MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085252 A) filed by Dr Uppara Manjulamma; Dr. Santosh Kumar Nayak; Dr D Saravanakumar; Senthil Kumar M; Dharani R; Dr. E. Janaki; Dr. B. Usharani; Dr. Madhuchhanda Nayak; Alisha Hashmi; Anvesh Perada; Ullal Akshatha Nayak; and Dr. Sreekanth Rallapalli on July 12, 2026, for Artificial Intelligence-Based System For Automated Faculty Candidate Shortlisting And Interview Recommendation In Higher Education.
Inventors include Dr Uppara Manjulamma; Dr. Santosh Kumar Nayak; Dr D Saravanakumar; Senthil Kumar M; Dharani R; Dr. E. Janaki; Dr. B. Usharani; Dr. Madhuchhanda Nayak; Alisha Hashmi; Anvesh Perada; Ullal Akshatha Nayak; and Dr. Sreekanth Rallapalli.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: This invention presents an Artificial Intelligence-Based System for the automated shortlisting of faculty candidates and interview recommendations in higher education, incorporating Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR) to streamline and improve the faculty recruitment process. The method allows candidates to submit applications via an online recruitment site, where resumes, academic certificates, teaching experience, research papers, patents, professional certifications, awards, and other supporting documents are gathered and evaluated. The system employs OCR and NLP techniques to extract and preprocess pertinent information, which is then assessed by machine learning algorithms according to established recruitment criteria, encompassing academic qualifications, teaching experience, research accomplishments, publications, patents, certifications, industrial experience, and institution-specific policies. The method calculates a candidate appropriateness score, ranks candidates, and categorises them as Interview Recommended, Waitlisted, Further Review Required, or Not Eligible. An Explainable Artificial Intelligence (XAI) module offers clear justifications for each recommendation, whereas an interview management module automates the scheduling and notifications of interviews. The system perpetually enhances its predictive precision using adaptive learning, utilising historical recruitment results and interviewer evaluations. The suggested innovation markedly decreases manual screening efforts, shortens recruitment duration, increases transparency, mitigates selection bias, promotes decision accuracy, and facilitates efficient, objective, and data-driven faculty recruitment in higher education institutions. FIG.1
Disclaimer: Curated by HT Syndication.
