MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641098830 A) filed by Madhankumar C; Dr. C. Udhaya Shankar - Sns College Of Technology; Ms V. V. Vishnupriya - Jai Shriram Engineering College; Ms B Thulir - Rathinam Technical Campus; Ms Dhivyalakshmi C - Dhanalakshmi Srinivasan University; Ms Srimathi T - Karpagam College Of Engineering; and Dr. G. R. Roshini - Nitte Deemed To Be University, Nitte Meenakshi on August 14, 2026, for Neuro-Symbolic Multi-Agent Disaster Intelligence System For Real-Time Emergency Response And Smart City Resilience.
Inventors include Dr. C. Udhaya Shankar - Sns College Of Technology; Ms V. V. Vishnupriya - Jai Shriram Engineering College; Ms B Thulir - Rathinam Technical Campus; Ms Dhivyalakshmi C - Dhanalakshmi Srinivasan University; Ms Srimathi T - Karpagam College Of Engineering; and Dr. G. R. Roshini - Nitte Deemed To Be University, Nitte Meenakshi Institute Of Technology.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: Neuro-Symbolic Multi-Agent Disaster Intelligence System for Real-Time Emergency Response and Smart City Resilience Abstract Natural and human-induced disasters such as floods, earthquakes, cyclones, urban fires, industrial accidents, and infrastructure failures require rapid, coordinated, and intelligent emergency response. Conventional disaster management systems often depend on isolated monitoring platforms, predefined rules, and centralized decision-making, which can limit their ability to adapt to rapidly changing conditions. This invention proposes a Neuro-Symbolic Multi Agent Disaster Intelligence System that integrates deep learning, symbolic reasoning, multi-agent artificial intelligence, real-time sensor analytics, geospatial intelligence, and predictive decision-making for intelligent emergency response and smart city resilience. The proposed system collects real-time information from IoT sensors, surveillance systems, weather services, emergency calls, social media, geographic information systems, and municipal infrastructure. A neural intelligence layer detects disaster events, estimates severity, predicts risk propagation, and identifies affected regions, while a symbolic reasoning layer applies emergency rules, spatial constraints, response protocols, and resource policies to validate and explain generated decisions. Multiple autonomous AI agents are assigned specialized functions such as disaster detection, risk assessment, resource allocation, evacuation planning, route optimization, infrastructure monitoring, and emergency communication. The agents collaboratively exchange information and dynamically coordinate response actions according to changing disaster conditions. A centralized disaster intelligence layer provides situation awareness, predictive alerts, explainable recommendations, and real-time coordination for emergency authorities. The system further supports continuous learning by incorporating response outcomes and sensor feedback to improve future predictions and decision-making. The proposed framework enables faster emergency response, optimized resource utilization, intelligent evacuation planning, improved situational awareness, and enhanced resilience of smart cities against dynamic and complex disaster scenarios.
Disclaimer: Curated by HT Syndication.
