Intellectual Property India Publishes Patent Application for System And Method For Ai-Enabled Autonomous Learning-Based Real-Time Anomaly Detection And Forecasting

MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641126036 A) filed by Dr. Bosco Nirmala Priya; Dr. Naseema Shaik; Dr Shaikh Afroz Fatima; Dr. Mohammad Alamgeer; D. Sophia Margaret; Dorathy Manjula; Ayesha Roohi; Sujatha L; Seema S Reshmi; and Dr. Fathima Begum F R on October 22, 2026, for System And Method For Ai-Enabled Autonomous Learning-Based Real-Time Anomaly Detection And Forecasting.

Inventors include Dr. Bosco Nirmala Priya; Dr. Naseema Shaik; Dr Shaikh Afroz Fatima; Dr. Mohammad Alamgeer; D. Sophia Margaret; Dorathy Manjula; Ayesha Roohi; Sujatha L; Seema S Reshmi; and Dr. Fathima Begum F R.

The application for the patent was published on October 30, 2026, under issue no. 44/2026.

Abstract: ABSTRACT SYSTEM AND METHOD FOR AI-ENABLED AUTONOMOUS LEARNING-BASED REAL-TIME ANOMALY DETECTION AND FORECASTING An AI-enabled autonomous learning system for real-time anomaly detection and forecasting is disclosed. The system comprises a data acquisition module, preprocessing engine, feature extraction module, autonomous learning engine, anomaly detection engine, predictive forecasting module, feedback optimization engine, and decision-support interface. Real-time data collected from multiple sources is normalized and analyzed to establish adaptive behavioral models. Incoming operational information is continuously evaluated against the learned models to identify anomalies, deviations, and abnormal conditions. Detected anomalies are utilized in conjunction with historical and current operational data to generate predictive forecasts regarding future system states. A feedback optimization mechanism continuously refines the analytical models and forecasting algorithms based on observed outcomes and performance metrics. The invention is applicable to industrial automation, infrastructure monitoring, healthcare systems, financial analytics, agriculture, smart cities, and other environments requiring intelligent monitoring, anomaly detection, and predictive decision-support capabilities.

Disclaimer: Curated by HT Syndication.