MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641063229 A) filed by Rajasrinivas Mallu; V. Madhulatha; P Durga Bhavani; G Vasanthi; Pindi Venkanna; Ravindrababu Muvva; and Pippirisetti Pavani on May 19, 2026, for Multimodal Artificial Intelligence Framework For Ecosystem Damage Prediction And Environmental Protection.
Inventors include Rajasrinivas Mallu; V. Madhulatha; P Durga Bhavani; G Vasanthi; Pindi Venkanna; Ravindrababu Muvva; and Pippirisetti Pavani.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: ABSTRACT: The present invention provides a multimodal Artificial Intelligence (AI) framework for ecosystem damage prediction and environmental protection that can be used to fuse disparate environmental data streams, build integrated ecological intelligence representations, and forecast the degradation paths of the ecosystem over different time horizons. It is a mixed data acquisition infrastructure designed to receive simultaneous data about the environment from satellite multispectral data, aerial hyperspectral sensing, Internet-of-Things environmental sensor networks, bioacoustic monitoring arrays and socioeconomic land-use databases. The multimodal fusion engine processes and aligns the heterogenous environmental modalities and then generates a unified spatiotemporal ecological feature tensor which represents the ecological conditions in space and time. A multi-branch deep learning prediction architecture is formed, which consists of shared representation layers, biome- specific prediction branches, temporal convolution modules, and graph neural network spatial reasoning modules, to forecast probabilistic ecosystem damage, biodiversity disruption, habitat degradation trajectory and estimation of ecological collapse risk with confidence interval for prediction horizon of up to one hundred and eighty days. The framework also includes a hierarchical ecological risk stratification module to categorise ecosystem damage that is predicted into several environmental risk classes and to provide scientific environmental protection suggestions to conservation agencies and regulatory bodies. In some embodiments, the invention also comprises a cross-biome generalization engine based on the use of meta-learning techniques to predictively adapt across a wide variety of ecosystem categories, and an explainability module for creating interpretable ecological attribution maps and environmental feature importance analysis. The invention provides a novel method for converting existing environmental monitoring systems that operate in pieces into a single platform that turns environmental monitoring into a predictive ecological intelligence, thereby enhancing the capability of biodiversity conservation, ecological risk forecasting accuracy, effectiveness of environmental protection, and evidence-based sustainability decision-making by detecting ecosystem degradation precursor signals before environmental collapse.
Disclaimer: Curated by HT Syndication.
