MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641113830 A) filed by Mrs. Manasa Madipeddi; Mrs. Priyanka Atul Deshmukh; Dr. G. Naga Chandrika; Mr. K. Naresh Babu; Kumar Devapogu; Suresh Talwar; Dr. P N V Syamala Rao M; Mrs. T. Vanitha; and Dr. K. Sunitha on September 23, 2026, for An Explainable Deep Learning System For Real-Time Critical Infrastructure Failure Prediction.
Inventors include Mrs. Manasa Madipeddi; Mrs. Priyanka Atul Deshmukh; Dr. G. Naga Chandrika; Mr. K. Naresh Babu; Kumar Devapogu; Suresh Talwar; Dr. P N V Syamala Rao M; Mrs. T. Vanitha; and Dr. K. Sunitha.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: [046] An explainable deep learning system (100) predicts impending failure in interconnected critical infrastructure using multirate telemetry acquired by edge nodes (104). An integrity and alignment engine (110) produces synchronized observation windows with missingness masks and sensor-trust values. A versioned topology registry (108) represents operational dependencies, and a graph-conditioned temporal prediction engine (112) combines temporal features with propagation-delay-aware dependency messages to generate failure-horizon risk and uncertainty. An explanation validation engine (114) identifies influential measurements and dependency paths and verifies them using counterfactual observation windows constrained by equipment operating envelopes and measurement coupling. An alert interface (116) generates qualified alerts and auditable evidence capsules, while local edge inference permits continued monitoring during communication outages. The system supports traceable, latency-conscious prediction and inspection prioritization for distributed infrastructure. Accompanied Drawing [FIGS. 1-2].
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