MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641124488 A) filed by Thanga Kumaran M; Pasupuleti Venkata Ramana; P. Tamilarasan; Dr P. Pavani; S. Rajeswari; S. Divya; Dr. Retna Mony R; Mrs S. Peerani; Dr. Abu Farah Hoque; Dr. I. D. Soubache; Dr. Blessy Queen Mary M; and S. Nageswari on October 17, 2026, for Machine Learning-Based Intelligent Framework For Personalized English Literature Learning In Higher Education.
Inventors include Thanga Kumaran M; Pasupuleti Venkata Ramana; P. Tamilarasan; Dr P. Pavani; S. Rajeswari; S. Divya; Dr. Retna Mony R; Mrs S. Peerani; Dr. Abu Farah Hoque; Dr. I. D. Soubache; Dr. Blessy Queen Mary M; and S. Nageswari.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: The study of English literature in higher education is a process of interpreting literary texts, building up vocabulary, explaining the themes and expressing one's thoughts by critical analysis. The availability of digital learning platforms, online educational resources, and available teaching methods have increased access to literary content and learning activities. But students often receive the same learning materials, even when they have different reading abilities, language skills and knowledge of literary concepts. General assessment scores may fail to identify specific difficulties, and fixed learning sequences may not respond to changes in individual performance. To overcome these limitations, the present invention provides a Machine Learning-Based Intelligent Framework for Individualized English Literature Learning in Higher Education. The framework gathers and analyzes student data, including assessment scores, reading comprehension, vocabulary performance, written responses, learning activities completed, and academic growth. It produces learning metrics including a Literary Comprehension Score (LCS), Vocabulary Development Index (VDI), Literary Analysis Skill Score (LASS), Learning Progress Index (LPI), Individualized Learning Suitability Score (PLSS), and Learning Engagement Index (LEI). These indicators are used in an analysis module based on machine learning algorithms to identify individual learning strengths and difficulties and suitable learning support. Based on the analysis, the framework recommends suitable literary texts, explanations, vocabulary exercises, guided reading activities, analytical tasks, writing exercises and revision materials. The system tracks progress and gives feedback, measuring updated student performance and changing future recommendations to match changing learning needs. The invention proposed herein is aimed at making the learning of English literature more responsive to the individual student, facilitating the development of comprehension and analytical skills, improving the relevance of learning materials, and assisting educators in identifying areas needing additional guidance. The framework may be implemented through a digital educational platform or learning management system for use in higher education. FIG.1
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