Intellectual Property India Publishes Patent Application for Intelligent Root Cause Analysis And Self-Healing Framework For Java-Based Enterprise Applications

MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641106524 A) filed by Dr. Siva Kumar Nendranbakam; Dr. Venkata Prasad Sandu; Dr. Venkata Lakshmi Narasimha Sarma Ikkurthy; Dr. Mokkarala Ravi Kumar; and Dr. Nimushakavi Satya Venkata Surya Narayana Murthy on September 03, 2026, for Intelligent Root Cause Analysis And Self-Healing Framework For Java-Based Enterprise Applications.

Inventors include Dr. Siva Kumar Nendranbakam; Dr. Venkata Prasad Sandu; Dr. Venkata Lakshmi Narasimha Sarma Ikkurthy; Dr. Mokkarala Ravi Kumar; and Dr. Nimushakavi Satya Venkata Surya Narayana Murthy.

The application for the patent was published on September 11, 2026, under issue no. 37/2026.

Abstract: ABSTRACT: Title: Intelligent Root Cause Analysis and Self-Healing Framework for Java-Based Enterprise Applications The present invention discloses an intelligent root cause analysis and self-healing framework for Java-based enterprise applications. The framework collects heterogeneous telemetry comprising application logs, exception stack traces, distributed traces, performance metrics, Java Virtual Machine information, thread states, database metrics, deployment events, configuration changes, and infrastructure events. The collected information is normalized and correlated using temporal, transactional, dependency, and contextual relationships to generate an incident context. An application dependency graph represents relationships among application services, Java modules, databases, APIs, queues, containers, and infrastructure resources. An intelligent root cause analysis engine generates and ranks candidate root causes using anomaly detection, historical patterns, dependency analysis, temporal correlation, and confidence scoring. A remediation decision engine selects a controlled remediation action based on root cause confidence, incident severity, operational policies, and historical outcomes. A remediation execution module performs the action, while a verification module determines recovery. A feedback learning module records remediation outcomes and improves subsequent diagnosis and self-healing decisions, thereby enabling closed-loop, intelligent, and controlled recovery of enterprise applications.

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