MUMBAI, India, Sept. 7 -- Intellectual Property India has published a patent application (202641104152 A) filed by Sns College Of Technology on August 29, 2026, for Integrated Ai And Nlp-Based Decision Support System For College Admission Demand Prediction And Applicant Resume Evaluation.
Inventors include Dr. P. Sumathi; and Dr. T. R. Lekhaa.
The application for the patent was published on September 04, 2026, under issue no. 36/2026.
Abstract: The present invention relates to an integrated Artificial Intelligence (AI) and Natural Language Processing (NLP)-based decision support system for college admission demand prediction and applicant resume evaluation. The proposed system provides a unified intelligent platform that assists higher educational institutions in forecasting future admission demand while simultaneously automating the evaluation of applicant resumes to support data-driven admission and recruitment decisions. The invention employs machine learning and deep learning techniques to analyze historical admission records, demographic trends, academic performance indicators, institutional preferences, economic factors, and other relevant datasets to predict future admission demand with improved accuracy. The demand forecasting module utilizes predictive analytics to estimate application volumes, enrollment probabilities, program-wise demand, and resource requirements, enabling institutions to optimize admission planning, faculty allocation, and infrastructure utilization. In parallel, the NLP-based resume evaluation module automatically extracts, processes, and analyzes structured and unstructured information from applicant resumes. Advanced natural language processing algorithms identify educational qualifications, technical skills, certifications, internships, projects, research contributions, achievements, and relevant experience. The extracted information is semantically matched with predefined institutional or organizational eligibility criteria using intelligent scoring and ranking algorithms to generate objective applicant evaluations. The integrated decision support engine combines admission demand predictions with applicant evaluation results to generate comprehensive dashboards, ranking reports, recommendation scores, admission insights, and resource planning suggestions. The system incorporates explainable AI techniques to provide transparent decision reasoning and confidence scores for both forecasting and resume evaluation outcomes. Continuous learning mechanisms enable the models to improve prediction accuracy and evaluation performance using newly available admission and applicant data. The proposed invention reduces manual effort, minimizes subjective bias, enhances decision consistency, improves operational efficiency, and supports strategic planning within higher education institutions. By integrating predictive analytics, machine learning, natural language processing, and intelligent recommendation capabilities into a single platform, the invention offers a scalable and adaptable solution for modern admission management and applicant assessment across diverse academic programs and institutional environments.
Disclaimer: Curated by HT Syndication.
