MUMBAI, India, Aug. 1 -- Intellectual Property India has published a patent application (202441005572 A) filed by Siddamma CM; Amruta Prabhugouda; Manjunath Reddy; Dr Harish Harsurkar; Husain Bavasab Shaikh; Priti Tukaram Chorade; and Dr Sharanbasappa Shetkar, Hyderabad, Telangana, on Jan. 28, 2024, for 'biased online admission system for colleges and universities.'
Inventor(s) include Siddamma CM; Amruta Prabhugouda; Manjunath Reddy; Dr Harish Harsurkar; Husain Bavasab Shaikh; Priti Tukaram Chorade; and Dr Sharanbasappa Shetkar.
The application for the patent was published on Aug. 1, under issue no. 31/2025.
According to the abstract released by the Intellectual Property India: "This Invention related to "BIASED ONLINE ADMISSION SYSTEM FOR COLLEGES AND UNIVERSITIES" has been claimed. In the rapidly evolving landscape of education, the traditional admission process is undergoing a transformative shift towards efficiency and objectivity through the integration of Artificial Intelligence (AI) technologies. This abstract introduces an BIASED ONLINE ADMISSION SYSTEM FOR COLLEGES AND UNIVERSITIES designed to streamline and enhance the admission process for educational institutions. The proposed system leverages machine learning algorithms to analyze and evaluate admission applications, thereby reducing manual intervention and expediting decision-making. The core components of the system include data pre-processing, feature extraction, and a predictive model trained on historical admission data. The data pre-processing phase involves the cleaning and normalization of application data, ensuring consistency and reliability. Feature extraction techniques are applied to extract relevant information from application documents, transcripts, and other supporting materials. Natural Language Processing (NLP) algorithms are employed to understand and extract meaningful insights from written components such as essays and recommendation letters.The heart of the system lies in the predictive model, which is trained on a diverse dataset encompassing successful and unsuccessful admission outcomes. Supervised learning techniques are employed to enable the model to recognize patterns and correlations between various application attributes and admission success. The model is continuously refined through iterative training to adapt to changing admission criteria and preferences.The BIASED ONLINE ADMISSION SYSTEM FOR COLLEGES AND UNIVERSITIESoffers several advantages. Firstly, it significantly reduces the time and resources required for manual evaluation, allowing educational institutions to process a higher volume of applications efficiently. Secondly, the system enhances objectivity by standardizing the evaluation process, minimizing biases that may arise from subjective human judgment..Moreover, the system provides valuable insights into the factors influencing admission decisions, enabling institutions to optimize their admission criteria and improve the overall quality of admitted students. Additionally, the automated screening process allows for quick identification of exceptional candidates, facilitating a more personalized and targeted approach to admissions.In conclusion, the BIASED ONLINE ADMISSION SYSTEM FOR COLLEGES AND UNIVERSITIESrepresents a paradigm shift in the admission process, combining advanced technologies to improve efficiency, objectivity, and decision-making. The integration of machine learning and NLP algorithms positions this system at the forefront of modern educational practices, ensuring a fair and data-driven approach to student admissions."
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