MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202511117236 A) filed by Harshit Saini; Tejswee Rauthan; Samarth Sahni; Ravi Prakash Chaturvedi; Anurag Rai; and Annu Mishra, Greater Noida, Uttar Pradesh, on Nov. 26, 2025, for 'an explainable two-tier machine learning framework for early detection and refined assessment of anxiety levels.'
Inventor(s) include Harshit Saini; Tejswee Rauthan; Samarth Sahni; Ravi Prakash Chaturvedi; Anurag Rai; and Annu Mishra.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "The present invention relates to an explainable two-tier machine learning framework designed for early detection and refined assessment of anxiety levels in individuals. The framework employs a hierarchical approach wherein the first tier performs binary classification to identify the presence or absence of anxiety, while the second tier conducts multi-class classification to determine specific anxiety severity levels. The system integrates multiple machine learning algorithms including Random Forest, Support Vector Machines, and Neural Networks, combined with explainability techniques such as SHAP and LIME to provide interpretable results. The framework processes physiological signals, behavioral patterns, and self-reported data to generate comprehensive anxiety assessments. By utilizing feature importance analysis and visualization tools, the invention enables healthcare professionals to understand the decision-making process and identify key contributing factors to anxiety levels. The two-tier architecture improves diagnostic accuracy while maintaining computational efficiency, making it suitable for real-time clinical applications and remote monitoring systems."
Disclaimer: Curated by HT Syndication.
