Intellectual Property India Publishes Patent Application for Machine Learning-Based Intelligent English Learning And Performance Evaluation System For Higher Education

MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641074724 A) filed by Pradeepa Dhanakumar; J Vasavi; Mrs. L. V. Naga Sailaja. Konathala; Svinodhini; Vaishnavi S; Arunpandian N.; Thanga Kumaran M; Eknath Prasath M; S. Peerani; Udayakumar N; Dr. K. Ramprathap; and Dr. Mohit Kumar Tiwari on June 16, 2026, for Machine Learning-Based Intelligent English Learning And Performance Evaluation System For Higher Education.

Inventors include Pradeepa Dhanakumar; J Vasavi; Mrs. L. V. Naga Sailaja. Konathala; Svinodhini; Vaishnavi S; Arunpandian N.; Thanga Kumaran M; Eknath Prasath M; S. Peerani; Udayakumar N; Dr. K. Ramprathap; and Dr. Mohit Kumar Tiwari.

The application for the patent was published on July 03, 2026, under issue no. 27/2026.

Abstract: Machine Learning-Based Intelligent English Learning and Performance Evaluation System for Higher Education is the proposed invention. The proposed invention focuses utilizes a Gradient Boosting Machine (GBM) model to analyze student learning behaviours, assessment outcomes, engagement patterns, and language proficiency indicators collected from various educational activities, including reading, writing, listening, speaking, grammar, and vocabulary exercises. Based on the analysis, the system generates personalized learning pathways and recommends targeted instructional content tailored to individual learner requirements. An intelligent evaluation module automatically assesses language competency and produces objective performance scores, progress reports, and skill-level indicators. The system further incorporates predictive analytics to forecast academic performance, identify learning difficulties at an early stage, and support timely educational interventions. A continuous feedback mechanism enables dynamic model refinement using real-time learner data, thereby improving recommendation accuracy and assessment reliability. Additionally, analytical dashboards provide educators with comprehensive insights into student progress, engagement trends, and instructional effectiveness.

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