Intellectual Property India Publishes Patent Application for Ai And Machine Learning-Based Hrm System For Faculty Performance Evaluation And Enhancement In Higher Education

MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641071885 A) filed by Kambham Pratap Joshi; Dr. M. Sivanadh; Reshma Pai A; Ashwini; Prof. Dr. Yogeshver Prasad Sharma; Dr. Himanshu Sanghavi; Dr. Rajvardhan; Dr. Syed Kumail Sajjad; S M Balaji; Dr. Muralidharan J; S Nagoorkani; and Kasthuri C on June 10, 2026, for Ai And Machine Learning-Based Hrm System For Faculty Performance Evaluation And Enhancement In Higher Education.

Inventors include Kambham Pratap Joshi; Dr. M. Sivanadh; Reshma Pai A; Ashwini; Prof. Dr. Yogeshver Prasad Sharma; Dr. Himanshu Sanghavi; Dr. Rajvardhan; Dr. Syed Kumail Sajjad; S M Balaji; Dr. Muralidharan J; S Nagoorkani; and Kasthuri C.

The application for the patent was published on June 19, 2026, under issue no. 25/2026.

Abstract: AI and Machine Learning-Based HRM System for Faculty Performance Evaluation and Enhancement in Higher Education is the proposed invention. The proposed invention is closes an AI and machine learning-based HRM system for faculty performance evaluation and enhancement in higher education using transformer-based deep learning and explainable artificial intelligence techniques. The system collects and analyzes multidimensional academic data including teaching activities, research productivity, student engagement, mentoring records, administrative contributions, and qualitative feedback to generate accurate and context-aware faculty performance assessments. Advanced natural language processing and predictive analytics models are employed to identify performance trends, teaching effectiveness, professional development requirements, and institutional contribution patterns. An adaptive recommendation engine generates personalized enhancement strategies including training programs, pedagogical improvements, collaborative research opportunities, and workload optimization plans. The invention further incorporates explainable AI mechanisms to ensure transparency, fairness, and accountability in faculty evaluation processes. The proposed system improves academic quality, institutional efficiency, professional growth, and data-driven decision-making within higher education human resource management environments.

Disclaimer: Curated by HT Syndication.