MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641051003 A) filed by Dr. S. V. Divya; Dr. P. Venkadesh; D. Sriharan; Vikash Reddy P; Sathiyamoorthi S; Zahira Shirin S; and Varshini. M, Coimbatore, Tamil Nadu, on April 21, for 'context-aware deep learning for text prediction & gec.'
Inventor(s) include Dr. S. V. Divya; Dr. P. Venkadesh; D. Sriharan; Vikash Reddy P; Sathiyamoorthi S; Zahira Shirin S; and Varshini. M.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "Text prediction and grammatical error correction (GEC) are important tasks in natural language processing that improve writing quality and user productivity. Traditional methods often rely on rule-based systems or simple statistical models, which struggle to capture complex language patterns and contextual meaning. This project proposes a context-aware deep learning approach to enhance both text prediction and grammatical error correction. The proposed system uses deep learning techniques to understand the context of a sentence and generate more accurate predictions while also identifying and correcting grammatical errors. A neural network-based model is trained on large text datasets to learn linguistic patterns, sentence structure, and contextual relationships between words. By considering surrounding words and sentence meaning, the model can predict the next word in a sequence and suggest corrections for grammar, spelling, and syntax errors. The architecture integrates components such as embedding layers for word representation, sequence modeling using recurrent or transformer-based networks, and a correction module that detects and fixes grammatical mistakes. The system is evaluated using metrics such as accuracy, precision, recall, and error correction rate to measure its effectiveness. Experimental results demonstrate that the context-aware model significantly improves prediction accuracy and grammatical correction compared to traditional approaches. The system can assist users in writing emails, reports, and messages by providing intelligent suggestions and automatic corrections. This approach contributes to the development of smarter writing assistance tools that improve communication efficiency and reduce language errors. Overall, the proposed framework highlights the potential of deep learning and contextual understanding in building advanced text prediction and grammar correction systems."
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