MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641071713 A) filed by Dr. M. Beulah Viji Christiana; Mr. Kurra Venkateswara Rao; Ms. K Ashwini; Mrs. Sowmya Pd; Reva University; Dr. A Nirmal Kumar; and B. Vijaya Ramnath on June 09, 2026, for Adaptive Quantum-Ai Infrastructure Orchestration System For Autonomous Resource Management In Cloud Computing Environments.
Inventors include Dr. M. Beulah Viji Christiana; Mr. Kurra Venkateswara Rao; Ms. K Ashwini; Mrs. Sowmya Pd; Dr. A Nirmal Kumar; and B. Vijaya Ramnath.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: The present invention discloses an Adaptive Quantum-AI Infrastructure Orchestration System for Autonomous Resource Management in Cloud Computing Environments configured to intelligently monitor, predict, optimize, and manage cloud infrastructure resources through the integration of artificial intelligence, quantum-assisted optimization, autonomous orchestration, and adaptive learning mechanisms. The system comprises an infrastructure monitoring layer, telemetry collection engine, artificial intelligence prediction module, quantum optimization engine, hybrid quantum-classical processing layer, autonomous orchestration controller, resource allocation manager, self-healing infrastructure module, security and compliance engine, adaptive learning repository, and distributed cloud resource pool. Operational telemetry collected from cloud resources is analyzed to forecast workload demands, identify infrastructure risks, and generate predictive insights. The quantum optimization engine processes resource allocation, workload scheduling, capacity planning, and network optimization problems to determine efficient infrastructure configurations. Based on optimization outputs, the orchestration controller autonomously performs resource provisioning, workload migration, scaling operations, and infrastructure reconfiguration. The self-healing module detects anomalies and initiates automated recovery actions to maintain service continuity and infrastructure resilience. Adaptive learning mechanisms continuously improve prediction accuracy and optimization effectiveness through feedback-driven model refinement. The invention further incorporates energy-aware resource management and security-integrated orchestration capabilities, enabling enhanced scalability, reliability, operational efficiency, fault tolerance, and cost optimization across public, private, hybrid, edge, and multi-cloud computing environments. The disclosed system provides a unified framework for next-generation autonomous cloud infrastructure management utilizing hybrid quantum-classical computational intelligence. Accompanied Drawing [FIGS. 1-2]
Disclaimer: Curated by HT Syndication.
