Intellectual Property India Publishes Patent Application for Ai-Driven Predictive Hrm System For Employee Stress Management, Preventive Intervention, And Organizational Financial Performance Optimization

MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202641111858 A) filed by Dr. G. Palanikumar; Prof Narayan Malakar; Aavleen Bakshi; Dr. T. V. Ambuli; Dr. Monika Arya; Dr. P. Malathi Latha; Dayananda Sagar Academy Of Technology And Management; Dr. J. Arun Prasad; Dr Abha Shukla; Dr K. Rajesh Kumar; Dr. B. Senthilnayaki; and Mr. S. Nagoorkani on September 17, 2026, for Ai-Driven Predictive Hrm System For Employee Stress Management, Preventive Intervention, And Organizational Financial Performance Optimization.

Inventors include Dr. G. Palanikumar; Prof Narayan Malakar; Aavleen Bakshi; Dr. T. V. Ambuli; Dr. Monika Arya; Dr. P. Malathi Latha; Dr. J. Arun Prasad; Dr Abha Shukla; Dr K. Rajesh Kumar; Dr. B. Senthilnayaki; and Mr. S. Nagoorkani.

The application for the patent was published on October 02, 2026, under issue no. 40/2026.

Abstract: AI-Driven Predictive HRM System for Employee Stress Management, Preventive Intervention, and Organizational Financial Performance Optimization is the proposed invention. The proposed invention employs a hybrid PatchTST and XGBoost machine learning architecture to analyze temporal and structured workforce information including workload patterns, working hours, overtime, attendance, leave behavior, task completion, performance trends, and other authorized organizational parameters. PatchTST processes sequential HR observations to identify evolving temporal patterns associated with employee stress and workforce risk, while XGBoost integrates the learned temporal representations with structured organizational features to generate stress-risk, absenteeism-risk, productivity-impact, and workload-risk predictions. Based on the predicted risk levels, a preventive intervention module evaluates configurable actions including workload redistribution, scheduling modification, resource allocation, task reassignment, and employee-support measures. A financial optimization module estimates intervention costs and projected organizational effects on productivity, absenteeism-related expenditure, staffing requirements, and operational efficiency. The system further provides an HR decision- support interface for monitoring risk trends, intervention scenarios, and projected financial outcomes, thereby enabling proactive and data-driven workforce management.

Disclaimer: Curated by HT Syndication.