Pinsetter Error Detection Using Real-Time Video Analysis

The patent uses video cameras to capture optical copies/images of bowling pins and lane conditions, processing these visual copies to detect errors. This replaces the need for complex physical sensors with simpler optical copying and image processing, achieving error detection through software analysis of visual data rather than hardware sensor arrays.

The patent substitutes mechanical sensor-based detection systems with an optical-electronic system using video cameras and machine learning algorithms. Instead of physical sensors contacting or measuring lane conditions directly, the system uses visual capture and computational analysis to detect pinsetter errors, lane oil patterns, and other conditions.

The patent implements continuous automated video monitoring and analysis that operates without interruption during bowling alley operations. The machine learning model continuously processes video feeds to detect errors in real-time, eliminating the gaps inherent in manual inspections and ensuring continuous detection capability without requiring staff intervention.

The system performs self-monitoring and self-diagnosis by automatically analyzing video data to detect pinsetter errors, lane conditions, and potential issues. The machine learning model autonomously identifies problems without human assistance, enabling the system to monitor itself and alert operators only when actual errors are detected, rather than requiring continuous manual oversight.