MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641107041 A) filed by Sns College Of Technology on September 05, 2026, for Combined Ai And Nlp-Based College Admission Decision Support System Demand Prediction And Applicant Resume Assessment.
Inventors include Ms. N. Ramyadevi; Dr. T. R. Lekhaa; Sobikan K; Santhosh V; Saravanan M; Mugesh P; Surya M; Tharun S; Shreesaran. M; and Vamsithar K.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The current invention pertains to a cohesive decision support system that utilises Artificial Intelligence (AI) and Natural Language Processing (NLP) for forecasting college admission demand and assessing candidate resumes. The suggested solution offers a cohesive intelligent platform that aids higher education institutions in predicting future enrolment needs while concurrently automating the assessment of applicant resumes to facilitate data-informed admission and recruitment choices. The innovation combines machine learning and deep learning methodologies to scrutinise past admission data, demographic patterns, academic performance metrics, institutional inclinations, economic variables, and additional pertinent statistics to forecast future admission demand with enhanced precision. The demand forecasting module use predictive analytics to project application volumes, enrolment likelihoods, program-specific demand, and resource needs, allowing institutions to enhance admission strategies, faculty distribution, and infrastructure efficiency. Simultaneously, the NLP-driven resume assessment component autonomously retrieves, processes, and scrutinises both structured and unstructured data from candidate resumes. Sophisticated natural language processing algorithms discern educational credentials, technical proficiencies, certifications, internships, projects, research contributions, accomplishments, and pertinent experience. The retrieved data is aligned semantically with established institutional or organisational eligibility standards through sophisticated scoring and ranking algorithms to produce impartial assessments of applicants. The unified decision support system amalgamates admission demand forecasts with candidate assessment outcomes to produce detailed dashboards, ranking analyses, recommendation metrics, admission perspectives, and resource allocation proposals. The solution employs explainable AI methodologies to deliver clear decision rationales and confidence metrics for both predictive analytics and resume assessment results. Ongoing learning systems allow the models to enhance predictive precision and assessment efficacy by utilising newly accessible admission and application information. The suggested innovation diminishes manual labour, lessens personal bias, increases decision-making uniformity, boosts operational effectiveness, and aids strategic planning in higher education establishments. The innovation provides a scalable and flexible solution for contemporary admission management and applicant evaluation across various academic programs and institutional settings by amalgamating predictive analytics, machine learning, natural language processing, and intelligent recommendation functionalities into a unified platform.
Disclaimer: Curated by HT Syndication.
