MUMBAI, India, Feb. 13 -- Intellectual Property India has published a patent application (202641007525 A) filed by Prathyusha Engineering College; Ms. C. Kamatchi; Ms. R. Anitha; Ms. A. Jayashree; Ms. D. Banupriya; Mr. B. B. Senthil Kumar; and Mrs. R. Kannamma, Thiruvallur, Tamil Nadu, on Jan. 27, for 'ai-enabled real-time quality control system for cool drink production using deep learning-based brand and defect detection.'
Inventor(s) include Ms. C. Kamatchi; Ms. R. Anitha; Ms. A. Jayashree; Ms. D. Banupriya; Mr. B. B. Senthil Kumar; and Mrs. R. Kannamma.
The application for the patent was published on Feb. 13, under issue no. 07/2026.
According to the abstract released by the Intellectual Property India: "The invention presents an advanced artificial intelligence (AI)-powered system designed to automate quality control in cool drink production. Utilizing the state-of-the-art YOLOv8 deep learning model for object detection, the system performs real-time identification of different beverage brands and detection of common defects such as missing caps, broken labels, and bottle deformities. The system is deployed through a user-friendly Flask web application, enabling both live webcam image capture and offline photo uploads for processing. Detection results, including brand classification and defect status, are stored securely in an SQLite database for historical analysis and audit purposes. This seamless integration supports continuous monitoring on production lines, reducing the reliance on labor-intensive, error-prone manual inspections and thereby enhancing operational efficiency and product consistency. Further, the invention features a Streamlit-based analytics dashboard that provides comprehensive quality insights, such as shift-wise defect trends and brand-wise distribution statistics, empowering manufacturers to make data-driven decisions quickly. The modular architecture includes role-based authentication for secure access and is optimized to run efficiently on standard CPU hardware. Trained on bespoke datasets tailored for beverage industry needs, the system achieves over 95% accuracy in brand recognition and 92% in defect detection under varied production conditions. This scalable and adaptable solution addresses the challenges of automated quality assurance in beverage manufacturing and can be extended to other product inspection applications requiring high reliability and real-time monitoring.cool-drink-detection-and-analysis-using-AI."
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