MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202511133028 A) filed by Prof. Vijyant Agarwal on December 29, 2025, for Ai-Assisted, Uncertainty-Aware Multi-Modal Fusion For Air-Pollution Hotspot Detection, Dual-Stream Compliance Operations, And Outcome-Verified Closed-Loop Intervention Control System And Method.
Inventor includes Prof. Vijyant Agarwal.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: The invention provides an Artificial Intelligence (AI) assisted system and method for urban air-pollution hotspot detection and compliance operations using uncertainty- aware multi-modal fusion of heterogeneous inputs and closed-loop intervention control. The system ingests AI-derived camera event metadata, ambient air-quality sensor time-series, meteorological parameters, satellite-derived gridded layers, geo-tagged citizen and field reports, and compliance history. A data quality module assigns quality flags and uncertainty measures, and a spatiotemporal alignment module maps all inputs to common spatial regions and time windows. An uncertainty- weighted fusion engine computes fused hotspot indicators and generates confidence-scored hotspot rankings and recommended operational actions selected from predefined playbooks. The system executes a dual-stream governance framework separating targeted enforcement actions from independent random or stratified audit sampling for representative compliance estimation, thereby preventing biased reporting. For each action, the system generates a tamper-evident evidence pack with cryptographic integrity and performs outcome verification using before/after deltas from nearby sensors and fused indicators. In one embodiment, the system further comprises a closed-loop intervention controller that evaluates whether a selected action produced a measurable improvement relative to a target reference (AQI_ref) and, when improvement criteria are not satisfied, automatically escalates, substitutes, or sequences new actions. In one embodiment, the system further comprises a feedforward module that predicts near-future air-quality degradation using meteorology and exogenous drivers and pre-stages preventive actions. The invention improves operational latency, reliability, traceability, privacy preservation, measured impact verification, and credibility of compliance reporting.
Disclaimer: Curated by HT Syndication.
