MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641069094 A) filed by Dayananda Sagar University on June 02, 2026, for Systems And Methods For Efficient Fine-Tuning Of Large Language Models Using Low-Rank Adaptation.
Inventors include Sushma D. S; Manish Nandy; Pritam Biswas; Yashaswini H C; Nagalaxmi; Kaveri A; Shubhrajyoti Paul; D. Srikanth Deivasigamani; Abhishek Kumar Pandey; Soumyajit Chakraborty; and Dr. Santosh Kumar J.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: The invention relates to systems and methods for efficient fine-tuning of large language models using a low-rank adaptation framework to reduce computational complexity, memory requirements, and training costs. The method includes freezing pre-trained model parameters while introducing trainable low-rank matrices into selected neural network layers to enable task-specific adaptation. The low-rank adaptation mechanism decomposes weight updates into compact matrices that significantly reduce the number of trainable parameters while maintaining model performance. The system further incorporates parameter-efficient optimization techniques, adaptive rank selection, and scalable deployment mechanisms to support fine-tuning across diverse natural language processing tasks such as text generation, summarization, sentiment analysis, question answering, and conversational AI. The adapted model can be trained with reduced GPU memory consumption and faster convergence compared to conventional full-model fine-tuning approaches. The invention provides improved scalability, cost efficiency, and deployment flexibility for large language models in cloud, edge, and enterprise computing environments. The proposed method is applicable to domain-specific AI systems, multilingual language processing, personalized assistants, and resource-constrained artificial intelligence applications.
Disclaimer: Curated by HT Syndication.
