MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641097966 A) filed by Sri Eshwar College Of Engineering on August 12, 2026, for An Offline Curriculum-Aware Ai-Based System For Secure And Personalized Learning.
Inventors include Mr. Gowrisanker M; Devaranjan J; Birundha S; and Harini S.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: The present invention relates to an offline curriculum-aware Artificial Intelligence (AI) educational platform designed to provide personalised, secure, and intelligent learning assistance without depending on cloud-based services or continuous internet connectivity. Existing AI-based educational platforms mainly rely on cloud-hosted Large Language Models (LLMs), which create challenges related to accessibility, privacy, cost, latency, and curriculum alignment. The proposed invention integrates a locally deployed Small Language Model (SLM) with a Retrieval-Augmented Generation (RAG) framework, local knowledge repository, multimodal processing modules, mathematical reasoning engine, and personalised learning system. Educational resources such as textbooks, syllabus documents, reference materials, and question banks are processed and stored locally. The RAG framework retrieves relevant curriculum-specific information and provides contextual knowledge to the SLM to generate accurate and syllabus-aligned responses. The system further integrates Optical Character Recognition (OCR) for handwritten answer analysis, document processing for PDF-based learning, mathematical reasoning for step-by-step problem solving, speech recognition, and text-to-speech modules for voice-based interaction. These capabilities allow students to interact with the platform using text, voice, images, handwritten answers, and documents through a single unified system. The invention also includes a personalised learning engine that analyses student interactions and learning patterns to provide customised explanations, notes, quizzes, assessments, and recommendations. By performing AI processing and data storage locally, the system ensures improved privacy, reduced latency, lower operational costs, and continuous availability even in low-connectivity environments. The proposed invention provides a secure, scalable, and accessible AI-powered educational solution that combines offline AI inference, curriculum-aware knowledge retrieval, multimodal interaction, and personalised learning support to enhance the overall digital learning experience.
Disclaimer: Curated by HT Syndication.
