Intellectual Property India Publishes Patent Application for A Deep Learning–based Machine Translation System For Converting English Text Into Multiple Indian Languages

MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202641073705 A) filed by Dr. Karimisetty Sujatha; Dr. Peluru Janardhana Rao; Mrs. Kondamudi Naga Neeraja; Dr. Charushila Axay Patel; Dr. Zeenat Taher; Dr. K. Balaji Sunil; and Dr. Nakul Sharma on June 13, 2026, for A Deep Learning–based Machine Translation System For Converting English Text Into Multiple Indian Languages.

Inventors include Dr. Karimisetty Sujatha; Dr. Peluru Janardhana Rao; Mrs. Kondamudi Naga Neeraja; Dr. Charushila Axay Patel; Dr. Zeenat Taher; Dr. K. Balaji Sunil; and Dr. Nakul Sharma.

The application for the patent was published on June 26, 2026, under issue no. 26/2026.

Abstract: The present invention discloses a deep learning–based machine translation system for converting English text into multiple Indian languages with improved contextual understanding, semantic accuracy, grammatical correctness, and linguistic fluency. The system comprises an input acquisition module, text preprocessing unit, contextual analysis engine, multilingual neural translation model, and post-processing framework. The preprocessing unit performs text normalization, tokenization, spelling correction, and sentence segmentation, while the contextual analysis engine extracts semantic and syntactic relationships from the source text. A transformer- based deep learning architecture equipped with attention mechanisms generates accurate translations in multiple Indian languages including Tamil, Hindi, Telugu, Kannada, Malayalam, Bengali, Marathi, Gujarati, and Punjabi. The system further incorporates domain adaptation, feedback-driven learning, quality validation, speech processing, and optical character recognition functionalities. The invention enables scalable, real-time, and context-aware multilingual communication across educational, governmental, healthcare, commercial, and digital platforms, thereby reducing language barriers and enhancing accessibility to information for diverse linguistic communities.

Disclaimer: Curated by HT Syndication.