Intellectual Property India Publishes Patent Application for Predicting Student Startup Success And Entrepreneurial Potential In Higher Education Using Machine Learning Techniques

MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641091743 A) filed by Dr. M. Kathiravan; Dr. M. Shabeena Begam; Dr. Suriya Kumari A; Dr. Pooja Krishna J; Dr. Candida Smitha; Ashapu Bhavani; Dr. B. Usharani; Dr. Akanksha Shivaji Bhanji; Dr. Santosh Kumar Samantaray; Ratnesh Kumar Sharma; Ms. S. Sivaselvi; and Maheshwari Vishal Patne on July 29, 2026, for Predicting Student Startup Success And Entrepreneurial Potential In Higher Education Using Machine Learning Techniques.

Inventors include Dr. M. Kathiravan; Dr. M. Shabeena Begam; Dr. Suriya Kumari A; Dr. Pooja Krishna J; Dr. Candida Smitha; Ashapu Bhavani; Dr. B. Usharani; Dr. Akanksha Shivaji Bhanji; Dr. Santosh Kumar Samantaray; Ratnesh Kumar Sharma; Ms. S. Sivaselvi; and Maheshwari Vishal Patne.

The application for the patent was published on August 21, 2026, under issue no. 34/2026.

Abstract: Predicting Student Startup Success and Entrepreneurial Potential in Higher Education Using Machine Learning Techniques is the proposed invention. The proposed invention system utilizes a Foundation Model for Tabular Data (TabPFN) integrated with multimodal feature fusion and Explainable Artificial Intelligence (XAI) to accurately assess the likelihood of startup success and entrepreneurial capability. The framework collects and processes multidimensional data, including academic performance, innovation activities, leadership skills, entrepreneurial mindset, business plan quality, mentorship records, incubation participation, funding readiness, intellectual property, market validation, and behavioral attributes. Following data preprocessing, feature engineering, and embedding generation, the TabPFN model learns complex nonlinear relationships among heterogeneous variables to generate a Startup Success Probability Score, Entrepreneurial Potential Index, Investment Readiness Score, and Risk Assessment Score. Explainable AI modules identify the key factors influencing each prediction and provide personalized recommendations for skill development, mentorship, funding opportunities, team formation, and market readiness. The system continuously updates its predictive capability through feedback from startup outcomes, enabling adaptive learning and improved accuracy over time.

Disclaimer: Curated by HT Syndication.