MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641086894 A) filed by Vallurupalli Nageswara Rao Vignana Jyothi Institute Of Engineering And Technology on July 16, 2026, for System And Method For Automated Vehicle Damage Detection, Repair Cost Estimation, And Insurance Claim Evaluation Using Deep Learning.
Inventors include N Venkata Sailaja; Chalumuru Suresh; Karnam Akhil; Singam Vamshi Krishna; Shaik Mahimood Pasha; T Varshitha; and Jangamgari Sandeep Kumar.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: In the Vehicle Insurance Industry, damage analysis following accidents is often inefficient and error-prone. Manual inspections are time-consuming, labor-intensive, and susceptible to subjective mistakes, leading to delays in claim settlements and higher operational costs. This highlights the need for a more efficient and accurate solution. To address this, there is a need for an AI-based System to detect vehicle damage, damaged parts, estimate repair cost with repair vs replacement decisions and insurance claims using advanced deep learning methods. Leveraging models like YOLOV5, VGG16 and Detectron2 with the Mask R-CNN framework allows for precise identification and segmentation of damaged areas in images from video input. The system maps damage severity, predicts repair costs, and supports data-driven decision-making for insurers. The use of AI ensures consistency, precision, and scalability, reducing reliance on manual inspections and accelerating the claims process. One of the key advantages of this solution is its self-service feature, allowing policyholders to easily submit photos and videos through a user-friendly interface, eliminating the need for physical inspections and speeding up claim processing. By incorporating contextual information such as vehicle make, model, and damage extent, the system generates accurate cost estimates, helping insurers make informed repair-versus-replacement decisions. The automation of damage evaluation enhances efficiency, transparency, and customer satisfaction, while simplifying workflows, reducing fraud risk, and enabling faster, more accurate claim settlements. This AI-based system addresses the industry's challenges, transforming the traditional claims process with state-of-the-art deep learning techniques.
Disclaimer: Curated by HT Syndication.
