MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641088181 A) filed by Karpaga Vinayaga College Of Engineering And Technology; Dr. A. B. Hajira Be; Ms. S. Shahinabanu; Mr. Naveen E; Ms. Poongodi V; and Ms. Sowmiya C on July 20, 2026, for Skintellix: An Ai Based Skin Analysis And Product Recommendation System Thereof.
Inventors include Dr. A. B. Hajira Be; Ms. S. Shahinabanu; Mr. Naveen E; Ms. Poongodi V; and Ms. Sowmiya C.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: The invention describes a full-stack, cloud-connected, data-driven intelligent skincare analysis and e-commerce price-optimization ecosystem designed to simplify personal dermatology, aesthetic care and consumer shopping habits by leveraging a robust multi-module stack. Running over a secure, decoupled Service Oriented Architecture (SOA), the platform starts off with an automatic user authentication layer which uses stateless sessions verified using JSON Web Tokens (JWT) to protect user privacy while simultaneously creating isolated, individual cloud database partitions for personal skin and sessions. Users can use a reactive, mobile first web app interface that acts as their “one-stop shop” wellness portal to effortlessly set up, modify or view their profile records, logs and buying preferences. When onboarding and setting up profiles, the platform captures biological profile details, including base skin type, underlying skin tone and primary skin health goals along with strict buying parameters, such as Budget vs Premium levels and number of steps. At the heart of the app is an asynchronous imageprocessing and computer vision pipeline optimized for on-device and cloud execution to enable real-time dermatological assessment. Users can take and upload digital “payloads” of stressedout faces directly to the app portal using secure file-stream middleware. The back end instantly runs image prep sequences to crop, resize, and normalize the image matrix then sends the payload through a deep convolutional neural network (CNN) trained to distinguish different skin issues, like acne, eczema, and rosacea. To avoid the black-box nature of typical deep learning models, the engine passes the network’s activated weights through a Gradientweighted Class Activation Mapping (Grad-CAM) generator that outputs a heat map directly overlaid on the image that shows exactly which spots made the AI classify the photo. The logic engine also filters out all the recommended products against the users’ own personal preferences from cost to safety guidelines and offers a list of friendly lifestyle suggestions, such as using makeup with green color correction layers to calm down areas where inflammation was detected. Everything you do every milestone you reach gets saved back to the database layer, which triggers an analytics engine that goes back through your entries over time to draw up an ongoing, weighted skin recovery slope.
Disclaimer: Curated by HT Syndication.
