MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641069307 A) filed by Sr University on June 02, 2026, for A Scalable Distributed Machine Learning Framework For Large-Scale Zoonotic Disease Surveillance Using Advanced Topic Modeling And Cluster-Based Matrix Factorization Techniques On Hadoop Ecosystems.
Inventors include Bedudoori Dwarakanath; Dr C Madan Kumar; and Dr. Praveen Barmavatu.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: The present invention relates to a scalable distributed machine learning framework configured for large-scale zoonotic disease surveillance using advanced topic modeling and cluster-based matrix factorization techniques on Hadoop ecosystems. The proposed framework integrates distributed computing infrastructures, predictive epidemiological analytics, intelligent healthcare monitoring systems, and parallel analytical processing technologies for supporting real-time outbreak detection and adaptive public healthcare management. The system continuously acquires healthcare datasets from hospitals, veterinary institutions, environmental monitoring systems, research laboratories, and public health organizations for generating predictive disease intelligence and epidemiological insights.The invention utilizes advanced topic modeling algorithms, natural language processing technologies, and semantic analytical frameworks for extracting hidden outbreak patterns, disease transmission trends, and epidemiological relationships from structured and unstructured healthcare information. Cluster-based matrix factorization techniques continuously analyze multidimensional healthcare datasets for improving disease classification accuracy, transmission clustering efficiency, and predictive analytical performance during large-scale epidemiological surveillance operations. Hadoop ecosystem infrastructures support distributed storage, scalable analytical processing, and low-latency computational execution across interconnected healthcare environments.Additionally, the framework incorporates environmental monitoring technologies, geospatial analytical systems, anomaly detection mechanisms, and intelligent outbreak forecasting engines for identifying zoonotic transmission risks before widespread healthcare emergencies occur.
Disclaimer: Curated by HT Syndication.
