MUMBAI, India, April 17 -- Intellectual Property India has published a patent application (202611021041 A) filed by Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, on Feb. 23, for 'an enhanced encoder-decoder neural network system and method for optimized sequence-to-sequence learning.'
Inventor(s) include Rudraksh; Saksham; Samee Raza Ansari; Samiksha Rawat; Dr. Inderjeet Kaur; and Ms. Shraddha Pandey.
The application for the patent was published on April 17, under issue no. 16/2026.
According to the abstract released by the Intellectual Property India: "The present invention discloses an enhanced encoder-decoder neural network system and method for optimized sequence-to-sequence learning. The system comprises interconnected hardware and software modules including a dual-stage encoder with bidirectional recurrent processing and self-attention mechanisms, a context refinement module for semantic enhancement and noise suppression, and a dynamic decoder incorporating adaptive attention and selective context alignment. The architecture is executed over heterogeneous computing platforms comprising central processing units, graphical processing units, and tensor processing units, enabling parallelized computation, reduced memory latency, and optimized resource utilization. The invention provides a technical solution to computational bottlenecks, long-range dependency loss, and memory inefficiency inherent in conventional sequence modeling systems. The proposed system achieves improved contextual accuracy, faster convergence, and enhanced scalability across multilingual translation, speech recognition, document summarization, and multimodal sequence processing applications, thereby demonstrating substantial technical advancement and industrial applicability."
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