MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641087387 A) filed by Madhankumar C; S. Kanimozhi; Dr. R. Reena Rose; Dr. V. S. Harilakshmi; S. Tharani; Dr. Biswajit Datta; Manigandan M; Angeline Yuvancy S; and Sanjavee M on July 17, 2026, for Neuro Fusion X: A Hybrid Neuro-Symbolic Generative Intelligence Frame Work For Explainable Autonomous Artificial Intelligence.
Inventors include S. Kanimozhi; Dr. R. Reena Rose; Dr. V. S. Harilakshmi; S. Tharani; Dr. Biswajit Datta; Manigandan M; Angeline Yuvancy S; and Sanjavee M.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: Neuro Fusion X: A Hybrid Neuro-Symbolic Generative Intelligence Frame work for Explainable Autonomous Artificial Intelligence Abstract The present invention discloses NeuroFusionX, a Hybrid Neuro-Symbolic Generative Intelligence Framework designed to enable explainable, autonomous, and adaptive artificial intelligence by integrating deep neural networks, symbolic reasoning, generative AI, knowledge graphs, and reinforcement learning within a unified cognitive architecture. Conventional AI systems primarily rely on data-driven neural models that often lack interpretability, logical reasoning, and transparent decision-making, while symbolic AI systems possess reasoning capabilities but are limited in handling unstructured data and large-scale learning. The proposed invention overcomes these limitations by combining neural perception with symbolic knowledge representation and generative intelligence to provide accurate, explainable, and context-aware autonomous decision-making. The framework comprises a multimodal data acquisition module, neural perception engine, symbolic reasoning engine, generative intelligence module, dynamic knowledge graph repository, explainability and verification engine, autonomous decision controller, continuous self-learning module, and cloud-edge computing infrastructure. The neural perception engine processes structured and unstructured data including text, images, audio, sensor streams, and video, while the symbolic reasoning engine applies logical inference, rule-based reasoning, ontological knowledge, and causal analysis to validate and refine AI-generated outputs. A generative intelligence module synthesizes new knowledge, predicts future scenarios, generates human-readable explanations, and autonomously recommends optimal actions. The explainability engine provides transparent reasoning paths, confidence scores, and decision traceability to improve user trust and regulatory compliance. The invention further incorporates reinforcement learning, federated learning, digital twin integration, cybersecurity mechanisms, and continuous model evolution to enhance adaptability, robustness, and scalability across dynamic environments. The proposed framework is applicable to healthcare, autonomous vehicles, smart manufacturing, robotics, cybersecurity, finance, scientific research, education, intelligent transportation, and sustainable smart infrastructure. By integrating neural intelligence, symbolic reasoning, generative AI, and explainable decision-making into a unified self-evolving architecture, NeuroFusionX significantly improves reasoning accuracy, transparency, adaptability, and autonomous intelligence, thereby providing a scalable foundation for next- generation trustworthy artificial intelligence systems.
Disclaimer: Curated by HT Syndication.
