MUMBAI, India, Aug. 12 -- Intellectual Property India has published a patent application (202641093405 A) filed by Satheesh Babu on July 31, 2026, for Ai-Powered Autonomous Operations Platform For Satellite Network Monitoring, Fault Prediction, And Self-Healing Remediation.
Inventor includes Satheesh Babu.
The application for the patent was published on August 07, 2026, under issue no. 32/2026.
Abstract: The present invention is an Artificial Intelligence-powered Autonomous Network Operations Center (AI NOC) platform specifically designed for satellite communication infrastructure management. The invention addresses three critical failures of conventional NOC environments: alarm fatigue arising from thousands of undifferentiated raw alarms per hour; slow mean-time-to-detect (MTTD) and mean-time-to-restore (MTTR) caused by the absence of automated root cause identification; and reactive incident management that allows failures to propagate before intervention. The platform operates as an intelligent middleware layer above existing monitoring infrastructure, ingesting alarm telemetry from heterogeneous sources including Simple Network Management Protocol (SNMP), Syslog, NetFlow, satellite management systems, and cloud monitoring APIs from Amazon Web Services, Microsoft Azure, and Alibaba Cloud. An intelligent alarm correlation engine aggregates thousands of discrete alarms into single prioritized master incidents carrying confidence scores and estimated restoration times. A graph-based dependency mapping engine identifies fault origin nodes within seconds. A machine learning ensemble assigns failure probability scores to satellite network nodes across six telemetry dimensions -- CPU utilization, memory utilization, power levels, signal quality, temperature, and firmware lifecycle status -- enabling predictive intervention with a minimum lead time of twenty-four hours. A generative AI Operations Copilot built on a large language model with retrieval-augmented generation (RAG) provides conversational support and automated executive reporting. A continuous learning engine retrains predictive models on resolved incident data. In its highest operational mode, the system executes autonomous self-healing remediation -- including service restart, configuration rollback, and traffic rerouting -- without human intervention. Deployment is supported in SaaS, on-premises, and hybrid configurations.
Disclaimer: Curated by HT Syndication.
