MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641125097 A) filed by J. J College Of Engineering And Technology on October 19, 2026, for Ai-Based Smart Waste Segregation, Collection And Route Optimization System.
Inventors include Ms Rajeshwari S; Abinaya. N; Mrs Mahalakshmi P; Suganya. P; Yobu D; Mrs M Saranya; Sathiya Priya T; and Mrs A Apoorvavalli.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: The present invention relates to an Artificial Intelligence (AI)-based smart waste management system for automated waste segregation, intelligent collection monitoring, and optimized waste transportation. The system comprises a waste detection unit, an image acquisition module, an AI-based classification processor, a sensor-integrated smart waste bin, a communication module, a centralized waste management server, and a route optimization engine. The image acquisition module captures images of waste materials, and the AI-based classification processor identifies and categorizes the waste into biodegradable, recyclable, non-recyclable, and hazardous categories based on visual characteristics and trained machine learning models. Sensor units monitor the fill level, weight, and operational status of waste bins and transmit the collected data to the centralized server through an Internet of Things (IoT) communication network. Based on the classification results, bin status, waste accumulation rate, vehicle capacity, traffic conditions, and geographical location, the system determines collection priorities and generates optimized collection routes. The route optimization engine reduces unnecessary vehicle movement, fuel consumption, collection time, and operational costs while improving waste collection efficiency. The system further provides real-time monitoring, collection scheduling, and analytical reports through a user interface accessible to municipal authorities and waste management operators. The proposed invention enables efficient, data-driven, and environmentally sustainable waste management in residential areas, educational institutions, hospitals, commercial establishments, and urban environments.
Disclaimer: Curated by HT Syndication.
