MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641078397 A) filed by Dr. G. Haridoss; Dr. A. H. Nandhu Kishore; Dr. A. Sasireka; Dr Sujatha Dandu; Marisetti Sri Durga; Dr Ranjith Kumar Varre; Dr. Ashish B. Patel; Mr Durairaji V; Dr. T. Prabakaran; A. Kiruthika; Brindha G; and Dr Vetrivel M on June 25, 2026, for Machine Learning-Based System For Early Prediction Of Faculty Stress Caused By Academic Overload In Higher Education.
Inventors include Dr. G. Haridoss; Dr. A. H. Nandhu Kishore; Dr. A. Sasireka; Dr Sujatha Dandu; Marisetti Sri Durga; Dr Ranjith Kumar Varre; Dr. Ashish B. Patel; Mr Durairaji V; Dr. T. Prabakaran; A. Kiruthika; Brindha G; and Dr Vetrivel M.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: Machine Learning-Based System for Early Prediction of Faculty Stress Caused by Academic Overload in Higher Education is the proposed invention. The proposed invention discloses a Machine Learning-Based System for Early Prediction of Faculty Stress Caused by Academic Overload in Higher Education. The system is designed to proactively identify stress risks among faculty members by analyzing academic, research, administrative, and institutional workload data collected from multiple sources. The proposed framework employs a hybrid deep learning architecture that integrates Transformer Networks for temporal workload pattern analysis and Graph Neural Networks (GNNs) for modeling complex relationships among faculty members, departments, courses, and institutional responsibilities. The system generates a stress risk score by identifying hidden workload patterns and predicting potential stress conditions before they escalate into burnout. A Reinforcement Learning-based recommendation engine further provides personalized interventions, including workload redistribution, schedule optimization, teaching assistance allocation, research support, and wellness recommendations.
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