MUMBAI, India, Feb. 13 -- Intellectual Property India has published a patent application (202641008768 A) filed by Srinivas Institute Of Technology, Mangaluru, Karnataka, on Jan. 28, for 'natural language-driven sql query automation platform.'

Inventor(s) include Dr. Parvathraj K M M; Dr. Shrinivasa Mayya D; Ms. Pranamya V Rao; Mr. Harshith S; Mr. Nuthan; Ms. Greeshma; Mr. Amshith; Ms. Swathi S Bhat; and Mr. Shithej S Suvarna.

The application for the patent was published on Feb. 13, under issue no. 07/2026.

According to the abstract released by the Intellectual Property India: "The "Natural Language-Driven SQL Query Automation Platform" is an advanced system designed to convert natural language prompts into precise SQL queries, making database management accessible to users without technical expertise. Operating entirely offline, it ensures data privacy, security, and availability even in restricted environments. The platform is built on a Sequence-to-Sequence (Seq2Seq) architecture, enhanced by key Natural Language Processing (NLP) techniques such as tokenization, embedding, and vector databases. Tokenization breaks user input into manageable components, while embeddings transform words into numerical vectors that capture semantic meaning. The Seq2Seq model processes these vectors using attention mechanisms, allowing it to focus on contextually important parts of the input when generating queries. A custom vector database is employed for efficient embedding storage and retrieval, ensuring high contextual understanding and query precision. The platform supports various SQL tasks, including data retrieval, filtering, aggregation, and conditional queries, demonstrating exceptional adaptability through extensive testing. Its offline nature eliminates dependency on cloud services, making it ideal for enterprise, academic, and research applications requiring privacy, scalability, and real-time performance. With its intuitive interface and robust backend, this platform redefines database interaction by automating complex query formulation through natural language input. Future enhancements include adding NoSQL support for diverse data handling and enabling voice commands for hands-free query generation, enhancing accessibility and versatility."

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