full display area without being positioned directly in front, thus avoiding perspective distortion while maintaining comprehensive capture capability
Solution Approach 2:
The camera is positioned in a different spatial dimension (to the side rather than directly in front) and uses a mirror to achieve the desired view. This dimensional repositioning allows the camera to capture the display from an angle that minimizes distortion while still covering the required area
2Loss of information
If image processing and machine learning algorithms are used to analyze captured images, then information can be determined from the display, but the system complexity increases
Solution Approach 1:
Traditional manual or rule-based image analysis methods are replaced with machine learning algorithms that automatically extract information from the captured display images. This substitution enables more accurate and robust information extraction (game elements, values, user interactions) while the modular implementation keeps system complexity manageable
Solution Approach 2:
The system uses captured images as copies of the display content to perform analysis, rather than requiring direct access to the gaming device's internal data. This copying approach allows information extraction from visual representations while maintaining system independence and reducing complexity
Data Source
AI summary
A camera captures a display of a gaming device and determines information that appears on the display. The camera is mounted on a video gaming device, and the camera continuously or at various intervals captures images of the screen of the video gaming device. Those images are analyzed to determine information displayed on the video gaming device, such as game speed (e.g., time between handle pulls, total time of play, handle pulls during a session, etc.), bet amounts, bet lines, credits, etc. This information may be determined in various ways, such as by using image processing of images captured by the camera. Machine learning algorithms may also be used to infer key information displayed on the screen of the video gaming device to capture and/or analyze. A housing of the camera may also have a secondary display oriented in a similar direction as the screen of the video gaming device.