MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202621058646 A) filed by Ankit Upadhyay; Burra Venkata Sesha Talpa Sai; Dr. Saranya J; Nazia Sarfaraz; Shaivyaa Sharma; Dr. M M Kavitha; Dr. K. M. Padmapriya; Dr Karunaiah Bonigala; Megha Chaitanya Singru; Mrs. Bobbili Pathrisamma; Pothineni Venkateswara Rao; and Thulasimani T on May 08, 2026, for Ai-Driven Predictive Analytics System For Faculty Stress Monitoring And Human Resource Optimization In Academic Institutions.
Inventors include Ankit Upadhyay; Burra Venkata Sesha Talpa Sai; Dr. Saranya J; Nazia Sarfaraz; Shaivyaa Sharma; Dr. M M Kavitha; Dr. K. M. Padmapriya; Dr Karunaiah Bonigala; Megha Chaitanya Singru; Mrs. Bobbili Pathrisamma; Pothineni Venkateswara Rao; and Thulasimani T.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: AI-DRIVEN PREDICTIVE ANALYTICS SYSTEM FOR FACULTY STRESS MONITORING AND HUMAN RESOURCE OPTIMIZATION IN ACADEMIC INSTITUTIONS Therefore, academic institutions have adopted digital systems as a successive step towards the management of various faculty activities, schedules and performance records. Although these systems represent a useful toolkit for data storage and organization purposes, they primarily serve as administrative tools that do not provide greater insight into faculty well-being or the conditions of their workload. At many institutions, stress and burnout in faculty members only become apparent when productivity slips or absenteeism occurs or people express being dissatisfied with their jobs. The existing approaches are also contingent on time-based feedback and manual reviews that do not correlate with the day-to-day pressure of workload or those whose stress levels might keep changing. As a consequence, it becomes challenging for institutions to act quickly and establish an acceptable degree of work-life balance. The present invention provides an artificial-intelligence based predictive analytics system for monitoring faculty stress and optimizing human resources in academic institutions. The system is running continuously tracking faculty activity data including teaching load, administrative responsibilities in university governance and planning thematically related to course development plans — such as attendance at meeting too with details of communication levels by email within department patterns degree students continue throughout their discipline engagement. After the raw data processing, different meaningful indicators are expressed such as Faculty Stress Index (FSI), Workload Imbalance Score (WIS), Engagement Consistency Metric (ECM) and Burnout Risk Indicator (BRI). This is done using a machine learning based prediction engine that analyzes these indicators to identify workload trends and potential stress conditions before they reach systemic thresholds. It will also give you recommendations to optimize workloads, monitoring dashboards and adaptive feedback mechanisms with predictions that become more accurate over time. This invention aims to allow institutions a better opportunity to not only improve workforce balance but also support faculty well-being as it relates both indirectly and directly through helping reduce risk of burnout, aid academic productivity, and care for the overall welfare of its professors—all in an increasingly proactive manner that is data-driven based rather than reactive. FIG.1
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