MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085503 A) filed by Malla Reddy Engineering College Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be University; and Malla Reddy Vishwavidyapeeth Deemed on July 13, 2026, for Detection Of Blood Group From Fingerprint Using Machine.
Inventors include Dr. Y. Madhaveelatha; Dr. Nalli Vinaya Kumari; Mr. Kalyana Chakravarthi Agnihothram; Mr. Ch Sandeep Reddy; Dr. Raj Kumar Pogaku; Dr. A. S. N Murthy; Mr. Sateesh Kokkiligadda; and Ms. Mamidishetti Alekya.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: This project presents a machine learning-based approach for detecting human blood groups using fingerprint images as a non-invasive and efficient alternative to traditional blood testing methods. The main objective of the system is to analyze fingerprint patterns and use them to predict the blood group of an individual with the help of image processing and classification techniques. Since fingerprints are unique and permanent for every person, they can serve as a useful biometric feature for medical prediction systems. The proposed model aims to reduce the need for invasive procedures while providing quick and cost-effective preliminary blood group identification. The system works by collecting fingerprint images, preprocessing them to improve clarity, and extracting important features required for classification. These processed images are then given to a machine learning model, such as a Convolutional Neural Network (CNN), which learns the hidden patterns associated with different blood groups. During training, the model identifies relationships between fingerprint ridge structures and blood group classes, enabling it to make predictions on unseen samples. This approach combines biometrics and artificial intelligence to create an automated system that is simple, reliable, and suitable for real- world applications. The proposed blood group detection system can be useful in hospitals, blood banks, emergency services, and healthcare support systems where fast identification is important. It offers advantages such as reduced testing time, user convenience, and improved accessibility, especially in situations where immediate blood group estimation is needed. Although the accuracy of prediction depends on the quality of the dataset, preprocessing methods, and model performance, the project demonstrates the potential of machine learning in healthcare innovation. Overall, this work highlights how fingerprint-based blood group detection can contribute to the development of smart, contactless, and technology-driven medical solutions.
Disclaimer: Curated by HT Syndication.
