MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202641054535 A) filed by Peri College Of Arts And Science on April 29, 2026, for Machine Learning Based Workplace Stress Monitoring System With Secure Data Aggregation.
Inventors include Anupriya P; Thiruslevi S; Sneha D; Hemavarshini S; Kiruthiga Sree M; and Harinima S.
The application for the patent was published on June 26, 2026, under issue no. 26/2026.
Abstract: The invention concerns a system and method for stress monitoring, assessment and management in workplace settings by leveraging a privacy-preserving artificial intelligence (Al) system. In today's fast-paced workplaces, organizations are struggling to promote employee wellbeing, productivity and mental health because of the increasing workload, ever-changing workplaces and digital fatigue. Traditional methods of stress monitoring, including surveys and manual assessments, are often subjective, limited in frequency and lack real-time capabilities. The current invention provides an intelligent dashboard system that gathers anonymous physiological, behavioural, and digital interaction data from employees via wearable devices, usage patterns of software and environmental sensors. The platform integrates machine learning techniques to analyze the data and identify stress, outliers, and patterns. Rather than focusing on individual users, the system aggregates the data at the team or department level, with differential privacy and secure multi-party computation techniques to maintain privacy. The system offers real-time monitoring of stress indices, predictive stress management for potential burnout prevention, and dynamic suggestions such as workload balancing, optimal break times, and stress-relieving measures. The system also integrates with enterprise resource planning (ERP) and human resource management systems (HRMS) for easy installation. The innovation employs Al-powered analytics with robust privacy preservation techniques to ensure ethical monitoring practices and enable data-driven decision making. The system contributes to employee satisfaction, burnout prevention, productivity enhancement, and a positive working environment, making it perfectly applicable to contemporary enterprises, distributed work environments and hybrid workplaces.
Disclaimer: Curated by HT Syndication.
