MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641122208 A) filed by Dr. S. Ganapathy; Rajeev Chandra; Dr. V. Asunee Kumari; E Janani; Thirumavalavan Rajagopalan; V. Keerthiga; Dr. S. Uma Maheswari; Sathish Kumar Jayaraman; Dr. Abu Farah Hoque; Thulasimani T; S Nagoorkani; and Amala P on October 12, 2026, for An Intelligent Ai And Machine Learning Apparatus For Student Performance Monitoring And Academic Outcome Prediction.
Inventors include Dr. S. Ganapathy; Rajeev Chandra; Dr. V. Asunee Kumari; E Janani; Thirumavalavan Rajagopalan; V. Keerthiga; Dr. S. Uma Maheswari; Sathish Kumar Jayaraman; Dr. Abu Farah Hoque; Thulasimani T; S Nagoorkani; and Amala P.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: An Intelligent AI and Machine Learning Apparatus for Student Performance Monitoring and Academic Outcome Prediction is the proposed invention. The proposed invention relates to an intelligent artificial intelligence and machine learning apparatus for student performance monitoring and academic outcome prediction using a hybrid Long Short-Term Memory (LSTM) and Kolmogorov–Arnold Network (KAN) architecture. The apparatus comprises a data acquisition module, preprocessing unit, temporal feature extraction module, academic prediction engine, explainability module, and intervention recommendation unit. The system processes student academic records, including examination scores, attendance, assignment completion, and assessment history, to generate structured representations of learning progress. The LSTM network analyzes sequential academic data to identify temporal dependencies and performance variations, while the KAN module models nonlinear relationships among educational indicators to predict academic outcomes and identify potential learning difficulties. An explainability module identifies influential factors associated with predicted results, enabling educators to interpret the predictions. Based on the prediction results, the apparatus generates personalized academic support recommendations and presents performance trends through an interactive dashboard. A feedback mechanism evaluates prediction accuracy using subsequent academic records. The invention supports proactive academic monitoring, individualized learning assistance, and data-driven educational decision-making while incorporating appropriate privacy safeguards and educator oversight.
Disclaimer: Curated by HT Syndication.
