MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641082524 A) filed by Krishna Kumar N. J; E. Christina Dally; D. Swaroopa; Dasari Srinivasa Rao; Rajesh A; Dr B Gayathri; Kalaguru Mamatha; G. Ramachandra Kumar; Dr. Shaista Parveen; Dr R Senthil Kumar; Hemalatha R; and P. Umamaheswari on July 04, 2026, for Ai-Assisted Self-Healing Electronic Architecture For Autonomous Fault Detection And Recovery In Next-Generation 6g Networks.
Inventors include Krishna Kumar N. J; E. Christina Dally; D. Swaroopa; Dasari Srinivasa Rao; Rajesh A; Dr B Gayathri; Kalaguru Mamatha; G. Ramachandra Kumar; Dr. Shaista Parveen; Dr R Senthil Kumar; Hemalatha R; and P. Umamaheswari.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: AI-ASSISTED SELF-HEALING ELECTRONIC ARCHITECTURE FOR AUTONOMOUS FAULT DETECTION AND RECOVERY IN NEXT- GENERATION 6G NETWORKS This innovation presents an AI-Assisted Self-Healing Electronic Architecture for Autonomous Fault Detection and Recovery in Next-Generation 6G Networks, aimed at delivering intelligent, proactive, and autonomous fault management for future communication infrastructures. The invention amalgamates distributed network monitoring systems, edge intelligence modules, artificial intelligence analytics engines, digital twin technology, root-cause analysis mechanisms, autonomous recovery controllers, security management frameworks, and continuous learning modules into a cohesive self-healing architecture. The distributed monitoring layer perpetually gathers real-time operational data from diverse network entities, including base stations, user equipment, IoT devices, edge servers, cloud platforms, satellite communication nodes, and virtual network functions. The gathered data is processed and analysed utilising sophisticated machine learning and deep learning models, including Deep Neural Networks (DNN), Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNN), Transformer architectures, and Reinforcement Learning algorithms, to identify anomalies, anticipate potential failures, and project network performance deterioration prior to service interruption. A digital twin simulation tool preserves a virtual counterpart of the physical 6G network and assesses recovery procedures under simulated settings before execution. Upon detecting a fault, a root-cause analysis module determines the origin of the issue, while an autonomous recovery controller executes corrective measures such as traffic rerouting, resource optimisation, network slice reconfiguration, virtual network function migration, spectrum allocation, and self-reconfiguration. The architecture also integrates AI-driven security systems and blockchain-based trust management to guarantee secure and dependable functioning. Continuous learning skills facilitate adaptive enhancement in predictive precision and recovery efficacy. The invention markedly improves network reliability, availability, resilience, security, and Quality of Service, while reducing downtime and operational expenses, rendering it exceptionally appropriates for smart cities, autonomous transportation systems, healthcare networks, industrial automation, satellite communications, and extensive Internet of Things ecosystems in forthcoming 6G environments. FIG.1.
Disclaimer: Curated by HT Syndication.
