MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641118575 A) filed by Madhankumar C; Mr. T. Udhayakumar - Hindusthan Institute Of Technology; Dr. D. Sharmila - Erode Sengunthar Engineering College; Dr. S. Prabakaran - V. S. B. Engineering College; Mrs. A. Martina - Kings Engineering College; Mrs. S. Suganya - Jct College Of Engineering And Technology; Mr. K. Mahadevan - Care College Of Engineering Autonomous; Dr. B. Nagarajan - Madanapalle Institute Of Technology & Science; Dr. M. Maheswaran - Nehru Institute Of Engineering And Technology; and M. S. Vinu - Jct College Of Engineering And Technology on October 04, 2026, for Cognitive Digital Twin Framework With Meta-Learning Enabled Predictive Intelligence For Smart Cities.
Inventors include Mr. T. Udhayakumar - Hindusthan Institute Of Technology; Dr. D. Sharmila - Erode Sengunthar Engineering College; Dr. S. Prabakaran - V. S. B. Engineering College; Mrs. A. Martina - Kings Engineering College; Mrs. S. Suganya - Jct College Of Engineering And Technology; Mr. K. Mahadevan - Care College Of Engineering Autonomous; Dr. B. Nagarajan - Madanapalle Institute Of Technology & Science Deemed To Be University; Dr. M. Maheswaran - Nehru Institute Of Engineering And Technology; and M. S. Vinu - Jct College Of Engineering And Technology.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: ABSTRACT Cognitive Digital Twin Framework with Meta-Learning Enabled Predictive Intelligence for Smart Cities The present invention relates to an intelligent Cognitive Digital Twin framework with Meta Learning enabled predictive intelligence for real-time monitoring, simulation, prediction, optimization, and autonomous decision support in smart-city environments. The proposed framework integrates Internet of Things (IoT) sensor networks, edge computing, digital twin technology, artificial intelligence, meta-learning, multi-source data fusion, predictive analytics, knowledge representation, and an intelligent decision-support module to create a continuously updated virtual representation of physical urban infrastructure and services. The system acquires real-time information from heterogeneous sources including traffic sensors, environmental sensors, energy meters, water-management systems, public transportation systems, surveillance devices, weather information, infrastructure-monitoring sensors, and other authorized urban data sources. An edge-processing layer performs data preprocessing, synchronization, feature extraction, anomaly identification, and event detection before transferring selected information to the Cognitive Digital Twin platform. The digital twin maintains a dynamic representation of urban assets, infrastructure states, environmental conditions, resource utilization, and interdependencies between different city services. A principal feature of the invention is the Meta-Learning enabled predictive intelligence engine, configured to learn from multiple urban scenarios and rapidly adapt predictive models to new locations, infrastructure conditions, seasonal variations, traffic patterns, environmental changes, and emerging operational situations with reduced retraining requirements. The engine can perform traffic prediction, energy-demand forecasting, infrastructure-health prediction, environmental-risk assessment, water- demand forecasting, congestion prediction, anomaly detection, and other city-service predictions. The Cognitive Digital Twin further incorporates a context-awareness and knowledge-analysis mechanism that correlates real-time sensor observations with historical information, infrastructure relationships, operational rules, and simulated scenarios. The system can generate alternative future scenarios within the digital twin to estimate the potential effects of infrastructure changes, traffic- management strategies, resource-allocation decisions, or environmental conditions before implementing corresponding actions in the physical city environment. An intelligent decision-support module evaluates predicted conditions and provides prioritized recommendations to authorized city administrators and infrastructure operators. The system may dynamically optimize selected city operations according to configured objectives such as traffic efficiency, energy utilization, infrastructure reliability, environmental sustainability, public-service availability, and resource utilization. An explainability mechanism may additionally identify the principal factors contributing to predictions and recommended actions. Accordingly, the proposed invention provides a self-adaptive, predictive, simulation-driven, and intelligent Cognitive Digital Twin framework capable of continuously learning from diverse smart-city environments and rapidly adapting to changing urban conditions. The framework enables proactive infrastructure management, predictive urban planning, efficient resource utilization, improved situational awareness, and intelligent decision support for resilient and sustainable smart-city operations.
Disclaimer: Curated by HT Syndication.
