Intellectual Property India Publishes Patent Application for Machine Learning-Based System For Predicting Student Psychological Behavior And Enhancing Examination Performance In Higher Education

MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202641107066 A) filed by Dr. Suresh Talamala; Bhadrachalam Anusha; Dr. Sunil Kumar; Prof. Dr. Santosh Kumar Sharma; Someswari Perla; Aviral Goswami; Vineeta Yadav; S V Kanchana; Lalit Sharma; S. Peerani; Dr. Sohel Ibrahim Shaikh; and Dr. G. Balaji on September 05, 2026, for Machine Learning-Based System For Predicting Student Psychological Behavior And Enhancing Examination Performance In Higher Education.

Inventors include Dr. Suresh Talamala; Bhadrachalam Anusha; Dr. Sunil Kumar; Prof. Dr. Santosh Kumar Sharma; Someswari Perla; Aviral Goswami; Vineeta Yadav; S V Kanchana; Lalit Sharma; S. Peerani; Dr. Sohel Ibrahim Shaikh; and Dr. G. Balaji.

The application for the patent was published on October 02, 2026, under issue no. 40/2026.

Abstract: Machine Learning-Based System for Predicting Student Psychological Behavior and Enhancing Examination Performance in Higher Education is the proposed invention. The proposed invention system acquires authorized academic and learning-interaction data comprising attendance, assessment results, assignment activity, learning-resource utilization, quiz attempts, study-session characteristics, and other time-dependent educational indicators. The acquired data are pre-processed and transformed into chronological behavioral sequences. A Transformer-based temporal learning module analyzes relationships among successive student activities and generates a contextual representation of the student's learning trajectory. The representation is utilized to estimate observable behavioral indicators and predict future examination performance. An adaptive intervention module generates personalized academic recommendations based on the predicted performance and behavioral trajectory. Subsequent student activities and assessment outcomes are monitored to determine the effectiveness of the intervention, and the feedback is used to update the predictive model. The system thereby provides continuous student-risk prediction, personalized academic intervention, and adaptive examination-performance enhancement within higher educational environments.

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