MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641087920 A) filed by Madhankumar C; Mr. P. Palanisamy; Ms. Pradheeba P; Dr. D. Karthikeswaran; Dr. Anand M; Ms. Rajeshwari P; Mrs. N. Nandhini; Mrs. T. Cowsalya; Mrs. M. Priyakumari; Dr. R. Arun Kumar; and M. S. Vinu on July 18, 2026, for Neuro-Symbolic Quantum Federated Digital Twin Architecture For Self Evolving Explainable Aiot Systems.
Inventors include Mr. P. Palanisamy; Ms. Pradheeba P; Dr. D. Karthikeswaran; Dr. Anand M; Ms. Rajeshwari P; Mrs. N. Nandhini; Mrs. T. Cowsalya; Mrs. M. Priyakumari; Dr. R. Arun Kumar; and M. S. Vinu.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: Neuro-Symbolic Quantum Federated Digital Twin Architecture for Self Evolving Explainable AIoT Systems Abstract The rapid expansion of Artificial Intelligence of Things (AIoT) has enabled intelligent, interconnected cyber-physical systems across healthcare, smart manufacturing, smart cities, transportation, and industrial automation. However, existing AIoT architectures face significant challenges related to data privacy, limited explainability, centralized learning, computational scalability, and adaptation to continuously evolving environments. This work proposes a Neuro Symbolic Quantum Federated Digital Twin Architecture (NSQF-DT) that integrates neural learning, symbolic reasoning, quantum-inspired optimization, federated learning, and digital twin technology into a unified self-evolving intelligent framework for next-generation Explainable AIoT systems. The proposed architecture employs distributed AIoT edge devices to continuously collect multimodal sensor data, while federated learning enables collaborative model training without transferring sensitive raw data, thereby preserving privacy and reducing communication overhead. A digital twin layer constructs real-time virtual replicas of physical assets, enabling continuous monitoring, simulation, anomaly prediction, and decision support. Deep neural networks extract complex patterns from heterogeneous data streams, whereas a neuro-symbolic reasoning engine combines learned knowledge with domain rules and ontologies to generate transparent, interpretable, and explainable decisions. A quantum-inspired optimization module dynamically optimizes resource allocation, communication efficiency, task scheduling, and model convergence across distributed AIoT networks. The self-evolving learning engine continuously updates system knowledge through reinforcement feedback, adaptive knowledge graphs, and incremental learning, ensuring long-term adaptability without complete retraining. Experimental evaluation demonstrates that the proposed framework significantly improves prediction accuracy, model interpretability, privacy preservation, computational efficiency, and real-time decision-making compared with conventional centralized AIoT architectures. The integration of neuro- symbolic intelligence, quantum-inspired optimization, federated learning, and digital twin technology provides robust, scalable, and trustworthy AI for complex cyber physical environments. The proposed architecture is well suited for applications including Industry 5.0, autonomous transportation, precision healthcare, smart energy management, intelligent manufacturing, and resilient smart city infrastructures, offering a comprehensive foundation for secure, adaptive, and explainable next- generation AIoT ecosystems.
Disclaimer: Curated by HT Syndication.
