Intellectual Property India Publishes Patent Application for Disaster Relief Resource Optimizer (drro): A Machine Learning-Based Framework For Intelligent Disaster Relief Resource Operations With Multi-Modal Input Processing And Optimized Allocation

MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641083387 A) filed by Sahana R.; Abhijitha G. S.; Dr. Archana Naik; Yash M. Dalal; and Nitte Meenakshi Institute Of Technology, Nitte Deemed To on July 07, 2026, for Disaster Relief Resource Optimizer (drro): A Machine Learning-Based Framework For Intelligent Disaster Relief Resource Operations With Multi-Modal Input Processing And Optimized Allocation.

Inventors include Sahana R.; Abhijitha G. S.; Dr. Archana Naik; Yash M. Dalal; and Nitte Meenakshi Institute Of Technology, Nitte Deemed To Be University, Bengaluru.

The application for the patent was published on July 10, 2026, under issue no. 28/2026.

Abstract: ABSTRACT OF THE INVENTION Title: DISASTER RELIEF RESOURCE OPTIMIZER (DRRO): A MACHINE LEARNING- BASED FRAMEWORK FOR INTELLIGENT DISASTER RELIEF RESOURCE OPERATIONS The present invention relates to a machine learning-based Disaster Relief Resource Optimizer (DRRO) system that combines image analysis, SMS processing, and intelligent resource allocation for effective disaster response operations. The system comprises a Field and User Input Layer with mobile application and SMS gateway capabilities; a Machine Learning Processing Layer employing CNN for disaster image classification, NLP for SMS emergency triage, and Random Forest algorithms for resource optimization; Core Backend Services for authentication, request management, and inventory tracking; a Data Layer with specialized databases; and an Admin Dashboard with GIS-integrated real-time monitoring capabilities. The system processes multi-modal inputs including disaster images classified by CNN for type and severity, and SMS messages parsed using NLP for emergency triage in low-connectivity environments. The Resource Optimization Engine uses Random Forest prediction to recommend optimal allocation of food, water, shelter, and medical supplies based on demand, location, and availability. The present invention ensures scalable, fault-tolerant, and efficient disaster response through intelligent automation, real-time situational awareness, and coordinated volunteer deployment, significantly improving emergency response times and resource utilization during natural and man-made disasters.

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