MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202621052713 A) filed by Vishalkumar Subhash Patil; Prof. Vrushali Ravi More; Ms. Akhila Ohmkumar; Ms. Anjali Santosh Barge; Ms. Neha Nana Dhurgude; Ms. Dhanashree Tanaji Jadhav; Ms. Tanvi Vasudev Powar; and Mrs Asmita Vishalkumar Patil on April 24, 2026, for A Multimodal Artificial Intelligence System For Biodiversity Monitoring And Ecosystem Stress Assessment Using Integrated Environmental, Acoustic, And Species Data.
Inventors include Prof. Vrushali Ravi More; Ms. Akhila Ohmkumar; Ms. Anjali Santosh Barge; Ms. Neha Nana Dhurgude; Ms. Dhanashree Tanaji Jadhav; Ms. Tanvi Vasudev Powar; and Mrs Asmita Vishalkumar Patil.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: The present invention introduces an advanced multimodal artificial intelligence system designed for effective biodiversity monitoring and ecosystem stress assessment. The system integrates diverse sources of environmental and biological data, including satellite-derived vegetation indicators, acoustic biodiversity signals, and species occurrence records, into a unified analytical framework. This integration enables a more comprehensive understanding of ecosystem health by combining both environmental and ecological perspectives. Initially, the collected data from multiple modalities undergoes preprocessing steps such as data cleaning, normalization, and temporal alignment to ensure consistency and accuracy. The processed data is then merged to form a structured multimodal dataset that captures variations in environmental conditions and biodiversity patterns over time. A key component of the system is the computation of a composite Eco-Stress Index, which quantitatively represents the overall condition of the ecosystem. This index is derived by combining vegetation health metrics, acoustic diversity patterns, and species distribution data, allowing for a holistic evaluation of ecological stress levels. Furthermore, machine learning models are employed to analyze complex relationships between environmental variables and biodiversity indicators. These models help in identifying hidden patterns, correlations, and anomalies that may indicate early signs of ecosystem degradation or stress. The system also incorporates a classification mechanism that categorizes ecosystem conditions into different states such as stable, moderate stress, or high stress. This classification supports early warning capabilities, enabling timely detection of environmental risks and facilitating proactive intervention strategies. In addition, an interactive visualization interface is provided to present ecosystem trends, comparative analyses, and stress indicators across different time periods. This interface enhances interpretability and allows researchers, policymakers, and environmental agencies to make informed decisions. Overall, the proposed system offers a data-driven, intelligent, and scalable approach for ecosystem monitoring, improving the accuracy of stress detection and supporting sustainable environmental management and decision-making.
Disclaimer: Curated by HT Syndication.
