MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641069291 A) filed by Sr University on June 02, 2026, for Design And Development Of A Hadoop-Based Distributed Topic Modeling And Cluster Visualization Framework For Intelligent Analysis And Early Detection Of Zoonotic Disease Patterns In Big Data Environments.
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 proposed framework integrates Hadoop ecosystem infrastructures, distributed machine learning technologies, epidemiological analytics engines, predictive outbreak monitoring systems, and intelligent healthcare visualization mechanisms for supporting adaptive public healthcare management and scalable disease surveillance operations. The system continuously acquires epidemiological datasets from hospitals, veterinary healthcare institutions, environmental monitoring systems, research laboratories, wildlife surveillance infrastructures, and public healthcare agencies for generating predictive disease intelligence and outbreak analytics. The invention utilizes advanced topic modeling algorithms, semantic analytical frameworks, and natural language processing technologies for extracting hidden epidemiological themes, disease transmission relationships, and outbreak-related healthcare patterns from structured and unstructured healthcare information. Intelligent cluster visualization technologies dynamically generate graphical outbreak maps, healthcare trend clusters, and disease relationship analytics for improving epidemiological interpretation and healthcare decision-making efficiency during large-scale zoonotic disease monitoring operations. Hadoop-based distributed processing infrastructures support scalable healthcare storage, parallel analytical execution, and low-latency computational performance across interconnected healthcare ecosystems. Additionally, the framework incorporates anomaly detection mechanisms, geospatial analytical systems, environmental monitoring technologies, and predictive epidemiological forecasting engines for identifying zoonotic transmission risks before widespread outbreak conditions occur. Real-time healthcare alerts, adaptive outbreak recommendations, and intelligent visualization interfaces significantly improve public healthcare preparedness, disease surveillance accuracy, epidemiological responsiveness, and operational scalability within modern healthcare big data environments.
Disclaimer: Curated by HT Syndication.
